diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/Uniop Exor Designer 6 __EXCLUSIVE__.md b/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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- Here is a possible title and article with html formatting for the keyword "Uniop exor designer 6":  
-
-# How to Use Uniop Exor Designer 6 to Create Stunning HMI Applications
-
- 
-
-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.
-
- 
-
-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.
-
- 
-
-## Step 1: Create a New Project
-
- 
-
-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.
-
- 
-
-## Step 2: Add Pages and Objects
-
- 
-
-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.
-
- 
-
-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.
-
- 
-
-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.
-
- 
-
-## Step 3: Define Tags and Variables
-
- 
-
-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.
-
- 
-
-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).
-
- 
-
-## Step 4: Assign Actions and Expressions
-
- 
-
-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.
-
- 
-
-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.
-
- 
-
-For this example, we will assign actions and expressions to our objects as follows:
-
- 
-
-- 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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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocom 2.12.2 Keygen Download and Install the Latest Version of Autocom Software.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocom 2.12.2 Keygen Download and Install the Latest Version of Autocom Software.md
deleted file mode 100644
index 1f719d01efe2c5f74c0f299b2d937bd70f44b02d..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocom 2.12.2 Keygen Download and Install the Latest Version of Autocom Software.md	
+++ /dev/null
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-<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>However, to use Autocom, you need a valid activation file that matches your device serial number and software version. This is where a <strong>keygen</strong> comes in handy. A keygen is a program that generates a unique activation file for you, so you can use Autocom without any limitations or restrictions.</p>
-<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>To run the keygen, you need to double-click on <code>Autocom 2020_23.exe</code>. You will see a window like this:</p>
-<img src="https://i.imgur.com/8ZyXkQa.png" alt="Autocom 2020_23.exe window">
-<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>
-<h2>How to Use Autocom 2.12.2 Keygen</h2>
-<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>
-<p>The next thing you need to do is run <code>date_patch.exe</code>, which is located in the same folder as the keygen. This patch will change your system date temporarily to match the activation date of your file (usually 01/01/2020). This is necessary because Autocom checks your system date every time you run it.</p>
-<p>After running <code>date_patch.exe</code>, you will see a window like this:</p>
-<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>
-<ul>
-<li>If you encounter any errors or problems with Autocom, such as communication failure, license error, hardware fault, etc., try restarting your device and/or computer, reconnecting your device via USB or Bluetooth, running <code>date_patch.exe</code> again, or reinstalling Autocom.</li>
-<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>
-</ul>
-<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>
-<p>By following this article, you should be able to download, install, and use Autocom 2.12.2 keygen without any hassle or difficulty.</p>
-<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>
- <p>If you are looking for alternatives to Autocom 2.12.2 keygen, there are some options that you can consider. For example, you can try:</p>
-<ul>
-<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>
-<li><strong>Other diagnostic tools</strong>: There are many other diagnostic tools that you can use for your car or truck, such as OBDLink, BlueDriver, Launch X431, etc. These tools have different features, functions, prices, and compatibility than Autocom. You can compare and choose the best one for your needs and budget.</li>
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-<li><strong>Where can I find more information about Autocom 2.12.2 keygen?</strong></li>
- <p>If you want to find more information about Autocom 2.12.2 keygen, such as reviews, feedback, tutorials, etc., you can visit some online forums or websites that specialize in automotive diagnostics and software. For example, you can check out:</p>
-<ul>
-<li><strong>MHH AUTO</strong>: This is a forum where you can find various topics and discussions about automotive software, repair manuals, coding, programming, chip tuning, etc. You can also find the download link and the password for Autocom 2.12.2 keygen here.</li>
-<li><strong>DHT Auto</strong>: This is a website where you can find various automotive software, repair manuals, coding, programming, chip tuning, etc. You can also find the download link and the password for Autocom Version 23.2020 (Full + Kegen) + Firmware 1 & 2 Platine here.</li>
-<li><strong>Lymuna</strong>: This is a website where you can find various automotive software, repair manuals, coding, programming, chip tuning, etc. You can also find the download link and the password for Autocom + Delphi 2021.11 software + Keygen here.</li>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Download Windows Xp Home Edition Ulcpc Iso _BEST_.md b/spaces/1gistliPinn/ChatGPT4/Examples/Download Windows Xp Home Edition Ulcpc Iso _BEST_.md
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-<br />
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-<p>You can try to download Windows XP Home Edition ULCPC ISO from a trustworthy source if you have the product key of your license. You can also try to create your own ISO file from another edition of Windows XP if you have the skills and tools. However, you should also consider the drawbacks of using an outdated operating system that is no longer supported or compatible with modern software and web standards.</p>
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-<p>We hope this article has helped you learn more about Windows XP Home Edition ULCPC ISO and how to download, install, and use it on your netbook. If you have any questions or feedback, please feel free to leave a comment below.</p>
-<p>Windows XP Home Edition ULCPC was a special edition of Windows XP that was designed for low-cost netbooks with limited hardware specifications. It had a regular license of Windows XP Home Edition with Service Pack 3 included. However, finding a legitimate and safe download of Windows XP Home Edition ULCPC ISO can be challenging, as Microsoft has discontinued support for Windows XP since 2014.</p>
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CSR Classics MOD APK and Enjoy Unlimited Money and Gold.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CSR Classics MOD APK and Enjoy Unlimited Money and Gold.md
deleted file mode 100644
index dfc9b9ab20e0cbcaeb9925ecba550bada2cce758..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CSR Classics MOD APK and Enjoy Unlimited Money and Gold.md	
+++ /dev/null
@@ -1,83 +0,0 @@
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-<p>In this article, we will tell you everything you need to know about CSR Racing Classics Mod APK, a modified version of the original game that gives you unlimited money, all cars unlocked, and other features. We will also show you how to download and install it on your Android device easily and safely. So, let's get started!</p>
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-<p>CSR Racing Classics is a racing game developed by NaturalMotionGames Ltd, the same company behind other popular games like CSR Racing 2, CSR Racing, and Clumsy Ninja. The game was released in 2014 and has been downloaded over 50 million times on Google Play Store. It has a rating of 4.4 out of 5 stars from more than 900 thousand reviews.</p>
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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>Yes, CSR Racing Classics Mod APK is safe to use as long as you download it from a trusted source like the one we have provided. It does not contain any viruses, malware, or spyware that can harm your device or data. However, you should always be careful when downloading and installing any modded apps or games from unknown sources.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download 2go APK and Enjoy Live Online Hangouts with Friends.md b/spaces/1phancelerku/anime-remove-background/Download 2go APK and Enjoy Live Online Hangouts with Friends.md
deleted file mode 100644
index b41753c2cdcae1b3b5040395512b7843f74e314c..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download 2go APK and Enjoy Live Online Hangouts with Friends.md	
+++ /dev/null
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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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-<ol>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Bitcoin Loophole App and Start Trading Cryptocurrencies Today.md b/spaces/1phancelerku/anime-remove-background/Download Bitcoin Loophole App and Start Trading Cryptocurrencies Today.md
deleted file mode 100644
index 01063bac07877a8f927415f984bf7f99722fdf6f..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Bitcoin Loophole App and Start Trading Cryptocurrencies Today.md	
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-</ul>
-<h3>How Much Money Can You Make with Bitcoin Loophole?</h3>
-<p>The amount of money you can make with Bitcoin Loophole depends on several factors, such as:</p>
-<ul>
-<li><b>Your initial investment:</b> The more money you invest, the more profits you can potentially make. However, you should never invest more than you can afford to lose, as trading involves risks.</li>
-<li><b>Your trading settings:</b> The app allows you to customize your trading settings, such as the amount per trade, the number of trades per day, the risk level, and the assets to trade. These settings can affect your profitability and performance.</li>
-<li><b>The market conditions:</b> The crypto markets are 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>
-</ul>
-<p>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. You should always do your own research and test the app before investing real money.</p> <h2>How to Get Started with Bitcoin Loophole?</h2>
-<p>Getting started with Bitcoin Loophole is easy and fast. You just need to follow these simple steps:</p>
-<ol>
-<li><b>Register:</b> Visit the official website of Bitcoin Loophole and fill out the registration form with your name, email, phone number, and password. You will receive a confirmation email with a link to verify your account.</li>
-<li><b>Deposit:</b> Log in to your account and choose a broker from the list of partners. You will be redirected to the broker's website, where you can make a deposit of at least $250 using various payment methods, such as credit cards, debit cards, e-wallets, or bank transfers.</li>
-<li><b>Trade:</b> Go back to your Bitcoin Loophole dashboard and activate the automated trading mode. The app will start placing trades on your behalf based on your settings and preferences. You can also switch to the manual trading mode if you want to have more control over your trades.</li>
-</ol>
-<p>Congratulations! You are now ready to trade crypto with Bitcoin Loophole!</p>
-<h2>How to Download the Bitcoin Loophole App?</h2>
-<p>If you want to download the Bitcoin Loophole app for more convenience and security, you can do so by following these steps:</p>
-<ul>
-<li><b>For Android devices:</b> Go to the Google Play Store and search for Bitcoin Loophole. Tap on the app icon and click on Install. Wait for the app to download and install on your device. Open the app and log in with your credentials.</li>
-<li><b>For iOS devices:</b> Go to the App Store and search for Bitcoin Loophole. Tap on the app icon and click on Get. Wait for the app to download and install on your device. Open the app and log in with your credentials.</li>
-<li><b>For Windows devices:</b> Go to the Microsoft Store and search for Bitcoin Loophole. Tap on the app icon and click on Get. Wait for the app to download and install on your device. Open the app and log in with your credentials.</li>
-</ul>
-<p>Note: The app may not be available in some countries or regions due to legal restrictions. If you can't find the app in your store, you can still access it from any browser.</p>
-<h2>How to Use the Bitcoin Loophole App?</h2>
-<p>The Bitcoin Loophole app is user-friendly and has a simple interface that anyone can navigate. Here are some of the features and settings that you can use from the app:</p>
-<ul>
-<li><b>Dashboard:</b> This is where you can monitor your trades and profits, as well as access other features of the app.</li>
-<li><b>Trading history:</b> This is where you can view your past trades and results, as well as analyze your performance and strategies.</li>
-<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 />
\ No newline at end of file
diff --git a/spaces/232labs/VToonify/vtoonify/model/raft/core/utils/frame_utils.py b/spaces/232labs/VToonify/vtoonify/model/raft/core/utils/frame_utils.py
deleted file mode 100644
index 6c491135efaffc25bd61ec3ecde99d236f5deb12..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/raft/core/utils/frame_utils.py
+++ /dev/null
@@ -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 []
\ No newline at end of file
diff --git a/spaces/839871171w/newbingAI/README.md b/spaces/839871171w/newbingAI/README.md
deleted file mode 100644
index 8c237e12cea82a34d064a2ffadd40e4854369441..0000000000000000000000000000000000000000
--- a/spaces/839871171w/newbingAI/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/AFOL/GigaGan/README.md b/spaces/AFOL/GigaGan/README.md
deleted file mode 100644
index c2774b0db4e443e3a8db35bd9a7a3bab280b7b34..0000000000000000000000000000000000000000
--- a/spaces/AFOL/GigaGan/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/AI4PD/hexviz/hexviz/models.py b/spaces/AI4PD/hexviz/hexviz/models.py
deleted file mode 100644
index 8c8e1c332d0126cdf671bb7ae9880bb0b54ac334..0000000000000000000000000000000000000000
--- a/spaces/AI4PD/hexviz/hexviz/models.py
+++ /dev/null
@@ -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
diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/config.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/config.py
deleted file mode 100644
index 934be1c68f4e1562e5fcef81d2f8db131cb39b9f..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/config.py
+++ /dev/null
@@ -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])
diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/web-ui.bat b/spaces/AbandonedMuse/UnlimitedMusicGen/web-ui.bat
deleted file mode 100644
index dd4e071c7b4ce465ca3535807530960dba47c5b5..0000000000000000000000000000000000000000
--- a/spaces/AbandonedMuse/UnlimitedMusicGen/web-ui.bat
+++ /dev/null
@@ -1 +0,0 @@
-py -m app
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/executor/tool_using.py b/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/executor/tool_using.py
deleted file mode 100644
index 667de12f194dd80f94082e6d936d67302b3654cf..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/executor/tool_using.py
+++ /dev/null
@@ -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)
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.d.ts
deleted file mode 100644
index 94240ec543f7b1232355e85ac3cf3fbf6341734e..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import Chart from './Chart';
-
-export default function ChartFactory(
-    config?: Chart.IConfig
-): Chart;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/Factory.d.ts
deleted file mode 100644
index 66c0f9145079da0cec900daf4c724f72ae4392d2..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/Factory.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import GridTable from './GridTable';
-
-export default function (
-    config?: GridTable.IConfig
-): GridTable;
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/instructpix2pix.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/instructpix2pix.md
deleted file mode 100644
index f9557c7bea2e8d8b20d666af9353b561e9774650..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/instructpix2pix.md
+++ /dev/null
@@ -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). 
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/cityscapes/README.md b/spaces/Andy1621/uniformer_image_detection/configs/cityscapes/README.md
deleted file mode 100644
index d892fc93aaca82cbd84cc38a88ad52c30641bbcc..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/cityscapes/README.md
+++ /dev/null
@@ -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) &#124; [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) &#124; [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) |
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py
deleted file mode 100644
index 8dfdbb44a49bf75e7460c49285046b7f38cdfc75..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py
+++ /dev/null
@@ -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)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
deleted file mode 100644
index 53964a3e1d2c9fd2e9eb905c6d6f645f62ea9957..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+++ /dev/null
@@ -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))
diff --git a/spaces/Andy1621/uniformer_image_detection/tools/test.py b/spaces/Andy1621/uniformer_image_detection/tools/test.py
deleted file mode 100644
index d7d9418d090be36935a65ab21914af0c7c657591..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/tools/test.py
+++ /dev/null
@@ -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()
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_hr18.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_hr18.py
deleted file mode 100644
index c3e299bc89ada56ca14bbffcbdb08a586b8ed9e9..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_hr18.py
+++ /dev/null
@@ -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'))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 332d9cfb79fb698c7867f0f80053c1fd29bf2c1d..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/data_preprocessor.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/data_preprocessor.py
deleted file mode 100644
index cbd14b6b0c9cfbbaedc2ebbcbe5883ed6253c5f9..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/data_preprocessor.py
+++ /dev/null
@@ -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
\ No newline at end of file
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py
deleted file mode 100644
index ee511ff20d73bb245fe7ae0c1fc31a41c33e7d29..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py
+++ /dev/null
@@ -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()
diff --git a/spaces/Aveygo/AstroSleuth/training.md b/spaces/Aveygo/AstroSleuth/training.md
deleted file mode 100644
index 73e6429d8a385607ca87fdd0b4783c5472926a45..0000000000000000000000000000000000000000
--- a/spaces/Aveygo/AstroSleuth/training.md
+++ /dev/null
@@ -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.
\ No newline at end of file
diff --git a/spaces/Awesimo/jojogan/e4e/criteria/__init__.py b/spaces/Awesimo/jojogan/e4e/criteria/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py
deleted file mode 100644
index 74ac123a7aed6cd77d6d833446a831d9048745b2..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py
+++ /dev/null
@@ -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
diff --git a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/__init__.py b/spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/__init__.py
deleted file mode 100644
index 75603d26cf2b8d6196f5a68a89f9e49d8e519bc8..0000000000000000000000000000000000000000
--- a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/__init__.py
+++ /dev/null
@@ -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)
diff --git a/spaces/Benson/text-generation/Examples/Casos Criminales Misterios Del Pasado Mod Apk ltima Versin.md b/spaces/Benson/text-generation/Examples/Casos Criminales Misterios Del Pasado Mod Apk ltima Versin.md
deleted file mode 100644
index 10b10407da9bd8cb992ce985f98b0249863fefa0..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Casos Criminales Misterios Del Pasado Mod Apk ltima Versin.md	
+++ /dev/null
@@ -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 />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/__init__.py
deleted file mode 100644
index f631ae6df4747b808cac7c03b38e3e1d48bea00b..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/__init__.py
+++ /dev/null
@@ -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__ = "eric@ionrock.org"
-__version__ = "0.12.11"
-
-from .wrapper import CacheControl
-from .adapter import CacheControlAdapter
-from .controller import CacheController
-
-import logging
-logging.getLogger(__name__).addHandler(logging.NullHandler())
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/wheel.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/wheel.py
deleted file mode 100644
index 028c2d99b57782ed3bb268ce522ede37c1704d98..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/wheel.py
+++ /dev/null
@@ -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
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/live.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/live.py
deleted file mode 100644
index 3ebbbc4ccbe47043eb62f8dd770f079745d3b743..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/live.py
+++ /dev/null
@@ -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))
diff --git a/spaces/BigDL/bigdl_nano_demo/data.py b/spaces/BigDL/bigdl_nano_demo/data.py
deleted file mode 100644
index d301a5d3bbdda1ebc888bd5e823cb1ada41ae15c..0000000000000000000000000000000000000000
--- a/spaces/BigDL/bigdl_nano_demo/data.py
+++ /dev/null
@@ -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)
\ No newline at end of file
diff --git a/spaces/BraydenMoore/MARCI-NFL-Betting/Source/Train/xgboost_ML.py b/spaces/BraydenMoore/MARCI-NFL-Betting/Source/Train/xgboost_ML.py
deleted file mode 100644
index 71e67e432f5c09ff12af7416c54f70b997656b52..0000000000000000000000000000000000000000
--- a/spaces/BraydenMoore/MARCI-NFL-Betting/Source/Train/xgboost_ML.py
+++ /dev/null
@@ -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')
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/README.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/README.md
deleted file mode 100644
index 62efc315204137f726dc1043afd6bd4cacbc2941..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/README.md
+++ /dev/null
@@ -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 git@github.com: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).
\ No newline at end of file
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/model.py b/spaces/ChrisCaviar/ControlNet-v1-1/model.py
deleted file mode 100644
index a9239489a9ee2d1a082f701847dccd209f0477ac..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/model.py
+++ /dev/null
@@ -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
diff --git a/spaces/CobaltZvc/Hyper_Bot/README.md b/spaces/CobaltZvc/Hyper_Bot/README.md
deleted file mode 100644
index 98c9ab277c44a08e374d6ff57e61fe1f6c5bb9e1..0000000000000000000000000000000000000000
--- a/spaces/CobaltZvc/Hyper_Bot/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Fakeopen.py b/spaces/CofAI/chat.b4/g4f/Provider/Providers/Fakeopen.py
deleted file mode 100644
index da6743ac3d3d5270cda55a88137ce2798d1be468..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Fakeopen.py
+++ /dev/null
@@ -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]])  
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/dcn_v2_cpu.cpp b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/dcn_v2_cpu.cpp
deleted file mode 100644
index 8ab4e24a3b08f9f91d73c9eb0cc6036dab2ecf1b..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/dcn_v2_cpu.cpp
+++ /dev/null
@@ -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
-    };
-}
\ No newline at end of file
diff --git a/spaces/DD0101/Disfluency-base/README.md b/spaces/DD0101/Disfluency-base/README.md
deleted file mode 100644
index b500edddd736440143097a3d33511b0db40dc4e7..0000000000000000000000000000000000000000
--- a/spaces/DD0101/Disfluency-base/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/teePen.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/teePen.py
deleted file mode 100644
index 2828175a7c02c1858db5cbfc45c8686f3187a50e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/teePen.py
+++ /dev/null
@@ -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()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-329f8260.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-329f8260.css
deleted file mode 100644
index 3b53ee465e192f512a964e9050e9aab81384add8..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-329f8260.css
+++ /dev/null
@@ -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}
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-9d98c4c0.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-9d98c4c0.js
deleted file mode 100644
index 713d4339ba7be205d15b2744672c0a182e7ffe2f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-9d98c4c0.js
+++ /dev/null
@@ -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
diff --git a/spaces/DataScienceEngineering/2-GradioLiveASR/README.md b/spaces/DataScienceEngineering/2-GradioLiveASR/README.md
deleted file mode 100644
index a2b084f56702ec8e401ffd943c97b4c07198ae4a..0000000000000000000000000000000000000000
--- a/spaces/DataScienceEngineering/2-GradioLiveASR/README.md
+++ /dev/null
@@ -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
\ No newline at end of file
diff --git a/spaces/DevashishBhake/Question_Generation/README.md b/spaces/DevashishBhake/Question_Generation/README.md
deleted file mode 100644
index 865f791983ce83f50e50d09576d60791678c80f7..0000000000000000000000000000000000000000
--- a/spaces/DevashishBhake/Question_Generation/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/modelconfig.py b/spaces/Dinoking/Guccio-AI-Designer/netdissect/modelconfig.py
deleted file mode 100644
index d0ee37a809ea1bcbd803cd7d4e100e1bb93290c9..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/modelconfig.py
+++ /dev/null
@@ -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
diff --git a/spaces/Dorado607/ChuanhuChatGPT/readme/README_ja.md b/spaces/Dorado607/ChuanhuChatGPT/readme/README_ja.md
deleted file mode 100644
index 394002636488f265c60faa906fd777c20a7e281b..0000000000000000000000000000000000000000
--- a/spaces/Dorado607/ChuanhuChatGPT/readme/README_ja.md
+++ /dev/null
@@ -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とチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/README.md b/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/README.md
deleted file mode 100644
index cb2bfae1054140d2863b10738e7ae5b91c52b28a..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/README.md
+++ /dev/null
@@ -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. )
-
diff --git a/spaces/Eddycrack864/Applio-Inference/lib/uvr5_pack/lib_v5/layers_33966KB.py b/spaces/Eddycrack864/Applio-Inference/lib/uvr5_pack/lib_v5/layers_33966KB.py
deleted file mode 100644
index a38b7bb3ae3136b07eadfc2db445fef4c2de186b..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/lib/uvr5_pack/lib_v5/layers_33966KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/Epoching/3D_Photo_Inpainting/main.py b/spaces/Epoching/3D_Photo_Inpainting/main.py
deleted file mode 100644
index 184bc0a5f51cb7c18335fcc5d5636481e6215e9d..0000000000000000000000000000000000000000
--- a/spaces/Epoching/3D_Photo_Inpainting/main.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import numpy as np
-import argparse
-import glob
-import os
-from functools import partial
-import vispy
-import scipy.misc as misc
-from tqdm import tqdm
-import yaml
-import time
-import sys
-from mesh import write_ply, read_ply, output_3d_photo
-from utils import get_MiDaS_samples, read_MiDaS_depth
-import torch
-import cv2
-from skimage.transform import resize
-import imageio
-import copy
-from networks import Inpaint_Color_Net, Inpaint_Depth_Net, Inpaint_Edge_Net
-from MiDaS.run import run_depth
-from boostmonodepth_utils import run_boostmonodepth
-from MiDaS.monodepth_net import MonoDepthNet
-import MiDaS.MiDaS_utils as MiDaS_utils
-from bilateral_filtering import sparse_bilateral_filtering
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--config', type=str, default='argument.yml',help='Configure of post processing')
-args = parser.parse_args()
-config = yaml.load(open(args.config, 'r'))
-if config['offscreen_rendering'] is True:
-    vispy.use(app='egl')
-os.makedirs(config['mesh_folder'], exist_ok=True)
-os.makedirs(config['video_folder'], exist_ok=True)
-os.makedirs(config['depth_folder'], exist_ok=True)
-sample_list = get_MiDaS_samples(config['src_folder'], config['depth_folder'], config, config['specific'])
-normal_canvas, all_canvas = None, None
-
-if isinstance(config["gpu_ids"], int) and (config["gpu_ids"] >= 0):
-    device = config["gpu_ids"]
-else:
-    device = "cpu"
-
-print(f"running on device {device}")
-
-for idx in tqdm(range(len(sample_list))):
-    depth = None
-    sample = sample_list[idx]
-    print("Current Source ==> ", sample['src_pair_name'])
-    mesh_fi = os.path.join(config['mesh_folder'], sample['src_pair_name'] +'.ply')
-    image = imageio.imread(sample['ref_img_fi'])
-
-    print(f"Running depth extraction at {time.time()}")
-    if config['use_boostmonodepth'] is True:
-        run_boostmonodepth(sample['ref_img_fi'], config['src_folder'], config['depth_folder'])
-    elif config['require_midas'] is True:
-        run_depth([sample['ref_img_fi']], config['src_folder'], config['depth_folder'],
-                  config['MiDaS_model_ckpt'], MonoDepthNet, MiDaS_utils, target_w=640)
-
-    if 'npy' in config['depth_format']:
-        config['output_h'], config['output_w'] = np.load(sample['depth_fi']).shape[:2]
-    else:
-        config['output_h'], config['output_w'] = imageio.imread(sample['depth_fi']).shape[:2]
-    frac = config['longer_side_len'] / max(config['output_h'], config['output_w'])
-    config['output_h'], config['output_w'] = int(config['output_h'] * frac), int(config['output_w'] * frac)
-    config['original_h'], config['original_w'] = config['output_h'], config['output_w']
-    if image.ndim == 2:
-        image = image[..., None].repeat(3, -1)
-    if np.sum(np.abs(image[..., 0] - image[..., 1])) == 0 and np.sum(np.abs(image[..., 1] - image[..., 2])) == 0:
-        config['gray_image'] = True
-    else:
-        config['gray_image'] = False
-    image = cv2.resize(image, (config['output_w'], config['output_h']), interpolation=cv2.INTER_AREA)
-    depth = read_MiDaS_depth(sample['depth_fi'], 3.0, config['output_h'], config['output_w'])
-    mean_loc_depth = depth[depth.shape[0]//2, depth.shape[1]//2]
-    if not(config['load_ply'] is True and os.path.exists(mesh_fi)):
-        vis_photos, vis_depths = sparse_bilateral_filtering(depth.copy(), image.copy(), config, num_iter=config['sparse_iter'], spdb=False)
-        depth = vis_depths[-1]
-        model = None
-        torch.cuda.empty_cache()
-        print("Start Running 3D_Photo ...")
-        print(f"Loading edge model at {time.time()}")
-        depth_edge_model = Inpaint_Edge_Net(init_weights=True)
-        depth_edge_weight = torch.load(config['depth_edge_model_ckpt'],
-                                       map_location=torch.device(device))
-        depth_edge_model.load_state_dict(depth_edge_weight)
-        depth_edge_model = depth_edge_model.to(device)
-        depth_edge_model.eval()
-
-        print(f"Loading depth model at {time.time()}")
-        depth_feat_model = Inpaint_Depth_Net()
-        depth_feat_weight = torch.load(config['depth_feat_model_ckpt'],
-                                       map_location=torch.device(device))
-        depth_feat_model.load_state_dict(depth_feat_weight, strict=True)
-        depth_feat_model = depth_feat_model.to(device)
-        depth_feat_model.eval()
-        depth_feat_model = depth_feat_model.to(device)
-        print(f"Loading rgb model at {time.time()}")
-        rgb_model = Inpaint_Color_Net()
-        rgb_feat_weight = torch.load(config['rgb_feat_model_ckpt'],
-                                     map_location=torch.device(device))
-        rgb_model.load_state_dict(rgb_feat_weight)
-        rgb_model.eval()
-        rgb_model = rgb_model.to(device)
-        graph = None
-
-
-        print(f"Writing depth ply (and basically doing everything) at {time.time()}")
-        rt_info = write_ply(image,
-                              depth,
-                              sample['int_mtx'],
-                              mesh_fi,
-                              config,
-                              rgb_model,
-                              depth_edge_model,
-                              depth_edge_model,
-                              depth_feat_model)
-
-        if rt_info is False:
-            continue
-        rgb_model = None
-        color_feat_model = None
-        depth_edge_model = None
-        depth_feat_model = None
-        torch.cuda.empty_cache()
-    if config['save_ply'] is True or config['load_ply'] is True:
-        verts, colors, faces, Height, Width, hFov, vFov = read_ply(mesh_fi)
-    else:
-        verts, colors, faces, Height, Width, hFov, vFov = rt_info
-
-
-    print(f"Making video at {time.time()}")
-    videos_poses, video_basename = copy.deepcopy(sample['tgts_poses']), sample['tgt_name']
-    top = (config.get('original_h') // 2 - sample['int_mtx'][1, 2] * config['output_h'])
-    left = (config.get('original_w') // 2 - sample['int_mtx'][0, 2] * config['output_w'])
-    down, right = top + config['output_h'], left + config['output_w']
-    border = [int(xx) for xx in [top, down, left, right]]
-    normal_canvas, all_canvas = output_3d_photo(verts.copy(), colors.copy(), faces.copy(), copy.deepcopy(Height), copy.deepcopy(Width), copy.deepcopy(hFov), copy.deepcopy(vFov),
-                        copy.deepcopy(sample['tgt_pose']), sample['video_postfix'], copy.deepcopy(sample['ref_pose']), copy.deepcopy(config['video_folder']),
-                        image.copy(), copy.deepcopy(sample['int_mtx']), config, image,
-                        videos_poses, video_basename, config.get('original_h'), config.get('original_w'), border=border, depth=depth, normal_canvas=normal_canvas, all_canvas=all_canvas,
-                        mean_loc_depth=mean_loc_depth)
diff --git a/spaces/Epoching/GLIDE_Inpaint/glide_text2im/text2im_model.py b/spaces/Epoching/GLIDE_Inpaint/glide_text2im/text2im_model.py
deleted file mode 100644
index c74394090a1bd61054f9aeabf15075e701d81601..0000000000000000000000000000000000000000
--- a/spaces/Epoching/GLIDE_Inpaint/glide_text2im/text2im_model.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .nn import timestep_embedding
-from .unet import UNetModel
-from .xf import LayerNorm, Transformer, convert_module_to_f16
-
-
-class Text2ImUNet(UNetModel):
-    """
-    A UNetModel that conditions on text with an encoding transformer.
-
-    Expects an extra kwarg `tokens` of text.
-
-    :param text_ctx: number of text tokens to expect.
-    :param xf_width: width of the transformer.
-    :param xf_layers: depth of the transformer.
-    :param xf_heads: heads in the transformer.
-    :param xf_final_ln: use a LayerNorm after the output layer.
-    :param tokenizer: the text tokenizer for sampling/vocab size.
-    """
-
-    def __init__(
-        self,
-        text_ctx,
-        xf_width,
-        xf_layers,
-        xf_heads,
-        xf_final_ln,
-        tokenizer,
-        *args,
-        cache_text_emb=False,
-        xf_ar=0.0,
-        xf_padding=False,
-        share_unemb=False,
-        **kwargs,
-    ):
-        self.text_ctx = text_ctx
-        self.xf_width = xf_width
-        self.xf_ar = xf_ar
-        self.xf_padding = xf_padding
-        self.tokenizer = tokenizer
-
-        if not xf_width:
-            super().__init__(*args, **kwargs, encoder_channels=None)
-        else:
-            super().__init__(*args, **kwargs, encoder_channels=xf_width)
-        if self.xf_width:
-            self.transformer = Transformer(
-                text_ctx,
-                xf_width,
-                xf_layers,
-                xf_heads,
-            )
-            if xf_final_ln:
-                self.final_ln = LayerNorm(xf_width)
-            else:
-                self.final_ln = None
-
-            self.token_embedding = nn.Embedding(self.tokenizer.n_vocab, xf_width)
-            self.positional_embedding = nn.Parameter(th.empty(text_ctx, xf_width, dtype=th.float32))
-            self.transformer_proj = nn.Linear(xf_width, self.model_channels * 4)
-
-            if self.xf_padding:
-                self.padding_embedding = nn.Parameter(
-                    th.empty(text_ctx, xf_width, dtype=th.float32)
-                )
-            if self.xf_ar:
-                self.unemb = nn.Linear(xf_width, self.tokenizer.n_vocab)
-                if share_unemb:
-                    self.unemb.weight = self.token_embedding.weight
-
-        self.cache_text_emb = cache_text_emb
-        self.cache = None
-
-    def convert_to_fp16(self):
-        super().convert_to_fp16()
-        if self.xf_width:
-            self.transformer.apply(convert_module_to_f16)
-            self.transformer_proj.to(th.float16)
-            self.token_embedding.to(th.float16)
-            self.positional_embedding.to(th.float16)
-            if self.xf_padding:
-                self.padding_embedding.to(th.float16)
-            if self.xf_ar:
-                self.unemb.to(th.float16)
-
-    def get_text_emb(self, tokens, mask):
-        assert tokens is not None
-
-        if self.cache_text_emb and self.cache is not None:
-            assert (
-                tokens == self.cache["tokens"]
-            ).all(), f"Tokens {tokens.cpu().numpy().tolist()} do not match cache {self.cache['tokens'].cpu().numpy().tolist()}"
-            return self.cache
-
-        xf_in = self.token_embedding(tokens.long())
-        xf_in = xf_in + self.positional_embedding[None]
-        if self.xf_padding:
-            assert mask is not None
-            xf_in = th.where(mask[..., None], xf_in, self.padding_embedding[None])
-        xf_out = self.transformer(xf_in.to(self.dtype))
-        if self.final_ln is not None:
-            xf_out = self.final_ln(xf_out)
-        xf_proj = self.transformer_proj(xf_out[:, -1])
-        xf_out = xf_out.permute(0, 2, 1)  # NLC -> NCL
-
-        outputs = dict(xf_proj=xf_proj, xf_out=xf_out)
-
-        if self.cache_text_emb:
-            self.cache = dict(
-                tokens=tokens,
-                xf_proj=xf_proj.detach(),
-                xf_out=xf_out.detach() if xf_out is not None else None,
-            )
-
-        return outputs
-
-    def del_cache(self):
-        self.cache = None
-
-    def forward(self, x, timesteps, tokens=None, mask=None):
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-        if self.xf_width:
-            text_outputs = self.get_text_emb(tokens, mask)
-            xf_proj, xf_out = text_outputs["xf_proj"], text_outputs["xf_out"]
-            emb = emb + xf_proj.to(emb)
-        else:
-            xf_out = None
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, xf_out)
-            hs.append(h)
-        h = self.middle_block(h, emb, xf_out)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, xf_out)
-        h = h.type(x.dtype)
-        h = self.out(h)
-        return h
-
-
-class SuperResText2ImUNet(Text2ImUNet):
-    """
-    A text2im model that performs super-resolution.
-    Expects an extra kwarg `low_res` to condition on a low-resolution image.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 2
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 2
-        super().__init__(*args, **kwargs)
-
-    def forward(self, x, timesteps, low_res=None, **kwargs):
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(
-            low_res, (new_height, new_width), mode="bilinear", align_corners=False
-        )
-        x = th.cat([x, upsampled], dim=1)
-        return super().forward(x, timesteps, **kwargs)
-
-
-class InpaintText2ImUNet(Text2ImUNet):
-    """
-    A text2im model which can perform inpainting.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 2 + 1
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 2 + 1
-        super().__init__(*args, **kwargs)
-
-    def forward(self, x, timesteps, inpaint_image=None, inpaint_mask=None, **kwargs):
-        if inpaint_image is None:
-            inpaint_image = th.zeros_like(x)
-        if inpaint_mask is None:
-            inpaint_mask = th.zeros_like(x[:, :1])
-        return super().forward(
-            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask], dim=1),
-            timesteps,
-            **kwargs,
-        )
-
-
-class SuperResInpaintText2ImUnet(Text2ImUNet):
-    """
-    A text2im model which can perform both upsampling and inpainting.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 3 + 1
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 3 + 1
-        super().__init__(*args, **kwargs)
-
-    def forward(
-        self,
-        x,
-        timesteps,
-        inpaint_image=None,
-        inpaint_mask=None,
-        low_res=None,
-        **kwargs,
-    ):
-        if inpaint_image is None:
-            inpaint_image = th.zeros_like(x)
-        if inpaint_mask is None:
-            inpaint_mask = th.zeros_like(x[:, :1])
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(
-            low_res, (new_height, new_width), mode="bilinear", align_corners=False
-        )
-        return super().forward(
-            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask, upsampled], dim=1),
-            timesteps,
-            **kwargs,
-        )
diff --git a/spaces/FSDL-Fashion/fashion_img_search/fis/feature_extraction/detection/dummy.py b/spaces/FSDL-Fashion/fashion_img_search/fis/feature_extraction/detection/dummy.py
deleted file mode 100644
index 3032b4e21535433b2d1fcf80f67634814e115cff..0000000000000000000000000000000000000000
--- a/spaces/FSDL-Fashion/fashion_img_search/fis/feature_extraction/detection/dummy.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from typing import List, Tuple
-
-from PIL import Image
-
-from fis.feature_extraction.detection.base import BaseDetector
-
-
-class DummyDetector(BaseDetector):
-    """Dummy detection model."""
-
-    def __call__(self, image: Image) -> List[Tuple[int]]:
-        """Return a bounding box with the same size as the image.
-
-        Args:
-            image: Image
-
-        Returns:
-            Dummy bounding box the same size as the image
-        """
-        x_min, y_min = 0, 0
-        x_max, y_max = image.size
-
-        return [(x_min, y_min, x_max, y_max)]
diff --git a/spaces/Gavnoed/Kaloed/README.md b/spaces/Gavnoed/Kaloed/README.md
deleted file mode 100644
index 8bb7855776233f830bbc16efe1ca786c1c1d8b42..0000000000000000000000000000000000000000
--- a/spaces/Gavnoed/Kaloed/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Kaloed
-emoji: 🌍
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gladiator/Text-Summarizer/extractive_summarizer/cluster_features.py b/spaces/Gladiator/Text-Summarizer/extractive_summarizer/cluster_features.py
deleted file mode 100644
index 3f76f04295749a082fa1a5283658b3d7895742d4..0000000000000000000000000000000000000000
--- a/spaces/Gladiator/Text-Summarizer/extractive_summarizer/cluster_features.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from typing import Dict, List
-
-import numpy as np
-from numpy import ndarray
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-from sklearn.mixture import GaussianMixture
-
-
-class ClusterFeatures(object):
-    """
-    Basic handling of clustering features.
-    """
-
-    def __init__(
-        self,
-        features: ndarray,
-        algorithm: str = 'kmeans',
-        pca_k: int = None,
-        random_state: int = 12345,
-    ):
-        """
-        :param features: the embedding matrix created by bert parent.
-        :param algorithm: Which clustering algorithm to use.
-        :param pca_k: If you want the features to be ran through pca, this is the components number.
-        :param random_state: Random state.
-        """
-        if pca_k:
-            self.features = PCA(n_components=pca_k).fit_transform(features)
-        else:
-            self.features = features
-
-        self.algorithm = algorithm
-        self.pca_k = pca_k
-        self.random_state = random_state
-
-    def __get_model(self, k: int):
-        """
-        Retrieve clustering model.
-
-        :param k: amount of clusters.
-        :return: Clustering model.
-        """
-
-        if self.algorithm == 'gmm':
-            return GaussianMixture(n_components=k, random_state=self.random_state)
-        return KMeans(n_clusters=k, random_state=self.random_state)
-
-    def __get_centroids(self, model):
-        """
-        Retrieve centroids of model.
-
-        :param model: Clustering model.
-        :return: Centroids.
-        """
-        if self.algorithm == 'gmm':
-            return model.means_
-        return model.cluster_centers_
-
-    def __find_closest_args(self, centroids: np.ndarray) -> Dict:
-        """
-        Find the closest arguments to centroid.
-
-        :param centroids: Centroids to find closest.
-        :return: Closest arguments.
-        """
-        centroid_min = 1e10
-        cur_arg = -1
-        args = {}
-        used_idx = []
-
-        for j, centroid in enumerate(centroids):
-
-            for i, feature in enumerate(self.features):
-                value = np.linalg.norm(feature - centroid)
-
-                if value < centroid_min and i not in used_idx:
-                    cur_arg = i
-                    centroid_min = value
-
-            used_idx.append(cur_arg)
-            args[j] = cur_arg
-            centroid_min = 1e10
-            cur_arg = -1
-
-        return args
-
-    def calculate_elbow(self, k_max: int) -> List[float]:
-        """
-        Calculates elbow up to the provided k_max.
-
-        :param k_max: K_max to calculate elbow for.
-        :return: The inertias up to k_max.
-        """
-        inertias = []
-
-        for k in range(1, min(k_max, len(self.features))):
-            model = self.__get_model(k).fit(self.features)
-
-            inertias.append(model.inertia_)
-
-        return inertias
-
-    def calculate_optimal_cluster(self, k_max: int):
-        """
-        Calculates the optimal cluster based on Elbow.
-
-        :param k_max: The max k to search elbow for.
-        :return: The optimal cluster size.
-        """
-        delta_1 = []
-        delta_2 = []
-
-        max_strength = 0
-        k = 1
-
-        inertias = self.calculate_elbow(k_max)
-
-        for i in range(len(inertias)):
-            delta_1.append(inertias[i] - inertias[i - 1] if i > 0 else 0.0)
-            delta_2.append(delta_1[i] - delta_1[i - 1] if i > 1 else 0.0)
-
-        for j in range(len(inertias)):
-            strength = 0 if j <= 1 or j == len(inertias) - 1 else delta_2[j + 1] - delta_1[j + 1]
-
-            if strength > max_strength:
-                max_strength = strength
-                k = j + 1
-
-        return k
-
-    def cluster(self, ratio: float = 0.1, num_sentences: int = None) -> List[int]:
-        """
-        Clusters sentences based on the ratio.
-
-        :param ratio: Ratio to use for clustering.
-        :param num_sentences: Number of sentences. Overrides ratio.
-        :return: Sentences index that qualify for summary.
-        """
-
-        if num_sentences is not None:
-            if num_sentences == 0:
-                return []
-
-            k = min(num_sentences, len(self.features))
-        else:
-            k = max(int(len(self.features) * ratio), 1)
-
-        model = self.__get_model(k).fit(self.features)
-
-        centroids = self.__get_centroids(model)
-        cluster_args = self.__find_closest_args(centroids)
-
-        sorted_values = sorted(cluster_args.values())
-        return sorted_values
-
-    def __call__(self, ratio: float = 0.1, num_sentences: int = None) -> List[int]:
-        """
-        Clusters sentences based on the ratio.
-
-        :param ratio: Ratio to use for clustering.
-        :param num_sentences: Number of sentences. Overrides ratio.
-        :return: Sentences index that qualify for summary.
-        """
-        return self.cluster(ratio)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/anime-colorization/scripts/image_sample.py b/spaces/Gradio-Blocks/anime-colorization/scripts/image_sample.py
deleted file mode 100644
index 289e06f074436bae8d9daf970315150e20f5a4d6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/anime-colorization/scripts/image_sample.py
+++ /dev/null
@@ -1,106 +0,0 @@
-"""
-Generate a large batch of image samples from a model and save them as a large
-numpy array. This can be used to produce samples for FID evaluation.
-"""
-
-import argparse
-import os
-
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-from pixel_guide_diffusion import dist_util, logger
-from pixel_guide_diffusion.script_util import (
-    NUM_CLASSES,
-    model_and_diffusion_defaults,
-    create_model_and_diffusion,
-    add_dict_to_argparser,
-    args_to_dict,
-)
-
-
-def main():
-    args = create_argparser().parse_args()
-
-    dist_util.setup_dist()
-    logger.configure()
-
-    logger.log("creating model and diffusion...")
-    model, diffusion = create_model_and_diffusion(
-        **args_to_dict(args, model_and_diffusion_defaults().keys())
-    )
-    model.load_state_dict(
-        dist_util.load_state_dict(args.model_path, map_location="cpu")
-    )
-    model.to(dist_util.dev())
-    model.eval()
-
-    logger.log("sampling...")
-    all_images = []
-    all_labels = []
-    while len(all_images) * args.batch_size < args.num_samples:
-        model_kwargs = {}
-        if args.class_cond:
-            classes = th.randint(
-                low=0, high=NUM_CLASSES, size=(args.batch_size,), device=dist_util.dev()
-            )
-            model_kwargs["y"] = classes
-        sample_fn = (
-            diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop
-        )
-        sample = sample_fn(
-            model,
-            (args.batch_size, 3, args.image_size, args.image_size),
-            clip_denoised=args.clip_denoised,
-            model_kwargs=model_kwargs,
-        )
-        sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8)
-        sample = sample.permute(0, 2, 3, 1)
-        sample = sample.contiguous()
-
-        gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
-        dist.all_gather(gathered_samples, sample)  # gather not supported with NCCL
-        all_images.extend([sample.cpu().numpy() for sample in gathered_samples])
-        if args.class_cond:
-            gathered_labels = [
-                th.zeros_like(classes) for _ in range(dist.get_world_size())
-            ]
-            dist.all_gather(gathered_labels, classes)
-            all_labels.extend([labels.cpu().numpy() for labels in gathered_labels])
-        logger.log(f"created {len(all_images) * args.batch_size} samples")
-
-    arr = np.concatenate(all_images, axis=0)
-    arr = arr[: args.num_samples]
-    if args.class_cond:
-        label_arr = np.concatenate(all_labels, axis=0)
-        label_arr = label_arr[: args.num_samples]
-    if dist.get_rank() == 0:
-        shape_str = "x".join([str(x) for x in arr.shape])
-        out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz")
-        logger.log(f"saving to {out_path}")
-        if args.class_cond:
-            np.savez(out_path, arr, label_arr)
-        else:
-            np.savez(out_path, arr)
-
-    dist.barrier()
-    logger.log("sampling complete")
-
-
-def create_argparser():
-    defaults = dict(
-        clip_denoised=True,
-        num_samples=10000,
-        batch_size=16,
-        use_ddim=False,
-        model_path="",
-    )
-    defaults.update(model_and_diffusion_defaults())
-    parser = argparse.ArgumentParser()
-    add_dict_to_argparser(parser, defaults)
-    return parser
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
deleted file mode 100644
index 81dfb4873bdb587626200a3007dc4d57a92c0fd9..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
+++ /dev/null
@@ -1,45 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
-# model settings
-model = dict(
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            aggregation='sum',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2)),
-        mask_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2))))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/vfnet.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/vfnet.py
deleted file mode 100644
index e23f89674c919921219ffd3486587a2d3c318fbd..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/vfnet.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class VFNet(SingleStageDetector):
-    """Implementation of `VarifocalNet
-    (VFNet).<https://arxiv.org/abs/2008.13367>`_"""
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(VFNet, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                    test_cfg, pretrained)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index fdff93f543af6bac93949e68532daea45e437167..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py
deleted file mode 100644
index 95f5c09567144db47e07fc802b114bedd6a00725..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/upernet_r50.py',
-    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_20k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
diff --git a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/env.py b/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/env.py
deleted file mode 100644
index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/env.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import shutil
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def build_env(config, config_name, path):
-    t_path = os.path.join(path, config_name)
-    if config != t_path:
-        os.makedirs(path, exist_ok=True)
-        shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/utils/__init__.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/__init__.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HMS1997/RepoGPT/README.md b/spaces/HMS1997/RepoGPT/README.md
deleted file mode 100644
index 81891abe5cb330901b41540f99d4d0b9b5a8ec3c..0000000000000000000000000000000000000000
--- a/spaces/HMS1997/RepoGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RepoGPT
-emoji: 🌍
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/utils/inference/api.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/utils/inference/api.py
deleted file mode 100644
index d6bcabd194a4531801941d5e1d248dc134ce255f..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/utils/inference/api.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from starlette.responses import StreamingResponse
-from tts import MelToWav, TextToMel
-from advanced_tts import load_all_models, run_tts_paragraph
-from typing import Optional
-from pydantic import BaseModel
-from fastapi import FastAPI, HTTPException
-import uvicorn
-import base64
-import argparse
-import json
-import time
-from argparse import Namespace
-
-app = FastAPI()
-
-
-class TextJson(BaseModel):
-    text: str
-    lang: Optional[str] = "hi"
-    noise_scale: Optional[float]=0.667
-    length_scale: Optional[float]=1.0
-    transliteration: Optional[int]=1
-    number_conversion: Optional[int]=1
-    split_sentences: Optional[int]=1
-
-
-
-
-@app.post("/TTS/")
-async def tts(input: TextJson):
-    text = input.text
-    lang = input.lang
-
-    args = Namespace(**input.dict())
-
-    args.wav = '../../results/api/'+str(int(time.time())) + '.wav'
-
-    if text:
-        sr, audio = run_tts_paragraph(args)
-    else:
-        raise HTTPException(status_code=400, detail={"error": "No text"})
-
-    ## to return outpur as a file
-    audio = open(args.wav, mode='rb')
-    return StreamingResponse(audio, media_type="audio/wav")
-
-    # with open(args.wav, "rb") as audio_file:
-    #     encoded_bytes = base64.b64encode(audio_file.read())
-    #     encoded_string = encoded_bytes.decode()
-    # return {"encoding": "base64", "data": encoded_string, "sr": sr}
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-a", "--acoustic", required=True, type=str)
-    parser.add_argument("-v", "--vocoder", required=True, type=str)
-    parser.add_argument("-d", "--device", type=str, default="cpu")
-    parser.add_argument("-L", "--lang", type=str, required=True)
-
-    args = parser.parse_args()
-
-    load_all_models(args)
-
-    uvicorn.run(
-        "api:app", host="0.0.0.0", port=6006, log_level="debug"
-    )
diff --git a/spaces/HighCWu/colorful-ascii-art/test_video.py b/spaces/HighCWu/colorful-ascii-art/test_video.py
deleted file mode 100644
index bfe1bfbb66fd2cd6cfe8960a8f5c42debcabfed4..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/colorful-ascii-art/test_video.py
+++ /dev/null
@@ -1,182 +0,0 @@
-'''
-Refer to https://huggingface.co/spaces/dt/ascii-art/blob/main/app.py
-'''
-
-# Python code to convert an image to ASCII image.
-import sys, random, argparse
-import numpy as np
-import math
-import base64
-from PIL import Image, ImageFont, ImageDraw
-from moviepy.editor import *
-from tqdm.auto import tqdm
-
-import gradio as gr
-
-# 70 levels of gray
-gscale1 = "$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\|()1{}[]?-_+~<>i!lI;:,\"^`'. "
- 
-# 10 levels of gray
-gscale2 = '@%#*+=-:. '
-
-font = ImageFont.load_default()
- 
-def getAverageL(image):
- 
-    """
-    Given PIL Image, return average value of grayscale value
-    """
-    # get image as numpy array
-    im = np.array(image)
-    # get shape
-    w,h = im.shape
- 
-    # get average
-    return np.average(im.reshape(w*h))
- 
-def covertImageToAscii(input_img, cols, scale, moreLevels):
-    """
-    Given Image and dims (rows, cols) returns an m*n list of Images
-    """
-    # declare globals
-    global gscale1, gscale2
- 
-    # open image and convert to grayscale
-    image = input_img.convert('L')
- 
-    # store dimensions
-    # store dimensions
-    W, H = image.size[0], image.size[1]
- 
-    # compute width of tile
-    w = W/cols
- 
-    # compute tile height based on aspect ratio and scale
-    h = w/scale
- 
-    # compute number of rows
-    rows = int(H/h)
- 
-    # check if image size is too small
-    if cols > W or rows > H:
-        print("Image too small for specified cols!")
-        exit(0)
- 
-    # ascii image is a list of character strings
-    aimg = []
-    # generate list of dimensions
-    for j in range(rows):
-        y1 = int(j*h)
-        y2 = int((j+1)*h)
- 
-        # correct last tile
-        if j == rows-1:
-            y2 = H
- 
-        # append an empty string
-        aimg.append("")
- 
-        for i in range(cols):
- 
-            # crop image to tile
-            x1 = int(i*w)
-            x2 = int((i+1)*w)
- 
-            # correct last tile
-            if i == cols-1:
-                x2 = W
- 
-            # crop image to extract tile
-            img = image.crop((x1, y1, x2, y2))
- 
-            # get average luminance
-            avg = int(getAverageL(img))
- 
-            # look up ascii char
-            if moreLevels:
-                gsval = gscale1[int((avg*69)/255)]
-            else:
-                gsval = gscale2[int((avg*9)/255)]
- 
-            # append ascii char to string
-            aimg[j] += gsval
-     
-    # return txt image
-    return aimg
-
-
-def colorizeTextImage(input_img, text_img):
-    input_img = np.asarray(input_img)
-    input_img = input_img.reshape((
-        input_img.shape[0]//11, 
-        11, 
-        input_img.shape[1]//6, 
-        6,
-        3
-    ))
-    input_img = np.float32(input_img)
-    text_img = np.asarray(text_img)
-    text_img = text_img.reshape((
-        input_img.shape[0], 
-        11, 
-        input_img.shape[2], 
-        6, 
-        3
-    ))
-    alpha = np.float32(text_img)[...,:1] / 255
-    alpha[alpha < 0.125] = 0
-    alpha[alpha >= 0.125] = 1
-    out_img = input_img * alpha
-    out_colors = out_img.sum((1,3), keepdims=True) / (alpha.sum((1,3), keepdims=True) + 1e-12)
-    out_img = out_colors * alpha
-    out_img = out_img.reshape((
-        out_img.shape[0] * out_img.shape[1],
-        out_img.shape[2] * out_img.shape[3],
-        3
-    ))
-    out_img = np.clip(out_img, 0, 255)
-    out_img = np.uint8(out_img)
-
-    return out_img
- 
-
-def sepia(input_img, no_colors=False):
-    input_img = Image.fromarray(input_img).convert('RGB')
-    aimg = covertImageToAscii(input_img, 200, 6/11, True)
-    blank_image = Image.new(mode="RGB", size=(len(aimg[0])*6, len(aimg)*11), color=(0, 0, 0))
-
-    my_image = blank_image.copy()
-    image_editable = ImageDraw.Draw(my_image)
-    
-    image_editable.text((0, 0), "\n".join(aimg), (255, 255, 255), font=font, spacing=0)
-    if no_colors:
-        return np.asarray(my_image)
-
-    input_img_resize = input_img.resize((len(aimg[0])*6, len(aimg)*11), Image.BICUBIC)
-    w, h = input_img.size
-    scale = 200 * 6 / w
-    w = 200 * 6
-    h = int(round(h*scale))
-    input_img = input_img.resize((200 * 6, h), Image.BICUBIC)
-    input_img_resize.paste(input_img, (0, 0, w, h))
-    input_img = input_img_resize
-
-    my_image = colorizeTextImage(input_img, my_image)
-
-    return my_image
-
-
-def sepia_video(video_file, no_colors=False):
-    clip = VideoFileClip(video_file)
-    audioclip = clip.audio
-    frames = int(clip.fps * clip.duration)
-    imgs = []
-    for i in tqdm(range(frames)):
-        imgs.append(sepia(clip.get_frame(i/clip.fps), no_colors))
-    video = ImageSequenceClip(imgs, fps=clip.fps)
-    video = video.set_audio(audioclip)
-    video.write_videofile("out.mp4", fps=clip.fps)
-
-    return "out.mp4"
-
-sepia_video("input.mp4", no_colors=False)
diff --git a/spaces/Hila/RobustViT/SegmentationTest/utils/render.py b/spaces/Hila/RobustViT/SegmentationTest/utils/render.py
deleted file mode 100644
index 70c0e1a87614b0f07bf18aedf4df3ed765ac25e8..0000000000000000000000000000000000000000
--- a/spaces/Hila/RobustViT/SegmentationTest/utils/render.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import numpy as np
-import matplotlib.cm
-import skimage.io
-import skimage.feature
-import skimage.filters
-
-
-def vec2im(V, shape=()):
-    '''
-    Transform an array V into a specified shape - or if no shape is given assume a square output format.
-
-    Parameters
-    ----------
-
-    V : numpy.ndarray
-        an array either representing a matrix or vector to be reshaped into an two-dimensional image
-
-    shape : tuple or list
-        optional. containing the shape information for the output array if not given, the output is assumed to be square
-
-    Returns
-    -------
-
-    W : numpy.ndarray
-        with W.shape = shape or W.shape = [np.sqrt(V.size)]*2
-
-    '''
-
-    if len(shape) < 2:
-        shape = [np.sqrt(V.size)] * 2
-        shape = map(int, shape)
-    return np.reshape(V, shape)
-
-
-def enlarge_image(img, scaling=3):
-    '''
-    Enlarges a given input matrix by replicating each pixel value scaling times in horizontal and vertical direction.
-
-    Parameters
-    ----------
-
-    img : numpy.ndarray
-        array of shape [H x W] OR [H x W x D]
-
-    scaling : int
-        positive integer value > 0
-
-    Returns
-    -------
-
-    out : numpy.ndarray
-        two-dimensional array of shape [scaling*H x scaling*W]
-        OR
-        three-dimensional array of shape [scaling*H x scaling*W x D]
-        depending on the dimensionality of the input
-    '''
-
-    if scaling < 1 or not isinstance(scaling, int):
-        print('scaling factor needs to be an int >= 1')
-
-    if len(img.shape) == 2:
-        H, W = img.shape
-
-        out = np.zeros((scaling * H, scaling * W))
-        for h in range(H):
-            fh = scaling * h
-            for w in range(W):
-                fw = scaling * w
-                out[fh:fh + scaling, fw:fw + scaling] = img[h, w]
-
-    elif len(img.shape) == 3:
-        H, W, D = img.shape
-
-        out = np.zeros((scaling * H, scaling * W, D))
-        for h in range(H):
-            fh = scaling * h
-            for w in range(W):
-                fw = scaling * w
-                out[fh:fh + scaling, fw:fw + scaling, :] = img[h, w, :]
-
-    return out
-
-
-def repaint_corner_pixels(rgbimg, scaling=3):
-    '''
-    DEPRECATED/OBSOLETE.
-
-    Recolors the top left and bottom right pixel (groups) with the average rgb value of its three neighboring pixel (groups).
-    The recoloring visually masks the opposing pixel values which are a product of stabilizing the scaling.
-    Assumes those image ares will pretty much never show evidence.
-
-    Parameters
-    ----------
-
-    rgbimg : numpy.ndarray
-        array of shape [H x W x 3]
-
-    scaling : int
-        positive integer value > 0
-
-    Returns
-    -------
-
-    rgbimg : numpy.ndarray
-        three-dimensional array of shape [scaling*H x scaling*W x 3]
-    '''
-
-    # top left corner.
-    rgbimg[0:scaling, 0:scaling, :] = (rgbimg[0, scaling, :] + rgbimg[scaling, 0, :] + rgbimg[scaling, scaling,
-                                                                                       :]) / 3.0
-    # bottom right corner
-    rgbimg[-scaling:, -scaling:, :] = (rgbimg[-1, -1 - scaling, :] + rgbimg[-1 - scaling, -1, :] + rgbimg[-1 - scaling,
-                                                                                                   -1 - scaling,
-                                                                                                   :]) / 3.0
-    return rgbimg
-
-
-def digit_to_rgb(X, scaling=3, shape=(), cmap='binary'):
-    '''
-    Takes as input an intensity array and produces a rgb image due to some color map
-
-    Parameters
-    ----------
-
-    X : numpy.ndarray
-        intensity matrix as array of shape [M x N]
-
-    scaling : int
-        optional. positive integer value > 0
-
-    shape: tuple or list of its , length = 2
-        optional. if not given, X is reshaped to be square.
-
-    cmap : str
-        name of color map of choice. default is 'binary'
-
-    Returns
-    -------
-
-    image : numpy.ndarray
-        three-dimensional array of shape [scaling*H x scaling*W x 3] , where H*W == M*N
-    '''
-
-    # create color map object from name string
-    cmap = eval('matplotlib.cm.{}'.format(cmap))
-
-    image = enlarge_image(vec2im(X, shape), scaling)  # enlarge
-    image = cmap(image.flatten())[..., 0:3].reshape([image.shape[0], image.shape[1], 3])  # colorize, reshape
-
-    return image
-
-
-def hm_to_rgb(R, X=None, scaling=3, shape=(), sigma=2, cmap='bwr', normalize=True):
-    '''
-    Takes as input an intensity array and produces a rgb image for the represented heatmap.
-    optionally draws the outline of another input on top of it.
-
-    Parameters
-    ----------
-
-    R : numpy.ndarray
-        the heatmap to be visualized, shaped [M x N]
-
-    X : numpy.ndarray
-        optional. some input, usually the data point for which the heatmap R is for, which shall serve
-        as a template for a black outline to be drawn on top of the image
-        shaped [M x N]
-
-    scaling: int
-        factor, on how to enlarge the heatmap (to control resolution and as a inverse way to control outline thickness)
-        after reshaping it using shape.
-
-    shape: tuple or list, length = 2
-        optional. if not given, X is reshaped to be square.
-
-    sigma : double
-        optional. sigma-parameter for the canny algorithm used for edge detection. the found edges are drawn as outlines.
-
-    cmap : str
-        optional. color map of choice
-
-    normalize : bool
-        optional. whether to normalize the heatmap to [-1 1] prior to colorization or not.
-
-    Returns
-    -------
-
-    rgbimg : numpy.ndarray
-        three-dimensional array of shape [scaling*H x scaling*W x 3] , where H*W == M*N
-    '''
-
-    # create color map object from name string
-    cmap = eval('matplotlib.cm.{}'.format(cmap))
-
-    if normalize:
-        R = R / np.max(np.abs(R))  # normalize to [-1,1] wrt to max relevance magnitude
-        R = (R + 1.) / 2.  # shift/normalize to [0,1] for color mapping
-
-    R = enlarge_image(R, scaling)
-    rgb = cmap(R.flatten())[..., 0:3].reshape([R.shape[0], R.shape[1], 3])
-    # rgb = repaint_corner_pixels(rgb, scaling) #obsolete due to directly calling the color map with [0,1]-normalized inputs
-
-    if not X is None:  # compute the outline of the input
-        # X = enlarge_image(vec2im(X,shape), scaling)
-        xdims = X.shape
-        Rdims = R.shape
-
-        # if not np.all(xdims == Rdims):
-        #     print 'transformed heatmap and data dimension mismatch. data dimensions differ?'
-        #     print 'R.shape = ',Rdims, 'X.shape = ', xdims
-        #     print 'skipping drawing of outline\n'
-        # else:
-        #     #edges = skimage.filters.canny(X, sigma=sigma)
-        #     edges = skimage.feature.canny(X, sigma=sigma)
-        #     edges = np.invert(np.dstack([edges]*3))*1.0
-        #     rgb *= edges # set outline pixels to black color
-
-    return rgb
-
-
-def save_image(rgb_images, path, gap=2):
-    '''
-    Takes as input a list of rgb images, places them next to each other with a gap and writes out the result.
-
-    Parameters
-    ----------
-
-    rgb_images : list , tuple, collection. such stuff
-        each item in the collection is expected to be an rgb image of dimensions [H x _ x 3]
-        where the width is variable
-
-    path : str
-        the output path of the assembled image
-
-    gap : int
-        optional. sets the width of a black area of pixels realized as an image shaped [H x gap x 3] in between the input images
-
-    Returns
-    -------
-
-    image : numpy.ndarray
-        the assembled image as written out to path
-    '''
-
-    sz = []
-    image = []
-    for i in range(len(rgb_images)):
-        if not sz:
-            sz = rgb_images[i].shape
-            image = rgb_images[i]
-            gap = np.zeros((sz[0], gap, sz[2]))
-            continue
-        if not sz[0] == rgb_images[i].shape[0] and sz[1] == rgb_images[i].shape[2]:
-            print('image', i, 'differs in size. unable to perform horizontal alignment')
-            print('expected: Hx_xD = {0}x_x{1}'.format(sz[0], sz[1]))
-            print('got     : Hx_xD = {0}x_x{1}'.format(rgb_images[i].shape[0], rgb_images[i].shape[1]))
-            print('skipping image\n')
-        else:
-            image = np.hstack((image, gap, rgb_images[i]))
-
-    image *= 255
-    image = image.astype(np.uint8)
-
-    print('saving image to ', path)
-    skimage.io.imsave(path, image)
-    return image
diff --git a/spaces/HuSusu/SuperResolution/README.md b/spaces/HuSusu/SuperResolution/README.md
deleted file mode 100644
index 18f8ae1b0e7caf112e450c8bcfeda21f170fab21..0000000000000000000000000000000000000000
--- a/spaces/HuSusu/SuperResolution/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SuperResolution
-emoji: 🐠
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.0
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/IAMTFRMZA/DreamlikeArt-Diffusion-1.0/README.md b/spaces/IAMTFRMZA/DreamlikeArt-Diffusion-1.0/README.md
deleted file mode 100644
index 43bb4c92438a78f83eca8f7f06051c81b01bb4ce..0000000000000000000000000000000000000000
--- a/spaces/IAMTFRMZA/DreamlikeArt-Diffusion-1.0/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: DreamlikeArt-Diffusion 1.0
-emoji: 🧘🏻‍♂️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-duplicated_from: phenomenon1981/DreamlikeArt-Diffusion-1.0
----
----
-title: DreamlikeArt-Diffusion .0
-emoji: 🧘🏻‍♂️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py
deleted file mode 100644
index 062bb82f669f63a537b6ee8df4d42d292eb2575e..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import nltk
-from misc.bleu_utils import sentence_bleu
-import warnings
-
-
-def get_target_sequences(manifest, ground_truth, to_take=1000):
-    import json
-    import pathlib
-
-    with open(ground_truth, 'r') as fin:
-        original_continuations = json.loads(fin.read())
-
-    sequence2length = [(k, v[0]) for k, v in original_continuations.items()]
-    assert all(float(v) >= 6.0 for (_, v) in sequence2length)  # 6 seconds
-
-    sequence2length.sort(key=lambda x: x[1])
-    to_take_sequences = set(v[0] for v in sequence2length[:to_take])
-    to_take_ids = []
-
-    with open(manifest, 'r') as f:
-        f.readline()
-
-        for i, line in enumerate(f.readlines()):
-            seq_id = line.split()[0]
-            seq_id = pathlib.Path(seq_id).name.split('__')[0]
-
-            if seq_id in to_take_sequences:
-                to_take_ids.append(i)
-
-    print(f'Took {len(to_take_ids)} ids')
-    return set(to_take_ids)
-
-
-def get_args():
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--asr-transcript', type=str,
-                        help='Path to the transcript file.')
-
-    parser.add_argument('--manifest', required=True)
-    parser.add_argument('--prompts-description', required=True)
-
-    parser.add_argument('--cut-id', action='store_true',
-                        help='Whether cut the first token (typically a seq id)')
-    parser.add_argument('--cut-tail', action='store_true',
-                        help='Whether cut the last token (typically a speaker id)')
-    parser.add_argument('--debug', action='store_true')
-
-    args = parser.parse_args()
-
-    return args
-
-
-def get_self_bleu(utterances, averaging_mode, weights):
-    self_bleu = []
-
-    for i in range(len(utterances)):
-        hypo = utterances[i]
-        rest = utterances[:i] + utterances[i+1:]
-
-        self_bleu.append(sentence_bleu(rest, hypo, weights,
-                         no_length_penalty=True, averaging_mode=averaging_mode))
-
-    return self_bleu
-
-
-def get_self_bleu2_arithmetic(utterances):
-    weights = (0.5, 0.5)  # equal weight for unigrams and bigrams
-    return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights)
-
-
-def get_self_bleu2_geometric(utterances):
-    weights = (0.5, 0.5)
-    return get_self_bleu(utterances, averaging_mode='geometric', weights=weights)
-
-
-def get_auto_bleu2_arithmetic(utterances):
-    weights = (0.5, 0.5)
-    return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances]
-
-
-def get_auto_bleu2_geometric(utterances):
-    weights = (0.5, 0.5)
-    return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances]
-
-
-def get_auto_bleu3_geometric(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances]
-
-
-def get_auto_bleu3_arithmetic(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances]
-
-
-def get_self_bleu3_arithmetic(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights)
-
-
-def get_self_bleu3_geometric(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return get_self_bleu(utterances, averaging_mode='geometric', weights=weights)
-
-
-def auto_bleu(sentence, weights, mean_mode='arithmetic'):
-    if len(sentence) <= 1:
-        return 0
-
-    N = len(weights)
-
-    bleu_n = np.zeros([N])
-    for n in range(N):
-        targ_ngrams = list(nltk.ngrams(sentence, n+1))
-        for p in range(len(targ_ngrams)):
-            left = sentence[:p]
-            right = sentence[(p+n+1):]
-            rest_ngrams = list(nltk.ngrams(left, n+1)) + \
-                list(nltk.ngrams(right, n+1))
-            # compute the nb of matching ngrams
-            bleu_n[n] += targ_ngrams[p] in rest_ngrams
-        bleu_n[n] /= len(targ_ngrams)  # average them to get a proportion
-
-    weights = np.array(weights)
-    if mean_mode == 'arithmetic':
-        return (bleu_n * weights).sum()
-    elif mean_mode == 'geometric':
-        return (bleu_n ** weights).prod()
-    else:
-        raise ValueError(f'Unknown agggregation mode {mean_mode}')
-
-
-def main():
-    from multiprocessing import Pool
-
-    args = get_args()
-    target_ids = get_target_sequences(args.manifest, args.prompts_description)
-
-    with open(args.asr_transcript, 'r') as fin:
-        lines = fin.readlines()
-
-    terms = [x.strip().split() for x in lines]
-    filtered = []
-    for term in terms:
-        line_id = int(term[-1].split('-')[1][:-1])
-        if line_id in target_ids:
-            filtered.append(term)
-    terms = filtered
-
-    if args.cut_id:
-        terms = [x[1:] for x in terms]
-    if args.cut_tail:
-        terms = [x[:-1] for x in terms]
-
-    if args.debug:
-        terms = terms[:10]
-
-    tasks = [
-        ('Self-BLEU2-arithmetic', get_self_bleu2_arithmetic),
-        ('Self-BLEU2-geometric', get_self_bleu2_geometric),
-        ('Auto-BLEU2-arithmetic', get_auto_bleu2_arithmetic),
-        ('Auto-BLEU2-geometric', get_auto_bleu2_geometric),
-
-        ('Self-BLEU3-arithmetic', get_self_bleu3_arithmetic),
-        ('Self-BLEU3-geometric', get_self_bleu3_geometric),
-        ('Auto-BLEU3-arithmetic', get_auto_bleu3_arithmetic),
-        ('Auto-BLEU3-geometric', get_auto_bleu3_geometric),
-    ]
-
-    n_processes = min(16, len(tasks))
-    with Pool(n_processes) as pool:
-        metrics = pool.map(run_f, [(t[1], terms) for t in tasks])
-
-    for (metric_name, _), metric in zip(tasks, metrics):
-        metric, sem = np.mean(metric), np.std(metric) / np.sqrt(len(metric))
-
-        metric, sem = [
-            round(100 * x, 2) for x in [metric, sem]
-        ]
-
-        print(f'{metric_name} {metric} +- {sem}')
-
-
-def run_f(task_params):
-    f, terms = task_params
-    return f(terms)
-
-
-if __name__ == '__main__':
-    # NLTK produces warnings
-    warnings.filterwarnings("ignore")
-
-    main()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/speech_to_text/modules/emformer.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/speech_to_text/modules/emformer.py
deleted file mode 100644
index 6ef76bd012ba40b0395fec2ca9ae9e9c136ffe40..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/speech_to_text/modules/emformer.py
+++ /dev/null
@@ -1,1837 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-
-import math
-import re
-from functools import partial
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from fairseq.models import (
-    FairseqEncoder,
-)
-from fairseq.models.speech_to_text.utils import (
-    NoOp,
-    lengths_to_padding_mask,
-    segments_to_sequence,
-)
-from fairseq.models.speech_to_text.utils import (
-    attention_suppression,
-    layer_norm_backward_hook,
-)
-from torch import Tensor, device as Device
-from torch.quantization.qconfig import (
-    default_dynamic_qconfig,
-    per_channel_dynamic_qconfig,
-)
-
-
-class RelativePositionEmbedding(nn.Module):
-    """
-    Implementation according to https://arxiv.org/abs/1803.02155
-    """
-
-    def __init__(self, head_dim, max_position, norm_init=True):
-        super().__init__()
-        self.head_dim = head_dim
-        self.max_position = max_position
-        self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim))
-        if norm_init:
-            nn.init.xavier_normal_(self.embeddings)
-        else:
-            nn.init.xavier_uniform_(self.embeddings)
-
-    def forward(self, input: Tensor):
-        output = nn.functional.embedding(input.long(), self.embeddings)
-        return output
-
-
-class Fp32LayerNorm(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        clamp_grad=True,
-        max_grad_value=256,
-        eps=1e-5,
-        elementwise_affine=True,
-    ):
-        super().__init__()
-        self.torch_module = torch.nn.LayerNorm(
-            input_dim, eps=eps, elementwise_affine=elementwise_affine
-        )
-        if clamp_grad:
-            hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value)
-            self.torch_module.register_backward_hook(hook)
-
-    def forward(self, input):
-        output = torch.nn.functional.layer_norm(
-            input.float(),
-            self.torch_module.normalized_shape,
-            self.torch_module.weight.float()
-            if self.torch_module.weight is not None
-            else None,
-            self.torch_module.bias.float()
-            if self.torch_module.bias is not None
-            else None,
-            self.torch_module.eps,
-        ).type_as(input)
-        return output
-
-
-# ------------------------------------------------------------------------------
-#   PositionwiseFF
-# ------------------------------------------------------------------------------
-
-
-class PositionwiseFF(nn.Module):
-    """
-    FFN layer in transformer.
-
-    Args:
-        input_dim: input embedding dimension
-        ffn_dim: FFN layer inner dimension
-        dropout_on_fc1: dropout for first linear layer
-        dropout_on_fc2: dropout fr second linear layer
-        activation_fn: activation function used after first linear layer. \
-                Only relu or gelu is supported.
-
-    """
-
-    def __init__(
-        self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn
-    ):
-        super(PositionwiseFF, self).__init__()
-
-        self.input_dim = input_dim
-        self.ffn_dim = ffn_dim
-        if activation_fn == "relu":
-            ac = nn.ReLU()
-        elif activation_fn == "gelu":
-            ac = nn.GELU()
-        else:
-            raise ValueError("Unsupported activation_fn = ({})".format(activation_fn))
-
-        # fc1 -> ac -> dropout -> fc2 -> dropout
-        self.module = nn.Sequential(
-            nn.Linear(input_dim, ffn_dim),
-            ac,
-            nn.Dropout(dropout_on_fc1),
-            nn.Linear(ffn_dim, input_dim),
-            nn.Dropout(dropout_on_fc2),
-        )
-
-        self.layer_norm = Fp32LayerNorm(input_dim)
-
-    def forward(self, input):
-        module_out = self.module(self.layer_norm(input))
-        output = module_out + input
-
-        return output
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-# ------------------------------------------------------------------------------
-#   SummarizationLayer
-# ------------------------------------------------------------------------------
-
-
-class SummarizationLayer(nn.Module):
-    def __init__(self, method, segment_size, embedding_dim):
-        super(SummarizationLayer, self).__init__()
-        self.segment_size = segment_size
-        self.embedding_dim = embedding_dim
-        nonlin_match = re.match(r"nonlinear\((?P<act>[a-z]+),(?P<dim>[0-9]+)\)", method)
-        self.method = method
-        if method == "mean":
-            self.module = nn.AvgPool1d(
-                kernel_size=segment_size,
-                stride=segment_size,
-                ceil_mode=True,
-            )
-        elif method == "max":
-            self.module = nn.MaxPool1d(
-                kernel_size=segment_size,
-                stride=segment_size,
-                ceil_mode=True,
-            )
-        elif method == "linear":
-            self.module = nn.Linear(segment_size, 1)
-        elif nonlin_match:
-            nonlin_args = nonlin_match.groupdict()
-            act_type = nonlin_args["act"]
-            hid_dim = int(nonlin_args["dim"])
-            if act_type == "relu":
-                act = nn.ReLU()
-            elif act_type == "gelu":
-                act = nn.GELU()
-            else:
-                raise ValueError("Unsupported activation_fn = ({})".format(act_type))
-            self.module = nn.Sequential(
-                nn.Linear(segment_size, hid_dim),
-                act,
-                nn.Linear(hid_dim, 1),
-            )
-        else:
-            raise ValueError("Unsupported summarization method = ({})".format(method))
-
-    def forward(self, input):
-        # T, B, D -> B, D, T
-        input = input.permute(1, 2, 0)
-
-        if self.method == "mean" or self.method == "max":
-            output = self.module(input)
-            output = output.permute(2, 0, 1)
-            return output
-
-        full_seg_length = input.size(2) // self.segment_size * self.segment_size
-        if full_seg_length > 0:
-            # at least one seg is full
-            B = input.size(0)
-            D = input.size(1)
-            input_todo = (
-                input[:, :, :full_seg_length]
-                .contiguous()
-                .view(B, -1, self.segment_size)
-            )
-            output = self.module(input_todo)
-            output = output.view(B, D, -1)
-        else:
-            output = input.new_zeros(input.size(0), input.size(1), 0)
-        left = input.size(2) - full_seg_length
-        if left > 0:
-            # when last seg is not full, use zeros as last memory placeholder
-            zeros = input.new_zeros(input.size(0), input.size(1), 1)
-            output = torch.cat([output, zeros], dim=2)
-        output = output.permute(2, 0, 1)
-        return output
-
-
-# ------------------------------------------------------------------------------
-#   NoSegAugmentedMemoryMultiheadAttentionBmm
-# ------------------------------------------------------------------------------
-
-
-class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module):
-    """
-    Whole utterance augmented memory multihead attention using BMM.
-
-    Different with previous augmented memory multihead attention where
-    the utterance is chunked into segments. Here we use attention mask
-    achieve so. The input embedding [right_context, utterance, summary]
-    is a concatenation of right context, utterance and summary.
-
-    Right context block is the concatenation of all the right context for
-    each segments. [right_context_0, right_context_1, ..., right_context_n]
-    For example, if we have utterance = [v0, v1, v2, ...., v20]. segment
-    size 8, right_context size 4. Then the right context blocks =
-    [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10,
-    and v11 are the right context for first segment. v16, v17, v18 and v19
-    are the right context for second segment. 0, 0, 0 and 0 are right context
-    for the last segment.
-
-    utterance is corresponding to input embedding sequence
-
-    summary is concatenation of average of each segments. [summary_0,
-    summary_1, ..., ].
-
-    In augmented memory multihead attention, the query is [right_context,
-    utterance, summary], key is [memory, right_context, utterance]. Different
-    with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from
-    previous attention layer. For the first attention layer, memory is average
-    of each segment.
-
-    Memory is a concatenation of memory from each segments in previous attention
-    layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n].
-    Each m_k is the output from seg_k in layer i-1.
-
-    args:
-        input_dim: input embedding dimension
-        num_heads: number of heads in multihead self-attention
-        dropout: attention dropout
-        std_scale: if std_scale is not None. The weak attention suppression is
-            turned on. For std_scale = 0.5, all the attention smaller than
-            mean + 0.5 * std will be suppressed.
-        scaled_init: whether to use scaled init for linear weight
-        tanh_on_mem: whether to use tanh on memory output
-        use_mem: whether to use memory or not. When max_memory_size is 0, then
-            we don't have memory anymore.
-        layer_index: current self-attention layer index that is used in depth
-            initialization
-        max_relative_position: max relative position used in relative position
-            embedding
-        rpe_old_option: To be compatible with previous model. The previous model
-            was trained with attention += attention + rpe. The correct equation
-            should be attention = attention + rpe
-
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        dropout=0.0,
-        std_scale=None,
-        scaled_init=False,
-        tanh_on_mem=False,
-        use_mem=True,
-        mini_batches=False,
-        negative_inf="-inf",
-        layer_index=-1,
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        if input_dim % num_heads:
-            raise ValueError(
-                "input_dim ({}) must be divisible by num_heads ({})".format(
-                    input_dim, num_heads
-                )
-            )
-
-        super().__init__()
-
-        embed_dim = input_dim
-        self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
-        self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True)
-        self.rpe_old_option = rpe_old_option
-        if max_relative_position > 0:
-            self.use_rpe = True
-            self.rpe_k = RelativePositionEmbedding(
-                head_dim=input_dim // num_heads,
-                max_position=max_relative_position,
-            )
-            self.rpe_v = RelativePositionEmbedding(
-                head_dim=input_dim // num_heads,
-                max_position=max_relative_position,
-            )
-        else:
-            self.use_rpe = False
-            self.rpe_k = None
-            self.rpe_v = None
-        if scaled_init:
-            if layer_index == -1:
-                gain = 1.0 / math.sqrt(2)
-            else:
-                # https://arxiv.org/abs/2005.09684 depthwise initialization
-                # stablize the training greatly. Use depthwise initialization to
-                # replace incremental loss.
-                gain = 1.0 / math.sqrt(layer_index + 1)
-            torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain)
-            torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain)
-
-        self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True)
-
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-
-        self.head_dim = embed_dim // num_heads
-        self.scaling = self.head_dim ** -0.5
-
-        self.std_scale = std_scale
-        self.use_mem = use_mem
-        self.mini_batches = mini_batches
-        self.negative_inf = negative_inf
-
-        if tanh_on_mem:
-            self.squash_mem = torch.tanh
-            self.nonlinear_squash_mem = True
-        else:
-            self.squash_mem = NoOp()
-            self.nonlinear_squash_mem = False
-
-    def prepare_qkv(
-        self,
-        input: Tensor,
-        mems: Tensor,
-        lengths: Tensor,
-        summary_length: int,
-        lc_length: int,
-    ):
-        # T: right_context length + utterance_length  + summary_length
-        T, B, D = input.shape
-        mem_length = mems.size(0)
-        utterance_length = torch.max(lengths)
-
-        right_context_blocks_length = T - utterance_length - summary_length
-        rc_block = input[:right_context_blocks_length, :, :]
-        utterance_block = input[right_context_blocks_length : T - summary_length, :, :]
-
-        if B == 1:
-            padding_mask = None
-        else:
-            klengths = lengths + mem_length + right_context_blocks_length + lc_length
-            padding_mask = lengths_to_padding_mask(lengths=klengths)
-
-        mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0)
-
-        # In training lc_length = 0
-        key_length = mem_rc_input.size(0) + lc_length
-        rc_input_sum = input
-        q = self.e2h_q(rc_input_sum)
-        kv = self.e2h_kv(mem_rc_input)
-        k, v = kv.chunk(chunks=2, dim=2)
-        result_qkv = (q, k, v)
-        input_shape = (T, B, D)
-        result_lengths_info = (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        )
-        if padding_mask is not None:
-            assert padding_mask.size(0) == B
-            assert padding_mask.size(1) == key_length
-
-        return result_qkv, input_shape, result_lengths_info, padding_mask
-
-    def prepare_attention_weights(
-        self,
-        q: Tensor,
-        new_k: Tensor,
-        new_v: Tensor,
-        input_shape: Tuple[int, int, int],
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor]:
-        T, B, D = input_shape
-        q = (
-            q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1)
-            * self.scaling
-        )
-
-        k = (
-            new_k.contiguous()
-            .view(-1, B * self.num_heads, self.head_dim)
-            .transpose(0, 1)
-        )
-
-        v = (
-            new_v.contiguous()
-            .view(-1, B * self.num_heads, self.head_dim)
-            .transpose(0, 1)
-        )
-
-        attention_weights = torch.bmm(q, k.transpose(1, 2))
-        if self.use_rpe and rpe is not None and self.rpe_v is not None:
-            r_k = self.rpe_k(rpe)
-            # [q, B*h, d] * [q, k, d] -> [B*h, q, k]
-            attention_weights_rpe = torch.matmul(
-                q.transpose(0, 1), r_k.transpose(1, 2)
-            ).transpose(0, 1)
-            attention_weights = attention_weights + attention_weights_rpe
-        attention_weights_float = attention_weights.float()
-
-        return attention_weights, attention_weights_float, v
-
-    def prepare_attention_output(
-        self,
-        attention_weights: Tensor,
-        attention_weights_float: Tensor,
-        v: Tensor,
-        input_shape: Tuple[int, int, int],
-        key_length: int,
-        padding_mask: Optional[Tensor],
-        rpe: Optional[Tensor],
-    ) -> Tensor:
-        T, B, D = input_shape
-        if padding_mask is not None:
-            attention_weights_float = attention_weights_float.view(
-                B, self.num_heads, T, key_length
-            )
-            attention_weights_float = attention_weights_float.masked_fill(
-                padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
-            )
-            attention_weights_float = attention_weights_float.view(
-                B * self.num_heads, T, key_length
-            )
-
-        if self.std_scale is not None:
-            attention_weights_float = attention_suppression(
-                attention_weights_float, self.std_scale
-            )
-
-        attention_weights_float = torch.nn.functional.softmax(
-            attention_weights_float, dim=-1
-        )
-        attention_weights = attention_weights_float.type_as(attention_weights)
-
-        attention_probs = torch.nn.functional.dropout(
-            attention_weights, p=self.dropout, training=self.training
-        )
-
-        # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head]
-        # -> [T, B, n_head, d_head]
-        attention = torch.bmm(attention_probs, v)
-        if self.use_rpe and rpe is not None and self.rpe_v is not None:
-            r_v = self.rpe_v(rpe)
-            attention_rpe = torch.matmul(
-                attention_probs.transpose(0, 1), r_v
-            ).transpose(0, 1)
-
-            if self.rpe_old_option:
-                attention += attention + attention_rpe
-            else:
-                attention = attention + attention_rpe
-
-        assert list(attention.shape) == [B * self.num_heads, T, self.head_dim]
-
-        attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim)
-
-        rc_output_memory = self.out_proj(attention)
-        return rc_output_memory
-
-    @torch.jit.unused
-    def forward(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        attention_mask: Tensor,
-        pre_mems: Optional[Tensor] = None,
-        left_context_key: Optional[Tensor] = None,
-        left_context_val: Optional[Tensor] = None,
-        rpe: Optional[Tensor] = None,
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        """
-        forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training.
-
-        args:
-            input: formed in the following way
-                [right_context_0, right_contex_1, ..., seg_0, seg_1,
-                ..., summary_0, summary_1,..]
-            lengths: the length of query which is [seg_0, seg_1, ....]
-            mems: [mem_0, mem_1, ...].
-            attention_mask: attention mask for query = [right_context, query, summary]
-                key = [mem, right_context, query]. This is only used for traing.
-
-        """
-        if self.use_mem:
-            mem_length = mems.size(0)
-            summary_length = mem_length + 1
-            if pre_mems is not None:
-                mems = torch.cat([pre_mems, mems], dim=0)
-        else:
-            mem_length = 0
-            summary_length = 0
-
-        # In training, lc_length = 0
-        if left_context_key is not None:
-            lc_length = left_context_key.size(0)
-        else:
-            lc_length = 0
-        results = self.prepare_qkv(
-            input=input,
-            mems=mems,
-            lengths=lengths,
-            summary_length=summary_length,
-            lc_length=lc_length,
-        )
-        result_qkv, input_shape, result_lengths_info, padding_mask = results
-        q, k, v = result_qkv
-        (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        ) = result_lengths_info
-
-        if left_context_key is not None:
-            # add the cache key and value
-            new_k = torch.cat(
-                [
-                    k[: mem_length + right_context_blocks_length, :, :],
-                    left_context_key,
-                    k[-utterance_length:, :, :],
-                ],
-                dim=0,
-            )
-            new_v = torch.cat(
-                [
-                    v[: mem_length + right_context_blocks_length, :, :],
-                    left_context_val,
-                    v[-utterance_length:, :, :],
-                ],
-                dim=0,
-            )
-            next_k = new_k[mem_length + right_context_blocks_length :, :, :]
-            next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-        else:
-            new_k = k
-            new_v = v
-            next_k = None
-            next_v = None
-
-        attention_weights, attention_weights_float, v = self.prepare_attention_weights(
-            q=q,
-            new_k=new_k,
-            new_v=new_v,
-            input_shape=input_shape,
-            rpe=rpe,
-        )
-
-        # mask attention
-        attention_mask = attention_mask.unsqueeze(0)
-        attention_weights_float = attention_weights_float.masked_fill(
-            attention_mask, float(self.negative_inf)
-        )
-
-        rc_output_memory = self.prepare_attention_output(
-            attention_weights=attention_weights,
-            attention_weights_float=attention_weights_float,
-            v=v,
-            input_shape=input_shape,
-            key_length=key_length,
-            padding_mask=padding_mask,
-            rpe=rpe,
-        )
-
-        if self.use_mem:
-            # next_m length equals to summary length - 1
-            # last memory is ignored
-            if self.mini_batches:
-                next_m = rc_output_memory[-summary_length:]
-            else:
-                next_m = rc_output_memory[-summary_length:-1]
-
-            next_m = self.squash_mem(next_m)
-            # rc and output
-            rc_output = rc_output_memory[:-summary_length]
-            if not self.nonlinear_squash_mem:
-                next_m = torch.clamp(next_m, min=-10, max=10)
-        else:
-            next_m = mems
-            rc_output = rc_output_memory
-
-        return rc_output, next_m, next_k, next_v
-
-    @torch.jit.export
-    def forward_jit(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        left_context_key: Tensor,
-        left_context_val: Tensor,
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        """
-        forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding.
-
-        args:
-            input: formed in the following way
-                [right_context_0, right_contex_1, ..., seg_0, seg_1,
-                ..., summary_0, summary_1,..]
-            lengths: the length of query which is [seg_0, seg_1, ....]
-            mems: [mem_0, mem_1, ...].
-            left_context_key: left_context for key part. This is only used for online
-                decoding. In training, this is empty tensor
-            left_context_val: left_context for value part. This is only used for online
-                decoding. In training, this is empty tensor
-
-        """
-        lc_length = left_context_key.size(0)
-
-        # In decoding, summary_length = 1 or 0
-        if self.use_mem:
-            summary_length = 1
-        else:
-            summary_length = 0
-
-        results = self.prepare_qkv(
-            input=input,
-            mems=mems,
-            lengths=lengths,
-            summary_length=summary_length,
-            lc_length=lc_length,
-        )
-        result_qkv, input_shape, result_lengths_info, padding_mask = results
-        q, k, v = result_qkv
-        (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        ) = result_lengths_info
-
-        # add the cache key and value
-        new_k = torch.cat(
-            [
-                k[: mem_length + right_context_blocks_length, :, :],
-                left_context_key,
-                k[-utterance_length:, :, :],
-            ],
-            dim=0,
-        )
-        new_v = torch.cat(
-            [
-                v[: mem_length + right_context_blocks_length, :, :],
-                left_context_val,
-                v[-utterance_length:, :, :],
-            ],
-            dim=0,
-        )
-        next_k = new_k[mem_length + right_context_blocks_length :, :, :]
-        next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-
-        attention_weights, attention_weights_float, v = self.prepare_attention_weights(
-            q=q,
-            new_k=new_k,
-            new_v=new_v,
-            input_shape=input_shape,
-            rpe=rpe,
-        )
-        # In online decoding, we don't have attention mask. But we still need
-        # to disable the attention from summary query to memory
-        attention_weights_float[:, -1, :mem_length] = float(self.negative_inf)
-        rc_output_memory = self.prepare_attention_output(
-            attention_weights=attention_weights,
-            attention_weights_float=attention_weights_float,
-            v=v,
-            input_shape=input_shape,
-            key_length=key_length,
-            padding_mask=padding_mask,
-            rpe=rpe,
-        )
-
-        # In decoding, summary length is 1
-        if self.use_mem:
-            next_m = rc_output_memory[-1:]
-            next_m = self.squash_mem(next_m)
-            # rc and output
-            rc_output = rc_output_memory[:-1]
-            if not self.nonlinear_squash_mem:
-                next_m = torch.clamp(next_m, min=-10, max=10)
-        else:
-            rc_output = rc_output_memory
-            # empty tensor as input mems
-            next_m = mems
-
-        return rc_output, next_m, next_k, next_v
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-class NoSegAugmentedMemoryTransformer(nn.Module):
-    """
-    Whole utterance augmented memory transformer.
-
-    This is not pyspeech nn layer. It is used as a module in a master layer where
-    multiple transformers is used.
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        ffn_dim,
-        dropout_in_attn=0.0,
-        dropout_on_attn=None,
-        dropout_on_fc1=None,
-        dropout_on_fc2=None,
-        activation_fn="relu",
-        tanh_on_mem=False,
-        std_scale=None,
-        scaled_init=False,
-        segment_size=128,
-        use_mem=True,
-        mini_batches=False,
-        negative_inf="-inf",
-        layer_index=-1,
-        summarization_method="mean",
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        super(NoSegAugmentedMemoryTransformer, self).__init__()
-
-        self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm(
-            input_dim=input_dim,
-            num_heads=num_heads,
-            dropout=dropout_in_attn,
-            scaled_init=scaled_init,
-            tanh_on_mem=tanh_on_mem,
-            std_scale=std_scale,
-            use_mem=use_mem,
-            mini_batches=mini_batches,
-            negative_inf=negative_inf,
-            layer_index=layer_index,
-            max_relative_position=max_relative_position,
-        )
-        self.dropout = nn.Dropout(dropout_on_attn)
-        self.pos_ff = PositionwiseFF(
-            input_dim=input_dim,
-            ffn_dim=ffn_dim,
-            dropout_on_fc1=dropout_on_fc1,
-            dropout_on_fc2=dropout_on_fc2,
-            activation_fn=activation_fn,
-        )
-        self.layer_norm_pre = Fp32LayerNorm(input_dim)
-        self.layer_norm = Fp32LayerNorm(input_dim)
-        self.segment_size = segment_size
-        self.use_mem = use_mem
-
-        self.memory_op = SummarizationLayer(
-            summarization_method, segment_size, input_dim
-        )
-
-    def set_mini_batches(self, mini_batches):
-        self.attention.mini_batches = mini_batches
-
-    def gen_summary_queries(self, input):
-        sum_input = self.memory_op(input)
-        return sum_input
-
-    def pre_attention_ops(self, input, right_context_blocks):
-        rc_length = right_context_blocks.size(0)
-        input_length = input.size(0)
-
-        rc_and_input = torch.cat([right_context_blocks, input], dim=0)
-        residual_input = rc_and_input
-        rc_and_input = self.layer_norm_pre(rc_and_input)
-
-        query_input = rc_and_input[-input_length:, :, :]
-        return rc_length, input_length, residual_input, query_input, rc_and_input
-
-    def after_attention_ops(self, attention_output, residual_input):
-        output = self.dropout(attention_output)
-        output = output + residual_input
-        output = self.pos_ff(output)
-        output = self.layer_norm(output)
-        return output
-
-    @torch.jit.export
-    def forward_jit(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        left_context_key: Tensor,
-        left_context_val: Tensor,
-        right_context_blocks: Tensor,
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
-
-        results = self.pre_attention_ops(input, right_context_blocks)
-        rc_length, input_length, residual_input, query_input, rc_and_input = results
-
-        # In online decoding, the summary query size is always 1 or 0
-        if self.use_mem:
-            summary_query = self.gen_summary_queries(query_input)
-            summary_query = summary_query[0:1, :, :]
-            rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
-        else:
-            rc_qu_su = rc_and_input
-
-        rc_output, next_m, next_k, next_v = self.attention.forward_jit(
-            input=rc_qu_su,
-            lengths=lengths,
-            mems=mems,
-            left_context_key=left_context_key,
-            left_context_val=left_context_val,
-            rpe=rpe,
-        )
-        rc_output = self.after_attention_ops(rc_output, residual_input)
-        results = (
-            rc_output[-input_length:, :, :],
-            next_m,
-            rc_output[0:rc_length, :, :],
-            next_k,
-            next_v,
-        )
-        return results
-
-    @torch.jit.unused
-    def forward(
-        self,
-        input,
-        lengths,
-        mems,
-        right_context_blocks,
-        attention_mask,
-        pre_mems,
-        left_context_key,
-        left_context_val,
-        rpe,
-    ):
-
-        results = self.pre_attention_ops(input, right_context_blocks)
-        rc_length, input_length, residual_input, query_input, rc_and_input = results
-        if self.use_mem:
-            summary_query = self.gen_summary_queries(query_input)
-            rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
-        else:
-            rc_qu_su = rc_and_input
-
-        rc_output, next_m, next_k, next_v = self.attention(
-            input=rc_qu_su,
-            lengths=lengths,
-            mems=mems,
-            attention_mask=attention_mask,
-            pre_mems=pre_mems,
-            left_context_key=left_context_key,
-            left_context_val=left_context_val,
-            rpe=rpe,
-        )
-
-        # [TODO] Note memory did not go through pos_ff. What happen if we pass
-        # memory through the pos_ff as well?
-        rc_output = self.after_attention_ops(rc_output, residual_input)
-        results = (
-            rc_output[-input_length:, :, :],
-            next_m,
-            rc_output[0:rc_length, :, :],
-            next_k,
-            next_v,
-        )
-
-        return results
-
-
-class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder):
-    """
-    Whole utterance augmented memory transformer encoder layer. This is a master layer
-    where we can define multiple augmented memory transformers. There are two reasons
-    to setup the master layer.
-    1. We only need to define once about the attention mask. All the layers in the master
-       layer share the same mask.
-    2. pyspeech nn layer has special input and output format. Defining one master layer is
-       easier to passing memory between different layes inside the master layer
-
-    args:
-        input_dim: input embedding dimension
-        num_heads: number of heads in multihead self-attention
-        ffn_dim: ffn dimension in FFN layer
-        num_layers: number of augmented memory transformer layers
-        dropout_in_attn: dropout used in multi-head self-attention
-        dropout_on_attn: dropout used for output from te multihead self-attention
-        dropout_on_fc1: dropout used in FFN layer for the first linear layer
-        dropout_on_fc2: dropout used in FFN layer for the second linear layer
-        segment_size: segment size for each segment
-        context_config: (left_context_size, right_context_size) defines the surround context size
-            for each segment
-        max_memory_size: maximum memory size used for each segment
-        scaled_init: whether use scaled init for weight initialization in attention layer
-        std_scale: if std_scale is not None. The weak attention suppression is
-            turned on. For std_scale = 0.5, all the attention smaller than
-            mean + 0.5 * std will be suppressed.
-        activation_fn: activation function used in FFN layer. [ReLU, GELU] supported
-        tanh_on_mem: whether use tanh on memory
-        mini_batches: use mini-btach training
-        negative_inf: the negative infinity value used in attention masking. default is "-inf".
-            For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue.
-        summarization_method: method to generate segment summrization embedding
-        max_relative_position: max relatie position for relative position embedding
-        rpe_old_option: To be compatible with previous model. The previous model
-            was trained with attention += attention + rpe. The correct equation
-            should be attention = attention + rpe
-        [TODO]: remove the rpe_old_option by the end of 2021 Q1.
-
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        ffn_dim,
-        num_layers=1,
-        dropout_in_attn=0.0,
-        dropout_on_attn=0.0,
-        dropout_on_fc1=0.0,
-        dropout_on_fc2=0.0,
-        segment_size=128,
-        context_config=(0, 0),
-        max_memory_size=0,
-        scaled_init=True,
-        std_scale=None,
-        activation_fn="relu",
-        tanh_on_mem=False,
-        mini_batches=False,
-        negative_inf="-inf",
-        deep_init=True,
-        summarization_method="mean",
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        super().__init__(None)
-        if input_dim % num_heads:
-            raise ValueError(
-                "input_dim ({}) must be divisible by num_heads ({})".format(
-                    input_dim, num_heads
-                )
-            )
-
-        # we used to support growing memory size. However, it will cause
-        # cross stream batching failure. Now we need to have exact max memory size
-        if max_memory_size < 0:
-            raise ValueError("max_memory_size must be >= 0")
-
-        # Only assign right_context. In decoding, left context will be cached.
-        # No need to let the online decoder to re-assign the left context
-        self.left_context, self.right_context = context_config
-        self.segment_size = segment_size
-        self.memory_dim = input_dim
-        self.max_memory_size = max_memory_size
-        self.mini_batches = mini_batches
-        if self.max_memory_size != 0:
-            self.use_mem = True
-        else:
-            self.use_mem = False
-
-        self.memory_op = SummarizationLayer(
-            summarization_method, segment_size, input_dim
-        )
-
-        self.layers = torch.nn.ModuleList()
-        self.num_layers = num_layers
-        self.max_relative_position = max_relative_position
-        if self.max_relative_position > 0:
-            self.use_rpe = True
-        else:
-            self.use_rpe = False
-        for i in range(self.num_layers):
-            if deep_init:
-                layer_index = i
-            else:
-                layer_index = -1
-
-            self.layers.append(
-                NoSegAugmentedMemoryTransformer(
-                    num_heads=num_heads,
-                    input_dim=input_dim,
-                    ffn_dim=ffn_dim,
-                    dropout_in_attn=dropout_in_attn,
-                    dropout_on_attn=dropout_on_attn,
-                    dropout_on_fc1=dropout_on_fc1,
-                    dropout_on_fc2=dropout_on_fc2,
-                    segment_size=segment_size,
-                    std_scale=std_scale,
-                    activation_fn=activation_fn,
-                    tanh_on_mem=tanh_on_mem,
-                    scaled_init=scaled_init,
-                    use_mem=self.use_mem,
-                    mini_batches=mini_batches,
-                    negative_inf=negative_inf,
-                    layer_index=layer_index,
-                    summarization_method=summarization_method,
-                    max_relative_position=max_relative_position,
-                    rpe_old_option=rpe_old_option,
-                )
-            )
-
-    def set_mini_batches(self, mini_batches):
-        # handy function only used for unit test
-        self.mini_batches = mini_batches
-        for layer in self.layers:
-            layer.set_mini_batches(mini_batches)
-
-    def _get_relative_position(
-        self,
-        input: Tensor,
-        max_relative_position: int,
-        left_context_length: int,
-        past_length: int,
-        is_decoding: bool,
-    ):
-        # For training, we copy the right context to the start of the utterance
-        # First dimension in distance is corresponding to query.
-        # [right context, utterance, summary vector]
-        # Second dimension in distance is corresponding to key.
-        # [Memory bank, right context, utterance]
-        # For summary vector in query part, the distance with
-        # all other position is 2*max_position. For memory bank in key,
-        # the distance with all other positions is 0.
-
-        T, B, D = input.shape
-        num_segs = math.ceil((T - self.right_context) / self.segment_size)
-
-        # utterance
-        u_st = past_length * self.segment_size
-        u_ed = u_st + T
-        utterance_ranges = torch.arange(u_st, u_ed - self.right_context)
-
-        # left context. Only in minibatch or decoding
-        left_context_ranges = torch.arange(u_st - left_context_length, u_st)
-
-        # Right context block
-        # right context + utterance
-        right_context_blocks = []
-        for i in range(0, num_segs - 1):
-            st = (i + 1) * self.segment_size + u_st
-            ed = st + self.right_context
-            assert ed < u_ed
-            temp = torch.arange(st, ed)
-            right_context_blocks.append(temp)
-        right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed))
-        right_context_ranges = torch.cat(right_context_blocks)
-
-        if self.use_mem:
-            # Memory bank
-            # The position for memory -n, .., -1
-            if is_decoding:
-                memory_size = min(past_length, self.max_memory_size)
-            else:
-                memory_size = num_segs + past_length - 1
-            memory_bank_ranges = torch.arange(
-                -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1
-            )
-
-            # summary vector
-            # The position for summary vector as the T+max_relative_position+1.
-            # After the clamping, the relative position is max_relative_position
-            summary_pos_st = u_ed + max_relative_position + 1
-            summary_vector_ranges = torch.arange(
-                summary_pos_st, summary_pos_st + num_segs
-            )
-
-            key_ranges = torch.cat(
-                [
-                    memory_bank_ranges,
-                    right_context_ranges,
-                    left_context_ranges,
-                    utterance_ranges,
-                ]
-            )
-
-            query_ranges = torch.cat(
-                [right_context_ranges, utterance_ranges, summary_vector_ranges]
-            )
-        else:
-            key_ranges = torch.cat(
-                [right_context_ranges, left_context_ranges, utterance_ranges]
-            )
-
-            query_ranges = torch.cat([right_context_ranges, utterance_ranges])
-
-        distance = key_ranges[None, :] - query_ranges[:, None]
-        distance_clamp = (
-            torch.clamp(distance, -max_relative_position, max_relative_position)
-            + max_relative_position
-        )
-        distance_clamp = distance_clamp.to(input.device).long().detach()
-        return distance_clamp
-
-    def _get_attention_mask(self, input, past_length=0, left_context_cache=0):
-        # attention mask for each query contains three parts:
-        # 1. memory part
-        # 2. left_context + segment
-        # 3. right_context_block
-        # so for each segment and its correspoinding right context block,
-        # the attention matrix is formed by 9 parts:
-        # [0, m, 0, 0, right_context, 0, 0, seg, 0]
-        # [before memory, memory, after memory, before right context, right_context,
-        #  after right context, before seg, seg, after seg]
-        #
-        # Query is formed in the way as [right_context_blocks, utterance, summary]
-        #
-        # Note: put m and right_context before segment is convenient
-        # for padding_mask operation.
-        # Key lengths = m_length + right_context_block_length + lengths
-        utterance_length, batch_size, _ = input.shape
-        summary_length = math.ceil(utterance_length / self.segment_size)
-        num_segs = summary_length
-        rc_length = self.right_context * num_segs
-        rc = self.right_context
-        lc = self.left_context
-
-        # using mini-batches, there is left context cache available for current
-        # sequence.
-        lcc = left_context_cache
-
-        # max_memory_size is 0 then we don't have memory and summary
-        # past_length is the memory carry from previous sequence
-        if self.use_mem:
-            mem_length = num_segs - 1 + past_length
-        else:
-            mem_length = 0
-        rc_mask = []
-        query_mask = []
-        summary_mask = []
-        for j in range(0, num_segs):
-            ssize = min(self.segment_size, utterance_length - j * self.segment_size)
-
-            rc_size = rc
-            rc_mat = []
-            q_mat = []
-            s_mat = []
-            m_start = max(j + past_length - self.max_memory_size, 0)
-
-            # max_memory_size is 0, then we don't use memory
-            if self.use_mem:
-                # part 0: before memory
-                rc_mat.append(input.new_zeros(rc_size, m_start))
-                q_mat.append(input.new_zeros(ssize, m_start))
-                s_mat.append(input.new_zeros(1, m_start))
-
-                # part 1: memory
-                col_1 = j + past_length - m_start
-                rc_mat.append(torch.ones(rc_size, col_1, device=input.device))
-                q_mat.append(torch.ones(ssize, col_1, device=input.device))
-                # based on D22875746, disable summary query attention
-                # on memeory is better for long form utterance
-                s_mat.append(input.new_zeros(1, col_1))
-
-                # part 2: after memory
-                col_2 = mem_length - (j + past_length)
-                rc_mat.append(input.new_zeros(rc_size, col_2))
-                q_mat.append(input.new_zeros(ssize, col_2))
-                s_mat.append(input.new_zeros(1, col_2))
-
-            # part 3: before right context
-            rc_start = j * rc
-            rc_mat.append(input.new_zeros(rc_size, rc_start))
-            q_mat.append(input.new_zeros(ssize, rc_start))
-            s_mat.append(input.new_zeros(1, rc_start))
-
-            # part 4: right context
-            rc_end = rc_start + rc
-            col_4 = rc
-            rc_mat.append(torch.ones(rc_size, col_4, device=input.device))
-            q_mat.append(torch.ones(ssize, col_4, device=input.device))
-            s_mat.append(torch.ones(1, col_4, device=input.device))
-
-            # part 5: after right context
-            col_5 = rc_length - rc_end
-            rc_mat.append(input.new_zeros(rc_size, col_5))
-            q_mat.append(input.new_zeros(ssize, col_5))
-            s_mat.append(input.new_zeros(1, col_5))
-
-            # part 6: before query segment
-            seg_start = max(j * self.segment_size + lcc - lc, 0)
-            rc_mat.append(input.new_zeros(rc_size, seg_start))
-            q_mat.append(input.new_zeros(ssize, seg_start))
-            s_mat.append(input.new_zeros(1, seg_start))
-
-            # part 7: query segment
-            # note: right context is put in right context block
-            # here we only need to consider about left context
-            seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc)
-            col_7 = seg_end - seg_start
-            rc_mat.append(torch.ones(rc_size, col_7, device=input.device))
-            q_mat.append(torch.ones(ssize, col_7, device=input.device))
-            s_mat.append(torch.ones(1, col_7, device=input.device))
-
-            # part 8: after query segment
-            col_8 = utterance_length + lcc - seg_end
-            rc_mat.append(input.new_zeros(rc_size, col_8))
-            q_mat.append(input.new_zeros(ssize, col_8))
-            s_mat.append(input.new_zeros(1, col_8))
-
-            rc_mask.append(torch.cat(rc_mat, dim=1))
-            query_mask.append(torch.cat(q_mat, dim=1))
-            summary_mask.append(torch.cat(s_mat, dim=1))
-
-        # no memory, then we don't need summary either
-        if self.use_mem:
-            attention_mask = (
-                1
-                - torch.cat(
-                    [
-                        torch.cat(rc_mask, dim=0),
-                        torch.cat(query_mask, dim=0),
-                        torch.cat(summary_mask, dim=0),
-                    ],
-                    dim=0,
-                )
-            ).to(torch.bool)
-        else:
-            attention_mask = (
-                1
-                - torch.cat(
-                    [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0
-                )
-            ).to(torch.bool)
-
-        return attention_mask
-
-    @torch.jit.export
-    def init_state(
-        self, batch_size: int, device: Optional[Device] = None
-    ) -> List[Tensor]:
-        empty_memory = torch.zeros(
-            self.num_layers,
-            self.max_memory_size,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        left_context_key = torch.zeros(
-            self.num_layers,
-            self.left_context,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        left_context_val = torch.zeros(
-            self.num_layers,
-            self.left_context,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
-
-        return [empty_memory, left_context_key, left_context_val, past_length]
-
-    @torch.jit.export
-    def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]:
-        if len(states) == 0:
-            return []
-        batched_m = []
-        batched_lc_key = []
-        batched_lc_val = []
-        batched_past_length = []
-        for state in states:
-            if len(state) == 0:
-                continue
-            m, lc_key, lc_val, past_length = state
-            batched_m.append(m)
-            batched_lc_key.append(lc_key)
-            batched_lc_val.append(lc_val)
-            batched_past_length.append(past_length)
-
-        if (
-            (len(batched_m) == 0)
-            or (len(batched_lc_key) == 0)
-            or (len(batched_lc_val) == 0)
-            or (len(batched_past_length) == 0)
-        ):
-            return [
-                torch.tensor([]),
-                torch.tensor([]),
-                torch.tensor([]),
-                torch.tensor([]),
-            ]
-
-        batched_m = torch.cat(batched_m, dim=2)
-        batched_lc_key = torch.cat(batched_lc_key, dim=2)
-        batched_lc_val = torch.cat(batched_lc_val, dim=2)
-        batched_past_length = torch.cat(batched_past_length, dim=1)
-        return [batched_m, batched_lc_key, batched_lc_val, batched_past_length]
-
-    @torch.jit.export
-    def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
-        if len(state) == 0:
-            return []
-        m, lc_key, lc_val, past_length = state
-        indices = indices.to(device=m.device)
-        reord_m = torch.index_select(m, 2, indices)
-        reord_lc_key = torch.index_select(lc_key, 2, indices)
-        reord_lc_val = torch.index_select(lc_val, 2, indices)
-        reord_past_length = torch.index_select(past_length, 1, indices)
-        return [reord_m, reord_lc_key, reord_lc_val, reord_past_length]
-
-    @torch.jit.export
-    def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
-        m, lc_key, lc_val, past_length = state
-        m = m.index_fill(dim=2, index=indices, value=0.0)
-        lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0)
-        lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0)
-        past_length = past_length.index_fill(dim=1, index=indices, value=0)
-
-        return [m, lc_key, lc_val, past_length]
-
-    @torch.jit.export
-    def state_size(self) -> int:
-        return 4
-
-    @torch.jit.export
-    def batch_size_in_state(
-        self, state: Optional[List[Tensor]], sloppy: bool = True
-    ) -> Optional[int]:
-        if state is None:
-            return None
-        return state[0].size(2)
-
-    def gen_summary_queries(self, input):
-        sum_input = self.memory_op(input)
-        return sum_input
-
-    def _gen_right_context_padded_input(self, input):
-        # This function deals with input that is already
-        # padded with right context (e.g. minibatch training)
-        right_context_blocks = []
-        T, B, D = input.shape
-        num_segs = math.ceil((T - self.right_context) / self.segment_size)
-        for i in range(0, num_segs - 1):
-            st = (i + 1) * self.segment_size
-            ed = st + self.right_context
-            assert ed < T
-            temp = input[st:ed, :, :]
-            right_context_blocks.append(temp)
-
-        # last segment right context is already available
-        right_context_blocks.append(input[T - self.right_context :, :, :])
-        return torch.cat(right_context_blocks, dim=0)
-
-    def _gen_segs_right_context(self, input, lengths):
-        segments = []
-        T, B, D = input.size()
-        nT = T - self.right_context
-
-        # assume input is right context padded
-        num_segs = math.ceil(nT / self.segment_size)
-        # pad zeros to the utterance to make sure each
-        # segment has the same right context. For the
-        for i in range(0, num_segs - 1):
-            st = i * self.segment_size
-            ed = min(T, st + self.segment_size + self.right_context)
-            temp = input[st:ed, :, :]
-            rest_lengths = torch.clamp(
-                lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size
-            )
-            segments.append((temp, lengths - rest_lengths + self.right_context))
-            lengths = rest_lengths
-
-        last_seg = input[st + self.segment_size :, :, :]
-        segments.append((last_seg, rest_lengths + self.right_context))
-
-        return segments
-
-    @torch.jit.unused
-    def forward(
-        self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
-        # Xutai: originally the second argument is lengths.
-        lengths = (~padding_masks).sum(dim=1).long()
-        # mini batch training.
-        if self.mini_batches:
-            return self.forward_mini_batches(input, lengths, state)
-
-        # regular full sequence training. Note, assume the right context in provided
-        # in the input.
-        T, B, D = input.size()
-        right_context_blocks = self._gen_right_context_padded_input(input)
-
-        # generate the relative positional embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=0,
-                past_length=0,
-                is_decoding=False,
-            )
-        else:
-            rpe = None
-        input = input[: T - self.right_context, :, :]
-
-        attention_mask = self._get_attention_mask(input)
-
-        # firt layer use each segment mean as memory
-        # ignore the last one seg average
-        if self.use_mem:
-            mems = self.gen_summary_queries(input)[:-1, :, :]
-        else:
-            mems = torch.zeros(0, input.size(1), input.size(2), device=input.device)
-            mems = mems.type_as(input)
-
-        output = input
-        all_outputs = []
-
-        for layer in self.layers:
-            output, mems, right_context_blocks, _, _ = layer(
-                input=output,
-                lengths=lengths,
-                attention_mask=attention_mask,
-                mems=mems,
-                right_context_blocks=right_context_blocks,
-                pre_mems=None,
-                left_context_key=None,
-                left_context_val=None,
-                rpe=rpe,
-            )
-            all_outputs.append(output)
-        return output, padding_masks, [], all_outputs
-
-    def forward_jit_mini_batch_init(
-        self,
-        seg: Tensor,
-        state: Optional[List[Tensor]] = None,
-        is_decoding: bool = False,
-    ):
-        # Prepare state. In whole sequence training, state is ignored.
-        # For minibatch training, we need to prepare state
-        if state is None:
-            state = self.init_state(batch_size=seg.size(1), device=seg.device)
-            if seg.dtype == torch.half:
-                state = [state[0].half(), state[1].half(), state[2].half(), state[3]]
-
-        if self.use_mem:
-            # note input average only on seg, not on right context
-            # first layer use each segmetn mean as memory. the last
-            # one segment average is used in state
-            full_mems = self.gen_summary_queries(seg)
-            if is_decoding:
-                mems = full_mems[0:1, :, :]
-                state_mems = torch.cat([state[0][0], mems], dim=0)
-            else:
-                mems = full_mems[:-1, :, :]
-                state_mems = torch.cat([state[0][0], full_mems], dim=0)
-        else:
-            mems = state[0][0]
-            state_mems = mems
-
-        # track processed segment number or memory number
-        # the same batch as the same bumber of past length
-        past_length = state[3][0][0].item()
-        past_left_context = min(past_length * self.segment_size, self.left_context)
-        past_length = min(self.max_memory_size, past_length)
-
-        return state, mems, state_mems, past_length, past_left_context
-
-    def state_update_before(
-        self, layer: int, state: List[Tensor], past_length: int, past_left_context: int
-    ):
-        pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :]
-        lc_key = state[1][layer][self.left_context - past_left_context :, :, :]
-        lc_val = state[2][layer][self.left_context - past_left_context :, :, :]
-        return pre_mems, lc_key, lc_val
-
-    def state_update_after(
-        self,
-        layer: int,
-        state: List[Tensor],
-        mems: Tensor,
-        next_key: Tensor,
-        next_val: Tensor,
-        mems_list: List[Tensor],
-        lc_key_list: List[Tensor],
-        lc_val_list: List[Tensor],
-    ):
-        # mems is used for next layer
-        if layer < self.num_layers - 1:
-            state_mems = torch.cat([state[0][layer + 1], mems], dim=0)
-            mems_list.append(state_mems[-self.max_memory_size :, :, :])
-
-        # when mems pass to next sequence, we need the last memory. when mems
-        # use for the next layer, we can ignore the last memory
-        mems = mems[:-1, :, :]
-
-        # note state[1][i] and state[2][i] original length equals to self.left_context
-        new_k = torch.cat([state[1][layer], next_key], dim=0)
-        new_v = torch.cat([state[2][layer], next_val], dim=0)
-        lc_key_list.append(new_k[-self.left_context :, :, :])
-        lc_val_list.append(new_v[-self.left_context :, :, :])
-        return mems_list, lc_key_list, lc_val_list, mems
-
-    def state_update_after_loop(
-        self,
-        state: List[Tensor],
-        mems_list: List[Tensor],
-        lc_key_list: List[Tensor],
-        lc_val_list: List[Tensor],
-        update_length: int,
-    ):
-        state[0] = torch.stack(mems_list, dim=0)
-        state[1] = torch.stack(lc_key_list, dim=0)
-        state[2] = torch.stack(lc_val_list, dim=0)
-        state[3] = state[3] + update_length
-        return state
-
-    @torch.jit.unused
-    def forward_mini_batches(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
-        T, B, D = input.size()
-
-        # input without right context
-        seg = input[: T - self.right_context, :, :]
-
-        # get right context blocks
-        right_context_blocks = self._gen_right_context_padded_input(input)
-
-        mems_list = []
-        lc_key_list = []
-        lc_val_list = []
-        results = self.forward_jit_mini_batch_init(seg, state, False)
-        state, mems, state_mems, past_length, past_left_context = results
-
-        # relative position embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=past_left_context,
-                past_length=past_length,
-                is_decoding=False,
-            )
-        else:
-            rpe = None
-
-        # get attention mask based on seg (not include right context) and available
-        # left context
-        attention_mask = self._get_attention_mask(seg, past_length, past_left_context)
-        mems_list.append(state_mems[-self.max_memory_size :, :, :])
-        output = seg
-        i = 0
-        all_outputs = []
-        for layer in self.layers:
-            # In order to make cross stream batching work, mem, left context key
-            # and left context value in the state should always be the same shape.
-            # We use the past length to track the processed segment number. In this
-            # way, we take out the essential memory, left context key and left
-            # context val from the state. After finish the forward for current segment
-            # we add the new memory, left context key and left context value into the
-            # staate and trim out the oldest part to keep the shape consistent.
-            pre_mems, lc_key, lc_val = self.state_update_before(
-                i, state, past_length, past_left_context
-            )
-
-            output, mems, right_context_blocks, next_key, next_val = layer.forward(
-                input=output,
-                lengths=lengths,
-                attention_mask=attention_mask,
-                mems=mems,
-                right_context_blocks=right_context_blocks,
-                pre_mems=pre_mems,
-                left_context_key=lc_key,
-                left_context_val=lc_val,
-                rpe=rpe,
-            )
-            all_outputs.append(output)
-            mems_list, lc_key_list, lc_val_list, mems = self.state_update_after(
-                layer=i,
-                state=state,
-                mems=mems,
-                next_key=next_key,
-                next_val=next_val,
-                mems_list=mems_list,
-                lc_key_list=lc_key_list,
-                lc_val_list=lc_val_list,
-            )
-
-            i += 1
-
-        # update state
-        update_length = math.ceil((T - self.right_context) / self.segment_size)
-        state = self.state_update_after_loop(
-            state=state,
-            mems_list=mems_list,
-            lc_key_list=lc_key_list,
-            lc_val_list=lc_val_list,
-            update_length=update_length,
-        )
-
-        return output, lengths, state, all_outputs
-
-    def forward_jit_test(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """
-        This one simulate sequence encoder forward jit. This is for unit test purpose.
-        It is not used in training or decoding. Note, extra_right_context is set in
-        the model. In unit test, input = [utterance, right_context], lengths =
-        [utterance_length].
-        args:
-            input: input utterance
-            lengths: utterance input length
-            state: None here. input is whole utterance
-        """
-        # [TODO] sequence_to_segment has bug in lengths.
-        seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths)
-
-        seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = []
-        state: Optional[List[Tensor]] = None
-        for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths:
-            seg_enc_tokens, seg_enc_lengths, state = self.forward_jit(
-                input=seg_src_tokens, lengths=seg_src_lengths, state=state
-            )
-            seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths))
-
-        enc_tokens, enc_lengths = segments_to_sequence(
-            segments=seg_enc_tokens_lengths, time_axis=0
-        )
-
-        state = []  # returns trivial state
-
-        return enc_tokens, enc_lengths, state
-
-    @torch.jit.export
-    def forward_jit(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """
-        Forward helper for online decoding.
-
-        args:
-            input: [seg, right_context]. We assume in online we
-                always padding the right context to the preset right context size.
-                For the last segment, we may have short segment size, but right
-                context size is the same as other segments
-            lengths: utterance input length is the utterance segment length and
-                     right context size
-            state: [memory, left_context_key, left_context_val]. To improve throughput,
-                in addition to memory, we also cache key and value for left_context in
-                multihead self-attention
-        """
-        # In online decoding, input = [segment, right_context]
-        # Lengths = [segment_length, right_context_length]
-        # so we need strip right context in output
-        T, B, D = input.size()
-        rc_str = T - self.right_context
-        rc_end = T
-        right_context_blocks = input[rc_str:rc_end, :, :]
-        seg = input[:rc_str, :, :]
-        lengths = torch.clamp(lengths - self.right_context, min=0)
-        mems_list = []
-        lc_key_list = []
-        lc_val_list = []
-
-        results = self.forward_jit_mini_batch_init(seg, state, True)
-        state, mems, state_mems, past_length, past_left_context = results
-
-        # relative position embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=past_left_context,
-                past_length=past_length,
-                is_decoding=True,
-            )
-        else:
-            rpe = None
-
-        # memory for first layer.
-        mems_list.append(state_mems[-self.max_memory_size :, :, :])
-        output = seg
-        i = 0
-        for layer in self.layers:
-            # In order to make cross stream batching work, mem, left context key
-            # and left context value in the state should always be the same shape.
-            # We use the past length to track the processed segment number. In this
-            # way, we take out the essential memory, left context key and left
-            # context val from the state. After finish the forward for current segment
-            # we add the new memory, left context key and left context value into the
-            # staate and trim out the oldest part to keep the shape consistent.
-            true_mems, lc_key, lc_val = self.state_update_before(
-                layer=i,
-                state=state,
-                past_length=past_length,
-                past_left_context=past_left_context,
-            )
-
-            output, mems, right_context_blocks, next_key, next_val = layer.forward_jit(
-                input=output,
-                lengths=lengths,
-                mems=true_mems,
-                right_context_blocks=right_context_blocks,
-                left_context_key=lc_key,
-                left_context_val=lc_val,
-                rpe=rpe,
-            )
-            # mems is used for next layer
-            mems_list, lc_key_list, lc_val_list, _ = self.state_update_after(
-                layer=i,
-                state=state,
-                mems_list=mems_list,
-                mems=mems,
-                next_key=next_key,
-                next_val=next_val,
-                lc_key_list=lc_key_list,
-                lc_val_list=lc_val_list,
-            )
-            i += 1
-
-        # update state
-        state = self.state_update_after_loop(
-            state=state,
-            mems_list=mems_list,
-            lc_key_list=lc_key_list,
-            lc_val_list=lc_val_list,
-            update_length=1,
-        )
-
-        return output, lengths, state
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-# ------------------------------------------------------------------------------
-#   Emformer encoder for seq2seq model
-#   This is a wrapper over the original emformer
-# ------------------------------------------------------------------------------
-def emformer_encoder(klass):
-    class SpeechEncoder(klass):
-        def __init__(self, args):
-            super().__init__(args)
-            stride = SpeechEncoder.conv_layer_stride(args)
-            trf_left_context = args.segment_left_context // stride
-            trf_right_context = args.segment_right_context // stride
-            context_config = [trf_left_context, trf_right_context]
-            self.transformer_layers = nn.ModuleList(
-                [
-                    NoSegAugmentedMemoryTransformerEncoderLayer(
-                        input_dim=args.encoder_embed_dim,
-                        num_heads=args.encoder_attention_heads,
-                        ffn_dim=args.encoder_ffn_embed_dim,
-                        num_layers=args.encoder_layers,
-                        dropout_in_attn=args.dropout,
-                        dropout_on_attn=args.dropout,
-                        dropout_on_fc1=args.dropout,
-                        dropout_on_fc2=args.dropout,
-                        activation_fn=args.activation_fn,
-                        context_config=context_config,
-                        segment_size=args.segment_length,
-                        max_memory_size=args.max_memory_size,
-                        scaled_init=True,  # TODO: use constant for now.
-                        tanh_on_mem=args.amtrf_tanh_on_mem,
-                    )
-                ]
-            )
-
-        def forward(self, src_tokens, src_lengths):
-            encoder_out = super().forward(src_tokens, src_lengths)
-            output = encoder_out["encoder_out"][0]
-            encoder_padding_masks = encoder_out["encoder_padding_mask"][0]
-
-            # This is because that in the original implementation
-            # the output didn't consider the last segment as right context.
-            encoder_padding_masks = encoder_padding_masks[:, : output.size(0)]
-
-            return {
-                "encoder_out": [output],
-                "encoder_padding_mask": [encoder_padding_masks],
-                "encoder_embedding": [],
-                "encoder_states": [],
-                "src_tokens": [],
-                "src_lengths": [],
-            }
-
-        @staticmethod
-        def conv_layer_stride(args):
-            # TODO: make it configurable from the args
-            return 4
-
-    SpeechEncoder.__name__ = klass.__name__
-    return SpeechEncoder
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/sequence_scorer.py b/spaces/ICML2022/OFA/fairseq/fairseq/sequence_scorer.py
deleted file mode 100644
index 411d4df4445ef8dd3f1907ad56f9de6943d1fed8..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/sequence_scorer.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-import torch
-from fairseq import utils
-
-
-class SequenceScorer(object):
-    """Scores the target for a given source sentence."""
-
-    def __init__(
-        self,
-        tgt_dict,
-        softmax_batch=None,
-        compute_alignment=False,
-        eos=None,
-        symbols_to_strip_from_output=None,
-    ):
-        self.pad = tgt_dict.pad()
-        self.eos = tgt_dict.eos() if eos is None else eos
-        self.softmax_batch = softmax_batch or sys.maxsize
-        assert self.softmax_batch > 0
-        self.compute_alignment = compute_alignment
-        self.symbols_to_strip_from_output = (
-            symbols_to_strip_from_output.union({self.eos})
-            if symbols_to_strip_from_output is not None
-            else {self.eos}
-        )
-
-    @torch.no_grad()
-    def generate(self, models, sample, **kwargs):
-        """Score a batch of translations."""
-        net_input = sample["net_input"]
-
-        def batch_for_softmax(dec_out, target):
-            # assumes decoder_out[0] is the only thing needed (may not be correct for future models!)
-            first, rest = dec_out[0], dec_out[1:]
-            bsz, tsz, dim = first.shape
-            if bsz * tsz < self.softmax_batch:
-                yield dec_out, target, True
-            else:
-                flat = first.contiguous().view(1, -1, dim)
-                flat_tgt = target.contiguous().view(flat.shape[:-1])
-                s = 0
-                while s < flat.size(1):
-                    e = s + self.softmax_batch
-                    yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False
-                    s = e
-
-        def gather_target_probs(probs, target):
-            probs = probs.gather(
-                dim=2,
-                index=target.unsqueeze(-1),
-            )
-            return probs
-
-        orig_target = sample["target"]
-
-        # compute scores for each model in the ensemble
-        avg_probs = None
-        avg_attn = None
-        for model in models:
-            model.eval()
-            decoder_out = model(**net_input)
-            attn = decoder_out[1] if len(decoder_out) > 1 else None
-            if type(attn) is dict:
-                attn = attn.get("attn", None)
-
-            batched = batch_for_softmax(decoder_out, orig_target)
-            probs, idx = None, 0
-            for bd, tgt, is_single in batched:
-                sample["target"] = tgt
-                curr_prob = model.get_normalized_probs(
-                    bd, log_probs=len(models) == 1, sample=sample
-                ).data
-                if is_single:
-                    probs = gather_target_probs(curr_prob, orig_target)
-                else:
-                    if probs is None:
-                        probs = curr_prob.new(orig_target.numel())
-                    step = curr_prob.size(0) * curr_prob.size(1)
-                    end = step + idx
-                    tgt_probs = gather_target_probs(
-                        curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt
-                    )
-                    probs[idx:end] = tgt_probs.view(-1)
-                    idx = end
-                sample["target"] = orig_target
-
-            probs = probs.view(sample["target"].shape)
-
-            if avg_probs is None:
-                avg_probs = probs
-            else:
-                avg_probs.add_(probs)
-            if attn is not None:
-                if torch.is_tensor(attn):
-                    attn = attn.data
-                else:
-                    attn = attn[0]
-                if avg_attn is None:
-                    avg_attn = attn
-                else:
-                    avg_attn.add_(attn)
-        if len(models) > 1:
-            avg_probs.div_(len(models))
-            avg_probs.log_()
-            if avg_attn is not None:
-                avg_attn.div_(len(models))
-
-        bsz = avg_probs.size(0)
-        hypos = []
-        start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz
-        for i in range(bsz):
-            # remove padding from ref
-            ref = (
-                utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad)
-                if sample["target"] is not None
-                else None
-            )
-            tgt_len = ref.numel()
-            avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len]
-            score_i = avg_probs_i.sum() / tgt_len
-            if avg_attn is not None:
-                avg_attn_i = avg_attn[i]
-                if self.compute_alignment:
-                    alignment = utils.extract_hard_alignment(
-                        avg_attn_i,
-                        sample["net_input"]["src_tokens"][i],
-                        sample["target"][i],
-                        self.pad,
-                        self.eos,
-                    )
-                else:
-                    alignment = None
-            else:
-                avg_attn_i = alignment = None
-            hypos.append(
-                [
-                    {
-                        "tokens": ref,
-                        "score": score_i,
-                        "attention": avg_attn_i,
-                        "alignment": alignment,
-                        "positional_scores": avg_probs_i,
-                    }
-                ]
-            )
-        return hypos
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/modules/mel_processing.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros/modules/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/modules/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/Illumotion/Koboldcpp/Dockerfile b/spaces/Illumotion/Koboldcpp/Dockerfile
deleted file mode 100644
index 72f0d94acac9a929a397e20af740f6a6733cb4af..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python
-WORKDIR /app
-COPY . .
-RUN apt update \
- && apt install build-essential wget libopenblas-dev make cmake tar -y \
- && mkdir build \
- && wget https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF/resolve/main/pygmalion-2-7b.Q6_K.gguf https://github.com/mozilla/sccache/releases/download/v0.5.4/sccache-v0.5.4-x86_64-unknown-linux-musl.tar.gz \
- && tar -vxzf sccache-v0.5.4-x86_64-unknown-linux-musl.tar.gz \
- && mv sccache-v0.5.4-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
- && chmod +x /usr/bin/sccache \
- && cd build \
- && cmake .. -DCMAKE_C_COMPILER_LAUNCHER=sccache -DCMAKE_CXX_COMPILER_LAUNCHER=sccache \
- && cmake --build . \
- && cd .. \
- && apt remove build-essential wget make cmake -y \
- && rm -fr *.bat convert-* ci docs examples otherarchs tests sccache-dist-v0.5.4-x86_64-unknown-linux-musl*
-
- ENTRYPOINT ["python", "koboldcpp.py", "pygmalion-2-7b.Q6_K.gguf", "--port", "7860", "--smartcontext"]
\ No newline at end of file
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/paper_runfiles/blur_tests.sh b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/paper_runfiles/blur_tests.sh
deleted file mode 100644
index 8f204a4c643d08935e5561ed27a286536643958d..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/paper_runfiles/blur_tests.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-##!/usr/bin/env bash
-#
-## !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst
-#
-## paths to data are valid for mml7
-#PLACES_ROOT="/data/inpainting/Places365"
-#OUT_DIR="/data/inpainting/paper_data/Places365_val_test"
-#
-#source "$(dirname $0)/env.sh"
-#
-#for datadir in test_large_30k  # val_large
-#do
-#    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-#    do
-#        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-#            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8
-#
-#        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-#    done
-#
-#    for conf in segm_256 segm_512
-#    do
-#        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-#            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2
-#
-#        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-#    done
-#done
-#
-#IN_DIR="/data/inpainting/paper_data/Places365_val_test/test_large_30k/random_medium_512"
-#PRED_DIR="/data/inpainting/predictions/final/images/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37/random_medium_512"
-#BLUR_OUT_DIR="/data/inpainting/predictions/final/blur/images"
-#
-#for b in 0.1
-#
-#"$BINDIR/blur_predicts.py" "$BASEDIR/../../configs/eval2.yaml" "$CUR_IN_DIR" "$CUR_OUT_DIR" "$CUR_EVAL_DIR"
-#
diff --git a/spaces/Isuru623/CardioScanPro/utils-preprocessing.py b/spaces/Isuru623/CardioScanPro/utils-preprocessing.py
deleted file mode 100644
index 054308caedd3a1d6e67e6ec16519e05aabd9bd08..0000000000000000000000000000000000000000
--- a/spaces/Isuru623/CardioScanPro/utils-preprocessing.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import scipy.io
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pandas as pd
-import pathlib as pl
-import numpy as np
-import random
-
-
-#=====================================================================================================
-
-def read_heafile(file_name):
-    # Function to read a .hea file and return its content as a list of strings
-    # Open the .hea file
-    with open(file_name, 'r') as file:
-        # Read the content of the .hea file
-        hea_content = file.readlines()
-
-    return hea_content
-
-#====================================================================================================
-
-def create_array(hea_content):
-    ID = hea_content[0].strip().split()[0]
-    
-    # Extract 'Age' from .hea file content
-    age_info = hea_content[13].strip().split()
-    age = int(age_info[2]) if len(age_info) > 2 and age_info[2].isdigit() else 0
-    
-    # Extract 'Gender' from .hea file content
-    gender = hea_content[14].strip().split()[2] if len(hea_content) > 14 else 'Unknown'
-    
-    # Extract 'Abnormality' from .hea file content
-    abnormality = hea_content[15].strip().split()[2] if len(hea_content) > 15 else 'Unknown'
-    
-    return [ID, age, gender, abnormality]
-
-#===================================================================================================
-
-
-
-#===================================================================================================
-def create_dataframes(training_directory):
-    dataframes = {}
-
-    subdirectories = [subdir for subdir in pl.Path(training_directory).iterdir() if subdir.is_dir()]
-    
-    for source_folder_path in subdirectories:
-        source_folder_name = source_folder_path.name
-        columns = ['ID', 'Age', 'Gender', 'Abnormality']
-        source_dataframe = pd.DataFrame(columns=columns)
-        patient_data = {}  # To collect patient information
-        
-        for subdir in source_folder_path.iterdir():
-            if subdir.is_dir():
-                data_dir = pl.Path(subdir)
-                header_files = list(data_dir.glob('*.hea'))
-
-                for header_file in header_files:
-                    header_path = data_dir.joinpath(header_file.name)
-                    hea_content = read_heafile(header_path)
-                    patient_info = create_array(hea_content)
-                    patient_id = patient_info[0]
-                    
-                    # Collect patient information
-                    for i, column_name in enumerate(['Age', 'Gender', 'Abnormality']):
-                        patient_data.setdefault(patient_id, {})[column_name] = patient_info[i + 1]
-                        
-        # Create a list of patient data dictionaries
-        patient_rows = []
-        for patient_id, info in patient_data.items():
-            row = {'ID': patient_id, 'Age': info.get('Age'), 'Gender': info.get('Gender'), 'Abnormality': info.get('Abnormality')}
-            patient_rows.append(row)
-            
-        # Concatenate patient data into the dataframe
-        source_dataframe = pd.concat([source_dataframe, pd.DataFrame(patient_rows)])
-        
-        dataframes[f'{source_folder_name}_df'] = source_dataframe
-        
-    return dataframes
-
-def preprocess_dataframes(result_dataframes):
-    cpsc_2018_df = result_dataframes['cpsc_2018_df']
-    cpsc_2018_extra_df = result_dataframes['cpsc_2018_extra_df']
-    georgia_df = result_dataframes['georgia_df']
-    ptb_df = result_dataframes['ptb_df']
-    ptb_xl_df = result_dataframes['ptb-xl_df']
-    st_petersburg_incart_df = result_dataframes['st_petersburg_incart_df']
-
-    dataframes = [cpsc_2018_df, cpsc_2018_extra_df, georgia_df, ptb_df, ptb_xl_df, st_petersburg_incart_df]
-
-    # Replace invalid age values with NaN (preidentified that outliers are only in ptb_xl_df dataframe)
-    invalid_age_indices = ptb_xl_df['Age'] == 300
-    ptb_xl_df.loc[invalid_age_indices, 'Age'] = np.nan
-
-    # Replace placeholders with NaN
-    for df in dataframes:
-        df['Age'] = df['Age'].replace(0, np.nan)
-        df['Gender'] = df['Gender'].replace('Unknown', np.nan)
-        df['Abnormality'] = df['Abnormality'].replace('Unknown', np.nan)
-
-    return dataframes
-
-# Assuming you have already created the 'result_dataframes' dictionary
-from sklearn.linear_model import LinearRegression
-from sklearn.preprocessing import OneHotEncoder
-
-def fill_missing_ages_with_regression(df):
-    # Create a copy of the dataframe to modify
-    df['Age'] = pd.to_numeric(df['Age'], errors='coerce')
-    df_copy = df.copy()
-    
-    # Create a one-hot encoded matrix for 'Abnormality'
-    encoder = OneHotEncoder()
-    abnormality_encoded = encoder.fit_transform(df_copy['Abnormality'].values.reshape(-1, 1)).toarray()
-    
-    # Define features and target variables
-    X = abnormality_encoded
-    y = df_copy['Age']
-    
-    # Identify rows with missing 'Age' values
-    missing_age_indices = np.isnan(y)
-    
-    if np.sum(missing_age_indices) == 0:
-        return df  # Skip if there are no missing 'Age' values
-    
-    # Build a linear regression model
-    regressor = LinearRegression()
-    regressor.fit(X[~missing_age_indices], y[~missing_age_indices])
-    
-    # Fill missing 'Age' values using the regression model
-    for index, row in df.iterrows():
-        if np.isnan(row['Age']):
-            abnormality = row['Abnormality']
-            abnormality_encoded = encoder.transform(np.array(abnormality).reshape(1, -1)).toarray()
-            predicted_age = regressor.predict(abnormality_encoded)[0]
-            df.loc[index, 'Age'] = predicted_age
-    
-    return df
-
-# Assuming 'result_dataframes' is a dictionary of DataFrames
-# You can call this function for each DataFrame in the dictionary
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Izal887/Konci887/infer_pack/models.py b/spaces/Izal887/Konci887/infer_pack/models.py
deleted file mode 100644
index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000
--- a/spaces/Izal887/Konci887/infer_pack/models.py
+++ /dev/null
@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Jaehan/Text2Text-Text-Summarization/README.md b/spaces/Jaehan/Text2Text-Text-Summarization/README.md
deleted file mode 100644
index b2b7854d47d77679b1f1770ee5696da770729ac1..0000000000000000000000000000000000000000
--- a/spaces/Jaehan/Text2Text-Text-Summarization/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text2Text Text Summarization
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/JammyMachina/streamlit-jam-machine/playback.py b/spaces/JammyMachina/streamlit-jam-machine/playback.py
deleted file mode 100644
index 2dd8d857f8cf846961abe5a7836ffe160318c92b..0000000000000000000000000000000000000000
--- a/spaces/JammyMachina/streamlit-jam-machine/playback.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import matplotlib.pyplot as plt
-import librosa.display
-from pretty_midi import PrettyMIDI
-
-
-# Note: these functions are meant to be played within an interactive Python shell
-# Please refer to the synth.ipynb for an example of how to use them
-
-
-def get_music(midi_file):
-    """
-    Load a midi file and return the PrettyMIDI object and the audio signal
-    """
-    music = PrettyMIDI(midi_file=midi_file)
-    waveform = music.fluidsynth()
-    return music, waveform
-
-
-def show_piano_roll(music_notes, fs=100):
-    """
-    Show the piano roll of a music piece, with all instruments squashed onto a single 128xN matrix
-    :param music_notes: PrettyMIDI object
-    :param fs: sampling frequency
-    """
-    # get the piano roll
-    piano_roll = music_notes.get_piano_roll(fs)
-    print("Piano roll shape: {}".format(piano_roll.shape))
-
-    # plot the piano roll
-    plt.figure(figsize=(12, 4))
-    librosa.display.specshow(piano_roll, sr=100, x_axis="time", y_axis="cqt_note")
-    plt.colorbar()
-    plt.title("Piano roll")
-    plt.tight_layout()
-    plt.show()
diff --git a/spaces/JunchuanYu/SegRS/segment_anything/modeling/transformer.py b/spaces/JunchuanYu/SegRS/segment_anything/modeling/transformer.py
deleted file mode 100644
index f1a2812f613cc55b1d0b3e3e1d0c84a760d1fb87..0000000000000000000000000000000000000000
--- a/spaces/JunchuanYu/SegRS/segment_anything/modeling/transformer.py
+++ /dev/null
@@ -1,240 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from torch import Tensor, nn
-
-import math
-from typing import Tuple, Type
-
-from .common import MLPBlock
-
-
-class TwoWayTransformer(nn.Module):
-    def __init__(
-        self,
-        depth: int,
-        embedding_dim: int,
-        num_heads: int,
-        mlp_dim: int,
-        activation: Type[nn.Module] = nn.ReLU,
-        attention_downsample_rate: int = 2,
-    ) -> None:
-        """
-        A transformer decoder that attends to an input image using
-        queries whose positional embedding is supplied.
-
-        Args:
-          depth (int): number of layers in the transformer
-          embedding_dim (int): the channel dimension for the input embeddings
-          num_heads (int): the number of heads for multihead attention. Must
-            divide embedding_dim
-          mlp_dim (int): the channel dimension internal to the MLP block
-          activation (nn.Module): the activation to use in the MLP block
-        """
-        super().__init__()
-        self.depth = depth
-        self.embedding_dim = embedding_dim
-        self.num_heads = num_heads
-        self.mlp_dim = mlp_dim
-        self.layers = nn.ModuleList()
-
-        for i in range(depth):
-            self.layers.append(
-                TwoWayAttentionBlock(
-                    embedding_dim=embedding_dim,
-                    num_heads=num_heads,
-                    mlp_dim=mlp_dim,
-                    activation=activation,
-                    attention_downsample_rate=attention_downsample_rate,
-                    skip_first_layer_pe=(i == 0),
-                )
-            )
-
-        self.final_attn_token_to_image = Attention(
-            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
-        )
-        self.norm_final_attn = nn.LayerNorm(embedding_dim)
-
-    def forward(
-        self,
-        image_embedding: Tensor,
-        image_pe: Tensor,
-        point_embedding: Tensor,
-    ) -> Tuple[Tensor, Tensor]:
-        """
-        Args:
-          image_embedding (torch.Tensor): image to attend to. Should be shape
-            B x embedding_dim x h x w for any h and w.
-          image_pe (torch.Tensor): the positional encoding to add to the image. Must
-            have the same shape as image_embedding.
-          point_embedding (torch.Tensor): the embedding to add to the query points.
-            Must have shape B x N_points x embedding_dim for any N_points.
-
-        Returns:
-          torch.Tensor: the processed point_embedding
-          torch.Tensor: the processed image_embedding
-        """
-        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
-        bs, c, h, w = image_embedding.shape
-        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
-        image_pe = image_pe.flatten(2).permute(0, 2, 1)
-
-        # Prepare queries
-        queries = point_embedding
-        keys = image_embedding
-
-        # Apply transformer blocks and final layernorm
-        for layer in self.layers:
-            queries, keys = layer(
-                queries=queries,
-                keys=keys,
-                query_pe=point_embedding,
-                key_pe=image_pe,
-            )
-
-        # Apply the final attenion layer from the points to the image
-        q = queries + point_embedding
-        k = keys + image_pe
-        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
-        queries = queries + attn_out
-        queries = self.norm_final_attn(queries)
-
-        return queries, keys
-
-
-class TwoWayAttentionBlock(nn.Module):
-    def __init__(
-        self,
-        embedding_dim: int,
-        num_heads: int,
-        mlp_dim: int = 2048,
-        activation: Type[nn.Module] = nn.ReLU,
-        attention_downsample_rate: int = 2,
-        skip_first_layer_pe: bool = False,
-    ) -> None:
-        """
-        A transformer block with four layers: (1) self-attention of sparse
-        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
-        block on sparse inputs, and (4) cross attention of dense inputs to sparse
-        inputs.
-
-        Arguments:
-          embedding_dim (int): the channel dimension of the embeddings
-          num_heads (int): the number of heads in the attention layers
-          mlp_dim (int): the hidden dimension of the mlp block
-          activation (nn.Module): the activation of the mlp block
-          skip_first_layer_pe (bool): skip the PE on the first layer
-        """
-        super().__init__()
-        self.self_attn = Attention(embedding_dim, num_heads)
-        self.norm1 = nn.LayerNorm(embedding_dim)
-
-        self.cross_attn_token_to_image = Attention(
-            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
-        )
-        self.norm2 = nn.LayerNorm(embedding_dim)
-
-        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
-        self.norm3 = nn.LayerNorm(embedding_dim)
-
-        self.norm4 = nn.LayerNorm(embedding_dim)
-        self.cross_attn_image_to_token = Attention(
-            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
-        )
-
-        self.skip_first_layer_pe = skip_first_layer_pe
-
-    def forward(
-        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
-    ) -> Tuple[Tensor, Tensor]:
-        # Self attention block
-        if self.skip_first_layer_pe:
-            queries = self.self_attn(q=queries, k=queries, v=queries)
-        else:
-            q = queries + query_pe
-            attn_out = self.self_attn(q=q, k=q, v=queries)
-            queries = queries + attn_out
-        queries = self.norm1(queries)
-
-        # Cross attention block, tokens attending to image embedding
-        q = queries + query_pe
-        k = keys + key_pe
-        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
-        queries = queries + attn_out
-        queries = self.norm2(queries)
-
-        # MLP block
-        mlp_out = self.mlp(queries)
-        queries = queries + mlp_out
-        queries = self.norm3(queries)
-
-        # Cross attention block, image embedding attending to tokens
-        q = queries + query_pe
-        k = keys + key_pe
-        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
-        keys = keys + attn_out
-        keys = self.norm4(keys)
-
-        return queries, keys
-
-
-class Attention(nn.Module):
-    """
-    An attention layer that allows for downscaling the size of the embedding
-    after projection to queries, keys, and values.
-    """
-
-    def __init__(
-        self,
-        embedding_dim: int,
-        num_heads: int,
-        downsample_rate: int = 1,
-    ) -> None:
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.internal_dim = embedding_dim // downsample_rate
-        self.num_heads = num_heads
-        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
-
-        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
-        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
-        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
-        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
-
-    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
-        b, n, c = x.shape
-        x = x.reshape(b, n, num_heads, c // num_heads)
-        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
-
-    def _recombine_heads(self, x: Tensor) -> Tensor:
-        b, n_heads, n_tokens, c_per_head = x.shape
-        x = x.transpose(1, 2)
-        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
-
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
-        # Input projections
-        q = self.q_proj(q)
-        k = self.k_proj(k)
-        v = self.v_proj(v)
-
-        # Separate into heads
-        q = self._separate_heads(q, self.num_heads)
-        k = self._separate_heads(k, self.num_heads)
-        v = self._separate_heads(v, self.num_heads)
-
-        # Attention
-        _, _, _, c_per_head = q.shape
-        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
-        attn = attn / math.sqrt(c_per_head)
-        attn = torch.softmax(attn, dim=-1)
-
-        # Get output
-        out = attn @ v
-        out = self._recombine_heads(out)
-        out = self.out_proj(out)
-
-        return out
diff --git a/spaces/Kevin676/Clone-Your-Voice/synthesizer/audio.py b/spaces/Kevin676/Clone-Your-Voice/synthesizer/audio.py
deleted file mode 100644
index 83dc96c63c962bc8e13c446d05e27c009fb3239f..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Clone-Your-Voice/synthesizer/audio.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import librosa
-import librosa.filters
-import numpy as np
-from scipy import signal
-from scipy.io import wavfile
-import soundfile as sf
-
-
-def load_wav(path, sr):
-    return librosa.core.load(path, sr=sr)[0]
-
-def save_wav(wav, path, sr):
-    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
-    #proposed by @dsmiller
-    wavfile.write(path, sr, wav.astype(np.int16))
-
-def save_wavenet_wav(wav, path, sr):
-    sf.write(path, wav.astype(np.float32), sr)
-
-def preemphasis(wav, k, preemphasize=True):
-    if preemphasize:
-        return signal.lfilter([1, -k], [1], wav)
-    return wav
-
-def inv_preemphasis(wav, k, inv_preemphasize=True):
-    if inv_preemphasize:
-        return signal.lfilter([1], [1, -k], wav)
-    return wav
-
-#From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py
-def start_and_end_indices(quantized, silence_threshold=2):
-    for start in range(quantized.size):
-        if abs(quantized[start] - 127) > silence_threshold:
-            break
-    for end in range(quantized.size - 1, 1, -1):
-        if abs(quantized[end] - 127) > silence_threshold:
-            break
-    
-    assert abs(quantized[start] - 127) > silence_threshold
-    assert abs(quantized[end] - 127) > silence_threshold
-    
-    return start, end
-
-def get_hop_size(hparams):
-    hop_size = hparams.hop_size
-    if hop_size is None:
-        assert hparams.frame_shift_ms is not None
-        hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
-    return hop_size
-
-def linearspectrogram(wav, hparams):
-    D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
-    S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db
-    
-    if hparams.signal_normalization:
-        return _normalize(S, hparams)
-    return S
-
-def melspectrogram(wav, hparams):
-    D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
-    S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db
-    
-    if hparams.signal_normalization:
-        return _normalize(S, hparams)
-    return S
-
-def inv_linear_spectrogram(linear_spectrogram, hparams):
-    """Converts linear spectrogram to waveform using librosa"""
-    if hparams.signal_normalization:
-        D = _denormalize(linear_spectrogram, hparams)
-    else:
-        D = linear_spectrogram
-    
-    S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear
-    
-    if hparams.use_lws:
-        processor = _lws_processor(hparams)
-        D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
-        y = processor.istft(D).astype(np.float32)
-        return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
-    else:
-        return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
-
-def inv_mel_spectrogram(mel_spectrogram, hparams):
-    """Converts mel spectrogram to waveform using librosa"""
-    if hparams.signal_normalization:
-        D = _denormalize(mel_spectrogram, hparams)
-    else:
-        D = mel_spectrogram
-    
-    S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams)  # Convert back to linear
-    
-    if hparams.use_lws:
-        processor = _lws_processor(hparams)
-        D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
-        y = processor.istft(D).astype(np.float32)
-        return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
-    else:
-        return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
-
-def _lws_processor(hparams):
-    import lws
-    return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech")
-
-def _griffin_lim(S, hparams):
-    """librosa implementation of Griffin-Lim
-    Based on https://github.com/librosa/librosa/issues/434
-    """
-    angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
-    S_complex = np.abs(S).astype(np.complex)
-    y = _istft(S_complex * angles, hparams)
-    for i in range(hparams.griffin_lim_iters):
-        angles = np.exp(1j * np.angle(_stft(y, hparams)))
-        y = _istft(S_complex * angles, hparams)
-    return y
-
-def _stft(y, hparams):
-    if hparams.use_lws:
-        return _lws_processor(hparams).stft(y).T
-    else:
-        return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
-
-def _istft(y, hparams):
-    return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
-
-##########################################################
-#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
-def num_frames(length, fsize, fshift):
-    """Compute number of time frames of spectrogram
-    """
-    pad = (fsize - fshift)
-    if length % fshift == 0:
-        M = (length + pad * 2 - fsize) // fshift + 1
-    else:
-        M = (length + pad * 2 - fsize) // fshift + 2
-    return M
-
-
-def pad_lr(x, fsize, fshift):
-    """Compute left and right padding
-    """
-    M = num_frames(len(x), fsize, fshift)
-    pad = (fsize - fshift)
-    T = len(x) + 2 * pad
-    r = (M - 1) * fshift + fsize - T
-    return pad, pad + r
-##########################################################
-#Librosa correct padding
-def librosa_pad_lr(x, fsize, fshift):
-    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
-
-# Conversions
-_mel_basis = None
-_inv_mel_basis = None
-
-def _linear_to_mel(spectogram, hparams):
-    global _mel_basis
-    if _mel_basis is None:
-        _mel_basis = _build_mel_basis(hparams)
-    return np.dot(_mel_basis, spectogram)
-
-def _mel_to_linear(mel_spectrogram, hparams):
-    global _inv_mel_basis
-    if _inv_mel_basis is None:
-        _inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
-    return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
-
-def _build_mel_basis(hparams):
-    assert hparams.fmax <= hparams.sample_rate // 2
-    return librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels,
-                               fmin=hparams.fmin, fmax=hparams.fmax)
-
-def _amp_to_db(x, hparams):
-    min_level = np.exp(hparams.min_level_db / 20 * np.log(10))
-    return 20 * np.log10(np.maximum(min_level, x))
-
-def _db_to_amp(x):
-    return np.power(10.0, (x) * 0.05)
-
-def _normalize(S, hparams):
-    if hparams.allow_clipping_in_normalization:
-        if hparams.symmetric_mels:
-            return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
-                           -hparams.max_abs_value, hparams.max_abs_value)
-        else:
-            return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)
-    
-    assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
-    if hparams.symmetric_mels:
-        return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
-    else:
-        return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))
-
-def _denormalize(D, hparams):
-    if hparams.allow_clipping_in_normalization:
-        if hparams.symmetric_mels:
-            return (((np.clip(D, -hparams.max_abs_value,
-                              hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value))
-                    + hparams.min_level_db)
-        else:
-            return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
-    
-    if hparams.symmetric_mels:
-        return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
-    else:
-        return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
diff --git a/spaces/LAKSJAKLCNDWNVWHEFKJH/asdfghjkl/info.md b/spaces/LAKSJAKLCNDWNVWHEFKJH/asdfghjkl/info.md
deleted file mode 100644
index f4aa7a73e0d0292ffa9b4a51ad7333043d3bcce6..0000000000000000000000000000000000000000
--- a/spaces/LAKSJAKLCNDWNVWHEFKJH/asdfghjkl/info.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# 😌 [Edit info.md - Your app's title here]
-Cuisine Recommendation
-
-### 🧐 Problem Statement and Research Summary
-[add info about your problem statement and your research here!]
-
-### 🎣 Data Collection Plan
-[Edit info.md - add info about what data you collected and why here!]
-
-### 💥 Ethical Considerations (Data Privacy and Bias)
-* Data privacy: [Edit info.md - add info about you considered users' privacy here!]
-* Bias: [Edit info.md - add info about you considered bias here!]
-
-### 👻 Our Team
-[Edit info.md - add info about your team members here!]
-
-![aiEDU logo](https://images.squarespace-cdn.com/content/v1/5e4efdef6d10420691f02bc1/5db5a8a3-1761-4fce-a096-bd5f2515162f/aiEDU+_black+logo+stacked.png?format=100w)
diff --git a/spaces/Laronix/Laronix_ASR_TTS_VC/local/PAL_dataset.py b/spaces/Laronix/Laronix_ASR_TTS_VC/local/PAL_dataset.py
deleted file mode 100644
index 753e3129075f04f2f2b6074b37179dd261467ad4..0000000000000000000000000000000000000000
--- a/spaces/Laronix/Laronix_ASR_TTS_VC/local/PAL_dataset.py
+++ /dev/null
@@ -1,34 +0,0 @@
-## ADD dataset appendning
-from datasets import load_dataset, Dataset, DatasetDict, concatenate_datasets
-import pdb
-
-import numpy as np
-# to_dataset = load_dataset("KevinGeng/testdataset")
-base_dataset = load_dataset("../laronix_automos/data/Patient_sil_trim_16k_normed_5_snr_40")
-base_extra_dataset = load_dataset("../laronix_automos/data/John_p326_large")
-
-PAL_dataset = DatasetDict({"base": base_dataset['train'], "base_extra": base_extra_dataset['train']})
-# PAL_dataset.push_to_hub("KevinGeng/PAL_dataset")
-concatenate_datasets(base_dataset['train'], base_extra_dataset['train'])
-pdb.set_trace()
-
-new_record = {"audio": 
-    {'path': 'Arthur_set1_001_noisy.wav',
-     'array': np.array([0.02526855, 0.04602051, 0.04873657, 0.00045776, 0.00201416, 0.00167847]),
-     'sampling_rate': 16000},
-    "transcription": "TOD"}
-pdb.set_trace()
-
-import requests
-headers = {"Authorization": f"KevinGeng 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"}
-# headers = {"Authorization": "Haopeng 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"}
-# pdb.set_trace()
-API_URL = "https://datasets-server.huggingface.co/is-valid?dataset=KevinGeng/testdataset"
-
-def query():
-    response = requests.request("GET", API_URL, headers=headers)
-    # pdb.set_trace()
-    return response.json()
-data = query()
-
-pdb.set_trace()
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/brokers/__init__.py b/spaces/Lianjd/stock_dashboard/backtrader/brokers/__init__.py
deleted file mode 100644
index ba060a5603f4eef65197bee513c877e28df57c4f..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/brokers/__init__.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-# The modules below should/must define __all__ with the objects wishes
-# or prepend an "_" (underscore) to private classes/variables
-
-from .bbroker import BackBroker, BrokerBack
-
-try:
-    from .ibbroker import IBBroker
-except ImportError:
-    pass  # The user may not have ibpy installed
-
-try:
-    from .vcbroker import VCBroker
-except ImportError:
-    pass  # The user may not have something installed
-
-try:
-    from .oandabroker import OandaBroker
-except ImportError as e:
-    pass  # The user may not have something installed
diff --git a/spaces/LittleYuan/My-Real-Bot/tests/test_utils.py b/spaces/LittleYuan/My-Real-Bot/tests/test_utils.py
deleted file mode 100644
index 7919b74905495b4b6f4aa957a1f0b5d7a174c782..0000000000000000000000000000000000000000
--- a/spaces/LittleYuan/My-Real-Bot/tests/test_utils.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import numpy as np
-from basicsr.archs.rrdbnet_arch import RRDBNet
-
-from realesrgan.utils import RealESRGANer
-
-
-def test_realesrganer():
-    # initialize with default model
-    restorer = RealESRGANer(
-        scale=4,
-        model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth',
-        model=None,
-        tile=10,
-        tile_pad=10,
-        pre_pad=2,
-        half=False)
-    assert isinstance(restorer.model, RRDBNet)
-    assert restorer.half is False
-    # initialize with user-defined model
-    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-    restorer = RealESRGANer(
-        scale=4,
-        model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth',
-        model=model,
-        tile=10,
-        tile_pad=10,
-        pre_pad=2,
-        half=True)
-    # test attribute
-    assert isinstance(restorer.model, RRDBNet)
-    assert restorer.half is True
-
-    # ------------------ test pre_process ---------------- #
-    img = np.random.random((12, 12, 3)).astype(np.float32)
-    restorer.pre_process(img)
-    assert restorer.img.shape == (1, 3, 14, 14)
-    # with modcrop
-    restorer.scale = 1
-    restorer.pre_process(img)
-    assert restorer.img.shape == (1, 3, 16, 16)
-
-    # ------------------ test process ---------------- #
-    restorer.process()
-    assert restorer.output.shape == (1, 3, 64, 64)
-
-    # ------------------ test post_process ---------------- #
-    restorer.mod_scale = 4
-    output = restorer.post_process()
-    assert output.shape == (1, 3, 60, 60)
-
-    # ------------------ test tile_process ---------------- #
-    restorer.scale = 4
-    img = np.random.random((12, 12, 3)).astype(np.float32)
-    restorer.pre_process(img)
-    restorer.tile_process()
-    assert restorer.output.shape == (1, 3, 64, 64)
-
-    # ------------------ test enhance ---------------- #
-    img = np.random.random((12, 12, 3)).astype(np.float32)
-    result = restorer.enhance(img, outscale=2)
-    assert result[0].shape == (24, 24, 3)
-    assert result[1] == 'RGB'
-
-    # ------------------ test enhance with 16-bit image---------------- #
-    img = np.random.random((4, 4, 3)).astype(np.uint16) + 512
-    result = restorer.enhance(img, outscale=2)
-    assert result[0].shape == (8, 8, 3)
-    assert result[1] == 'RGB'
-
-    # ------------------ test enhance with gray image---------------- #
-    img = np.random.random((4, 4)).astype(np.float32)
-    result = restorer.enhance(img, outscale=2)
-    assert result[0].shape == (8, 8)
-    assert result[1] == 'L'
-
-    # ------------------ test enhance with RGBA---------------- #
-    img = np.random.random((4, 4, 4)).astype(np.float32)
-    result = restorer.enhance(img, outscale=2)
-    assert result[0].shape == (8, 8, 4)
-    assert result[1] == 'RGBA'
-
-    # ------------------ test enhance with RGBA, alpha_upsampler---------------- #
-    restorer.tile_size = 0
-    img = np.random.random((4, 4, 4)).astype(np.float32)
-    result = restorer.enhance(img, outscale=2, alpha_upsampler=None)
-    assert result[0].shape == (8, 8, 4)
-    assert result[1] == 'RGBA'
diff --git "a/spaces/LuxOAI/ChatGpt-Web/.github/ISSUE_TEMPLATE/\345\217\215\351\246\210\351\227\256\351\242\230.md" "b/spaces/LuxOAI/ChatGpt-Web/.github/ISSUE_TEMPLATE/\345\217\215\351\246\210\351\227\256\351\242\230.md"
deleted file mode 100644
index 73ad4b2c6a7546e3f051721dabb2c81de0d0ca25..0000000000000000000000000000000000000000
--- "a/spaces/LuxOAI/ChatGpt-Web/.github/ISSUE_TEMPLATE/\345\217\215\351\246\210\351\227\256\351\242\230.md"
+++ /dev/null
@@ -1,32 +0,0 @@
----
-name: 反馈问题
-about: 请告诉我们你遇到的问题
-title: "[Bug] "
-labels: ''
-assignees: ''
-
----
-
-**反馈须知**
-
-⚠️ 注意：不遵循此模板的任何帖子都会被立即关闭。
-
-请在下方中括号内输入 x 来表示你已经知晓相关内容。
-- [ ] 我确认已经在 [常见问题](https://github.com/Yidadaa/ChatGPT-Next-Web/blob/main/docs/faq-cn.md) 中搜索了此次反馈的问题，没有找到解答；
-- [ ] 我确认已经在 [Issues](https://github.com/Yidadaa/ChatGPT-Next-Web/issues) 列表（包括已经 Close 的）中搜索了此次反馈的问题，没有找到解答。
-- [ ] 我确认已经在 [Vercel 使用教程](https://github.com/Yidadaa/ChatGPT-Next-Web/blob/main/docs/vercel-cn.md) 中搜索了此次反馈的问题，没有找到解答。
-
-**描述问题**
-请在此描述你遇到了什么问题。
-
-**如何复现**
-请告诉我们你是通过什么操作触发的该问题。
-
-**截图**
-请在此提供控制台截图、屏幕截图或者服务端的 log 截图。
-
-**一些必要的信息**
- - 系统：[比如 windows 10/ macos 12/ linux / android 11 / ios 16]
- - 浏览器： [比如 chrome, safari]
- - 版本： [填写设置页面的版本号]
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diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/audio/comb_filters.c b/spaces/Marshalls/testmtd/feature_extraction/madmom/audio/comb_filters.c
deleted file mode 100644
index a4449f28424cf63a419217de07356c400a4780ea..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/audio/comb_filters.c
+++ /dev/null
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-#define __PYX_MARK_ERR_POS(f_index, lineno) \
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-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
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-
-#define __PYX_HAVE__madmom__audio__comb_filters
-#define __PYX_HAVE_API__madmom__audio__comb_filters
-/* Early includes */
-#include <string.h>
-#include <stdio.h>
-#include "numpy/arrayobject.h"
-#include "numpy/ndarrayobject.h"
-#include "numpy/ndarraytypes.h"
-#include "numpy/arrayscalars.h"
-#include "numpy/ufuncobject.h"
-
-    /* NumPy API declarations from "numpy/__init__.pxd" */
-    
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
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-    (sizeof(type) > sizeof(Py_ssize_t) &&\
-          likely(v < (type)PY_SSIZE_T_MAX ||\
-                 v == (type)PY_SSIZE_T_MAX)  &&\
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-                                v == (type)PY_SSIZE_T_MIN)))  ||\
-    (sizeof(type) == sizeof(Py_ssize_t) &&\
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
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-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
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-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
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-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-static PyObject *__pyx_m = NULL;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_cython_runtime = NULL;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static PyObject *__pyx_empty_unicode;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-/* Header.proto */
-#if !defined(CYTHON_CCOMPLEX)
-  #if defined(__cplusplus)
-    #define CYTHON_CCOMPLEX 1
-  #elif defined(_Complex_I)
-    #define CYTHON_CCOMPLEX 1
-  #else
-    #define CYTHON_CCOMPLEX 0
-  #endif
-#endif
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #include <complex>
-  #else
-    #include <complex.h>
-  #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
-  #undef _Complex_I
-  #define _Complex_I 1.0fj
-#endif
-
-
-static const char *__pyx_f[] = {
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-  "__init__.pxd",
-  "type.pxd",
-};
-/* BufferFormatStructs.proto */
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-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
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-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
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-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
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-typedef struct {
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-} __Pyx_BufFmt_StackElem;
-typedef struct {
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-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
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-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-
-/* "../../../.local/lib/python3.8/site-packages/numpy/__init__.pxd":690
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- * 
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- * ctypedef npy_int32      int32_t
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-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-/* "../../../.local/lib/python3.8/site-packages/numpy/__init__.pxd":691
- * 
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t
- */
-typedef npy_int16 __pyx_t_5numpy_int16_t;
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- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int64      int64_t
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-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* SliceObject.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
-        PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
-        int has_cstart, int has_cstop, int wraparound);
-
-/* PyIntCompare.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, long inplace);
-
-/* ArgTypeTest.proto */
-#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
-    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
-        __Pyx__ArgTypeTest(obj, type, name, exact))
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* BufferGetAndValidate.proto */
-#define __Pyx_GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack)\
-    ((obj == Py_None || obj == NULL) ?\
-    (__Pyx_ZeroBuffer(buf), 0) :\
-    __Pyx__GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack))
-static int  __Pyx__GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
-    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
-static void __Pyx_ZeroBuffer(Py_buffer* buf);
-static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
-static Py_ssize_t __Pyx_minusones[] = { -1, -1, -1, -1, -1, -1, -1, -1 };
-static Py_ssize_t __Pyx_zeros[] = { 0, 0, 0, 0, 0, 0, 0, 0 };
-
-/* PyObjectCallNoArg.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-#else
-#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL)
-#endif
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
-
-#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
-#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1)
-/* PyIntCompare.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_NeObjC(PyObject *op1, PyObject *op2, long intval, long inplace);
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* IterFinish.proto */
-static CYTHON_INLINE int __Pyx_IterFinish(void);
-
-/* UnpackItemEndCheck.proto */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PyObjectSetAttrStr.proto */
-#if CYTHON_USE_TYPE_SLOTS
-#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o, n, NULL)
-static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value);
-#else
-#define __Pyx_PyObject_DelAttrStr(o,n)   PyObject_DelAttr(o,n)
-#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
-#endif
-
-/* IncludeStringH.proto */
-#include <string.h>
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* StrEquals.proto */
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
-#else
-#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
-#endif
-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
-
-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* PyErrExceptionMatches.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
-#else
-#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* TypeImport.proto */
-#ifndef __PYX_HAVE_RT_ImportType_proto
-#define __PYX_HAVE_RT_ImportType_proto
-enum __Pyx_ImportType_CheckSize {
-   __Pyx_ImportType_CheckSize_Error = 0,
-   __Pyx_ImportType_CheckSize_Warn = 1,
-   __Pyx_ImportType_CheckSize_Ignore = 2
-};
-static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* CalculateMetaclass.proto */
-static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases);
-
-/* FetchCommonType.proto */
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED 1
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#define __Pyx_CyFunction_GetClassObj(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-    PyCFunctionObject func;
-#if PY_VERSION_HEX < 0x030500A0
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-    PyObject *func_classobj;
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-} __pyx_CyFunctionObject;
-static PyTypeObject *__pyx_CyFunctionType = 0;
-#define __Pyx_CyFunction_Check(obj)  (__Pyx_TypeCheck(obj, __pyx_CyFunctionType))
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *self,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(void);
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* SetNameInClass.proto */
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-#define __Pyx_SetNameInClass(ns, name, value)\
-    (likely(PyDict_CheckExact(ns)) ? _PyDict_SetItem_KnownHash(ns, name, value, ((PyASCIIObject *) name)->hash) : PyObject_SetItem(ns, name, value))
-#elif CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_SetNameInClass(ns, name, value)\
-    (likely(PyDict_CheckExact(ns)) ? PyDict_SetItem(ns, name, value) : PyObject_SetItem(ns, name, value))
-#else
-#define __Pyx_SetNameInClass(ns, name, value)  PyObject_SetItem(ns, name, value)
-#endif
-
-/* Py3ClassCreate.proto */
-static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname,
-                                           PyObject *mkw, PyObject *modname, PyObject *doc);
-static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict,
-                                      PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass);
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-/* BufferStructDeclare.proto */
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-/* GCCDiagnostics.proto */
-#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* RealImag.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if defined(__cplusplus) && CYTHON_CCOMPLEX\
-        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_float(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_float(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_float(a, b) ((a)-(b))
-    #define __Pyx_c_prod_float(a, b) ((a)*(b))
-    #define __Pyx_c_quot_float(a, b) ((a)/(b))
-    #define __Pyx_c_neg_float(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)
-    #define __Pyx_c_conj_float(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_float(z)     (::std::abs(z))
-        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_float(z) ((z)==0)
-    #define __Pyx_c_conj_float(z)    (conjf(z))
-    #if 1
-        #define __Pyx_c_abs_float(z)     (cabsf(z))
-        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    #endif
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_double(a, b) ((a)-(b))
-    #define __Pyx_c_prod_double(a, b) ((a)*(b))
-    #define __Pyx_c_quot_double(a, b) ((a)/(b))
-    #define __Pyx_c_neg_double(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
-    #define __Pyx_c_conj_double(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (::std::abs(z))
-        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_double(z) ((z)==0)
-    #define __Pyx_c_conj_double(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (cabs(z))
-        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    #endif
-#endif
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'cpython.buffer' */
-
-/* Module declarations from 'libc.string' */
-
-/* Module declarations from 'libc.stdio' */
-
-/* Module declarations from '__builtin__' */
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-/* "madmom/audio/comb_filters.pyx":19
- * 
- * # feed forward comb filter
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-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* PyCFunctionFastCall */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
-    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
-    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
-    PyObject *self = PyCFunction_GET_SELF(func);
-    int flags = PyCFunction_GET_FLAGS(func);
-    assert(PyCFunction_Check(func));
-    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
-    assert(nargs >= 0);
-    assert(nargs == 0 || args != NULL);
-    /* _PyCFunction_FastCallDict() must not be called with an exception set,
-       because it may clear it (directly or indirectly) and so the
-       caller loses its exception */
-    assert(!PyErr_Occurred());
-    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
-        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
-    } else {
-        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
-    }
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args, *result = NULL;
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyFunction_FastCall(function, args, 2);
-    }
-    #endif
-    #if CYTHON_FAST_PYCCALL
-    if (__Pyx_PyFastCFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyCFunction_FastCall(function, args, 2);
-    }
-    #endif
-    args = PyTuple_New(2);
-    if (unlikely(!args)) goto done;
-    Py_INCREF(arg1);
-    PyTuple_SET_ITEM(args, 0, arg1);
-    Py_INCREF(arg2);
-    PyTuple_SET_ITEM(args, 1, arg2);
-    Py_INCREF(function);
-    result = __Pyx_PyObject_Call(function, args, NULL);
-    Py_DECREF(args);
-    Py_DECREF(function);
-done:
-    return result;
-}
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallOneArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_New(1);
-    if (unlikely(!args)) return NULL;
-    Py_INCREF(arg);
-    PyTuple_SET_ITEM(args, 0, arg);
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, &arg, 1);
-    }
-#endif
-    if (likely(PyCFunction_Check(func))) {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-            return __Pyx_PyObject_CallMethO(func, arg);
-#if CYTHON_FAST_PYCCALL
-        } else if (__Pyx_PyFastCFunction_Check(func)) {
-            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
-#endif
-        }
-    }
-    return __Pyx__PyObject_CallOneArg(func, arg);
-}
-#else
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_Pack(1, arg);
-    if (unlikely(!args)) return NULL;
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-#endif
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
-    PyObject *runerr;
-    Py_ssize_t key_value;
-    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
-    if (unlikely(!(m && m->sq_item))) {
-        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
-        return NULL;
-    }
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
-    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(m && m->mp_subscript)) {
-        return m->mp_subscript(obj, key);
-    }
-    return __Pyx_PyObject_GetIndex(obj, key);
-}
-#endif
-
-/* SliceObject */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
-        Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
-        int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
-#if CYTHON_USE_TYPE_SLOTS
-    PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
-    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
-    if (likely(ms && ms->sq_slice)) {
-        if (!has_cstart) {
-            if (_py_start && (*_py_start != Py_None)) {
-                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
-                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstart = 0;
-        }
-        if (!has_cstop) {
-            if (_py_stop && (*_py_stop != Py_None)) {
-                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
-                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstop = PY_SSIZE_T_MAX;
-        }
-        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
-            Py_ssize_t l = ms->sq_length(obj);
-            if (likely(l >= 0)) {
-                if (cstop < 0) {
-                    cstop += l;
-                    if (cstop < 0) cstop = 0;
-                }
-                if (cstart < 0) {
-                    cstart += l;
-                    if (cstart < 0) cstart = 0;
-                }
-            } else {
-                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                    goto bad;
-                PyErr_Clear();
-            }
-        }
-        return ms->sq_slice(obj, cstart, cstop);
-    }
-#endif
-    mp = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(mp && mp->mp_subscript))
-#endif
-    {
-        PyObject* result;
-        PyObject *py_slice, *py_start, *py_stop;
-        if (_py_slice) {
-            py_slice = *_py_slice;
-        } else {
-            PyObject* owned_start = NULL;
-            PyObject* owned_stop = NULL;
-            if (_py_start) {
-                py_start = *_py_start;
-            } else {
-                if (has_cstart) {
-                    owned_start = py_start = PyInt_FromSsize_t(cstart);
-                    if (unlikely(!py_start)) goto bad;
-                } else
-                    py_start = Py_None;
-            }
-            if (_py_stop) {
-                py_stop = *_py_stop;
-            } else {
-                if (has_cstop) {
-                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
-                    if (unlikely(!py_stop)) {
-                        Py_XDECREF(owned_start);
-                        goto bad;
-                    }
-                } else
-                    py_stop = Py_None;
-            }
-            py_slice = PySlice_New(py_start, py_stop, Py_None);
-            Py_XDECREF(owned_start);
-            Py_XDECREF(owned_stop);
-            if (unlikely(!py_slice)) goto bad;
-        }
-#if CYTHON_USE_TYPE_SLOTS
-        result = mp->mp_subscript(obj, py_slice);
-#else
-        result = PyObject_GetItem(obj, py_slice);
-#endif
-        if (!_py_slice) {
-            Py_DECREF(py_slice);
-        }
-        return result;
-    }
-    PyErr_Format(PyExc_TypeError,
-        "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
-bad:
-    return NULL;
-}
-
-/* PyIntCompare */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED long inplace) {
-    if (op1 == op2) {
-        Py_RETURN_TRUE;
-    }
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long a = PyInt_AS_LONG(op1);
-        if (a == b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        int unequal;
-        unsigned long uintval;
-        Py_ssize_t size = Py_SIZE(op1);
-        const digit* digits = ((PyLongObject*)op1)->ob_digit;
-        if (intval == 0) {
-            if (size == 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-        } else if (intval < 0) {
-            if (size >= 0)
-                Py_RETURN_FALSE;
-            intval = -intval;
-            size = -size;
-        } else {
-            if (size <= 0)
-                Py_RETURN_FALSE;
-        }
-        uintval = (unsigned long) intval;
-#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 4)) {
-            unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 3)) {
-            unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 2)) {
-            unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 1)) {
-            unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
-        if (unequal == 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-        double a = PyFloat_AS_DOUBLE(op1);
-        if ((double)a == (double)b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-    }
-    return (
-        PyObject_RichCompare(op1, op2, Py_EQ));
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
-        name, type->tp_name, Py_TYPE(obj)->tp_name);
-    return 0;
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparseable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* BufferGetAndValidate */
-  static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
-  if (unlikely(info->buf == NULL)) return;
-  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
-  __Pyx_ReleaseBuffer(info);
-}
-static void __Pyx_ZeroBuffer(Py_buffer* buf) {
-  buf->buf = NULL;
-  buf->obj = NULL;
-  buf->strides = __Pyx_zeros;
-  buf->shape = __Pyx_zeros;
-  buf->suboffsets = __Pyx_minusones;
-}
-static int __Pyx__GetBufferAndValidate(
-        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
-        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
-{
-  buf->buf = NULL;
-  if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {
-    __Pyx_ZeroBuffer(buf);
-    return -1;
-  }
-  if (unlikely(buf->ndim != nd)) {
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer has wrong number of dimensions (expected %d, got %d)",
-                 nd, buf->ndim);
-    goto fail;
-  }
-  if (!cast) {
-    __Pyx_BufFmt_Context ctx;
-    __Pyx_BufFmt_Init(&ctx, stack, dtype);
-    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
-  }
-  if (unlikely((size_t)buf->itemsize != dtype->size)) {
-    PyErr_Format(PyExc_ValueError,
-      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
-      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
-      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
-    goto fail;
-  }
-  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
-  return 0;
-fail:;
-  __Pyx_SafeReleaseBuffer(buf);
-  return -1;
-}
-
-/* PyObjectCallNoArg */
-  #if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, NULL, 0);
-    }
-#endif
-#ifdef __Pyx_CyFunction_USED
-    if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func)))
-#else
-    if (likely(PyCFunction_Check(func)))
-#endif
-    {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-            return __Pyx_PyObject_CallMethO(func, NULL);
-        }
-    }
-    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
-}
-#endif
-
-/* ExtTypeTest */
-  static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
-}
-
-/* PyIntCompare */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_NeObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED long inplace) {
-    if (op1 == op2) {
-        Py_RETURN_FALSE;
-    }
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long a = PyInt_AS_LONG(op1);
-        if (a != b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        int unequal;
-        unsigned long uintval;
-        Py_ssize_t size = Py_SIZE(op1);
-        const digit* digits = ((PyLongObject*)op1)->ob_digit;
-        if (intval == 0) {
-            if (size != 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-        } else if (intval < 0) {
-            if (size >= 0)
-                Py_RETURN_TRUE;
-            intval = -intval;
-            size = -size;
-        } else {
-            if (size <= 0)
-                Py_RETURN_TRUE;
-        }
-        uintval = (unsigned long) intval;
-#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 4)) {
-            unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 3)) {
-            unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 2)) {
-            unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * 1)) {
-            unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
-        } else
-#endif
-            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
-        if (unequal != 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-        double a = PyFloat_AS_DOUBLE(op1);
-        if ((double)a != (double)b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
-    }
-    return (
-        PyObject_RichCompare(op1, op2, Py_NE));
-}
-
-/* RaiseTooManyValuesToUnpack */
-  static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-  static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* IterFinish */
-  static CYTHON_INLINE int __Pyx_IterFinish(void) {
-#if CYTHON_FAST_THREAD_STATE
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject* exc_type = tstate->curexc_type;
-    if (unlikely(exc_type)) {
-        if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) {
-            PyObject *exc_value, *exc_tb;
-            exc_value = tstate->curexc_value;
-            exc_tb = tstate->curexc_traceback;
-            tstate->curexc_type = 0;
-            tstate->curexc_value = 0;
-            tstate->curexc_traceback = 0;
-            Py_DECREF(exc_type);
-            Py_XDECREF(exc_value);
-            Py_XDECREF(exc_tb);
-            return 0;
-        } else {
-            return -1;
-        }
-    }
-    return 0;
-#else
-    if (unlikely(PyErr_Occurred())) {
-        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
-            PyErr_Clear();
-            return 0;
-        } else {
-            return -1;
-        }
-    }
-    return 0;
-#endif
-}
-
-/* UnpackItemEndCheck */
-  static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
-    if (unlikely(retval)) {
-        Py_DECREF(retval);
-        __Pyx_RaiseTooManyValuesError(expected);
-        return -1;
-    } else {
-        return __Pyx_IterFinish();
-    }
-    return 0;
-}
-
-/* PyObjectSetAttrStr */
-  #if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_setattro))
-        return tp->tp_setattro(obj, attr_name, value);
-#if PY_MAJOR_VERSION < 3
-    if (likely(tp->tp_setattr))
-        return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
-#endif
-    return PyObject_SetAttr(obj, attr_name, value);
-}
-#endif
-
-/* BytesEquals */
-  static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-  static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* GetTopmostException */
-  #if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-  #if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    #endif
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-#endif
-
-/* PyErrExceptionMatches */
-  #if CYTHON_FAST_THREAD_STATE
-static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
-    PyObject *exc_type = tstate->curexc_type;
-    if (exc_type == err) return 1;
-    if (unlikely(!exc_type)) return 0;
-    if (unlikely(PyTuple_Check(err)))
-        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
-    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetException */
-  #if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* TypeImport */
-  #ifndef __PYX_HAVE_RT_ImportType
-#define __PYX_HAVE_RT_ImportType
-static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,
-    size_t size, enum __Pyx_ImportType_CheckSize check_size)
-{
-    PyObject *result = 0;
-    char warning[200];
-    Py_ssize_t basicsize;
-#ifdef Py_LIMITED_API
-    PyObject *py_basicsize;
-#endif
-    result = PyObject_GetAttrString(module, class_name);
-    if (!result)
-        goto bad;
-    if (!PyType_Check(result)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s.%.200s is not a type object",
-            module_name, class_name);
-        goto bad;
-    }
-#ifndef Py_LIMITED_API
-    basicsize = ((PyTypeObject *)result)->tp_basicsize;
-#else
-    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
-    if (!py_basicsize)
-        goto bad;
-    basicsize = PyLong_AsSsize_t(py_basicsize);
-    Py_DECREF(py_basicsize);
-    py_basicsize = 0;
-    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
-        goto bad;
-#endif
-    if ((size_t)basicsize < size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        goto bad;
-    }
-    if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        goto bad;
-    }
-    else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {
-        PyOS_snprintf(warning, sizeof(warning),
-            "%s.%s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
-    }
-    return (PyTypeObject *)result;
-bad:
-    Py_XDECREF(result);
-    return NULL;
-}
-#endif
-
-/* Import */
-  static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *empty_list = 0;
-    PyObject *module = 0;
-    PyObject *global_dict = 0;
-    PyObject *empty_dict = 0;
-    PyObject *list;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (!py_import)
-        goto bad;
-    #endif
-    if (from_list)
-        list = from_list;
-    else {
-        empty_list = PyList_New(0);
-        if (!empty_list)
-            goto bad;
-        list = empty_list;
-    }
-    global_dict = PyModule_GetDict(__pyx_m);
-    if (!global_dict)
-        goto bad;
-    empty_dict = PyDict_New();
-    if (!empty_dict)
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                module = PyImport_ImportModuleLevelObject(
-                    name, global_dict, empty_dict, list, 1);
-                if (!module) {
-                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (!py_level)
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, global_dict, empty_dict, list, level);
-            #endif
-        }
-    }
-bad:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    Py_XDECREF(empty_list);
-    Py_XDECREF(empty_dict);
-    return module;
-}
-
-/* ImportFrom */
-  static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* CalculateMetaclass */
-  static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) {
-    Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases);
-    for (i=0; i < nbases; i++) {
-        PyTypeObject *tmptype;
-        PyObject *tmp = PyTuple_GET_ITEM(bases, i);
-        tmptype = Py_TYPE(tmp);
-#if PY_MAJOR_VERSION < 3
-        if (tmptype == &PyClass_Type)
-            continue;
-#endif
-        if (!metaclass) {
-            metaclass = tmptype;
-            continue;
-        }
-        if (PyType_IsSubtype(metaclass, tmptype))
-            continue;
-        if (PyType_IsSubtype(tmptype, metaclass)) {
-            metaclass = tmptype;
-            continue;
-        }
-        PyErr_SetString(PyExc_TypeError,
-                        "metaclass conflict: "
-                        "the metaclass of a derived class "
-                        "must be a (non-strict) subclass "
-                        "of the metaclasses of all its bases");
-        return NULL;
-    }
-    if (!metaclass) {
-#if PY_MAJOR_VERSION < 3
-        metaclass = &PyClass_Type;
-#else
-        metaclass = &PyType_Type;
-#endif
-    }
-    Py_INCREF((PyObject*) metaclass);
-    return (PyObject*) metaclass;
-}
-
-/* FetchCommonType */
-  static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* fake_module;
-    PyTypeObject* cached_type = NULL;
-    fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
-    if (!fake_module) return NULL;
-    Py_INCREF(fake_module);
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name);
-    if (cached_type) {
-        if (!PyType_Check((PyObject*)cached_type)) {
-            PyErr_Format(PyExc_TypeError,
-                "Shared Cython type %.200s is not a type object",
-                type->tp_name);
-            goto bad;
-        }
-        if (cached_type->tp_basicsize != type->tp_basicsize) {
-            PyErr_Format(PyExc_TypeError,
-                "Shared Cython type %.200s has the wrong size, try recompiling",
-                type->tp_name);
-            goto bad;
-        }
-    } else {
-        if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-        PyErr_Clear();
-        if (PyType_Ready(type) < 0) goto bad;
-        if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0)
-            goto bad;
-        Py_INCREF(type);
-        cached_type = type;
-    }
-done:
-    Py_DECREF(fake_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-
-/* CythonFunctionShared */
-  #include <structmember.h>
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure)
-{
-    if (unlikely(op->func_doc == NULL)) {
-        if (op->func.m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(op->func.m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp = op->func_doc;
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    op->func_doc = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(op->func.m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    tmp = op->func_name;
-    Py_INCREF(value);
-    op->func_name = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    tmp = op->func_qualname;
-    Py_INCREF(value);
-    op->func_qualname = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure)
-{
-    PyObject *self;
-    self = m->func_closure;
-    if (self == NULL)
-        self = Py_None;
-    Py_INCREF(self);
-    return self;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    tmp = op->func_dict;
-    Py_INCREF(value);
-    op->func_dict = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) {
-    PyObject* tmp;
-    if (!value) {
-        value = Py_None;
-    } else if (value != Py_None && !PyTuple_Check(value)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    Py_INCREF(value);
-    tmp = op->defaults_tuple;
-    op->defaults_tuple = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) {
-    PyObject* result = op->defaults_tuple;
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) {
-    PyObject* tmp;
-    if (!value) {
-        value = Py_None;
-    } else if (value != Py_None && !PyDict_Check(value)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    Py_INCREF(value);
-    tmp = op->defaults_kwdict;
-    op->defaults_kwdict = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) {
-    PyObject* result = op->defaults_kwdict;
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) {
-    PyObject* tmp;
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (!PyDict_Check(value)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    tmp = op->func_annotations;
-    op->func_annotations = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) {
-    PyObject* result = op->func_annotations;
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0},
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromString(m->func.m_ml->ml_name);
-#else
-    return PyString_FromString(m->func.m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    op->func.m_ml = ml;
-    op->func.m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    op->func.m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-    op->func_classobj = NULL;
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(m->func.m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-    Py_CLEAR(m->func_classobj);
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    PyObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(m->func.m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(m->func_classobj);
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type)
-{
-#if PY_MAJOR_VERSION < 3
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) {
-        Py_INCREF(func);
-        return func;
-    }
-    if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) {
-        if (type == NULL)
-            type = (PyObject *)(Py_TYPE(obj));
-        return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type)));
-    }
-    if (obj == Py_None)
-        obj = NULL;
-#endif
-    return __Pyx_PyMethod_New(func, obj, type);
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags in "
-                        "__Pyx_CyFunction_Call. METH_OLDARGS is no "
-                        "longer supported!");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-    0,
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_CyFunction_descr_get,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-    0,
-#endif
-};
-static int __pyx_CyFunction_init(void) {
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-  static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* Py3ClassCreate */
-  static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name,
-                                           PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) {
-    PyObject *ns;
-    if (metaclass) {
-        PyObject *prep = __Pyx_PyObject_GetAttrStr(metaclass, __pyx_n_s_prepare);
-        if (prep) {
-            PyObject *pargs = PyTuple_Pack(2, name, bases);
-            if (unlikely(!pargs)) {
-                Py_DECREF(prep);
-                return NULL;
-            }
-            ns = PyObject_Call(prep, pargs, mkw);
-            Py_DECREF(prep);
-            Py_DECREF(pargs);
-        } else {
-            if (unlikely(!PyErr_ExceptionMatches(PyExc_AttributeError)))
-                return NULL;
-            PyErr_Clear();
-            ns = PyDict_New();
-        }
-    } else {
-        ns = PyDict_New();
-    }
-    if (unlikely(!ns))
-        return NULL;
-    if (unlikely(PyObject_SetItem(ns, __pyx_n_s_module, modname) < 0)) goto bad;
-    if (unlikely(PyObject_SetItem(ns, __pyx_n_s_qualname, qualname) < 0)) goto bad;
-    if (unlikely(doc && PyObject_SetItem(ns, __pyx_n_s_doc, doc) < 0)) goto bad;
-    return ns;
-bad:
-    Py_DECREF(ns);
-    return NULL;
-}
-static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases,
-                                      PyObject *dict, PyObject *mkw,
-                                      int calculate_metaclass, int allow_py2_metaclass) {
-    PyObject *result, *margs;
-    PyObject *owned_metaclass = NULL;
-    if (allow_py2_metaclass) {
-        owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass);
-        if (owned_metaclass) {
-            metaclass = owned_metaclass;
-        } else if (likely(PyErr_ExceptionMatches(PyExc_KeyError))) {
-            PyErr_Clear();
-        } else {
-            return NULL;
-        }
-    }
-    if (calculate_metaclass && (!metaclass || PyType_Check(metaclass))) {
-        metaclass = __Pyx_CalculateMetaclass((PyTypeObject*) metaclass, bases);
-        Py_XDECREF(owned_metaclass);
-        if (unlikely(!metaclass))
-            return NULL;
-        owned_metaclass = metaclass;
-    }
-    margs = PyTuple_Pack(3, name, bases, dict);
-    if (unlikely(!margs)) {
-        result = NULL;
-    } else {
-        result = PyObject_Call(metaclass, margs, mkw);
-        Py_DECREF(margs);
-    }
-    Py_XDECREF(owned_metaclass);
-    return result;
-}
-
-/* CLineInTraceback */
-  #ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-  static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-/* AddTraceback */
-  #include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyObject *py_srcfile = 0;
-    PyObject *py_funcname = 0;
-    #if PY_MAJOR_VERSION < 3
-    py_srcfile = PyString_FromString(filename);
-    #else
-    py_srcfile = PyUnicode_FromString(filename);
-    #endif
-    if (!py_srcfile) goto bad;
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        #else
-        py_funcname = PyUnicode_FromString(funcname);
-        #endif
-    }
-    if (!py_funcname) goto bad;
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    Py_DECREF(py_funcname);
-    return py_code;
-bad:
-    Py_XDECREF(py_srcfile);
-    Py_XDECREF(py_funcname);
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) goto bad;
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-  /* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* Declarations */
-  #if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return ::std::complex< float >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return x + y*(__pyx_t_float_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      __pyx_t_float_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-  #if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabsf(b.real) >= fabsf(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                float r = b.imag / b.real;
-                float s = (float)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_float_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            float r = b.real / b.imag;
-            float s = (float)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_float_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            float denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_float_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrtf(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypotf(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-            __pyx_t_float_complex z;
-            float r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    float denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_float(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_float(a, a);
-                        return __Pyx_c_prod_float(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_float(a, a);
-                        return __Pyx_c_prod_float(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if (b.imag == 0) {
-                    z.real = powf(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2f(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_float(a);
-                theta = atan2f(a.imag, a.real);
-            }
-            lnr = logf(r);
-            z_r = expf(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cosf(z_theta);
-            z.imag = z_r * sinf(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* Declarations */
-  #if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-  #if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabs(b.real) >= fabs(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                double r = b.imag / b.real;
-                double s = (double)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_double_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            double r = b.real / b.imag;
-            double s = (double)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_double_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            double denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_double_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_double(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if (b.imag == 0) {
-                    z.real = pow(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_double(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* CIntFromPy */
-  static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(unsigned int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(unsigned int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (unsigned int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (unsigned int) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) >= 2 * PyLong_SHIFT) {
-                            return (unsigned int) (((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) >= 3 * PyLong_SHIFT) {
-                            return (unsigned int) (((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) >= 4 * PyLong_SHIFT) {
-                            return (unsigned int) (((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (unsigned int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(unsigned int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (unsigned int) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(unsigned int, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(unsigned int,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(unsigned int) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) {
-                            return (unsigned int) (((unsigned int)-1)*(((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) {
-                            return (unsigned int) ((((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) {
-                            return (unsigned int) (((unsigned int)-1)*(((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) {
-                            return (unsigned int) ((((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) {
-                            return (unsigned int) (((unsigned int)-1)*(((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) {
-                            return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(unsigned int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            unsigned int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (unsigned int) -1;
-        }
-    } else {
-        unsigned int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (unsigned int) -1;
-        val = __Pyx_PyInt_As_unsigned_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to unsigned int");
-    return (unsigned int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to unsigned int");
-    return (unsigned int) -1;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(unsigned int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(unsigned int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(unsigned int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(unsigned int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
-                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
-                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
-                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
-                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
-                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
-                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* FastTypeChecks */
-  #if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = a->tp_base;
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
-    if (!res) {
-        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-    return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[4], rtversion[4];
-    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
-    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
-        char message[200];
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type %.200s).  "
-                "The ability to return an instance of a strict subclass of int "
-                "is deprecated, and may be removed in a future version of Python.",
-                Py_TYPE(result)->tp_name)) {
-            Py_DECREF(result);
-            return NULL;
-        }
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type %.200s)",
-                 type_name, type_name, Py_TYPE(result)->tp_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    const digit* digits = ((PyLongObject*)b)->ob_digit;
-    const Py_ssize_t size = Py_SIZE(b);
-    if (likely(__Pyx_sst_abs(size) <= 1)) {
-        ival = likely(size) ? digits[0] : 0;
-        if (size == -1) ival = -ival;
-        return ival;
-    } else {
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/__init__.py b/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MathysL/AutoGPT4/autogpt/memory/milvus.py b/spaces/MathysL/AutoGPT4/autogpt/memory/milvus.py
deleted file mode 100644
index 44aa72b956224fa4c2a16d5f40b0eaeb35e98581..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/autogpt/memory/milvus.py
+++ /dev/null
@@ -1,115 +0,0 @@
-""" Milvus memory storage provider."""
-from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections
-
-from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
-
-
-class MilvusMemory(MemoryProviderSingleton):
-    """Milvus memory storage provider."""
-
-    def __init__(self, cfg) -> None:
-        """Construct a milvus memory storage connection.
-
-        Args:
-            cfg (Config): Auto-GPT global config.
-        """
-        # connect to milvus server.
-        connections.connect(address=cfg.milvus_addr)
-        fields = [
-            FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
-            FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
-            FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535),
-        ]
-
-        # create collection if not exist and load it.
-        self.milvus_collection = cfg.milvus_collection
-        self.schema = CollectionSchema(fields, "auto-gpt memory storage")
-        self.collection = Collection(self.milvus_collection, self.schema)
-        # create index if not exist.
-        if not self.collection.has_index():
-            self.collection.release()
-            self.collection.create_index(
-                "embeddings",
-                {
-                    "metric_type": "IP",
-                    "index_type": "HNSW",
-                    "params": {"M": 8, "efConstruction": 64},
-                },
-                index_name="embeddings",
-            )
-        self.collection.load()
-
-    def add(self, data) -> str:
-        """Add an embedding of data into memory.
-
-        Args:
-            data (str): The raw text to construct embedding index.
-
-        Returns:
-            str: log.
-        """
-        embedding = get_ada_embedding(data)
-        result = self.collection.insert([[embedding], [data]])
-        _text = (
-            "Inserting data into memory at primary key: "
-            f"{result.primary_keys[0]}:\n data: {data}"
-        )
-        return _text
-
-    def get(self, data):
-        """Return the most relevant data in memory.
-        Args:
-            data: The data to compare to.
-        """
-        return self.get_relevant(data, 1)
-
-    def clear(self) -> str:
-        """Drop the index in memory.
-
-        Returns:
-            str: log.
-        """
-        self.collection.drop()
-        self.collection = Collection(self.milvus_collection, self.schema)
-        self.collection.create_index(
-            "embeddings",
-            {
-                "metric_type": "IP",
-                "index_type": "HNSW",
-                "params": {"M": 8, "efConstruction": 64},
-            },
-            index_name="embeddings",
-        )
-        self.collection.load()
-        return "Obliviated"
-
-    def get_relevant(self, data: str, num_relevant: int = 5):
-        """Return the top-k relevant data in memory.
-        Args:
-            data: The data to compare to.
-            num_relevant (int, optional): The max number of relevant data.
-                Defaults to 5.
-
-        Returns:
-            list: The top-k relevant data.
-        """
-        # search the embedding and return the most relevant text.
-        embedding = get_ada_embedding(data)
-        search_params = {
-            "metrics_type": "IP",
-            "params": {"nprobe": 8},
-        }
-        result = self.collection.search(
-            [embedding],
-            "embeddings",
-            search_params,
-            num_relevant,
-            output_fields=["raw_text"],
-        )
-        return [item.entity.value_of_field("raw_text") for item in result[0]]
-
-    def get_stats(self) -> str:
-        """
-        Returns: The stats of the milvus cache.
-        """
-        return f"Entities num: {self.collection.num_entities}"
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/decode_heads/decode_head.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/decode_heads/decode_head.py
deleted file mode 100644
index 88a661b8f6fec5d4c031d3d85e80777ee63951a6..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/decode_heads/decode_head.py
+++ /dev/null
@@ -1,234 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import normal_init
-from annotator.uniformer.mmcv.runner import auto_fp16, force_fp32
-
-from annotator.uniformer.mmseg.core import build_pixel_sampler
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import build_loss
-from ..losses import accuracy
-
-
-class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
-    """Base class for BaseDecodeHead.
-
-    Args:
-        in_channels (int|Sequence[int]): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        num_classes (int): Number of classes.
-        dropout_ratio (float): Ratio of dropout layer. Default: 0.1.
-        conv_cfg (dict|None): Config of conv layers. Default: None.
-        norm_cfg (dict|None): Config of norm layers. Default: None.
-        act_cfg (dict): Config of activation layers.
-            Default: dict(type='ReLU')
-        in_index (int|Sequence[int]): Input feature index. Default: -1
-        input_transform (str|None): Transformation type of input features.
-            Options: 'resize_concat', 'multiple_select', None.
-            'resize_concat': Multiple feature maps will be resize to the
-                same size as first one and than concat together.
-                Usually used in FCN head of HRNet.
-            'multiple_select': Multiple feature maps will be bundle into
-                a list and passed into decode head.
-            None: Only one select feature map is allowed.
-            Default: None.
-        loss_decode (dict): Config of decode loss.
-            Default: dict(type='CrossEntropyLoss').
-        ignore_index (int | None): The label index to be ignored. When using
-            masked BCE loss, ignore_index should be set to None. Default: 255
-        sampler (dict|None): The config of segmentation map sampler.
-            Default: None.
-        align_corners (bool): align_corners argument of F.interpolate.
-            Default: False.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 channels,
-                 *,
-                 num_classes,
-                 dropout_ratio=0.1,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 act_cfg=dict(type='ReLU'),
-                 in_index=-1,
-                 input_transform=None,
-                 loss_decode=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     loss_weight=1.0),
-                 ignore_index=255,
-                 sampler=None,
-                 align_corners=False):
-        super(BaseDecodeHead, self).__init__()
-        self._init_inputs(in_channels, in_index, input_transform)
-        self.channels = channels
-        self.num_classes = num_classes
-        self.dropout_ratio = dropout_ratio
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.in_index = in_index
-        self.loss_decode = build_loss(loss_decode)
-        self.ignore_index = ignore_index
-        self.align_corners = align_corners
-        if sampler is not None:
-            self.sampler = build_pixel_sampler(sampler, context=self)
-        else:
-            self.sampler = None
-
-        self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1)
-        if dropout_ratio > 0:
-            self.dropout = nn.Dropout2d(dropout_ratio)
-        else:
-            self.dropout = None
-        self.fp16_enabled = False
-
-    def extra_repr(self):
-        """Extra repr."""
-        s = f'input_transform={self.input_transform}, ' \
-            f'ignore_index={self.ignore_index}, ' \
-            f'align_corners={self.align_corners}'
-        return s
-
-    def _init_inputs(self, in_channels, in_index, input_transform):
-        """Check and initialize input transforms.
-
-        The in_channels, in_index and input_transform must match.
-        Specifically, when input_transform is None, only single feature map
-        will be selected. So in_channels and in_index must be of type int.
-        When input_transform
-
-        Args:
-            in_channels (int|Sequence[int]): Input channels.
-            in_index (int|Sequence[int]): Input feature index.
-            input_transform (str|None): Transformation type of input features.
-                Options: 'resize_concat', 'multiple_select', None.
-                'resize_concat': Multiple feature maps will be resize to the
-                    same size as first one and than concat together.
-                    Usually used in FCN head of HRNet.
-                'multiple_select': Multiple feature maps will be bundle into
-                    a list and passed into decode head.
-                None: Only one select feature map is allowed.
-        """
-
-        if input_transform is not None:
-            assert input_transform in ['resize_concat', 'multiple_select']
-        self.input_transform = input_transform
-        self.in_index = in_index
-        if input_transform is not None:
-            assert isinstance(in_channels, (list, tuple))
-            assert isinstance(in_index, (list, tuple))
-            assert len(in_channels) == len(in_index)
-            if input_transform == 'resize_concat':
-                self.in_channels = sum(in_channels)
-            else:
-                self.in_channels = in_channels
-        else:
-            assert isinstance(in_channels, int)
-            assert isinstance(in_index, int)
-            self.in_channels = in_channels
-
-    def init_weights(self):
-        """Initialize weights of classification layer."""
-        normal_init(self.conv_seg, mean=0, std=0.01)
-
-    def _transform_inputs(self, inputs):
-        """Transform inputs for decoder.
-
-        Args:
-            inputs (list[Tensor]): List of multi-level img features.
-
-        Returns:
-            Tensor: The transformed inputs
-        """
-
-        if self.input_transform == 'resize_concat':
-            inputs = [inputs[i] for i in self.in_index]
-            upsampled_inputs = [
-                resize(
-                    input=x,
-                    size=inputs[0].shape[2:],
-                    mode='bilinear',
-                    align_corners=self.align_corners) for x in inputs
-            ]
-            inputs = torch.cat(upsampled_inputs, dim=1)
-        elif self.input_transform == 'multiple_select':
-            inputs = [inputs[i] for i in self.in_index]
-        else:
-            inputs = inputs[self.in_index]
-
-        return inputs
-
-    @auto_fp16()
-    @abstractmethod
-    def forward(self, inputs):
-        """Placeholder of forward function."""
-        pass
-
-    def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):
-        """Forward function for training.
-        Args:
-            inputs (list[Tensor]): List of multi-level img features.
-            img_metas (list[dict]): List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmseg/datasets/pipelines/formatting.py:Collect`.
-            gt_semantic_seg (Tensor): Semantic segmentation masks
-                used if the architecture supports semantic segmentation task.
-            train_cfg (dict): The training config.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        seg_logits = self.forward(inputs)
-        losses = self.losses(seg_logits, gt_semantic_seg)
-        return losses
-
-    def forward_test(self, inputs, img_metas, test_cfg):
-        """Forward function for testing.
-
-        Args:
-            inputs (list[Tensor]): List of multi-level img features.
-            img_metas (list[dict]): List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmseg/datasets/pipelines/formatting.py:Collect`.
-            test_cfg (dict): The testing config.
-
-        Returns:
-            Tensor: Output segmentation map.
-        """
-        return self.forward(inputs)
-
-    def cls_seg(self, feat):
-        """Classify each pixel."""
-        if self.dropout is not None:
-            feat = self.dropout(feat)
-        output = self.conv_seg(feat)
-        return output
-
-    @force_fp32(apply_to=('seg_logit', ))
-    def losses(self, seg_logit, seg_label):
-        """Compute segmentation loss."""
-        loss = dict()
-        seg_logit = resize(
-            input=seg_logit,
-            size=seg_label.shape[2:],
-            mode='bilinear',
-            align_corners=self.align_corners)
-        if self.sampler is not None:
-            seg_weight = self.sampler.sample(seg_logit, seg_label)
-        else:
-            seg_weight = None
-        seg_label = seg_label.squeeze(1)
-        loss['loss_seg'] = self.loss_decode(
-            seg_logit,
-            seg_label,
-            weight=seg_weight,
-            ignore_index=self.ignore_index)
-        loss['acc_seg'] = accuracy(seg_logit, seg_label)
-        return loss
diff --git a/spaces/Miuzarte/SUI-svc-3.0/commons.py b/spaces/Miuzarte/SUI-svc-3.0/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/Miuzarte/SUI-svc-3.0/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, idx_str:idx_end]
-  return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
-  return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/backbone/oclip/README.md b/spaces/Mountchicken/MAERec-Gradio/configs/backbone/oclip/README.md
deleted file mode 100644
index e29cf971f6f8e6ba6c4fc640e6d06c5583d2909d..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/backbone/oclip/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# oCLIP
-
-> [Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136880282.pdf)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text images. However, these methods cannot well cope with OCR tasks because of the difficulty in both instance-level text encoding and image-text pair acquisition (i.e. images and captured texts in them). This paper presents a weakly supervised pre-training method, oCLIP, which can acquire effective scene text representations by jointly learning and aligning visual and textual information. Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features, respectively, as well as a visual-textual decoder that models the interaction among textual and visual features for learning effective scene text representations. With the learning of textual features, the pre-trained model can attend texts in images well with character awareness. Besides, these designs enable the learning from weakly annotated texts (i.e. partial texts in images without text bounding boxes) which mitigates the data annotation constraint greatly. Experiments over the weakly annotated images in ICDAR2019-LSVT show that our pre-trained model improves F-score by +2.5% and +4.8% while transferring its weights to other text detection and spotting networks, respectively. In addition, the proposed method outperforms existing pre-training techniques consistently across multiple public datasets (e.g., +3.2% and +1.3% for Total-Text and CTW1500).
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/24622904/199475057-aa688422-518d-4d7a-86fc-1be0cc1b5dc6.png"/>
-</div>
-
-## Models
-
-| Backbone  | Pre-train Data |                                       Model                                       |
-| :-------: | :------------: | :-------------------------------------------------------------------------------: |
-| ResNet-50 |   SynthText    | [Link](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) |
-
-```{note}
-The model is converted from the official [oCLIP](https://github.com/bytedance/oclip.git).
-```
-
-## Supported Text Detection Models
-
-|           | [DBNet](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#dbnet) | [DBNet++](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#dbnetpp) | [FCENet](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#fcenet) | [TextSnake](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#fcenet) | [PSENet](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#psenet) | [DRRG](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#drrg) | [Mask R-CNN](https://mmocr.readthedocs.io/en/dev-1.x/textdet_models.html#mask-r-cnn) |
-| :-------: | :------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :--------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | :--------------------------------------------------------------------------: | :----------------------------------------------------------------------: | :----------------------------------------------------------------------------------: |
-| ICDAR2015 |                                     ✓                                      |                                       ✓                                        |                                      ✓                                       |                                                                                 |                                      ✓                                       |                                                                          |                                          ✓                                           |
-|  CTW1500  |                                                                            |                                                                                |                                      ✓                                       |                                        ✓                                        |                                      ✓                                       |                                    ✓                                     |                                          ✓                                           |
-
-## Citation
-
-```bibtex
-@article{xue2022language,
-  title={Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting},
-  author={Xue, Chuhui and Zhang, Wenqing and Hao, Yu and Lu, Shijian and Torr, Philip and Bai, Song},
-  journal={Proceedings of the European Conference on Computer Vision (ECCV)},
-  year={2022}
-}
-```
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/sequence_attention_decoder.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/sequence_attention_decoder.py
deleted file mode 100644
index dfbf293f730e7c729511afa6ea24d494b86fe1b2..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/sequence_attention_decoder.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-from typing import Dict, Optional, Sequence, Union
-
-import torch
-import torch.nn as nn
-
-from mmocr.models.common.dictionary import Dictionary
-from mmocr.models.textrecog.layers import DotProductAttentionLayer
-from mmocr.registry import MODELS
-from mmocr.structures import TextRecogDataSample
-from .base import BaseDecoder
-
-
-@MODELS.register_module()
-class SequenceAttentionDecoder(BaseDecoder):
-    """Sequence attention decoder for RobustScanner.
-
-    RobustScanner: `RobustScanner: Dynamically Enhancing Positional Clues for
-    Robust Text Recognition <https://arxiv.org/abs/2007.07542>`_
-
-    Args:
-        dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
-            the instance of `Dictionary`.
-        module_loss (dict, optional): Config to build module_loss. Defaults
-            to None.
-        postprocessor (dict, optional): Config to build postprocessor.
-            Defaults to None.
-        rnn_layers (int): Number of RNN layers. Defaults to 2.
-        dim_input (int): Dimension :math:`D_i` of input vector ``feat``.
-            Defaults to 512.
-        dim_model (int): Dimension :math:`D_m` of the model. Should also be the
-            same as encoder output vector ``out_enc``. Defaults to 128.
-        max_seq_len (int): Maximum output sequence length :math:`T`.
-            Defaults to 40.
-        mask (bool): Whether to mask input features according to
-            ``data_sample.valid_ratio``. Defaults to True.
-        dropout (float): Dropout rate for LSTM layer. Defaults to 0.
-        return_feature (bool): Return feature or logic as the result.
-            Defaults to True.
-        encode_value (bool): Whether to use the output of encoder ``out_enc``
-            as `value` of attention layer. If False, the original feature
-            ``feat`` will be used. Defaults to False.
-        init_cfg (dict or list[dict], optional): Initialization configs.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 dictionary: Union[Dictionary, Dict],
-                 module_loss: Optional[Dict] = None,
-                 postprocessor: Optional[Dict] = None,
-                 rnn_layers: int = 2,
-                 dim_input: int = 512,
-                 dim_model: int = 128,
-                 max_seq_len: int = 40,
-                 mask: bool = True,
-                 dropout: int = 0,
-                 return_feature: bool = True,
-                 encode_value: bool = False,
-                 init_cfg: Optional[Union[Dict,
-                                          Sequence[Dict]]] = None) -> None:
-        super().__init__(
-            dictionary=dictionary,
-            module_loss=module_loss,
-            postprocessor=postprocessor,
-            max_seq_len=max_seq_len,
-            init_cfg=init_cfg)
-
-        self.dim_input = dim_input
-        self.dim_model = dim_model
-        self.return_feature = return_feature
-        self.encode_value = encode_value
-        self.mask = mask
-
-        self.embedding = nn.Embedding(
-            self.dictionary.num_classes,
-            self.dim_model,
-            padding_idx=self.dictionary.padding_idx)
-
-        self.sequence_layer = nn.LSTM(
-            input_size=dim_model,
-            hidden_size=dim_model,
-            num_layers=rnn_layers,
-            batch_first=True,
-            dropout=dropout)
-
-        self.attention_layer = DotProductAttentionLayer()
-
-        self.prediction = None
-        if not self.return_feature:
-            self.prediction = nn.Linear(
-                dim_model if encode_value else dim_input,
-                self.dictionary.num_classes)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward_train(
-        self,
-        feat: torch.Tensor,
-        out_enc: torch.Tensor,
-        data_samples: Optional[Sequence[TextRecogDataSample]] = None
-    ) -> torch.Tensor:
-        """
-        Args:
-            feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
-            out_enc (Tensor): Encoder output of shape
-                :math:`(N, D_m, H, W)`.
-            targets_dict (dict): A dict with the key ``padded_targets``, a
-                tensor of shape :math:`(N, T)`. Each element is the index of a
-                character.
-            data_samples (list[TextRecogDataSample], optional): Batch of
-                TextRecogDataSample, containing gt_text information. Defaults
-                to None.
-
-        Returns:
-            Tensor: A raw logit tensor of shape :math:`(N, T, C)` if
-            ``return_feature=False``. Otherwise it would be the hidden feature
-            before the prediction projection layer, whose shape is
-            :math:`(N, T, D_m)`.
-        """
-
-        valid_ratios = [
-            data_sample.get('valid_ratio', 1.0) for data_sample in data_samples
-        ] if self.mask else None
-
-        padded_targets = [
-            data_sample.gt_text.padded_indexes for data_sample in data_samples
-        ]
-        padded_targets = torch.stack(padded_targets, dim=0).to(feat.device)
-        tgt_embedding = self.embedding(padded_targets)
-
-        n, c_enc, h, w = out_enc.size()
-        assert c_enc == self.dim_model
-        _, c_feat, _, _ = feat.size()
-        assert c_feat == self.dim_input
-        _, len_q, c_q = tgt_embedding.size()
-        assert c_q == self.dim_model
-        assert len_q <= self.max_seq_len
-
-        query, _ = self.sequence_layer(tgt_embedding)
-        query = query.permute(0, 2, 1).contiguous()
-        key = out_enc.view(n, c_enc, h * w)
-        if self.encode_value:
-            value = key
-        else:
-            value = feat.view(n, c_feat, h * w)
-
-        mask = None
-        if valid_ratios is not None:
-            mask = query.new_zeros((n, h, w))
-            for i, valid_ratio in enumerate(valid_ratios):
-                valid_width = min(w, math.ceil(w * valid_ratio))
-                mask[i, :, valid_width:] = 1
-            mask = mask.bool()
-            mask = mask.view(n, h * w)
-
-        attn_out = self.attention_layer(query, key, value, mask)
-        attn_out = attn_out.permute(0, 2, 1).contiguous()
-
-        if self.return_feature:
-            return attn_out
-
-        out = self.prediction(attn_out)
-
-        return out
-
-    def forward_test(self, feat: torch.Tensor, out_enc: torch.Tensor,
-                     data_samples: Optional[Sequence[TextRecogDataSample]]
-                     ) -> torch.Tensor:
-        """
-        Args:
-            feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
-            out_enc (Tensor): Encoder output of shape
-                :math:`(N, D_m, H, W)`.
-            data_samples (list[TextRecogDataSample], optional): Batch of
-                TextRecogDataSample, containing gt_text information. Defaults
-                to None.
-
-        Returns:
-            Tensor: Character probabilities. of shape
-            :math:`(N, self.max_seq_len, C)` where :math:`C` is
-            ``num_classes``.
-        """
-        seq_len = self.max_seq_len
-        batch_size = feat.size(0)
-
-        decode_sequence = (feat.new_ones(
-            (batch_size, seq_len)) * self.dictionary.start_idx).long()
-        assert not self.return_feature
-        outputs = []
-        for i in range(seq_len):
-            step_out = self.forward_test_step(feat, out_enc, decode_sequence,
-                                              i, data_samples)
-            outputs.append(step_out)
-            _, max_idx = torch.max(step_out, dim=1, keepdim=False)
-            if i < seq_len - 1:
-                decode_sequence[:, i + 1] = max_idx
-
-        outputs = torch.stack(outputs, 1)
-
-        return self.softmax(outputs)
-
-    def forward_test_step(self, feat: torch.Tensor, out_enc: torch.Tensor,
-                          decode_sequence: torch.Tensor, current_step: int,
-                          data_samples: Sequence[TextRecogDataSample]
-                          ) -> torch.Tensor:
-        """
-        Args:
-            feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
-            out_enc (Tensor): Encoder output of shape
-                :math:`(N, D_m, H, W)`.
-            decode_sequence (Tensor): Shape :math:`(N, T)`. The tensor that
-                stores history decoding result.
-            current_step (int): Current decoding step.
-            data_samples (list[TextRecogDataSample], optional): Batch of
-                TextRecogDataSample, containing gt_text information. Defaults
-                to None.
-
-        Returns:
-            Tensor: Shape :math:`(N, C)`. The logit tensor of predicted
-            tokens at current time step.
-        """
-        valid_ratios = [
-            img_meta.get('valid_ratio', 1.0) for img_meta in data_samples
-        ] if self.mask else None
-
-        embed = self.embedding(decode_sequence)
-
-        n, c_enc, h, w = out_enc.size()
-        assert c_enc == self.dim_model
-        _, c_feat, _, _ = feat.size()
-        assert c_feat == self.dim_input
-        _, _, c_q = embed.size()
-        assert c_q == self.dim_model
-
-        query, _ = self.sequence_layer(embed)
-        query = query.permute(0, 2, 1).contiguous()
-        key = out_enc.view(n, c_enc, h * w)
-        if self.encode_value:
-            value = key
-        else:
-            value = feat.view(n, c_feat, h * w)
-
-        mask = None
-        if valid_ratios is not None:
-            mask = query.new_zeros((n, h, w))
-            for i, valid_ratio in enumerate(valid_ratios):
-                valid_width = min(w, math.ceil(w * valid_ratio))
-                mask[i, :, valid_width:] = 1
-            mask = mask.bool()
-            mask = mask.view(n, h * w)
-
-        # [n, c, l]
-        attn_out = self.attention_layer(query, key, value, mask)
-
-        out = attn_out[:, :, current_step]
-
-        if not self.return_feature:
-            out = self.prediction(out)
-
-        return out
diff --git a/spaces/NATSpeech/PortaSpeech/utils/commons/tensor_utils.py b/spaces/NATSpeech/PortaSpeech/utils/commons/tensor_utils.py
deleted file mode 100644
index be4b69a4f135b95fcf18618668ed909314f24871..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/utils/commons/tensor_utils.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import torch.distributed as dist
-
-
-def reduce_tensors(metrics):
-    new_metrics = {}
-    for k, v in metrics.items():
-        if isinstance(v, torch.Tensor):
-            dist.all_reduce(v)
-            v = v / dist.get_world_size()
-        if type(v) is dict:
-            v = reduce_tensors(v)
-        new_metrics[k] = v
-    return new_metrics
-
-
-def tensors_to_scalars(tensors):
-    if isinstance(tensors, torch.Tensor):
-        tensors = tensors.item()
-        return tensors
-    elif isinstance(tensors, dict):
-        new_tensors = {}
-        for k, v in tensors.items():
-            v = tensors_to_scalars(v)
-            new_tensors[k] = v
-        return new_tensors
-    elif isinstance(tensors, list):
-        return [tensors_to_scalars(v) for v in tensors]
-    else:
-        return tensors
-
-
-def tensors_to_np(tensors):
-    if isinstance(tensors, dict):
-        new_np = {}
-        for k, v in tensors.items():
-            if isinstance(v, torch.Tensor):
-                v = v.cpu().numpy()
-            if type(v) is dict:
-                v = tensors_to_np(v)
-            new_np[k] = v
-    elif isinstance(tensors, list):
-        new_np = []
-        for v in tensors:
-            if isinstance(v, torch.Tensor):
-                v = v.cpu().numpy()
-            if type(v) is dict:
-                v = tensors_to_np(v)
-            new_np.append(v)
-    elif isinstance(tensors, torch.Tensor):
-        v = tensors
-        if isinstance(v, torch.Tensor):
-            v = v.cpu().numpy()
-        if type(v) is dict:
-            v = tensors_to_np(v)
-        new_np = v
-    else:
-        raise Exception(f'tensors_to_np does not support type {type(tensors)}.')
-    return new_np
-
-
-def move_to_cpu(tensors):
-    ret = {}
-    for k, v in tensors.items():
-        if isinstance(v, torch.Tensor):
-            v = v.cpu()
-        if type(v) is dict:
-            v = move_to_cpu(v)
-        ret[k] = v
-    return ret
-
-
-def move_to_cuda(batch, gpu_id=0):
-    # base case: object can be directly moved using `cuda` or `to`
-    if callable(getattr(batch, 'cuda', None)):
-        return batch.cuda(gpu_id, non_blocking=True)
-    elif callable(getattr(batch, 'to', None)):
-        return batch.to(torch.device('cuda', gpu_id), non_blocking=True)
-    elif isinstance(batch, list):
-        for i, x in enumerate(batch):
-            batch[i] = move_to_cuda(x, gpu_id)
-        return batch
-    elif isinstance(batch, tuple):
-        batch = list(batch)
-        for i, x in enumerate(batch):
-            batch[i] = move_to_cuda(x, gpu_id)
-        return tuple(batch)
-    elif isinstance(batch, dict):
-        for k, v in batch.items():
-            batch[k] = move_to_cuda(v, gpu_id)
-        return batch
-    return batch
diff --git a/spaces/NCTCMumbai/NCTC/models/official/benchmark/benchmark_wrappers.py b/spaces/NCTCMumbai/NCTC/models/official/benchmark/benchmark_wrappers.py
deleted file mode 100644
index 3d38b690c7865e0ab560e59422a2454e44be052d..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/benchmark/benchmark_wrappers.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Utils to annotate and trace benchmarks."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from absl import flags
-from absl import logging
-from absl.testing import flagsaver
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_multi_string(
-    'benchmark_method_flags', None,
-    'Optional list of runtime flags of the form key=value. Specify '
-    'multiple times to specify different flags. These will override the FLAGS '
-    'object directly after hardcoded settings in individual benchmark methods '
-    'before they call _run_and_report benchmark. Example if we set '
-    '--benchmark_method_flags=train_steps=10 and a benchmark method hardcodes '
-    'FLAGS.train_steps=10000 and later calls _run_and_report_benchmark, '
-    'it\'ll only run for 10 steps. This is useful for '
-    'debugging/profiling workflows.')
-
-
-def enable_runtime_flags(decorated_func):
-  """Sets attributes from --benchmark_method_flags for method execution.
-
-  @enable_runtime_flags decorator temporarily adds flags passed in via
-  --benchmark_method_flags and runs the decorated function in that context.
-
-  A user can set --benchmark_method_flags=train_steps=5 to run the benchmark
-  method in the snippet below with FLAGS.train_steps=5 for debugging (without
-  modifying the benchmark code).
-
-  class ModelBenchmark():
-
-    @benchmark_wrappers.enable_runtime_flags
-    def _run_and_report_benchmark(self):
-      # run benchmark ...
-      # report benchmark results ...
-
-    def benchmark_method(self):
-      FLAGS.train_steps = 1000
-      ...
-      self._run_and_report_benchmark()
-
-  Args:
-    decorated_func: The method that runs the benchmark after previous setup
-      execution that set some flags.
-
-  Returns:
-    new_func: The same method which executes in a temporary context where flag
-      overrides from --benchmark_method_flags are active.
-  """
-
-  def runner(*args, **kwargs):
-    """Creates a temporary context to activate --benchmark_method_flags."""
-    if FLAGS.benchmark_method_flags:
-      saved_flag_values = flagsaver.save_flag_values()
-      for key_value in FLAGS.benchmark_method_flags:
-        key, value = key_value.split('=', 1)
-        try:
-          numeric_float = float(value)
-          numeric_int = int(numeric_float)
-          if abs(numeric_int) == abs(numeric_float):
-            flag_value = numeric_int
-          else:
-            flag_value = numeric_float
-        except ValueError:
-          flag_value = value
-        logging.info('Setting --%s=%s', key, flag_value)
-        setattr(FLAGS, key, flag_value)
-    else:
-      saved_flag_values = None
-    try:
-      result = decorated_func(*args, **kwargs)
-      return result
-    finally:
-      if saved_flag_values:
-        flagsaver.restore_flag_values(saved_flag_values)
-
-  return runner
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/configs_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/configs_test.py
deleted file mode 100644
index 2b855ec6a955cd7f2a50fb173f7f5efb68b84263..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/configs_test.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for configs."""
-
-import tensorflow as tf
-from official.nlp.nhnet import configs
-
-BERT2BERT_CONFIG = {
-    "vocab_size": 30522,
-    "hidden_size": 768,
-    "num_hidden_layers": 12,
-    "num_attention_heads": 12,
-    "intermediate_size": 3072,
-    "hidden_act": "gelu",
-    "hidden_dropout_prob": 0.1,
-    "attention_probs_dropout_prob": 0.1,
-    "max_position_embeddings": 512,
-    "type_vocab_size": 2,
-    "initializer_range": 0.02,
-
-    # model params
-    "decoder_intermediate_size": 3072,
-    "num_decoder_attn_heads": 12,
-    "num_decoder_layers": 12,
-
-    # training params
-    "label_smoothing": 0.1,
-    "learning_rate": 0.05,
-    "learning_rate_warmup_steps": 20000,
-    "optimizer": "Adam",
-    "adam_beta1": 0.9,
-    "adam_beta2": 0.997,
-    "adam_epsilon": 1e-09,
-
-    # predict params
-    "beam_size": 5,
-    "alpha": 0.6,
-    "initializer_gain": 1.0,
-    "use_cache": True,
-
-    # input params
-    "input_sharding": False,
-    "input_data_not_padded": False,
-    "pad_token_id": 0,
-    "end_token_id": 102,
-    "start_token_id": 101,
-}
-
-NHNET_CONFIG = {
-    "vocab_size": 30522,
-    "hidden_size": 768,
-    "num_hidden_layers": 12,
-    "num_attention_heads": 12,
-    "intermediate_size": 3072,
-    "hidden_act": "gelu",
-    "hidden_dropout_prob": 0.1,
-    "attention_probs_dropout_prob": 0.1,
-    "max_position_embeddings": 512,
-    "type_vocab_size": 2,
-    "initializer_range": 0.02,
-
-    # model params
-    "decoder_intermediate_size": 3072,
-    "num_decoder_attn_heads": 12,
-    "num_decoder_layers": 12,
-    "multi_channel_cross_attention": True,
-
-    # training params
-    "label_smoothing": 0.1,
-    "learning_rate": 0.05,
-    "learning_rate_warmup_steps": 20000,
-    "optimizer": "Adam",
-    "adam_beta1": 0.9,
-    "adam_beta2": 0.997,
-    "adam_epsilon": 1e-09,
-
-    # predict params
-    "beam_size": 5,
-    "alpha": 0.6,
-    "initializer_gain": 1.0,
-    "use_cache": True,
-
-    # input params
-    "passage_list": ["b", "c", "d", "e", "f"],
-    "input_sharding": False,
-    "input_data_not_padded": False,
-    "pad_token_id": 0,
-    "end_token_id": 102,
-    "start_token_id": 101,
-
-    "init_from_bert2bert": True,
-}
-
-
-class ConfigsTest(tf.test.TestCase):
-
-  def test_configs(self):
-    cfg = configs.BERT2BERTConfig()
-    cfg.validate()
-    self.assertEqual(cfg.as_dict(), BERT2BERT_CONFIG)
-
-  def test_nhnet_config(self):
-    cfg = configs.NHNetConfig()
-    cfg.validate()
-    self.assertEqual(cfg.as_dict(), NHNET_CONFIG)
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/preprocessing/inception_preprocessing.py b/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/preprocessing/inception_preprocessing.py
deleted file mode 100644
index 846b81b3cd30985f2aa55e6f7c471195ac6e046b..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/preprocessing/inception_preprocessing.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Provides utilities to preprocess images for the Inception networks."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from tensorflow.python.ops import control_flow_ops
-
-
-def apply_with_random_selector(x, func, num_cases):
-  """Computes func(x, sel), with sel sampled from [0...num_cases-1].
-
-  Args:
-    x: input Tensor.
-    func: Python function to apply.
-    num_cases: Python int32, number of cases to sample sel from.
-
-  Returns:
-    The result of func(x, sel), where func receives the value of the
-    selector as a python integer, but sel is sampled dynamically.
-  """
-  sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
-  # Pass the real x only to one of the func calls.
-  return control_flow_ops.merge([
-      func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
-      for case in range(num_cases)])[0]
-
-
-def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
-  """Distort the color of a Tensor image.
-
-  Each color distortion is non-commutative and thus ordering of the color ops
-  matters. Ideally we would randomly permute the ordering of the color ops.
-  Rather then adding that level of complication, we select a distinct ordering
-  of color ops for each preprocessing thread.
-
-  Args:
-    image: 3-D Tensor containing single image in [0, 1].
-    color_ordering: Python int, a type of distortion (valid values: 0-3).
-    fast_mode: Avoids slower ops (random_hue and random_contrast)
-    scope: Optional scope for name_scope.
-  Returns:
-    3-D Tensor color-distorted image on range [0, 1]
-  Raises:
-    ValueError: if color_ordering not in [0, 3]
-  """
-  with tf.name_scope(scope, 'distort_color', [image]):
-    if fast_mode:
-      if color_ordering == 0:
-        image = tf.image.random_brightness(image, max_delta=32. / 255.)
-        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
-      else:
-        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
-        image = tf.image.random_brightness(image, max_delta=32. / 255.)
-    else:
-      if color_ordering == 0:
-        image = tf.image.random_brightness(image, max_delta=32. / 255.)
-        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
-        image = tf.image.random_hue(image, max_delta=0.2)
-        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
-      elif color_ordering == 1:
-        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
-        image = tf.image.random_brightness(image, max_delta=32. / 255.)
-        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
-        image = tf.image.random_hue(image, max_delta=0.2)
-      elif color_ordering == 2:
-        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
-        image = tf.image.random_hue(image, max_delta=0.2)
-        image = tf.image.random_brightness(image, max_delta=32. / 255.)
-        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
-      elif color_ordering == 3:
-        image = tf.image.random_hue(image, max_delta=0.2)
-        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
-        image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
-        image = tf.image.random_brightness(image, max_delta=32. / 255.)
-      else:
-        raise ValueError('color_ordering must be in [0, 3]')
-
-    # The random_* ops do not necessarily clamp.
-    return tf.clip_by_value(image, 0.0, 1.0)
-
-
-def distorted_bounding_box_crop(image,
-                                bbox,
-                                min_object_covered=0.1,
-                                aspect_ratio_range=(0.75, 1.33),
-                                area_range=(0.05, 1.0),
-                                max_attempts=100,
-                                scope=None):
-  """Generates cropped_image using a one of the bboxes randomly distorted.
-
-  See `tf.image.sample_distorted_bounding_box` for more documentation.
-
-  Args:
-    image: 3-D Tensor of image (it will be converted to floats in [0, 1]).
-    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
-      where each coordinate is [0, 1) and the coordinates are arranged
-      as [ymin, xmin, ymax, xmax]. If num_boxes is 0 then it would use the whole
-      image.
-    min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
-      area of the image must contain at least this fraction of any bounding box
-      supplied.
-    aspect_ratio_range: An optional list of `floats`. The cropped area of the
-      image must have an aspect ratio = width / height within this range.
-    area_range: An optional list of `floats`. The cropped area of the image
-      must contain a fraction of the supplied image within in this range.
-    max_attempts: An optional `int`. Number of attempts at generating a cropped
-      region of the image of the specified constraints. After `max_attempts`
-      failures, return the entire image.
-    scope: Optional scope for name_scope.
-  Returns:
-    A tuple, a 3-D Tensor cropped_image and the distorted bbox
-  """
-  with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
-    # Each bounding box has shape [1, num_boxes, box coords] and
-    # the coordinates are ordered [ymin, xmin, ymax, xmax].
-
-    # A large fraction of image datasets contain a human-annotated bounding
-    # box delineating the region of the image containing the object of interest.
-    # We choose to create a new bounding box for the object which is a randomly
-    # distorted version of the human-annotated bounding box that obeys an
-    # allowed range of aspect ratios, sizes and overlap with the human-annotated
-    # bounding box. If no box is supplied, then we assume the bounding box is
-    # the entire image.
-    sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
-        tf.shape(image),
-        bounding_boxes=bbox,
-        min_object_covered=min_object_covered,
-        aspect_ratio_range=aspect_ratio_range,
-        area_range=area_range,
-        max_attempts=max_attempts,
-        use_image_if_no_bounding_boxes=True)
-    bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
-
-    # Crop the image to the specified bounding box.
-    cropped_image = tf.slice(image, bbox_begin, bbox_size)
-    return cropped_image, distort_bbox
-
-
-def preprocess_for_train(image, height, width, bbox,
-                         fast_mode=True,
-                         scope=None,
-                         add_image_summaries=True):
-  """Distort one image for training a network.
-
-  Distorting images provides a useful technique for augmenting the data
-  set during training in order to make the network invariant to aspects
-  of the image that do not effect the label.
-
-  Additionally it would create image_summaries to display the different
-  transformations applied to the image.
-
-  Args:
-    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
-      [0, 1], otherwise it would converted to tf.float32 assuming that the range
-      is [0, MAX], where MAX is largest positive representable number for
-      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
-    height: integer
-    width: integer
-    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
-      where each coordinate is [0, 1) and the coordinates are arranged
-      as [ymin, xmin, ymax, xmax].
-    fast_mode: Optional boolean, if True avoids slower transformations (i.e.
-      bi-cubic resizing, random_hue or random_contrast).
-    scope: Optional scope for name_scope.
-    add_image_summaries: Enable image summaries.
-  Returns:
-    3-D float Tensor of distorted image used for training with range [-1, 1].
-  """
-  with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]):
-    if bbox is None:
-      bbox = tf.constant([0.0, 0.0, 1.0, 1.0],
-                         dtype=tf.float32,
-                         shape=[1, 1, 4])
-    if image.dtype != tf.float32:
-      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
-    # Each bounding box has shape [1, num_boxes, box coords] and
-    # the coordinates are ordered [ymin, xmin, ymax, xmax].
-    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
-                                                  bbox)
-    if add_image_summaries:
-      tf.summary.image('image_with_bounding_boxes', image_with_box)
-
-    distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
-    # Restore the shape since the dynamic slice based upon the bbox_size loses
-    # the third dimension.
-    distorted_image.set_shape([None, None, 3])
-    image_with_distorted_box = tf.image.draw_bounding_boxes(
-        tf.expand_dims(image, 0), distorted_bbox)
-    if add_image_summaries:
-      tf.summary.image('images_with_distorted_bounding_box',
-                       image_with_distorted_box)
-
-    # This resizing operation may distort the images because the aspect
-    # ratio is not respected. We select a resize method in a round robin
-    # fashion based on the thread number.
-    # Note that ResizeMethod contains 4 enumerated resizing methods.
-
-    # We select only 1 case for fast_mode bilinear.
-    num_resize_cases = 1 if fast_mode else 4
-    distorted_image = apply_with_random_selector(
-        distorted_image,
-        lambda x, method: tf.image.resize_images(x, [height, width], method),
-        num_cases=num_resize_cases)
-
-    if add_image_summaries:
-      tf.summary.image('cropped_resized_image',
-                       tf.expand_dims(distorted_image, 0))
-
-    # Randomly flip the image horizontally.
-    distorted_image = tf.image.random_flip_left_right(distorted_image)
-
-    # Randomly distort the colors. There are 1 or 4 ways to do it.
-    num_distort_cases = 1 if fast_mode else 4
-    distorted_image = apply_with_random_selector(
-        distorted_image,
-        lambda x, ordering: distort_color(x, ordering, fast_mode),
-        num_cases=num_distort_cases)
-
-    if add_image_summaries:
-      tf.summary.image('final_distorted_image',
-                       tf.expand_dims(distorted_image, 0))
-    distorted_image = tf.subtract(distorted_image, 0.5)
-    distorted_image = tf.multiply(distorted_image, 2.0)
-    return distorted_image
-
-
-def preprocess_for_eval(image, height, width,
-                        central_fraction=0.875, scope=None):
-  """Prepare one image for evaluation.
-
-  If height and width are specified it would output an image with that size by
-  applying resize_bilinear.
-
-  If central_fraction is specified it would crop the central fraction of the
-  input image.
-
-  Args:
-    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
-      [0, 1], otherwise it would converted to tf.float32 assuming that the range
-      is [0, MAX], where MAX is largest positive representable number for
-      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
-    height: integer
-    width: integer
-    central_fraction: Optional Float, fraction of the image to crop.
-    scope: Optional scope for name_scope.
-  Returns:
-    3-D float Tensor of prepared image.
-  """
-  with tf.name_scope(scope, 'eval_image', [image, height, width]):
-    if image.dtype != tf.float32:
-      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
-    # Crop the central region of the image with an area containing 87.5% of
-    # the original image.
-    if central_fraction:
-      image = tf.image.central_crop(image, central_fraction=central_fraction)
-
-    if height and width:
-      # Resize the image to the specified height and width.
-      image = tf.expand_dims(image, 0)
-      image = tf.image.resize_bilinear(image, [height, width],
-                                       align_corners=False)
-      image = tf.squeeze(image, [0])
-    image = tf.subtract(image, 0.5)
-    image = tf.multiply(image, 2.0)
-    return image
-
-
-def preprocess_image(image, height, width,
-                     is_training=False,
-                     bbox=None,
-                     fast_mode=True,
-                     add_image_summaries=True):
-  """Pre-process one image for training or evaluation.
-
-  Args:
-    image: 3-D Tensor [height, width, channels] with the image. If dtype is
-      tf.float32 then the range should be [0, 1], otherwise it would converted
-      to tf.float32 assuming that the range is [0, MAX], where MAX is largest
-      positive representable number for int(8/16/32) data type (see
-      `tf.image.convert_image_dtype` for details).
-    height: integer, image expected height.
-    width: integer, image expected width.
-    is_training: Boolean. If true it would transform an image for train,
-      otherwise it would transform it for evaluation.
-    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
-      where each coordinate is [0, 1) and the coordinates are arranged as
-      [ymin, xmin, ymax, xmax].
-    fast_mode: Optional boolean, if True avoids slower transformations.
-    add_image_summaries: Enable image summaries.
-
-  Returns:
-    3-D float Tensor containing an appropriately scaled image
-
-  Raises:
-    ValueError: if user does not provide bounding box
-  """
-  if is_training:
-    return preprocess_for_train(image, height, width, bbox, fast_mode,
-                                add_image_summaries=add_image_summaries)
-  else:
-    return preprocess_for_eval(image, height, width)
diff --git a/spaces/NN-BRD/OWL-ViT/README.md b/spaces/NN-BRD/OWL-ViT/README.md
deleted file mode 100644
index c1d4a79105b531c599ed4e40e76fc4ed16bf0800..0000000000000000000000000000000000000000
--- a/spaces/NN-BRD/OWL-ViT/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: OWL-ViT Demo
-emoji: 🔥
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-python_version: 3.9
-license: apache-2.0
-duplicated_from: kellyxiaowei/OWL-ViT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NN520/AI/src/components/toaster.tsx b/spaces/NN520/AI/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/Nee001/bing0/src/lib/utils.ts b/spaces/Nee001/bing0/src/lib/utils.ts
deleted file mode 100644
index 8de2eba94bf0bc93579d4f489e8b810dbf6ce92a..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/src/lib/utils.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-// @ts-ignore
-import randomip from 'random-ip'
-import cidr from './cidr.json'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.floor(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  // return `104.${random(0, 21)}.${random(0, 127)}.${random(1, 255)}`
-  const [ip, range] = cidr.at(random(0, cidr.length))?.split('/')!
-  return randomip(ip, range)
-}
-
-export const defaultUID = 'xxx'
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function setCookie(key: string, value: string) {
-  const maxAge = value ? 86400 * 30 : 0
-  document.cookie = `${key}=${value || ''}; Path=/; Max-Age=${maxAge}; SameSite=None; Secure`
-}
-
-export function getCookie(cookieName: string) {
-  const re = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`)
-  return re.test(document.cookie) ? RegExp.$1 : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function mockUser(cookies: Partial<{ [key: string]: string }>) {
-  const {
-    BING_UA = process.env.BING_UA,
-    BING_IP,
-    _U = defaultUID,
-  } = cookies
-  const ua = parseUA(BING_UA)
-
-  return {
-    'x-forwarded-for': BING_IP!,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.3 OS/Win32',
-    cookie: `_U=${_U}` || '',
-  }
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, type?: string) {
-  let {
-    BING_HEADER = process.env.BING_HEADER,
-    BING_IP,
-    IMAGE_ONLY = process.env.IMAGE_ONLY ?? '1',
-  } = cookies
-  const imageOnly = /^(1|true|yes)$/.test(String(IMAGE_ONLY))
-  if (BING_HEADER) {
-    if (
-      (imageOnly && type === 'image')
-      || !imageOnly
-    ) {
-      const headers = extraHeadersFromCookie({
-        BING_HEADER,
-        ...cookies,
-      }) || {}
-      headers['x-forward-for'] = BING_IP!
-      return headers
-    }
-  }
-  return mockUser(cookies)
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}
diff --git a/spaces/Nesip/Aeala-GPT4-x-AlpacaDente2-30b/Dockerfile b/spaces/Nesip/Aeala-GPT4-x-AlpacaDente2-30b/Dockerfile
deleted file mode 100644
index 672b72c017c38c280701ebfb62cedb8d658eff9d..0000000000000000000000000000000000000000
--- a/spaces/Nesip/Aeala-GPT4-x-AlpacaDente2-30b/Dockerfile
+++ /dev/null
@@ -1,17 +0,0 @@
-FROM rocker/shiny-verse:latest
-
-WORKDIR /code
-
-# Install stable packages from CRAN
-RUN install2.r --error \
-    ggExtra \
-    shiny
-
-# Install development packages from GitHub
-RUN installGithub.r \
-    rstudio/bslib \
-    rstudio/httpuv
-
-COPY . .
-
-CMD ["R", "--quiet", "-e", "shiny::runApp(host='0.0.0.0', port=7860)"]
diff --git a/spaces/NiiCole/FireExtinguishers/app.py b/spaces/NiiCole/FireExtinguishers/app.py
deleted file mode 100644
index e122372b370a0b98e3bf330be8627ded05c2a12a..0000000000000000000000000000000000000000
--- a/spaces/NiiCole/FireExtinguishers/app.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import gradio as gr
-import torch
-from PIL import Image
-
-# Images
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
-
-# Model
-model_name ='best.pt'  # force_reload=True to update
-
-if model_name:
-    model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_name, force_reload=True)
-else:
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
-    
-
-def yolo(im, size=640):
-    g = (size / max(im.size))  # gain
-    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
-
-    results = model(im)  # inference
-    results.render()  # updates results.imgs with boxes and labels
-    return Image.fromarray(results.imgs[0])
-
-
-inputs = gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Image(type="pil", label="Output Image")
-
-title = "YOLOv5"
-description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
-article = "<p style='text-align: center'> THis Demo is meant to detect specific models of fire extinguishers , trained on an artificially generated dataset from IFC MODEL with Blender" \
-          "The aim is to simulate real world fire extinguishers as much possible in order for the object detector to recognizeit" \
-        
-         
-
-examples = [['ex1.jfif']]
-gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(
-    debug=True)
-
-
-# try again
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/basicsr/__init__.py b/spaces/OAOA/DifFace/basicsr/__init__.py
deleted file mode 100644
index 28437544a254656cca7fb7021ef7bbf724cf2879..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# https://github.com/xinntao/BasicSR
-# flake8: noqa
-from .archs import *
-from .data import *
-from .losses import *
-from .metrics import *
-from .models import *
-from .ops import *
-from .test import *
-from .train import *
-from .utils import *
-# from .version import __gitsha__, __version__
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/discriminative_reranking_nmt/models/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/discriminative_reranking_nmt/models/__init__.py
deleted file mode 100644
index c593ea5f1842794bfcc952fc93c679a5f16aeb98..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/discriminative_reranking_nmt/models/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .discriminative_reranking_model import DiscriminativeNMTReranker
-
-
-__all__ = [
-    "DiscriminativeNMTReranker",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/m2m_100/install_dependecies.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/m2m_100/install_dependecies.sh
deleted file mode 100644
index 82a1054745264a56fbec4a8eb593884f8a42bd08..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/m2m_100/install_dependecies.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-CWD=`pwd`
-INSTALL_PATH=$CWD/tokenizers/thirdparty
-
-MOSES=$INSTALL_PATH/mosesdecoder
-if [ ! -d $MOSES ]; then
-    echo 'Cloning Moses github repository (for tokenization scripts)...'
-    git clone https://github.com/moses-smt/mosesdecoder.git $MOSES
-    cd $MOSES
-    # To deal with differences in handling ' vs "
-    git checkout 03578921cc1a03402
-    cd -
-fi
-
-WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts
-if [ ! -d $WMT16_SCRIPTS ]; then
-    echo 'Cloning Romanian tokenization scripts'
-    git clone https://github.com/rsennrich/wmt16-scripts.git $WMT16_SCRIPTS
-fi
-
-KYTEA=$INSTALL_PATH/kytea
-if [ ! -f $KYTEA/bin/kytea ]; then
-    git clone https://github.com/neubig/kytea.git $KYTEA
-    cd $KYTEA
-    autoreconf -i
-    ./configure --prefix=`pwd`
-    make
-    make install
-    cd ..
-fi
-
-export MECAB=$INSTALL_PATH/mecab-0.996-ko-0.9.2
-if [ ! -f $MECAB/bin/mecab ]; then
-    cd $INSTALL_PATH
-    curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz
-    tar zxfv mecab-0.996-ko-0.9.2.tar.gz
-    cd mecab-0.996-ko-0.9.2/
-    ./configure --prefix=`pwd`
-    make
-    make install
-
-    cd ..
-    curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz
-    tar zxfv mecab-ko-dic-2.1.1-20180720.tar.gz
-    cd mecab-ko-dic-2.1.1-20180720/
-    ./autogen.sh
-    ./configure --prefix=`pwd` --with-dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic --with-mecab-config=$MECAB/bin/mecab-config
-    make
-    sh -c 'echo "dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic" > $MECAB/etc/mecabrc'
-    make install
-    cd $CWD
-fi
-
-INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources
-if [ ! -d $INDIC_RESOURCES_PATH ]; then
-    echo 'Cloning indic_nlp_resources'
-    git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git $INDIC_RESOURCES_PATH
-fi
-
-
-if [ ! -f $INSTALL_PATH/seg_my.py ]; then
-    cd $INSTALL_PATH
-    wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip
-    unzip wat2020.my-en.zip
-    # switch to python3
-    cat wat2020.my-en/myseg.py  |sed 's/^sys.std/###sys.std/g' | sed 's/### sys/sys/g' | sed 's/unichr/chr/g' > seg_my.py
-    cd $CWD
-fi
-
-
-pip install pythainlp sacrebleu indic-nlp-library
-
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/file_utils.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/file_utils.py
deleted file mode 100644
index d1d5ea65746682881264e4a9c462854dcfb3413f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/file_utils.py
+++ /dev/null
@@ -1,369 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Utilities for working with the local dataset cache.
-This file is adapted from `AllenNLP <https://github.com/allenai/allennlp>`_.
-and `huggingface <https://github.com/huggingface>`_.
-"""
-
-import fnmatch
-import json
-import logging
-import os
-import shutil
-import tarfile
-import tempfile
-from functools import partial, wraps
-from hashlib import sha256
-from io import open
-
-
-try:
-    from torch.hub import _get_torch_home
-
-    torch_cache_home = _get_torch_home()
-except ImportError:
-    torch_cache_home = os.path.expanduser(
-        os.getenv(
-            "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch")
-        )
-    )
-default_cache_path = os.path.join(torch_cache_home, "pytorch_fairseq")
-
-try:
-    from urllib.parse import urlparse
-except ImportError:
-    from urlparse import urlparse
-
-try:
-    from pathlib import Path
-
-    PYTORCH_FAIRSEQ_CACHE = Path(os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path))
-except (AttributeError, ImportError):
-    PYTORCH_FAIRSEQ_CACHE = os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path)
-
-CONFIG_NAME = "config.json"
-WEIGHTS_NAME = "pytorch_model.bin"
-
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
-
-def load_archive_file(archive_file):
-    # redirect to the cache, if necessary
-    try:
-        resolved_archive_file = cached_path(archive_file, cache_dir=None)
-    except EnvironmentError:
-        logger.info(
-            "Archive name '{}' was not found in archive name list. "
-            "We assumed '{}' was a path or URL but couldn't find any file "
-            "associated to this path or URL.".format(
-                archive_file,
-                archive_file,
-            )
-        )
-        return None
-
-    if resolved_archive_file == archive_file:
-        logger.info("loading archive file {}".format(archive_file))
-    else:
-        logger.info(
-            "loading archive file {} from cache at {}".format(
-                archive_file, resolved_archive_file
-            )
-        )
-
-    # Extract archive to temp dir and replace .tar.bz2 if necessary
-    tempdir = None
-    if not os.path.isdir(resolved_archive_file):
-        tempdir = tempfile.mkdtemp()
-        logger.info(
-            "extracting archive file {} to temp dir {}".format(
-                resolved_archive_file, tempdir
-            )
-        )
-        ext = os.path.splitext(archive_file)[1][1:]
-        with tarfile.open(resolved_archive_file, "r:" + ext) as archive:
-            top_dir = os.path.commonprefix(archive.getnames())
-            archive.extractall(tempdir)
-        os.remove(resolved_archive_file)
-        shutil.move(os.path.join(tempdir, top_dir), resolved_archive_file)
-        shutil.rmtree(tempdir)
-
-    return resolved_archive_file
-
-
-def url_to_filename(url, etag=None):
-    """
-    Convert `url` into a hashed filename in a repeatable way.
-    If `etag` is specified, append its hash to the URL's, delimited
-    by a period.
-    """
-    url_bytes = url.encode("utf-8")
-    url_hash = sha256(url_bytes)
-    filename = url_hash.hexdigest()
-
-    if etag:
-        etag_bytes = etag.encode("utf-8")
-        etag_hash = sha256(etag_bytes)
-        filename += "." + etag_hash.hexdigest()
-
-    return filename
-
-
-def filename_to_url(filename, cache_dir=None):
-    """
-    Return the url and etag (which may be ``None``) stored for `filename`.
-    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_FAIRSEQ_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    cache_path = os.path.join(cache_dir, filename)
-    if not os.path.exists(cache_path):
-        raise EnvironmentError("file {} not found".format(cache_path))
-
-    meta_path = cache_path + ".json"
-    if not os.path.exists(meta_path):
-        raise EnvironmentError("file {} not found".format(meta_path))
-
-    with open(meta_path, encoding="utf-8") as meta_file:
-        metadata = json.load(meta_file)
-    url = metadata["url"]
-    etag = metadata["etag"]
-
-    return url, etag
-
-
-def cached_path_from_pm(url_or_filename):
-    """
-    Tries to cache the specified URL using PathManager class.
-    Returns the cached path if success otherwise failure.
-    """
-    try:
-        from fairseq.file_io import PathManager
-        local_path = PathManager.get_local_path(url_or_filename)
-        return local_path
-    except Exception:
-        return None
-
-
-def cached_path(url_or_filename, cache_dir=None):
-    """
-    Given something that might be a URL (or might be a local path),
-    determine which. If it's a URL, download the file and cache it, and
-    return the path to the cached file. If it's already a local path,
-    make sure the file exists and then return the path.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_FAIRSEQ_CACHE
-    if isinstance(url_or_filename, Path):
-        url_or_filename = str(url_or_filename)
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    parsed = urlparse(url_or_filename)
-
-    if parsed.scheme in ("http", "https", "s3"):
-        # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
-    elif os.path.exists(url_or_filename):
-        # File, and it exists.
-        return url_or_filename
-    elif parsed.scheme == "":
-        # File, but it doesn't exist.
-        raise EnvironmentError("file {} not found".format(url_or_filename))
-    else:
-        cached_path = cached_path_from_pm(url_or_filename)
-        if cached_path:
-            return cached_path
-        # Something unknown
-        raise ValueError(
-            "unable to parse {} as a URL or as a local path".format(url_or_filename)
-        )
-
-
-def split_s3_path(url):
-    """Split a full s3 path into the bucket name and path."""
-    parsed = urlparse(url)
-    if not parsed.netloc or not parsed.path:
-        raise ValueError("bad s3 path {}".format(url))
-    bucket_name = parsed.netloc
-    s3_path = parsed.path
-    # Remove '/' at beginning of path.
-    if s3_path.startswith("/"):
-        s3_path = s3_path[1:]
-    return bucket_name, s3_path
-
-
-def s3_request(func):
-    """
-    Wrapper function for s3 requests in order to create more helpful error
-    messages.
-    """
-
-    @wraps(func)
-    def wrapper(url, *args, **kwargs):
-        from botocore.exceptions import ClientError
-
-        try:
-            return func(url, *args, **kwargs)
-        except ClientError as exc:
-            if int(exc.response["Error"]["Code"]) == 404:
-                raise EnvironmentError("file {} not found".format(url))
-            else:
-                raise
-
-    return wrapper
-
-
-@s3_request
-def s3_etag(url):
-    """Check ETag on S3 object."""
-    import boto3
-
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_object = s3_resource.Object(bucket_name, s3_path)
-    return s3_object.e_tag
-
-
-@s3_request
-def s3_get(url, temp_file):
-    """Pull a file directly from S3."""
-    import boto3
-
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
-
-
-def request_wrap_timeout(func, url):
-    import requests
-
-    for attempt, timeout in enumerate([10, 20, 40, 60, 60]):
-        try:
-            return func(timeout=timeout)
-        except requests.exceptions.Timeout as e:
-            logger.warning(
-                "Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs",
-                url,
-                attempt,
-                timeout,
-                exc_info=e,
-            )
-            continue
-    raise RuntimeError(f"Unable to fetch file {url}")
-
-
-def http_get(url, temp_file):
-    import requests
-    from tqdm import tqdm
-
-    req = request_wrap_timeout(partial(requests.get, url, stream=True), url)
-    content_length = req.headers.get("Content-Length")
-    total = int(content_length) if content_length is not None else None
-    progress = tqdm(unit="B", total=total)
-    for chunk in req.iter_content(chunk_size=1024):
-        if chunk:  # filter out keep-alive new chunks
-            progress.update(len(chunk))
-            temp_file.write(chunk)
-    progress.close()
-
-
-def get_from_cache(url, cache_dir=None):
-    """
-    Given a URL, look for the corresponding dataset in the local cache.
-    If it's not there, download it. Then return the path to the cached file.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_FAIRSEQ_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    if not os.path.exists(cache_dir):
-        os.makedirs(cache_dir)
-
-    # Get eTag to add to filename, if it exists.
-    if url.startswith("s3://"):
-        etag = s3_etag(url)
-    else:
-        try:
-            import requests
-
-            response = request_wrap_timeout(
-                partial(requests.head, url, allow_redirects=True), url
-            )
-            if response.status_code != 200:
-                etag = None
-            else:
-                etag = response.headers.get("ETag")
-        except RuntimeError:
-            etag = None
-
-    filename = url_to_filename(url, etag)
-
-    # get cache path to put the file
-    cache_path = os.path.join(cache_dir, filename)
-
-    # If we don't have a connection (etag is None) and can't identify the file
-    # try to get the last downloaded one
-    if not os.path.exists(cache_path) and etag is None:
-        matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*")
-        matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files))
-        if matching_files:
-            cache_path = os.path.join(cache_dir, matching_files[-1])
-
-    if not os.path.exists(cache_path):
-        # Download to temporary file, then copy to cache dir once finished.
-        # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with tempfile.NamedTemporaryFile() as temp_file:
-            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
-
-            # GET file object
-            if url.startswith("s3://"):
-                s3_get(url, temp_file)
-            else:
-                http_get(url, temp_file)
-
-            # we are copying the file before closing it, so flush to avoid truncation
-            temp_file.flush()
-            # shutil.copyfileobj() starts at the current position, so go to the start
-            temp_file.seek(0)
-
-            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
-            with open(cache_path, "wb") as cache_file:
-                shutil.copyfileobj(temp_file, cache_file)
-
-            logger.info("creating metadata file for %s", cache_path)
-            meta = {"url": url, "etag": etag}
-            meta_path = cache_path + ".json"
-            with open(meta_path, "w") as meta_file:
-                output_string = json.dumps(meta)
-                meta_file.write(output_string)
-
-            logger.info("removing temp file %s", temp_file.name)
-
-    return cache_path
-
-
-def read_set_from_file(filename):
-    """
-    Extract a de-duped collection (set) of text from a file.
-    Expected file format is one item per line.
-    """
-    collection = set()
-    with open(filename, "r", encoding="utf-8") as file_:
-        for line in file_:
-            collection.add(line.rstrip())
-    return collection
-
-
-def get_file_extension(path, dot=True, lower=True):
-    ext = os.path.splitext(path)[1]
-    ext = ext if dot else ext[1:]
-    return ext.lower() if lower else ext
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/downsampled_multihead_attention.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/downsampled_multihead_attention.py
deleted file mode 100644
index 2cdece3f7fca2b830eb72999ce93f58667ed595b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/downsampled_multihead_attention.py
+++ /dev/null
@@ -1,316 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-#
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from fairseq.modules.scalar_bias import scalar_bias
-
-
-class SingleHeadAttention(nn.Module):
-    """
-    Single-head attention that supports Gating and Downsampling
-    """
-
-    def __init__(
-        self,
-        out_channels,
-        embed_dim,
-        head_dim,
-        head_index,
-        dropout=0.0,
-        bias=True,
-        project_input=True,
-        gated=False,
-        downsample=False,
-        num_heads=1,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.dropout_module = FairseqDropout(
-            dropout, module_name=self.__class__.__name__
-        )
-        self.head_index = head_index
-        self.head_dim = head_dim
-        self.project_input = project_input
-        self.gated = gated
-        self.downsample = downsample
-        self.num_heads = num_heads
-        self.projection = None
-
-        k_layers = []
-        v_layers = []
-        if self.downsample:
-            k_layers.append(Downsample(self.head_index))
-            v_layers.append(Downsample(self.head_index))
-            out_proj_size = self.head_dim
-        else:
-            out_proj_size = self.head_dim * self.num_heads
-        if self.gated:
-            k_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
-            self.in_proj_q = GatedLinear(self.embed_dim, out_proj_size, bias=bias)
-            v_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
-        else:
-            k_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
-            self.in_proj_q = Linear(self.embed_dim, out_proj_size, bias=bias)
-            v_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
-
-        self.in_proj_k = nn.Sequential(*k_layers)
-        self.in_proj_v = nn.Sequential(*v_layers)
-
-        if self.downsample:
-            self.out_proj = Linear(out_proj_size, self.head_dim, bias=bias)
-        else:
-            self.out_proj = Linear(out_proj_size, out_channels, bias=bias)
-
-        self.scaling = self.head_dim ** -0.5
-
-    def forward(
-        self,
-        query,
-        key,
-        value,
-        mask_future_timesteps=False,
-        key_padding_mask=None,
-        use_scalar_bias=False,
-    ):
-        """Input shape: Time x Batch x Channel
-        Self-attention can be implemented by passing in the same arguments for
-        query, key and value. Future timesteps can be masked with the
-        `mask_future_timesteps` argument. Padding elements can be excluded from
-        the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
-        batch x src_len, where padding elements are indicated by 1s.
-        """
-        src_len, bsz, out_channels = key.size()
-        tgt_len = query.size(0)
-        assert list(query.size()) == [tgt_len, bsz, out_channels]
-        assert key.size() == value.size()
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.size(0) == bsz
-            assert key_padding_mask.size(1) == src_len
-
-        if self.downsample:
-            size = bsz
-        else:
-            size = bsz * self.num_heads
-
-        k = key
-        v = value
-        q = query
-        if self.project_input:
-            q = self.in_proj_q(q)
-            k = self.in_proj_k(k)
-            v = self.in_proj_v(v)
-            src_len = k.size()[0]
-        q *= self.scaling
-
-        if not self.downsample:
-            q = q.view(tgt_len, size, self.head_dim)
-            k = k.view(src_len, size, self.head_dim)
-            v = v.view(src_len, size, self.head_dim)
-
-        q = q.transpose(0, 1)
-        k = k.transpose(0, 1)
-        v = v.transpose(0, 1)
-
-        attn_weights = torch.bmm(q, k.transpose(1, 2))
-        if mask_future_timesteps:
-            assert (
-                query.size() == key.size()
-            ), "mask_future_timesteps only applies to self-attention"
-            attn_weights *= torch.tril(
-                attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(),
-                diagonal=-1,
-            )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0)
-            attn_weights += torch.triu(
-                attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(),
-                diagonal=0,
-            )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0)
-        tgt_size = tgt_len
-        if use_scalar_bias:
-            attn_weights = scalar_bias(attn_weights, 2)
-            v = scalar_bias(v, 1)
-            tgt_size += 1
-
-        if key_padding_mask is not None:
-            # don't attend to padding symbols
-            if key_padding_mask.max() > 0:
-                if self.downsample:
-                    attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len)
-                else:
-                    attn_weights = attn_weights.view(
-                        size, self.num_heads, tgt_len, src_len
-                    )
-                attn_weights = attn_weights.masked_fill(
-                    key_padding_mask.unsqueeze(1).unsqueeze(2),
-                    -math.inf,
-                )
-                attn_weights = attn_weights.view(size, tgt_len, src_len)
-        attn_weights = F.softmax(attn_weights, dim=-1)
-        attn_weights = self.dropout_module(attn_weights)
-
-        attn = torch.bmm(attn_weights, v)
-        if self.downsample:
-            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim)
-        else:
-            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
-
-        attn = self.out_proj(attn)
-
-        return attn, attn_weights
-
-
-class DownsampledMultiHeadAttention(nn.ModuleList):
-    """
-    Multi-headed attention with Gating and Downsampling
-    """
-
-    def __init__(
-        self,
-        out_channels,
-        embed_dim,
-        num_heads,
-        dropout=0.0,
-        bias=True,
-        project_input=True,
-        gated=False,
-        downsample=False,
-    ):
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.head_dim = embed_dim // num_heads
-        self.downsample = downsample
-        self.gated = gated
-        self.project_input = project_input
-        assert self.head_dim * num_heads == embed_dim
-
-        if self.downsample:
-            attention_heads = []
-            for index in range(self.num_heads):
-                attention_heads.append(
-                    SingleHeadAttention(
-                        out_channels,
-                        self.embed_dim,
-                        self.head_dim,
-                        index,
-                        dropout,
-                        bias,
-                        self.project_input,
-                        self.gated,
-                        self.downsample,
-                        self.num_heads,
-                    )
-                )
-            super().__init__(modules=attention_heads)
-            self.out_proj = Linear(embed_dim, out_channels, bias=bias)
-        else:
-            # either we have a list of attention heads, or just one attention head
-            # if not being downsampled, we can do the heads with one linear layer instead of separate ones
-            super().__init__()
-            self.attention_module = SingleHeadAttention(
-                out_channels,
-                self.embed_dim,
-                self.head_dim,
-                1,
-                dropout,
-                bias,
-                self.project_input,
-                self.gated,
-                self.downsample,
-                self.num_heads,
-            )
-
-    def forward(
-        self,
-        query,
-        key,
-        value,
-        mask_future_timesteps=False,
-        key_padding_mask=None,
-        use_scalar_bias=False,
-    ):
-        src_len, bsz, embed_dim = key.size()
-        tgt_len = query.size(0)
-        assert embed_dim == self.embed_dim
-        assert list(query.size()) == [tgt_len, bsz, embed_dim]
-        assert key.size() == value.size()
-
-        tgt_size = tgt_len
-        if use_scalar_bias:
-            tgt_size += 1
-
-        attn = []
-        attn_weights = []
-        if self.downsample:
-            for attention_head_number in range(self.num_heads):
-                # call the forward of each attention head
-                _attn, _attn_weight = self[attention_head_number](
-                    query,
-                    key,
-                    value,
-                    mask_future_timesteps,
-                    key_padding_mask,
-                    use_scalar_bias,
-                )
-                attn.append(_attn)
-                attn_weights.append(_attn_weight)
-            full_attn = torch.cat(attn, dim=2)
-            full_attn = self.out_proj(full_attn)
-            return full_attn, attn_weights[0].clone()
-        else:
-            _attn, _attn_weight = self.attention_module(
-                query,
-                key,
-                value,
-                mask_future_timesteps,
-                key_padding_mask,
-                use_scalar_bias,
-            )
-            attn.append(_attn)
-            attn_weights.append(_attn_weight)
-            full_attn = torch.cat(attn, dim=2)
-            full_attn_weights = torch.cat(attn_weights)
-            full_attn_weights = full_attn_weights.view(
-                bsz, self.num_heads, tgt_size, src_len
-            )
-            full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads
-            return full_attn, full_attn_weights
-
-
-class Downsample(nn.Module):
-    """
-    Selects every nth element, where n is the index
-    """
-
-    def __init__(self, index):
-        super().__init__()
-        self.index = index
-
-    def forward(self, x):
-        return x[:: self.index + 1]
-
-
-def Linear(in_features, out_features, dropout=0.0, bias=True):
-    """Weight-normalized Linear layer (input: B x T x C)"""
-    m = nn.Linear(in_features, out_features, bias=bias)
-    m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
-    m.bias.data.zero_()
-    return nn.utils.weight_norm(m)
-
-
-def GatedLinear(in_features, out_features, dropout=0.0, bias=True):
-    """Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units"""
-    return nn.Sequential(
-        Linear(in_features, out_features * 4, dropout, bias),
-        nn.GLU(),
-        Linear(out_features * 2, out_features * 2, dropout, bias),
-        nn.GLU(),
-        Linear(out_features, out_features, dropout, bias),
-    )
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/learned_positional_embedding.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/learned_positional_embedding.py
deleted file mode 100644
index 378d0f707183dd344dbb9288dda394b11053acf0..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/learned_positional_embedding.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from torch import Tensor
-
-
-class LearnedPositionalEmbedding(nn.Embedding):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    Padding ids are ignored by either offsetting based on padding_idx
-    or by setting padding_idx to None and ensuring that the appropriate
-    position ids are passed to the forward function.
-    """
-
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        super().__init__(num_embeddings, embedding_dim, padding_idx)
-        self.onnx_trace = False
-        if self.padding_idx is not None:
-            self.max_positions = self.num_embeddings - self.padding_idx - 1
-        else:
-            self.max_positions = self.num_embeddings
-
-    def forward(
-        self,
-        input: Tensor,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        positions: Optional[Tensor] = None,
-    ):
-        """Input is expected to be of size [bsz x seqlen]."""
-        assert (positions is None) or (
-            self.padding_idx is None
-        ), "If positions is pre-computed then padding_idx should not be set."
-
-        if positions is None:
-            if incremental_state is not None:
-                # positions is the same for every token when decoding a single step
-                # Without the int() cast, it doesn't work in some cases when exporting to ONNX
-                positions = torch.zeros(
-                    (1, 1), device=input.device, dtype=input.dtype
-                ).fill_(int(self.padding_idx + input.size(1)))
-            else:
-                positions = utils.make_positions(
-                    input, self.padding_idx, onnx_trace=self.onnx_trace
-                )
-        return F.embedding(
-            positions,
-            self.weight,
-            self.padding_idx,
-            self.max_norm,
-            self.norm_type,
-            self.scale_grad_by_freq,
-            self.sparse,
-        )
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_checkpoint_utils.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_checkpoint_utils.py
deleted file mode 100644
index 0f28222633a68943497616507ce412ead76864d6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_checkpoint_utils.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import contextlib
-import logging
-import os
-import tempfile
-import unittest
-from io import StringIO
-from unittest.mock import patch
-
-from fairseq import checkpoint_utils
-from omegaconf import OmegaConf
-
-from tests.utils import (
-    create_dummy_data,
-    preprocess_translation_data,
-    train_translation_model,
-)
-
-
-class TestCheckpointUtils(unittest.TestCase):
-    def setUp(self):
-        logging.disable(logging.CRITICAL)
-
-    def tearDown(self):
-        logging.disable(logging.NOTSET)
-
-    @contextlib.contextmanager
-    def _train_transformer(self, seed, extra_args=None):
-        if extra_args is None:
-            extra_args = []
-        with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir:
-            create_dummy_data(data_dir)
-            preprocess_translation_data(data_dir)
-            train_translation_model(
-                data_dir,
-                "transformer_iwslt_de_en",
-                [
-                    "--encoder-layers",
-                    "3",
-                    "--decoder-layers",
-                    "3",
-                    "--encoder-embed-dim",
-                    "8",
-                    "--decoder-embed-dim",
-                    "8",
-                    "--seed",
-                    str(seed),
-                ]
-                + extra_args,
-            )
-            yield os.path.join(data_dir, "checkpoint_last.pt")
-
-    def test_load_model_ensemble_and_task(self):
-        # with contextlib.redirect_stdout(StringIO()):
-            with self._train_transformer(seed=123) as model1:
-                with self._train_transformer(seed=456) as model2:
-                    ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-                        filenames=[model1, model2]
-                    )
-                    self.assertEqual(len(ensemble), 2)
-
-                    # after Transformer has been migrated to Hydra, this will probably
-                    # become cfg.common.seed
-                    self.assertEqual(ensemble[0].args.seed, 123)
-                    self.assertEqual(ensemble[1].args.seed, 456)
-
-                    # the task from the first model should be returned
-                    self.assertTrue("seed123" in task.cfg.data)
-
-                    # last cfg is saved
-                    self.assertEqual(cfg.common.seed, 456)
-
-    def test_prune_state_dict(self):
-        with contextlib.redirect_stdout(StringIO()):
-            extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"]
-            with self._train_transformer(seed=1, extra_args=extra_args) as model:
-                ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-                    filenames=[model],
-                    arg_overrides={
-                        "encoder_layers_to_keep": "0,2",
-                        "decoder_layers_to_keep": "1",
-                    },
-                )
-                self.assertEqual(len(ensemble), 1)
-                self.assertEqual(len(ensemble[0].encoder.layers), 2)
-                self.assertEqual(len(ensemble[0].decoder.layers), 1)
-
-    def test_torch_persistent_save_async(self):
-        state_dict = {}
-        filename = "async_checkpoint.pt"
-
-        with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena:
-            with patch(f"{checkpoint_utils.__name__}._torch_persistent_save") as mock_save:
-                checkpoint_utils.torch_persistent_save(
-                    state_dict, filename, async_write=True
-                )
-                mock_opena.assert_called_with(filename, "wb")
-                mock_save.assert_called()
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/constrained_decoding/normalize.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/constrained_decoding/normalize.py
deleted file mode 100644
index 4ae2b5111ba025acb9e1613865c92fdc339a58d5..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/constrained_decoding/normalize.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-from sacremoses.normalize import MosesPunctNormalizer
-
-
-def main(args):
-    normalizer = MosesPunctNormalizer(lang=args.lang, penn=args.penn)
-    for line in sys.stdin:
-        print(normalizer.normalize(line.rstrip()), flush=True)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--lang", "-l", default="en")
-    parser.add_argument("--penn", "-p", action="store_true")
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/round_robin_zip_datasets.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/round_robin_zip_datasets.py
deleted file mode 100644
index 2cb7447ea955a7c3ae7372f09ee426c08acd430e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/round_robin_zip_datasets.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from collections import OrderedDict
-from typing import Dict, Sequence
-
-import numpy as np
-
-from . import FairseqDataset, LanguagePairDataset
-
-logger = logging.getLogger(__name__)
-
-
-class RoundRobinZipDatasets(FairseqDataset):
-    """Zip multiple :class:`~fairseq.data.FairseqDataset` instances together.
-
-    Shorter datasets are repeated in a round-robin fashion to match the length
-    of the longest one.
-
-    Args:
-        datasets (Dict[~fairseq.data.FairseqDataset]): a dictionary of
-            :class:`~fairseq.data.FairseqDataset` instances.
-        eval_key (str, optional): a key used at evaluation time that causes
-            this instance to pass-through batches from *datasets[eval_key]*.
-    """
-
-    def __init__(self, datasets, eval_key=None):
-        super().__init__()
-        if isinstance(datasets, dict):
-            datasets = OrderedDict(datasets)
-        assert isinstance(datasets, OrderedDict)
-        assert datasets, "Can't make a RoundRobinZipDatasets out of nothing"
-        for dataset in datasets.values():
-            assert isinstance(dataset, FairseqDataset)
-
-        self.datasets = datasets
-        self.eval_key = eval_key
-
-        self.longest_dataset_key = max(datasets, key=lambda k: len(datasets[k]))
-        self.longest_dataset = datasets[self.longest_dataset_key]
-        self._ordered_indices: Dict[str, Sequence[int]] = None
-
-    def _map_index(self, key, index):
-        assert (
-            self._ordered_indices is not None
-        ), "Must call RoundRobinZipDatasets.ordered_indices() first"
-        o = self._ordered_indices[key]
-        return o[index % len(o)]
-
-    def __getitem__(self, index):
-        if self.eval_key is None:
-            return OrderedDict(
-                [
-                    (key, dataset[self._map_index(key, index)])
-                    for key, dataset in self.datasets.items()
-                ]
-            )
-        else:
-            # at evaluation time it's useful to pass-through batches from a single key
-            return self.datasets[self.eval_key][self._map_index(self.eval_key, index)]
-
-    def __len__(self):
-        if self._ordered_indices is not None:
-            return len(self._ordered_indices[self.longest_dataset_key])
-        return len(self.longest_dataset)
-
-    def collater(self, samples):
-        """Merge a list of samples to form a mini-batch."""
-        if len(samples) == 0:
-            return None
-        if self.eval_key is None:
-            return OrderedDict(
-                [
-                    (key, dataset.collater([sample[key] for sample in samples]))
-                    for key, dataset in self.datasets.items()
-                ]
-            )
-        else:
-            # at evaluation time it's useful to pass-through batches from a single key
-            return self.datasets[self.eval_key].collater(samples)
-
-    def num_tokens(self, index):
-        """Return an example's length (number of tokens), used for batching."""
-        # TODO make it configurable whether to use max() or sum() here
-        return max(
-            dataset.num_tokens(self._map_index(key, index))
-            for key, dataset in self.datasets.items()
-        )
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with ``--max-positions``."""
-        return {
-            key: dataset.size(self._map_index(key, index))
-            for key, dataset in self.datasets.items()
-        }
-
-    def ordered_indices(self):
-        """Ordered indices for batching."""
-        if self._ordered_indices is None:
-            # Call the underlying dataset's ordered_indices() here, so that we
-            # get the same random ordering as we would have from using the
-            # underlying sub-datasets directly.
-            self._ordered_indices = OrderedDict(
-                [
-                    (key, dataset.ordered_indices())
-                    for key, dataset in self.datasets.items()
-                ]
-            )
-        return np.arange(len(self))
-
-    def filter_indices_by_size(self, indices, max_positions=None):
-        """
-        Filter each sub-dataset independently, then update the round robin to work
-        on the filtered sub-datasets.
-        """
-
-        def _deep_until_language_pair(dataset):
-            if isinstance(dataset, LanguagePairDataset):
-                return dataset
-            if hasattr(dataset, "tgt_dataset"):
-                return _deep_until_language_pair(dataset.tgt_dataset)
-            if hasattr(dataset, "dataset"):
-                return _deep_until_language_pair(dataset.dataset)
-            raise Exception(f"Don't know how to unwrap this dataset: {dataset}")
-
-        if not isinstance(max_positions, dict):
-            max_positions = {k: max_positions for k in self.datasets.keys()}
-        ignored_some = False
-        for key, dataset in self.datasets.items():
-            dataset = _deep_until_language_pair(dataset)
-            self._ordered_indices[key], ignored = dataset.filter_indices_by_size(
-                self._ordered_indices[key], max_positions[key]
-            )
-            if len(ignored) > 0:
-                ignored_some = True
-                logger.warning(
-                    f"{len(ignored)} samples from {key} have invalid sizes and will be skipped, "
-                    f"max_positions={max_positions[key]}, first few sample ids={ignored[:10]}"
-                )
-        # Since we are modifying in place the _ordered_indices,
-        # it's not possible anymore to return valid ignored indices.
-        # Hopefully the extra debug information print above should be enough to debug.
-        # Ideally we would receive ignore_invalid_inputs so that we could have
-        # a proper error message.
-        return (np.arange(len(self)), [0] if ignored_some else [])
-
-    @property
-    def supports_prefetch(self):
-        return all(
-            getattr(dataset, "supports_prefetch", False)
-            for dataset in self.datasets.values()
-        )
-
-    def prefetch(self, indices):
-        for key, dataset in self.datasets.items():
-            dataset.prefetch([self._map_index(key, index) for index in indices])
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/criterions/scst_loss.py b/spaces/OFA-Sys/OFA-Visual_Grounding/criterions/scst_loss.py
deleted file mode 100644
index 9eaa3b8e82237e91a309db170aaf9b3170d91f62..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/criterions/scst_loss.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import string
-from dataclasses import dataclass, field
-from collections import OrderedDict
-from typing import Optional
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-from data import data_utils
-from utils.cider.pyciderevalcap.ciderD.ciderD import CiderD
-
-
-def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
-    loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
-    if ignore_index is not None:
-        pad_mask = target.eq(ignore_index)
-        loss.masked_fill_(pad_mask, 0.0)
-        ntokens = (~pad_mask).sum()
-    else:
-        loss = loss.squeeze(-1)
-        ntokens = target.numel()
-    if reduce:
-        loss = loss.sum()
-    return loss, ntokens
-
-@dataclass
-class ScstRewardCriterionConfig(FairseqDataclass):
-    scst_cider_cached_tokens: str = field(
-        default="coco-train-words.p",
-        metadata={"help": "path to cached cPickle file used to calculate CIDEr scores"},
-    )
-    ignore_prefix_size: int = field(
-        default=0,
-        metadata={"help": "Ignore first N tokens"},
-    )
-    sentence_avg: bool = II("optimization.sentence_avg")
-    constraint_range: Optional[str] = field(
-        default=None,
-        metadata={"help": "constraint range"}
-    )
-
-
-@register_criterion(
-    "scst_reward_criterion", dataclass=ScstRewardCriterionConfig
-)
-class ScstRewardCriterion(FairseqCriterion):
-    CIDER_REWARD_WEIGHT = 1
-
-    def __init__(
-        self,
-        task,
-        scst_cider_cached_tokens,
-        sentence_avg,
-        ignore_prefix_size=0,
-        constraint_range=None
-    ):
-        super().__init__(task)
-        self.scst_cider_scorer = CiderD(df=scst_cider_cached_tokens)
-        self.sentence_avg = sentence_avg
-        self.ignore_prefix_size = ignore_prefix_size
-        self.transtab = str.maketrans({key: None for key in string.punctuation})
-
-        self.constraint_start = None
-        self.constraint_end = None
-        if constraint_range is not None:
-            constraint_start, constraint_end = constraint_range.split(',')
-            self.constraint_start = int(constraint_start)
-            self.constraint_end = int(constraint_end)
-
-    def forward(self, model, sample, update_num=0, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
-
-        sample_size = (
-            nsentences if self.sentence_avg else ntokens
-        )
-        logging_output = {
-            "loss": loss.data,
-            "score": score,
-            "ntokens": ntokens,
-            "nsentences": nsentences,
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    def _calculate_eval_scores(self, gen_res, gt_idx, gt_res):
-        '''
-        gen_res: generated captions, list of str
-        gt_idx: list of int, of the same length as gen_res
-        gt_res: ground truth captions, list of list of str.
-            gen_res[i] corresponds to gt_res[gt_idx[i]]
-            Each image can have multiple ground truth captions
-        '''
-        gen_res_size = len(gen_res)
-
-        res = OrderedDict()
-        for i in range(gen_res_size):
-            res[i] = [self._wrap_sentence(gen_res[i].strip().translate(self.transtab))]
-
-        gts = OrderedDict()
-        gt_res_ = [
-            [self._wrap_sentence(gt_res[i][j].strip().translate(self.transtab)) for j in range(len(gt_res[i]))]
-                for i in range(len(gt_res))
-        ]
-        for i in range(gen_res_size):
-            gts[i] = gt_res_[gt_idx[i]]
-
-        res_ = [{'image_id':i, 'caption': res[i]} for i in range(len(res))]
-        _, batch_cider_scores = self.scst_cider_scorer.compute_score(gts, res_)
-        scores = self.CIDER_REWARD_WEIGHT * batch_cider_scores
-        return scores
-
-    @classmethod
-    def _wrap_sentence(self, s):
-        # ensure the sentence ends with <eos> token
-        # in order to keep consisitent with cider_cached_tokens
-        r = s.strip()
-        if r.endswith('.'):
-            r = r[:-1]
-        r += ' <eos>'
-        return r
-
-    def get_generator_out(self, model, sample):
-        def decode(toks):
-            hypo = toks.int().cpu()
-            hypo_str = self.task.tgt_dict.string(hypo)
-            hypo_str = self.task.bpe.decode(hypo_str).strip()
-            return hypo, hypo_str
-
-        model.eval()
-        with torch.no_grad():
-            self.task.scst_generator.model.eval()
-            gen_out = self.task.scst_generator.generate([model], sample)
-
-        gen_target = []
-        gen_res = []
-        gt_res = []
-        for i in range(len(gen_out)):
-            for j in range(len(gen_out[i])):
-                hypo, hypo_str = decode(gen_out[i][j]["tokens"])
-                gen_target.append(hypo)
-                gen_res.append(hypo_str)
-            gt_res.append(
-                decode(utils.strip_pad(sample["target"][i], self.padding_idx))[1].split('&&')
-            )
-
-        return gen_target, gen_res, gt_res
-
-    def get_reward_and_scores(self, gen_res, gt_res, device):
-        batch_size = len(gt_res)
-        gen_res_size = len(gen_res)
-        seq_per_img = gen_res_size // batch_size
-
-        gt_idx = [i // seq_per_img for i in range(gen_res_size)]
-        scores = self._calculate_eval_scores(gen_res, gt_idx, gt_res)
-        sc_ = scores.reshape(batch_size, seq_per_img)
-        baseline = (sc_.sum(1, keepdims=True) - sc_) / (sc_.shape[1] - 1)
-        # sample - baseline
-        reward = scores.reshape(batch_size, seq_per_img)
-        reward = reward - baseline
-        reward = reward.reshape(gen_res_size)
-        reward = torch.as_tensor(reward, device=device, dtype=torch.float64)
-
-        return reward, scores
-
-    def get_net_output(self, model, sample, gen_target):
-        def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
-            return data_utils.collate_tokens(
-                sample_list,
-                pad_idx=self.padding_idx,
-                eos_idx=eos,
-                left_pad=False,
-                move_eos_to_beginning=move_eos_to_beginning,
-            )
-
-        batch_size = len(sample["target"])
-        gen_target_size = len(gen_target)
-        seq_per_img = gen_target_size // batch_size
-
-        model.train()
-        sample_src_tokens = torch.repeat_interleave(
-            sample['net_input']['src_tokens'], seq_per_img, dim=0
-        )
-        sample_src_lengths = torch.repeat_interleave(
-            sample['net_input']['src_lengths'], seq_per_img, dim=0
-        )
-        sample_patch_images = torch.repeat_interleave(
-            sample['net_input']['patch_images'], seq_per_img, dim=0
-        )
-        sample_patch_masks = torch.repeat_interleave(
-            sample['net_input']['patch_masks'], seq_per_img, dim=0
-        )
-        gen_prev_output_tokens = torch.as_tensor(
-            merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
-            device=sample["target"].device, dtype=torch.int64
-        )
-        gen_target_tokens = torch.as_tensor(
-            merge(gen_target), device=sample["target"].device, dtype=torch.int64
-        )
-        net_output = model(
-            src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
-            patch_images=sample_patch_images, patch_masks=sample_patch_masks,
-            prev_output_tokens=gen_prev_output_tokens
-        )
-
-        return net_output, gen_target_tokens
-
-    def get_lprobs_and_target(self, model, net_output, gen_target):
-        if self.constraint_start is not None and self.constraint_end is not None:
-            net_output[0][:, :, 4:self.constraint_start] = -math.inf
-            net_output[0][:, :, self.constraint_end:] = -math.inf
-        lprobs = model.get_normalized_probs(net_output, log_probs=True)
-        if self.ignore_prefix_size > 0:
-            if getattr(lprobs, "batch_first", False):
-                lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
-                gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
-            else:
-                lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
-                gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
-        return lprobs, gen_target
-
-    def compute_loss(self, model, sample, reduce=True):
-        gen_target, gen_res, gt_res = self.get_generator_out(model, sample)
-        reward, scores = self.get_reward_and_scores(gen_res, gt_res, device=sample["target"].device)
-        net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
-        gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
-        loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
-        nsentences = gen_target_tokens.size(0)
-
-        return loss, scores.sum(), ntokens, nsentences
-
-    @classmethod
-    def reduce_metrics(cls, logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        score_sum = sum(log.get("score", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size, sample_size, round=3
-        )
-        metrics.log_scalar(
-            "score", score_sum / nsentences, nsentences, round=3
-        )
-
-        metrics.log_scalar(
-            "ntokens", ntokens, 1, round=3
-        )
-        metrics.log_scalar(
-            "nsentences", nsentences, 1, round=3
-        )
-        metrics.log_scalar(
-            "sample_size", sample_size, 1, round=3
-        )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/demo/README.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/demo/README.md
deleted file mode 100644
index 133d8d38e5e9f5f44aca92c59f73309e166d7132..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/demo/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-## Detectron2 Demo
-
-We provide a command line tool to run a simple demo of builtin configs.
-The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md).
-
-See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-)
-for a high-quality demo generated with this tool.
diff --git a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/tool_using/README.md b/spaces/Osborn-bh/ChatGLM3-6B-Osborn/tool_using/README.md
deleted file mode 100644
index 3a3eb9ea78e58b8479d45ffedb8092b7f29c25df..0000000000000000000000000000000000000000
--- a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/tool_using/README.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# 工具调用
-本文档将介绍如何使用 ChatGLM3-6B 进行工具调用。目前只有 ChatGLM3-6B 模型支持工具调用，而 ChatGLM3-6B-Base 和 ChatGLM3-6B-32K 模型不支持。
-
-## 构建 System Prompt
-这里以两个工具调用为例，首先准备好要构建的数据的描述信息。
-
-```python
-tools = [
-    {
-        "name": "track",
-        "description": "追踪指定股票的实时价格",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "symbol": {
-                    "description": "需要追踪的股票代码"
-                }
-            },
-            "required": ['symbol']
-        }
-    },
-    {
-        "name": "text-to-speech",
-        "description": "将文本转换为语音",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "text": {
-                    "description": "需要转换成语音的文本"
-                },
-                "voice": {
-                    "description": "要使用的语音类型（男声、女声等）"
-                },
-                "speed": {
-                    "description": "语音的速度（快、中等、慢等）"
-                }
-            },
-            "required": ['text']
-        }
-    }
-]
-system_info = {"role": "system", "content": "Answer the following questions as best as you can. You have access to the following tools:", "tools": tools}
-```
-请确保工具的定义格式与例子中一致以获得最优的性能
-
-## 提出问题
-注意：目前 ChatGLM3-6B 的工具调用只支持通过 `chat` 方法，不支持  `stream_chat` 方法。
-```python
-history = [system_info]
-query = "帮我查询股票10111的价格"
-response, history = model.chat(tokenizer, query, history=history)
-print(response)
-```
-这里期望得到的输出为
-```json
-{"name": "track", "parameters": {"symbol": "10111"}}
-```
-这表示模型需要调用工具 `track`，并且需要传入参数 `symbol`。
-
-## 调用工具，生成回复
-这里需要自行实现调用工具的逻辑。假设已经得到了返回结果，将结果以 json 格式返回给模型并得到回复。
-```python
-result = json.dumps({"price": 12412}, ensure_ascii=False)
-response, history = model.chat(tokenizer, result, history=history, role="observation")
-print(response)
-```
-这里 `role="observation"` 表示输入的是工具调用的返回值而不是用户输入，不能省略。
-
-期望得到的输出为
-```
-根据您的查询，经过API的调用，股票10111的价格是12412。
-```
-
-这表示本次工具调用已经结束，模型根据返回结果生成回复。对于比较复杂的问题，模型可能需要进行多次工具调用。这时，可以根据返回的 `response` 是 `str` 还是 `dict` 来判断返回的是生成的回复还是工具调用请求。
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/lr_updater.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/lr_updater.py
deleted file mode 100644
index 6365908ddf6070086de2ffc0afada46ed2f32256..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/lr_updater.py
+++ /dev/null
@@ -1,670 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numbers
-from math import cos, pi
-
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-
-
-class LrUpdaterHook(Hook):
-    """LR Scheduler in MMCV.
-
-    Args:
-        by_epoch (bool): LR changes epoch by epoch
-        warmup (string): Type of warmup used. It can be None(use no warmup),
-            'constant', 'linear' or 'exp'
-        warmup_iters (int): The number of iterations or epochs that warmup
-            lasts
-        warmup_ratio (float): LR used at the beginning of warmup equals to
-            warmup_ratio * initial_lr
-        warmup_by_epoch (bool): When warmup_by_epoch == True, warmup_iters
-            means the number of epochs that warmup lasts, otherwise means the
-            number of iteration that warmup lasts
-    """
-
-    def __init__(self,
-                 by_epoch=True,
-                 warmup=None,
-                 warmup_iters=0,
-                 warmup_ratio=0.1,
-                 warmup_by_epoch=False):
-        # validate the "warmup" argument
-        if warmup is not None:
-            if warmup not in ['constant', 'linear', 'exp']:
-                raise ValueError(
-                    f'"{warmup}" is not a supported type for warming up, valid'
-                    ' types are "constant" and "linear"')
-        if warmup is not None:
-            assert warmup_iters > 0, \
-                '"warmup_iters" must be a positive integer'
-            assert 0 < warmup_ratio <= 1.0, \
-                '"warmup_ratio" must be in range (0,1]'
-
-        self.by_epoch = by_epoch
-        self.warmup = warmup
-        self.warmup_iters = warmup_iters
-        self.warmup_ratio = warmup_ratio
-        self.warmup_by_epoch = warmup_by_epoch
-
-        if self.warmup_by_epoch:
-            self.warmup_epochs = self.warmup_iters
-            self.warmup_iters = None
-        else:
-            self.warmup_epochs = None
-
-        self.base_lr = []  # initial lr for all param groups
-        self.regular_lr = []  # expected lr if no warming up is performed
-
-    def _set_lr(self, runner, lr_groups):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                for param_group, lr in zip(optim.param_groups, lr_groups[k]):
-                    param_group['lr'] = lr
-        else:
-            for param_group, lr in zip(runner.optimizer.param_groups,
-                                       lr_groups):
-                param_group['lr'] = lr
-
-    def get_lr(self, runner, base_lr):
-        raise NotImplementedError
-
-    def get_regular_lr(self, runner):
-        if isinstance(runner.optimizer, dict):
-            lr_groups = {}
-            for k in runner.optimizer.keys():
-                _lr_group = [
-                    self.get_lr(runner, _base_lr)
-                    for _base_lr in self.base_lr[k]
-                ]
-                lr_groups.update({k: _lr_group})
-
-            return lr_groups
-        else:
-            return [self.get_lr(runner, _base_lr) for _base_lr in self.base_lr]
-
-    def get_warmup_lr(self, cur_iters):
-
-        def _get_warmup_lr(cur_iters, regular_lr):
-            if self.warmup == 'constant':
-                warmup_lr = [_lr * self.warmup_ratio for _lr in regular_lr]
-            elif self.warmup == 'linear':
-                k = (1 - cur_iters / self.warmup_iters) * (1 -
-                                                           self.warmup_ratio)
-                warmup_lr = [_lr * (1 - k) for _lr in regular_lr]
-            elif self.warmup == 'exp':
-                k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
-                warmup_lr = [_lr * k for _lr in regular_lr]
-            return warmup_lr
-
-        if isinstance(self.regular_lr, dict):
-            lr_groups = {}
-            for key, regular_lr in self.regular_lr.items():
-                lr_groups[key] = _get_warmup_lr(cur_iters, regular_lr)
-            return lr_groups
-        else:
-            return _get_warmup_lr(cur_iters, self.regular_lr)
-
-    def before_run(self, runner):
-        # NOTE: when resuming from a checkpoint, if 'initial_lr' is not saved,
-        # it will be set according to the optimizer params
-        if isinstance(runner.optimizer, dict):
-            self.base_lr = {}
-            for k, optim in runner.optimizer.items():
-                for group in optim.param_groups:
-                    group.setdefault('initial_lr', group['lr'])
-                _base_lr = [
-                    group['initial_lr'] for group in optim.param_groups
-                ]
-                self.base_lr.update({k: _base_lr})
-        else:
-            for group in runner.optimizer.param_groups:
-                group.setdefault('initial_lr', group['lr'])
-            self.base_lr = [
-                group['initial_lr'] for group in runner.optimizer.param_groups
-            ]
-
-    def before_train_epoch(self, runner):
-        if self.warmup_iters is None:
-            epoch_len = len(runner.data_loader)
-            self.warmup_iters = self.warmup_epochs * epoch_len
-
-        if not self.by_epoch:
-            return
-
-        self.regular_lr = self.get_regular_lr(runner)
-        self._set_lr(runner, self.regular_lr)
-
-    def before_train_iter(self, runner):
-        cur_iter = runner.iter
-        if not self.by_epoch:
-            self.regular_lr = self.get_regular_lr(runner)
-            if self.warmup is None or cur_iter >= self.warmup_iters:
-                self._set_lr(runner, self.regular_lr)
-            else:
-                warmup_lr = self.get_warmup_lr(cur_iter)
-                self._set_lr(runner, warmup_lr)
-        elif self.by_epoch:
-            if self.warmup is None or cur_iter > self.warmup_iters:
-                return
-            elif cur_iter == self.warmup_iters:
-                self._set_lr(runner, self.regular_lr)
-            else:
-                warmup_lr = self.get_warmup_lr(cur_iter)
-                self._set_lr(runner, warmup_lr)
-
-
-@HOOKS.register_module()
-class FixedLrUpdaterHook(LrUpdaterHook):
-
-    def __init__(self, **kwargs):
-        super(FixedLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        return base_lr
-
-
-@HOOKS.register_module()
-class StepLrUpdaterHook(LrUpdaterHook):
-    """Step LR scheduler with min_lr clipping.
-
-    Args:
-        step (int | list[int]): Step to decay the LR. If an int value is given,
-            regard it as the decay interval. If a list is given, decay LR at
-            these steps.
-        gamma (float, optional): Decay LR ratio. Default: 0.1.
-        min_lr (float, optional): Minimum LR value to keep. If LR after decay
-            is lower than `min_lr`, it will be clipped to this value. If None
-            is given, we don't perform lr clipping. Default: None.
-    """
-
-    def __init__(self, step, gamma=0.1, min_lr=None, **kwargs):
-        if isinstance(step, list):
-            assert mmcv.is_list_of(step, int)
-            assert all([s > 0 for s in step])
-        elif isinstance(step, int):
-            assert step > 0
-        else:
-            raise TypeError('"step" must be a list or integer')
-        self.step = step
-        self.gamma = gamma
-        self.min_lr = min_lr
-        super(StepLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        progress = runner.epoch if self.by_epoch else runner.iter
-
-        # calculate exponential term
-        if isinstance(self.step, int):
-            exp = progress // self.step
-        else:
-            exp = len(self.step)
-            for i, s in enumerate(self.step):
-                if progress < s:
-                    exp = i
-                    break
-
-        lr = base_lr * (self.gamma**exp)
-        if self.min_lr is not None:
-            # clip to a minimum value
-            lr = max(lr, self.min_lr)
-        return lr
-
-
-@HOOKS.register_module()
-class ExpLrUpdaterHook(LrUpdaterHook):
-
-    def __init__(self, gamma, **kwargs):
-        self.gamma = gamma
-        super(ExpLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        progress = runner.epoch if self.by_epoch else runner.iter
-        return base_lr * self.gamma**progress
-
-
-@HOOKS.register_module()
-class PolyLrUpdaterHook(LrUpdaterHook):
-
-    def __init__(self, power=1., min_lr=0., **kwargs):
-        self.power = power
-        self.min_lr = min_lr
-        super(PolyLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        if self.by_epoch:
-            progress = runner.epoch
-            max_progress = runner.max_epochs
-        else:
-            progress = runner.iter
-            max_progress = runner.max_iters
-        coeff = (1 - progress / max_progress)**self.power
-        return (base_lr - self.min_lr) * coeff + self.min_lr
-
-
-@HOOKS.register_module()
-class InvLrUpdaterHook(LrUpdaterHook):
-
-    def __init__(self, gamma, power=1., **kwargs):
-        self.gamma = gamma
-        self.power = power
-        super(InvLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        progress = runner.epoch if self.by_epoch else runner.iter
-        return base_lr * (1 + self.gamma * progress)**(-self.power)
-
-
-@HOOKS.register_module()
-class CosineAnnealingLrUpdaterHook(LrUpdaterHook):
-
-    def __init__(self, min_lr=None, min_lr_ratio=None, **kwargs):
-        assert (min_lr is None) ^ (min_lr_ratio is None)
-        self.min_lr = min_lr
-        self.min_lr_ratio = min_lr_ratio
-        super(CosineAnnealingLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        if self.by_epoch:
-            progress = runner.epoch
-            max_progress = runner.max_epochs
-        else:
-            progress = runner.iter
-            max_progress = runner.max_iters
-
-        if self.min_lr_ratio is not None:
-            target_lr = base_lr * self.min_lr_ratio
-        else:
-            target_lr = self.min_lr
-        return annealing_cos(base_lr, target_lr, progress / max_progress)
-
-
-@HOOKS.register_module()
-class FlatCosineAnnealingLrUpdaterHook(LrUpdaterHook):
-    """Flat + Cosine lr schedule.
-
-    Modified from https://github.com/fastai/fastai/blob/master/fastai/callback/schedule.py#L128 # noqa: E501
-
-    Args:
-        start_percent (float): When to start annealing the learning rate
-            after the percentage of the total training steps.
-            The value should be in range [0, 1).
-            Default: 0.75
-        min_lr (float, optional): The minimum lr. Default: None.
-        min_lr_ratio (float, optional): The ratio of minimum lr to the base lr.
-            Either `min_lr` or `min_lr_ratio` should be specified.
-            Default: None.
-    """
-
-    def __init__(self,
-                 start_percent=0.75,
-                 min_lr=None,
-                 min_lr_ratio=None,
-                 **kwargs):
-        assert (min_lr is None) ^ (min_lr_ratio is None)
-        if start_percent < 0 or start_percent > 1 or not isinstance(
-                start_percent, float):
-            raise ValueError(
-                'expected float between 0 and 1 start_percent, but '
-                f'got {start_percent}')
-        self.start_percent = start_percent
-        self.min_lr = min_lr
-        self.min_lr_ratio = min_lr_ratio
-        super(FlatCosineAnnealingLrUpdaterHook, self).__init__(**kwargs)
-
-    def get_lr(self, runner, base_lr):
-        if self.by_epoch:
-            start = round(runner.max_epochs * self.start_percent)
-            progress = runner.epoch - start
-            max_progress = runner.max_epochs - start
-        else:
-            start = round(runner.max_iters * self.start_percent)
-            progress = runner.iter - start
-            max_progress = runner.max_iters - start
-
-        if self.min_lr_ratio is not None:
-            target_lr = base_lr * self.min_lr_ratio
-        else:
-            target_lr = self.min_lr
-
-        if progress < 0:
-            return base_lr
-        else:
-            return annealing_cos(base_lr, target_lr, progress / max_progress)
-
-
-@HOOKS.register_module()
-class CosineRestartLrUpdaterHook(LrUpdaterHook):
-    """Cosine annealing with restarts learning rate scheme.
-
-    Args:
-        periods (list[int]): Periods for each cosine anneling cycle.
-        restart_weights (list[float], optional): Restart weights at each
-            restart iteration. Default: [1].
-        min_lr (float, optional): The minimum lr. Default: None.
-        min_lr_ratio (float, optional): The ratio of minimum lr to the base lr.
-            Either `min_lr` or `min_lr_ratio` should be specified.
-            Default: None.
-    """
-
-    def __init__(self,
-                 periods,
-                 restart_weights=[1],
-                 min_lr=None,
-                 min_lr_ratio=None,
-                 **kwargs):
-        assert (min_lr is None) ^ (min_lr_ratio is None)
-        self.periods = periods
-        self.min_lr = min_lr
-        self.min_lr_ratio = min_lr_ratio
-        self.restart_weights = restart_weights
-        assert (len(self.periods) == len(self.restart_weights)
-                ), 'periods and restart_weights should have the same length.'
-        super(CosineRestartLrUpdaterHook, self).__init__(**kwargs)
-
-        self.cumulative_periods = [
-            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
-        ]
-
-    def get_lr(self, runner, base_lr):
-        if self.by_epoch:
-            progress = runner.epoch
-        else:
-            progress = runner.iter
-
-        if self.min_lr_ratio is not None:
-            target_lr = base_lr * self.min_lr_ratio
-        else:
-            target_lr = self.min_lr
-
-        idx = get_position_from_periods(progress, self.cumulative_periods)
-        current_weight = self.restart_weights[idx]
-        nearest_restart = 0 if idx == 0 else self.cumulative_periods[idx - 1]
-        current_periods = self.periods[idx]
-
-        alpha = min((progress - nearest_restart) / current_periods, 1)
-        return annealing_cos(base_lr, target_lr, alpha, current_weight)
-
-
-def get_position_from_periods(iteration, cumulative_periods):
-    """Get the position from a period list.
-
-    It will return the index of the right-closest number in the period list.
-    For example, the cumulative_periods = [100, 200, 300, 400],
-    if iteration == 50, return 0;
-    if iteration == 210, return 2;
-    if iteration == 300, return 3.
-
-    Args:
-        iteration (int): Current iteration.
-        cumulative_periods (list[int]): Cumulative period list.
-
-    Returns:
-        int: The position of the right-closest number in the period list.
-    """
-    for i, period in enumerate(cumulative_periods):
-        if iteration < period:
-            return i
-    raise ValueError(f'Current iteration {iteration} exceeds '
-                     f'cumulative_periods {cumulative_periods}')
-
-
-@HOOKS.register_module()
-class CyclicLrUpdaterHook(LrUpdaterHook):
-    """Cyclic LR Scheduler.
-
-    Implement the cyclical learning rate policy (CLR) described in
-    https://arxiv.org/pdf/1506.01186.pdf
-
-    Different from the original paper, we use cosine annealing rather than
-    triangular policy inside a cycle. This improves the performance in the
-    3D detection area.
-
-    Args:
-        by_epoch (bool): Whether to update LR by epoch.
-        target_ratio (tuple[float]): Relative ratio of the highest LR and the
-            lowest LR to the initial LR.
-        cyclic_times (int): Number of cycles during training
-        step_ratio_up (float): The ratio of the increasing process of LR in
-            the total cycle.
-        anneal_strategy (str): {'cos', 'linear'}
-            Specifies the annealing strategy: 'cos' for cosine annealing,
-            'linear' for linear annealing. Default: 'cos'.
-    """
-
-    def __init__(self,
-                 by_epoch=False,
-                 target_ratio=(10, 1e-4),
-                 cyclic_times=1,
-                 step_ratio_up=0.4,
-                 anneal_strategy='cos',
-                 **kwargs):
-        if isinstance(target_ratio, float):
-            target_ratio = (target_ratio, target_ratio / 1e5)
-        elif isinstance(target_ratio, tuple):
-            target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
-                if len(target_ratio) == 1 else target_ratio
-        else:
-            raise ValueError('target_ratio should be either float '
-                             f'or tuple, got {type(target_ratio)}')
-
-        assert len(target_ratio) == 2, \
-            '"target_ratio" must be list or tuple of two floats'
-        assert 0 <= step_ratio_up < 1.0, \
-            '"step_ratio_up" must be in range [0,1)'
-
-        self.target_ratio = target_ratio
-        self.cyclic_times = cyclic_times
-        self.step_ratio_up = step_ratio_up
-        self.lr_phases = []  # init lr_phases
-        # validate anneal_strategy
-        if anneal_strategy not in ['cos', 'linear']:
-            raise ValueError('anneal_strategy must be one of "cos" or '
-                             f'"linear", instead got {anneal_strategy}')
-        elif anneal_strategy == 'cos':
-            self.anneal_func = annealing_cos
-        elif anneal_strategy == 'linear':
-            self.anneal_func = annealing_linear
-
-        assert not by_epoch, \
-            'currently only support "by_epoch" = False'
-        super(CyclicLrUpdaterHook, self).__init__(by_epoch, **kwargs)
-
-    def before_run(self, runner):
-        super(CyclicLrUpdaterHook, self).before_run(runner)
-        # initiate lr_phases
-        # total lr_phases are separated as up and down
-        max_iter_per_phase = runner.max_iters // self.cyclic_times
-        iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
-        self.lr_phases.append(
-            [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
-        self.lr_phases.append([
-            iter_up_phase, max_iter_per_phase, max_iter_per_phase,
-            self.target_ratio[0], self.target_ratio[1]
-        ])
-
-    def get_lr(self, runner, base_lr):
-        curr_iter = runner.iter
-        for (start_iter, end_iter, max_iter_per_phase, start_ratio,
-             end_ratio) in self.lr_phases:
-            curr_iter %= max_iter_per_phase
-            if start_iter <= curr_iter < end_iter:
-                progress = curr_iter - start_iter
-                return self.anneal_func(base_lr * start_ratio,
-                                        base_lr * end_ratio,
-                                        progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleLrUpdaterHook(LrUpdaterHook):
-    """One Cycle LR Scheduler.
-
-    The 1cycle learning rate policy changes the learning rate after every
-    batch. The one cycle learning rate policy is described in
-    https://arxiv.org/pdf/1708.07120.pdf
-
-    Args:
-        max_lr (float or list): Upper learning rate boundaries in the cycle
-            for each parameter group.
-        total_steps (int, optional): The total number of steps in the cycle.
-            Note that if a value is not provided here, it will be the max_iter
-            of runner. Default: None.
-        pct_start (float): The percentage of the cycle (in number of steps)
-            spent increasing the learning rate.
-            Default: 0.3
-        anneal_strategy (str): {'cos', 'linear'}
-            Specifies the annealing strategy: 'cos' for cosine annealing,
-            'linear' for linear annealing.
-            Default: 'cos'
-        div_factor (float): Determines the initial learning rate via
-            initial_lr = max_lr/div_factor
-            Default: 25
-        final_div_factor (float): Determines the minimum learning rate via
-            min_lr = initial_lr/final_div_factor
-            Default: 1e4
-        three_phase (bool): If three_phase is True, use a third phase of the
-            schedule to annihilate the learning rate according to
-            final_div_factor instead of modifying the second phase (the first
-            two phases will be symmetrical about the step indicated by
-            pct_start).
-            Default: False
-    """
-
-    def __init__(self,
-                 max_lr,
-                 total_steps=None,
-                 pct_start=0.3,
-                 anneal_strategy='cos',
-                 div_factor=25,
-                 final_div_factor=1e4,
-                 three_phase=False,
-                 **kwargs):
-        # validate by_epoch, currently only support by_epoch = False
-        if 'by_epoch' not in kwargs:
-            kwargs['by_epoch'] = False
-        else:
-            assert not kwargs['by_epoch'], \
-                'currently only support "by_epoch" = False'
-        if not isinstance(max_lr, (numbers.Number, list, dict)):
-            raise ValueError('the type of max_lr must be the one of list or '
-                             f'dict, but got {type(max_lr)}')
-        self._max_lr = max_lr
-        if total_steps is not None:
-            if not isinstance(total_steps, int):
-                raise ValueError('the type of total_steps must be int, but'
-                                 f'got {type(total_steps)}')
-            self.total_steps = total_steps
-        # validate pct_start
-        if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
-            raise ValueError('expected float between 0 and 1 pct_start, but '
-                             f'got {pct_start}')
-        self.pct_start = pct_start
-        # validate anneal_strategy
-        if anneal_strategy not in ['cos', 'linear']:
-            raise ValueError('anneal_strategy must be one of "cos" or '
-                             f'"linear", instead got {anneal_strategy}')
-        elif anneal_strategy == 'cos':
-            self.anneal_func = annealing_cos
-        elif anneal_strategy == 'linear':
-            self.anneal_func = annealing_linear
-        self.div_factor = div_factor
-        self.final_div_factor = final_div_factor
-        self.three_phase = three_phase
-        self.lr_phases = []  # init lr_phases
-        super(OneCycleLrUpdaterHook, self).__init__(**kwargs)
-
-    def before_run(self, runner):
-        if hasattr(self, 'total_steps'):
-            total_steps = self.total_steps
-        else:
-            total_steps = runner.max_iters
-        if total_steps < runner.max_iters:
-            raise ValueError(
-                'The total steps must be greater than or equal to max '
-                f'iterations {runner.max_iters} of runner, but total steps '
-                f'is {total_steps}.')
-
-        if isinstance(runner.optimizer, dict):
-            self.base_lr = {}
-            for k, optim in runner.optimizer.items():
-                _max_lr = format_param(k, optim, self._max_lr)
-                self.base_lr[k] = [lr / self.div_factor for lr in _max_lr]
-                for group, lr in zip(optim.param_groups, self.base_lr[k]):
-                    group.setdefault('initial_lr', lr)
-        else:
-            k = type(runner.optimizer).__name__
-            _max_lr = format_param(k, runner.optimizer, self._max_lr)
-            self.base_lr = [lr / self.div_factor for lr in _max_lr]
-            for group, lr in zip(runner.optimizer.param_groups, self.base_lr):
-                group.setdefault('initial_lr', lr)
-
-        if self.three_phase:
-            self.lr_phases.append(
-                [float(self.pct_start * total_steps) - 1, 1, self.div_factor])
-            self.lr_phases.append([
-                float(2 * self.pct_start * total_steps) - 2, self.div_factor, 1
-            ])
-            self.lr_phases.append(
-                [total_steps - 1, 1, 1 / self.final_div_factor])
-        else:
-            self.lr_phases.append(
-                [float(self.pct_start * total_steps) - 1, 1, self.div_factor])
-            self.lr_phases.append(
-                [total_steps - 1, self.div_factor, 1 / self.final_div_factor])
-
-    def get_lr(self, runner, base_lr):
-        curr_iter = runner.iter
-        start_iter = 0
-        for i, (end_iter, start_lr, end_lr) in enumerate(self.lr_phases):
-            if curr_iter <= end_iter:
-                pct = (curr_iter - start_iter) / (end_iter - start_iter)
-                lr = self.anneal_func(base_lr * start_lr, base_lr * end_lr,
-                                      pct)
-                break
-            start_iter = end_iter
-        return lr
-
-
-def annealing_cos(start, end, factor, weight=1):
-    """Calculate annealing cos learning rate.
-
-    Cosine anneal from `weight * start + (1 - weight) * end` to `end` as
-    percentage goes from 0.0 to 1.0.
-
-    Args:
-        start (float): The starting learning rate of the cosine annealing.
-        end (float): The ending learing rate of the cosine annealing.
-        factor (float): The coefficient of `pi` when calculating the current
-            percentage. Range from 0.0 to 1.0.
-        weight (float, optional): The combination factor of `start` and `end`
-            when calculating the actual starting learning rate. Default to 1.
-    """
-    cos_out = cos(pi * factor) + 1
-    return end + 0.5 * weight * (start - end) * cos_out
-
-
-def annealing_linear(start, end, factor):
-    """Calculate annealing linear learning rate.
-
-    Linear anneal from `start` to `end` as percentage goes from 0.0 to 1.0.
-
-    Args:
-        start (float): The starting learning rate of the linear annealing.
-        end (float): The ending learing rate of the linear annealing.
-        factor (float): The coefficient of `pi` when calculating the current
-            percentage. Range from 0.0 to 1.0.
-    """
-    return start + (end - start) * factor
-
-
-def format_param(name, optim, param):
-    if isinstance(param, numbers.Number):
-        return [param] * len(optim.param_groups)
-    elif isinstance(param, (list, tuple)):  # multi param groups
-        if len(param) != len(optim.param_groups):
-            raise ValueError(f'expected {len(optim.param_groups)} '
-                             f'values for {name}, got {len(param)}')
-        return param
-    else:  # multi optimizers
-        if name not in param:
-            raise KeyError(f'{name} is not found in {param.keys()}')
-        return param[name]
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/optimizer.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/optimizer.py
deleted file mode 100644
index 4ef3e9ff8f9c6926e32bdf027612267b64ed80df..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/optimizer.py
+++ /dev/null
@@ -1,508 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-from collections import defaultdict
-from itertools import chain
-
-from torch.nn.utils import clip_grad
-
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
-from ..dist_utils import allreduce_grads
-from ..fp16_utils import LossScaler, wrap_fp16_model
-from .hook import HOOKS, Hook
-
-try:
-    # If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported
-    # and used; otherwise, auto fp16 will adopt mmcv's implementation.
-    from torch.cuda.amp import GradScaler
-except ImportError:
-    pass
-
-
-@HOOKS.register_module()
-class OptimizerHook(Hook):
-
-    def __init__(self, grad_clip=None):
-        self.grad_clip = grad_clip
-
-    def clip_grads(self, params):
-        params = list(
-            filter(lambda p: p.requires_grad and p.grad is not None, params))
-        if len(params) > 0:
-            return clip_grad.clip_grad_norm_(params, **self.grad_clip)
-
-    def after_train_iter(self, runner):
-        runner.optimizer.zero_grad()
-        runner.outputs['loss'].backward()
-        if self.grad_clip is not None:
-            grad_norm = self.clip_grads(runner.model.parameters())
-            if grad_norm is not None:
-                # Add grad norm to the logger
-                runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                         runner.outputs['num_samples'])
-        runner.optimizer.step()
-
-
-@HOOKS.register_module()
-class GradientCumulativeOptimizerHook(OptimizerHook):
-    """Optimizer Hook implements multi-iters gradient cumulating.
-
-    Args:
-        cumulative_iters (int, optional): Num of gradient cumulative iters.
-            The optimizer will step every `cumulative_iters` iters.
-            Defaults to 1.
-
-    Examples:
-        >>> # Use cumulative_iters to simulate a large batch size
-        >>> # It is helpful when the hardware cannot handle a large batch size.
-        >>> loader = DataLoader(data, batch_size=64)
-        >>> optim_hook = GradientCumulativeOptimizerHook(cumulative_iters=4)
-        >>> # almost equals to
-        >>> loader = DataLoader(data, batch_size=256)
-        >>> optim_hook = OptimizerHook()
-    """
-
-    def __init__(self, cumulative_iters=1, **kwargs):
-        super(GradientCumulativeOptimizerHook, self).__init__(**kwargs)
-
-        assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \
-            f'cumulative_iters only accepts positive int, but got ' \
-            f'{type(cumulative_iters)} instead.'
-
-        self.cumulative_iters = cumulative_iters
-        self.divisible_iters = 0
-        self.remainder_iters = 0
-        self.initialized = False
-
-    def has_batch_norm(self, module):
-        if isinstance(module, _BatchNorm):
-            return True
-        for m in module.children():
-            if self.has_batch_norm(m):
-                return True
-        return False
-
-    def _init(self, runner):
-        if runner.iter % self.cumulative_iters != 0:
-            runner.logger.warning(
-                'Resume iter number is not divisible by cumulative_iters in '
-                'GradientCumulativeOptimizerHook, which means the gradient of '
-                'some iters is lost and the result may be influenced slightly.'
-            )
-
-        if self.has_batch_norm(runner.model) and self.cumulative_iters > 1:
-            runner.logger.warning(
-                'GradientCumulativeOptimizerHook may slightly decrease '
-                'performance if the model has BatchNorm layers.')
-
-        residual_iters = runner.max_iters - runner.iter
-
-        self.divisible_iters = (
-            residual_iters // self.cumulative_iters * self.cumulative_iters)
-        self.remainder_iters = residual_iters - self.divisible_iters
-
-        self.initialized = True
-
-    def after_train_iter(self, runner):
-        if not self.initialized:
-            self._init(runner)
-
-        if runner.iter < self.divisible_iters:
-            loss_factor = self.cumulative_iters
-        else:
-            loss_factor = self.remainder_iters
-        loss = runner.outputs['loss']
-        loss = loss / loss_factor
-        loss.backward()
-
-        if (self.every_n_iters(runner, self.cumulative_iters)
-                or self.is_last_iter(runner)):
-
-            if self.grad_clip is not None:
-                grad_norm = self.clip_grads(runner.model.parameters())
-                if grad_norm is not None:
-                    # Add grad norm to the logger
-                    runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                             runner.outputs['num_samples'])
-            runner.optimizer.step()
-            runner.optimizer.zero_grad()
-
-
-if (TORCH_VERSION != 'parrots'
-        and digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
-
-    @HOOKS.register_module()
-    class Fp16OptimizerHook(OptimizerHook):
-        """FP16 optimizer hook (using PyTorch's implementation).
-
-        If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
-        to take care of the optimization procedure.
-
-        Args:
-            loss_scale (float | str | dict): Scale factor configuration.
-                If loss_scale is a float, static loss scaling will be used with
-                the specified scale. If loss_scale is a string, it must be
-                'dynamic', then dynamic loss scaling will be used.
-                It can also be a dict containing arguments of GradScalar.
-                Defaults to 512. For Pytorch >= 1.6, mmcv uses official
-                implementation of GradScaler. If you use a dict version of
-                loss_scale to create GradScaler, please refer to:
-                https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler
-                for the parameters.
-
-        Examples:
-            >>> loss_scale = dict(
-            ...     init_scale=65536.0,
-            ...     growth_factor=2.0,
-            ...     backoff_factor=0.5,
-            ...     growth_interval=2000
-            ... )
-            >>> optimizer_hook = Fp16OptimizerHook(loss_scale=loss_scale)
-        """
-
-        def __init__(self,
-                     grad_clip=None,
-                     coalesce=True,
-                     bucket_size_mb=-1,
-                     loss_scale=512.,
-                     distributed=True):
-            self.grad_clip = grad_clip
-            self.coalesce = coalesce
-            self.bucket_size_mb = bucket_size_mb
-            self.distributed = distributed
-            self._scale_update_param = None
-            if loss_scale == 'dynamic':
-                self.loss_scaler = GradScaler()
-            elif isinstance(loss_scale, float):
-                self._scale_update_param = loss_scale
-                self.loss_scaler = GradScaler(init_scale=loss_scale)
-            elif isinstance(loss_scale, dict):
-                self.loss_scaler = GradScaler(**loss_scale)
-            else:
-                raise ValueError('loss_scale must be of type float, dict, or '
-                                 f'"dynamic", got {loss_scale}')
-
-        def before_run(self, runner):
-            """Preparing steps before Mixed Precision Training."""
-            # wrap model mode to fp16
-            wrap_fp16_model(runner.model)
-            # resume from state dict
-            if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
-                scaler_state_dict = runner.meta['fp16']['loss_scaler']
-                self.loss_scaler.load_state_dict(scaler_state_dict)
-
-        def copy_grads_to_fp32(self, fp16_net, fp32_weights):
-            """Copy gradients from fp16 model to fp32 weight copy."""
-            for fp32_param, fp16_param in zip(fp32_weights,
-                                              fp16_net.parameters()):
-                if fp16_param.grad is not None:
-                    if fp32_param.grad is None:
-                        fp32_param.grad = fp32_param.data.new(
-                            fp32_param.size())
-                    fp32_param.grad.copy_(fp16_param.grad)
-
-        def copy_params_to_fp16(self, fp16_net, fp32_weights):
-            """Copy updated params from fp32 weight copy to fp16 model."""
-            for fp16_param, fp32_param in zip(fp16_net.parameters(),
-                                              fp32_weights):
-                fp16_param.data.copy_(fp32_param.data)
-
-        def after_train_iter(self, runner):
-            """Backward optimization steps for Mixed Precision Training. For
-            dynamic loss scaling, please refer to
-            https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler.
-
-            1. Scale the loss by a scale factor.
-            2. Backward the loss to obtain the gradients.
-            3. Unscale the optimizer’s gradient tensors.
-            4. Call optimizer.step() and update scale factor.
-            5. Save loss_scaler state_dict for resume purpose.
-            """
-            # clear grads of last iteration
-            runner.model.zero_grad()
-            runner.optimizer.zero_grad()
-
-            self.loss_scaler.scale(runner.outputs['loss']).backward()
-            self.loss_scaler.unscale_(runner.optimizer)
-            # grad clip
-            if self.grad_clip is not None:
-                grad_norm = self.clip_grads(runner.model.parameters())
-                if grad_norm is not None:
-                    # Add grad norm to the logger
-                    runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                             runner.outputs['num_samples'])
-            # backward and update scaler
-            self.loss_scaler.step(runner.optimizer)
-            self.loss_scaler.update(self._scale_update_param)
-
-            # save state_dict of loss_scaler
-            runner.meta.setdefault(
-                'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-    @HOOKS.register_module()
-    class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
-                                              Fp16OptimizerHook):
-        """Fp16 optimizer Hook (using PyTorch's implementation) implements
-        multi-iters gradient cumulating.
-
-        If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
-        to take care of the optimization procedure.
-        """
-
-        def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
-
-        def after_train_iter(self, runner):
-            if not self.initialized:
-                self._init(runner)
-
-            if runner.iter < self.divisible_iters:
-                loss_factor = self.cumulative_iters
-            else:
-                loss_factor = self.remainder_iters
-            loss = runner.outputs['loss']
-            loss = loss / loss_factor
-
-            self.loss_scaler.scale(loss).backward()
-
-            if (self.every_n_iters(runner, self.cumulative_iters)
-                    or self.is_last_iter(runner)):
-
-                # copy fp16 grads in the model to fp32 params in the optimizer
-                self.loss_scaler.unscale_(runner.optimizer)
-
-                if self.grad_clip is not None:
-                    grad_norm = self.clip_grads(runner.model.parameters())
-                    if grad_norm is not None:
-                        # Add grad norm to the logger
-                        runner.log_buffer.update(
-                            {'grad_norm': float(grad_norm)},
-                            runner.outputs['num_samples'])
-
-                # backward and update scaler
-                self.loss_scaler.step(runner.optimizer)
-                self.loss_scaler.update(self._scale_update_param)
-
-                # save state_dict of loss_scaler
-                runner.meta.setdefault(
-                    'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-                # clear grads
-                runner.model.zero_grad()
-                runner.optimizer.zero_grad()
-
-else:
-
-    @HOOKS.register_module()
-    class Fp16OptimizerHook(OptimizerHook):
-        """FP16 optimizer hook (mmcv's implementation).
-
-        The steps of fp16 optimizer is as follows.
-        1. Scale the loss value.
-        2. BP in the fp16 model.
-        2. Copy gradients from fp16 model to fp32 weights.
-        3. Update fp32 weights.
-        4. Copy updated parameters from fp32 weights to fp16 model.
-
-        Refer to https://arxiv.org/abs/1710.03740 for more details.
-
-        Args:
-            loss_scale (float | str | dict): Scale factor configuration.
-                If loss_scale is a float, static loss scaling will be used with
-                the specified scale. If loss_scale is a string, it must be
-                'dynamic', then dynamic loss scaling will be used.
-                It can also be a dict containing arguments of LossScaler.
-                Defaults to 512.
-        """
-
-        def __init__(self,
-                     grad_clip=None,
-                     coalesce=True,
-                     bucket_size_mb=-1,
-                     loss_scale=512.,
-                     distributed=True):
-            self.grad_clip = grad_clip
-            self.coalesce = coalesce
-            self.bucket_size_mb = bucket_size_mb
-            self.distributed = distributed
-            if loss_scale == 'dynamic':
-                self.loss_scaler = LossScaler(mode='dynamic')
-            elif isinstance(loss_scale, float):
-                self.loss_scaler = LossScaler(
-                    init_scale=loss_scale, mode='static')
-            elif isinstance(loss_scale, dict):
-                self.loss_scaler = LossScaler(**loss_scale)
-            else:
-                raise ValueError('loss_scale must be of type float, dict, or '
-                                 f'"dynamic", got {loss_scale}')
-
-        def before_run(self, runner):
-            """Preparing steps before Mixed Precision Training.
-
-            1. Make a master copy of fp32 weights for optimization.
-            2. Convert the main model from fp32 to fp16.
-            """
-            # keep a copy of fp32 weights
-            old_groups = runner.optimizer.param_groups
-            runner.optimizer.param_groups = copy.deepcopy(
-                runner.optimizer.param_groups)
-            state = defaultdict(dict)
-            p_map = {
-                old_p: p
-                for old_p, p in zip(
-                    chain(*(g['params'] for g in old_groups)),
-                    chain(*(g['params']
-                            for g in runner.optimizer.param_groups)))
-            }
-            for k, v in runner.optimizer.state.items():
-                state[p_map[k]] = v
-            runner.optimizer.state = state
-            # convert model to fp16
-            wrap_fp16_model(runner.model)
-            # resume from state dict
-            if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
-                scaler_state_dict = runner.meta['fp16']['loss_scaler']
-                self.loss_scaler.load_state_dict(scaler_state_dict)
-
-        def copy_grads_to_fp32(self, fp16_net, fp32_weights):
-            """Copy gradients from fp16 model to fp32 weight copy."""
-            for fp32_param, fp16_param in zip(fp32_weights,
-                                              fp16_net.parameters()):
-                if fp16_param.grad is not None:
-                    if fp32_param.grad is None:
-                        fp32_param.grad = fp32_param.data.new(
-                            fp32_param.size())
-                    fp32_param.grad.copy_(fp16_param.grad)
-
-        def copy_params_to_fp16(self, fp16_net, fp32_weights):
-            """Copy updated params from fp32 weight copy to fp16 model."""
-            for fp16_param, fp32_param in zip(fp16_net.parameters(),
-                                              fp32_weights):
-                fp16_param.data.copy_(fp32_param.data)
-
-        def after_train_iter(self, runner):
-            """Backward optimization steps for Mixed Precision Training. For
-            dynamic loss scaling, please refer `loss_scalar.py`
-
-            1. Scale the loss by a scale factor.
-            2. Backward the loss to obtain the gradients (fp16).
-            3. Copy gradients from the model to the fp32 weight copy.
-            4. Scale the gradients back and update the fp32 weight copy.
-            5. Copy back the params from fp32 weight copy to the fp16 model.
-            6. Save loss_scaler state_dict for resume purpose.
-            """
-            # clear grads of last iteration
-            runner.model.zero_grad()
-            runner.optimizer.zero_grad()
-            # scale the loss value
-            scaled_loss = runner.outputs['loss'] * self.loss_scaler.loss_scale
-            scaled_loss.backward()
-            # copy fp16 grads in the model to fp32 params in the optimizer
-
-            fp32_weights = []
-            for param_group in runner.optimizer.param_groups:
-                fp32_weights += param_group['params']
-            self.copy_grads_to_fp32(runner.model, fp32_weights)
-            # allreduce grads
-            if self.distributed:
-                allreduce_grads(fp32_weights, self.coalesce,
-                                self.bucket_size_mb)
-
-            has_overflow = self.loss_scaler.has_overflow(fp32_weights)
-            # if has overflow, skip this iteration
-            if not has_overflow:
-                # scale the gradients back
-                for param in fp32_weights:
-                    if param.grad is not None:
-                        param.grad.div_(self.loss_scaler.loss_scale)
-                if self.grad_clip is not None:
-                    grad_norm = self.clip_grads(fp32_weights)
-                    if grad_norm is not None:
-                        # Add grad norm to the logger
-                        runner.log_buffer.update(
-                            {'grad_norm': float(grad_norm)},
-                            runner.outputs['num_samples'])
-                # update fp32 params
-                runner.optimizer.step()
-                # copy fp32 params to the fp16 model
-                self.copy_params_to_fp16(runner.model, fp32_weights)
-            self.loss_scaler.update_scale(has_overflow)
-            if has_overflow:
-                runner.logger.warning('Check overflow, downscale loss scale '
-                                      f'to {self.loss_scaler.cur_scale}')
-
-            # save state_dict of loss_scaler
-            runner.meta.setdefault(
-                'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-    @HOOKS.register_module()
-    class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
-                                              Fp16OptimizerHook):
-        """Fp16 optimizer Hook (using mmcv implementation) implements multi-
-        iters gradient cumulating."""
-
-        def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
-
-        def after_train_iter(self, runner):
-            if not self.initialized:
-                self._init(runner)
-
-            if runner.iter < self.divisible_iters:
-                loss_factor = self.cumulative_iters
-            else:
-                loss_factor = self.remainder_iters
-
-            loss = runner.outputs['loss']
-            loss = loss / loss_factor
-
-            # scale the loss value
-            scaled_loss = loss * self.loss_scaler.loss_scale
-            scaled_loss.backward()
-
-            if (self.every_n_iters(runner, self.cumulative_iters)
-                    or self.is_last_iter(runner)):
-
-                # copy fp16 grads in the model to fp32 params in the optimizer
-                fp32_weights = []
-                for param_group in runner.optimizer.param_groups:
-                    fp32_weights += param_group['params']
-                self.copy_grads_to_fp32(runner.model, fp32_weights)
-                # allreduce grads
-                if self.distributed:
-                    allreduce_grads(fp32_weights, self.coalesce,
-                                    self.bucket_size_mb)
-
-                has_overflow = self.loss_scaler.has_overflow(fp32_weights)
-                # if has overflow, skip this iteration
-                if not has_overflow:
-                    # scale the gradients back
-                    for param in fp32_weights:
-                        if param.grad is not None:
-                            param.grad.div_(self.loss_scaler.loss_scale)
-                    if self.grad_clip is not None:
-                        grad_norm = self.clip_grads(fp32_weights)
-                        if grad_norm is not None:
-                            # Add grad norm to the logger
-                            runner.log_buffer.update(
-                                {'grad_norm': float(grad_norm)},
-                                runner.outputs['num_samples'])
-                    # update fp32 params
-                    runner.optimizer.step()
-                    # copy fp32 params to the fp16 model
-                    self.copy_params_to_fp16(runner.model, fp32_weights)
-                else:
-                    runner.logger.warning(
-                        'Check overflow, downscale loss scale '
-                        f'to {self.loss_scaler.cur_scale}')
-
-                self.loss_scaler.update_scale(has_overflow)
-
-                # save state_dict of loss_scaler
-                runner.meta.setdefault(
-                    'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-                # clear grads
-                runner.model.zero_grad()
-                runner.optimizer.zero_grad()
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/backbones/skip/non_local_dot_product.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/backbones/skip/non_local_dot_product.py
deleted file mode 100644
index 0ebed5e5021b75b14c6fba39f27a23ccc94a22a0..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/backbones/skip/non_local_dot_product.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import torch
-from torch import nn
-
-
-class _NonLocalBlockND(nn.Module):
-    def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
-        super(_NonLocalBlockND, self).__init__()
-
-        assert dimension in [1, 2, 3]
-
-        self.dimension = dimension
-        self.sub_sample = sub_sample
-
-        self.in_channels = in_channels
-        self.inter_channels = inter_channels
-
-        if self.inter_channels is None:
-            self.inter_channels = in_channels // 2
-            if self.inter_channels == 0:
-                self.inter_channels = 1
-
-        if dimension == 3:
-            conv_nd = nn.Conv3d
-            max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
-            bn = nn.BatchNorm3d
-        elif dimension == 2:
-            conv_nd = nn.Conv2d
-            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
-            bn = nn.BatchNorm2d
-        else:
-            conv_nd = nn.Conv1d
-            max_pool_layer = nn.MaxPool1d(kernel_size=(2))
-            bn = nn.BatchNorm1d
-
-        self.g = conv_nd(
-            in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0
-        )
-
-        if bn_layer:
-            self.W = nn.Sequential(
-                conv_nd(
-                    in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0
-                ),
-                bn(self.in_channels),
-            )
-            nn.init.constant_(self.W[1].weight, 0)
-            nn.init.constant_(self.W[1].bias, 0)
-        else:
-            self.W = conv_nd(
-                in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0
-            )
-            nn.init.constant_(self.W.weight, 0)
-            nn.init.constant_(self.W.bias, 0)
-
-        self.theta = conv_nd(
-            in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0
-        )
-
-        self.phi = conv_nd(
-            in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0
-        )
-
-        if sub_sample:
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            self.phi = nn.Sequential(self.phi, max_pool_layer)
-
-    def forward(self, x):
-        """
-        :param x: (b, c, t, h, w)
-        :return:
-        """
-
-        batch_size = x.size(0)
-
-        g_x = self.g(x).view(batch_size, self.inter_channels, -1)
-        g_x = g_x.permute(0, 2, 1)
-
-        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
-        theta_x = theta_x.permute(0, 2, 1)
-        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
-        f = torch.matmul(theta_x, phi_x)
-        N = f.size(-1)
-        f_div_C = f / N
-
-        y = torch.matmul(f_div_C, g_x)
-        y = y.permute(0, 2, 1).contiguous()
-        y = y.view(batch_size, self.inter_channels, *x.size()[2:])
-        W_y = self.W(y)
-        z = W_y + x
-
-        return z
-
-
-class NONLocalBlock1D(_NonLocalBlockND):
-    def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True):
-        super(NONLocalBlock1D, self).__init__(
-            in_channels, inter_channels=inter_channels, dimension=1, sub_sample=sub_sample, bn_layer=bn_layer
-        )
-
-
-class NONLocalBlock2D(_NonLocalBlockND):
-    def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True):
-        super(NONLocalBlock2D, self).__init__(
-            in_channels, inter_channels=inter_channels, dimension=2, sub_sample=sub_sample, bn_layer=bn_layer
-        )
-
-
-class NONLocalBlock3D(_NonLocalBlockND):
-    def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True):
-        super(NONLocalBlock3D, self).__init__(
-            in_channels, inter_channels=inter_channels, dimension=3, sub_sample=sub_sample, bn_layer=bn_layer
-        )
-
-
-if __name__ == "__main__":
-    for (sub_sample, bn_layer) in [(True, True), (False, False), (True, False), (False, True)]:
-        img = torch.zeros(2, 3, 20)
-        net = NONLocalBlock1D(3, sub_sample=sub_sample, bn_layer=bn_layer)
-        out = net(img)
-        print(out.size())
-
-        img = torch.zeros(2, 3, 20, 20)
-        net = NONLocalBlock2D(3, sub_sample=sub_sample, bn_layer=bn_layer)
-        out = net(img)
-        print(out.size())
-
-        img = torch.randn(2, 3, 8, 20, 20)
-        net = NONLocalBlock3D(3, sub_sample=sub_sample, bn_layer=bn_layer)
-        out = net(img)
-        print(out.size())
diff --git a/spaces/PaulHilders/CLIPGroundingExplainability/README.md b/spaces/PaulHilders/CLIPGroundingExplainability/README.md
deleted file mode 100644
index 0c265567898f6541ba2264dbb8eccba0cf5b0bcc..0000000000000000000000000000000000000000
--- a/spaces/PaulHilders/CLIPGroundingExplainability/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CLIPGroundingExplainabilityDemo
-emoji: 💩
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.22
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pippoz/Hugging_Space/README.md b/spaces/Pippoz/Hugging_Space/README.md
deleted file mode 100644
index fdada61bf83568c1fa2efc5adb2f0e2d044a6f3c..0000000000000000000000000000000000000000
--- a/spaces/Pippoz/Hugging_Space/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: OPT- Meta
-emoji: 🤖
-colorFrom: blue
-colorTo: white
-sdk: streamlit
-sdk_version: 1.9.0
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/QINGFNEG/Real-CUGAN/README.md b/spaces/QINGFNEG/Real-CUGAN/README.md
deleted file mode 100644
index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000
--- a/spaces/QINGFNEG/Real-CUGAN/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Real CUGAN
-emoji: 🐢
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: DianXian/Real-CUGAN
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/demucs/test.py b/spaces/RMXK/RVC_HFF/demucs/test.py
deleted file mode 100644
index 4140914ddbff3543b4056ca0cb1b5e887434a40a..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/demucs/test.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import gzip
-import sys
-from concurrent import futures
-
-import musdb
-import museval
-import torch as th
-import tqdm
-from scipy.io import wavfile
-from torch import distributed
-
-from .audio import convert_audio
-from .utils import apply_model
-
-
-def evaluate(model,
-             musdb_path,
-             eval_folder,
-             workers=2,
-             device="cpu",
-             rank=0,
-             save=False,
-             shifts=0,
-             split=False,
-             overlap=0.25,
-             is_wav=False,
-             world_size=1):
-    """
-    Evaluate model using museval. Run the model
-    on a single GPU, the bottleneck being the call to museval.
-    """
-
-    output_dir = eval_folder / "results"
-    output_dir.mkdir(exist_ok=True, parents=True)
-    json_folder = eval_folder / "results/test"
-    json_folder.mkdir(exist_ok=True, parents=True)
-
-    # we load tracks from the original musdb set
-    test_set = musdb.DB(musdb_path, subsets=["test"], is_wav=is_wav)
-    src_rate = 44100  # hardcoded for now...
-
-    for p in model.parameters():
-        p.requires_grad = False
-        p.grad = None
-
-    pendings = []
-    with futures.ProcessPoolExecutor(workers or 1) as pool:
-        for index in tqdm.tqdm(range(rank, len(test_set), world_size), file=sys.stdout):
-            track = test_set.tracks[index]
-
-            out = json_folder / f"{track.name}.json.gz"
-            if out.exists():
-                continue
-
-            mix = th.from_numpy(track.audio).t().float()
-            ref = mix.mean(dim=0)  # mono mixture
-            mix = (mix - ref.mean()) / ref.std()
-            mix = convert_audio(mix, src_rate, model.samplerate, model.audio_channels)
-            estimates = apply_model(model, mix.to(device),
-                                    shifts=shifts, split=split, overlap=overlap)
-            estimates = estimates * ref.std() + ref.mean()
-
-            estimates = estimates.transpose(1, 2)
-            references = th.stack(
-                [th.from_numpy(track.targets[name].audio).t() for name in model.sources])
-            references = convert_audio(references, src_rate,
-                                       model.samplerate, model.audio_channels)
-            references = references.transpose(1, 2).numpy()
-            estimates = estimates.cpu().numpy()
-            win = int(1. * model.samplerate)
-            hop = int(1. * model.samplerate)
-            if save:
-                folder = eval_folder / "wav/test" / track.name
-                folder.mkdir(exist_ok=True, parents=True)
-                for name, estimate in zip(model.sources, estimates):
-                    wavfile.write(str(folder / (name + ".wav")), 44100, estimate)
-
-            if workers:
-                pendings.append((track.name, pool.submit(
-                    museval.evaluate, references, estimates, win=win, hop=hop)))
-            else:
-                pendings.append((track.name, museval.evaluate(
-                    references, estimates, win=win, hop=hop)))
-            del references, mix, estimates, track
-
-        for track_name, pending in tqdm.tqdm(pendings, file=sys.stdout):
-            if workers:
-                pending = pending.result()
-            sdr, isr, sir, sar = pending
-            track_store = museval.TrackStore(win=44100, hop=44100, track_name=track_name)
-            for idx, target in enumerate(model.sources):
-                values = {
-                    "SDR": sdr[idx].tolist(),
-                    "SIR": sir[idx].tolist(),
-                    "ISR": isr[idx].tolist(),
-                    "SAR": sar[idx].tolist()
-                }
-
-                track_store.add_target(target_name=target, values=values)
-                json_path = json_folder / f"{track_name}.json.gz"
-                gzip.open(json_path, "w").write(track_store.json.encode('utf-8'))
-    if world_size > 1:
-        distributed.barrier()
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/models/link.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/models/link.py
deleted file mode 100644
index c792d128bcf9423016d6582c0eb67153c13f9cc8..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/models/link.py
+++ /dev/null
@@ -1,507 +0,0 @@
-import functools
-import itertools
-import logging
-import os
-import posixpath
-import re
-import urllib.parse
-from dataclasses import dataclass
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    List,
-    Mapping,
-    NamedTuple,
-    Optional,
-    Tuple,
-    Union,
-)
-
-from pip._internal.utils.filetypes import WHEEL_EXTENSION
-from pip._internal.utils.hashes import Hashes
-from pip._internal.utils.misc import (
-    pairwise,
-    redact_auth_from_url,
-    split_auth_from_netloc,
-    splitext,
-)
-from pip._internal.utils.models import KeyBasedCompareMixin
-from pip._internal.utils.urls import path_to_url, url_to_path
-
-if TYPE_CHECKING:
-    from pip._internal.index.collector import IndexContent
-
-logger = logging.getLogger(__name__)
-
-
-# Order matters, earlier hashes have a precedence over later hashes for what
-# we will pick to use.
-_SUPPORTED_HASHES = ("sha512", "sha384", "sha256", "sha224", "sha1", "md5")
-
-
-@dataclass(frozen=True)
-class LinkHash:
-    """Links to content may have embedded hash values. This class parses those.
-
-    `name` must be any member of `_SUPPORTED_HASHES`.
-
-    This class can be converted to and from `ArchiveInfo`. While ArchiveInfo intends to
-    be JSON-serializable to conform to PEP 610, this class contains the logic for
-    parsing a hash name and value for correctness, and then checking whether that hash
-    conforms to a schema with `.is_hash_allowed()`."""
-
-    name: str
-    value: str
-
-    _hash_re = re.compile(
-        # NB: we do not validate that the second group (.*) is a valid hex
-        # digest. Instead, we simply keep that string in this class, and then check it
-        # against Hashes when hash-checking is needed. This is easier to debug than
-        # proactively discarding an invalid hex digest, as we handle incorrect hashes
-        # and malformed hashes in the same place.
-        r"({choices})=(.*)".format(
-            choices="|".join(re.escape(hash_name) for hash_name in _SUPPORTED_HASHES)
-        ),
-    )
-
-    def __post_init__(self) -> None:
-        assert self._hash_re.match(f"{self.name}={self.value}")
-
-    @classmethod
-    @functools.lru_cache(maxsize=None)
-    def split_hash_name_and_value(cls, url: str) -> Optional["LinkHash"]:
-        """Search a string for a checksum algorithm name and encoded output value."""
-        match = cls._hash_re.search(url)
-        if match is None:
-            return None
-        name, value = match.groups()
-        return cls(name=name, value=value)
-
-    def as_hashes(self) -> Hashes:
-        """Return a Hashes instance which checks only for the current hash."""
-        return Hashes({self.name: [self.value]})
-
-    def is_hash_allowed(self, hashes: Optional[Hashes]) -> bool:
-        """
-        Return True if the current hash is allowed by `hashes`.
-        """
-        if hashes is None:
-            return False
-        return hashes.is_hash_allowed(self.name, hex_digest=self.value)
-
-
-def _clean_url_path_part(part: str) -> str:
-    """
-    Clean a "part" of a URL path (i.e. after splitting on "@" characters).
-    """
-    # We unquote prior to quoting to make sure nothing is double quoted.
-    return urllib.parse.quote(urllib.parse.unquote(part))
-
-
-def _clean_file_url_path(part: str) -> str:
-    """
-    Clean the first part of a URL path that corresponds to a local
-    filesystem path (i.e. the first part after splitting on "@" characters).
-    """
-    # We unquote prior to quoting to make sure nothing is double quoted.
-    # Also, on Windows the path part might contain a drive letter which
-    # should not be quoted. On Linux where drive letters do not
-    # exist, the colon should be quoted. We rely on urllib.request
-    # to do the right thing here.
-    return urllib.request.pathname2url(urllib.request.url2pathname(part))
-
-
-# percent-encoded:                   /
-_reserved_chars_re = re.compile("(@|%2F)", re.IGNORECASE)
-
-
-def _clean_url_path(path: str, is_local_path: bool) -> str:
-    """
-    Clean the path portion of a URL.
-    """
-    if is_local_path:
-        clean_func = _clean_file_url_path
-    else:
-        clean_func = _clean_url_path_part
-
-    # Split on the reserved characters prior to cleaning so that
-    # revision strings in VCS URLs are properly preserved.
-    parts = _reserved_chars_re.split(path)
-
-    cleaned_parts = []
-    for to_clean, reserved in pairwise(itertools.chain(parts, [""])):
-        cleaned_parts.append(clean_func(to_clean))
-        # Normalize %xx escapes (e.g. %2f -> %2F)
-        cleaned_parts.append(reserved.upper())
-
-    return "".join(cleaned_parts)
-
-
-def _ensure_quoted_url(url: str) -> str:
-    """
-    Make sure a link is fully quoted.
-    For example, if ' ' occurs in the URL, it will be replaced with "%20",
-    and without double-quoting other characters.
-    """
-    # Split the URL into parts according to the general structure
-    # `scheme://netloc/path;parameters?query#fragment`.
-    result = urllib.parse.urlparse(url)
-    # If the netloc is empty, then the URL refers to a local filesystem path.
-    is_local_path = not result.netloc
-    path = _clean_url_path(result.path, is_local_path=is_local_path)
-    return urllib.parse.urlunparse(result._replace(path=path))
-
-
-class Link(KeyBasedCompareMixin):
-    """Represents a parsed link from a Package Index's simple URL"""
-
-    __slots__ = [
-        "_parsed_url",
-        "_url",
-        "_hashes",
-        "comes_from",
-        "requires_python",
-        "yanked_reason",
-        "dist_info_metadata",
-        "link_hash",
-        "cache_link_parsing",
-    ]
-
-    def __init__(
-        self,
-        url: str,
-        comes_from: Optional[Union[str, "IndexContent"]] = None,
-        requires_python: Optional[str] = None,
-        yanked_reason: Optional[str] = None,
-        dist_info_metadata: Optional[str] = None,
-        link_hash: Optional[LinkHash] = None,
-        cache_link_parsing: bool = True,
-        hashes: Optional[Mapping[str, str]] = None,
-    ) -> None:
-        """
-        :param url: url of the resource pointed to (href of the link)
-        :param comes_from: instance of IndexContent where the link was found,
-            or string.
-        :param requires_python: String containing the `Requires-Python`
-            metadata field, specified in PEP 345. This may be specified by
-            a data-requires-python attribute in the HTML link tag, as
-            described in PEP 503.
-        :param yanked_reason: the reason the file has been yanked, if the
-            file has been yanked, or None if the file hasn't been yanked.
-            This is the value of the "data-yanked" attribute, if present, in
-            a simple repository HTML link. If the file has been yanked but
-            no reason was provided, this should be the empty string. See
-            PEP 592 for more information and the specification.
-        :param dist_info_metadata: the metadata attached to the file, or None if no such
-            metadata is provided. This is the value of the "data-dist-info-metadata"
-            attribute, if present, in a simple repository HTML link. This may be parsed
-            into its own `Link` by `self.metadata_link()`. See PEP 658 for more
-            information and the specification.
-        :param link_hash: a checksum for the content the link points to. If not
-            provided, this will be extracted from the link URL, if the URL has
-            any checksum.
-        :param cache_link_parsing: A flag that is used elsewhere to determine
-                                   whether resources retrieved from this link
-                                   should be cached. PyPI index urls should
-                                   generally have this set to False, for
-                                   example.
-        :param hashes: A mapping of hash names to digests to allow us to
-                       determine the validity of a download.
-        """
-
-        # url can be a UNC windows share
-        if url.startswith("\\\\"):
-            url = path_to_url(url)
-
-        self._parsed_url = urllib.parse.urlsplit(url)
-        # Store the url as a private attribute to prevent accidentally
-        # trying to set a new value.
-        self._url = url
-        self._hashes = hashes if hashes is not None else {}
-
-        self.comes_from = comes_from
-        self.requires_python = requires_python if requires_python else None
-        self.yanked_reason = yanked_reason
-        self.dist_info_metadata = dist_info_metadata
-        self.link_hash = link_hash or LinkHash.split_hash_name_and_value(self._url)
-
-        super().__init__(key=url, defining_class=Link)
-
-        self.cache_link_parsing = cache_link_parsing
-
-    @classmethod
-    def from_json(
-        cls,
-        file_data: Dict[str, Any],
-        page_url: str,
-    ) -> Optional["Link"]:
-        """
-        Convert an pypi json document from a simple repository page into a Link.
-        """
-        file_url = file_data.get("url")
-        if file_url is None:
-            return None
-
-        url = _ensure_quoted_url(urllib.parse.urljoin(page_url, file_url))
-        pyrequire = file_data.get("requires-python")
-        yanked_reason = file_data.get("yanked")
-        dist_info_metadata = file_data.get("dist-info-metadata")
-        hashes = file_data.get("hashes", {})
-
-        # The Link.yanked_reason expects an empty string instead of a boolean.
-        if yanked_reason and not isinstance(yanked_reason, str):
-            yanked_reason = ""
-        # The Link.yanked_reason expects None instead of False.
-        elif not yanked_reason:
-            yanked_reason = None
-
-        return cls(
-            url,
-            comes_from=page_url,
-            requires_python=pyrequire,
-            yanked_reason=yanked_reason,
-            hashes=hashes,
-            dist_info_metadata=dist_info_metadata,
-        )
-
-    @classmethod
-    def from_element(
-        cls,
-        anchor_attribs: Dict[str, Optional[str]],
-        page_url: str,
-        base_url: str,
-    ) -> Optional["Link"]:
-        """
-        Convert an anchor element's attributes in a simple repository page to a Link.
-        """
-        href = anchor_attribs.get("href")
-        if not href:
-            return None
-
-        url = _ensure_quoted_url(urllib.parse.urljoin(base_url, href))
-        pyrequire = anchor_attribs.get("data-requires-python")
-        yanked_reason = anchor_attribs.get("data-yanked")
-        dist_info_metadata = anchor_attribs.get("data-dist-info-metadata")
-
-        return cls(
-            url,
-            comes_from=page_url,
-            requires_python=pyrequire,
-            yanked_reason=yanked_reason,
-            dist_info_metadata=dist_info_metadata,
-        )
-
-    def __str__(self) -> str:
-        if self.requires_python:
-            rp = f" (requires-python:{self.requires_python})"
-        else:
-            rp = ""
-        if self.comes_from:
-            return "{} (from {}){}".format(
-                redact_auth_from_url(self._url), self.comes_from, rp
-            )
-        else:
-            return redact_auth_from_url(str(self._url))
-
-    def __repr__(self) -> str:
-        return f"<Link {self}>"
-
-    @property
-    def url(self) -> str:
-        return self._url
-
-    @property
-    def filename(self) -> str:
-        path = self.path.rstrip("/")
-        name = posixpath.basename(path)
-        if not name:
-            # Make sure we don't leak auth information if the netloc
-            # includes a username and password.
-            netloc, user_pass = split_auth_from_netloc(self.netloc)
-            return netloc
-
-        name = urllib.parse.unquote(name)
-        assert name, f"URL {self._url!r} produced no filename"
-        return name
-
-    @property
-    def file_path(self) -> str:
-        return url_to_path(self.url)
-
-    @property
-    def scheme(self) -> str:
-        return self._parsed_url.scheme
-
-    @property
-    def netloc(self) -> str:
-        """
-        This can contain auth information.
-        """
-        return self._parsed_url.netloc
-
-    @property
-    def path(self) -> str:
-        return urllib.parse.unquote(self._parsed_url.path)
-
-    def splitext(self) -> Tuple[str, str]:
-        return splitext(posixpath.basename(self.path.rstrip("/")))
-
-    @property
-    def ext(self) -> str:
-        return self.splitext()[1]
-
-    @property
-    def url_without_fragment(self) -> str:
-        scheme, netloc, path, query, fragment = self._parsed_url
-        return urllib.parse.urlunsplit((scheme, netloc, path, query, ""))
-
-    _egg_fragment_re = re.compile(r"[#&]egg=([^&]*)")
-
-    @property
-    def egg_fragment(self) -> Optional[str]:
-        match = self._egg_fragment_re.search(self._url)
-        if not match:
-            return None
-        return match.group(1)
-
-    _subdirectory_fragment_re = re.compile(r"[#&]subdirectory=([^&]*)")
-
-    @property
-    def subdirectory_fragment(self) -> Optional[str]:
-        match = self._subdirectory_fragment_re.search(self._url)
-        if not match:
-            return None
-        return match.group(1)
-
-    def metadata_link(self) -> Optional["Link"]:
-        """Implementation of PEP 658 parsing."""
-        # Note that Link.from_element() parsing the "data-dist-info-metadata" attribute
-        # from an HTML anchor tag is typically how the Link.dist_info_metadata attribute
-        # gets set.
-        if self.dist_info_metadata is None:
-            return None
-        metadata_url = f"{self.url_without_fragment}.metadata"
-        link_hash: Optional[LinkHash] = None
-        # If data-dist-info-metadata="true" is set, then the metadata file exists,
-        # but there is no information about its checksum or anything else.
-        if self.dist_info_metadata != "true":
-            link_hash = LinkHash.split_hash_name_and_value(self.dist_info_metadata)
-        return Link(metadata_url, link_hash=link_hash)
-
-    def as_hashes(self) -> Optional[Hashes]:
-        if self.link_hash is not None:
-            return self.link_hash.as_hashes()
-        return None
-
-    @property
-    def hash(self) -> Optional[str]:
-        if self.link_hash is not None:
-            return self.link_hash.value
-        return None
-
-    @property
-    def hash_name(self) -> Optional[str]:
-        if self.link_hash is not None:
-            return self.link_hash.name
-        return None
-
-    @property
-    def show_url(self) -> str:
-        return posixpath.basename(self._url.split("#", 1)[0].split("?", 1)[0])
-
-    @property
-    def is_file(self) -> bool:
-        return self.scheme == "file"
-
-    def is_existing_dir(self) -> bool:
-        return self.is_file and os.path.isdir(self.file_path)
-
-    @property
-    def is_wheel(self) -> bool:
-        return self.ext == WHEEL_EXTENSION
-
-    @property
-    def is_vcs(self) -> bool:
-        from pip._internal.vcs import vcs
-
-        return self.scheme in vcs.all_schemes
-
-    @property
-    def is_yanked(self) -> bool:
-        return self.yanked_reason is not None
-
-    @property
-    def has_hash(self) -> bool:
-        return self.link_hash is not None
-
-    def is_hash_allowed(self, hashes: Optional[Hashes]) -> bool:
-        """
-        Return True if the link has a hash and it is allowed by `hashes`.
-        """
-        if self.link_hash is None:
-            return False
-        return self.link_hash.is_hash_allowed(hashes)
-
-
-class _CleanResult(NamedTuple):
-    """Convert link for equivalency check.
-
-    This is used in the resolver to check whether two URL-specified requirements
-    likely point to the same distribution and can be considered equivalent. This
-    equivalency logic avoids comparing URLs literally, which can be too strict
-    (e.g. "a=1&b=2" vs "b=2&a=1") and produce conflicts unexpecting to users.
-
-    Currently this does three things:
-
-    1. Drop the basic auth part. This is technically wrong since a server can
-       serve different content based on auth, but if it does that, it is even
-       impossible to guarantee two URLs without auth are equivalent, since
-       the user can input different auth information when prompted. So the
-       practical solution is to assume the auth doesn't affect the response.
-    2. Parse the query to avoid the ordering issue. Note that ordering under the
-       same key in the query are NOT cleaned; i.e. "a=1&a=2" and "a=2&a=1" are
-       still considered different.
-    3. Explicitly drop most of the fragment part, except ``subdirectory=`` and
-       hash values, since it should have no impact the downloaded content. Note
-       that this drops the "egg=" part historically used to denote the requested
-       project (and extras), which is wrong in the strictest sense, but too many
-       people are supplying it inconsistently to cause superfluous resolution
-       conflicts, so we choose to also ignore them.
-    """
-
-    parsed: urllib.parse.SplitResult
-    query: Dict[str, List[str]]
-    subdirectory: str
-    hashes: Dict[str, str]
-
-
-def _clean_link(link: Link) -> _CleanResult:
-    parsed = link._parsed_url
-    netloc = parsed.netloc.rsplit("@", 1)[-1]
-    # According to RFC 8089, an empty host in file: means localhost.
-    if parsed.scheme == "file" and not netloc:
-        netloc = "localhost"
-    fragment = urllib.parse.parse_qs(parsed.fragment)
-    if "egg" in fragment:
-        logger.debug("Ignoring egg= fragment in %s", link)
-    try:
-        # If there are multiple subdirectory values, use the first one.
-        # This matches the behavior of Link.subdirectory_fragment.
-        subdirectory = fragment["subdirectory"][0]
-    except (IndexError, KeyError):
-        subdirectory = ""
-    # If there are multiple hash values under the same algorithm, use the
-    # first one. This matches the behavior of Link.hash_value.
-    hashes = {k: fragment[k][0] for k in _SUPPORTED_HASHES if k in fragment}
-    return _CleanResult(
-        parsed=parsed._replace(netloc=netloc, query="", fragment=""),
-        query=urllib.parse.parse_qs(parsed.query),
-        subdirectory=subdirectory,
-        hashes=hashes,
-    )
-
-
-@functools.lru_cache(maxsize=None)
-def links_equivalent(link1: Link, link2: Link) -> bool:
-    return _clean_link(link1) == _clean_link(link2)
diff --git a/spaces/RobLi/ControlNet-v1-1/utils.py b/spaces/RobLi/ControlNet-v1-1/utils.py
deleted file mode 100644
index a626d25c3f4eb92d10bdb66d3c28059a0927a8cd..0000000000000000000000000000000000000000
--- a/spaces/RobLi/ControlNet-v1-1/utils.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import random
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, 1000000)
-    return seed
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/fileio/handlers/base.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/fileio/handlers/base.py
deleted file mode 100644
index 288878bc57282fbb2f12b32290152ca8e9d3cab0..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/fileio/handlers/base.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta, abstractmethod
-
-
-class BaseFileHandler(metaclass=ABCMeta):
-    # `str_like` is a flag to indicate whether the type of file object is
-    # str-like object or bytes-like object. Pickle only processes bytes-like
-    # objects but json only processes str-like object. If it is str-like
-    # object, `StringIO` will be used to process the buffer.
-    str_like = True
-
-    @abstractmethod
-    def load_from_fileobj(self, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_str(self, obj, **kwargs):
-        pass
-
-    def load_from_path(self, filepath, mode='r', **kwargs):
-        with open(filepath, mode) as f:
-            return self.load_from_fileobj(f, **kwargs)
-
-    def dump_to_path(self, obj, filepath, mode='w', **kwargs):
-        with open(filepath, mode) as f:
-            self.dump_to_fileobj(obj, f, **kwargs)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/bbox/iou_calculators/iou2d_calculator.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/bbox/iou_calculators/iou2d_calculator.py
deleted file mode 100644
index 158b702c234f5c10c4f5f03e08e8794ac7b8dcad..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/bbox/iou_calculators/iou2d_calculator.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import torch
-
-from .builder import IOU_CALCULATORS
-
-
-@IOU_CALCULATORS.register_module()
-class BboxOverlaps2D(object):
-    """2D Overlaps (e.g. IoUs, GIoUs) Calculator."""
-
-    def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
-        """Calculate IoU between 2D bboxes.
-
-        Args:
-            bboxes1 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
-                format, or shape (m, 5) in <x1, y1, x2, y2, score> format.
-            bboxes2 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
-                format, shape (m, 5) in <x1, y1, x2, y2, score> format, or be
-                empty. If ``is_aligned `` is ``True``, then m and n must be
-                equal.
-            mode (str): "iou" (intersection over union), "iof" (intersection
-                over foreground), or "giou" (generalized intersection over
-                union).
-            is_aligned (bool, optional): If True, then m and n must be equal.
-                Default False.
-
-        Returns:
-            Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
-        """
-        assert bboxes1.size(-1) in [0, 4, 5]
-        assert bboxes2.size(-1) in [0, 4, 5]
-        if bboxes2.size(-1) == 5:
-            bboxes2 = bboxes2[..., :4]
-        if bboxes1.size(-1) == 5:
-            bboxes1 = bboxes1[..., :4]
-        return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
-
-    def __repr__(self):
-        """str: a string describing the module"""
-        repr_str = self.__class__.__name__ + '()'
-        return repr_str
-
-
-def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6):
-    """Calculate overlap between two set of bboxes.
-
-    If ``is_aligned `` is ``False``, then calculate the overlaps between each
-    bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
-    pair of bboxes1 and bboxes2.
-
-    Args:
-        bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
-        bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
-            B indicates the batch dim, in shape (B1, B2, ..., Bn).
-            If ``is_aligned `` is ``True``, then m and n must be equal.
-        mode (str): "iou" (intersection over union), "iof" (intersection over
-            foreground) or "giou" (generalized intersection over union).
-            Default "iou".
-        is_aligned (bool, optional): If True, then m and n must be equal.
-            Default False.
-        eps (float, optional): A value added to the denominator for numerical
-            stability. Default 1e-6.
-
-    Returns:
-        Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
-
-    Example:
-        >>> bboxes1 = torch.FloatTensor([
-        >>>     [0, 0, 10, 10],
-        >>>     [10, 10, 20, 20],
-        >>>     [32, 32, 38, 42],
-        >>> ])
-        >>> bboxes2 = torch.FloatTensor([
-        >>>     [0, 0, 10, 20],
-        >>>     [0, 10, 10, 19],
-        >>>     [10, 10, 20, 20],
-        >>> ])
-        >>> overlaps = bbox_overlaps(bboxes1, bboxes2)
-        >>> assert overlaps.shape == (3, 3)
-        >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True)
-        >>> assert overlaps.shape == (3, )
-
-    Example:
-        >>> empty = torch.empty(0, 4)
-        >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]])
-        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
-        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
-        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
-    """
-
-    assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}'
-    # Either the boxes are empty or the length of boxes' last dimension is 4
-    assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
-    assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
-
-    # Batch dim must be the same
-    # Batch dim: (B1, B2, ... Bn)
-    assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
-    batch_shape = bboxes1.shape[:-2]
-
-    rows = bboxes1.size(-2)
-    cols = bboxes2.size(-2)
-    if is_aligned:
-        assert rows == cols
-
-    if rows * cols == 0:
-        if is_aligned:
-            return bboxes1.new(batch_shape + (rows, ))
-        else:
-            return bboxes1.new(batch_shape + (rows, cols))
-
-    area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (
-        bboxes1[..., 3] - bboxes1[..., 1])
-    area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (
-        bboxes2[..., 3] - bboxes2[..., 1])
-
-    if is_aligned:
-        lt = torch.max(bboxes1[..., :2], bboxes2[..., :2])  # [B, rows, 2]
-        rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:])  # [B, rows, 2]
-
-        wh = (rb - lt).clamp(min=0)  # [B, rows, 2]
-        overlap = wh[..., 0] * wh[..., 1]
-
-        if mode in ['iou', 'giou']:
-            union = area1 + area2 - overlap
-        else:
-            union = area1
-        if mode == 'giou':
-            enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2])
-            enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:])
-    else:
-        lt = torch.max(bboxes1[..., :, None, :2],
-                       bboxes2[..., None, :, :2])  # [B, rows, cols, 2]
-        rb = torch.min(bboxes1[..., :, None, 2:],
-                       bboxes2[..., None, :, 2:])  # [B, rows, cols, 2]
-
-        wh = (rb - lt).clamp(min=0)  # [B, rows, cols, 2]
-        overlap = wh[..., 0] * wh[..., 1]
-
-        if mode in ['iou', 'giou']:
-            union = area1[..., None] + area2[..., None, :] - overlap
-        else:
-            union = area1[..., None]
-        if mode == 'giou':
-            enclosed_lt = torch.min(bboxes1[..., :, None, :2],
-                                    bboxes2[..., None, :, :2])
-            enclosed_rb = torch.max(bboxes1[..., :, None, 2:],
-                                    bboxes2[..., None, :, 2:])
-
-    eps = union.new_tensor([eps])
-    union = torch.max(union, eps)
-    ious = overlap / union
-    if mode in ['iou', 'iof']:
-        return ious
-    # calculate gious
-    enclose_wh = (enclosed_rb - enclosed_lt).clamp(min=0)
-    enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
-    enclose_area = torch.max(enclose_area, eps)
-    gious = ious - (enclose_area - union) / enclose_area
-    return gious
diff --git a/spaces/Roboflow/Annotators/app.py b/spaces/Roboflow/Annotators/app.py
deleted file mode 100644
index 8b83e2b86f9fd91845f1f6caff12d8ec1627bd95..0000000000000000000000000000000000000000
--- a/spaces/Roboflow/Annotators/app.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import gradio as gr
-import supervision as sv
-from ultralytics import YOLO
-import os #added for cache_examples
-from PIL import Image, ImageColor
-import numpy as np
-
-def load_model(img):
-  # Load model, get results and return detections/labels
-  model = YOLO('yolov8s-seg.pt')
-  result = model(img, verbose=False, imgsz=1280)[0]
-  detections = sv.Detections.from_ultralytics(result)
-  labels = [
-            f"{model.model.names[class_id]} {confidence:.2f}"
-            for class_id, confidence in zip(detections.class_id, detections.confidence)
-           ]
-
-  return detections, labels
-
-def calculate_crop_dim(a,b):
-  #Calculates the crop dimensions of the image resultant
-  if a>b:
-    width= a
-    height = a
-  else:
-    width = b
-    height = b
-
-  return width, height
-
-def annotator(img,annotators,colorbb,colormask,colorellipse,colorbc,colorcir,colorlabel):
-
-    """
-    Function that changes the color of annotators
-    Args:
-        annotators: Icon whose color needs to be changed.
-        color: Chosen color with which to edit the input icon in Hex.
-        img: Input image is numpy matrix in BGR.
-    Returns:
-        annotators: annotated image
-    """
-
-
-
-    img = img[...,::-1].copy() # BGR to RGB using numpy
-
-
-    detections, labels = load_model(img)
-
-
-    if "Blur" in annotators:
-        # Apply Blur
-        blur_annotator = sv.BlurAnnotator()
-        img = blur_annotator.annotate(img, detections=detections)
-
-    if "BoundingBox" in annotators:
-        # Draw Bounding box
-        box_annotator = sv.BoundingBoxAnnotator(sv.Color.from_hex(str(colorbb)))
-        img = box_annotator.annotate(img, detections=detections)
-
-    if "Mask" in annotators:
-        # Draw Mask
-        mask_annotator = sv.MaskAnnotator(sv.Color.from_hex(str(colormask)))
-        img = mask_annotator.annotate(img, detections=detections)
-
-    if "Ellipse" in annotators:
-        # Draw ellipse
-        ellipse_annotator = sv.EllipseAnnotator(sv.Color.from_hex(str(colorellipse)))
-        img = ellipse_annotator.annotate(img, detections=detections)
-
-    if "BoxCorner" in annotators:
-        # Draw Box corner
-        corner_annotator = sv.BoxCornerAnnotator(sv.Color.from_hex(str(colorbc)))
-        img = corner_annotator.annotate(img, detections=detections)
-
-
-    if "Circle" in annotators: 
-        # Draw circle
-        circle_annotator = sv.CircleAnnotator(sv.Color.from_hex(str(colorcir)))
-        img = circle_annotator.annotate(img, detections=detections)
-
-    if "Label" in annotators:
-        # Draw Label
-        label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
-        label_annotator = sv.LabelAnnotator(sv.Color.from_hex(str(colorlabel)))
-        img = label_annotator.annotate(img, detections=detections, labels=labels)
-
-
-
-    #crop image for the largest possible square
-    res_img = Image.fromarray(img)
-    print(type(res_img))
-
-    x=0
-    y=0
-
-
-    print("size of the pil im=", res_img.size)
-    (v1,v2) = res_img.size
-    width, height = calculate_crop_dim(v1, v2)
-    print(width, height)
-    my_img = np.array(res_img)
-
-    crop_img = my_img[y:y+height, x:x+width]
-    print(type(crop_img))
-
-    return crop_img[...,::-1].copy() # BGR to RGB using numpy
-
-
-with gr.Blocks(theme=gr.themes.Soft(primary_hue=gr.themes.colors.purple)
-    .set(
-      button_primary_background_fill="*primary_600",
-      button_primary_background_fill_hover="*primary_700",
-      checkbox_label_background_fill_selected="*primary_600",
-      checkbox_background_color_selected="*primary_400",
-    )) as demo:
-    gr.Markdown("""# Supervision Annotators""")
-    annotators = gr.CheckboxGroup(choices=["BoundingBox", "Mask", "Ellipse", "BoxCorner", "Circle", "Label", "Blur"], value=["BoundingBox", "Mask"], label="Select Annotators:")
-
-    with gr.Accordion("**Color Picker**"):
-      with gr.Row():
-        with gr.Column():
-          colorbb = gr.ColorPicker(value="#A351FB",label="BoundingBox")
-        with gr.Column():
-          colormask = gr.ColorPicker(value="#A351FB",label="Mask")
-        with gr.Column():
-          colorellipse = gr.ColorPicker(value="#A351FB",label="Ellipse")
-        with gr.Column():
-          colorbc = gr.ColorPicker(value="#A351FB",label="BoxCorner")
-        with gr.Column():
-          colorcir = gr.ColorPicker(value="#A351FB",label="Circle")
-        with gr.Column():
-          colorlabel = gr.ColorPicker(value="#A351FB",label="Label")
-    
-    with gr.Row():
-      with gr.Column():
-        with gr.Tab("Input image"):
-          image_input = gr.Image(type="numpy", show_label=False)
-      with gr.Column():
-        with gr.Tab("Result image"):
-          image_output = gr.Image(type="numpy", show_label=False)
-    image_button = gr.Button(value="Annotate it!", variant="primary")
-
-    image_button.click(annotator, inputs=[image_input,annotators,colorbb,colormask,colorellipse,colorbc,colorcir,colorlabel], outputs=image_output)
-
-    gr.Markdown("## Image Examples")
-    gr.Examples(
-        examples=[os.path.join(os.path.abspath(''), "city.jpg"), 
-                  os.path.join(os.path.abspath(''), "household.jpg"), 
-                  os.path.join(os.path.abspath(''), "industry.jpg"),
-                  os.path.join(os.path.abspath(''), "retail.jpg"),
-                  os.path.join(os.path.abspath(''), "aerodefence.jpg")],
-        inputs=image_input,
-        outputs=image_output,
-        fn=annotator,
-        cache_examples=False,
-    )
-
-
-demo.launch(debug=False)
\ No newline at end of file
diff --git a/spaces/Rongjiehuang/ProDiff/utils/ckpt_utils.py b/spaces/Rongjiehuang/ProDiff/utils/ckpt_utils.py
deleted file mode 100644
index fc321f9ba891ffffc374df65871c3085bf898afb..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/utils/ckpt_utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import logging
-import os
-import re
-import torch
-
-
-def get_last_checkpoint(work_dir, steps=None):
-    checkpoint = None
-    last_ckpt_path = None
-    ckpt_paths = get_all_ckpts(work_dir, steps)
-    if len(ckpt_paths) > 0:
-        last_ckpt_path = ckpt_paths[0]
-        checkpoint = torch.load(last_ckpt_path, map_location='cpu')
-        logging.info(f'load module from checkpoint: {last_ckpt_path}')
-    return checkpoint, last_ckpt_path
-
-
-def get_all_ckpts(work_dir, steps=None):
-    if steps is None:
-        ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_*.ckpt'
-    else:
-        ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_{steps}.ckpt'
-    return sorted(glob.glob(ckpt_path_pattern),
-                  key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0]))
-
-
-def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True):
-    if os.path.isfile(ckpt_base_dir):
-        base_dir = os.path.dirname(ckpt_base_dir)
-        ckpt_path = ckpt_base_dir
-        checkpoint = torch.load(ckpt_base_dir, map_location='cpu')
-    else:
-        base_dir = ckpt_base_dir
-        checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir)
-    if checkpoint is not None:
-        state_dict = checkpoint["state_dict"]
-        if len([k for k in state_dict.keys() if '.' in k]) > 0:
-            state_dict = {k[len(model_name) + 1:]: v for k, v in state_dict.items()
-                          if k.startswith(f'{model_name}.')}
-        else:
-            if '.' not in model_name:
-                state_dict = state_dict[model_name]
-            else:
-                base_model_name = model_name.split('.')[0]
-                rest_model_name = model_name[len(base_model_name) + 1:]
-                state_dict = {
-                    k[len(rest_model_name) + 1:]: v for k, v in state_dict[base_model_name].items()
-                    if k.startswith(f'{rest_model_name}.')}
-        if not strict:
-            cur_model_state_dict = cur_model.state_dict()
-            unmatched_keys = []
-            for key, param in state_dict.items():
-                if key in cur_model_state_dict:
-                    new_param = cur_model_state_dict[key]
-                    if new_param.shape != param.shape:
-                        unmatched_keys.append(key)
-                        print("| Unmatched keys: ", key, new_param.shape, param.shape)
-            for key in unmatched_keys:
-                del state_dict[key]
-        cur_model.load_state_dict(state_dict, strict=strict)
-        print(f"| load '{model_name}' from '{ckpt_path}'.")
-    else:
-        e_msg = f"| ckpt not found in {base_dir}."
-        if force:
-            assert False, e_msg
-        else:
-            print(e_msg)
diff --git a/spaces/Saketh-Reddy/webhook_space/models.py b/spaces/Saketh-Reddy/webhook_space/models.py
deleted file mode 100644
index f2c5adfec92dab6c3fa2fd2a1097111e56c4d1b4..0000000000000000000000000000000000000000
--- a/spaces/Saketh-Reddy/webhook_space/models.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-from pydantic import BaseModel
-from typing import Literal
-
-
-
-class WebhookPayloadEvent(BaseModel):
-	action: Literal["create", "update", "delete"]
-	scope: str
-
-class WebhookPayloadRepo(BaseModel):
-	type: Literal["dataset", "model", "space"]
-	name: str
-	id: str
-	private: bool
-	headSha: str
-
-class WebhookPayload(BaseModel):
-	event: WebhookPayloadEvent
-	repo: WebhookPayloadRepo
-
diff --git a/spaces/Sambhavnoobcoder/stable-diffusion-inpainting/README.md b/spaces/Sambhavnoobcoder/stable-diffusion-inpainting/README.md
deleted file mode 100644
index 9671a6056b5a55672f2a7c566d6e95f4fc2c2553..0000000000000000000000000000000000000000
--- a/spaces/Sambhavnoobcoder/stable-diffusion-inpainting/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Runway Inpainting
-emoji: 🔥
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-duplicated_from: runwayml/stable-diffusion-inpainting
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
diff --git a/spaces/SeViLA/SeViLA/lavis/models/timesformer/vit.py b/spaces/SeViLA/SeViLA/lavis/models/timesformer/vit.py
deleted file mode 100644
index a6202b40636684b4a9c4feb8af5be227180ca966..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/timesformer/vit.py
+++ /dev/null
@@ -1,634 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-
- Based on https://github.com/facebookresearch/TimeSformer
-"""
-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# Copyright 2020 Ross Wightman
-# Modified Model definition
-
-import logging
-from functools import partial
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils
-import torch.utils.checkpoint
-from einops import rearrange
-from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
-
-from .helpers import load_pretrained, load_pretrained_imagenet, load_pretrained_kinetics
-from .vit_utils import (
-    IMAGENET_DEFAULT_MEAN,
-    IMAGENET_DEFAULT_STD,
-    DropPath,
-    to_2tuple,
-    trunc_normal_,
-)
-
-
-def _cfg(url="", **kwargs):
-    return {
-        "url": url,
-        "num_classes": 1000,
-        "input_size": (3, 224, 224),
-        "pool_size": None,
-        "crop_pct": 0.9,
-        "interpolation": "bicubic",
-        "mean": IMAGENET_DEFAULT_MEAN,
-        "std": IMAGENET_DEFAULT_STD,
-        "first_conv": "patch_embed.proj",
-        "classifier": "head",
-        **kwargs,
-    }
-
-
-default_cfgs = {
-    "vit_base_patch16_224": _cfg(
-        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth",
-        mean=(0.5, 0.5, 0.5),
-        std=(0.5, 0.5, 0.5),
-    ),
-}
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=False,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-        with_qkv=True,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-        self.with_qkv = with_qkv
-        if self.with_qkv:
-            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-            self.proj = nn.Linear(dim, dim)
-            self.proj_drop = nn.Dropout(proj_drop)
-        self.attn_drop = nn.Dropout(attn_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        if self.with_qkv:
-            qkv = (
-                self.qkv(x)
-                .reshape(B, N, 3, self.num_heads, C // self.num_heads)
-                .permute(2, 0, 3, 1, 4)
-            )
-            q, k, v = qkv[0], qkv[1], qkv[2]
-        else:
-            qkv = x.reshape(B, N, self.num_heads, C // self.num_heads).permute(
-                0, 2, 1, 3
-            )
-            q, k, v = qkv, qkv, qkv
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        if self.with_qkv:
-            x = self.proj(x)
-            x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        layer_num,
-        mlp_ratio=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.1,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        attention_type="divided_space_time",
-        use_grad_checkpointing=False,
-    ):
-        super().__init__()
-        self.attention_type = attention_type
-        assert attention_type in [
-            "divided_space_time",
-            "space_only",
-            "joint_space_time",
-        ]
-
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        # Temporal Attention Parameters
-        if self.attention_type == "divided_space_time":
-            self.temporal_norm1 = norm_layer(dim)
-            self.temporal_attn = Attention(
-                dim,
-                num_heads=num_heads,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                attn_drop=attn_drop,
-                proj_drop=drop,
-            )
-            self.temporal_fc = nn.Linear(dim, dim)
-
-        # drop path
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        # [dxli]
-        self.layer_num = layer_num
-        self.use_grad_checkpointing = use_grad_checkpointing
-
-        if use_grad_checkpointing:
-            self.temporal_attn = checkpoint_wrapper(self.temporal_attn)
-            self.attn = checkpoint_wrapper(self.attn)
-            self.mlp = checkpoint_wrapper(self.mlp)
-
-    def forward(self, x, B, T, W):
-        num_spatial_tokens = (x.size(1) - 1) // T
-        H = num_spatial_tokens // W
-
-        if self.attention_type in ["space_only", "joint_space_time"]:
-            x = x + self.drop_path(self.attn(self.norm1(x)))
-            x = x + self.drop_path(self.mlp(self.norm2(x)))
-            return x
-        elif self.attention_type == "divided_space_time":
-            # Temporal
-            xt = x[:, 1:, :]
-            xt = rearrange(xt, "b (h w t) m -> (b h w) t m", b=B, h=H, w=W, t=T)
-
-            temporal_attn_out = self.temporal_attn(self.temporal_norm1(xt))
-
-            res_temporal = self.drop_path(temporal_attn_out)
-
-            res_temporal = rearrange(
-                res_temporal, "(b h w) t m -> b (h w t) m", b=B, h=H, w=W, t=T
-            )
-            res_temporal = self.temporal_fc(res_temporal)
-            xt = x[:, 1:, :] + res_temporal
-
-            # Spatial
-            init_cls_token = x[:, 0, :].unsqueeze(1)
-            cls_token = init_cls_token.repeat(1, T, 1)
-            cls_token = rearrange(cls_token, "b t m -> (b t) m", b=B, t=T).unsqueeze(1)
-            xs = xt
-            xs = rearrange(xs, "b (h w t) m -> (b t) (h w) m", b=B, h=H, w=W, t=T)
-            xs = torch.cat((cls_token, xs), 1)
-
-            spatial_attn_out = self.attn(self.norm1(xs))
-            res_spatial = self.drop_path(spatial_attn_out)
-
-            # Taking care of CLS token
-            cls_token = res_spatial[:, 0, :]
-            cls_token = rearrange(cls_token, "(b t) m -> b t m", b=B, t=T)
-            # averaging for every frame
-            cls_token = torch.mean(cls_token, 1, True)
-            res_spatial = res_spatial[:, 1:, :]
-            res_spatial = rearrange(
-                res_spatial, "(b t) (h w) m -> b (h w t) m", b=B, h=H, w=W, t=T
-            )
-            res = res_spatial
-            x = xt
-
-            # Mlp
-            x = torch.cat((init_cls_token, x), 1) + torch.cat((cls_token, res), 1)
-
-            x_res = x
-
-            x = self.norm2(x)
-            # x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-            # MLP
-            mlp_out = self.mlp(x)
-
-            x = x_res + self.drop_path(mlp_out)
-            return x
-
-
-class PatchEmbed(nn.Module):
-    """Image to Patch Embedding"""
-
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
-        )
-
-    def forward(self, x):
-        B, C, T, H, W = x.shape
-        x = rearrange(x, "b c t h w -> (b t) c h w")
-        x = self.proj(x)
-        W = x.size(-1)
-        x = x.flatten(2).transpose(1, 2)
-        return x, T, W
-
-
-class VisionTransformer(nn.Module):
-    """Vision Transformere"""
-
-    def __init__(
-        self,
-        img_size=224,
-        patch_size=16,
-        in_chans=3,
-        num_classes=1000,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        mlp_ratio=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.1,
-        hybrid_backbone=None,
-        norm_layer=nn.LayerNorm,
-        num_frames=8,
-        attention_type="divided_space_time",
-        dropout=0.0,
-        use_grad_checkpointing=False,
-        ckpt_layer=0,
-    ):
-        super().__init__()
-
-        self.attention_type = attention_type
-        self.depth = depth
-        self.dropout = nn.Dropout(dropout)
-        self.num_classes = num_classes
-        # num_features for consistency with other models
-        self.num_features = self.embed_dim = embed_dim
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-        )
-        num_patches = self.patch_embed.num_patches
-
-        # Positional Embeddings
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-        if self.attention_type != "space_only":
-            self.time_embed = nn.Parameter(torch.zeros(1, num_frames, embed_dim))
-            self.time_drop = nn.Dropout(p=drop_rate)
-
-        # Attention Blocks
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, self.depth)
-        ]  # stochastic depth decay rule
-        self.blocks = nn.ModuleList(
-            [
-                Block(
-                    layer_num=i,
-                    use_grad_checkpointing=(
-                        use_grad_checkpointing and i >= self.depth - ckpt_layer
-                    ),
-                    dim=embed_dim,
-                    num_heads=num_heads,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop_rate,
-                    attn_drop=attn_drop_rate,
-                    drop_path=dpr[i],
-                    norm_layer=norm_layer,
-                    attention_type=self.attention_type,
-                )
-                for i in range(self.depth)
-            ]
-        )
-        self.norm = norm_layer(embed_dim)
-
-        # Classifier head
-        self.head = (
-            nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-        )
-
-        trunc_normal_(self.pos_embed, std=0.02)
-        trunc_normal_(self.cls_token, std=0.02)
-        self.apply(self._init_weights)
-
-        # initialization of temporal attention weights
-        if self.attention_type == "divided_space_time":
-            i = 0
-            for m in self.blocks.modules():
-                m_str = str(m)
-                if "Block" in m_str:
-                    if i > 0:
-                        nn.init.constant_(m.temporal_fc.weight, 0)
-                        nn.init.constant_(m.temporal_fc.bias, 0)
-                    i += 1
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {"pos_embed", "cls_token", "time_embed"}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=""):
-        self.num_classes = num_classes
-        self.head = (
-            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-        )
-
-    def remove_classifier(self):
-        self.num_classes = 0
-        self.head = None
-
-    def forward_features(self, x):
-        B = x.shape[0]
-        x, T, W = self.patch_embed(x)
-        cls_tokens = self.cls_token.expand(x.size(0), -1, -1)
-        x = torch.cat((cls_tokens, x), dim=1)
-
-        # resizing the positional embeddings in case they don't match the input at inference
-        if x.size(1) != self.pos_embed.size(1):
-            pos_embed = self.pos_embed
-            cls_pos_embed = pos_embed[0, 0, :].unsqueeze(0).unsqueeze(1)
-            other_pos_embed = pos_embed[0, 1:, :].unsqueeze(0).transpose(1, 2)
-            P = int(other_pos_embed.size(2) ** 0.5)
-            H = x.size(1) // W
-            other_pos_embed = other_pos_embed.reshape(1, x.size(2), P, P)
-            new_pos_embed = F.interpolate(other_pos_embed, size=(H, W), mode="nearest")
-            new_pos_embed = new_pos_embed.flatten(2)
-            new_pos_embed = new_pos_embed.transpose(1, 2)
-            new_pos_embed = torch.cat((cls_pos_embed, new_pos_embed), 1)
-            x = x + new_pos_embed
-        else:
-            x = x + self.pos_embed
-        x = self.pos_drop(x)
-
-        # Time Embeddings
-        if self.attention_type != "space_only":
-            cls_tokens = x[:B, 0, :].unsqueeze(1)
-            x = x[:, 1:]
-            x = rearrange(x, "(b t) n m -> (b n) t m", b=B, t=T)
-            # Resizing time embeddings in case they don't match
-            if T != self.time_embed.size(1):
-                time_embed = self.time_embed.transpose(1, 2)
-                new_time_embed = F.interpolate(time_embed, size=(T), mode="nearest")
-                new_time_embed = new_time_embed.transpose(1, 2)
-                x = x + new_time_embed
-            else:
-                x = x + self.time_embed
-            x = self.time_drop(x)
-            x = rearrange(x, "(b n) t m -> b (n t) m", b=B, t=T)
-            x = torch.cat((cls_tokens, x), dim=1)
-
-        # Attention blocks
-        for blk in self.blocks:
-            x = blk(x, B, T, W)
-
-        # Predictions for space-only baseline
-        if self.attention_type == "space_only":
-            x = rearrange(x, "(b t) n m -> b t n m", b=B, t=T)
-            x = torch.mean(x, 1)  # averaging predictions for every frame
-
-        x = self.norm(x)
-
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def _conv_filter(state_dict, patch_size=16):
-    """convert patch embedding weight from manual patchify + linear proj to conv"""
-    out_dict = {}
-    for k, v in state_dict.items():
-        if "patch_embed.proj.weight" in k:
-            if v.shape[-1] != patch_size:
-                patch_size = v.shape[-1]
-            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
-        out_dict[k] = v
-    return out_dict
-
-
-class vit_base_patch16_224(nn.Module):
-    def __init__(self, cfg, **kwargs):
-        super(vit_base_patch16_224, self).__init__()
-        self.pretrained = True
-        patch_size = 16
-        self.model = VisionTransformer(
-            img_size=cfg.DATA.TRAIN_CROP_SIZE,
-            num_classes=cfg.MODEL.NUM_CLASSES,
-            patch_size=patch_size,
-            embed_dim=768,
-            depth=12,
-            num_heads=12,
-            mlp_ratio=4,
-            qkv_bias=True,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6),
-            drop_rate=0.0,
-            attn_drop_rate=0.0,
-            drop_path_rate=0.1,
-            num_frames=cfg.DATA.NUM_FRAMES,
-            attention_type=cfg.TIMESFORMER.ATTENTION_TYPE,
-            **kwargs,
-        )
-
-        self.attention_type = cfg.TIMESFORMER.ATTENTION_TYPE
-        self.model.default_cfg = default_cfgs["vit_base_patch16_224"]
-        self.num_patches = (cfg.DATA.TRAIN_CROP_SIZE // patch_size) * (
-            cfg.DATA.TRAIN_CROP_SIZE // patch_size
-        )
-        pretrained_model = cfg.TIMESFORMER.PRETRAINED_MODEL
-        if self.pretrained:
-            load_pretrained(
-                self.model,
-                num_classes=self.model.num_classes,
-                in_chans=kwargs.get("in_chans", 3),
-                filter_fn=_conv_filter,
-                img_size=cfg.DATA.TRAIN_CROP_SIZE,
-                num_patches=self.num_patches,
-                attention_type=self.attention_type,
-                pretrained_model=pretrained_model,
-            )
-
-    def forward(self, x):
-        x = self.model(x)
-        return x
-
-
-class TimeSformer(nn.Module):
-    def __init__(
-        self,
-        image_size=224,
-        patch_size=16,
-        n_frms=8,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.1,
-        drop_rate=0,
-        use_grad_ckpt=False,
-        ckpt_layer=0,
-        remove_classifier=True,
-        **kwargs,
-    ):
-        super(TimeSformer, self).__init__()
-
-        self.img_size = image_size
-        self.patch_size = patch_size
-        self.num_frames = n_frms
-        self.attn_drop_rate = attn_drop_rate
-        self.drop_path_rate = drop_path_rate
-        self.drop_rate = drop_rate
-        self.use_grad_ckpt = use_grad_ckpt
-        self.ckpt_layer = ckpt_layer
-
-        self.attention_type = "divided_space_time"
-
-        logging.info(
-            f"Initializing TimeSformer with img_size={self.img_size}, patch_size={self.patch_size}, num_frames={self.num_frames}"
-        )
-
-        # will be ignored when loading official pretrained ckpt
-        self.num_classes = 400
-
-        self.model = VisionTransformer(
-            img_size=self.img_size,
-            num_classes=self.num_classes,
-            patch_size=self.patch_size,
-            embed_dim=768,
-            depth=12,
-            num_heads=12,
-            mlp_ratio=4,
-            qkv_bias=True,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6),
-            drop_rate=self.drop_rate,
-            attn_drop_rate=self.attn_drop_rate,
-            drop_path_rate=self.drop_path_rate,
-            num_frames=self.num_frames,
-            attention_type=self.attention_type,
-            use_grad_checkpointing=self.use_grad_ckpt,
-            ckpt_layer=self.ckpt_layer,
-            **kwargs,
-        )
-
-        if remove_classifier:
-            self.model.remove_classifier()
-
-        self.model.default_cfg = default_cfgs[
-            "vit_base_patch" + str(self.patch_size) + "_224"
-        ]
-        self.num_patches = (self.img_size // self.patch_size) * (
-            self.img_size // self.patch_size
-        )
-
-    def forward(self, x):
-        x = self.model(x)
-        return x
-
-    def forward_features(self, x):
-        # b, c, t, h, w = x.shape
-        x = self.model.forward_features(x)
-
-        ## apply pooling
-        W = H = self.img_size // self.patch_size
-        T = self.num_frames
-
-        cls_tokens = x[:, 0, :].unsqueeze(1)
-        other_tokens = x[:, 1:, :]
-
-        x = rearrange(other_tokens, "b (h w t) m -> b t (h w) m", h=H, w=W, t=T)
-
-        x = torch.mean(x, dim=1)
-        x = torch.cat((cls_tokens, x), dim=1)
-
-        return x
-
-    def load_state_dict(self, pretrained_ckpt_path):
-        logging.info(
-            "Loading TimeSformer checkpoints from {}".format(pretrained_ckpt_path)
-        )
-
-        if pretrained_ckpt_path == "vit_base_patch16_224":
-            load_ckpt_func = load_pretrained_imagenet
-        else:
-            load_ckpt_func = load_pretrained_kinetics
-
-        load_ckpt_func(
-            self.model,
-            num_classes=self.model.num_classes,
-            in_chans=3,
-            filter_fn=_conv_filter,
-            img_size=self.img_size,
-            num_frames=self.num_frames,
-            num_patches=self.num_patches,
-            attention_type=self.attention_type,
-            pretrained_model=pretrained_ckpt_path,
-        )
diff --git a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/util/misc.py b/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/util/misc.py
deleted file mode 100644
index d64b84ef24bea0c98e76824feb1903f6bfebe7a5..0000000000000000000000000000000000000000
--- a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/util/misc.py
+++ /dev/null
@@ -1,717 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import colorsys
-import datetime
-import functools
-import io
-import json
-import os
-import pickle
-import subprocess
-import time
-from collections import OrderedDict, defaultdict, deque
-from typing import List, Optional
-
-import numpy as np
-import torch
-import torch.distributed as dist
-
-# needed due to empty tensor bug in pytorch and torchvision 0.5
-import torchvision
-from torch import Tensor
-
-__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
-if __torchvision_need_compat_flag:
-    from torchvision.ops import _new_empty_tensor
-    from torchvision.ops.misc import _output_size
-
-
-class SmoothedValue(object):
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        if d.shape[0] == 0:
-            return 0
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        if os.environ.get("SHILONG_AMP", None) == "1":
-            eps = 1e-4
-        else:
-            eps = 1e-6
-        return self.total / (self.count + eps)
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median,
-            avg=self.avg,
-            global_avg=self.global_avg,
-            max=self.max,
-            value=self.value,
-        )
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
-    """
-    Return a process group based on gloo backend, containing all the ranks
-    The result is cached.
-    """
-
-    if dist.get_backend() == "nccl":
-        return dist.new_group(backend="gloo")
-
-    return dist.group.WORLD
-
-
-def all_gather_cpu(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    cpu_group = _get_global_gloo_group()
-
-    buffer = io.BytesIO()
-    torch.save(data, buffer)
-    data_view = buffer.getbuffer()
-    device = "cuda" if cpu_group is None else "cpu"
-    tensor = torch.ByteTensor(data_view).to(device)
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
-    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
-    if cpu_group is None:
-        dist.all_gather(size_list, local_size)
-    else:
-        print("gathering on cpu")
-        dist.all_gather(size_list, local_size, group=cpu_group)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-    assert isinstance(local_size.item(), int)
-    local_size = int(local_size.item())
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
-        tensor = torch.cat((tensor, padding), dim=0)
-    if cpu_group is None:
-        dist.all_gather(tensor_list, tensor)
-    else:
-        dist.all_gather(tensor_list, tensor, group=cpu_group)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
-        buffer = io.BytesIO(tensor.cpu().numpy())
-        obj = torch.load(buffer)
-        data_list.append(obj)
-
-    return data_list
-
-
-def all_gather(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    if os.getenv("CPU_REDUCE") == "1":
-        return all_gather_cpu(data)
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    # serialized to a Tensor
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to("cuda")
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device="cuda")
-    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-        tensor = torch.cat((tensor, padding), dim=0)
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Args:
-        input_dict (dict): all the values will be reduced
-        average (bool): whether to do average or sum
-    Reduce the values in the dictionary from all processes so that all processes
-    have the averaged results. Returns a dict with the same fields as
-    input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.all_reduce(values)
-        if average:
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values)}
-    return reduced_dict
-
-
-class MetricLogger(object):
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            # print(name, str(meter))
-            # import ipdb;ipdb.set_trace()
-            if meter.count > 0:
-                loss_str.append("{}: {}".format(name, str(meter)))
-        return self.delimiter.join(loss_str)
-
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None, logger=None):
-        if logger is None:
-            print_func = print
-        else:
-            print_func = logger.info
-
-        i = 0
-        if not header:
-            header = ""
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt="{avg:.4f}")
-        data_time = SmoothedValue(fmt="{avg:.4f}")
-        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
-        if torch.cuda.is_available():
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                    "max mem: {memory:.0f}",
-                ]
-            )
-        else:
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                ]
-            )
-        MB = 1024.0 * 1024.0
-        for obj in iterable:
-            data_time.update(time.time() - end)
-            yield obj
-            # import ipdb; ipdb.set_trace()
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                            memory=torch.cuda.max_memory_allocated() / MB,
-                        )
-                    )
-                else:
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                        )
-                    )
-            i += 1
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print_func(
-            "{} Total time: {} ({:.4f} s / it)".format(
-                header, total_time_str, total_time / len(iterable)
-            )
-        )
-
-
-def get_sha():
-    cwd = os.path.dirname(os.path.abspath(__file__))
-
-    def _run(command):
-        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
-
-    sha = "N/A"
-    diff = "clean"
-    branch = "N/A"
-    try:
-        sha = _run(["git", "rev-parse", "HEAD"])
-        subprocess.check_output(["git", "diff"], cwd=cwd)
-        diff = _run(["git", "diff-index", "HEAD"])
-        diff = "has uncommited changes" if diff else "clean"
-        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
-    except Exception:
-        pass
-    message = f"sha: {sha}, status: {diff}, branch: {branch}"
-    return message
-
-
-def collate_fn(batch):
-    # import ipdb; ipdb.set_trace()
-    batch = list(zip(*batch))
-    batch[0] = nested_tensor_from_tensor_list(batch[0])
-    return tuple(batch)
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-        if mask == "auto":
-            self.mask = torch.zeros_like(tensors).to(tensors.device)
-            if self.mask.dim() == 3:
-                self.mask = self.mask.sum(0).to(bool)
-            elif self.mask.dim() == 4:
-                self.mask = self.mask.sum(1).to(bool)
-            else:
-                raise ValueError(
-                    "tensors dim must be 3 or 4 but {}({})".format(
-                        self.tensors.dim(), self.tensors.shape
-                    )
-                )
-
-    def imgsize(self):
-        res = []
-        for i in range(self.tensors.shape[0]):
-            mask = self.mask[i]
-            maxH = (~mask).sum(0).max()
-            maxW = (~mask).sum(1).max()
-            res.append(torch.Tensor([maxH, maxW]))
-        return res
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def to_img_list_single(self, tensor, mask):
-        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
-        maxH = (~mask).sum(0).max()
-        maxW = (~mask).sum(1).max()
-        img = tensor[:, :maxH, :maxW]
-        return img
-
-    def to_img_list(self):
-        """remove the padding and convert to img list
-
-        Returns:
-            [type]: [description]
-        """
-        if self.tensors.dim() == 3:
-            return self.to_img_list_single(self.tensors, self.mask)
-        else:
-            res = []
-            for i in range(self.tensors.shape[0]):
-                tensor_i = self.tensors[i]
-                mask_i = self.mask[i]
-                res.append(self.to_img_list_single(tensor_i, mask_i))
-            return res
-
-    @property
-    def device(self):
-        return self.tensors.device
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-    @property
-    def shape(self):
-        return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ["WORLD_SIZE"])
-        args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
-
-        # launch by torch.distributed.launch
-        # Single node
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
-        # Multi nodes
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
-        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
-        # args.world_size = args.world_size * local_world_size
-        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
-        # args.rank = args.rank * local_world_size + args.local_rank
-        print(
-            "world size: {}, rank: {}, local rank: {}".format(
-                args.world_size, args.rank, args.local_rank
-            )
-        )
-        print(json.dumps(dict(os.environ), indent=2))
-    elif "SLURM_PROCID" in os.environ:
-        args.rank = int(os.environ["SLURM_PROCID"])
-        args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
-        args.world_size = int(os.environ["SLURM_NPROCS"])
-
-        print(
-            "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
-                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
-            )
-        )
-    else:
-        print("Not using distributed mode")
-        args.distributed = False
-        args.world_size = 1
-        args.rank = 0
-        args.local_rank = 0
-        return
-
-    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
-    args.distributed = True
-    torch.cuda.set_device(args.local_rank)
-    args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
-
-    torch.distributed.init_process_group(
-        backend=args.dist_backend,
-        world_size=args.world_size,
-        rank=args.rank,
-        init_method=args.dist_url,
-    )
-
-    print("Before torch.distributed.barrier()")
-    torch.distributed.barrier()
-    print("End torch.distributed.barrier()")
-    setup_for_distributed(args.rank == 0)
-
-
-@torch.no_grad()
-def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
-    if target.numel() == 0:
-        return [torch.zeros([], device=output.device)]
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(100.0 / batch_size))
-    return res
-
-
-@torch.no_grad()
-def accuracy_onehot(pred, gt):
-    """_summary_
-
-    Args:
-        pred (_type_): n, c
-        gt (_type_): n, c
-    """
-    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
-    acc = tp / gt.shape[0] * 100
-    return acc
-
-
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
-    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
-    """
-    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
-    This will eventually be supported natively by PyTorch, and this
-    class can go away.
-    """
-    if __torchvision_need_compat_flag < 0.7:
-        if input.numel() > 0:
-            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
-
-        output_shape = _output_size(2, input, size, scale_factor)
-        output_shape = list(input.shape[:-2]) + list(output_shape)
-        return _new_empty_tensor(input, output_shape)
-    else:
-        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
-
-
-class color_sys:
-    def __init__(self, num_colors) -> None:
-        self.num_colors = num_colors
-        colors = []
-        for i in np.arange(0.0, 360.0, 360.0 / num_colors):
-            hue = i / 360.0
-            lightness = (50 + np.random.rand() * 10) / 100.0
-            saturation = (90 + np.random.rand() * 10) / 100.0
-            colors.append(
-                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
-            )
-        self.colors = colors
-
-    def __call__(self, idx):
-        return self.colors[idx]
-
-
-def inverse_sigmoid(x, eps=1e-3):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def clean_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k[:7] == "module.":
-            k = k[7:]  # remove `module.`
-        new_state_dict[k] = v
-    return new_state_dict
diff --git a/spaces/Shrikrishna/Which_Bollywood_Celebrity_Are_You/app.py b/spaces/Shrikrishna/Which_Bollywood_Celebrity_Are_You/app.py
deleted file mode 100644
index 88be3945261119abe26013b91e0e30f48616f71f..0000000000000000000000000000000000000000
--- a/spaces/Shrikrishna/Which_Bollywood_Celebrity_Are_You/app.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from tensorflow import keras
-from keras.preprocessing import image
-import pickle
-from sklearn.metrics.pairwise import cosine_similarity
-import streamlit as st
-from PIL import Image
-import os
-import cv2
-from mtcnn import MTCNN
-import numpy as np
-
-
-#keras.applications.resnet50.ResNet50
-
-#VGGFace(model='resnet50',include_top=False,input_shape=(224,224,3),pooling='avg')
-
-#st.text("Hello Welcome")
-detector = MTCNN()
-model = keras.applications.resnet50.ResNet50(
-    include_top=False,
-    input_shape=(224,224,3),
-    pooling='avg',
-    weights='imagenet'
-)
-feature_list = pickle.load(open('embedding.pkl', 'rb'))
-filenames = pickle.load(open('filenames.pkl', 'rb'))
-
-filenames = [sub.replace('/kaggle/input/bollywood-celeb-localized-face-dataset/', 'https://technirmitisoftwares.com/img_data/data/') for sub in filenames]
-
-def save_uploaded_image(uploaded_image):
-    try:
-        with open(uploaded_image.name, 'wb') as f:
-            f.write(uploaded_image.getbuffer())
-        return True
-    except:
-        return False
-
-
-def extract_features(img_path, model, detector):
-    img = cv2.imread(img_path)
-    results = detector.detect_faces(img)
-
-    x, y, width, height = results[0]['box']
-
-    face = img[y:y + height, x:x + width]
-
-    #  extract its features
-    image = Image.fromarray(face)
-    image = image.resize((224, 224))
-
-    face_array = np.asarray(image)
-
-    face_array = face_array.astype('float32')
-
-    expanded_img = np.expand_dims(face_array, axis=0)
-    preprocessed_img = keras.applications.resnet50.preprocess_input(expanded_img)
-    result = model.predict(preprocessed_img).flatten()
-    return result
-
-
-def recommend(feature_list,features):
-    similarity = []
-    for i in range(len(feature_list)):
-        similarity.append(cosine_similarity(features.reshape(1, -1), feature_list[i].reshape(1, -1))[0][0])
-
-    index_pos = sorted(list(enumerate(similarity)), reverse=True, key=lambda x: x[1])[0][0]
-    return index_pos
-
-    
-
-st.title('Which bollywood celebrity are you?')
-
-uploaded_image = st.file_uploader('Choose an image')
-
-if uploaded_image is not None:
-    # save the image in a directory
-    if save_uploaded_image(uploaded_image):
-        display_image = Image.open(uploaded_image)
-        st.header("Image Uploded!, Processing...")
-        
-        # extract the features
-        features = extract_features(uploaded_image.name, model, detector)
-        
-        # recommend
-        index_pos = recommend(feature_list,features)
-        predicted_actor = filenames[index_pos]
-        
-        # display
-        display_image_resized = display_image.resize((150,150))
-        col1,col2 = st.columns(2)
-
-        with col1:
-            st.subheader('Your uploaded image')
-            st.image(display_image_resized,width=150, caption='Uploaded Image')
-        with col2:
-            st.subheader("Look Like: " + predicted_actor.split("/")[7])
-            st.image(filenames[index_pos],width=150, caption='Predicted Look Alike Image')
diff --git a/spaces/Singularity666/RadiXGPT_/README.md b/spaces/Singularity666/RadiXGPT_/README.md
deleted file mode 100644
index 7a4b755b4f9b4f2091aecfc24935a319e6e286cf..0000000000000000000000000000000000000000
--- a/spaces/Singularity666/RadiXGPT_/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RadiXGPT
-emoji: 🏢
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SpacesExamples/docker-examples/app.py b/spaces/SpacesExamples/docker-examples/app.py
deleted file mode 100644
index 791f031bdd2db497065a71a8a2b7ce1068044dfa..0000000000000000000000000000000000000000
--- a/spaces/SpacesExamples/docker-examples/app.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import gradio as gr
-
-
-def name_to_hf_markdown(name):
-    return f"[{name}](https://huggingface.co/{name})"
-
-
-def show_template(name, description, authors, url, image_url, more_info=None):
-    if isinstance(authors, str):
-        authors = [authors]
-    authors_md = ", ".join([name_to_hf_markdown(author) for author in authors])
-    with gr.Box():
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.HTML(f"""<img src="{image_url}" alt="{name}-thumbnail" height=256 width=256>""")
-            with gr.Column(scale=4):
-                gr.Markdown(
-                    f"""
-                ## {name}
-                [![Open In Colab](https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&amp;style=flat&amp;logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&amp;logoWidth=14)]({url})
-
-                #### {description}
-
-                **Author(s):** {authors_md}
-            """
-                )
-                if more_info:
-                    with gr.Row():
-                        with gr.Accordion("👀 More Details", open=False):
-                            gr.Markdown(more_info)
-
-
-title_and_description = """
-# Spaces Templates
-
-<div align="center">
-  <a src="https://img.shields.io/github/stars/nateraw/spaces-docker-templates?style=social" href="https://github.com/nateraw/spaces-docker-templates" target="_blank">
-    <img src="https://img.shields.io/github/stars/nateraw/spaces-docker-templates?label=Contribute&style=social" alt="GitHub Stars">
-  </a>
-
-  <h4>🚀 A collection of templates for <a href="https://huggingface.co/spaces">Hugging Face Spaces</a></h4>
-
-  The templates below are designed to help you get started with Docker Spaces. Duplicate them to get started with your own project. 🤗
-</div>
-"""
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(title_and_description)
-    show_template(
-        name="JupyterLab",
-        description="Spin up a JupyterLab instance with just a couple clicks. This template is great for data exploration, model training, and more. Works on CPU and GPU hardware.",
-        authors=["camenduru", "nateraw"],
-        url="https://huggingface.co/spaces/DockerTemplates/jupyterlab?duplicate=true",
-        image_url="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/1767px-Jupyter_logo.svg.png",
-        more_info="""
-        ### Configuration
-        - You can add dependencies to your JupyterLab instance by editing the `requirements.txt` file.
-        - You can add linux packages to your JupyterLab instance by editing the `packages.txt` file.
-        - You can add custom startup commands to your JupyterLab instance by editing the `on_startup.sh` file. These run with the root user.
-        """,
-    )
-    show_template(
-        name="VSCode",
-        description="Spin up a VSCode instance with just a couple clicks. This template is great for data exploration, model training, and more. Works on CPU and GPU hardware.",
-        authors=["camenduru", "nateraw"],
-        url="https://huggingface.co/spaces/DockerTemplates/vscode?duplicate=true",
-        image_url="https://upload.wikimedia.org/wikipedia/commons/thumb/9/9a/Visual_Studio_Code_1.35_icon.svg/1200px-Visual_Studio_Code_1.35_icon.svg.png",
-        more_info="""
-        ### Configuration
-        - You can add dependencies to your VSCode instance by editing the `requirements.txt` file.
-        - You can add linux packages to your VSCode instance by editing the `packages.txt` file.
-        - You can add custom startup commands to your VSCode instance by editing the `on_startup.sh` file. These run with the root user.
-        """,
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/Sumit7864/Image-Enhancer/realesrgan/data/realesrgan_dataset.py b/spaces/Sumit7864/Image-Enhancer/realesrgan/data/realesrgan_dataset.py
deleted file mode 100644
index 4cf2d9e6583a6789b771679734ce55bb8a22e628..0000000000000000000000000000000000000000
--- a/spaces/Sumit7864/Image-Enhancer/realesrgan/data/realesrgan_dataset.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import os.path as osp
-import random
-import time
-import torch
-from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANDataset(data.Dataset):
-    """Dataset used for Real-ESRGAN model:
-    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It loads gt (Ground-Truth) images, and augments them.
-    It also generates blur kernels and sinc kernels for generating low-quality images.
-    Note that the low-quality images are processed in tensors on GPUS for faster processing.
-
-    Args:
-        opt (dict): Config for train datasets. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            meta_info (str): Path for meta information file.
-            io_backend (dict): IO backend type and other kwarg.
-            use_hflip (bool): Use horizontal flips.
-            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
-            Please see more options in the codes.
-    """
-
-    def __init__(self, opt):
-        super(RealESRGANDataset, self).__init__()
-        self.opt = opt
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-        self.gt_folder = opt['dataroot_gt']
-
-        # file client (lmdb io backend)
-        if self.io_backend_opt['type'] == 'lmdb':
-            self.io_backend_opt['db_paths'] = [self.gt_folder]
-            self.io_backend_opt['client_keys'] = ['gt']
-            if not self.gt_folder.endswith('.lmdb'):
-                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
-            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
-                self.paths = [line.split('.')[0] for line in fin]
-        else:
-            # disk backend with meta_info
-            # Each line in the meta_info describes the relative path to an image
-            with open(self.opt['meta_info']) as fin:
-                paths = [line.strip().split(' ')[0] for line in fin]
-                self.paths = [os.path.join(self.gt_folder, v) for v in paths]
-
-        # blur settings for the first degradation
-        self.blur_kernel_size = opt['blur_kernel_size']
-        self.kernel_list = opt['kernel_list']
-        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
-        self.blur_sigma = opt['blur_sigma']
-        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
-        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
-        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters
-
-        # blur settings for the second degradation
-        self.blur_kernel_size2 = opt['blur_kernel_size2']
-        self.kernel_list2 = opt['kernel_list2']
-        self.kernel_prob2 = opt['kernel_prob2']
-        self.blur_sigma2 = opt['blur_sigma2']
-        self.betag_range2 = opt['betag_range2']
-        self.betap_range2 = opt['betap_range2']
-        self.sinc_prob2 = opt['sinc_prob2']
-
-        # a final sinc filter
-        self.final_sinc_prob = opt['final_sinc_prob']
-
-        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
-        # TODO: kernel range is now hard-coded, should be in the configure file
-        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
-        self.pulse_tensor[10, 10] = 1
-
-    def __getitem__(self, index):
-        if self.file_client is None:
-            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
-        # -------------------------------- Load gt images -------------------------------- #
-        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
-        gt_path = self.paths[index]
-        # avoid errors caused by high latency in reading files
-        retry = 3
-        while retry > 0:
-            try:
-                img_bytes = self.file_client.get(gt_path, 'gt')
-            except (IOError, OSError) as e:
-                logger = get_root_logger()
-                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
-                # change another file to read
-                index = random.randint(0, self.__len__())
-                gt_path = self.paths[index]
-                time.sleep(1)  # sleep 1s for occasional server congestion
-            else:
-                break
-            finally:
-                retry -= 1
-        img_gt = imfrombytes(img_bytes, float32=True)
-
-        # -------------------- Do augmentation for training: flip, rotation -------------------- #
-        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])
-
-        # crop or pad to 400
-        # TODO: 400 is hard-coded. You may change it accordingly
-        h, w = img_gt.shape[0:2]
-        crop_pad_size = 400
-        # pad
-        if h < crop_pad_size or w < crop_pad_size:
-            pad_h = max(0, crop_pad_size - h)
-            pad_w = max(0, crop_pad_size - w)
-            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
-        # crop
-        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
-            h, w = img_gt.shape[0:2]
-            # randomly choose top and left coordinates
-            top = random.randint(0, h - crop_pad_size)
-            left = random.randint(0, w - crop_pad_size)
-            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]
-
-        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
-        kernel_size = random.choice(self.kernel_range)
-        if np.random.uniform() < self.opt['sinc_prob']:
-            # this sinc filter setting is for kernels ranging from [7, 21]
-            if kernel_size < 13:
-                omega_c = np.random.uniform(np.pi / 3, np.pi)
-            else:
-                omega_c = np.random.uniform(np.pi / 5, np.pi)
-            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
-        else:
-            kernel = random_mixed_kernels(
-                self.kernel_list,
-                self.kernel_prob,
-                kernel_size,
-                self.blur_sigma,
-                self.blur_sigma, [-math.pi, math.pi],
-                self.betag_range,
-                self.betap_range,
-                noise_range=None)
-        # pad kernel
-        pad_size = (21 - kernel_size) // 2
-        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
-
-        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
-        kernel_size = random.choice(self.kernel_range)
-        if np.random.uniform() < self.opt['sinc_prob2']:
-            if kernel_size < 13:
-                omega_c = np.random.uniform(np.pi / 3, np.pi)
-            else:
-                omega_c = np.random.uniform(np.pi / 5, np.pi)
-            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
-        else:
-            kernel2 = random_mixed_kernels(
-                self.kernel_list2,
-                self.kernel_prob2,
-                kernel_size,
-                self.blur_sigma2,
-                self.blur_sigma2, [-math.pi, math.pi],
-                self.betag_range2,
-                self.betap_range2,
-                noise_range=None)
-
-        # pad kernel
-        pad_size = (21 - kernel_size) // 2
-        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
-
-        # ------------------------------------- the final sinc kernel ------------------------------------- #
-        if np.random.uniform() < self.opt['final_sinc_prob']:
-            kernel_size = random.choice(self.kernel_range)
-            omega_c = np.random.uniform(np.pi / 3, np.pi)
-            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
-            sinc_kernel = torch.FloatTensor(sinc_kernel)
-        else:
-            sinc_kernel = self.pulse_tensor
-
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
-        kernel = torch.FloatTensor(kernel)
-        kernel2 = torch.FloatTensor(kernel2)
-
-        return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
-        return return_d
-
-    def __len__(self):
-        return len(self.paths)
diff --git a/spaces/Sumit7864/Image-Enhancer/realesrgan/models/realesrnet_model.py b/spaces/Sumit7864/Image-Enhancer/realesrgan/models/realesrnet_model.py
deleted file mode 100644
index d11668f3712bffcd062c57db14d22ca3a0e1e59d..0000000000000000000000000000000000000000
--- a/spaces/Sumit7864/Image-Enhancer/realesrgan/models/realesrnet_model.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.sr_model import SRModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRNetModel(SRModel):
-    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It is trained without GAN losses.
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-
-    def __init__(self, opt):
-        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
-        self.queue_size = opt.get('queue_size', 180)
-
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self):
-        """It is the training pair pool for increasing the diversity in a batch.
-
-        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
-        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
-        to increase the degradation diversity in a batch.
-        """
-        # initialize
-        b, c, h, w = self.lq.size()
-        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
-            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
-            _, c, h, w = self.gt.size()
-            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
-            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # the pool is full
-            # do dequeue and enqueue
-            # shuffle
-            idx = torch.randperm(self.queue_size)
-            self.queue_lr = self.queue_lr[idx]
-            self.queue_gt = self.queue_gt[idx]
-            # get first b samples
-            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
-            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update the queue
-            self.queue_lr[0:b, :, :, :] = self.lq.clone()
-            self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
-            self.lq = lq_dequeue
-            self.gt = gt_dequeue
-        else:
-            # only do enqueue
-            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
-            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
-            self.queue_ptr = self.queue_ptr + b
-
-    @torch.no_grad()
-    def feed_data(self, data):
-        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
-        """
-        if self.is_train and self.opt.get('high_order_degradation', True):
-            # training data synthesis
-            self.gt = data['gt'].to(self.device)
-            # USM sharpen the GT images
-            if self.opt['gt_usm'] is True:
-                self.gt = self.usm_sharpener(self.gt)
-
-            self.kernel1 = data['kernel1'].to(self.device)
-            self.kernel2 = data['kernel2'].to(self.device)
-            self.sinc_kernel = data['sinc_kernel'].to(self.device)
-
-            ori_h, ori_w = self.gt.size()[2:4]
-
-            # ----------------------- The first degradation process ----------------------- #
-            # blur
-            out = filter2D(self.gt, self.kernel1)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob']
-            if np.random.uniform() < self.opt['gaussian_noise_prob']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression
-            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
-            out = self.jpeger(out, quality=jpeg_p)
-
-            # ----------------------- The second degradation process ----------------------- #
-            # blur
-            if np.random.uniform() < self.opt['second_blur_prob']:
-                out = filter2D(out, self.kernel2)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range2'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(
-                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob2']
-            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range2'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-
-            # JPEG compression + the final sinc filter
-            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
-            # as one operation.
-            # We consider two orders:
-            #   1. [resize back + sinc filter] + JPEG compression
-            #   2. JPEG compression + [resize back + sinc filter]
-            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
-            if np.random.uniform() < 0.5:
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-            else:
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-
-            # clamp and round
-            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
-
-            # random crop
-            gt_size = self.opt['gt_size']
-            self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
-
-            # training pair pool
-            self._dequeue_and_enqueue()
-            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
-        else:
-            # for paired training or validation
-            self.lq = data['lq'].to(self.device)
-            if 'gt' in data:
-                self.gt = data['gt'].to(self.device)
-                self.gt_usm = self.usm_sharpener(self.gt)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        # do not use the synthetic process during validation
-        self.is_train = False
-        super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True
diff --git a/spaces/Superlang/ImageProcessor/annotator/denthPose/readme.md b/spaces/Superlang/ImageProcessor/annotator/denthPose/readme.md
deleted file mode 100644
index 59753a4c357d2e75ed8a354ed4be62ae2ad76508..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/denthPose/readme.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### 安装 detectron2
-pip install 'git+https://github.com/facebookresearch/detectron2.git'
-
-### 安装 DensePose
-pip install git+https://github.com/facebookresearch/detectron2@main#subdirectory=projects/DensePose
-
-
-#
-python apply_net.py show densepose_rcnn_R_50_FPN_s1x.yaml model_final_162be9.pkl demo.jpg dp_contour,bbox --output image_densepose_contour.png
-
diff --git a/spaces/TNR-5/chatorO/web.html b/spaces/TNR-5/chatorO/web.html
deleted file mode 100644
index 9e1fd00c7dd7aef4e03d88c14c8e8d0e67e808de..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/chatorO/web.html
+++ /dev/null
@@ -1,60 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>API Demo</title>
-  </head>
-  <body>
-    <h1>API Demo</h1>
-    <label for="day">Select a day:</label>
-    <select id="day">
-      <option value="monday">Monday</option>
-      <option value="tuesday">Tuesday</option>
-      <option value="wednesday">Wednesday</option>
-      <option value="thursday">Thursday</option>
-      <option value="friday">Friday</option>
-      <option value="saturday">Saturday</option>
-      <option value="sunday">Sunday</option>
-    </select>
-    <label for="data">Select data:</label>
-    <select id="data" name="data"></select>
-    <br><br>
-    <input type="button" value="Submit" onclick="fetchData()">
-
-    <!-- <br><br>
-    <button onclick="fetchAPI()">Submit</button>
-    <br><br> -->
-    <div id="result"></div>
-    <script>
-     function fetchData() {
-    const day = document.getElementById("day").value;
-    const dataSelect = document.getElementById("data");
-    const resultDiv = document.getElementById("result");
-
-    // Clear previous results
-    resultDiv.innerHTML = "";
-
-    // Make API request
-    fetch(`https://api.example.com/data/${day}`)
-        .then(response => response.json())
-        .then(data => {
-            // Populate data dropdown
-            dataSelect.innerHTML = "";
-            data.forEach(item => {
-                const option = document.createElement("option");
-                option.value = item.value;
-                option.textContent = item.label;
-                dataSelect.appendChild(option);
-            });
-
-            // Show fetched data
-            resultDiv.innerHTML = `Data for ${day}: ${JSON.stringify(data)}`;
-        })
-        .catch(error => {
-            console.error(error);
-            resultDiv.innerHTML = "Error fetching data.";
-        });
-}
-
-    </script>
-  </body>
-</html>
\ No newline at end of file
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/show.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/show.py
deleted file mode 100644
index 3f10701f6b28c72b62c9904fec37b96bdd199dcc..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/show.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import logging
-from optparse import Values
-from typing import Generator, Iterable, Iterator, List, NamedTuple, Optional
-
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import ERROR, SUCCESS
-from pip._internal.metadata import BaseDistribution, get_default_environment
-from pip._internal.utils.misc import write_output
-
-logger = logging.getLogger(__name__)
-
-
-class ShowCommand(Command):
-    """
-    Show information about one or more installed packages.
-
-    The output is in RFC-compliant mail header format.
-    """
-
-    usage = """
-      %prog [options] <package> ..."""
-    ignore_require_venv = True
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "-f",
-            "--files",
-            dest="files",
-            action="store_true",
-            default=False,
-            help="Show the full list of installed files for each package.",
-        )
-
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        if not args:
-            logger.warning("ERROR: Please provide a package name or names.")
-            return ERROR
-        query = args
-
-        results = search_packages_info(query)
-        if not print_results(
-            results, list_files=options.files, verbose=options.verbose
-        ):
-            return ERROR
-        return SUCCESS
-
-
-class _PackageInfo(NamedTuple):
-    name: str
-    version: str
-    location: str
-    editable_project_location: Optional[str]
-    requires: List[str]
-    required_by: List[str]
-    installer: str
-    metadata_version: str
-    classifiers: List[str]
-    summary: str
-    homepage: str
-    project_urls: List[str]
-    author: str
-    author_email: str
-    license: str
-    entry_points: List[str]
-    files: Optional[List[str]]
-
-
-def search_packages_info(query: List[str]) -> Generator[_PackageInfo, None, None]:
-    """
-    Gather details from installed distributions. Print distribution name,
-    version, location, and installed files. Installed files requires a
-    pip generated 'installed-files.txt' in the distributions '.egg-info'
-    directory.
-    """
-    env = get_default_environment()
-
-    installed = {dist.canonical_name: dist for dist in env.iter_all_distributions()}
-    query_names = [canonicalize_name(name) for name in query]
-    missing = sorted(
-        [name for name, pkg in zip(query, query_names) if pkg not in installed]
-    )
-    if missing:
-        logger.warning("Package(s) not found: %s", ", ".join(missing))
-
-    def _get_requiring_packages(current_dist: BaseDistribution) -> Iterator[str]:
-        return (
-            dist.metadata["Name"] or "UNKNOWN"
-            for dist in installed.values()
-            if current_dist.canonical_name
-            in {canonicalize_name(d.name) for d in dist.iter_dependencies()}
-        )
-
-    for query_name in query_names:
-        try:
-            dist = installed[query_name]
-        except KeyError:
-            continue
-
-        requires = sorted((req.name for req in dist.iter_dependencies()), key=str.lower)
-        required_by = sorted(_get_requiring_packages(dist), key=str.lower)
-
-        try:
-            entry_points_text = dist.read_text("entry_points.txt")
-            entry_points = entry_points_text.splitlines(keepends=False)
-        except FileNotFoundError:
-            entry_points = []
-
-        files_iter = dist.iter_declared_entries()
-        if files_iter is None:
-            files: Optional[List[str]] = None
-        else:
-            files = sorted(files_iter)
-
-        metadata = dist.metadata
-
-        yield _PackageInfo(
-            name=dist.raw_name,
-            version=str(dist.version),
-            location=dist.location or "",
-            editable_project_location=dist.editable_project_location,
-            requires=requires,
-            required_by=required_by,
-            installer=dist.installer,
-            metadata_version=dist.metadata_version or "",
-            classifiers=metadata.get_all("Classifier", []),
-            summary=metadata.get("Summary", ""),
-            homepage=metadata.get("Home-page", ""),
-            project_urls=metadata.get_all("Project-URL", []),
-            author=metadata.get("Author", ""),
-            author_email=metadata.get("Author-email", ""),
-            license=metadata.get("License", ""),
-            entry_points=entry_points,
-            files=files,
-        )
-
-
-def print_results(
-    distributions: Iterable[_PackageInfo],
-    list_files: bool,
-    verbose: bool,
-) -> bool:
-    """
-    Print the information from installed distributions found.
-    """
-    results_printed = False
-    for i, dist in enumerate(distributions):
-        results_printed = True
-        if i > 0:
-            write_output("---")
-
-        write_output("Name: %s", dist.name)
-        write_output("Version: %s", dist.version)
-        write_output("Summary: %s", dist.summary)
-        write_output("Home-page: %s", dist.homepage)
-        write_output("Author: %s", dist.author)
-        write_output("Author-email: %s", dist.author_email)
-        write_output("License: %s", dist.license)
-        write_output("Location: %s", dist.location)
-        if dist.editable_project_location is not None:
-            write_output(
-                "Editable project location: %s", dist.editable_project_location
-            )
-        write_output("Requires: %s", ", ".join(dist.requires))
-        write_output("Required-by: %s", ", ".join(dist.required_by))
-
-        if verbose:
-            write_output("Metadata-Version: %s", dist.metadata_version)
-            write_output("Installer: %s", dist.installer)
-            write_output("Classifiers:")
-            for classifier in dist.classifiers:
-                write_output("  %s", classifier)
-            write_output("Entry-points:")
-            for entry in dist.entry_points:
-                write_output("  %s", entry.strip())
-            write_output("Project-URLs:")
-            for project_url in dist.project_urls:
-                write_output("  %s", project_url)
-        if list_files:
-            write_output("Files:")
-            if dist.files is None:
-                write_output("Cannot locate RECORD or installed-files.txt")
-            else:
-                for line in dist.files:
-                    write_output("  %s", line.strip())
-    return results_printed
diff --git a/spaces/TheThanos/anything-v3.0_krn/README.md b/spaces/TheThanos/anything-v3.0_krn/README.md
deleted file mode 100644
index 15176bed26d36b4f9566c7102a5655e310f76036..0000000000000000000000000000000000000000
--- a/spaces/TheThanos/anything-v3.0_krn/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anything V3.0
-emoji: 🏃
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: akhaliq/anything-v3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TitleOS/Seahorse-350m/app.py b/spaces/TitleOS/Seahorse-350m/app.py
deleted file mode 100644
index a1fc1bd2115245c40cf9bebbaf654313a111fdc8..0000000000000000000000000000000000000000
--- a/spaces/TitleOS/Seahorse-350m/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import gradio as gr
-import transformers
-from transformers import pipeline
-
-def generate_text(prompt, temperature):
-    generator = pipeline('text-generation', model='titleos/Seahorse-350m')
-    generated_text = generator("### Instruction " + prompt + " ### Response ", max_length=100, temperature=temperature)[0]['generated_text']
-    response = generated_text.split("### Response")[-1]
-    return response
-
-interface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.inputs.Textbox(lines=1, label="Instruction", default="What are the three primary colors?"),
-        gr.inputs.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.5, label="Temperature")
-    ],
-    outputs=gr.outputs.Textbox(label="Response")
-)
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/TornikeO/dis-background-removal/Inference.py b/spaces/TornikeO/dis-background-removal/Inference.py
deleted file mode 100644
index 0b2907ddfc8475d477078b990efb6ae106c8ce1e..0000000000000000000000000000000000000000
--- a/spaces/TornikeO/dis-background-removal/Inference.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-import time
-import numpy as np
-from skimage import io
-import time
-from glob import glob
-from tqdm import tqdm
-
-import torch, gc
-import torch.nn as nn
-from torch.autograd import Variable
-import torch.optim as optim
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-
-from models import *
-
-
-if __name__ == "__main__":
-    dataset_path="../demo_datasets/your_dataset"  #Your dataset path
-    model_path="../saved_models/IS-Net/isnet-general-use.pth"  # the model path
-    result_path="../demo_datasets/your_dataset_result"  #The folder path that you want to save the results
-    input_size=[1024,1024]
-    net=ISNetDIS()
-
-    if torch.cuda.is_available():
-        net.load_state_dict(torch.load(model_path))
-        net=net.cuda()
-    else:
-        net.load_state_dict(torch.load(model_path,map_location="cpu"))
-    net.eval()
-    im_list = glob(dataset_path+"/*.jpg")+glob(dataset_path+"/*.JPG")+glob(dataset_path+"/*.jpeg")+glob(dataset_path+"/*.JPEG")+glob(dataset_path+"/*.png")+glob(dataset_path+"/*.PNG")+glob(dataset_path+"/*.bmp")+glob(dataset_path+"/*.BMP")+glob(dataset_path+"/*.tiff")+glob(dataset_path+"/*.TIFF")
-    with torch.no_grad():
-        for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
-            print("im_path: ", im_path)
-            im = io.imread(im_path)
-            if len(im.shape) < 3:
-                im = im[:, :, np.newaxis]
-            im_shp=im.shape[0:2]
-            im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
-            im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear").type(torch.uint8)
-            image = torch.divide(im_tensor,255.0)
-            image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
-
-            if torch.cuda.is_available():
-                image=image.cuda()
-            result=net(image)
-            result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
-            ma = torch.max(result)
-            mi = torch.min(result)
-            result = (result-mi)/(ma-mi)
-            im_name=im_path.split('/')[-1].split('.')[0]
-            io.imsave(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))
diff --git a/spaces/Tshackelton/IBMPlex-DenseReadable/README.md b/spaces/Tshackelton/IBMPlex-DenseReadable/README.md
deleted file mode 100644
index e3dafead5a98fc32eb7c6bd81b797652d105fc36..0000000000000000000000000000000000000000
--- a/spaces/Tshackelton/IBMPlex-DenseReadable/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
----
-tags: [gradio-theme]
-title: IBMPlex-DenseReadable
-colorFrom: orange
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-# IBMPlex-DenseReadable
-## Description
-Add a description of this theme here!
-## Contributions
-Thanks to [@Tshackelton](https://huggingface.co/Tshackelton) for adding this gradio theme!
diff --git a/spaces/UVA-MSBA/M4_Team8/README.md b/spaces/UVA-MSBA/M4_Team8/README.md
deleted file mode 100644
index e7de553d68d2211242d7d822f4c8dc085d799b80..0000000000000000000000000000000000000000
--- a/spaces/UVA-MSBA/M4_Team8/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: M4 Team8 ADR Detector
-emoji: 😻
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Fakeopen.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Fakeopen.py
deleted file mode 100644
index 5a82bf2cc0736384563332a279f5fbcbb120f676..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Fakeopen.py
+++ /dev/null
@@ -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]])  
diff --git a/spaces/WYF20618/Real-CUGAN/upcunet_v3.py b/spaces/WYF20618/Real-CUGAN/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/WYF20618/Real-CUGAN/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/WindVChen/INR-Harmon/model/base/conv_autoencoder.py b/spaces/WindVChen/INR-Harmon/model/base/conv_autoencoder.py
deleted file mode 100644
index bc30b98941037b4d6dc30ee177f5872daa1327c1..0000000000000000000000000000000000000000
--- a/spaces/WindVChen/INR-Harmon/model/base/conv_autoencoder.py
+++ /dev/null
@@ -1,519 +0,0 @@
-import torch
-import torchvision
-from torch import nn as nn
-import torch.nn.functional as F
-import numpy as np
-import math
-
-from .basic_blocks import ConvBlock, lineParams, convParams
-from .ops import MaskedChannelAttention, FeaturesConnector
-from .ops import PosEncodingNeRF, INRGAN_embed, RandomFourier, CIPS_embed
-from utils import misc
-from utils.misc import lin2img
-from ..lut_transformation_net import build_lut_transform
-
-
-class Sine(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return torch.sin(30 * input)
-
-
-class Leaky_relu(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return torch.nn.functional.leaky_relu(input, 0.01, inplace=True)
-
-
-def select_activation(type):
-    if type == 'sine':
-        return Sine()
-    elif type == 'leakyrelu_pe':
-        return Leaky_relu()
-    else:
-        raise NotImplementedError
-
-
-class ConvEncoder(nn.Module):
-    def __init__(
-            self,
-            depth, ch,
-            norm_layer, batchnorm_from, max_channels,
-            backbone_from, backbone_channels=None, backbone_mode='', INRDecode=False
-    ):
-        super(ConvEncoder, self).__init__()
-        self.depth = depth
-        self.INRDecode = INRDecode
-        self.backbone_from = backbone_from
-        backbone_channels = [] if backbone_channels is None else backbone_channels[::-1]
-
-        in_channels = 4
-        out_channels = ch
-
-        self.block0 = ConvBlock(in_channels, out_channels, norm_layer=norm_layer if batchnorm_from == 0 else None)
-        self.block1 = ConvBlock(out_channels, out_channels, norm_layer=norm_layer if 0 <= batchnorm_from <= 1 else None)
-        self.blocks_channels = [out_channels, out_channels]
-
-        self.blocks_connected = nn.ModuleDict()
-        self.connectors = nn.ModuleDict()
-        for block_i in range(2, depth):
-            if block_i % 2:
-                in_channels = out_channels
-            else:
-                in_channels, out_channels = out_channels, min(2 * out_channels, max_channels)
-
-            if 0 <= backbone_from <= block_i and len(backbone_channels):
-                if INRDecode:
-                    self.blocks_connected[f'block{block_i}_decode'] = ConvBlock(
-                        in_channels, out_channels,
-                        norm_layer=norm_layer if 0 <= batchnorm_from <= block_i else None,
-                        padding=int(block_i < depth - 1)
-                    )
-                    self.blocks_channels += [out_channels]
-                stage_channels = backbone_channels.pop()
-                connector = FeaturesConnector(backbone_mode, in_channels, stage_channels, in_channels)
-                self.connectors[f'connector{block_i}'] = connector
-                in_channels = connector.output_channels
-
-            self.blocks_connected[f'block{block_i}'] = ConvBlock(
-                in_channels, out_channels,
-                norm_layer=norm_layer if 0 <= batchnorm_from <= block_i else None,
-                padding=int(block_i < depth - 1)
-            )
-            self.blocks_channels += [out_channels]
-
-    def forward(self, x, backbone_features):
-        backbone_features = [] if backbone_features is None else backbone_features[::-1]
-
-        outputs = [self.block0(x)]
-        outputs += [self.block1(outputs[-1])]
-
-        for block_i in range(2, self.depth):
-            output = outputs[-1]
-            connector_name = f'connector{block_i}'
-            if connector_name in self.connectors:
-                if self.INRDecode:
-                    block = self.blocks_connected[f'block{block_i}_decode']
-                    outputs += [block(output)]
-
-                stage_features = backbone_features.pop()
-                connector = self.connectors[connector_name]
-                output = connector(output, stage_features)
-            block = self.blocks_connected[f'block{block_i}']
-            outputs += [block(output)]
-
-        return outputs[::-1]
-
-
-class DeconvDecoder(nn.Module):
-    def __init__(self, depth, encoder_blocks_channels, norm_layer, attend_from=-1, image_fusion=False):
-        super(DeconvDecoder, self).__init__()
-        self.image_fusion = image_fusion
-        self.deconv_blocks = nn.ModuleList()
-
-        in_channels = encoder_blocks_channels.pop()
-        out_channels = in_channels
-        for d in range(depth):
-            out_channels = encoder_blocks_channels.pop() if len(encoder_blocks_channels) else in_channels // 2
-            self.deconv_blocks.append(SEDeconvBlock(
-                in_channels, out_channels,
-                norm_layer=norm_layer,
-                padding=0 if d == 0 else 1,
-                with_se=0 <= attend_from <= d
-            ))
-            in_channels = out_channels
-
-        if self.image_fusion:
-            self.conv_attention = nn.Conv2d(out_channels, 1, kernel_size=1)
-        self.to_rgb = nn.Conv2d(out_channels, 3, kernel_size=1)
-
-    def forward(self, encoder_outputs, image, mask=None):
-        output = encoder_outputs[0]
-        for block, skip_output in zip(self.deconv_blocks[:-1], encoder_outputs[1:]):
-            output = block(output, mask)
-            output = output + skip_output
-        output = self.deconv_blocks[-1](output, mask)
-
-        if self.image_fusion:
-            attention_map = torch.sigmoid(3.0 * self.conv_attention(output))
-            output = attention_map * image + (1.0 - attention_map) * self.to_rgb(output)
-        else:
-            output = self.to_rgb(output)
-
-        return output
-
-
-class SEDeconvBlock(nn.Module):
-    def __init__(
-            self,
-            in_channels, out_channels,
-            kernel_size=4, stride=2, padding=1,
-            norm_layer=nn.BatchNorm2d, activation=nn.ELU,
-            with_se=False
-    ):
-        super(SEDeconvBlock, self).__init__()
-        self.with_se = with_se
-        self.block = nn.Sequential(
-            nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding),
-            norm_layer(out_channels) if norm_layer is not None else nn.Identity(),
-            activation(),
-        )
-        if self.with_se:
-            self.se = MaskedChannelAttention(out_channels)
-
-    def forward(self, x, mask=None):
-        out = self.block(x)
-        if self.with_se:
-            out = self.se(out, mask)
-        return out
-
-
-class INRDecoder(nn.Module):
-    def __init__(self, depth, encoder_blocks_channels, norm_layer, opt, attend_from):
-        super(INRDecoder, self).__init__()
-        self.INR_encoding = None
-        if opt.embedding_type == "PosEncodingNeRF":
-            self.INR_encoding = PosEncodingNeRF(in_features=2, sidelength=opt.input_size)
-        elif opt.embedding_type == "RandomFourier":
-            self.INR_encoding = RandomFourier(std_scale=10, embedding_length=64, device=opt.device)
-        elif opt.embedding_type == "CIPS_embed":
-            self.INR_encoding = CIPS_embed(size=opt.base_size, embedding_length=32)
-        elif opt.embedding_type == "INRGAN_embed":
-            self.INR_encoding = INRGAN_embed(resolution=opt.INR_input_size)
-        else:
-            raise NotImplementedError
-        encoder_blocks_channels = encoder_blocks_channels[::-1]
-        max_hidden_mlp_num = attend_from + 1
-        self.opt = opt
-        self.max_hidden_mlp_num = max_hidden_mlp_num
-        self.content_mlp_blocks = nn.ModuleDict()
-        for n in range(max_hidden_mlp_num):
-            if n != max_hidden_mlp_num - 1:
-                self.content_mlp_blocks[f"block{n}"] = convParams(encoder_blocks_channels.pop(),
-                                                                  [self.INR_encoding.out_dim + opt.INR_MLP_dim + (
-                                                                      4 if opt.isMoreINRInput else 0), opt.INR_MLP_dim],
-                                                                  opt, n + 1)
-            else:
-                self.content_mlp_blocks[f"block{n}"] = convParams(encoder_blocks_channels.pop(),
-                                                                  [self.INR_encoding.out_dim + (
-                                                                      4 if opt.isMoreINRInput else 0), opt.INR_MLP_dim],
-                                                                  opt, n + 1)
-
-        self.deconv_blocks = nn.ModuleList()
-
-        encoder_blocks_channels = encoder_blocks_channels[::-1]
-        in_channels = encoder_blocks_channels.pop()
-        out_channels = in_channels
-        for d in range(depth - attend_from):
-            out_channels = encoder_blocks_channels.pop() if len(encoder_blocks_channels) else in_channels // 2
-            self.deconv_blocks.append(SEDeconvBlock(
-                in_channels, out_channels,
-                norm_layer=norm_layer,
-                padding=0 if d == 0 else 1,
-                with_se=False
-            ))
-            in_channels = out_channels
-
-        self.appearance_mlps = lineParams(out_channels, [opt.INR_MLP_dim, opt.INR_MLP_dim],
-                                          (opt.base_size // (2 ** (max_hidden_mlp_num - 1))) ** 2,
-                                          opt, 2, toRGB=True)
-
-        self.lut_transform = build_lut_transform(self.appearance_mlps.output_dim, opt.LUT_dim,
-                                                 None, opt)
-
-        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
-
-    def forward(self, encoder_outputs, image=None, mask=None, coord_samples=None, start_proportion=None):
-        """For full resolution, do split."""
-        if self.opt.hr_train and not (self.training or hasattr(self.opt, 'split_num') or hasattr(self.opt,
-                                                                                                 'split_resolution')) and self.opt.isFullRes:
-            return self.forward_fullResInference(encoder_outputs, image=image, mask=mask, coord_samples=coord_samples)
-
-        encoder_outputs = encoder_outputs[::-1]
-        mlp_output = None
-        waitToRGB = []
-        for n in range(self.max_hidden_mlp_num):
-            if not self.opt.hr_train:
-                coord = misc.get_mgrid(self.opt.INR_input_size // (2 ** (self.max_hidden_mlp_num - n - 1))) \
-                    .unsqueeze(0).repeat(encoder_outputs[0].shape[0], 1, 1).to(self.opt.device)
-            else:
-                if self.training or hasattr(self.opt, 'split_num') or hasattr(self.opt, 'split_resolution'):
-                    coord = coord_samples[self.max_hidden_mlp_num - n - 1].permute(0, 2, 3, 1).view(
-                        encoder_outputs[0].shape[0], -1, 2)
-                else:
-                    coord = misc.get_mgrid(
-                        self.opt.INR_input_size // (2 ** (self.max_hidden_mlp_num - n - 1))).unsqueeze(0).repeat(
-                        encoder_outputs[0].shape[0], 1, 1).to(self.opt.device)
-
-            """Whether to leverage multiple input to INR decoder. See Section 3.4 in the paper."""
-            if self.opt.isMoreINRInput:
-                if not self.opt.isFullRes or (
-                        self.training or hasattr(self.opt, 'split_num') or hasattr(self.opt, 'split_resolution')):
-                    res_h = res_w = np.sqrt(coord.shape[1]).astype(int)
-                else:
-                    res_h = image.shape[-2] // (2 ** (self.max_hidden_mlp_num - n - 1))
-                    res_w = image.shape[-1] // (2 ** (self.max_hidden_mlp_num - n - 1))
-
-                res_image = torchvision.transforms.Resize([res_h, res_w])(image)
-                res_mask = torchvision.transforms.Resize([res_h, res_w])(mask)
-                coord = torch.cat([self.INR_encoding(coord), res_image.view(*res_image.shape[:2], -1).permute(0, 2, 1),
-                                   res_mask.view(*res_mask.shape[:2], -1).permute(0, 2, 1)], dim=-1)
-            else:
-                coord = self.INR_encoding(coord)
-
-            """============ LRIP structure, see Section 3.3 =============="""
-
-            """Local MLPs."""
-            if n == 0:
-                mlp_output = self.mlp_process(coord, self.INR_encoding.out_dim + (4 if self.opt.isMoreINRInput else 0),
-                                              self.opt, content_mlp=self.content_mlp_blocks[
-                        f"block{self.max_hidden_mlp_num - 1 - n}"](
-                        encoder_outputs.pop(self.max_hidden_mlp_num - 1 - n)), start_proportion=start_proportion)
-                waitToRGB.append(mlp_output[1])
-            else:
-                mlp_output = self.mlp_process(coord, self.opt.INR_MLP_dim + self.INR_encoding.out_dim + (
-                    4 if self.opt.isMoreINRInput else 0), self.opt, base_feat=mlp_output[0],
-                                              content_mlp=self.content_mlp_blocks[
-                                                  f"block{self.max_hidden_mlp_num - 1 - n}"](
-                                                  encoder_outputs.pop(self.max_hidden_mlp_num - 1 - n)),
-                                              start_proportion=start_proportion)
-                waitToRGB.append(mlp_output[1])
-
-        encoder_outputs = encoder_outputs[::-1]
-        output = encoder_outputs[0]
-        for block, skip_output in zip(self.deconv_blocks[:-1], encoder_outputs[1:]):
-            output = block(output)
-            output = output + skip_output
-        output = self.deconv_blocks[-1](output)
-
-        """Global MLPs."""
-        app_mlp, app_params = self.appearance_mlps(output)
-        harm_out = []
-        for id in range(len(waitToRGB)):
-            output = self.mlp_process(None, self.opt.INR_MLP_dim, self.opt, base_feat=waitToRGB[id],
-                                      appearance_mlp=app_mlp)
-            harm_out.append(output[0])
-
-        """Optional 3D LUT prediction."""
-        fit_lut3d, lut_transform_image = self.lut_transform(image, app_params, None)
-
-        return harm_out, fit_lut3d, lut_transform_image
-
-    def mlp_process(self, coorinates, INR_input_dim, opt, base_feat=None, content_mlp=None, appearance_mlp=None,
-                    resolution=None, start_proportion=None):
-
-        activation = select_activation(opt.activation)
-
-        output = None
-
-        if content_mlp is not None:
-            if base_feat is not None:
-                coorinates = torch.cat([coorinates, base_feat], dim=2)
-            coorinates = lin2img(coorinates, resolution)
-
-            if hasattr(opt, 'split_resolution'):
-                """
-                    Here we crop the needed MLPs according to the region of the split input patches. 
-                    Note that this only support inferencing square images.
-                """
-                for idx in range(len(content_mlp)):
-                    content_mlp[idx][0] = content_mlp[idx][0][:,
-                                          (content_mlp[idx][0].shape[1] * start_proportion[0]).int():(
-                                                      content_mlp[idx][0].shape[1] * start_proportion[2]).int(),
-                                          (content_mlp[idx][0].shape[2] * start_proportion[1]).int():(
-                                                      content_mlp[idx][0].shape[2] * start_proportion[3]).int(), :,
-                                          :]
-                    content_mlp[idx][1] = content_mlp[idx][1][:,
-                                          (content_mlp[idx][1].shape[1] * start_proportion[0]).int():(
-                                                      content_mlp[idx][1].shape[1] * start_proportion[2]).int(),
-                                          (content_mlp[idx][1].shape[2] * start_proportion[1]).int():(
-                                                      content_mlp[idx][1].shape[2] * start_proportion[3]).int(),
-                                          :,
-                                          :]
-                k_h = coorinates.shape[2] // content_mlp[0][0].shape[1]
-                k_w = coorinates.shape[3] // content_mlp[0][0].shape[1]
-                bs = coorinates.shape[0]
-                h_lr = w_lr = content_mlp[0][0].shape[1]
-                nci = INR_input_dim
-
-                coorinates = coorinates.unfold(2, k_h, k_h).unfold(3, k_w, k_w)
-                coorinates = coorinates.permute(0, 2, 3, 4, 5, 1).contiguous().view(
-                    bs, h_lr, w_lr, int(k_h * k_w), nci)
-
-                for id, layer in enumerate(content_mlp):
-                    if id == 0:
-                        output = torch.matmul(coorinates, layer[0]) + layer[1]
-                        output = activation(output)
-                    else:
-                        output = torch.matmul(output, layer[0]) + layer[1]
-                        output = activation(output)
-
-                output = output.view(bs, h_lr, w_lr, k_h, k_w, opt.INR_MLP_dim).permute(
-                    0, 1, 3, 2, 4, 5).contiguous().view(bs, -1, opt.INR_MLP_dim)
-
-                output_large = self.up(lin2img(output))
-
-                return output_large.view(bs, -1, opt.INR_MLP_dim), output
-
-            k_h = coorinates.shape[2] // content_mlp[0][0].shape[1]
-            k_w = coorinates.shape[3] // content_mlp[0][0].shape[1]
-            bs = coorinates.shape[0]
-            h_lr = w_lr = content_mlp[0][0].shape[1]
-            nci = INR_input_dim
-
-            """(evaluation or not HR training) and not fullres evaluation"""
-            if (not self.opt.hr_train or not (self.training or hasattr(self.opt, 'split_num'))) and not (
-                    not (self.training or hasattr(self.opt, 'split_num')) and self.opt.isFullRes and self.opt.hr_train):
-                coorinates = coorinates.unfold(2, k_h, k_h).unfold(3, k_w, k_w)
-                coorinates = coorinates.permute(0, 2, 3, 4, 5, 1).contiguous().view(
-                    bs, h_lr, w_lr, int(k_h * k_w), nci)
-
-                for id, layer in enumerate(content_mlp):
-                    if id == 0:
-                        output = torch.matmul(coorinates, layer[0]) + layer[1]
-                        output = activation(output)
-                    else:
-                        output = torch.matmul(output, layer[0]) + layer[1]
-                        output = activation(output)
-
-                output = output.view(bs, h_lr, w_lr, k_h, k_w, opt.INR_MLP_dim).permute(
-                    0, 1, 3, 2, 4, 5).contiguous().view(bs, -1, opt.INR_MLP_dim)
-
-                output_large = self.up(lin2img(output))
-
-                return output_large.view(bs, -1, opt.INR_MLP_dim), output
-            else:
-                coorinates = coorinates.permute(0, 2, 3, 1)
-                for id, layer in enumerate(content_mlp):
-                    weigt_shape = layer[0].shape
-                    bias_shape = layer[1].shape
-                    layer[0] = layer[0].view(*layer[0].shape[:-2], -1).permute(0, 3, 1, 2).contiguous()
-                    layer[1] = layer[1].view(*layer[1].shape[:-2], -1).permute(0, 3, 1, 2).contiguous()
-                    layer[0] = F.grid_sample(layer[0], coorinates[..., :2].flip(-1), mode='nearest' if True
-                    else 'bilinear', padding_mode='border', align_corners=False)
-                    layer[1] = F.grid_sample(layer[1], coorinates[..., :2].flip(-1), mode='nearest' if True
-                    else 'bilinear', padding_mode='border', align_corners=False)
-                    layer[0] = layer[0].permute(0, 2, 3, 1).contiguous().view(*coorinates.shape[:-1], *weigt_shape[-2:])
-                    layer[1] = layer[1].permute(0, 2, 3, 1).contiguous().view(*coorinates.shape[:-1], *bias_shape[-2:])
-
-                    if id == 0:
-                        output = torch.matmul(coorinates.unsqueeze(-2), layer[0]) + layer[1]
-                        output = activation(output)
-                    else:
-                        output = torch.matmul(output, layer[0]) + layer[1]
-                        output = activation(output)
-
-                output = output.squeeze(-2).view(bs, -1, opt.INR_MLP_dim)
-
-                output_large = self.up(lin2img(output, resolution))
-
-                return output_large.view(bs, -1, opt.INR_MLP_dim), output
-
-        elif appearance_mlp is not None:
-            output = base_feat
-            genMask = None
-            for id, layer in enumerate(appearance_mlp):
-                if id != len(appearance_mlp) - 1:
-                    output = torch.matmul(output, layer[0]) + layer[1]
-                    output = activation(output)
-                else:
-                    output = torch.matmul(output, layer[0]) + layer[1]  # last layer
-                    if opt.activation == 'leakyrelu_pe':
-                        output = torch.tanh(output)
-            return lin2img(output, resolution), None
-
-    def forward_fullResInference(self, encoder_outputs, image=None, mask=None, coord_samples=None):
-        encoder_outputs = encoder_outputs[::-1]
-        mlp_output = None
-        res_w = image.shape[-1]
-        res_h = image.shape[-2]
-        coord = misc.get_mgrid([image.shape[-2], image.shape[-1]]).unsqueeze(0).repeat(
-            encoder_outputs[0].shape[0], 1, 1).to(self.opt.device)
-
-        if self.opt.isMoreINRInput:
-            coord = torch.cat(
-                [self.INR_encoding(coord, (res_h, res_w)), image.view(*image.shape[:2], -1).permute(0, 2, 1),
-                 mask.view(*mask.shape[:2], -1).permute(0, 2, 1)], dim=-1)
-        else:
-            coord = self.INR_encoding(coord, (res_h, res_w))
-
-        total = coord.clone()
-
-        interval = 10
-        all_intervals = math.ceil(res_h / interval)
-        divisible = True
-        if res_h / interval != res_h // interval:
-            divisible = False
-
-        for n in range(self.max_hidden_mlp_num):
-            accum_mlp_output = []
-            for line in range(all_intervals):
-                if not divisible and line == all_intervals - 1:
-                    coord = total[:, line * interval * res_w:, :]
-                else:
-                    coord = total[:, line * interval * res_w: (line + 1) * interval * res_w, :]
-                if n == 0:
-                    accum_mlp_output.append(self.mlp_process(coord,
-                                                             self.INR_encoding.out_dim + (
-                                                                 4 if self.opt.isMoreINRInput else 0),
-                                                             self.opt, content_mlp=self.content_mlp_blocks[
-                            f"block{self.max_hidden_mlp_num - 1 - n}"](
-                            encoder_outputs.pop(self.max_hidden_mlp_num - 1 - n) if line == all_intervals - 1 else
-                            encoder_outputs[self.max_hidden_mlp_num - 1 - n]),
-                                                             resolution=(interval,
-                                                                         res_w) if divisible or line != all_intervals - 1 else (
-                                                                 res_h - interval * (all_intervals - 1), res_w))[1])
-
-                else:
-                    accum_mlp_output.append(self.mlp_process(coord, self.opt.INR_MLP_dim + self.INR_encoding.out_dim + (
-                        4 if self.opt.isMoreINRInput else 0), self.opt, base_feat=mlp_output[0][:,
-                                                                                  line * interval * res_w: (
-                                                                                                                   line + 1) * interval * res_w,
-                                                                                  :]
-                    if divisible or line != all_intervals - 1 else mlp_output[0][:, line * interval * res_w:, :],
-                                                             content_mlp=self.content_mlp_blocks[
-                                                                 f"block{self.max_hidden_mlp_num - 1 - n}"](
-                                                                 encoder_outputs.pop(
-                                                                     self.max_hidden_mlp_num - 1 - n) if line == all_intervals - 1 else
-                                                                 encoder_outputs[self.max_hidden_mlp_num - 1 - n]),
-                                                             resolution=(interval,
-                                                                         res_w) if divisible or line != all_intervals - 1 else (
-                                                                 res_h - interval * (all_intervals - 1), res_w))[1])
-
-            accum_mlp_output = torch.cat(accum_mlp_output, dim=1)
-            mlp_output = [accum_mlp_output, accum_mlp_output]
-
-        encoder_outputs = encoder_outputs[::-1]
-        output = encoder_outputs[0]
-        for block, skip_output in zip(self.deconv_blocks[:-1], encoder_outputs[1:]):
-            output = block(output)
-            output = output + skip_output
-        output = self.deconv_blocks[-1](output)
-
-        app_mlp, app_params = self.appearance_mlps(output)
-        harm_out = []
-
-        accum_mlp_output = []
-        for line in range(all_intervals):
-            if not divisible and line == all_intervals - 1:
-                base = mlp_output[1][:, line * interval * res_w:, :]
-            else:
-                base = mlp_output[1][:, line * interval * res_w: (line + 1) * interval * res_w, :]
-
-            accum_mlp_output.append(self.mlp_process(None, self.opt.INR_MLP_dim, self.opt, base_feat=base,
-                                                     appearance_mlp=app_mlp,
-                                                     resolution=(
-                                                         interval,
-                                                         res_w) if divisible or line != all_intervals - 1 else (
-                                                         res_h - interval * (all_intervals - 1), res_w))[0])
-
-        accum_mlp_output = torch.cat(accum_mlp_output, dim=2)
-        harm_out.append(accum_mlp_output)
-
-        fit_lut3d, lut_transform_image = self.lut_transform(image, app_params, None)
-
-        return harm_out, fit_lut3d, lut_transform_image
diff --git a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/text/cantonese.py b/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/text/cantonese.py
deleted file mode 100644
index b66d12138b81b70b86f18217d24a08fce76305c0..0000000000000000000000000000000000000000
--- a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/text/cantonese.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('jyutjyu')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ei˥'),
-    ('B', 'biː˥'),
-    ('C', 'siː˥'),
-    ('D', 'tiː˥'),
-    ('E', 'iː˥'),
-    ('F', 'e˥fuː˨˩'),
-    ('G', 'tsiː˥'),
-    ('H', 'ɪk̚˥tsʰyː˨˩'),
-    ('I', 'ɐi˥'),
-    ('J', 'tsei˥'),
-    ('K', 'kʰei˥'),
-    ('L', 'e˥llou˨˩'),
-    ('M', 'ɛːm˥'),
-    ('N', 'ɛːn˥'),
-    ('O', 'ou˥'),
-    ('P', 'pʰiː˥'),
-    ('Q', 'kʰiːu˥'),
-    ('R', 'aː˥lou˨˩'),
-    ('S', 'ɛː˥siː˨˩'),
-    ('T', 'tʰiː˥'),
-    ('U', 'juː˥'),
-    ('V', 'wiː˥'),
-    ('W', 'tʊk̚˥piː˥juː˥'),
-    ('X', 'ɪk̚˥siː˨˩'),
-    ('Y', 'waːi˥'),
-    ('Z', 'iː˨sɛːt̚˥')
-]]
-
-
-def number_to_cantonese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def cantonese_to_ipa(text):
-    text = number_to_cantonese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/XzJosh/Nana7mi-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Nana7mi-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Nana7mi-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/grid_sample_gradfix.py b/spaces/YotamNitzan/domain-expansion/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index ca6b3413ea72a734703c34382c023b84523601fd..0000000000000000000000000000000000000000
--- a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import warnings
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    if not enabled:
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
-    return False
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Zengyf-CVer/Streamlit_YOLOv5_Model2x/utils/autoanchor.py b/spaces/Zengyf-CVer/Streamlit_YOLOv5_Model2x/utils/autoanchor.py
deleted file mode 100644
index 0b49ab3319c00b9d58ef2200e7c7722b3cae567b..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Streamlit_YOLOv5_Model2x/utils/autoanchor.py
+++ /dev/null
@@ -1,169 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-AutoAnchor utils
-"""
-
-import random
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from utils import TryExcept
-from utils.general import LOGGER, colorstr
-
-PREFIX = colorstr('AutoAnchor: ')
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchors.prod(-1).mean(-1).view(-1)  # mean anchor area per output layer
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da and (da.sign() != ds.sign()):  # same order
-        LOGGER.info(f'{PREFIX}Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-
-
-@TryExcept(f'{PREFIX}ERROR: ')
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1 / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1 / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    stride = m.stride.to(m.anchors.device).view(-1, 1, 1)  # model strides
-    anchors = m.anchors.clone() * stride  # current anchors
-    bpr, aat = metric(anchors.cpu().view(-1, 2))
-    s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
-    if bpr > 0.98:  # threshold to recompute
-        LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅')
-    else:
-        LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')
-        na = m.anchors.numel() // 2  # number of anchors
-        anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchors[:] = anchors.clone().view_as(m.anchors)
-            check_anchor_order(m)  # must be in pixel-space (not grid-space)
-            m.anchors /= stride
-            s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)'
-        else:
-            s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
-        LOGGER.info(s)
-
-
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            dataset: path to data.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    from scipy.cluster.vq import kmeans
-
-    npr = np.random
-    thr = 1 / thr
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k, verbose=True):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
-            f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
-            f'past_thr={x[x > thr].mean():.3f}-mean: '
-        for x in k:
-            s += '%i,%i, ' % (round(x[0]), round(x[1]))
-        if verbose:
-            LOGGER.info(s[:-2])
-        return k
-
-    if isinstance(dataset, str):  # *.yaml file
-        with open(dataset, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)  # model dict
-        from utils.dataloaders import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
-    wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32)  # filter > 2 pixels
-    # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans init
-    try:
-        LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
-        assert n <= len(wh)  # apply overdetermined constraint
-        s = wh.std(0)  # sigmas for whitening
-        k = kmeans(wh / s, n, iter=30)[0] * s  # points
-        assert n == len(k)  # kmeans may return fewer points than requested if wh is insufficient or too similar
-    except Exception:
-        LOGGER.warning(f'{PREFIX}WARNING: switching strategies from kmeans to random init')
-        k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size  # random init
-    wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
-    k = print_results(k, verbose=False)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k, verbose)
-
-    return print_results(k).astype(np.float32)
diff --git a/spaces/abdvl/datahub_qa_bot/README.md b/spaces/abdvl/datahub_qa_bot/README.md
deleted file mode 100644
index c2aa95046ff4da58c835d78345c575848029fe9b..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Datahub Qa Bot
-emoji: 👁
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# DataHub documentation bot
-Using [OpenAI](https://platform.openai.com/docs/introduction), [Langchain](https://python.langchain.com/en/latest/index.html) and [streamlit](https://docs.streamlit.io/) to train DataHub documentation to provide a DataHub QA BOT on [huggingface space](https://huggingface.co/spaces/abdvl/datahub_qa_bot?logs=build)
-
-# How to run locally
-1. Clone the repo
-2. Run:
-```
-source .venv/bin/activate
-pip install -r requirements.txt
-streamlit run app.py
-```
-
-## How to train your own model
-1. Delete the db folder
-2. Copy the docs folder from [DataHub docs folder](https://github.com/datahub-project/datahub/tree/master/docs) to `./docs`
-3. Update the `os.environ["OPENAI_API_KEY"] ` in the `train.py`
-4. Run `python3 train.py`
-
-The training will take 15 seconds, and cost around $0.20
-```
-chromadb.db.duckdb: loaded in 236 embeddings
-chromadb.db.duckdb: loaded in 1 collections
-```
-
-
diff --git a/spaces/abhilash1910/CartoonGAN/README.md b/spaces/abhilash1910/CartoonGAN/README.md
deleted file mode 100644
index 58652a5d55d13e3af35893e67264e85a016b4f82..0000000000000000000000000000000000000000
--- a/spaces/abhilash1910/CartoonGAN/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: CartoonGAN
-emoji: 🚀
-colorFrom: red
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/embedding_rpn_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/embedding_rpn_head.py
deleted file mode 100644
index 200ce8d20c5503f98c5c21f30bb9d00437e25f34..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/embedding_rpn_head.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import torch
-import torch.nn as nn
-
-from mmdet.models.builder import HEADS
-from ...core import bbox_cxcywh_to_xyxy
-
-
-@HEADS.register_module()
-class EmbeddingRPNHead(nn.Module):
-    """RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011.12450>`_ .
-
-    Unlike traditional RPNHead, this module does not need FPN input, but just
-    decode `init_proposal_bboxes` and expand the first dimension of
-    `init_proposal_bboxes` and `init_proposal_features` to the batch_size.
-
-    Args:
-        num_proposals (int): Number of init_proposals. Default 100.
-        proposal_feature_channel (int): Channel number of
-            init_proposal_feature. Defaults to 256.
-    """
-
-    def __init__(self,
-                 num_proposals=100,
-                 proposal_feature_channel=256,
-                 **kwargs):
-        super(EmbeddingRPNHead, self).__init__()
-        self.num_proposals = num_proposals
-        self.proposal_feature_channel = proposal_feature_channel
-        self._init_layers()
-
-    def _init_layers(self):
-        """Initialize a sparse set of proposal boxes and proposal features."""
-        self.init_proposal_bboxes = nn.Embedding(self.num_proposals, 4)
-        self.init_proposal_features = nn.Embedding(
-            self.num_proposals, self.proposal_feature_channel)
-
-    def init_weights(self):
-        """Initialize the init_proposal_bboxes as normalized.
-
-        [c_x, c_y, w, h], and we initialize it to the size of  the entire
-        image.
-        """
-        nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5)
-        nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1)
-
-    def _decode_init_proposals(self, imgs, img_metas):
-        """Decode init_proposal_bboxes according to the size of images and
-        expand dimension of init_proposal_features to batch_size.
-
-        Args:
-            imgs (list[Tensor]): List of FPN features.
-            img_metas (list[dict]): List of meta-information of
-                images. Need the img_shape to decode the init_proposals.
-
-        Returns:
-            Tuple(Tensor):
-
-                - proposals (Tensor): Decoded proposal bboxes,
-                  has shape (batch_size, num_proposals, 4).
-                - init_proposal_features (Tensor): Expanded proposal
-                  features, has shape
-                  (batch_size, num_proposals, proposal_feature_channel).
-                - imgs_whwh (Tensor): Tensor with shape
-                  (batch_size, 4), the dimension means
-                  [img_width, img_height, img_width, img_height].
-        """
-        proposals = self.init_proposal_bboxes.weight.clone()
-        proposals = bbox_cxcywh_to_xyxy(proposals)
-        num_imgs = len(imgs[0])
-        imgs_whwh = []
-        for meta in img_metas:
-            h, w, _ = meta['img_shape']
-            imgs_whwh.append(imgs[0].new_tensor([[w, h, w, h]]))
-        imgs_whwh = torch.cat(imgs_whwh, dim=0)
-        imgs_whwh = imgs_whwh[:, None, :]
-
-        # imgs_whwh has shape (batch_size, 1, 4)
-        # The shape of proposals change from (num_proposals, 4)
-        # to (batch_size ,num_proposals, 4)
-        proposals = proposals * imgs_whwh
-
-        init_proposal_features = self.init_proposal_features.weight.clone()
-        init_proposal_features = init_proposal_features[None].expand(
-            num_imgs, *init_proposal_features.size())
-        return proposals, init_proposal_features, imgs_whwh
-
-    def forward_dummy(self, img, img_metas):
-        """Dummy forward function.
-
-        Used in flops calculation.
-        """
-        return self._decode_init_proposals(img, img_metas)
-
-    def forward_train(self, img, img_metas):
-        """Forward function in training stage."""
-        return self._decode_init_proposals(img, img_metas)
-
-    def simple_test_rpn(self, img, img_metas):
-        """Forward function in testing stage."""
-        return self._decode_init_proposals(img, img_metas)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/trident_faster_rcnn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/trident_faster_rcnn.py
deleted file mode 100644
index f0fd80d41407162df71ba5349fc659d4713cdb6e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/trident_faster_rcnn.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from ..builder import DETECTORS
-from .faster_rcnn import FasterRCNN
-
-
-@DETECTORS.register_module()
-class TridentFasterRCNN(FasterRCNN):
-    """Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
-
-    def __init__(self,
-                 backbone,
-                 rpn_head,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None):
-
-        super(TridentFasterRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
-        assert self.backbone.num_branch == self.roi_head.num_branch
-        assert self.backbone.test_branch_idx == self.roi_head.test_branch_idx
-        self.num_branch = self.backbone.num_branch
-        self.test_branch_idx = self.backbone.test_branch_idx
-
-    def simple_test(self, img, img_metas, proposals=None, rescale=False):
-        """Test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-        x = self.extract_feat(img)
-        if proposals is None:
-            num_branch = (self.num_branch if self.test_branch_idx == -1 else 1)
-            trident_img_metas = img_metas * num_branch
-            proposal_list = self.rpn_head.simple_test_rpn(x, trident_img_metas)
-        else:
-            proposal_list = proposals
-
-        return self.roi_head.simple_test(
-            x, proposal_list, trident_img_metas, rescale=rescale)
-
-    def aug_test(self, imgs, img_metas, rescale=False):
-        """Test with augmentations.
-
-        If rescale is False, then returned bboxes and masks will fit the scale
-        of imgs[0].
-        """
-        x = self.extract_feats(imgs)
-        num_branch = (self.num_branch if self.test_branch_idx == -1 else 1)
-        trident_img_metas = [img_metas * num_branch for img_metas in img_metas]
-        proposal_list = self.rpn_head.aug_test_rpn(x, trident_img_metas)
-        return self.roi_head.aug_test(
-            x, proposal_list, img_metas, rescale=rescale)
-
-    def forward_train(self, img, img_metas, gt_bboxes, gt_labels, **kwargs):
-        """make copies of img and gts to fit multi-branch."""
-        trident_gt_bboxes = tuple(gt_bboxes * self.num_branch)
-        trident_gt_labels = tuple(gt_labels * self.num_branch)
-        trident_img_metas = tuple(img_metas * self.num_branch)
-
-        return super(TridentFasterRCNN,
-                     self).forward_train(img, trident_img_metas,
-                                         trident_gt_bboxes, trident_gt_labels)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/bricks/swish.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/bricks/swish.py
deleted file mode 100644
index e2ca8ed7b749413f011ae54aac0cab27e6f0b51f..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/bricks/swish.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-
-from .registry import ACTIVATION_LAYERS
-
-
-@ACTIVATION_LAYERS.register_module()
-class Swish(nn.Module):
-    """Swish Module.
-
-    This module applies the swish function:
-
-    .. math::
-        Swish(x) = x * Sigmoid(x)
-
-    Returns:
-        Tensor: The output tensor.
-    """
-
-    def __init__(self):
-        super(Swish, self).__init__()
-
-    def forward(self, x):
-        return x * torch.sigmoid(x)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/pipelines/test_time_aug.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/pipelines/test_time_aug.py
deleted file mode 100644
index 8590bf3c1d64d543cf9e9ee3bc7ba53237306ade..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/pipelines/test_time_aug.py
+++ /dev/null
@@ -1,145 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import warnings
-
-import annotator.uniformer.mmcv as mmcv
-
-from ..builder import PIPELINES
-from .compose import Compose
-
-
-@PIPELINES.register_module()
-class MultiScaleFlipAug(object):
-    """Test-time augmentation with multiple scales and flipping.
-
-    An example configuration is as followed:
-
-    .. code-block::
-
-        img_scale=(2048, 1024),
-        img_ratios=[0.5, 1.0],
-        flip=True,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ]
-
-    After MultiScaleFLipAug with above configuration, the results are wrapped
-    into lists of the same length as followed:
-
-    .. code-block::
-
-        dict(
-            img=[...],
-            img_shape=[...],
-            scale=[(1024, 512), (1024, 512), (2048, 1024), (2048, 1024)]
-            flip=[False, True, False, True]
-            ...
-        )
-
-    Args:
-        transforms (list[dict]): Transforms to apply in each augmentation.
-        img_scale (None | tuple | list[tuple]): Images scales for resizing.
-        img_ratios (float | list[float]): Image ratios for resizing
-        flip (bool): Whether apply flip augmentation. Default: False.
-        flip_direction (str | list[str]): Flip augmentation directions,
-            options are "horizontal" and "vertical". If flip_direction is list,
-            multiple flip augmentations will be applied.
-            It has no effect when flip == False. Default: "horizontal".
-    """
-
-    def __init__(self,
-                 transforms,
-                 img_scale,
-                 img_ratios=None,
-                 flip=False,
-                 flip_direction='horizontal'):
-        self.transforms = Compose(transforms)
-        if img_ratios is not None:
-            img_ratios = img_ratios if isinstance(img_ratios,
-                                                  list) else [img_ratios]
-            assert mmcv.is_list_of(img_ratios, float)
-        if img_scale is None:
-            # mode 1: given img_scale=None and a range of image ratio
-            self.img_scale = None
-            assert mmcv.is_list_of(img_ratios, float)
-        elif isinstance(img_scale, tuple) and mmcv.is_list_of(
-                img_ratios, float):
-            assert len(img_scale) == 2
-            # mode 2: given a scale and a range of image ratio
-            self.img_scale = [(int(img_scale[0] * ratio),
-                               int(img_scale[1] * ratio))
-                              for ratio in img_ratios]
-        else:
-            # mode 3: given multiple scales
-            self.img_scale = img_scale if isinstance(img_scale,
-                                                     list) else [img_scale]
-        assert mmcv.is_list_of(self.img_scale, tuple) or self.img_scale is None
-        self.flip = flip
-        self.img_ratios = img_ratios
-        self.flip_direction = flip_direction if isinstance(
-            flip_direction, list) else [flip_direction]
-        assert mmcv.is_list_of(self.flip_direction, str)
-        if not self.flip and self.flip_direction != ['horizontal']:
-            warnings.warn(
-                'flip_direction has no effect when flip is set to False')
-        if (self.flip
-                and not any([t['type'] == 'RandomFlip' for t in transforms])):
-            warnings.warn(
-                'flip has no effect when RandomFlip is not in transforms')
-
-    def __call__(self, results):
-        """Call function to apply test time augment transforms on results.
-
-        Args:
-            results (dict): Result dict contains the data to transform.
-
-        Returns:
-           dict[str: list]: The augmented data, where each value is wrapped
-               into a list.
-        """
-
-        aug_data = []
-        if self.img_scale is None and mmcv.is_list_of(self.img_ratios, float):
-            h, w = results['img'].shape[:2]
-            img_scale = [(int(w * ratio), int(h * ratio))
-                         for ratio in self.img_ratios]
-        else:
-            img_scale = self.img_scale
-        flip_aug = [False, True] if self.flip else [False]
-        for scale in img_scale:
-            for flip in flip_aug:
-                for direction in self.flip_direction:
-                    _results = results.copy()
-                    _results['scale'] = scale
-                    _results['flip'] = flip
-                    _results['flip_direction'] = direction
-                    data = self.transforms(_results)
-                    aug_data.append(data)
-        # list of dict to dict of list
-        aug_data_dict = {key: [] for key in aug_data[0]}
-        for data in aug_data:
-            for key, val in data.items():
-                aug_data_dict[key].append(val)
-        return aug_data_dict
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(transforms={self.transforms}, '
-        repr_str += f'img_scale={self.img_scale}, flip={self.flip})'
-        repr_str += f'flip_direction={self.flip_direction}'
-        return repr_str
diff --git a/spaces/abidlabs/ControlNet/style.css b/spaces/abidlabs/ControlNet/style.css
deleted file mode 100644
index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/ControlNet/style.css
+++ /dev/null
@@ -1,3 +0,0 @@
-h1 {
-  text-align: center;
-}
diff --git a/spaces/abrar-lohia/text-2-character-anim/README.md b/spaces/abrar-lohia/text-2-character-anim/README.md
deleted file mode 100644
index 0c3a26d30b76a475d7a35eccb16a0622bcfbdf4d..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text 2 Character Anim
-emoji: 📚
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/extlibs/__init__.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/extlibs/__init__.py
deleted file mode 100644
index 416beddb2de99a57f1849afbf06772ed53369c5b..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/extlibs/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""
-External dependencies for Pyglet.
-
-These dependencies are included to publish Pyglet as a fully self-contained package.
-"""
diff --git a/spaces/adirik/stylemc-demo/encoder4editing/datasets/inference_dataset.py b/spaces/adirik/stylemc-demo/encoder4editing/datasets/inference_dataset.py
deleted file mode 100644
index fb577d7b538d634f27013c2784d2ea32143154cb..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/encoder4editing/datasets/inference_dataset.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from torch.utils.data import Dataset
-from PIL import Image
-from utils import data_utils
-
-
-class InferenceDataset(Dataset):
-
-	def __init__(self, root, opts, transform=None, preprocess=None):
-		self.paths = sorted(data_utils.make_dataset(root))
-		self.transform = transform
-		self.preprocess = preprocess
-		self.opts = opts
-
-	def __len__(self):
-		return len(self.paths)
-
-	def __getitem__(self, index):
-		from_path = self.paths[index]
-		if self.preprocess is not None:
-			from_im = self.preprocess(from_path)
-		else:
-			from_im = Image.open(from_path).convert('RGB')
-		if self.transform:
-			from_im = self.transform(from_im)
-		return from_im
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/datasets/audio_mel_dataset.py b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/datasets/audio_mel_dataset.py
deleted file mode 100644
index ccda58b6f76e5e40f3a07f219189be3e7bff88af..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/datasets/audio_mel_dataset.py
+++ /dev/null
@@ -1,343 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Dataset modules."""
-
-import logging
-import os
-
-from multiprocessing import Manager
-
-import numpy as np
-
-from torch.utils.data import Dataset
-
-from parallel_wavegan.utils import find_files
-from parallel_wavegan.utils import read_hdf5
-
-
-class AudioMelDataset(Dataset):
-    """PyTorch compatible audio and mel dataset."""
-
-    def __init__(
-        self,
-        root_dir,
-        audio_query="*.h5",
-        mel_query="*.h5",
-        audio_load_fn=lambda x: read_hdf5(x, "wave"),
-        mel_load_fn=lambda x: read_hdf5(x, "feats"),
-        audio_length_threshold=None,
-        mel_length_threshold=None,
-        return_utt_id=False,
-        allow_cache=False,
-    ):
-        """Initialize dataset.
-
-        Args:
-            root_dir (str): Root directory including dumped files.
-            audio_query (str): Query to find audio files in root_dir.
-            mel_query (str): Query to find feature files in root_dir.
-            audio_load_fn (func): Function to load audio file.
-            mel_load_fn (func): Function to load feature file.
-            audio_length_threshold (int): Threshold to remove short audio files.
-            mel_length_threshold (int): Threshold to remove short feature files.
-            return_utt_id (bool): Whether to return the utterance id with arrays.
-            allow_cache (bool): Whether to allow cache of the loaded files.
-
-        """
-        # find all of audio and mel files
-        audio_files = sorted(find_files(root_dir, audio_query))
-        mel_files = sorted(find_files(root_dir, mel_query))
-
-        # filter by threshold
-        if audio_length_threshold is not None:
-            audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]
-            idxs = [
-                idx
-                for idx in range(len(audio_files))
-                if audio_lengths[idx] > audio_length_threshold
-            ]
-            if len(audio_files) != len(idxs):
-                logging.warning(
-                    f"Some files are filtered by audio length threshold "
-                    f"({len(audio_files)} -> {len(idxs)})."
-                )
-            audio_files = [audio_files[idx] for idx in idxs]
-            mel_files = [mel_files[idx] for idx in idxs]
-        if mel_length_threshold is not None:
-            mel_lengths = [mel_load_fn(f).shape[0] for f in mel_files]
-            idxs = [
-                idx
-                for idx in range(len(mel_files))
-                if mel_lengths[idx] > mel_length_threshold
-            ]
-            if len(mel_files) != len(idxs):
-                logging.warning(
-                    f"Some files are filtered by mel length threshold "
-                    f"({len(mel_files)} -> {len(idxs)})."
-                )
-            audio_files = [audio_files[idx] for idx in idxs]
-            mel_files = [mel_files[idx] for idx in idxs]
-
-        # assert the number of files
-        assert len(audio_files) != 0, f"Not found any audio files in ${root_dir}."
-        assert len(audio_files) == len(
-            mel_files
-        ), f"Number of audio and mel files are different ({len(audio_files)} vs {len(mel_files)})."
-
-        self.audio_files = audio_files
-        self.audio_load_fn = audio_load_fn
-        self.mel_load_fn = mel_load_fn
-        self.mel_files = mel_files
-        if ".npy" in audio_query:
-            self.utt_ids = [
-                os.path.basename(f).replace("-wave.npy", "") for f in audio_files
-            ]
-        else:
-            self.utt_ids = [
-                os.path.splitext(os.path.basename(f))[0] for f in audio_files
-            ]
-        self.return_utt_id = return_utt_id
-        self.allow_cache = allow_cache
-        if allow_cache:
-            # NOTE(kan-bayashi): Manager is need to share memory in dataloader with num_workers > 0
-            self.manager = Manager()
-            self.caches = self.manager.list()
-            self.caches += [() for _ in range(len(audio_files))]
-
-    def __getitem__(self, idx):
-        """Get specified idx items.
-
-        Args:
-            idx (int): Index of the item.
-
-        Returns:
-            str: Utterance id (only in return_utt_id = True).
-            ndarray: Audio signal (T,).
-            ndarray: Feature (T', C).
-
-        """
-        if self.allow_cache and len(self.caches[idx]) != 0:
-            return self.caches[idx]
-
-        utt_id = self.utt_ids[idx]
-        audio = self.audio_load_fn(self.audio_files[idx])
-        mel = self.mel_load_fn(self.mel_files[idx])
-
-        if self.return_utt_id:
-            items = utt_id, audio, mel
-        else:
-            items = audio, mel
-
-        if self.allow_cache:
-            self.caches[idx] = items
-
-        return items
-
-    def __len__(self):
-        """Return dataset length.
-
-        Returns:
-            int: The length of dataset.
-
-        """
-        return len(self.audio_files)
-
-
-class AudioDataset(Dataset):
-    """PyTorch compatible audio dataset."""
-
-    def __init__(
-        self,
-        root_dir,
-        audio_query="*-wave.npy",
-        audio_length_threshold=None,
-        audio_load_fn=np.load,
-        return_utt_id=False,
-        allow_cache=False,
-    ):
-        """Initialize dataset.
-
-        Args:
-            root_dir (str): Root directory including dumped files.
-            audio_query (str): Query to find audio files in root_dir.
-            audio_load_fn (func): Function to load audio file.
-            audio_length_threshold (int): Threshold to remove short audio files.
-            return_utt_id (bool): Whether to return the utterance id with arrays.
-            allow_cache (bool): Whether to allow cache of the loaded files.
-
-        """
-        # find all of audio and mel files
-        audio_files = sorted(find_files(root_dir, audio_query))
-
-        # filter by threshold
-        if audio_length_threshold is not None:
-            audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]
-            idxs = [
-                idx
-                for idx in range(len(audio_files))
-                if audio_lengths[idx] > audio_length_threshold
-            ]
-            if len(audio_files) != len(idxs):
-                logging.waning(
-                    f"some files are filtered by audio length threshold "
-                    f"({len(audio_files)} -> {len(idxs)})."
-                )
-            audio_files = [audio_files[idx] for idx in idxs]
-
-        # assert the number of files
-        assert len(audio_files) != 0, f"Not found any audio files in ${root_dir}."
-
-        self.audio_files = audio_files
-        self.audio_load_fn = audio_load_fn
-        self.return_utt_id = return_utt_id
-        if ".npy" in audio_query:
-            self.utt_ids = [
-                os.path.basename(f).replace("-wave.npy", "") for f in audio_files
-            ]
-        else:
-            self.utt_ids = [
-                os.path.splitext(os.path.basename(f))[0] for f in audio_files
-            ]
-        self.allow_cache = allow_cache
-        if allow_cache:
-            # NOTE(kan-bayashi): Manager is need to share memory in dataloader with num_workers > 0
-            self.manager = Manager()
-            self.caches = self.manager.list()
-            self.caches += [() for _ in range(len(audio_files))]
-
-    def __getitem__(self, idx):
-        """Get specified idx items.
-
-        Args:
-            idx (int): Index of the item.
-
-        Returns:
-            str: Utterance id (only in return_utt_id = True).
-            ndarray: Audio (T,).
-
-        """
-        if self.allow_cache and len(self.caches[idx]) != 0:
-            return self.caches[idx]
-
-        utt_id = self.utt_ids[idx]
-        audio = self.audio_load_fn(self.audio_files[idx])
-
-        if self.return_utt_id:
-            items = utt_id, audio
-        else:
-            items = audio
-
-        if self.allow_cache:
-            self.caches[idx] = items
-
-        return items
-
-    def __len__(self):
-        """Return dataset length.
-
-        Returns:
-            int: The length of dataset.
-
-        """
-        return len(self.audio_files)
-
-
-class MelDataset(Dataset):
-    """PyTorch compatible mel dataset."""
-
-    def __init__(
-        self,
-        root_dir,
-        mel_query="*-feats.npy",
-        mel_length_threshold=None,
-        mel_load_fn=np.load,
-        return_utt_id=False,
-        allow_cache=False,
-    ):
-        """Initialize dataset.
-
-        Args:
-            root_dir (str): Root directory including dumped files.
-            mel_query (str): Query to find feature files in root_dir.
-            mel_load_fn (func): Function to load feature file.
-            mel_length_threshold (int): Threshold to remove short feature files.
-            return_utt_id (bool): Whether to return the utterance id with arrays.
-            allow_cache (bool): Whether to allow cache of the loaded files.
-
-        """
-        # find all of the mel files
-        mel_files = sorted(find_files(root_dir, mel_query))
-
-        # filter by threshold
-        if mel_length_threshold is not None:
-            mel_lengths = [mel_load_fn(f).shape[0] for f in mel_files]
-            idxs = [
-                idx
-                for idx in range(len(mel_files))
-                if mel_lengths[idx] > mel_length_threshold
-            ]
-            if len(mel_files) != len(idxs):
-                logging.warning(
-                    f"Some files are filtered by mel length threshold "
-                    f"({len(mel_files)} -> {len(idxs)})."
-                )
-            mel_files = [mel_files[idx] for idx in idxs]
-
-        # assert the number of files
-        assert len(mel_files) != 0, f"Not found any mel files in ${root_dir}."
-
-        self.mel_files = mel_files
-        self.mel_load_fn = mel_load_fn
-        self.utt_ids = [os.path.splitext(os.path.basename(f))[0] for f in mel_files]
-        if ".npy" in mel_query:
-            self.utt_ids = [
-                os.path.basename(f).replace("-feats.npy", "") for f in mel_files
-            ]
-        else:
-            self.utt_ids = [os.path.splitext(os.path.basename(f))[0] for f in mel_files]
-        self.return_utt_id = return_utt_id
-        self.allow_cache = allow_cache
-        if allow_cache:
-            # NOTE(kan-bayashi): Manager is need to share memory in dataloader with num_workers > 0
-            self.manager = Manager()
-            self.caches = self.manager.list()
-            self.caches += [() for _ in range(len(mel_files))]
-
-    def __getitem__(self, idx):
-        """Get specified idx items.
-
-        Args:
-            idx (int): Index of the item.
-
-        Returns:
-            str: Utterance id (only in return_utt_id = True).
-            ndarray: Feature (T', C).
-
-        """
-        if self.allow_cache and len(self.caches[idx]) != 0:
-            return self.caches[idx]
-
-        utt_id = self.utt_ids[idx]
-        mel = self.mel_load_fn(self.mel_files[idx])
-
-        if self.return_utt_id:
-            items = utt_id, mel
-        else:
-            items = mel
-
-        if self.allow_cache:
-            self.caches[idx] = items
-
-        return items
-
-    def __len__(self):
-        """Return dataset length.
-
-        Returns:
-            int: The length of dataset.
-
-        """
-        return len(self.mel_files)
diff --git a/spaces/akhaliq/deeplab2/model/layers/squeeze_and_excite.py b/spaces/akhaliq/deeplab2/model/layers/squeeze_and_excite.py
deleted file mode 100644
index d77d73b66dacd3faa47f59106a69d4da1bc6cc10..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/layers/squeeze_and_excite.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Squeeze and excite layer.
-
-This script implements the squeeze-and-excite (SE), proposed in
-- Squeeze-and-Excitation Networks, Jie Hu, Li Shen, Samuel Albanie,
-Gang Sun, Enhua Wu. In CVPR 2018.
-
-Recently, this SE operation is further simplied with a single fully
-connected layer, referred as simplified_squeeze_and_excite in our
-implementation. For details, please see
-- Lee and Park proposed to use only one fully connected layer in SE.
-CenterMask : Real-Time Anchor-Free Instance Segmentation.
-Youngwan Lee and Jongyoul Park. In CVPR 2020.
-"""
-from typing import Optional
-
-from absl import logging
-import tensorflow as tf
-
-from deeplab2.model import utils
-from deeplab2.model.layers import activations
-
-layers = tf.keras.layers
-
-
-class SimplifiedSqueezeAndExcite(tf.keras.layers.Layer):
-  """A simplified squeeze-and-excite layer.
-
-  Original squeeze-and-exciation (SE) is proposed in
-  Squeeze-and-Excitation Networks, Jie Hu, Li Shen, Samuel Albanie,
-  Gang Sun, Enhua Wu. In CVPR 2018.
-
-  Lee and Park proposed to use only one fully connected layer in SE.
-  CenterMask : Real-Time Anchor-Free Instance Segmentation.
-  Youngwan Lee and Jongyoul Park. In CVPR 2020.
-
-  In this function, we implement the simplified version of SE.
-
-  Additionally, we follow MobileNetv3 to use the hard sigmoid function.
-  """
-
-  def __init__(self, squeeze_channels, name=None):
-    """Initializes a simplified squeeze-and-excite layer.
-
-    Args:
-      squeeze_channels: Integer, channels for the squeezed features.
-      name: An optional string specifying the operation name.
-    """
-    super(SimplifiedSqueezeAndExcite, self).__init__(name=name)
-    self._squeeze_channels = squeeze_channels
-
-    self._se_conv = layers.Conv2D(self._squeeze_channels,
-                                  1,
-                                  name='squeeze_and_excite',
-                                  use_bias=True,
-                                  kernel_initializer='VarianceScaling')
-    self._hard_sigmoid = activations.get_activation('hard_sigmoid')
-
-  def call(self, input_tensor):
-    """Performs a forward pass.
-
-    Args:
-      input_tensor: An input tensor of type tf.Tensor with shape [batch, height,
-        width, channels].
-
-    Returns:
-      The output tensor.
-    """
-    pooled = tf.reduce_mean(input_tensor, [1, 2], keepdims=True)
-    squeezed = self._se_conv(pooled)
-    excited = self._hard_sigmoid(squeezed) * input_tensor
-    return excited
-
-  def get_config(self):
-    config = {
-        'squeeze_channels': self._squeeze_channels,
-    }
-    base_config = super(SimplifiedSqueezeAndExcite, self).get_config()
-    return dict(list(base_config.items()) + list(config.items()))
-
-
-class SqueezeAndExcite(tf.keras.layers.Layer):
-  """Creates a squeeze and excitation layer.
-
-  Reference: Squeeze-and-Excitation Networks, Jie Hu, Li Shen, Samuel Albanie,
-  Gang Sun, Enhua Wu. In CVPR 2018.
-  This implementation follows the original SE and differs from the above
-  simplified version.
-  """
-
-  def __init__(
-      self,
-      in_filters: int,
-      out_filters: int,
-      se_ratio: float,
-      divisible_by: int = 1,
-      kernel_initializer: str = 'VarianceScaling',
-      kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
-      bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
-      activation: str = 'relu',
-      gating_activation: str = 'sigmoid',
-      name: Optional[str] = None):
-    """Initializes a squeeze and excitation layer.
-
-    Args:
-      in_filters: The number of filters that se_ratio should be applied to.
-      out_filters: The number of filters of the output tensor.
-      se_ratio: The SE ratio for the squeeze and excitation layer.
-      divisible_by: An `int` that ensures all inner dimensions are divisible by
-        this number.
-      kernel_initializer: The kernel_initializer for convolutional
-        layers.
-      kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
-        Conv2D. Default to None.
-      bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2d.
-        Default to None.
-      activation: The name of the activation function.
-      gating_activation: The name of the activation function for final
-        gating function.
-      name: The layer name.
-    """
-    super(SqueezeAndExcite, self).__init__(name=name)
-
-    self._in_filters = in_filters
-    self._out_filters = out_filters
-    self._se_ratio = se_ratio
-    self._divisible_by = divisible_by
-    self._activation = activation
-    self._gating_activation = gating_activation
-    self._kernel_initializer = kernel_initializer
-    self._kernel_regularizer = kernel_regularizer
-    self._bias_regularizer = bias_regularizer
-    if tf.keras.backend.image_data_format() == 'channels_last':
-      self._spatial_axis = [1, 2]
-    else:
-      self._spatial_axis = [2, 3]
-    self._activation_fn = activations.get_activation(activation)
-    self._gating_activation_fn = activations.get_activation(gating_activation)
-
-    num_reduced_filters = utils.make_divisible(
-        max(1, int(self._in_filters * self._se_ratio)),
-        divisor=self._divisible_by)
-    if self._se_ratio > 1.0:
-      logging.warn('Squeezing ratio %d is larger than 1.0.', self._se_ratio)
-
-    self._se_reduce = tf.keras.layers.Conv2D(
-        filters=num_reduced_filters,
-        kernel_size=1,
-        strides=1,
-        padding='same',
-        use_bias=True,
-        kernel_initializer=self._kernel_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        name=name + '_reduce')
-
-    self._se_expand = tf.keras.layers.Conv2D(
-        filters=self._out_filters,
-        kernel_size=1,
-        strides=1,
-        padding='same',
-        use_bias=True,
-        kernel_initializer=self._kernel_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        name=name + '_expand')
-
-  def call(self, inputs):
-    x = tf.reduce_mean(inputs, self._spatial_axis, keepdims=True)
-    x = self._activation_fn(self._se_reduce(x))
-    x = self._gating_activation_fn(self._se_expand(x))
-    return x * inputs
diff --git a/spaces/alamin655/websurfx/public/static/colorschemes/dracula.css b/spaces/alamin655/websurfx/public/static/colorschemes/dracula.css
deleted file mode 100644
index 71739aba36cdaecad414b5a4c8a8eb1dd734f2a5..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/public/static/colorschemes/dracula.css
+++ /dev/null
@@ -1,11 +0,0 @@
-:root {
-  --background-color: #44475a;
-  --foreground-color: #8be9fd;
-  --color-one: #ff5555;
-  --color-two: #50fa7b;
-  --color-three: #ffb86c;
-  --color-four: #bd93f9;
-  --color-five: #ff79c6;
-  --color-six: #94a3a5;
-  --color-seven: #ffffff;
-}
diff --git a/spaces/ali-ghamdan/image-colors-corrector/evaluation/calc_mse.py b/spaces/ali-ghamdan/image-colors-corrector/evaluation/calc_mse.py
deleted file mode 100644
index 4352fdefcd8d6aad70d501839ad24646f265b90c..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/image-colors-corrector/evaluation/calc_mse.py
+++ /dev/null
@@ -1,27 +0,0 @@
-## Calculate mean squared error between source and target images.
-#
-# Copyright (c) 2018-present, Mahmoud Afifi
-# York University, Canada
-# mafifi@eecs.yorku.ca | m.3afifi@gmail.com
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# All rights reserved.
-#
-# Please cite the following work if this program is used:
-# Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown,
-# "When color constancy goes wrong: Correcting improperly white-balanced
-# images", CVPR 2019.
-#
-##########################################################################
-
-
-import numpy as np
-
-
-def calc_mse(source, target, color_chart_area):
-  source = np.reshape(source, [-1, 1]).astype(np.float64)
-  target = np.reshape(target, [-1, 1]).astype(np.float64)
-  mse = sum(np.power((source - target), 2))
-  return mse / ((np.shape(source)[
-    0]) - color_chart_area)
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/examples/paex_mono_asio_channel_select.c b/spaces/amarchheda/ChordDuplicate/portaudio/examples/paex_mono_asio_channel_select.c
deleted file mode 100644
index 4b55d318578a48ca241c4bcc7e2133126d27b28d..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/examples/paex_mono_asio_channel_select.c
+++ /dev/null
@@ -1,167 +0,0 @@
-/** @file paex_mono_asio_channel_select.c
-    @ingroup examples_src
-    @brief Play a monophonic sine wave on a specific ASIO channel.
-    @author Ross Bencina <rossb@audiomulch.com>
-    @author Phil Burk <philburk@softsynth.com>
-*/
-/*
- * $Id$
- *
- * Authors:
- *    Ross Bencina <rossb@audiomulch.com>
- *    Phil Burk <philburk@softsynth.com>
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-#include "pa_asio.h"
-
-#define NUM_SECONDS   (10)
-#define SAMPLE_RATE   (44100)
-#define AMPLITUDE     (0.8)
-#define FRAMES_PER_BUFFER  (64)
-#define OUTPUT_DEVICE Pa_GetDefaultOutputDevice()
-
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-
-#define TABLE_SIZE   (200)
-typedef struct
-{
-    float sine[TABLE_SIZE];
-    int phase;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
-                            unsigned long framesPerBuffer,
-                            const PaStreamCallbackTimeInfo* timeInfo,
-                            PaStreamCallbackFlags statusFlags,
-                            void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    float *out = (float*)outputBuffer;
-    unsigned long i;
-    int finished = 0;
-    /* avoid unused variable warnings */
-    (void) inputBuffer;
-    (void) timeInfo;
-    (void) statusFlags;
-    for( i=0; i<framesPerBuffer; i++ )
-    {
-        *out++ = data->sine[data->phase];  /* left */
-        data->phase += 1;
-        if( data->phase >= TABLE_SIZE ) data->phase -= TABLE_SIZE;
-    }
-    return finished;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    PaStreamParameters outputParameters;
-    PaAsioStreamInfo asioOutputInfo;
-    PaStream *stream;
-    PaError err;
-    paTestData data;
-    int outputChannelSelectors[1];
-    int i;
-    printf("PortAudio Test: output MONO sine wave. SR = %d, BufSize = %d\n", SAMPLE_RATE, FRAMES_PER_BUFFER);
-    /* initialise sinusoidal wavetable */
-    for( i=0; i<TABLE_SIZE; i++ )
-    {
-        data.sine[i] = (float) (AMPLITUDE * sin( ((double)i/(double)TABLE_SIZE) * M_PI * 2. ));
-    }
-    data.phase = 0;
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    outputParameters.device = OUTPUT_DEVICE;
-    outputParameters.channelCount = 1;       /* MONO output */
-    outputParameters.sampleFormat = paFloat32; /* 32 bit floating point output */
-    outputParameters.suggestedLatency = Pa_GetDeviceInfo( outputParameters.device )->defaultLowOutputLatency;
-
-    /* Use an ASIO specific structure. WARNING - this is not portable. */
-    asioOutputInfo.size = sizeof(PaAsioStreamInfo);
-    asioOutputInfo.hostApiType = paASIO;
-    asioOutputInfo.version = 1;
-    asioOutputInfo.flags = paAsioUseChannelSelectors;
-    outputChannelSelectors[0] = 1; /* skip channel 0 and use the second (right) ASIO device channel */
-    asioOutputInfo.channelSelectors = outputChannelSelectors;
-    outputParameters.hostApiSpecificStreamInfo = &asioOutputInfo;
-
-    err = Pa_OpenStream(
-              &stream,
-              NULL, /* no input */
-              &outputParameters,
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,
-              paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-              patestCallback,
-              &data );
-    if( err != paNoError ) goto error;
-
-    err = Pa_StartStream( stream );
-    if( err != paNoError ) goto error;
-
-    printf("Play for %d seconds.\n", NUM_SECONDS ); fflush(stdout);
-    Pa_Sleep( NUM_SECONDS * 1000 );
-
-    err = Pa_StopStream( stream );
-    if( err != paNoError ) goto error;
-
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto error;
-
-    Pa_Terminate();
-    printf("Test finished.\n");
-    return err;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/js/icons.js b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/js/icons.js
deleted file mode 100644
index 84fed38dd35e0d0203370a8314a360d27f350dd6..0000000000000000000000000000000000000000
--- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/js/icons.js
+++ /dev/null
@@ -1 +0,0 @@
-window.FontAwesomeKitConfig={asyncLoading:{enabled:!1},autoA11y:{enabled:!0},baseUrl:"https://ka-f.fontawesome.com",baseUrlKit:"https://kit-pro.fontawesome.com",detectConflictsUntil:null,iconUploads:{},id:96462084,license:"pro",method:"css",minify:{enabled:!0},token:"d0514f1901",v4FontFaceShim:{enabled:!0},v4shim:{enabled:!0},v5FontFaceShim:{enabled:!0},version:"6.1.1"},function(t){"function"==typeof define&&define.amd?define("kit-loader",t):t()}(function(){"use strict";function t(e){return(t="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(e)}function e(t,e,n){return e in t?Object.defineProperty(t,e,{value:n,enumerable:!0,configurable:!0,writable:!0}):t[e]=n,t}function n(t,e){var n=Object.keys(t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(t);e&&(o=o.filter(function(e){return 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diff --git a/spaces/aphenx/bingo/README.md b/spaces/aphenx/bingo/README.md
deleted file mode 100644
index 218767d1d7debd26932ffddca2ec0f421c0171a9..0000000000000000000000000000000000000000
--- a/spaces/aphenx/bingo/README.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: true
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-
diff --git a/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/app.py b/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/app.py
deleted file mode 100644
index 532db5fa106b431160846cf39f4056bad665838c..0000000000000000000000000000000000000000
--- a/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import io
-import os
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-import torch
-from inference.infer_tool import Svc
-import logging
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-model_path = "logs/48k/aqua.pth"
-config_path = "configs/config.json"
-
-svc_model = Svc(model_path, config_path)
-
-
-def vc_fn(input_audio, vc_transform, term):
-    if not term:
-        return "请阅读并同意《AI阿夸模型使用协议》", None
-    if input_audio is None:
-        return "请上传音频", None
-    sampling_rate, audio = input_audio
-    duration = audio.shape[0] / sampling_rate
-    if duration > 30:
-        return "请上传小于30s的音频，长音频的转换请在本地进行", None
-    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-    if len(audio.shape) > 1:
-        audio = librosa.to_mono(audio.transpose(1, 0))
-    if sampling_rate != 24000:
-        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=24000)
-    print(audio.shape)
-    out_wav_path = io.BytesIO()
-    soundfile.write(out_wav_path, audio, 24000, format="wav")
-    out_wav_path.seek(0)
-
-    # sid = sid_map[sid]
-    sid = "aqua"
-    out_audio, out_sr = svc_model.infer(sid, vc_transform, out_wav_path)
-    _audio = out_audio.cpu().numpy()
-    return "Success", (48000, _audio)
-
-
-inputs = [
-    gr.inputs.Audio(source="upload"),
-    gr.inputs.Number(default=0),
-    gr.Checkbox(label="您已阅读并同意《AI阿夸模型使用协议》")
-]
-
-outputs = [
-    "text",
-    gr.outputs.Audio(type="numpy")
-]
-
-example = [
-    ["./raw/大手拉小手.wav", 0, False]
-]
-
-des = """
-## 在使用此模型前请阅读[AI阿夸模型使用协议](https://huggingface.co/spaces/DoNotSelect/AI-minato_aqua/blob/main/terms.md)
-"""
-
-demo = gr.Interface(
-    fn=vc_fn,
-    inputs=inputs,
-    outputs=outputs,
-    layout="horizontal",
-    theme="huggingface",
-    description=des,
-    examples=example,
-    cache_examples=True
-)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/arkiitkgp/stablediff-demo/app_old.py b/spaces/arkiitkgp/stablediff-demo/app_old.py
deleted file mode 100644
index f95a3226be9a189bf1bd0ba12e9624a218a24278..0000000000000000000000000000000000000000
--- a/spaces/arkiitkgp/stablediff-demo/app_old.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import gradio as gr
-# import torch
-# from torch import autocast
-# from diffusers import StableDiffusionPipeline
-# from datasets import load_dataset
-from PIL import Image  
-import re
-import os
-
-# auth_token = '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' #os.getenv("auth_token")
-# model_id = "CompVis/stable-diffusion-v1-4"
-# device = "cpu"
-# pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=auth_token, revision="fp16", torch_dtype=torch.float16)
-# pipe = pipe.to(device)
-
-
-stable_diffusion = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion")
-
-def get_images(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    return [os.path.join(gallery_dir, img) for img in os.listdir(gallery_dir)]
-
-
-
-def infer(prompt, samples, steps, scale, seed):        
-    generator = torch.Generator(device=device).manual_seed(seed)
-    images_list = pipe(
-        [prompt] * samples,
-        num_inference_steps=steps,
-        guidance_scale=scale,
-        generator=generator,
-    )
-    images = []
-    # safe_image = Image.open(r"unsafe.png")
-    for i, image in enumerate(images_list["sample"]):
-        images.append(image)
-        # if(images_list["nsfw_content_detected"][i]):
-    #         images.append(safe_image)
-    #     else:
-    #         images.append(image)
-    return images
-    
-
-
-block = gr.Blocks()
-
-with block:
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Enter your prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                )
-        gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-
-        advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
-
-        with gr.Row(elem_id="advanced-options"):
-            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
-            steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
-            scale = gr.Slider(
-                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
-            )
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=2147483647,
-                step=1,
-                randomize=True,
-            )
-        text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
-        btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
-        advanced_button.click(
-            None,
-            [],
-            text,
-        )
-        
-block.launch()
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_Blowfish.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_Blowfish.py
deleted file mode 100644
index 4ce3a41555a620ad100568c68b2c90dad6a184a5..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_Blowfish.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  SelfTest/Cipher/test_Blowfish.py: Self-test for the Blowfish cipher
-#
-# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net>
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""Self-test suite for Crypto.Cipher.Blowfish"""
-
-import unittest
-
-from Crypto.Util.py3compat import bchr
-
-from Crypto.Cipher import Blowfish
-
-# This is a list of (plaintext, ciphertext, key) tuples.
-test_data = [
-    # Test vectors from http://www.schneier.com/code/vectors.txt
-    ('0000000000000000', '4ef997456198dd78', '0000000000000000'),
-    ('ffffffffffffffff', '51866fd5b85ecb8a', 'ffffffffffffffff'),
-    ('1000000000000001', '7d856f9a613063f2', '3000000000000000'),
-    ('1111111111111111', '2466dd878b963c9d', '1111111111111111'),
-    ('1111111111111111', '61f9c3802281b096', '0123456789abcdef'),
-    ('0123456789abcdef', '7d0cc630afda1ec7', '1111111111111111'),
-    ('0000000000000000', '4ef997456198dd78', '0000000000000000'),
-    ('0123456789abcdef', '0aceab0fc6a0a28d', 'fedcba9876543210'),
-    ('01a1d6d039776742', '59c68245eb05282b', '7ca110454a1a6e57'),
-    ('5cd54ca83def57da', 'b1b8cc0b250f09a0', '0131d9619dc1376e'),
-    ('0248d43806f67172', '1730e5778bea1da4', '07a1133e4a0b2686'),
-    ('51454b582ddf440a', 'a25e7856cf2651eb', '3849674c2602319e'),
-    ('42fd443059577fa2', '353882b109ce8f1a', '04b915ba43feb5b6'),
-    ('059b5e0851cf143a', '48f4d0884c379918', '0113b970fd34f2ce'),
-    ('0756d8e0774761d2', '432193b78951fc98', '0170f175468fb5e6'),
-    ('762514b829bf486a', '13f04154d69d1ae5', '43297fad38e373fe'),
-    ('3bdd119049372802', '2eedda93ffd39c79', '07a7137045da2a16'),
-    ('26955f6835af609a', 'd887e0393c2da6e3', '04689104c2fd3b2f'),
-    ('164d5e404f275232', '5f99d04f5b163969', '37d06bb516cb7546'),
-    ('6b056e18759f5cca', '4a057a3b24d3977b', '1f08260d1ac2465e'),
-    ('004bd6ef09176062', '452031c1e4fada8e', '584023641aba6176'),
-    ('480d39006ee762f2', '7555ae39f59b87bd', '025816164629b007'),
-    ('437540c8698f3cfa', '53c55f9cb49fc019', '49793ebc79b3258f'),
-    ('072d43a077075292', '7a8e7bfa937e89a3', '4fb05e1515ab73a7'),
-    ('02fe55778117f12a', 'cf9c5d7a4986adb5', '49e95d6d4ca229bf'),
-    ('1d9d5c5018f728c2', 'd1abb290658bc778', '018310dc409b26d6'),
-    ('305532286d6f295a', '55cb3774d13ef201', '1c587f1c13924fef'),
-    ('0123456789abcdef', 'fa34ec4847b268b2', '0101010101010101'),
-    ('0123456789abcdef', 'a790795108ea3cae', '1f1f1f1f0e0e0e0e'),
-    ('0123456789abcdef', 'c39e072d9fac631d', 'e0fee0fef1fef1fe'),
-    ('ffffffffffffffff', '014933e0cdaff6e4', '0000000000000000'),
-    ('0000000000000000', 'f21e9a77b71c49bc', 'ffffffffffffffff'),
-    ('0000000000000000', '245946885754369a', '0123456789abcdef'),
-    ('ffffffffffffffff', '6b5c5a9c5d9e0a5a', 'fedcba9876543210'),
-    #('fedcba9876543210', 'f9ad597c49db005e', 'f0'),
-    #('fedcba9876543210', 'e91d21c1d961a6d6', 'f0e1'),
-    #('fedcba9876543210', 'e9c2b70a1bc65cf3', 'f0e1d2'),
-    ('fedcba9876543210', 'be1e639408640f05', 'f0e1d2c3'),
-    ('fedcba9876543210', 'b39e44481bdb1e6e', 'f0e1d2c3b4'),
-    ('fedcba9876543210', '9457aa83b1928c0d', 'f0e1d2c3b4a5'),
-    ('fedcba9876543210', '8bb77032f960629d', 'f0e1d2c3b4a596'),
-    ('fedcba9876543210', 'e87a244e2cc85e82', 'f0e1d2c3b4a59687'),
-    ('fedcba9876543210', '15750e7a4f4ec577', 'f0e1d2c3b4a5968778'),
-    ('fedcba9876543210', '122ba70b3ab64ae0', 'f0e1d2c3b4a596877869'),
-    ('fedcba9876543210', '3a833c9affc537f6', 'f0e1d2c3b4a5968778695a'),
-    ('fedcba9876543210', '9409da87a90f6bf2', 'f0e1d2c3b4a5968778695a4b'),
-    ('fedcba9876543210', '884f80625060b8b4', 'f0e1d2c3b4a5968778695a4b3c'),
-    ('fedcba9876543210', '1f85031c19e11968', 'f0e1d2c3b4a5968778695a4b3c2d'),
-    ('fedcba9876543210', '79d9373a714ca34f', 'f0e1d2c3b4a5968778695a4b3c2d1e'),
-    ('fedcba9876543210', '93142887ee3be15c',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f'),
-    ('fedcba9876543210', '03429e838ce2d14b',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f00'),
-    ('fedcba9876543210', 'a4299e27469ff67b',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f0011'),
-    ('fedcba9876543210', 'afd5aed1c1bc96a8',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f001122'),
-    ('fedcba9876543210', '10851c0e3858da9f',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f00112233'),
-    ('fedcba9876543210', 'e6f51ed79b9db21f',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f0011223344'),
-    ('fedcba9876543210', '64a6e14afd36b46f',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f001122334455'),
-    ('fedcba9876543210', '80c7d7d45a5479ad',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f00112233445566'),
-    ('fedcba9876543210', '05044b62fa52d080',
-        'f0e1d2c3b4a5968778695a4b3c2d1e0f0011223344556677'),
-]
-
-
-class KeyLength(unittest.TestCase):
-
-    def runTest(self):
-        self.assertRaises(ValueError, Blowfish.new, bchr(0) * 3,
-                          Blowfish.MODE_ECB)
-        self.assertRaises(ValueError, Blowfish.new, bchr(0) * 57,
-                          Blowfish.MODE_ECB)
-
-
-class TestOutput(unittest.TestCase):
-
-    def runTest(self):
-        # Encrypt/Decrypt data and test output parameter
-
-        cipher = Blowfish.new(b'4'*16, Blowfish.MODE_ECB)
-
-        pt = b'5' * 16
-        ct = cipher.encrypt(pt)
-
-        output = bytearray(16)
-        res = cipher.encrypt(pt, output=output)
-        self.assertEqual(ct, output)
-        self.assertEqual(res, None)
-        
-        res = cipher.decrypt(ct, output=output)
-        self.assertEqual(pt, output)
-        self.assertEqual(res, None)
-
-        output = memoryview(bytearray(16))
-        cipher.encrypt(pt, output=output)
-        self.assertEqual(ct, output)
-        
-        cipher.decrypt(ct, output=output)
-        self.assertEqual(pt, output)
-
-        self.assertRaises(TypeError, cipher.encrypt, pt, output=b'0'*16)
-        self.assertRaises(TypeError, cipher.decrypt, ct, output=b'0'*16)
-
-        shorter_output = bytearray(7)
-        self.assertRaises(ValueError, cipher.encrypt, pt, output=shorter_output)
-        self.assertRaises(ValueError, cipher.decrypt, ct, output=shorter_output)
-
-
-def get_tests(config={}):
-    from .common import make_block_tests
-    tests = make_block_tests(Blowfish, "Blowfish", test_data)
-    tests.append(KeyLength())
-    tests += [TestOutput()]
-    return tests
-
-
-if __name__ == '__main__':
-    import unittest
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_line_chart.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_line_chart.py
deleted file mode 100644
index 7461362257f18c47c5d23aef81a44b7a1ba67677..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_line_chart.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""
-Simple Line Chart
------------------
-This chart shows the most basic line chart, made from a dataframe with two
-columns.
-"""
-# category: simple charts
-
-import altair as alt
-import numpy as np
-import pandas as pd
-
-x = np.arange(100)
-source = pd.DataFrame({
-  'x': x,
-  'f(x)': np.sin(x / 5)
-})
-
-alt.Chart(source).mark_line().encode(
-    x='x',
-    y='f(x)'
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/entrypoints.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/entrypoints.py
deleted file mode 100644
index eb7b86f12098856505796608860a779571a5789f..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/entrypoints.py
+++ /dev/null
@@ -1,247 +0,0 @@
-"""Discover and load entry points from installed packages."""
-# Copyright (c) Thomas Kluyver and contributors
-# Distributed under the terms of the MIT license; see LICENSE file.
-
-from contextlib import contextmanager
-import glob
-from importlib import import_module
-import io
-import itertools
-import os.path as osp
-import re
-import sys
-import warnings
-import zipfile
-
-import configparser
-
-entry_point_pattern = re.compile(r"""
-(?P<modulename>\w+(\.\w+)*)
-(:(?P<objectname>\w+(\.\w+)*))?
-\s*
-(\[(?P<extras>.+)\])?
-$
-""", re.VERBOSE)
-
-file_in_zip_pattern = re.compile(r"""
-(?P<dist_version>[^/\\]+)\.(dist|egg)-info
-[/\\]entry_points.txt$
-""", re.VERBOSE)
-
-__version__ = '0.4'
-
-class BadEntryPoint(Exception):
-    """Raised when an entry point can't be parsed.
-    """
-    def __init__(self, epstr):
-        self.epstr = epstr
-
-    def __str__(self):
-        return "Couldn't parse entry point spec: %r" % self.epstr
-
-    @staticmethod
-    @contextmanager
-    def err_to_warnings():
-        try:
-            yield
-        except BadEntryPoint as e:
-            warnings.warn(str(e))
-
-class NoSuchEntryPoint(Exception):
-    """Raised by :func:`get_single` when no matching entry point is found."""
-    def __init__(self, group, name):
-        self.group = group
-        self.name = name
-
-    def __str__(self):
-        return "No {!r} entry point found in group {!r}".format(self.name, self.group)
-
-
-class CaseSensitiveConfigParser(configparser.ConfigParser):
-    optionxform = staticmethod(str)
-
-
-class EntryPoint(object):
-    def __init__(self, name, module_name, object_name, extras=None, distro=None):
-        self.name = name
-        self.module_name = module_name
-        self.object_name = object_name
-        self.extras = extras
-        self.distro = distro
-
-    def __repr__(self):
-        return "EntryPoint(%r, %r, %r, %r)" % \
-            (self.name, self.module_name, self.object_name, self.distro)
-
-    def load(self):
-        """Load the object to which this entry point refers.
-        """
-        mod = import_module(self.module_name)
-        obj = mod
-        if self.object_name:
-            for attr in self.object_name.split('.'):
-                obj = getattr(obj, attr)
-        return obj
-
-    @classmethod
-    def from_string(cls, epstr, name, distro=None):
-        """Parse an entry point from the syntax in entry_points.txt
-
-        :param str epstr: The entry point string (not including 'name =')
-        :param str name: The name of this entry point
-        :param Distribution distro: The distribution in which the entry point was found
-        :rtype: EntryPoint
-        :raises BadEntryPoint: if *epstr* can't be parsed as an entry point.
-        """
-        m = entry_point_pattern.match(epstr)
-        if m:
-            mod, obj, extras = m.group('modulename', 'objectname', 'extras')
-            if extras is not None:
-                extras = re.split(r',\s*', extras)
-            return cls(name, mod, obj, extras, distro)
-        else:
-            raise BadEntryPoint(epstr)
-
-class Distribution(object):
-    def __init__(self, name, version):
-        self.name = name
-        self.version = version
-
-    @classmethod
-    def from_name_version(cls, name):
-        """Parse a distribution from a "name-version" string
-
-        :param str name: The name-version string (entrypoints-0.3)
-        Returns an :class:`Distribution` object
-        """
-        version = None
-        if '-' in name:
-            name, version = name.split('-', 1)
-        return cls(name, version)
-
-    def __repr__(self):
-        return "Distribution(%r, %r)" % (self.name, self.version)
-
-
-def iter_files_distros(path=None, repeated_distro='first'):
-    if path is None:
-        path = sys.path
-
-    # Distributions found earlier in path will shadow those with the same name
-    # found later. If these distributions used different module names, it may
-    # actually be possible to import both, but in most cases this shadowing
-    # will be correct.
-    distro_names_seen = set()
-
-    for folder in path:
-        if folder.rstrip('/\\').endswith('.egg'):
-            # Gah, eggs
-            egg_name = osp.basename(folder)
-            distro = Distribution.from_name_version(egg_name.split(".egg")[0])
-
-            if (repeated_distro == 'first') \
-                    and (distro.name in distro_names_seen):
-                continue
-            distro_names_seen.add(distro.name)
-
-            if osp.isdir(folder):
-                ep_path = osp.join(folder, 'EGG-INFO', 'entry_points.txt')
-                if osp.isfile(ep_path):
-                    cp = CaseSensitiveConfigParser(delimiters=('=',))
-                    cp.read([ep_path])
-                    yield cp, distro
-
-            elif zipfile.is_zipfile(folder):
-                z = zipfile.ZipFile(folder)
-                try:
-                    info = z.getinfo('EGG-INFO/entry_points.txt')
-                except KeyError:
-                    continue
-                cp = CaseSensitiveConfigParser(delimiters=('=',))
-                with z.open(info) as f:
-                    fu = io.TextIOWrapper(f)
-                    cp.read_file(fu, source=osp.join(
-                        folder, 'EGG-INFO', 'entry_points.txt'))
-                yield cp, distro
-
-        # zip imports, not egg
-        elif zipfile.is_zipfile(folder):
-            with zipfile.ZipFile(folder) as zf:
-                for info in zf.infolist():
-                    m = file_in_zip_pattern.match(info.filename)
-                    if not m:
-                        continue
-
-                    distro_name_version = m.group('dist_version')
-                    distro = Distribution.from_name_version(distro_name_version)
-
-                    if (repeated_distro == 'first') \
-                            and (distro.name in distro_names_seen):
-                        continue
-                    distro_names_seen.add(distro.name)
-
-                    cp = CaseSensitiveConfigParser(delimiters=('=',))
-                    with zf.open(info) as f:
-                        fu = io.TextIOWrapper(f)
-                        cp.read_file(fu, source=osp.join(folder, info.filename))
-                    yield cp, distro
-
-        # Regular file imports (not egg, not zip file)
-        for path in itertools.chain(
-            glob.iglob(osp.join(glob.escape(folder), '*.dist-info', 'entry_points.txt')),
-            glob.iglob(osp.join(glob.escape(folder), '*.egg-info', 'entry_points.txt'))
-        ):
-            distro_name_version = osp.splitext(osp.basename(osp.dirname(path)))[0]
-            distro = Distribution.from_name_version(distro_name_version)
-
-            if (repeated_distro == 'first') \
-                    and (distro.name in distro_names_seen):
-                continue
-            distro_names_seen.add(distro.name)
-
-            cp = CaseSensitiveConfigParser(delimiters=('=',))
-            cp.read([path])
-            yield cp, distro
-
-def get_single(group, name, path=None):
-    """Find a single entry point.
-
-    Returns an :class:`EntryPoint` object, or raises :exc:`NoSuchEntryPoint`
-    if no match is found.
-    """
-    for config, distro in iter_files_distros(path=path):
-        if (group in config) and (name in config[group]):
-            epstr = config[group][name]
-            with BadEntryPoint.err_to_warnings():
-                return EntryPoint.from_string(epstr, name, distro)
-
-    raise NoSuchEntryPoint(group, name)
-
-def get_group_named(group, path=None):
-    """Find a group of entry points with unique names.
-
-    Returns a dictionary of names to :class:`EntryPoint` objects.
-    """
-    result = {}
-    for ep in get_group_all(group, path=path):
-        if ep.name not in result:
-            result[ep.name] = ep
-    return result
-
-def get_group_all(group, path=None):
-    """Find all entry points in a group.
-
-    Returns a list of :class:`EntryPoint` objects.
-    """
-    result = []
-    for config, distro in iter_files_distros(path=path):
-        if group in config:
-            for name, epstr in config[group].items():
-                with BadEntryPoint.err_to_warnings():
-                    result.append(EntryPoint.from_string(epstr, name, distro))
-
-    return result
-
-if __name__ == '__main__':
-    import pprint
-    pprint.pprint(get_group_all('console_scripts'))
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/scripts/text_token_extractor/pretokenization.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/scripts/text_token_extractor/pretokenization.py
deleted file mode 100644
index 29ae5dc151085cc59de03e4e8bfc6e5341defb8b..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/scripts/text_token_extractor/pretokenization.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pickle
-import os
-import argparse
-import numpy as np
-
-from torch.utils.data import Dataset, DataLoader
-from mmpt.processors import PKLJSONStrTextProcessor
-from mmpt.utils import ShardedTensor, recursive_config
-
-
-class TokenizerDataset(Dataset):
-    def __init__(self, config):
-        self.text_processor = PKLJSONStrTextProcessor(config)
-        self.video_ids = list(self.text_processor.data.keys())
-
-    def __getitem__(self, idx):
-        video_id = self.video_ids[idx]
-        return video_id, self.text_processor(video_id)
-
-    def __len__(self):
-        return len(self.video_ids)
-
-
-def numpify(shard_idx, video_ids, captions, target_dir, split, prefix, max_cap_len=32):
-    startends = []
-    caps_ids = []
-    for video_id in video_ids:
-        caption = captions[video_id]
-        startend = []
-        cap_ids = []
-        for start, end, cap in zip(
-                caption["start"], caption["end"], caption["cap"]):
-            startend.append(np.array([start, end]).astype("float32"))
-            cap_id = np.full((max_cap_len,), -1, dtype=np.int32)
-            cap = cap[:max_cap_len]
-            cap_id[:len(cap)] = cap
-            cap_ids.append(cap_id)
-        startends.append(np.stack(startend))
-        caps_ids.append(np.stack(cap_ids))
-
-    startends = ShardedTensor.from_list(startends)
-    target_path = os.path.join(
-        target_dir,
-        prefix + split + "_" + str(shard_idx)
-    )
-    print("save to", target_path)
-    startends.save(target_path + ".startends")
-    caps_ids = ShardedTensor.from_list(caps_ids)
-    caps_ids.save(target_path + ".caps_ids")
-
-
-def sharding(config, out_file):
-    with open(out_file, "rb") as fr:
-        captions = pickle.load(fr)
-    target_dir = config.target_dir
-    prefix = os.path.basename(
-                os.path.splitext(config.caption_pkl_path)[0]
-            ) + "." + config.bert_name + "."
-    for split in ["train", "val"]:
-        target_path = os.path.join(target_dir, split + "_meta")
-        with open(target_path + ".pkl", "rb") as fr:
-            meta = pickle.load(fr)
-        print("load meta", target_path, len(meta))
-        for shard_id in meta:
-            numpify(
-                shard_id, meta[shard_id], captions,
-                target_dir, split, prefix
-            )
-
-
-def tokenize(config, out_file):
-    def collator(samples):
-        return samples
-    dataset = TokenizerDataset(config)
-    data = {}
-    for idx, batch in enumerate(
-            DataLoader(dataset, collate_fn=collator, num_workers=16)):
-        for video_id, caption in batch:
-            data[video_id] = caption
-        if idx % 5000 == 0:
-            print(idx)
-    with open(out_file, "wb") as fw:
-        pickle.dump(data, fw, pickle.HIGHEST_PROTOCOL)
-
-
-def main(args):
-    config = recursive_config(args.config).dataset
-
-    out_file = os.path.splitext(config.caption_pkl_path)[0] \
-        + "." + config.bert_name + ".pkl"
-    if not os.path.isfile(out_file):
-        tokenize(config, out_file)
-    sharding(config, out_file)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="pretokenize (raw_)caption.json into pkl.")
-    parser.add_argument('config', type=str)
-    args = parser.parse_args()
-    main(args)
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Cheng Qian.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Cheng Qian.html
deleted file mode 100644
index f6f92a3c36d88ba661516245dc69a6f29356dd12..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Cheng Qian.html	
+++ /dev/null
@@ -1,136 +0,0 @@
-<!DOCTYPE html><html lang="en"><head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no">
-    <title>Cheng Qian</title>
-    <style>
-      html {
-        padding: 1em;
-        background: #fff;
-        color: #222;
-        font-family: "Lucida Grande", "Lucida Sans Unicode", "Lucida Sans", Geneva, Verdana, sans-serif;
-        font-size: 100%;
-        line-height: 1.5;
-      }
-
-      h1,
-      p,
-      ul,
-      ol,
-      div,
-      figure {
-        margin: 0;
-      }
-
-      h1 { font-size: 1.2em; }
-
-      ul {
-        list-style: disc;
-        padding-left: 1.3em;
-      }
-
-      ol {
-        padding-left: 1.3em;
-        list-style: decimal;
-      }
-
-      blockquote {
-        border-left: 3px solid #000;
-        margin: 0;
-        padding: 0 0 0 1em;
-      }
-
-      pre {
-        font-family: monaco, monospace;
-        font-size: 0.875em;
-        margin: 0;
-        padding: 1rem;
-        background: #eee;
-        border-radius: 0.4rem;
-        white-space: pre;
-        word-wrap: normal;
-        word-break: normal;
-        overflow-x: auto;
-      }
-
-      code { border-radius: 0.4rem; }
-
-      figure {
-        display: inline-block;
-        width: 100%;
-        box-sizing: border-box;
-      }
-
-      figcaption { word-break: break-word; }
-
-      img {
-        display: block;
-        max-width: 100%;
-        margin: 0 auto;
-        padding: 1px;
-        border: 1px solid #eee;
-      }
-
-      a {
-        color: #1b6ac9;
-        text-decoration: underline;
-      }
-
-      a:active { color: #064ac9; }
-
-      .wrapper {
-        max-width: 650px;
-        margin: 0 auto;
-      }
-
-      .title {
-        font-size: 2em;
-        margin: 0.5em 0 1em;
-        line-height: 1.3;
-      }
-
-      .attachment--image {
-        text-align: center;
-        color: #888;
-        font-size: 0.875em;
-      }
-
-      .attachment--file {
-        position: relative;
-        padding: 1.4em 1em 1.5em;
-        border: 1px solid #ddd;
-        border-bottom-width: 3px;
-        border-radius: 6px;
-        color: #222;
-      }
-
-      .attachment__label {
-        position: absolute;
-        bottom: 0;
-        right: 0;
-        background: #ddd;
-        font-size: 10px;
-        padding: 2px 6px;
-        border-radius: 6px 0 4px 0;
-        color: #888;
-      }
-
-      .metadata { color: #888; }
-    </style>
-  </head>
-
-  <body>
-    <div class="wrapper">
-      <h1 class="title">Cheng Qian</h1>
-
-<div class="formatted_content">
-  <div><strong>How'd you hear about SM?</strong></div><ul><li>Little Red Book ? Chinese social media app - and someone posted about SM. So I checked out and liked it</li></ul><div><strong>Career?</strong></div><ul><li>DS at McDonald's<ul><li>Build ML models to support marketing team</li><li>e.g. match digital offers to customers</li></ul></li><li>background in analytics and DS</li><li>4-5 years in marketing analytics, then moved to DS and building model</li><li>previously at a bank, building ML models for operations team - fraud detection</li></ul><div><br><strong>Mentorship exp?</strong></div><ul><li>Have a heart to help ppl - esp. students</li><li>2019 - started to mentor students from my uni in Chicago<ul><li>review resume</li><li>high-level career goal-setting and skill building</li><li>prep for interviews</li><li>many of them landed jobs - really happy about that</li></ul></li><li>If I can do it for fun, and enjoy it. Maybe I can also benefit from mentoring</li></ul><div><br><strong>Beginner mistakes. How can you help?</strong></div><ul><li>A realistic understanding of the business world</li><li>The way they describe the entire experience is not relevant o industry</li><li>they write everything they did, or everything they know</li><li>look at the JD, and tailor it to their keywords</li><li>See above</li><li>every week we have a single goal</li><li>in between meetings I am available "how to respond", "I need help negotiating"</li><li>Give advice, but I won't do the work for them.&nbsp;</li><li>How to fish instead of give them a fish</li></ul><div><bc-attachment content-type="application/vnd.basecamp.horizontal-rule.html"><figure class="attachment attachment--content attachment--horizontal-rule">
-  <hr>
-</figure></bc-attachment><br><br><strong>Questions for me?</strong></div><ul><li>Profile of mentees?</li><li>How many mentors?</li><li>How long does it take for a mentor to be paired with a mentor?</li><li>totally okay with ISA - I'd do it for free - but... what if they get laid off?</li><li>How do you make sure they continue to pay?</li><li>What support does SM offer?</li></ul><div><br><bc-attachment content-type="application/vnd.basecamp.horizontal-rule.html"><figure class="attachment attachment--content attachment--horizontal-rule">
-  <hr>
-</figure></bc-attachment></div>
-</div>
-
-    </div>
-  
-
-</body></html>
\ No newline at end of file
diff --git a/spaces/avivdm1/AutoGPT/autogpt/commands/improve_code.py b/spaces/avivdm1/AutoGPT/autogpt/commands/improve_code.py
deleted file mode 100644
index e3440d8b7c6ee8cb62d73df48623ab757c973c59..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/commands/improve_code.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from __future__ import annotations
-
-import json
-
-from autogpt.llm_utils import call_ai_function
-
-
-def improve_code(suggestions: list[str], code: str) -> str:
-    """
-    A function that takes in code and suggestions and returns a response from create
-      chat completion api call.
-
-    Parameters:
-        suggestions (List): A list of suggestions around what needs to be improved.
-        code (str): Code to be improved.
-    Returns:
-        A result string from create chat completion. Improved code in response.
-    """
-
-    function_string = (
-        "def generate_improved_code(suggestions: List[str], code: str) -> str:"
-    )
-    args = [json.dumps(suggestions), code]
-    description_string = (
-        "Improves the provided code based on the suggestions"
-        " provided, making no other changes."
-    )
-
-    return call_ai_function(function_string, args, description_string)
diff --git a/spaces/awacke1/SentenceToGeneratedVideo/README.md b/spaces/awacke1/SentenceToGeneratedVideo/README.md
deleted file mode 100644
index 8ac37edfbe98673576244b4dc73be80bdfa8ffef..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SentenceToGeneratedVideo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 📑SentenceToGeneratedVideo📹
-emoji: 📑📹
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/TopTenAIGeneratedSolutionsAnyoneCouldUse/README.md b/spaces/awacke1/TopTenAIGeneratedSolutionsAnyoneCouldUse/README.md
deleted file mode 100644
index 9070e88ec6fc5a9353d51e3e0ca94a8f3c2608af..0000000000000000000000000000000000000000
--- a/spaces/awacke1/TopTenAIGeneratedSolutionsAnyoneCouldUse/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TopTenAIGeneratedSolutionsAnyoneCouldUse
-emoji: 😻
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/badayvedat/LLaVA/scripts/finetune_qlora.sh b/spaces/badayvedat/LLaVA/scripts/finetune_qlora.sh
deleted file mode 100644
index 1af397d40c925aa18794ffc9650c7cb50f0430d7..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/LLaVA/scripts/finetune_qlora.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/bin/bash
-
-# Uncomment and set the following variables correspondingly to run this script:
-
-################## VICUNA ##################
-# PROMPT_VERSION=v1
-# MODEL_VERSION="vicuna-v1-3-7b"
-################## VICUNA ##################
-
-################## LLaMA-2 ##################
-# PROMPT_VERSION="llava_llama_2"
-# MODEL_VERSION="llama-2-7b-chat"
-################## LLaMA-2 ##################
-
-deepspeed llava/train/train_mem.py \
-    --deepspeed ./scripts/zero2.json \
-    --lora_enable True \
-    --bits 4 \
-    --model_name_or_path ./checkpoints/$MODEL_VERSION \
-    --version $PROMPT_VERSION \
-    --data_path ./playground/data/llava_instruct_80k.json \
-    --image_folder /path/to/coco/train2017 \
-    --vision_tower openai/clip-vit-large-patch14 \
-    --pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \
-    --mm_vision_select_layer -2 \
-    --mm_use_im_start_end False \
-    --mm_use_im_patch_token False \
-    --bf16 True \
-    --output_dir ./checkpoints/llava-$MODEL_VERSION-finetune_lora \
-    --num_train_epochs 1 \
-    --per_device_train_batch_size 16 \
-    --per_device_eval_batch_size 4 \
-    --gradient_accumulation_steps 1 \
-    --evaluation_strategy "no" \
-    --save_strategy "steps" \
-    --save_steps 50000 \
-    --save_total_limit 1 \
-    --learning_rate 2e-5 \
-    --weight_decay 0. \
-    --warmup_ratio 0.03 \
-    --lr_scheduler_type "cosine" \
-    --logging_steps 1 \
-    --tf32 True \
-    --model_max_length 2048 \
-    --gradient_checkpointing True \
-    --lazy_preprocess True \
-    --dataloader_num_workers 4 \
-    --report_to wandb
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Vector3.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/math/Vector3.d.ts
deleted file mode 100644
index d665dec5680a91bbe9a6fded4cd7a619ee5896f4..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/math/Vector3.d.ts
+++ /dev/null
@@ -1,271 +0,0 @@
-import { Euler } from './Euler';
-import { Matrix3 } from './Matrix3';
-import { Matrix4 } from './Matrix4';
-import { Quaternion } from './Quaternion';
-import { Camera } from './../cameras/Camera';
-import { Spherical } from './Spherical';
-import { Cylindrical } from './Cylindrical';
-import { BufferAttribute } from './../core/BufferAttribute';
-import { Vector } from './Vector2';
-/**
- * 3D vector.
- *
- * @example
- * var a = new THREE.Vector3( 1, 0, 0 );
- * var b = new THREE.Vector3( 0, 1, 0 );
- * var c = new THREE.Vector3();
- * c.crossVectors( a, b );
- *
- * @see <a href="https://github.com/mrdoob/three.js/blob/master/src/math/Vector3.js">src/math/Vector3.js</a>
- *
- * ( class Vector3 implements Vector<Vector3> )
- */
-export class Vector3 implements Vector {
-  constructor(x?: number, y?: number, z?: number);
-
-  x: number;
-  y: number;
-  z: number;
-  isVector3: true;
-
-  /**
-   * Sets value of this vector.
-   */
-  set(x: number, y: number, z: number): this;
-
-  /**
-   * Sets all values of this vector.
-   */
-  setScalar(scalar: number): this;
-
-  /**
-   * Sets x value of this vector.
-   */
-  setX(x: number): Vector3;
-
-  /**
-   * Sets y value of this vector.
-   */
-  setY(y: number): Vector3;
-
-  /**
-   * Sets z value of this vector.
-   */
-  setZ(z: number): Vector3;
-
-  setComponent(index: number, value: number): this;
-
-  getComponent(index: number): number;
-
-  /**
-   * Clones this vector.
-   */
-  clone(): this;
-
-  /**
-   * Copies value of v to this vector.
-   */
-  copy(v: Vector3): this;
-
-  /**
-   * Adds v to this vector.
-   */
-  add(a: Vector3, b?: Vector3): this;
-
-  addScalar(s: number): this;
-
-  addScaledVector(v: Vector3, s: number): this;
-
-  /**
-   * Sets this vector to a + b.
-   */
-  addVectors(a: Vector3, b: Vector3): this;
-
-  /**
-   * Subtracts v from this vector.
-   */
-  sub(a: Vector3): this;
-
-  subScalar(s: number): this;
-
-  /**
-   * Sets this vector to a - b.
-   */
-  subVectors(a: Vector3, b: Vector3): this;
-
-  multiply(v: Vector3): this;
-
-  /**
-   * Multiplies this vector by scalar s.
-   */
-  multiplyScalar(s: number): this;
-
-  multiplyVectors(a: Vector3, b: Vector3): this;
-
-  applyEuler(euler: Euler): this;
-
-  applyAxisAngle(axis: Vector3, angle: number): this;
-
-  applyMatrix3(m: Matrix3): this;
-
-  applyMatrix4(m: Matrix4): this;
-
-  applyQuaternion(q: Quaternion): this;
-
-  project(camera: Camera): this;
-
-  unproject(camera: Camera): this;
-
-  transformDirection(m: Matrix4): this;
-
-  divide(v: Vector3): this;
-
-  /**
-   * Divides this vector by scalar s.
-   * Set vector to ( 0, 0, 0 ) if s == 0.
-   */
-  divideScalar(s: number): this;
-
-  min(v: Vector3): this;
-
-  max(v: Vector3): this;
-
-  clamp(min: Vector3, max: Vector3): this;
-
-  clampScalar(min: number, max: number): this;
-
-  clampLength(min: number, max: number): this;
-
-  floor(): this;
-
-  ceil(): this;
-
-  round(): this;
-
-  roundToZero(): this;
-
-  /**
-   * Inverts this vector.
-   */
-  negate(): this;
-
-  /**
-   * Computes dot product of this vector and v.
-   */
-  dot(v: Vector3): number;
-
-  /**
-   * Computes squared length of this vector.
-   */
-  lengthSq(): number;
-
-  /**
-   * Computes length of this vector.
-   */
-  length(): number;
-
-  /**
-   * Computes Manhattan length of this vector.
-   * http://en.wikipedia.org/wiki/Taxicab_geometry
-   *
-   * @deprecated Use {@link Vector3#manhattanLength .manhattanLength()} instead.
-   */
-  lengthManhattan(): number;
-
-  /**
-   * Computes the Manhattan length of this vector.
-   *
-   * @return {number}
-   *
-   * @see {@link http://en.wikipedia.org/wiki/Taxicab_geometry|Wikipedia: Taxicab Geometry}
-   */
-  manhattanLength(): number;
-
-  /**
-   * Computes the Manhattan length (distance) from this vector to the given vector v
-   *
-   * @param {Vector3} v
-   *
-   * @return {number}
-   *
-   * @see {@link http://en.wikipedia.org/wiki/Taxicab_geometry|Wikipedia: Taxicab Geometry}
-   */
-  manhattanDistanceTo(v: Vector3): number;
-
-  /**
-   * Normalizes this vector.
-   */
-  normalize(): this;
-
-  /**
-   * Normalizes this vector and multiplies it by l.
-   */
-  setLength(l: number): this;
-  lerp(v: Vector3, alpha: number): this;
-
-  lerpVectors(v1: Vector3, v2: Vector3, alpha: number): this;
-
-  /**
-   * Sets this vector to cross product of itself and v.
-   */
-  cross(a: Vector3, w?: Vector3): this;
-
-  /**
-   * Sets this vector to cross product of a and b.
-   */
-  crossVectors(a: Vector3, b: Vector3): this;
-  projectOnVector(v: Vector3): this;
-  projectOnPlane(planeNormal: Vector3): this;
-  reflect(vector: Vector3): this;
-  angleTo(v: Vector3): number;
-
-  /**
-   * Computes distance of this vector to v.
-   */
-  distanceTo(v: Vector3): number;
-
-  /**
-   * Computes squared distance of this vector to v.
-   */
-  distanceToSquared(v: Vector3): number;
-
-  /**
-   * @deprecated Use {@link Vector3#manhattanDistanceTo .manhattanDistanceTo()} instead.
-   */
-  distanceToManhattan(v: Vector3): number;
-
-  setFromSpherical(s: Spherical): this;
-  setFromCylindrical(s: Cylindrical): this;
-  setFromMatrixPosition(m: Matrix4): this;
-  setFromMatrixScale(m: Matrix4): this;
-  setFromMatrixColumn(matrix: Matrix4, index: number): this;
-
-  /**
-   * Checks for strict equality of this vector and v.
-   */
-  equals(v: Vector3): boolean;
-
-  fromArray(xyz: number[], offset?: number): Vector3;
-
-  /**
-   * Returns an array [x, y, z], or copies x, y and z into the provided array.
-   * @param array (optional) array to store the vector to. If this is not provided, a new array will be created.
-   * @param offset (optional) optional offset into the array.
-   * @return The created or provided array.
-   */
-  toArray(xyz?: number[], offset?: number): number[];
-
-  /**
-   * Copies x, y and z into the provided array-like.
-   * @param array array-like to store the vector to.
-   * @param offset (optional) optional offset into the array.
-   * @return The provided array-like.
-   */
-  toArray(xyz: ArrayLike<number>, offset?: number): ArrayLike<number>;
-
-  fromBufferAttribute(
-    attribute: BufferAttribute,
-    index: number,
-    offset?: number
-  ): this;
-}
diff --git a/spaces/bastiendechamps/geoguessr-bot/geoguessr_bot/guessr/abstract_guessr.py b/spaces/bastiendechamps/geoguessr-bot/geoguessr_bot/guessr/abstract_guessr.py
deleted file mode 100644
index ef84b1121179c78de81f680d219293b2806f97e4..0000000000000000000000000000000000000000
--- a/spaces/bastiendechamps/geoguessr-bot/geoguessr_bot/guessr/abstract_guessr.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from abc import abstractmethod
-from dataclasses import dataclass
-from typing import Optional
-
-from PIL import Image
-import plotly.graph_objects as go
-
-from geoguessr_bot.interfaces import Coordinate
-
-
-@dataclass
-class AbstractGuessr:
-    """Abstract class for a guessr
-    """
-    @abstractmethod
-    def guess(self, image: Image) -> Coordinate:
-        raise NotImplementedError
-
-    def guess_from_path(self, image_path: str) -> Coordinate:
-        image = Image.open(image_path)
-        return self.guess(image)
-
-    @staticmethod
-    def create_map(guess_coordinate: Optional[Coordinate] = None, **kwargs) -> go.Figure:
-        """Create an interactive map showing a coordinate
-        """
-        fig = go.Figure(go.Scattermapbox(
-            customdata=[str(guess_coordinate)] if guess_coordinate is not None else None,
-            lat=[guess_coordinate.latitude] if guess_coordinate is not None else None,
-            lon=[guess_coordinate.longitude] if guess_coordinate is not None else None,
-            mode="markers",
-            marker=go.scattermapbox.Marker(
-                size=6,
-            ),
-            hoverinfo="text",
-            hovertemplate='coord: %{customdata[0]}'
-        ))
-        fig.update_layout(
-            mapbox_style="open-street-map",
-            hovermode='closest',
-            mapbox=dict(
-                bearing=0,
-                center=go.layout.mapbox.Center(
-                    lat=guess_coordinate.latitude if guess_coordinate is not None else 0,
-                    lon=guess_coordinate.longitude if guess_coordinate is not None else 0
-                ),
-                pitch=0,
-                zoom=3 if guess_coordinate is not None else 0
-            ),
-        )
-        return fig
diff --git a/spaces/bigcode/Reasoning-with-StarCoder/prompt.py b/spaces/bigcode/Reasoning-with-StarCoder/prompt.py
deleted file mode 100644
index 10abdbd9e2770769f8f9f436fadc60caa90a496b..0000000000000000000000000000000000000000
--- a/spaces/bigcode/Reasoning-with-StarCoder/prompt.py
+++ /dev/null
@@ -1,329 +0,0 @@
-prompt = '''
-def solution():
-    #Ques: For Halloween Debby and her sister combined the candy they received. Debby had 32 pieces of candy while her sister had 42. If they ate 35 pieces the first night, how many pieces do they have left?
-    Debby_candies = 32
-    sister_candies = 42
-    candies_ate = 35
-    return ((Debby_candies + sister_candies) - candies_ate) 
-
-
-def solution():
-    #Ques: What are roots of the equation x^2 - 2x + 1?
-    import math
-    a = 1
-    b = -2
-    c = 1
-    root1 = (-b + math.sqrt(b**2 - 4*a*c)) / (2*a)
-    root2 = (-b - math.sqrt(b ** 2 - 4 * a * c)) / (2 * a)
-    return root1, root2
-
-
-def solution():
-    #Ques: A waiter had 22 customers in his section. If 14 of them left and the rest of his tables had 4 people at each table, how many tables did he have?
-    customers = 32
-    customers_left = 14
-    each_table = 4
-    total_tables = (customers - customers_left) / each_table
-    return total_tables
-
-
-def solution():
-    #Ques: What is the 5th number in Fibonacci sequence?
-    n = 5
-    a = 0
-    b = 1
-    if n < 0:
-        print("Incorrect input")
-    elif n == 0:
-        return a
-    elif n == 1:
-        return b
-    else:
-        for i in range(2, n):
-            c = a + b
-            a = b
-            b = c
-        return b
-print(solution())
-
-
-def solution():
-    #Ques: {question}
-'''
-
-algebric_prompt = '''
-#Ques: For Halloween Debby and her sister combined the candy they received. Debby had A pieces of candy while her sister had B. If they ate C pieces the first night, how many pieces do they have left?
-Answer = (A + B) - C
-
-
-#Ques: A waiter had A customers in his section. If B of them left and the rest of his tables had C people at each table, how many tables did he have?
-Answer = (A - B) / C
-
-
-#Ques: There were A friends playing a video game online when B players quit. If each player left had C lives, how many lives did they have total? "
-Answer = (A - B) * C
-
-
-#Ques: {question}
-'''
-
-
-python_prompt = '''
-# Ques: For Halloween Debby and her sister combined the candy they received. Debby had A pieces of candy while her sister had B . If they ate C pieces the first night, how many pieces do they have left?
-# Expression = (A + B) - C
-
-# Function for above Expression and Ques is:
-def solution(A, B, C):
-    return (A + B) - C
-
-
-# Ques: A waiter had A customers in his section. If B of them left and the rest of his tables had C people at each table, how many tables did he have?    
-# Expression = (A - B) / C
-
-# Function for above expression is:
-def solution(A, B, C):
-    return (A - B) / C
-
-
-# Ques: There were A friends playing a video game online when B more players joined the game. If each player had C lives, how many lives did they have total?
-# Expression = (A - B) * C
-
-# Function for above expression is:
-def solution(A, B, C):
-    return (A - B) * C
-
-
-#Ques: {question}
-# Expression = {expression}
-
-# Function for above expression is:
-'''
-
-TA_prompt = '''
-Below are a series of dialogues between various people and an AI technical assistant. The assistant tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with code questions, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-
------
-
-Human: Write a function that takes two lists and returns a list that has alternating elements from each input list.
-
-Assistant: Sure. Here is a function that does that.
-
-```
-def alternating(list1, list2):
-   results = []
-   for i in range(len(list1)):
-       results.append(list1[i])
-       results.append(list2[i])
-   return results
-```
-
-Human: Can you write some test cases for this function?
-
-Assistant: Sure, here are some tests.
-
-```
-assert alternating([10, 20, 30], [1, 2, 3]) == [10, 1, 20, 2, 30, 3]
-assert alternating([True, False], [4, 5]) == [True, 4, False, 5]
-assert alternating([], []) == []
-```
-
-Human: Modify the function so that it returns all input elements when the lists have uneven length. The elements from the longer list should be at the end.
-
-Assistant: Here is the modified function.
-
-```
-def alternating(list1, list2):
-   results = []
-   for i in range(min(len(list1), len(list2))):
-       results.append(list1[i])
-       results.append(list2[i])
-   if len(list1) > len(list2):
-       results.extend(list1[i+1:])
-   else:
-       results.extend(list2[i+1:])
-   return results
-```
-
------
-
-Human: My code below is not working. Can you see what may be wrong?
-
-```
-def factorial(n):
-   r = 0
-   for i in range(n):
-       r *= i
-   return r
-```
-
-Assistant: In factorial, the initial value of `r` must be `1`. The following version should work:
-
-```
-def factorial(n):
-   r = 1 # Changed here
-   for i in range(n):
-       r *= i
-   return r
-```
-Human: Can you use this function to create a code to compute the number of k-combinations of a set of n elements?
-
-Assistant: Yes, of course. The number of k-combinations of a set of elements can be written as C(n, k) and we have C(n, k) = \frac{n!}{(n-k)!k!} whenever k <= n. The code is as follows
-
-def combination(n, k):
-    assert k <= n
-    return factorial(n)/(factorial(n-k)*factorial(k))
-
-
-This should work pretty well. Keep in mind that you can use numpy or scipy to have a much better implementation.
-
-
-Human: Thanks.
-
-
-Assistant: You are welcome.
------
-
-Human: I have a CSV file that looks like this:
-
-Name,Salary
-Bob,12000
-Avantika,35000
-Alice,30000
-
-Write a program that returns the name of the person with the highest salary.
-
-Assistant: Here is a solution that assumes the filename is salaries.csv.
-
-
-```
-from csv import DictReader
-from pathlib import Path
-
-
-def max_salary(filename):
-   rows = DictReader(Path(filename).read_text().splitlines())
-   max_index = -1
-   for i, row in enumerate(rows):
-       if int(row['Salary']) > int(rows[max_index]['Salary']):
-           max_index = i
-   return rows[max_index]['Name']
-
-
-print(max_salary('salaries.csv'))
-```
------
-
-Human: What is the purpose of the code below ?
-
-```
-def f(n, p):
-   if p == 0:
-	return 1
-   else :
-   	return n*f(n, p-1)
-```
-Assistant: This python function is designed to raise a base value n to a power p, that is, computing the value n^p where p is a positive integer.
-
------
-
-Human: Write a program that returns the prime numbers less than 1000 that are congruent to 1 modulo 6.
-
-Assistant : The method I will adopt to solve this problem is to go through all the integers less than 1000 and check if they are prime and congruent to 1 modulo 6. For that purpose, I will need to write an utility function to check if a number is prime. Here it is:
-
-```
-def is_prime(p):
-   if p <= 1:
-	return False
-   else :
-   	for i in range(2, int(p**0.5)+1):
-	    if p%i == 0 :
-		 return False 
-      return True
-```
-Now, here is the final solution:
-
-```
-def primes_mod_6():
-   primes = []
-   for n in range(1000):
-	if is_prime(n) and n%6 == 1 :
-	   primes.append(n)
-   return primes
-``` 
-
------
-
-Human: Can you translate the code below in Java? 
-```
-print(“Hello world”)
-```
-Assistant: Sure, the Java translation of your code is as follows.
-```
-System.out.println(“Hello world”);
-```
-
------
-
-Human: The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning. They sold 93 loaves in the morning and 39 loaves in the afternoon. A grocery store returned 6 unsold loaves. How many loaves of bread did they have left?
-
-Assistant: Let’s think step by step. The bakers started with 200 loaves of bread. They sold 93 in the morning and 39 in the afternoon. So they sold 93+39=132 loaves of bread. Which means they had 200 - 132 = 68 loaves left. Finally, a grocery returned 6 unsold loaves to the store. Thus, they ended up with 68+6=74 loaves. The answer is 74.
-
-Human: Nice. Can you write a python function to solve this problem? You should consider 4 arguments, the initial amount of loaves baked, the amount of loaves sold in the morning, the amount of loaves sold in the afternoon and the number of loaves returned to the bakery.
-
-Assistant: Sure, here is the code.
-
-```
-def loaves_left(initial_number_of_loaves, loaves_sold_morning, loaves_sold_afternoon, loaves_returned):
-    loaves_sold_total = loaves_sold_morning + loaves_sold_afternoon
-    return initial_number_of_loaves - loaves_sold_total + loaves_returned
-```
-Human: Can you test your function with the following inputs?
-24, 5, 9, 3
-153, 57, 99, 32
-
-Assistant: Definitely, we have:
-loaves_left(24, 5, 9, 3) = 13
-loaves_left(153, 57, 99, 32) = 29
-
------
-
-Human: Can you write a python function to randomly decide between eating a pizza or a burger with probability 0.3 and 0.7 respectively?
-
-```
-import random
-def pizza_or_burger():
-    p = random.uniform(0, 1)
-    if p <= 0.3:
- print(“Let’s eat a pizza for lunch.”)
-    else:
- print(“Let’s eat a burger for lunch.”)
-```
-
------
-
-Human:  I have a dictionary with lots of dictionaries inside it, and so on, how can I make that into just one single dictionary?
-
-Assistant:  Are you talking about dictionaries in the context of python programming?
-
-Human:  Yeah these are dictionaries with keys and values, and the values themselves are dictionaries.
-
-Assistant:  Great, I think I understand what you’re asking.  We might say that we want to flatten the dictionaries, right?  And if there are several levels, we’ll want to do this recursively.
-
-But to do this we will need to combine the keys from the outer dictionary with the keys from the inner dictionary, so that the final resulting keys are unambiguous.  I’d suggest doing this by concatenating them together, separated by some character.  Will that work for you, and what character would you like to use?
-
-```
-def recursively_flatten_dicts(d, separator="_"):
-   result = {}
-   for k, v in d.items():
-       if isinstance(v, dict):
-           new_dict = recursively_flatten_dicts(
-               {k + separator + p: w for p, w in v.items()}, separator=separator
-           )
-           result.update(new_dict)
-       else:
-           result[k] = v
-   return result
-```
------
-
-'''
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/ui_components.py b/spaces/bigjoker/stable-diffusion-webui/modules/ui_components.py
deleted file mode 100644
index d239d3f70938942f625f5f49e9398fcde10016bf..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/ui_components.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-
-
-class ToolButton(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool-top", **kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-class FormRow(gr.Row, gr.components.FormComponent):
-    """Same as gr.Row but fits inside gradio forms"""
-
-    def get_block_name(self):
-        return "row"
-
-
-class FormGroup(gr.Group, gr.components.FormComponent):
-    """Same as gr.Row but fits inside gradio forms"""
-
-    def get_block_name(self):
-        return "group"
-
-
-class FormHTML(gr.HTML, gr.components.FormComponent):
-    """Same as gr.HTML but fits inside gradio forms"""
-
-    def get_block_name(self):
-        return "html"
-
-
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
-    """Same as gr.ColorPicker but fits inside gradio forms"""
-
-    def get_block_name(self):
-        return "colorpicker"
-
-
-class DropdownMulti(gr.Dropdown):
-    """Same as gr.Dropdown but always multiselect"""
-    def __init__(self, **kwargs):
-        super().__init__(multiselect=True, **kwargs)
-
-    def get_block_name(self):
-        return "dropdown"
diff --git a/spaces/bioriAsaeru/text-to-voice/Download Borland Delphi 2010 Full 52 A Complete Tutorial for Beginners.md b/spaces/bioriAsaeru/text-to-voice/Download Borland Delphi 2010 Full 52 A Complete Tutorial for Beginners.md
deleted file mode 100644
index 549763555c41543f561222d2ed9a2ef6c62624f5..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Download Borland Delphi 2010 Full 52 A Complete Tutorial for Beginners.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Program to display GINI format images received over the NOAAPORT system,and convert them to BMP format for subsequent import to other software (such as DigitalAtmosphere Workstation). The program will run with full function for30 days after download, but if you wish to use it after the trial period youwill have to register the software, for which there is a small charge.</p>
-<h2>download borland delphi 2010 full 52</h2><br /><p><b><b>Download Zip</b> &#9658;&#9658;&#9658; <a href="https://urloso.com/2uyRGu">https://urloso.com/2uyRGu</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Free Expression A Fundamental Human Right and Its Challenges.md b/spaces/bioriAsaeru/text-to-voice/Free Expression A Fundamental Human Right and Its Challenges.md
deleted file mode 100644
index c08eba082554d09ee26e0c503bd776335259ec35..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Free Expression A Fundamental Human Right and Its Challenges.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-
-<p>But in order to really participate, we need to know our rights - otherwise we may lose them. The highest law in our land is the U.S. Constitution, which has some amendments, known as the Bill of Rights. The Bill of Rights guarantees that the government can never deprive people in the U.S. of certain fundamental rights including the right to freedom of religion and to free speech and the due process of law. Many federal and state laws give us additional rights, too.</p>
-<h2>Free Expression</h2><br /><p><b><b>Download</b> &#9675; <a href="https://urloso.com/2uyO7i">https://urloso.com/2uyO7i</a></b></p><br /><br />
-<p>The First Amendment guarantees our <strong>right to free expression and free association, which means that the government does not have the right to forbid us from saying what we like and writing what we like; we can form clubs and organizations, and take part in demonstrations and rallies. </strong></p>
-<p>Some states -- including Colorado, California, Iowa, Kansas and Massachusetts -- have <strong>"High School Free Expression" laws that give students more free speech rights than the Constitution requires. Check with your local ACLU to find out if your state has such a law. </strong></p>
-<p>If you believe that your school is censoring books because of their viewpoints, you, your teachers and the school librarian can challenge book censorship at your school or in court. <strong>The freedom to read is the freedom to think - and that's totally worth fighting for!</strong></p>
-<p></p>
-<p>It is equally important not to stifle the dissemination of any ideas, even if other members of our community may find those ideas abhorrent. Individuals wishing to express ideas with which others may disagree must be free to do so, without fear of being bullied, threatened or silenced. This does not mean that such ideas should go unchallenged, as that is part of the learning process. And though we believe all members of our campus communities have a role to play in promoting civility and mutual respect in that type of discourse, we must not let concerns over civility or respect be used as a reason to silence expression. We should empower and enable one another to speak and listen, rather than interfere with or silence the open expression of ideas.</p>
-<p>The internet and new technologies give individuals the ability to publish and receive information, participate in political processes, and share knowledge. CDT has fought to extend the highest level of free speech protections to the internet and to keep new technologies free of government censorship and content gatekeepers. Private online service providers need to be protected from legal liability for content posted by users so that they will be willing to host it. User choice and control, which allow individuals to decide what to say, publish and access online, are essential parts of protecting free expression rights.</p>
-<p>The content throughout this website that originates with CDT can be freely copied and used as long as you make no substantive changes and clearly give us credit. More on CDT's content reuse policy is available here.</p>
-<p>Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble, and to petition the government for a redress of grievances.</p>
-<p>The Office of Instructional Resources has partnered with other university offices to create freedom of expression curriculum packet available for use in any Blackboard course. The curriculum is entirely customizable and can be fit to different class needs.</p>
-<p>It is inevitable that the views and ideas of our community will conflict. We are committed to maintaining an environment that is a marketplace of ideas to the benefit of all individuals, where freely exchanging ideas is not compromised because the ideas are to some offensive, unwise, disagreeable, too conservative, too liberal, too traditional, radical, or wrong-headed. It is for the individual members of our University community, not for the University as an institution, to make value judgments, and to act on those judgments not by seeking to suppress speech, but by openly and vigorously contesting the ideas that they oppose.</p>
-<p>We recognize that we may falter at times, but make no mistake that we are committed to continuing, and forever looking for ways to improve, the support and preservation of a lively and fearless freedom of debate and deliberation while protecting that freedom when others attempt to restrict it.</p>
-<p>These principles carry responsibilities. Academic freedom carries the responsibility for the faithful performance of professional duties and obligations. All members of the university community at each of the institutions in the University of Wisconsin System share in the responsibility for maintaining civility and a climate of mutual respect. Although members of the university community at each institution are free to criticize and contest the views expressed on campus, they may not obstruct or otherwise interfere with the freedom of others, including speakers who are invited to campus, to express views they reject or even loathe. Freedom of expression also carries the obligation to make clear that when speaking on matters of public interest or concern, one is speaking on behalf of oneself, not the institution.</p>
-<p>Chapter UWS 17.09 describes misconduct that may subject a student to discipline, including but not limited to: disruption of university-authorized activities; dangerous conduct; unauthorized use of or damage to property; violation of criminal law; serious and repeated violations of municipal law; violation of university rules; and violation of Chapter UWS 18. Chapter UWS 17.10(1) contains a range of disciplinary sanctions for students under the jurisdiction of the institution who engage in violent or other disorderly conduct that materially and substantially disrupts the free expression of others.</p>
-<p>Students and employees have the freedom to discuss any problem that presents itself, as the First Amendment of the U.S. Constitution and Article I of the Wisconsin Constitution permit. Students and employees shall be permitted to assemble and engage in spontaneous expressive activity as long as such activity does not materially and substantially disrupt the functioning of an institution.</p>
-<p>Protests and demonstrations that materially and substantially disrupt the rights of others to engage in or listen to expressive activity shall not be permitted and shall be subject to sanction. This policy shall not prohibit administrators, faculty, or other instructors from maintaining order. Access to UW institutions for purposes of free speech and expression shall occur within the limits of reasonable viewpoint-neutral and content-neutral restrictions on time, place, and manner of expression and the provisions of Chapter UWS 21 (Use of University Facilities) of the Wisconsin Administrative Code.</p>
-<p>Educational institutions play an essential role in sustaining democracy. They serve as crucibles of ideas, dialogue, and debate, committed to educating the rising generation as well as forwarding the pursuit of knowledge and truth. But amid rising polarization in the United States and around the world, educational institutions have become a site of conflict and challenge, particularly when it comes to the bedrock values of free speech and diversity, equity, and inclusion, and perceived tensions among them.</p>
-<p>Free speech has become a flashpoint for controversy on campuses across the U.S. We believe that for a campus to be open to all ideas, it must be genuinely open to all people, including those from less traditional backgrounds and those from previously excluded groups. In our trainings for university faculty and staff, and in our advocacy for free speech and academic freedom, we encourage campuses to robustly defend free speech and open inquiry at the same time that they advance principles of diversity and inclusion and address lingering legacies of discrimination.</p>
-<p>Bucknell University is committed to protecting the right to free expression on campus to further our mission to "educate students for a lifetime of critical thinking and strong leadership characterized by continued exploration, creativity and imagination."</p>
-<p>Diverse perspectives in the classroom and across campus enhance everyone's educational experience. We integrate a commitment to free expression and the principles of academic freedom throughout our policies and by specific actions that support lively and fearless debate. In particular, Bucknell:</p>
-<p>Encourages students to engage freely with course content with assurance that students will be evaluated on the merits of their arguments and performance, and not on their opinions or conduct in matters irrelevant to the academic mission of the University or course content.</p>
-<p>Respects each student's freedom to express reasoned opinions that differ from the opinions of faculty or the majority of other students without concern for arbitrary and capricious evaluation of their work.</p>
-<p>Supports academic freedom, freedom of expression, and freedom of association for all members of the University community, subject to reasonable restrictions of time, place and manner to ensure orderly scheduling of facilities and adequate preparation for the event, and that the activity is conducted in a manner appropriate to an academic community.</p>
-<p>Values a constituency composed of individuals with diversity of interests and opinions, bound together by respect for the individual and collective rights of others. All members of the University community are responsible for the maintenance of an environment in which people are free to learn and work without fear of discrimination or abuse.</p>
-<p>The Board of Trustees has adopted the statement of the American Association of University Professors which recognizes that members of the faculty are citizens with the right to speak and write as citizens free from institutional censorship or discipline. When speaking as a private citizen faculty must still remember that the public may judge their profession and Bucknell by their statements and therefore even when speaking as private citizens, members of the faculty are expected to be accurate, to exercise appropriate restraint, to show respect for the opinion of others and indicate that they are not speaking on behalf of Bucknell.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Iron Grip Warlord Torrent Download !FREE! [Password].md b/spaces/bioriAsaeru/text-to-voice/Iron Grip Warlord Torrent Download !FREE! [Password].md
deleted file mode 100644
index af3030714dc85ce9f439667388bfb4ad19d1b120..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Iron Grip Warlord Torrent Download !FREE! [Password].md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<h1>How to Download Iron Grip: Warlord for Free with a Password</h1>
-<p>Iron Grip: Warlord is a first-person shooter game that combines tower defense and survival elements. You play as one of the warlords, a leader of a resistance group fighting against the oppressive Confederation. You have to defend your stronghold from waves of enemies using various weapons and traps.</p>
-<p>If you want to download Iron Grip: Warlord for free, you might be tempted to look for a torrent file online. However, this is not a safe or legal option. Many torrent sites are full of malware and viruses that can harm your computer. Moreover, downloading copyrighted content without permission is a violation of the law and can result in fines or legal action.</p>
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-<p>Fortunately, there is a better way to get Iron Grip: Warlord for free. You can use a password that unlocks the full version of the game without any risk or hassle. The password is <strong>IGW2023</strong> and it works for both Windows and Mac versions of the game. Here are the steps to use the password:</p>
-<ol>
-<li>Go to the official website of Iron Grip: Warlord at <a href="https://www.isotx.com/iron-grip-warlord/">https://www.isotx.com/iron-grip-warlord/</a> and click on the "Download" button.</li>
-<li>Choose your preferred platform (Windows or Mac) and download the installer file.</li>
-<li>Run the installer file and follow the instructions to install the game on your computer.</li>
-<li>Launch the game and enter the password <strong>IGW2023</strong> when prompted.</li>
-<li>Enjoy playing Iron Grip: Warlord for free!</li>
-</ol>
-<p>This password is valid until April 30, 2023. After that date, you will need to purchase the game or use another password if available. You can also check out the official forums of Iron Grip: Warlord for more tips, tricks, and updates on the game.</p>
-<p>Iron Grip: Warlord is a fun and challenging game that will test your skills and strategy. Download it today with the password <strong>IGW2023</strong> and join the resistance!</p>
-
-<p>Iron Grip: Warlord is not just a shooter game. It also has elements of tower defense and survival. You have to build and upgrade your defenses, such as barricades, turrets, mines, and traps. You also have to manage your resources, such as ammo, health, and money. You can use the money to buy more weapons and upgrades from the shop.</p>
-<p>The game has four different modes: Campaign, Survival, Skirmish, and Co-op. In Campaign mode, you have to complete a series of missions with different objectives and scenarios. In Survival mode, you have to survive as long as possible against endless waves of enemies. In Skirmish mode, you can customize your own game settings and play against the AI or other players. In Co-op mode, you can team up with up to seven other players and work together to defend your stronghold.</p>
-<p>Iron Grip: Warlord also has a unique art style and atmosphere. The game is set in an alternate history where the world is divided into two factions: the Confederation and the Atelia. The Confederation is a powerful empire that seeks to conquer and oppress the world. The Atelia is a coalition of rebel groups that fight for freedom and independence. The game has a steampunk aesthetic that mixes industrial and Victorian elements. The game also has a dynamic soundtrack that changes according to the situation and mood.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/__init__.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/camenduru-com/sl/app.py b/spaces/camenduru-com/sl/app.py
deleted file mode 100644
index eba538048b283d046e0313befd7f4f9ea070f08a..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/sl/app.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import re
-from flask import Flask, request, Response
-# We use streamlink to catch video stream from web page or direct link.
-from streamlink import Streamlink
-from sys import argv
-from time import time
-from urllib.request import urlopen
-
-app = Flask(__name__)
-# Set video chunk size
-buff_size = 1 << 16
-pause = 600
-
-
-@app.route('/')
-def streamlink(request=request):
-    '''Parse query string, set options and get stream.'''
-    try:
-        # Get arguments passed with query string
-        args = request.args
-        # Available options
-        if 'help' in args:
-            return Response(Streamlink.set_option.__doc__, content_type='text/plain')
-        if 'version' in args:
-            return Response(Streamlink.version.fget('self') + '\n', content_type='text/plain')
-        # url should be either first argument or set explicitly with 'url' key.
-        if 'url' not in args:
-            url = next(iter(args))
-        else:
-            url = args['url']
-
-        # Split url to url itself (url[0]) and stream (url[1]) if present.
-        url = url.split()
-        session = Streamlink()
-        session.set_option('http-headers', 'User-Agent=Mozilla/5.0')
-        plugin = session.resolve_url(url[0])[0]
-        # Use remain arguments to set other options.
-        for key in args:
-            if re.match('[0-9]+$', args[key]):
-                value = int(args[key])
-            else:
-                value = args[key]
-            # Set session options described by help
-            session.set_option(key, value)
-            # Set plugin options if require (usually username and password)
-            session.set_plugin_option(plugin, key, value)
-        # Catch stream with given url
-        streams = session.streams(url[0])
-        # pick the stream
-        if len(url) > 1:
-            stream = streams[url[1]]
-        else:
-            # If specific stream is not provided in args, output list of available streams.
-            return Response('Available streams: ' + str(list(streams.keys())) + '\n', content_type='text/plain')
-
-        # Stream generator
-        url_root = request.url_root
-
-        def generate(fd):
-            chunk = True
-            # Iterate over stream
-            with fd:
-                last = time()
-                while chunk:
-                    now = time()
-                    # yank periodically server to keepalive
-                    if now - last > pause:
-                        urlopen(url_root)
-                        last = now
-                    chunk = fd.read(buff_size)
-                    # Read chunk of stream
-                    yield chunk
-
-        if 'link' in args:
-            # Redirect client to stream url
-            redirect_url = stream.url
-            response = Response('', content_type='')
-            response.headers['Location'] = redirect_url
-            response.status_code = 302
-            return response
-        else:
-            # Streaming to client
-            # Open file like object of stream
-            fd = stream.open()
-            return Response(generate(fd), content_type='video/mpeg', headers={'Access-Control-Allow-Origin':'*'})
-    except Exception or OSError as exception:
-        error = 'Exception {0}: {1}\n'.format(type(exception).__name__, exception)
-        return Response(error, content_type='text/plain')
-
-
-# call script from command line for testing
-if __name__ == '__main__':
-    if len(argv) > 1:
-        port = argv[1]
-    else:
-        port = 7860
-    app.run(debug=True, host='0.0.0.0', port=port)
diff --git a/spaces/ceckenrode/HTML5-Aframe-3D-Maps/README.md b/spaces/ceckenrode/HTML5-Aframe-3D-Maps/README.md
deleted file mode 100644
index 8dbf5dd27edbaa6c3f95f6848acc5c36fcbdc35a..0000000000000000000000000000000000000000
--- a/spaces/ceckenrode/HTML5-Aframe-3D-Maps/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: HTML5 Aframe 3D Maps
-emoji: 💩
-colorFrom: red
-colorTo: pink
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cenji1109285052/img-to-music/utils.py b/spaces/cenji1109285052/img-to-music/utils.py
deleted file mode 100644
index 58f6e0c1f9c6af926a3cacf090517d6a62d618be..0000000000000000000000000000000000000000
--- a/spaces/cenji1109285052/img-to-music/utils.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import json
-import numpy as np
-import httpx
-import os
-
-from constants import MUBERT_TAGS, MUBERT_MODE, MUBERT_LICENSE, MUBERT_TOKEN
-
-def get_mubert_tags_embeddings(w2v_model):
-    return w2v_model.encode(MUBERT_TAGS)
-
-
-def get_pat(email: str):
-    r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess',
-                   json={
-                       "method": "GetServiceAccess",
-                       "params": {
-                           "email": email,
-                           "license": MUBERT_LICENSE,
-                           "token": MUBERT_TOKEN,
-                           "mode": MUBERT_MODE,
-                       }
-                   })
-
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, "probably incorrect e-mail"
-    pat = rdata['data']['pat']
-    return pat
-
-
-def find_similar(em, embeddings, method='cosine'):
-    scores = []
-    for ref in embeddings:
-        if method == 'cosine':
-            scores.append(1 - np.dot(ref, em) / (np.linalg.norm(ref) * np.linalg.norm(em)))
-        if method == 'norm':
-            scores.append(np.linalg.norm(ref - em))
-    return np.array(scores), np.argsort(scores)
-
-
-def get_tags_for_prompts(w2v_model, mubert_tags_embeddings, prompts, top_n=3, debug=False):
-    prompts_embeddings = w2v_model.encode(prompts)
-    ret = []
-    for i, pe in enumerate(prompts_embeddings):
-        scores, idxs = find_similar(pe, mubert_tags_embeddings)
-        top_tags = MUBERT_TAGS[idxs[:top_n]]
-        top_prob = 1 - scores[idxs[:top_n]]
-        if debug:
-            print(f"Prompt: {prompts[i]}\nTags: {', '.join(top_tags)}\nScores: {top_prob}\n\n\n")
-        ret.append((prompts[i], list(top_tags)))
-    return ret
\ No newline at end of file
diff --git a/spaces/chasetank/owner-manual/app.py b/spaces/chasetank/owner-manual/app.py
deleted file mode 100644
index 1ae7f1cfbd711c8afa465a282e59c5a09a27ce45..0000000000000000000000000000000000000000
--- a/spaces/chasetank/owner-manual/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from InnovationHub.llm.vector_store import *
-from InnovationHub.llm.chain import *
-
-
-def start_ui():
-    chatbot_interface = gradio.Interface(
-        fn=chatbot.chat,
-        inputs=["text",
-                gradio.inputs.Dropdown(
-                    vehicle_options, label="Select Vehicle Model"),
-                gradio.inputs.Slider(minimum=1, maximum=10, step=1, label="k"),
-                gradio.inputs.Slider(
-                    minimum=0.0, maximum=1.0, step=0.01, label="Temperature")
-                ],
-        outputs="text",
-        title="Owner's Manual",
-        description="Ask your vehicle manual and get a response.",
-        examples=[["What are the different features of the dashboard console?", "S-Class", 2, 0.5],
-                  ["What is flacon?", "S-Class", 3, 0.8],
-                  ["What is hyperscreen?", "EQS", 2, 0.2],
-                  ["Where can I find my vin?", "EQS", 3, 0.3]
-                  ]
-    )
-
-    chatbot_interface.launch()
-
-if __name__ == '__main__':
-    start_ui()
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/models/altclip/processing_altclip.py b/spaces/chendl/compositional_test/transformers/src/transformers/models/altclip/processing_altclip.py
deleted file mode 100644
index 8fe49ad678e9c81efd7d34b7d3f952816bea2752..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/models/altclip/processing_altclip.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# coding=utf-8
-# Copyright 2022 WenXiang ZhongzhiCheng LedellWu LiuGuang BoWenZhang The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Image/Text processor class for AltCLIP
-"""
-import warnings
-
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding
-
-
-class AltCLIPProcessor(ProcessorMixin):
-    r"""
-    Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
-    processor.
-
-    [`AltCLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`XLMRobertaTokenizerFast`]. See
-    the [`~AltCLIPProcessor.__call__`] and [`~AltCLIPProcessor.decode`] for more information.
-
-    Args:
-        image_processor ([`CLIPImageProcessor`]):
-            The image processor is a required input.
-        tokenizer ([`XLMRobertaTokenizerFast`]):
-            The tokenizer is a required input.
-    """
-    attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "CLIPImageProcessor"
-    tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
-
-    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-        if image_processor is None:
-            raise ValueError("You need to specify an `image_processor`.")
-        if tokenizer is None:
-            raise ValueError("You need to specify a `tokenizer`.")
-
-        super().__init__(image_processor, tokenizer)
-
-    def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
-        """
-        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
-        and `kwargs` arguments to XLMRobertaTokenizerFast's [`~XLMRobertaTokenizerFast.__call__`] if `text` is not
-        `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
-        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
-        of the above two methods for more information.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
-                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
-                number of channels, H and W are image height and width.
-
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors of a particular framework. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return NumPy `np.ndarray` objects.
-                - `'jax'`: Return JAX `jnp.ndarray` objects.
-
-        Returns:
-            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-        """
-
-        if text is None and images is None:
-            raise ValueError("You have to specify either text or images. Both cannot be none.")
-
-        if text is not None:
-            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
-
-        if images is not None:
-            image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
-
-        if text is not None and images is not None:
-            encoding["pixel_values"] = image_features.pixel_values
-            return encoding
-        elif text is not None:
-            return encoding
-        else:
-            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
-
-    def batch_decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
-        Please refer to the docstring of this method for more information.
-        """
-        return self.tokenizer.batch_decode(*args, **kwargs)
-
-    def decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
-        refer to the docstring of this method for more information.
-        """
-        return self.tokenizer.decode(*args, **kwargs)
-
-    @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
diff --git a/spaces/chompionsawelo/whisper_transcribe/tool/gdrive_tool.py b/spaces/chompionsawelo/whisper_transcribe/tool/gdrive_tool.py
deleted file mode 100644
index e09aa988ca83934f13e6689a5e6da73f800d6ceb..0000000000000000000000000000000000000000
--- a/spaces/chompionsawelo/whisper_transcribe/tool/gdrive_tool.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from tool.file_name import *
-from ui.ui_component import current_ui_lang
-import gradio as gr
-import gdown
-
-
-def get_video_from_url(url):
-    # Download video from google drive with shareable link
-    gr.Info(current_ui_lang["get_video_start_info"])
-    download_result = gdown.download(
-        url, dir_download_file, quiet=False, fuzzy=True)
-
-    # Raise error if failed
-    if (download_result is None):
-        raise gr.Error(current_ui_lang["get_video_fail_info"])
-
-    gr.Info(current_ui_lang["get_video_finished_info"])
-    return dir_download_file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl   Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
-    This format cannot be automatically recognized, so the
-    class is not registered for use with :py:func:`PIL.Image.open()`.  To open a
-    gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
-    THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE.  This
-    implementation is provided for convenience and demonstrational
-    purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
-    """
-    Image plugin for the GD uncompressed format.  Note that this format
-    is not supported by the standard :py:func:`PIL.Image.open()` function.  To use
-    this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
-    use the :py:func:`PIL.GdImageFile.open()` function.
-    """
-
-    format = "GD"
-    format_description = "GD uncompressed images"
-
-    def _open(self):
-        # Header
-        s = self.fp.read(1037)
-
-        if i16(s) not in [65534, 65535]:
-            msg = "Not a valid GD 2.x .gd file"
-            raise SyntaxError(msg)
-
-        self.mode = "L"  # FIXME: "P"
-        self._size = i16(s, 2), i16(s, 4)
-
-        true_color = s[6]
-        true_color_offset = 2 if true_color else 0
-
-        # transparency index
-        tindex = i32(s, 7 + true_color_offset)
-        if tindex < 256:
-            self.info["transparency"] = tindex
-
-        self.palette = ImagePalette.raw(
-            "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
-        )
-
-        self.tile = [
-            (
-                "raw",
-                (0, 0) + self.size,
-                7 + true_color_offset + 4 + 256 * 4,
-                ("L", 0, 1),
-            )
-        ]
-
-
-def open(fp, mode="r"):
-    """
-    Load texture from a GD image file.
-
-    :param fp: GD file name, or an opened file handle.
-    :param mode: Optional mode.  In this version, if the mode argument
-        is given, it must be "r".
-    :returns: An image instance.
-    :raises OSError: If the image could not be read.
-    """
-    if mode != "r":
-        msg = "bad mode"
-        raise ValueError(msg)
-
-    try:
-        return GdImageFile(fp)
-    except SyntaxError as e:
-        msg = "cannot identify this image file"
-        raise UnidentifiedImageError(msg) from e
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/WmfImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/WmfImagePlugin.py
deleted file mode 100644
index 0ecab56a824fd3917067fd4b05c530f4abce75a3..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/WmfImagePlugin.py
+++ /dev/null
@@ -1,178 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# WMF stub codec
-#
-# history:
-# 1996-12-14 fl   Created
-# 2004-02-22 fl   Turned into a stub driver
-# 2004-02-23 fl   Added EMF support
-#
-# Copyright (c) Secret Labs AB 1997-2004.  All rights reserved.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-# WMF/EMF reference documentation:
-# https://winprotocoldoc.blob.core.windows.net/productionwindowsarchives/MS-WMF/[MS-WMF].pdf
-# http://wvware.sourceforge.net/caolan/index.html
-# http://wvware.sourceforge.net/caolan/ora-wmf.html
-
-from . import Image, ImageFile
-from ._binary import i16le as word
-from ._binary import si16le as short
-from ._binary import si32le as _long
-
-_handler = None
-
-
-def register_handler(handler):
-    """
-    Install application-specific WMF image handler.
-
-    :param handler: Handler object.
-    """
-    global _handler
-    _handler = handler
-
-
-if hasattr(Image.core, "drawwmf"):
-    # install default handler (windows only)
-
-    class WmfHandler:
-        def open(self, im):
-            im.mode = "RGB"
-            self.bbox = im.info["wmf_bbox"]
-
-        def load(self, im):
-            im.fp.seek(0)  # rewind
-            return Image.frombytes(
-                "RGB",
-                im.size,
-                Image.core.drawwmf(im.fp.read(), im.size, self.bbox),
-                "raw",
-                "BGR",
-                (im.size[0] * 3 + 3) & -4,
-                -1,
-            )
-
-    register_handler(WmfHandler())
-
-#
-# --------------------------------------------------------------------
-# Read WMF file
-
-
-def _accept(prefix):
-    return (
-        prefix[:6] == b"\xd7\xcd\xc6\x9a\x00\x00" or prefix[:4] == b"\x01\x00\x00\x00"
-    )
-
-
-##
-# Image plugin for Windows metafiles.
-
-
-class WmfStubImageFile(ImageFile.StubImageFile):
-    format = "WMF"
-    format_description = "Windows Metafile"
-
-    def _open(self):
-        self._inch = None
-
-        # check placable header
-        s = self.fp.read(80)
-
-        if s[:6] == b"\xd7\xcd\xc6\x9a\x00\x00":
-            # placeable windows metafile
-
-            # get units per inch
-            self._inch = word(s, 14)
-
-            # get bounding box
-            x0 = short(s, 6)
-            y0 = short(s, 8)
-            x1 = short(s, 10)
-            y1 = short(s, 12)
-
-            # normalize size to 72 dots per inch
-            self.info["dpi"] = 72
-            size = (
-                (x1 - x0) * self.info["dpi"] // self._inch,
-                (y1 - y0) * self.info["dpi"] // self._inch,
-            )
-
-            self.info["wmf_bbox"] = x0, y0, x1, y1
-
-            # sanity check (standard metafile header)
-            if s[22:26] != b"\x01\x00\t\x00":
-                msg = "Unsupported WMF file format"
-                raise SyntaxError(msg)
-
-        elif s[:4] == b"\x01\x00\x00\x00" and s[40:44] == b" EMF":
-            # enhanced metafile
-
-            # get bounding box
-            x0 = _long(s, 8)
-            y0 = _long(s, 12)
-            x1 = _long(s, 16)
-            y1 = _long(s, 20)
-
-            # get frame (in 0.01 millimeter units)
-            frame = _long(s, 24), _long(s, 28), _long(s, 32), _long(s, 36)
-
-            size = x1 - x0, y1 - y0
-
-            # calculate dots per inch from bbox and frame
-            xdpi = 2540.0 * (x1 - y0) / (frame[2] - frame[0])
-            ydpi = 2540.0 * (y1 - y0) / (frame[3] - frame[1])
-
-            self.info["wmf_bbox"] = x0, y0, x1, y1
-
-            if xdpi == ydpi:
-                self.info["dpi"] = xdpi
-            else:
-                self.info["dpi"] = xdpi, ydpi
-
-        else:
-            msg = "Unsupported file format"
-            raise SyntaxError(msg)
-
-        self.mode = "RGB"
-        self._size = size
-
-        loader = self._load()
-        if loader:
-            loader.open(self)
-
-    def _load(self):
-        return _handler
-
-    def load(self, dpi=None):
-        if dpi is not None and self._inch is not None:
-            self.info["dpi"] = dpi
-            x0, y0, x1, y1 = self.info["wmf_bbox"]
-            self._size = (
-                (x1 - x0) * self.info["dpi"] // self._inch,
-                (y1 - y0) * self.info["dpi"] // self._inch,
-            )
-        return super().load()
-
-
-def _save(im, fp, filename):
-    if _handler is None or not hasattr(_handler, "save"):
-        msg = "WMF save handler not installed"
-        raise OSError(msg)
-    _handler.save(im, fp, filename)
-
-
-#
-# --------------------------------------------------------------------
-# Registry stuff
-
-
-Image.register_open(WmfStubImageFile.format, WmfStubImageFile, _accept)
-Image.register_save(WmfStubImageFile.format, _save)
-
-Image.register_extensions(WmfStubImageFile.format, [".wmf", ".emf"])
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_exceptions.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_exceptions.py
deleted file mode 100644
index c640e1e7fbdf8c56a9e744492d99f8ca32988142..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_exceptions.py
+++ /dev/null
@@ -1,342 +0,0 @@
-"""HTTP related errors."""
-
-import asyncio
-import warnings
-from typing import TYPE_CHECKING, Any, Optional, Tuple, Union
-
-from .http_parser import RawResponseMessage
-from .typedefs import LooseHeaders
-
-try:
-    import ssl
-
-    SSLContext = ssl.SSLContext
-except ImportError:  # pragma: no cover
-    ssl = SSLContext = None  # type: ignore[assignment]
-
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .client_reqrep import ClientResponse, ConnectionKey, Fingerprint, RequestInfo
-else:
-    RequestInfo = ClientResponse = ConnectionKey = None
-
-__all__ = (
-    "ClientError",
-    "ClientConnectionError",
-    "ClientOSError",
-    "ClientConnectorError",
-    "ClientProxyConnectionError",
-    "ClientSSLError",
-    "ClientConnectorSSLError",
-    "ClientConnectorCertificateError",
-    "ServerConnectionError",
-    "ServerTimeoutError",
-    "ServerDisconnectedError",
-    "ServerFingerprintMismatch",
-    "ClientResponseError",
-    "ClientHttpProxyError",
-    "WSServerHandshakeError",
-    "ContentTypeError",
-    "ClientPayloadError",
-    "InvalidURL",
-)
-
-
-class ClientError(Exception):
-    """Base class for client connection errors."""
-
-
-class ClientResponseError(ClientError):
-    """Connection error during reading response.
-
-    request_info: instance of RequestInfo
-    """
-
-    def __init__(
-        self,
-        request_info: RequestInfo,
-        history: Tuple[ClientResponse, ...],
-        *,
-        code: Optional[int] = None,
-        status: Optional[int] = None,
-        message: str = "",
-        headers: Optional[LooseHeaders] = None,
-    ) -> None:
-        self.request_info = request_info
-        if code is not None:
-            if status is not None:
-                raise ValueError(
-                    "Both code and status arguments are provided; "
-                    "code is deprecated, use status instead"
-                )
-            warnings.warn(
-                "code argument is deprecated, use status instead",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        if status is not None:
-            self.status = status
-        elif code is not None:
-            self.status = code
-        else:
-            self.status = 0
-        self.message = message
-        self.headers = headers
-        self.history = history
-        self.args = (request_info, history)
-
-    def __str__(self) -> str:
-        return "{}, message={!r}, url={!r}".format(
-            self.status,
-            self.message,
-            self.request_info.real_url,
-        )
-
-    def __repr__(self) -> str:
-        args = f"{self.request_info!r}, {self.history!r}"
-        if self.status != 0:
-            args += f", status={self.status!r}"
-        if self.message != "":
-            args += f", message={self.message!r}"
-        if self.headers is not None:
-            args += f", headers={self.headers!r}"
-        return f"{type(self).__name__}({args})"
-
-    @property
-    def code(self) -> int:
-        warnings.warn(
-            "code property is deprecated, use status instead",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.status
-
-    @code.setter
-    def code(self, value: int) -> None:
-        warnings.warn(
-            "code property is deprecated, use status instead",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        self.status = value
-
-
-class ContentTypeError(ClientResponseError):
-    """ContentType found is not valid."""
-
-
-class WSServerHandshakeError(ClientResponseError):
-    """websocket server handshake error."""
-
-
-class ClientHttpProxyError(ClientResponseError):
-    """HTTP proxy error.
-
-    Raised in :class:`aiohttp.connector.TCPConnector` if
-    proxy responds with status other than ``200 OK``
-    on ``CONNECT`` request.
-    """
-
-
-class TooManyRedirects(ClientResponseError):
-    """Client was redirected too many times."""
-
-
-class ClientConnectionError(ClientError):
-    """Base class for client socket errors."""
-
-
-class ClientOSError(ClientConnectionError, OSError):
-    """OSError error."""
-
-
-class ClientConnectorError(ClientOSError):
-    """Client connector error.
-
-    Raised in :class:`aiohttp.connector.TCPConnector` if
-        a connection can not be established.
-    """
-
-    def __init__(self, connection_key: ConnectionKey, os_error: OSError) -> None:
-        self._conn_key = connection_key
-        self._os_error = os_error
-        super().__init__(os_error.errno, os_error.strerror)
-        self.args = (connection_key, os_error)
-
-    @property
-    def os_error(self) -> OSError:
-        return self._os_error
-
-    @property
-    def host(self) -> str:
-        return self._conn_key.host
-
-    @property
-    def port(self) -> Optional[int]:
-        return self._conn_key.port
-
-    @property
-    def ssl(self) -> Union[SSLContext, None, bool, "Fingerprint"]:
-        return self._conn_key.ssl
-
-    def __str__(self) -> str:
-        return "Cannot connect to host {0.host}:{0.port} ssl:{1} [{2}]".format(
-            self, self.ssl if self.ssl is not None else "default", self.strerror
-        )
-
-    # OSError.__reduce__ does too much black magick
-    __reduce__ = BaseException.__reduce__
-
-
-class ClientProxyConnectionError(ClientConnectorError):
-    """Proxy connection error.
-
-    Raised in :class:`aiohttp.connector.TCPConnector` if
-        connection to proxy can not be established.
-    """
-
-
-class UnixClientConnectorError(ClientConnectorError):
-    """Unix connector error.
-
-    Raised in :py:class:`aiohttp.connector.UnixConnector`
-    if connection to unix socket can not be established.
-    """
-
-    def __init__(
-        self, path: str, connection_key: ConnectionKey, os_error: OSError
-    ) -> None:
-        self._path = path
-        super().__init__(connection_key, os_error)
-
-    @property
-    def path(self) -> str:
-        return self._path
-
-    def __str__(self) -> str:
-        return "Cannot connect to unix socket {0.path} ssl:{1} [{2}]".format(
-            self, self.ssl if self.ssl is not None else "default", self.strerror
-        )
-
-
-class ServerConnectionError(ClientConnectionError):
-    """Server connection errors."""
-
-
-class ServerDisconnectedError(ServerConnectionError):
-    """Server disconnected."""
-
-    def __init__(self, message: Union[RawResponseMessage, str, None] = None) -> None:
-        if message is None:
-            message = "Server disconnected"
-
-        self.args = (message,)
-        self.message = message
-
-
-class ServerTimeoutError(ServerConnectionError, asyncio.TimeoutError):
-    """Server timeout error."""
-
-
-class ServerFingerprintMismatch(ServerConnectionError):
-    """SSL certificate does not match expected fingerprint."""
-
-    def __init__(self, expected: bytes, got: bytes, host: str, port: int) -> None:
-        self.expected = expected
-        self.got = got
-        self.host = host
-        self.port = port
-        self.args = (expected, got, host, port)
-
-    def __repr__(self) -> str:
-        return "<{} expected={!r} got={!r} host={!r} port={!r}>".format(
-            self.__class__.__name__, self.expected, self.got, self.host, self.port
-        )
-
-
-class ClientPayloadError(ClientError):
-    """Response payload error."""
-
-
-class InvalidURL(ClientError, ValueError):
-    """Invalid URL.
-
-    URL used for fetching is malformed, e.g. it doesn't contains host
-    part.
-    """
-
-    # Derive from ValueError for backward compatibility
-
-    def __init__(self, url: Any) -> None:
-        # The type of url is not yarl.URL because the exception can be raised
-        # on URL(url) call
-        super().__init__(url)
-
-    @property
-    def url(self) -> Any:
-        return self.args[0]
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} {self.url}>"
-
-
-class ClientSSLError(ClientConnectorError):
-    """Base error for ssl.*Errors."""
-
-
-if ssl is not None:
-    cert_errors = (ssl.CertificateError,)
-    cert_errors_bases = (
-        ClientSSLError,
-        ssl.CertificateError,
-    )
-
-    ssl_errors = (ssl.SSLError,)
-    ssl_error_bases = (ClientSSLError, ssl.SSLError)
-else:  # pragma: no cover
-    cert_errors = tuple()
-    cert_errors_bases = (
-        ClientSSLError,
-        ValueError,
-    )
-
-    ssl_errors = tuple()
-    ssl_error_bases = (ClientSSLError,)
-
-
-class ClientConnectorSSLError(*ssl_error_bases):  # type: ignore[misc]
-    """Response ssl error."""
-
-
-class ClientConnectorCertificateError(*cert_errors_bases):  # type: ignore[misc]
-    """Response certificate error."""
-
-    def __init__(
-        self, connection_key: ConnectionKey, certificate_error: Exception
-    ) -> None:
-        self._conn_key = connection_key
-        self._certificate_error = certificate_error
-        self.args = (connection_key, certificate_error)
-
-    @property
-    def certificate_error(self) -> Exception:
-        return self._certificate_error
-
-    @property
-    def host(self) -> str:
-        return self._conn_key.host
-
-    @property
-    def port(self) -> Optional[int]:
-        return self._conn_key.port
-
-    @property
-    def ssl(self) -> bool:
-        return self._conn_key.is_ssl
-
-    def __str__(self) -> str:
-        return (
-            "Cannot connect to host {0.host}:{0.port} ssl:{0.ssl} "
-            "[{0.certificate_error.__class__.__name__}: "
-            "{0.certificate_error.args}]".format(self)
-        )
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/oxml/text/paragraph.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/oxml/text/paragraph.py
deleted file mode 100644
index 5e4213776c3449af740c417616b74596f7376c03..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/oxml/text/paragraph.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# encoding: utf-8
-
-"""
-Custom element classes related to paragraphs (CT_P).
-"""
-
-from ..ns import qn
-from ..xmlchemy import BaseOxmlElement, OxmlElement, ZeroOrMore, ZeroOrOne
-
-
-class CT_P(BaseOxmlElement):
-    """
-    ``<w:p>`` element, containing the properties and text for a paragraph.
-    """
-    pPr = ZeroOrOne('w:pPr')
-    r = ZeroOrMore('w:r')
-
-    def _insert_pPr(self, pPr):
-        self.insert(0, pPr)
-        return pPr
-
-    def add_p_before(self):
-        """
-        Return a new ``<w:p>`` element inserted directly prior to this one.
-        """
-        new_p = OxmlElement('w:p')
-        self.addprevious(new_p)
-        return new_p
-
-    @property
-    def alignment(self):
-        """
-        The value of the ``<w:jc>`` grandchild element or |None| if not
-        present.
-        """
-        pPr = self.pPr
-        if pPr is None:
-            return None
-        return pPr.jc_val
-
-    @alignment.setter
-    def alignment(self, value):
-        pPr = self.get_or_add_pPr()
-        pPr.jc_val = value
-
-    def clear_content(self):
-        """
-        Remove all child elements, except the ``<w:pPr>`` element if present.
-        """
-        for child in self[:]:
-            if child.tag == qn('w:pPr'):
-                continue
-            self.remove(child)
-
-    def set_sectPr(self, sectPr):
-        """
-        Unconditionally replace or add *sectPr* as a grandchild in the
-        correct sequence.
-        """
-        pPr = self.get_or_add_pPr()
-        pPr._remove_sectPr()
-        pPr._insert_sectPr(sectPr)
-
-    @property
-    def style(self):
-        """
-        String contained in w:val attribute of ./w:pPr/w:pStyle grandchild,
-        or |None| if not present.
-        """
-        pPr = self.pPr
-        if pPr is None:
-            return None
-        return pPr.style
-
-    @style.setter
-    def style(self, style):
-        pPr = self.get_or_add_pPr()
-        pPr.style = style
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/t1Lib/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/t1Lib/__init__.py
deleted file mode 100644
index e98acb7c52e89a83b7750601c6d80cbd094637d7..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/t1Lib/__init__.py
+++ /dev/null
@@ -1,638 +0,0 @@
-"""fontTools.t1Lib.py -- Tools for PostScript Type 1 fonts (Python2 only)
-
-Functions for reading and writing raw Type 1 data:
-
-read(path)
-	reads any Type 1 font file, returns the raw data and a type indicator:
-	'LWFN', 'PFB' or 'OTHER', depending on the format of the file pointed
-	to by 'path'.
-	Raises an error when the file does not contain valid Type 1 data.
-
-write(path, data, kind='OTHER', dohex=False)
-	writes raw Type 1 data to the file pointed to by 'path'.
-	'kind' can be one of 'LWFN', 'PFB' or 'OTHER'; it defaults to 'OTHER'.
-	'dohex' is a flag which determines whether the eexec encrypted
-	part should be written as hexadecimal or binary, but only if kind
-	is 'OTHER'.
-"""
-import fontTools
-from fontTools.misc import eexec
-from fontTools.misc.macCreatorType import getMacCreatorAndType
-from fontTools.misc.textTools import bytechr, byteord, bytesjoin, tobytes
-from fontTools.misc.psOperators import (
-    _type1_pre_eexec_order,
-    _type1_fontinfo_order,
-    _type1_post_eexec_order,
-)
-from fontTools.encodings.StandardEncoding import StandardEncoding
-import os
-import re
-
-__author__ = "jvr"
-__version__ = "1.0b3"
-DEBUG = 0
-
-
-try:
-    try:
-        from Carbon import Res
-    except ImportError:
-        import Res  # MacPython < 2.2
-except ImportError:
-    haveMacSupport = 0
-else:
-    haveMacSupport = 1
-
-
-class T1Error(Exception):
-    pass
-
-
-class T1Font(object):
-
-    """Type 1 font class.
-
-    Uses a minimal interpeter that supports just about enough PS to parse
-    Type 1 fonts.
-    """
-
-    def __init__(self, path, encoding="ascii", kind=None):
-        if kind is None:
-            self.data, _ = read(path)
-        elif kind == "LWFN":
-            self.data = readLWFN(path)
-        elif kind == "PFB":
-            self.data = readPFB(path)
-        elif kind == "OTHER":
-            self.data = readOther(path)
-        else:
-            raise ValueError(kind)
-        self.encoding = encoding
-
-    def saveAs(self, path, type, dohex=False):
-        write(path, self.getData(), type, dohex)
-
-    def getData(self):
-        if not hasattr(self, "data"):
-            self.data = self.createData()
-        return self.data
-
-    def getGlyphSet(self):
-        """Return a generic GlyphSet, which is a dict-like object
-        mapping glyph names to glyph objects. The returned glyph objects
-        have a .draw() method that supports the Pen protocol, and will
-        have an attribute named 'width', but only *after* the .draw() method
-        has been called.
-
-        In the case of Type 1, the GlyphSet is simply the CharStrings dict.
-        """
-        return self["CharStrings"]
-
-    def __getitem__(self, key):
-        if not hasattr(self, "font"):
-            self.parse()
-        return self.font[key]
-
-    def parse(self):
-        from fontTools.misc import psLib
-        from fontTools.misc import psCharStrings
-
-        self.font = psLib.suckfont(self.data, self.encoding)
-        charStrings = self.font["CharStrings"]
-        lenIV = self.font["Private"].get("lenIV", 4)
-        assert lenIV >= 0
-        subrs = self.font["Private"]["Subrs"]
-        for glyphName, charString in charStrings.items():
-            charString, R = eexec.decrypt(charString, 4330)
-            charStrings[glyphName] = psCharStrings.T1CharString(
-                charString[lenIV:], subrs=subrs
-            )
-        for i in range(len(subrs)):
-            charString, R = eexec.decrypt(subrs[i], 4330)
-            subrs[i] = psCharStrings.T1CharString(charString[lenIV:], subrs=subrs)
-        del self.data
-
-    def createData(self):
-        sf = self.font
-
-        eexec_began = False
-        eexec_dict = {}
-        lines = []
-        lines.extend(
-            [
-                self._tobytes(f"%!FontType1-1.1: {sf['FontName']}"),
-                self._tobytes(f"%t1Font: ({fontTools.version})"),
-                self._tobytes(f"%%BeginResource: font {sf['FontName']}"),
-            ]
-        )
-        # follow t1write.c:writeRegNameKeyedFont
-        size = 3  # Headroom for new key addition
-        size += 1  # FontMatrix is always counted
-        size += 1 + 1  # Private, CharStings
-        for key in font_dictionary_keys:
-            size += int(key in sf)
-        lines.append(self._tobytes(f"{size} dict dup begin"))
-
-        for key, value in sf.items():
-            if eexec_began:
-                eexec_dict[key] = value
-                continue
-
-            if key == "FontInfo":
-                fi = sf["FontInfo"]
-                # follow t1write.c:writeFontInfoDict
-                size = 3  # Headroom for new key addition
-                for subkey in FontInfo_dictionary_keys:
-                    size += int(subkey in fi)
-                lines.append(self._tobytes(f"/FontInfo {size} dict dup begin"))
-
-                for subkey, subvalue in fi.items():
-                    lines.extend(self._make_lines(subkey, subvalue))
-                lines.append(b"end def")
-            elif key in _type1_post_eexec_order:  # usually 'Private'
-                eexec_dict[key] = value
-                eexec_began = True
-            else:
-                lines.extend(self._make_lines(key, value))
-        lines.append(b"end")
-        eexec_portion = self.encode_eexec(eexec_dict)
-        lines.append(bytesjoin([b"currentfile eexec ", eexec_portion]))
-
-        for _ in range(8):
-            lines.append(self._tobytes("0" * 64))
-        lines.extend([b"cleartomark", b"%%EndResource", b"%%EOF"])
-
-        data = bytesjoin(lines, "\n")
-        return data
-
-    def encode_eexec(self, eexec_dict):
-        lines = []
-
-        # '-|', '|-', '|'
-        RD_key, ND_key, NP_key = None, None, None
-
-        for key, value in eexec_dict.items():
-            if key == "Private":
-                pr = eexec_dict["Private"]
-                # follow t1write.c:writePrivateDict
-                size = 3  # for RD, ND, NP
-                for subkey in Private_dictionary_keys:
-                    size += int(subkey in pr)
-                lines.append(b"dup /Private")
-                lines.append(self._tobytes(f"{size} dict dup begin"))
-                for subkey, subvalue in pr.items():
-                    if not RD_key and subvalue == RD_value:
-                        RD_key = subkey
-                    elif not ND_key and subvalue == ND_value:
-                        ND_key = subkey
-                    elif not NP_key and subvalue == PD_value:
-                        NP_key = subkey
-
-                    if subkey == "OtherSubrs":
-                        # XXX: assert that no flex hint is used
-                        lines.append(self._tobytes(hintothers))
-                    elif subkey == "Subrs":
-                        # XXX: standard Subrs only
-                        lines.append(b"/Subrs 5 array")
-                        for i, subr_bin in enumerate(std_subrs):
-                            encrypted_subr, R = eexec.encrypt(
-                                bytesjoin([char_IV, subr_bin]), 4330
-                            )
-                            lines.append(
-                                bytesjoin(
-                                    [
-                                        self._tobytes(
-                                            f"dup {i} {len(encrypted_subr)} {RD_key} "
-                                        ),
-                                        encrypted_subr,
-                                        self._tobytes(f" {NP_key}"),
-                                    ]
-                                )
-                            )
-                        lines.append(b"def")
-
-                        lines.append(b"put")
-                    else:
-                        lines.extend(self._make_lines(subkey, subvalue))
-            elif key == "CharStrings":
-                lines.append(b"dup /CharStrings")
-                lines.append(
-                    self._tobytes(f"{len(eexec_dict['CharStrings'])} dict dup begin")
-                )
-                for glyph_name, char_bin in eexec_dict["CharStrings"].items():
-                    char_bin.compile()
-                    encrypted_char, R = eexec.encrypt(
-                        bytesjoin([char_IV, char_bin.bytecode]), 4330
-                    )
-                    lines.append(
-                        bytesjoin(
-                            [
-                                self._tobytes(
-                                    f"/{glyph_name} {len(encrypted_char)} {RD_key} "
-                                ),
-                                encrypted_char,
-                                self._tobytes(f" {ND_key}"),
-                            ]
-                        )
-                    )
-                lines.append(b"end put")
-            else:
-                lines.extend(self._make_lines(key, value))
-
-        lines.extend(
-            [
-                b"end",
-                b"dup /FontName get exch definefont pop",
-                b"mark",
-                b"currentfile closefile\n",
-            ]
-        )
-
-        eexec_portion = bytesjoin(lines, "\n")
-        encrypted_eexec, R = eexec.encrypt(bytesjoin([eexec_IV, eexec_portion]), 55665)
-
-        return encrypted_eexec
-
-    def _make_lines(self, key, value):
-        if key == "FontName":
-            return [self._tobytes(f"/{key} /{value} def")]
-        if key in ["isFixedPitch", "ForceBold", "RndStemUp"]:
-            return [self._tobytes(f"/{key} {'true' if value else 'false'} def")]
-        elif key == "Encoding":
-            if value == StandardEncoding:
-                return [self._tobytes(f"/{key} StandardEncoding def")]
-            else:
-                # follow fontTools.misc.psOperators._type1_Encoding_repr
-                lines = []
-                lines.append(b"/Encoding 256 array")
-                lines.append(b"0 1 255 {1 index exch /.notdef put} for")
-                for i in range(256):
-                    name = value[i]
-                    if name != ".notdef":
-                        lines.append(self._tobytes(f"dup {i} /{name} put"))
-                lines.append(b"def")
-                return lines
-        if isinstance(value, str):
-            return [self._tobytes(f"/{key} ({value}) def")]
-        elif isinstance(value, bool):
-            return [self._tobytes(f"/{key} {'true' if value else 'false'} def")]
-        elif isinstance(value, list):
-            return [self._tobytes(f"/{key} [{' '.join(str(v) for v in value)}] def")]
-        elif isinstance(value, tuple):
-            return [self._tobytes(f"/{key} {{{' '.join(str(v) for v in value)}}} def")]
-        else:
-            return [self._tobytes(f"/{key} {value} def")]
-
-    def _tobytes(self, s, errors="strict"):
-        return tobytes(s, self.encoding, errors)
-
-
-# low level T1 data read and write functions
-
-
-def read(path, onlyHeader=False):
-    """reads any Type 1 font file, returns raw data"""
-    _, ext = os.path.splitext(path)
-    ext = ext.lower()
-    creator, typ = getMacCreatorAndType(path)
-    if typ == "LWFN":
-        return readLWFN(path, onlyHeader), "LWFN"
-    if ext == ".pfb":
-        return readPFB(path, onlyHeader), "PFB"
-    else:
-        return readOther(path), "OTHER"
-
-
-def write(path, data, kind="OTHER", dohex=False):
-    assertType1(data)
-    kind = kind.upper()
-    try:
-        os.remove(path)
-    except os.error:
-        pass
-    err = 1
-    try:
-        if kind == "LWFN":
-            writeLWFN(path, data)
-        elif kind == "PFB":
-            writePFB(path, data)
-        else:
-            writeOther(path, data, dohex)
-        err = 0
-    finally:
-        if err and not DEBUG:
-            try:
-                os.remove(path)
-            except os.error:
-                pass
-
-
-# -- internal --
-
-LWFNCHUNKSIZE = 2000
-HEXLINELENGTH = 80
-
-
-def readLWFN(path, onlyHeader=False):
-    """reads an LWFN font file, returns raw data"""
-    from fontTools.misc.macRes import ResourceReader
-
-    reader = ResourceReader(path)
-    try:
-        data = []
-        for res in reader.get("POST", []):
-            code = byteord(res.data[0])
-            if byteord(res.data[1]) != 0:
-                raise T1Error("corrupt LWFN file")
-            if code in [1, 2]:
-                if onlyHeader and code == 2:
-                    break
-                data.append(res.data[2:])
-            elif code in [3, 5]:
-                break
-            elif code == 4:
-                with open(path, "rb") as f:
-                    data.append(f.read())
-            elif code == 0:
-                pass  # comment, ignore
-            else:
-                raise T1Error("bad chunk code: " + repr(code))
-    finally:
-        reader.close()
-    data = bytesjoin(data)
-    assertType1(data)
-    return data
-
-
-def readPFB(path, onlyHeader=False):
-    """reads a PFB font file, returns raw data"""
-    data = []
-    with open(path, "rb") as f:
-        while True:
-            if f.read(1) != bytechr(128):
-                raise T1Error("corrupt PFB file")
-            code = byteord(f.read(1))
-            if code in [1, 2]:
-                chunklen = stringToLong(f.read(4))
-                chunk = f.read(chunklen)
-                assert len(chunk) == chunklen
-                data.append(chunk)
-            elif code == 3:
-                break
-            else:
-                raise T1Error("bad chunk code: " + repr(code))
-            if onlyHeader:
-                break
-    data = bytesjoin(data)
-    assertType1(data)
-    return data
-
-
-def readOther(path):
-    """reads any (font) file, returns raw data"""
-    with open(path, "rb") as f:
-        data = f.read()
-    assertType1(data)
-    chunks = findEncryptedChunks(data)
-    data = []
-    for isEncrypted, chunk in chunks:
-        if isEncrypted and isHex(chunk[:4]):
-            data.append(deHexString(chunk))
-        else:
-            data.append(chunk)
-    return bytesjoin(data)
-
-
-# file writing tools
-
-
-def writeLWFN(path, data):
-    # Res.FSpCreateResFile was deprecated in OS X 10.5
-    Res.FSpCreateResFile(path, "just", "LWFN", 0)
-    resRef = Res.FSOpenResFile(path, 2)  # write-only
-    try:
-        Res.UseResFile(resRef)
-        resID = 501
-        chunks = findEncryptedChunks(data)
-        for isEncrypted, chunk in chunks:
-            if isEncrypted:
-                code = 2
-            else:
-                code = 1
-            while chunk:
-                res = Res.Resource(bytechr(code) + "\0" + chunk[: LWFNCHUNKSIZE - 2])
-                res.AddResource("POST", resID, "")
-                chunk = chunk[LWFNCHUNKSIZE - 2 :]
-                resID = resID + 1
-        res = Res.Resource(bytechr(5) + "\0")
-        res.AddResource("POST", resID, "")
-    finally:
-        Res.CloseResFile(resRef)
-
-
-def writePFB(path, data):
-    chunks = findEncryptedChunks(data)
-    with open(path, "wb") as f:
-        for isEncrypted, chunk in chunks:
-            if isEncrypted:
-                code = 2
-            else:
-                code = 1
-            f.write(bytechr(128) + bytechr(code))
-            f.write(longToString(len(chunk)))
-            f.write(chunk)
-        f.write(bytechr(128) + bytechr(3))
-
-
-def writeOther(path, data, dohex=False):
-    chunks = findEncryptedChunks(data)
-    with open(path, "wb") as f:
-        hexlinelen = HEXLINELENGTH // 2
-        for isEncrypted, chunk in chunks:
-            if isEncrypted:
-                code = 2
-            else:
-                code = 1
-            if code == 2 and dohex:
-                while chunk:
-                    f.write(eexec.hexString(chunk[:hexlinelen]))
-                    f.write(b"\r")
-                    chunk = chunk[hexlinelen:]
-            else:
-                f.write(chunk)
-
-
-# decryption tools
-
-EEXECBEGIN = b"currentfile eexec"
-# The spec allows for 512 ASCII zeros interrupted by arbitrary whitespace to
-# follow eexec
-EEXECEND = re.compile(b"(0[ \t\r\n]*){512}", flags=re.M)
-EEXECINTERNALEND = b"currentfile closefile"
-EEXECBEGINMARKER = b"%-- eexec start\r"
-EEXECENDMARKER = b"%-- eexec end\r"
-
-_ishexRE = re.compile(b"[0-9A-Fa-f]*$")
-
-
-def isHex(text):
-    return _ishexRE.match(text) is not None
-
-
-def decryptType1(data):
-    chunks = findEncryptedChunks(data)
-    data = []
-    for isEncrypted, chunk in chunks:
-        if isEncrypted:
-            if isHex(chunk[:4]):
-                chunk = deHexString(chunk)
-            decrypted, R = eexec.decrypt(chunk, 55665)
-            decrypted = decrypted[4:]
-            if (
-                decrypted[-len(EEXECINTERNALEND) - 1 : -1] != EEXECINTERNALEND
-                and decrypted[-len(EEXECINTERNALEND) - 2 : -2] != EEXECINTERNALEND
-            ):
-                raise T1Error("invalid end of eexec part")
-            decrypted = decrypted[: -len(EEXECINTERNALEND) - 2] + b"\r"
-            data.append(EEXECBEGINMARKER + decrypted + EEXECENDMARKER)
-        else:
-            if chunk[-len(EEXECBEGIN) - 1 : -1] == EEXECBEGIN:
-                data.append(chunk[: -len(EEXECBEGIN) - 1])
-            else:
-                data.append(chunk)
-    return bytesjoin(data)
-
-
-def findEncryptedChunks(data):
-    chunks = []
-    while True:
-        eBegin = data.find(EEXECBEGIN)
-        if eBegin < 0:
-            break
-        eBegin = eBegin + len(EEXECBEGIN) + 1
-        endMatch = EEXECEND.search(data, eBegin)
-        if endMatch is None:
-            raise T1Error("can't find end of eexec part")
-        eEnd = endMatch.start()
-        cypherText = data[eBegin : eEnd + 2]
-        if isHex(cypherText[:4]):
-            cypherText = deHexString(cypherText)
-        plainText, R = eexec.decrypt(cypherText, 55665)
-        eEndLocal = plainText.find(EEXECINTERNALEND)
-        if eEndLocal < 0:
-            raise T1Error("can't find end of eexec part")
-        chunks.append((0, data[:eBegin]))
-        chunks.append((1, cypherText[: eEndLocal + len(EEXECINTERNALEND) + 1]))
-        data = data[eEnd:]
-    chunks.append((0, data))
-    return chunks
-
-
-def deHexString(hexstring):
-    return eexec.deHexString(bytesjoin(hexstring.split()))
-
-
-# Type 1 assertion
-
-_fontType1RE = re.compile(rb"/FontType\s+1\s+def")
-
-
-def assertType1(data):
-    for head in [b"%!PS-AdobeFont", b"%!FontType1"]:
-        if data[: len(head)] == head:
-            break
-    else:
-        raise T1Error("not a PostScript font")
-    if not _fontType1RE.search(data):
-        raise T1Error("not a Type 1 font")
-    if data.find(b"currentfile eexec") < 0:
-        raise T1Error("not an encrypted Type 1 font")
-    # XXX what else?
-    return data
-
-
-# pfb helpers
-
-
-def longToString(long):
-    s = b""
-    for i in range(4):
-        s += bytechr((long & (0xFF << (i * 8))) >> i * 8)
-    return s
-
-
-def stringToLong(s):
-    if len(s) != 4:
-        raise ValueError("string must be 4 bytes long")
-    l = 0
-    for i in range(4):
-        l += byteord(s[i]) << (i * 8)
-    return l
-
-
-# PS stream helpers
-
-font_dictionary_keys = list(_type1_pre_eexec_order)
-# t1write.c:writeRegNameKeyedFont
-# always counts following keys
-font_dictionary_keys.remove("FontMatrix")
-
-FontInfo_dictionary_keys = list(_type1_fontinfo_order)
-# extend because AFDKO tx may use following keys
-FontInfo_dictionary_keys.extend(
-    [
-        "FSType",
-        "Copyright",
-    ]
-)
-
-Private_dictionary_keys = [
-    # We don't know what names will be actually used.
-    # "RD",
-    # "ND",
-    # "NP",
-    "Subrs",
-    "OtherSubrs",
-    "UniqueID",
-    "BlueValues",
-    "OtherBlues",
-    "FamilyBlues",
-    "FamilyOtherBlues",
-    "BlueScale",
-    "BlueShift",
-    "BlueFuzz",
-    "StdHW",
-    "StdVW",
-    "StemSnapH",
-    "StemSnapV",
-    "ForceBold",
-    "LanguageGroup",
-    "password",
-    "lenIV",
-    "MinFeature",
-    "RndStemUp",
-]
-
-# t1write_hintothers.h
-hintothers = """/OtherSubrs[{}{}{}{systemdict/internaldict known not{pop 3}{1183615869
-systemdict/internaldict get exec dup/startlock known{/startlock get exec}{dup
-/strtlck known{/strtlck get exec}{pop 3}ifelse}ifelse}ifelse}executeonly]def"""
-# t1write.c:saveStdSubrs
-std_subrs = [
-    # 3 0 callother pop pop setcurrentpoint return
-    b"\x8e\x8b\x0c\x10\x0c\x11\x0c\x11\x0c\x21\x0b",
-    # 0 1 callother return
-    b"\x8b\x8c\x0c\x10\x0b",
-    # 0 2 callother return
-    b"\x8b\x8d\x0c\x10\x0b",
-    # return
-    b"\x0b",
-    # 3 1 3 callother pop callsubr return
-    b"\x8e\x8c\x8e\x0c\x10\x0c\x11\x0a\x0b",
-]
-# follow t1write.c:writeRegNameKeyedFont
-eexec_IV = b"cccc"
-char_IV = b"\x0c\x0c\x0c\x0c"
-RD_value = ("string", "currentfile", "exch", "readstring", "pop")
-ND_value = ("def",)
-PD_value = ("put",)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/caching.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/caching.py
deleted file mode 100644
index 511de1dee8f3416cf89475c9393275748df00022..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/caching.py
+++ /dev/null
@@ -1,804 +0,0 @@
-import collections
-import functools
-import logging
-import math
-import os
-import threading
-import warnings
-from concurrent.futures import ThreadPoolExecutor
-
-logger = logging.getLogger("fsspec")
-
-
-class BaseCache(object):
-    """Pass-though cache: doesn't keep anything, calls every time
-
-    Acts as base class for other cachers
-
-    Parameters
-    ----------
-    blocksize: int
-        How far to read ahead in numbers of bytes
-    fetcher: func
-        Function of the form f(start, end) which gets bytes from remote as
-        specified
-    size: int
-        How big this file is
-    """
-
-    name = "none"
-
-    def __init__(self, blocksize, fetcher, size):
-        self.blocksize = blocksize
-        self.fetcher = fetcher
-        self.size = size
-
-    def _fetch(self, start, stop):
-        if start is None:
-            start = 0
-        if stop is None:
-            stop = self.size
-        if start >= self.size or start >= stop:
-            return b""
-        return self.fetcher(start, stop)
-
-
-class MMapCache(BaseCache):
-    """memory-mapped sparse file cache
-
-    Opens temporary file, which is filled blocks-wise when data is requested.
-    Ensure there is enough disc space in the temporary location.
-
-    This cache method might only work on posix
-    """
-
-    name = "mmap"
-
-    def __init__(self, blocksize, fetcher, size, location=None, blocks=None):
-        super().__init__(blocksize, fetcher, size)
-        self.blocks = set() if blocks is None else blocks
-        self.location = location
-        self.cache = self._makefile()
-
-    def _makefile(self):
-        import mmap
-        import tempfile
-
-        if self.size == 0:
-            return bytearray()
-
-        # posix version
-        if self.location is None or not os.path.exists(self.location):
-            if self.location is None:
-                fd = tempfile.TemporaryFile()
-                self.blocks = set()
-            else:
-                fd = open(self.location, "wb+")
-            fd.seek(self.size - 1)
-            fd.write(b"1")
-            fd.flush()
-        else:
-            fd = open(self.location, "rb+")
-
-        return mmap.mmap(fd.fileno(), self.size)
-
-    def _fetch(self, start, end):
-        logger.debug(f"MMap cache fetching {start}-{end}")
-        if start is None:
-            start = 0
-        if end is None:
-            end = self.size
-        if start >= self.size or start >= end:
-            return b""
-        start_block = start // self.blocksize
-        end_block = end // self.blocksize
-        need = [i for i in range(start_block, end_block + 1) if i not in self.blocks]
-        while need:
-            # TODO: not a for loop so we can consolidate blocks later to
-            # make fewer fetch calls; this could be parallel
-            i = need.pop(0)
-            sstart = i * self.blocksize
-            send = min(sstart + self.blocksize, self.size)
-            logger.debug(f"MMap get block #{i} ({sstart}-{send}")
-            self.cache[sstart:send] = self.fetcher(sstart, send)
-            self.blocks.add(i)
-
-        return self.cache[start:end]
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        # Remove the unpicklable entries.
-        del state["cache"]
-        return state
-
-    def __setstate__(self, state):
-        # Restore instance attributes
-        self.__dict__.update(state)
-        self.cache = self._makefile()
-
-
-class ReadAheadCache(BaseCache):
-    """Cache which reads only when we get beyond a block of data
-
-    This is a much simpler version of BytesCache, and does not attempt to
-    fill holes in the cache or keep fragments alive. It is best suited to
-    many small reads in a sequential order (e.g., reading lines from a file).
-    """
-
-    name = "readahead"
-
-    def __init__(self, blocksize, fetcher, size):
-        super().__init__(blocksize, fetcher, size)
-        self.cache = b""
-        self.start = 0
-        self.end = 0
-
-    def _fetch(self, start, end):
-        if start is None:
-            start = 0
-        if end is None or end > self.size:
-            end = self.size
-        if start >= self.size or start >= end:
-            return b""
-        l = end - start
-        if start >= self.start and end <= self.end:
-            # cache hit
-            return self.cache[start - self.start : end - self.start]
-        elif self.start <= start < self.end:
-            # partial hit
-            part = self.cache[start - self.start :]
-            l -= len(part)
-            start = self.end
-        else:
-            # miss
-            part = b""
-        end = min(self.size, end + self.blocksize)
-        self.cache = self.fetcher(start, end)  # new block replaces old
-        self.start = start
-        self.end = self.start + len(self.cache)
-        return part + self.cache[:l]
-
-
-class FirstChunkCache(BaseCache):
-    """Caches the first block of a file only
-
-    This may be useful for file types where the metadata is stored in the header,
-    but is randomly accessed.
-    """
-
-    name = "first"
-
-    def __init__(self, blocksize, fetcher, size):
-        super().__init__(blocksize, fetcher, size)
-        self.cache = None
-
-    def _fetch(self, start, end):
-        start = start or 0
-        end = end or self.size
-        if start < self.blocksize:
-            if self.cache is None:
-                if end > self.blocksize:
-                    data = self.fetcher(0, end)
-                    self.cache = data[: self.blocksize]
-                    return data[start:]
-                self.cache = self.fetcher(0, self.blocksize)
-            part = self.cache[start:end]
-            if end > self.blocksize:
-                part += self.fetcher(self.blocksize, end)
-            return part
-        else:
-            return self.fetcher(start, end)
-
-
-class BlockCache(BaseCache):
-    """
-    Cache holding memory as a set of blocks.
-
-    Requests are only ever made ``blocksize`` at a time, and are
-    stored in an LRU cache. The least recently accessed block is
-    discarded when more than ``maxblocks`` are stored.
-
-    Parameters
-    ----------
-    blocksize : int
-        The number of bytes to store in each block.
-        Requests are only ever made for ``blocksize``, so this
-        should balance the overhead of making a request against
-        the granularity of the blocks.
-    fetcher : Callable
-    size : int
-        The total size of the file being cached.
-    maxblocks : int
-        The maximum number of blocks to cache for. The maximum memory
-        use for this cache is then ``blocksize * maxblocks``.
-    """
-
-    name = "blockcache"
-
-    def __init__(self, blocksize, fetcher, size, maxblocks=32):
-        super().__init__(blocksize, fetcher, size)
-        self.nblocks = math.ceil(size / blocksize)
-        self.maxblocks = maxblocks
-        self._fetch_block_cached = functools.lru_cache(maxblocks)(self._fetch_block)
-
-    def __repr__(self):
-        return "<BlockCache blocksize={}, size={}, nblocks={}>".format(
-            self.blocksize, self.size, self.nblocks
-        )
-
-    def cache_info(self):
-        """
-        The statistics on the block cache.
-
-        Returns
-        -------
-        NamedTuple
-            Returned directly from the LRU Cache used internally.
-        """
-        return self._fetch_block_cached.cache_info()
-
-    def __getstate__(self):
-        state = self.__dict__
-        del state["_fetch_block_cached"]
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__.update(state)
-        self._fetch_block_cached = functools.lru_cache(state["maxblocks"])(
-            self._fetch_block
-        )
-
-    def _fetch(self, start, end):
-        if start is None:
-            start = 0
-        if end is None:
-            end = self.size
-        if start >= self.size or start >= end:
-            return b""
-
-        # byte position -> block numbers
-        start_block_number = start // self.blocksize
-        end_block_number = end // self.blocksize
-
-        # these are cached, so safe to do multiple calls for the same start and end.
-        for block_number in range(start_block_number, end_block_number + 1):
-            self._fetch_block_cached(block_number)
-
-        return self._read_cache(
-            start,
-            end,
-            start_block_number=start_block_number,
-            end_block_number=end_block_number,
-        )
-
-    def _fetch_block(self, block_number):
-        """
-        Fetch the block of data for `block_number`.
-        """
-        if block_number > self.nblocks:
-            raise ValueError(
-                "'block_number={}' is greater than the number of blocks ({})".format(
-                    block_number, self.nblocks
-                )
-            )
-
-        start = block_number * self.blocksize
-        end = start + self.blocksize
-        logger.info("BlockCache fetching block %d", block_number)
-        block_contents = super()._fetch(start, end)
-        return block_contents
-
-    def _read_cache(self, start, end, start_block_number, end_block_number):
-        """
-        Read from our block cache.
-
-        Parameters
-        ----------
-        start, end : int
-            The start and end byte positions.
-        start_block_number, end_block_number : int
-            The start and end block numbers.
-        """
-        start_pos = start % self.blocksize
-        end_pos = end % self.blocksize
-
-        if start_block_number == end_block_number:
-            block = self._fetch_block_cached(start_block_number)
-            return block[start_pos:end_pos]
-
-        else:
-            # read from the initial
-            out = []
-            out.append(self._fetch_block_cached(start_block_number)[start_pos:])
-
-            # intermediate blocks
-            # Note: it'd be nice to combine these into one big request. However
-            # that doesn't play nicely with our LRU cache.
-            for block_number in range(start_block_number + 1, end_block_number):
-                out.append(self._fetch_block_cached(block_number))
-
-            # final block
-            out.append(self._fetch_block_cached(end_block_number)[:end_pos])
-
-            return b"".join(out)
-
-
-class BytesCache(BaseCache):
-    """Cache which holds data in a in-memory bytes object
-
-    Implements read-ahead by the block size, for semi-random reads progressing
-    through the file.
-
-    Parameters
-    ----------
-    trim: bool
-        As we read more data, whether to discard the start of the buffer when
-        we are more than a blocksize ahead of it.
-    """
-
-    name = "bytes"
-
-    def __init__(self, blocksize, fetcher, size, trim=True):
-        super().__init__(blocksize, fetcher, size)
-        self.cache = b""
-        self.start = None
-        self.end = None
-        self.trim = trim
-
-    def _fetch(self, start, end):
-        # TODO: only set start/end after fetch, in case it fails?
-        # is this where retry logic might go?
-        if start is None:
-            start = 0
-        if end is None:
-            end = self.size
-        if start >= self.size or start >= end:
-            return b""
-        if (
-            self.start is not None
-            and start >= self.start
-            and self.end is not None
-            and end < self.end
-        ):
-            # cache hit: we have all the required data
-            offset = start - self.start
-            return self.cache[offset : offset + end - start]
-
-        if self.blocksize:
-            bend = min(self.size, end + self.blocksize)
-        else:
-            bend = end
-
-        if bend == start or start > self.size:
-            return b""
-
-        if (self.start is None or start < self.start) and (
-            self.end is None or end > self.end
-        ):
-            # First read, or extending both before and after
-            self.cache = self.fetcher(start, bend)
-            self.start = start
-        elif start < self.start:
-            if self.end - end > self.blocksize:
-                self.cache = self.fetcher(start, bend)
-                self.start = start
-            else:
-                new = self.fetcher(start, self.start)
-                self.start = start
-                self.cache = new + self.cache
-        elif bend > self.end:
-            if self.end > self.size:
-                pass
-            elif end - self.end > self.blocksize:
-                self.cache = self.fetcher(start, bend)
-                self.start = start
-            else:
-                new = self.fetcher(self.end, bend)
-                self.cache = self.cache + new
-
-        self.end = self.start + len(self.cache)
-        offset = start - self.start
-        out = self.cache[offset : offset + end - start]
-        if self.trim:
-            num = (self.end - self.start) // (self.blocksize + 1)
-            if num > 1:
-                self.start += self.blocksize * num
-                self.cache = self.cache[self.blocksize * num :]
-        return out
-
-    def __len__(self):
-        return len(self.cache)
-
-
-class AllBytes(BaseCache):
-    """Cache entire contents of the file"""
-
-    name = "all"
-
-    def __init__(self, blocksize=None, fetcher=None, size=None, data=None):
-        super().__init__(blocksize, fetcher, size)
-        if data is None:
-            data = self.fetcher(0, self.size)
-        self.data = data
-
-    def _fetch(self, start, end):
-        return self.data[start:end]
-
-
-class KnownPartsOfAFile(BaseCache):
-    """
-    Cache holding known file parts.
-
-    Parameters
-    ----------
-    blocksize: int
-        How far to read ahead in numbers of bytes
-    fetcher: func
-        Function of the form f(start, end) which gets bytes from remote as
-        specified
-    size: int
-        How big this file is
-    data: dict
-        A dictionary mapping explicit `(start, stop)` file-offset tuples
-        with known bytes.
-    strict: bool, default True
-        Whether to fetch reads that go beyond a known byte-range boundary.
-        If `False`, any read that ends outside a known part will be zero
-        padded. Note that zero padding will not be used for reads that
-        begin outside a known byte-range.
-    """
-
-    name = "parts"
-
-    def __init__(self, blocksize, fetcher, size, data={}, strict=True, **_):
-        super(KnownPartsOfAFile, self).__init__(blocksize, fetcher, size)
-        self.strict = strict
-
-        # simple consolidation of contiguous blocks
-        if data:
-            old_offsets = sorted(list(data.keys()))
-            offsets = [old_offsets[0]]
-            blocks = [data.pop(old_offsets[0])]
-            for start, stop in old_offsets[1:]:
-                start0, stop0 = offsets[-1]
-                if start == stop0:
-                    offsets[-1] = (start0, stop)
-                    blocks[-1] += data.pop((start, stop))
-                else:
-                    offsets.append((start, stop))
-                    blocks.append(data.pop((start, stop)))
-
-            self.data = dict(zip(offsets, blocks))
-        else:
-            self.data = data
-
-    def _fetch(self, start, stop):
-        out = b""
-        for (loc0, loc1), data in self.data.items():
-            # If self.strict=False, use zero-padded data
-            # for reads beyond the end of a "known" buffer
-            if loc0 <= start < loc1:
-                off = start - loc0
-                out = data[off : off + stop - start]
-                if not self.strict or loc0 <= stop <= loc1:
-                    # The request is within a known range, or
-                    # it begins within a known range, and we
-                    # are allowed to pad reads beyond the
-                    # buffer with zero
-                    out += b"\x00" * (stop - start - len(out))
-                    return out
-                else:
-                    # The request ends outside a known range,
-                    # and we are being "strict" about reads
-                    # beyond the buffer
-                    start = loc1
-                    break
-
-        # We only get here if there is a request outside the
-        # known parts of the file. In an ideal world, this
-        # should never happen
-        if self.fetcher is None:
-            # We cannot fetch the data, so raise an error
-            raise ValueError(f"Read is outside the known file parts: {(start, stop)}. ")
-        # We can fetch the data, but should warn the user
-        # that this may be slow
-        warnings.warn(
-            f"Read is outside the known file parts: {(start, stop)}. "
-            f"IO/caching performance may be poor!"
-        )
-        logger.debug(f"KnownPartsOfAFile cache fetching {start}-{stop}")
-        return out + super()._fetch(start, stop)
-
-
-class UpdatableLRU:
-    """
-    Custom implementation of LRU cache that allows updating keys
-
-    Used by BackgroudBlockCache
-    """
-
-    CacheInfo = collections.namedtuple(
-        "CacheInfo", ["hits", "misses", "maxsize", "currsize"]
-    )
-
-    def __init__(self, func, max_size=128):
-        self._cache = collections.OrderedDict()
-        self._func = func
-        self._max_size = max_size
-        self._hits = 0
-        self._misses = 0
-        self._lock = threading.Lock()
-
-    def __call__(self, *args):
-        with self._lock:
-            if args in self._cache:
-                self._cache.move_to_end(args)
-                self._hits += 1
-                return self._cache[args]
-
-        result = self._func(*args)
-
-        with self._lock:
-            self._cache[args] = result
-            self._misses += 1
-            if len(self._cache) > self._max_size:
-                self._cache.popitem(last=False)
-
-        return result
-
-    def is_key_cached(self, *args):
-        with self._lock:
-            return args in self._cache
-
-    def add_key(self, result, *args):
-        with self._lock:
-            self._cache[args] = result
-            if len(self._cache) > self._max_size:
-                self._cache.popitem(last=False)
-
-    def cache_info(self):
-        with self._lock:
-            return self.CacheInfo(
-                maxsize=self._max_size,
-                currsize=len(self._cache),
-                hits=self._hits,
-                misses=self._misses,
-            )
-
-
-class BackgroundBlockCache(BaseCache):
-    """
-    Cache holding memory as a set of blocks with pre-loading of
-    the next block in the background.
-
-    Requests are only ever made ``blocksize`` at a time, and are
-    stored in an LRU cache. The least recently accessed block is
-    discarded when more than ``maxblocks`` are stored. If the
-    next block is not in cache, it is loaded in a separate thread
-    in non-blocking way.
-
-    Parameters
-    ----------
-    blocksize : int
-        The number of bytes to store in each block.
-        Requests are only ever made for ``blocksize``, so this
-        should balance the overhead of making a request against
-        the granularity of the blocks.
-    fetcher : Callable
-    size : int
-        The total size of the file being cached.
-    maxblocks : int
-        The maximum number of blocks to cache for. The maximum memory
-        use for this cache is then ``blocksize * maxblocks``.
-    """
-
-    name = "background"
-
-    def __init__(self, blocksize, fetcher, size, maxblocks=32):
-        super().__init__(blocksize, fetcher, size)
-        self.nblocks = math.ceil(size / blocksize)
-        self.maxblocks = maxblocks
-        self._fetch_block_cached = UpdatableLRU(self._fetch_block, maxblocks)
-
-        self._thread_executor = ThreadPoolExecutor(max_workers=1)
-        self._fetch_future_block_number = None
-        self._fetch_future = None
-        self._fetch_future_lock = threading.Lock()
-
-    def __repr__(self):
-        return "<BackgroundBlockCache blocksize={}, size={}, nblocks={}>".format(
-            self.blocksize, self.size, self.nblocks
-        )
-
-    def cache_info(self):
-        """
-        The statistics on the block cache.
-
-        Returns
-        -------
-        NamedTuple
-            Returned directly from the LRU Cache used internally.
-        """
-        return self._fetch_block_cached.cache_info()
-
-    def __getstate__(self):
-        state = self.__dict__
-        del state["_fetch_block_cached"]
-        del state["_thread_executor"]
-        del state["_fetch_future_block_number"]
-        del state["_fetch_future"]
-        del state["_fetch_future_lock"]
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__.update(state)
-        self._fetch_block_cached = UpdatableLRU(self._fetch_block, state["maxblocks"])
-        self._thread_executor = ThreadPoolExecutor(max_workers=1)
-        self._fetch_future_block_number = None
-        self._fetch_future = None
-        self._fetch_future_lock = threading.Lock()
-
-    def _fetch(self, start, end):
-        if start is None:
-            start = 0
-        if end is None:
-            end = self.size
-        if start >= self.size or start >= end:
-            return b""
-
-        # byte position -> block numbers
-        start_block_number = start // self.blocksize
-        end_block_number = end // self.blocksize
-
-        fetch_future_block_number = None
-        fetch_future = None
-        with self._fetch_future_lock:
-            # Background thread is running. Check we we can or must join it.
-            if self._fetch_future is not None:
-                if self._fetch_future.done():
-                    logger.info("BlockCache joined background fetch without waiting.")
-                    self._fetch_block_cached.add_key(
-                        self._fetch_future.result(), self._fetch_future_block_number
-                    )
-                    # Cleanup the fetch variables. Done with fetching the block.
-                    self._fetch_future_block_number = None
-                    self._fetch_future = None
-                else:
-                    # Must join if we need the block for the current fetch
-                    must_join = bool(
-                        start_block_number
-                        <= self._fetch_future_block_number
-                        <= end_block_number
-                    )
-                    if must_join:
-                        # Copy to the local variables to release lock
-                        # before waiting for result
-                        fetch_future_block_number = self._fetch_future_block_number
-                        fetch_future = self._fetch_future
-
-                        # Cleanup the fetch variables. Have a local copy.
-                        self._fetch_future_block_number = None
-                        self._fetch_future = None
-
-        # Need to wait for the future for the current read
-        if fetch_future is not None:
-            logger.info("BlockCache waiting for background fetch.")
-            # Wait until result and put it in cache
-            self._fetch_block_cached.add_key(
-                fetch_future.result(), fetch_future_block_number
-            )
-
-        # these are cached, so safe to do multiple calls for the same start and end.
-        for block_number in range(start_block_number, end_block_number + 1):
-            self._fetch_block_cached(block_number)
-
-        # fetch next block in the background if nothing is running in the background,
-        # the block is within file and it is not already cached
-        end_block_plus_1 = end_block_number + 1
-        with self._fetch_future_lock:
-            if (
-                self._fetch_future is None
-                and end_block_plus_1 <= self.nblocks
-                and not self._fetch_block_cached.is_key_cached(end_block_plus_1)
-            ):
-                self._fetch_future_block_number = end_block_plus_1
-                self._fetch_future = self._thread_executor.submit(
-                    self._fetch_block, end_block_plus_1, "async"
-                )
-
-        return self._read_cache(
-            start,
-            end,
-            start_block_number=start_block_number,
-            end_block_number=end_block_number,
-        )
-
-    def _fetch_block(self, block_number, log_info="sync"):
-        """
-        Fetch the block of data for `block_number`.
-        """
-        if block_number > self.nblocks:
-            raise ValueError(
-                "'block_number={}' is greater than the number of blocks ({})".format(
-                    block_number, self.nblocks
-                )
-            )
-
-        start = block_number * self.blocksize
-        end = start + self.blocksize
-        logger.info("BlockCache fetching block (%s) %d", log_info, block_number)
-        block_contents = super()._fetch(start, end)
-        return block_contents
-
-    def _read_cache(self, start, end, start_block_number, end_block_number):
-        """
-        Read from our block cache.
-
-        Parameters
-        ----------
-        start, end : int
-            The start and end byte positions.
-        start_block_number, end_block_number : int
-            The start and end block numbers.
-        """
-        start_pos = start % self.blocksize
-        end_pos = end % self.blocksize
-
-        if start_block_number == end_block_number:
-            block = self._fetch_block_cached(start_block_number)
-            return block[start_pos:end_pos]
-
-        else:
-            # read from the initial
-            out = []
-            out.append(self._fetch_block_cached(start_block_number)[start_pos:])
-
-            # intermediate blocks
-            # Note: it'd be nice to combine these into one big request. However
-            # that doesn't play nicely with our LRU cache.
-            for block_number in range(start_block_number + 1, end_block_number):
-                out.append(self._fetch_block_cached(block_number))
-
-            # final block
-            out.append(self._fetch_block_cached(end_block_number)[:end_pos])
-
-            return b"".join(out)
-
-
-caches = {
-    # one custom case
-    None: BaseCache,
-}
-
-
-def register_cache(cls, clobber=False):
-    """'Register' cache implementation.
-
-    Parameters
-    ----------
-    clobber: bool, optional
-        If set to True (default is False) - allow to overwrite existing
-        entry.
-
-    Raises
-    ------
-    ValueError
-    """
-    name = cls.name
-    if not clobber and name in caches:
-        raise ValueError(f"Cache with name {name!r} is already known: {caches[name]}")
-    caches[name] = cls
-
-
-for c in (
-    BaseCache,
-    MMapCache,
-    BytesCache,
-    ReadAheadCache,
-    BlockCache,
-    FirstChunkCache,
-    AllBytes,
-    KnownPartsOfAFile,
-    BackgroundBlockCache,
-):
-    register_cache(c)
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cuviddec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cuviddec.c
deleted file mode 100644
index 76e70aa648302aad5f8a37ec0db8573bb319fe74..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cuviddec.c
+++ /dev/null
@@ -1,1185 +0,0 @@
-/*
- * Nvidia CUVID decoder
- * Copyright (c) 2016 Timo Rothenpieler <timo@rothenpieler.org>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "config_components.h"
-
-#include "compat/cuda/dynlink_loader.h"
-
-#include "libavutil/buffer.h"
-#include "libavutil/mathematics.h"
-#include "libavutil/hwcontext.h"
-#include "libavutil/hwcontext_cuda_internal.h"
-#include "libavutil/cuda_check.h"
-#include "libavutil/fifo.h"
-#include "libavutil/log.h"
-#include "libavutil/opt.h"
-#include "libavutil/pixdesc.h"
-
-#include "avcodec.h"
-#include "bsf.h"
-#include "codec_internal.h"
-#include "decode.h"
-#include "hwconfig.h"
-#include "nvdec.h"
-#include "internal.h"
-
-#if !NVDECAPI_CHECK_VERSION(9, 0)
-#define cudaVideoSurfaceFormat_YUV444 2
-#define cudaVideoSurfaceFormat_YUV444_16Bit 3
-#endif
-
-#if NVDECAPI_CHECK_VERSION(11, 0)
-#define CUVID_HAS_AV1_SUPPORT
-#endif
-
-typedef struct CuvidContext
-{
-    AVClass *avclass;
-
-    CUvideodecoder cudecoder;
-    CUvideoparser cuparser;
-
-    /* This packet coincides with AVCodecInternal.in_pkt
-     * and is not owned by us. */
-    AVPacket *pkt;
-
-    char *cu_gpu;
-    int nb_surfaces;
-    int drop_second_field;
-    char *crop_expr;
-    char *resize_expr;
-
-    struct {
-        int left;
-        int top;
-        int right;
-        int bottom;
-    } crop;
-
-    struct {
-        int width;
-        int height;
-    } resize;
-
-    AVBufferRef *hwdevice;
-    AVBufferRef *hwframe;
-
-    AVFifo      *frame_queue;
-
-    int deint_mode;
-    int deint_mode_current;
-    int64_t prev_pts;
-    int progressive_sequence;
-
-    int internal_error;
-    int decoder_flushing;
-
-    int *key_frame;
-
-    cudaVideoCodec codec_type;
-    cudaVideoChromaFormat chroma_format;
-
-    CUVIDDECODECAPS caps8, caps10, caps12;
-
-    CUVIDPARSERPARAMS cuparseinfo;
-    CUVIDEOFORMATEX *cuparse_ext;
-
-    CudaFunctions *cudl;
-    CuvidFunctions *cvdl;
-} CuvidContext;
-
-typedef struct CuvidParsedFrame
-{
-    CUVIDPARSERDISPINFO dispinfo;
-    int second_field;
-    int is_deinterlacing;
-} CuvidParsedFrame;
-
-#define CHECK_CU(x) FF_CUDA_CHECK_DL(avctx, ctx->cudl, x)
-
-static int CUDAAPI cuvid_handle_video_sequence(void *opaque, CUVIDEOFORMAT* format)
-{
-    AVCodecContext *avctx = opaque;
-    CuvidContext *ctx = avctx->priv_data;
-    AVHWFramesContext *hwframe_ctx = (AVHWFramesContext*)ctx->hwframe->data;
-    CUVIDDECODECAPS *caps = NULL;
-    CUVIDDECODECREATEINFO cuinfo;
-    int surface_fmt;
-    int chroma_444;
-
-    int old_width = avctx->width;
-    int old_height = avctx->height;
-
-    enum AVPixelFormat pix_fmts[3] = { AV_PIX_FMT_CUDA,
-                                       AV_PIX_FMT_NONE,  // Will be updated below
-                                       AV_PIX_FMT_NONE };
-
-    av_log(avctx, AV_LOG_TRACE, "pfnSequenceCallback, progressive_sequence=%d\n", format->progressive_sequence);
-
-    memset(&cuinfo, 0, sizeof(cuinfo));
-
-    ctx->internal_error = 0;
-
-    avctx->coded_width = cuinfo.ulWidth = format->coded_width;
-    avctx->coded_height = cuinfo.ulHeight = format->coded_height;
-
-    // apply cropping
-    cuinfo.display_area.left = format->display_area.left + ctx->crop.left;
-    cuinfo.display_area.top = format->display_area.top + ctx->crop.top;
-    cuinfo.display_area.right = format->display_area.right - ctx->crop.right;
-    cuinfo.display_area.bottom = format->display_area.bottom - ctx->crop.bottom;
-
-    // width and height need to be set before calling ff_get_format
-    if (ctx->resize_expr) {
-        avctx->width = ctx->resize.width;
-        avctx->height = ctx->resize.height;
-    } else {
-        avctx->width = cuinfo.display_area.right - cuinfo.display_area.left;
-        avctx->height = cuinfo.display_area.bottom - cuinfo.display_area.top;
-    }
-
-    // target width/height need to be multiples of two
-    cuinfo.ulTargetWidth = avctx->width = (avctx->width + 1) & ~1;
-    cuinfo.ulTargetHeight = avctx->height = (avctx->height + 1) & ~1;
-
-    // aspect ratio conversion, 1:1, depends on scaled resolution
-    cuinfo.target_rect.left = 0;
-    cuinfo.target_rect.top = 0;
-    cuinfo.target_rect.right = cuinfo.ulTargetWidth;
-    cuinfo.target_rect.bottom = cuinfo.ulTargetHeight;
-
-    chroma_444 = format->chroma_format == cudaVideoChromaFormat_444;
-
-    switch (format->bit_depth_luma_minus8) {
-    case 0: // 8-bit
-        pix_fmts[1] = chroma_444 ? AV_PIX_FMT_YUV444P : AV_PIX_FMT_NV12;
-        caps = &ctx->caps8;
-        break;
-    case 2: // 10-bit
-        pix_fmts[1] = chroma_444 ? AV_PIX_FMT_YUV444P16 : AV_PIX_FMT_P010;
-        caps = &ctx->caps10;
-        break;
-    case 4: // 12-bit
-        pix_fmts[1] = chroma_444 ? AV_PIX_FMT_YUV444P16 : AV_PIX_FMT_P016;
-        caps = &ctx->caps12;
-        break;
-    default:
-        break;
-    }
-
-    if (!caps || !caps->bIsSupported) {
-        av_log(avctx, AV_LOG_ERROR, "unsupported bit depth: %d\n",
-               format->bit_depth_luma_minus8 + 8);
-        ctx->internal_error = AVERROR(EINVAL);
-        return 0;
-    }
-
-    surface_fmt = ff_get_format(avctx, pix_fmts);
-    if (surface_fmt < 0) {
-        av_log(avctx, AV_LOG_ERROR, "ff_get_format failed: %d\n", surface_fmt);
-        ctx->internal_error = AVERROR(EINVAL);
-        return 0;
-    }
-
-    av_log(avctx, AV_LOG_VERBOSE, "Formats: Original: %s | HW: %s | SW: %s\n",
-           av_get_pix_fmt_name(avctx->pix_fmt),
-           av_get_pix_fmt_name(surface_fmt),
-           av_get_pix_fmt_name(avctx->sw_pix_fmt));
-
-    avctx->pix_fmt = surface_fmt;
-
-    // Update our hwframe ctx, as the get_format callback might have refreshed it!
-    if (avctx->hw_frames_ctx) {
-        av_buffer_unref(&ctx->hwframe);
-
-        ctx->hwframe = av_buffer_ref(avctx->hw_frames_ctx);
-        if (!ctx->hwframe) {
-            ctx->internal_error = AVERROR(ENOMEM);
-            return 0;
-        }
-
-        hwframe_ctx = (AVHWFramesContext*)ctx->hwframe->data;
-    }
-
-    ff_set_sar(avctx, av_div_q(
-        (AVRational){ format->display_aspect_ratio.x, format->display_aspect_ratio.y },
-        (AVRational){ avctx->width, avctx->height }));
-
-    ctx->deint_mode_current = format->progressive_sequence
-                              ? cudaVideoDeinterlaceMode_Weave
-                              : ctx->deint_mode;
-
-    ctx->progressive_sequence = format->progressive_sequence;
-
-    if (!format->progressive_sequence && ctx->deint_mode_current == cudaVideoDeinterlaceMode_Weave)
-        avctx->flags |= AV_CODEC_FLAG_INTERLACED_DCT;
-    else
-        avctx->flags &= ~AV_CODEC_FLAG_INTERLACED_DCT;
-
-    if (format->video_signal_description.video_full_range_flag)
-        avctx->color_range = AVCOL_RANGE_JPEG;
-    else
-        avctx->color_range = AVCOL_RANGE_MPEG;
-
-    avctx->color_primaries = format->video_signal_description.color_primaries;
-    avctx->color_trc = format->video_signal_description.transfer_characteristics;
-    avctx->colorspace = format->video_signal_description.matrix_coefficients;
-
-    if (format->bitrate)
-        avctx->bit_rate = format->bitrate;
-
-    if (format->frame_rate.numerator && format->frame_rate.denominator) {
-        avctx->framerate.num = format->frame_rate.numerator;
-        avctx->framerate.den = format->frame_rate.denominator;
-    }
-
-    if (ctx->cudecoder
-            && avctx->coded_width == format->coded_width
-            && avctx->coded_height == format->coded_height
-            && avctx->width == old_width
-            && avctx->height == old_height
-            && ctx->chroma_format == format->chroma_format
-            && ctx->codec_type == format->codec)
-        return 1;
-
-    if (ctx->cudecoder) {
-        av_log(avctx, AV_LOG_TRACE, "Re-initializing decoder\n");
-        ctx->internal_error = CHECK_CU(ctx->cvdl->cuvidDestroyDecoder(ctx->cudecoder));
-        if (ctx->internal_error < 0)
-            return 0;
-        ctx->cudecoder = NULL;
-    }
-
-    if (hwframe_ctx->pool && (
-            hwframe_ctx->width < avctx->width ||
-            hwframe_ctx->height < avctx->height ||
-            hwframe_ctx->format != AV_PIX_FMT_CUDA ||
-            hwframe_ctx->sw_format != avctx->sw_pix_fmt)) {
-        av_log(avctx, AV_LOG_ERROR, "AVHWFramesContext is already initialized with incompatible parameters\n");
-        av_log(avctx, AV_LOG_DEBUG, "width: %d <-> %d\n", hwframe_ctx->width, avctx->width);
-        av_log(avctx, AV_LOG_DEBUG, "height: %d <-> %d\n", hwframe_ctx->height, avctx->height);
-        av_log(avctx, AV_LOG_DEBUG, "format: %s <-> cuda\n", av_get_pix_fmt_name(hwframe_ctx->format));
-        av_log(avctx, AV_LOG_DEBUG, "sw_format: %s <-> %s\n",
-               av_get_pix_fmt_name(hwframe_ctx->sw_format), av_get_pix_fmt_name(avctx->sw_pix_fmt));
-        ctx->internal_error = AVERROR(EINVAL);
-        return 0;
-    }
-
-    ctx->chroma_format = format->chroma_format;
-
-    cuinfo.CodecType = ctx->codec_type = format->codec;
-    cuinfo.ChromaFormat = format->chroma_format;
-
-    switch (avctx->sw_pix_fmt) {
-    case AV_PIX_FMT_NV12:
-        cuinfo.OutputFormat = cudaVideoSurfaceFormat_NV12;
-        break;
-    case AV_PIX_FMT_P010:
-    case AV_PIX_FMT_P016:
-        cuinfo.OutputFormat = cudaVideoSurfaceFormat_P016;
-        break;
-    case AV_PIX_FMT_YUV444P:
-        cuinfo.OutputFormat = cudaVideoSurfaceFormat_YUV444;
-        break;
-    case AV_PIX_FMT_YUV444P16:
-        cuinfo.OutputFormat = cudaVideoSurfaceFormat_YUV444_16Bit;
-        break;
-    default:
-        av_log(avctx, AV_LOG_ERROR, "Unsupported output format: %s\n",
-               av_get_pix_fmt_name(avctx->sw_pix_fmt));
-        ctx->internal_error = AVERROR(EINVAL);
-        return 0;
-    }
-
-    cuinfo.ulNumDecodeSurfaces = ctx->nb_surfaces;
-    cuinfo.ulNumOutputSurfaces = 1;
-    cuinfo.ulCreationFlags = cudaVideoCreate_PreferCUVID;
-    cuinfo.bitDepthMinus8 = format->bit_depth_luma_minus8;
-    cuinfo.DeinterlaceMode = ctx->deint_mode_current;
-
-    if (ctx->deint_mode_current != cudaVideoDeinterlaceMode_Weave && !ctx->drop_second_field)
-        avctx->framerate = av_mul_q(avctx->framerate, (AVRational){2, 1});
-
-    ctx->internal_error = CHECK_CU(ctx->cvdl->cuvidCreateDecoder(&ctx->cudecoder, &cuinfo));
-    if (ctx->internal_error < 0)
-        return 0;
-
-    if (!hwframe_ctx->pool) {
-        hwframe_ctx->format = AV_PIX_FMT_CUDA;
-        hwframe_ctx->sw_format = avctx->sw_pix_fmt;
-        hwframe_ctx->width = avctx->width;
-        hwframe_ctx->height = avctx->height;
-
-        if ((ctx->internal_error = av_hwframe_ctx_init(ctx->hwframe)) < 0) {
-            av_log(avctx, AV_LOG_ERROR, "av_hwframe_ctx_init failed\n");
-            return 0;
-        }
-    }
-
-    return 1;
-}
-
-static int CUDAAPI cuvid_handle_picture_decode(void *opaque, CUVIDPICPARAMS* picparams)
-{
-    AVCodecContext *avctx = opaque;
-    CuvidContext *ctx = avctx->priv_data;
-
-    av_log(avctx, AV_LOG_TRACE, "pfnDecodePicture\n");
-
-    if(picparams->intra_pic_flag)
-        ctx->key_frame[picparams->CurrPicIdx] = picparams->intra_pic_flag;
-
-    ctx->internal_error = CHECK_CU(ctx->cvdl->cuvidDecodePicture(ctx->cudecoder, picparams));
-    if (ctx->internal_error < 0)
-        return 0;
-
-    return 1;
-}
-
-static int CUDAAPI cuvid_handle_picture_display(void *opaque, CUVIDPARSERDISPINFO* dispinfo)
-{
-    AVCodecContext *avctx = opaque;
-    CuvidContext *ctx = avctx->priv_data;
-    CuvidParsedFrame parsed_frame = { { 0 } };
-
-    parsed_frame.dispinfo = *dispinfo;
-    ctx->internal_error = 0;
-
-    // For some reason, dispinfo->progressive_frame is sometimes wrong.
-    parsed_frame.dispinfo.progressive_frame = ctx->progressive_sequence;
-
-    if (ctx->deint_mode_current == cudaVideoDeinterlaceMode_Weave) {
-        av_fifo_write(ctx->frame_queue, &parsed_frame, 1);
-    } else {
-        parsed_frame.is_deinterlacing = 1;
-        av_fifo_write(ctx->frame_queue, &parsed_frame, 1);
-        if (!ctx->drop_second_field) {
-            parsed_frame.second_field = 1;
-            av_fifo_write(ctx->frame_queue, &parsed_frame, 1);
-        }
-    }
-
-    return 1;
-}
-
-static int cuvid_is_buffer_full(AVCodecContext *avctx)
-{
-    CuvidContext *ctx = avctx->priv_data;
-
-    int delay = ctx->cuparseinfo.ulMaxDisplayDelay;
-    if (ctx->deint_mode != cudaVideoDeinterlaceMode_Weave && !ctx->drop_second_field)
-        delay *= 2;
-
-    return av_fifo_can_read(ctx->frame_queue) + delay >= ctx->nb_surfaces;
-}
-
-static int cuvid_decode_packet(AVCodecContext *avctx, const AVPacket *avpkt)
-{
-    CuvidContext *ctx = avctx->priv_data;
-    AVHWDeviceContext *device_ctx = (AVHWDeviceContext*)ctx->hwdevice->data;
-    AVCUDADeviceContext *device_hwctx = device_ctx->hwctx;
-    CUcontext dummy, cuda_ctx = device_hwctx->cuda_ctx;
-    CUVIDSOURCEDATAPACKET cupkt;
-    int ret = 0, eret = 0, is_flush = ctx->decoder_flushing;
-
-    av_log(avctx, AV_LOG_TRACE, "cuvid_decode_packet\n");
-
-    if (is_flush && avpkt && avpkt->size)
-        return AVERROR_EOF;
-
-    if (cuvid_is_buffer_full(avctx) && avpkt && avpkt->size)
-        return AVERROR(EAGAIN);
-
-    ret = CHECK_CU(ctx->cudl->cuCtxPushCurrent(cuda_ctx));
-    if (ret < 0) {
-        return ret;
-    }
-
-    memset(&cupkt, 0, sizeof(cupkt));
-
-    if (avpkt && avpkt->size) {
-        cupkt.payload_size = avpkt->size;
-        cupkt.payload = avpkt->data;
-
-        if (avpkt->pts != AV_NOPTS_VALUE) {
-            cupkt.flags = CUVID_PKT_TIMESTAMP;
-            if (avctx->pkt_timebase.num && avctx->pkt_timebase.den)
-                cupkt.timestamp = av_rescale_q(avpkt->pts, avctx->pkt_timebase, (AVRational){1, 10000000});
-            else
-                cupkt.timestamp = avpkt->pts;
-        }
-    } else {
-        cupkt.flags = CUVID_PKT_ENDOFSTREAM;
-        ctx->decoder_flushing = 1;
-    }
-
-    ret = CHECK_CU(ctx->cvdl->cuvidParseVideoData(ctx->cuparser, &cupkt));
-
-    if (ret < 0)
-        goto error;
-
-    // cuvidParseVideoData doesn't return an error just because stuff failed...
-    if (ctx->internal_error) {
-        av_log(avctx, AV_LOG_ERROR, "cuvid decode callback error\n");
-        ret = ctx->internal_error;
-        goto error;
-    }
-
-error:
-    eret = CHECK_CU(ctx->cudl->cuCtxPopCurrent(&dummy));
-
-    if (eret < 0)
-        return eret;
-    else if (ret < 0)
-        return ret;
-    else if (is_flush)
-        return AVERROR_EOF;
-    else
-        return 0;
-}
-
-static int cuvid_output_frame(AVCodecContext *avctx, AVFrame *frame)
-{
-    CuvidContext *ctx = avctx->priv_data;
-    AVHWDeviceContext *device_ctx = (AVHWDeviceContext*)ctx->hwdevice->data;
-    AVCUDADeviceContext *device_hwctx = device_ctx->hwctx;
-    CUcontext dummy, cuda_ctx = device_hwctx->cuda_ctx;
-    CuvidParsedFrame parsed_frame;
-    CUdeviceptr mapped_frame = 0;
-    int ret = 0, eret = 0;
-
-    av_log(avctx, AV_LOG_TRACE, "cuvid_output_frame\n");
-
-    if (ctx->decoder_flushing) {
-        ret = cuvid_decode_packet(avctx, NULL);
-        if (ret < 0 && ret != AVERROR_EOF)
-            return ret;
-    }
-
-    if (!cuvid_is_buffer_full(avctx)) {
-        AVPacket *const pkt = ctx->pkt;
-        ret = ff_decode_get_packet(avctx, pkt);
-        if (ret < 0 && ret != AVERROR_EOF)
-            return ret;
-        ret = cuvid_decode_packet(avctx, pkt);
-        av_packet_unref(pkt);
-        // cuvid_is_buffer_full() should avoid this.
-        if (ret == AVERROR(EAGAIN))
-            ret = AVERROR_EXTERNAL;
-        if (ret < 0 && ret != AVERROR_EOF)
-            return ret;
-    }
-
-    ret = CHECK_CU(ctx->cudl->cuCtxPushCurrent(cuda_ctx));
-    if (ret < 0)
-        return ret;
-
-    if (av_fifo_read(ctx->frame_queue, &parsed_frame, 1) >= 0) {
-        const AVPixFmtDescriptor *pixdesc;
-        CUVIDPROCPARAMS params;
-        unsigned int pitch = 0;
-        int offset = 0;
-        int i;
-
-        memset(&params, 0, sizeof(params));
-        params.progressive_frame = parsed_frame.dispinfo.progressive_frame;
-        params.second_field = parsed_frame.second_field;
-        params.top_field_first = parsed_frame.dispinfo.top_field_first;
-
-        ret = CHECK_CU(ctx->cvdl->cuvidMapVideoFrame(ctx->cudecoder, parsed_frame.dispinfo.picture_index, &mapped_frame, &pitch, &params));
-        if (ret < 0)
-            goto error;
-
-        if (avctx->pix_fmt == AV_PIX_FMT_CUDA) {
-            ret = av_hwframe_get_buffer(ctx->hwframe, frame, 0);
-            if (ret < 0) {
-                av_log(avctx, AV_LOG_ERROR, "av_hwframe_get_buffer failed\n");
-                goto error;
-            }
-
-            ret = ff_decode_frame_props(avctx, frame);
-            if (ret < 0) {
-                av_log(avctx, AV_LOG_ERROR, "ff_decode_frame_props failed\n");
-                goto error;
-            }
-
-            pixdesc = av_pix_fmt_desc_get(avctx->sw_pix_fmt);
-
-            for (i = 0; i < pixdesc->nb_components; i++) {
-                int height = avctx->height >> (i ? pixdesc->log2_chroma_h : 0);
-                CUDA_MEMCPY2D cpy = {
-                    .srcMemoryType = CU_MEMORYTYPE_DEVICE,
-                    .dstMemoryType = CU_MEMORYTYPE_DEVICE,
-                    .srcDevice     = mapped_frame,
-                    .dstDevice     = (CUdeviceptr)frame->data[i],
-                    .srcPitch      = pitch,
-                    .dstPitch      = frame->linesize[i],
-                    .srcY          = offset,
-                    .WidthInBytes  = FFMIN(pitch, frame->linesize[i]),
-                    .Height        = height,
-                };
-
-                ret = CHECK_CU(ctx->cudl->cuMemcpy2DAsync(&cpy, device_hwctx->stream));
-                if (ret < 0)
-                    goto error;
-
-                offset += height;
-            }
-        } else if (avctx->pix_fmt == AV_PIX_FMT_NV12      ||
-                   avctx->pix_fmt == AV_PIX_FMT_P010      ||
-                   avctx->pix_fmt == AV_PIX_FMT_P016      ||
-                   avctx->pix_fmt == AV_PIX_FMT_YUV444P   ||
-                   avctx->pix_fmt == AV_PIX_FMT_YUV444P16) {
-            unsigned int offset = 0;
-            AVFrame *tmp_frame = av_frame_alloc();
-            if (!tmp_frame) {
-                av_log(avctx, AV_LOG_ERROR, "av_frame_alloc failed\n");
-                ret = AVERROR(ENOMEM);
-                goto error;
-            }
-
-            pixdesc = av_pix_fmt_desc_get(avctx->sw_pix_fmt);
-
-            tmp_frame->format        = AV_PIX_FMT_CUDA;
-            tmp_frame->hw_frames_ctx = av_buffer_ref(ctx->hwframe);
-            if (!tmp_frame->hw_frames_ctx) {
-                ret = AVERROR(ENOMEM);
-                av_frame_free(&tmp_frame);
-                goto error;
-            }
-
-            tmp_frame->width         = avctx->width;
-            tmp_frame->height        = avctx->height;
-
-            /*
-             * Note that the following logic would not work for three plane
-             * YUV420 because the pitch value is different for the chroma
-             * planes.
-             */
-            for (i = 0; i < pixdesc->nb_components; i++) {
-                tmp_frame->data[i]     = (uint8_t*)mapped_frame + offset;
-                tmp_frame->linesize[i] = pitch;
-                offset += pitch * (avctx->height >> (i ? pixdesc->log2_chroma_h : 0));
-            }
-
-            ret = ff_get_buffer(avctx, frame, 0);
-            if (ret < 0) {
-                av_log(avctx, AV_LOG_ERROR, "ff_get_buffer failed\n");
-                av_frame_free(&tmp_frame);
-                goto error;
-            }
-
-            ret = av_hwframe_transfer_data(frame, tmp_frame, 0);
-            if (ret) {
-                av_log(avctx, AV_LOG_ERROR, "av_hwframe_transfer_data failed\n");
-                av_frame_free(&tmp_frame);
-                goto error;
-            }
-            av_frame_free(&tmp_frame);
-        } else {
-            ret = AVERROR_BUG;
-            goto error;
-        }
-
-        frame->key_frame = ctx->key_frame[parsed_frame.dispinfo.picture_index];
-        ctx->key_frame[parsed_frame.dispinfo.picture_index] = 0;
-
-        frame->width = avctx->width;
-        frame->height = avctx->height;
-        if (avctx->pkt_timebase.num && avctx->pkt_timebase.den)
-            frame->pts = av_rescale_q(parsed_frame.dispinfo.timestamp, (AVRational){1, 10000000}, avctx->pkt_timebase);
-        else
-            frame->pts = parsed_frame.dispinfo.timestamp;
-
-        if (parsed_frame.second_field) {
-            if (ctx->prev_pts == INT64_MIN) {
-                ctx->prev_pts = frame->pts;
-                frame->pts += (avctx->pkt_timebase.den * avctx->framerate.den) / (avctx->pkt_timebase.num * avctx->framerate.num);
-            } else {
-                int pts_diff = (frame->pts - ctx->prev_pts) / 2;
-                ctx->prev_pts = frame->pts;
-                frame->pts += pts_diff;
-            }
-        }
-
-        /* CUVIDs opaque reordering breaks the internal pkt logic.
-         * So set pkt_pts and clear all the other pkt_ fields.
-         */
-        frame->duration = 0;
-#if FF_API_FRAME_PKT
-FF_DISABLE_DEPRECATION_WARNINGS
-        frame->pkt_pos = -1;
-        frame->pkt_size = -1;
-FF_ENABLE_DEPRECATION_WARNINGS
-#endif
-
-        frame->interlaced_frame = !parsed_frame.is_deinterlacing && !parsed_frame.dispinfo.progressive_frame;
-
-        if (frame->interlaced_frame)
-            frame->top_field_first = parsed_frame.dispinfo.top_field_first;
-    } else if (ctx->decoder_flushing) {
-        ret = AVERROR_EOF;
-    } else {
-        ret = AVERROR(EAGAIN);
-    }
-
-error:
-    if (ret < 0)
-        av_frame_unref(frame);
-
-    if (mapped_frame)
-        eret = CHECK_CU(ctx->cvdl->cuvidUnmapVideoFrame(ctx->cudecoder, mapped_frame));
-
-    eret = CHECK_CU(ctx->cudl->cuCtxPopCurrent(&dummy));
-
-    if (eret < 0)
-        return eret;
-    else
-        return ret;
-}
-
-static av_cold int cuvid_decode_end(AVCodecContext *avctx)
-{
-    CuvidContext *ctx = avctx->priv_data;
-    AVHWDeviceContext *device_ctx = ctx->hwdevice ? (AVHWDeviceContext *)ctx->hwdevice->data : NULL;
-    AVCUDADeviceContext *device_hwctx = device_ctx ? device_ctx->hwctx : NULL;
-    CUcontext dummy, cuda_ctx = device_hwctx ? device_hwctx->cuda_ctx : NULL;
-
-    av_fifo_freep2(&ctx->frame_queue);
-
-    if (cuda_ctx) {
-        ctx->cudl->cuCtxPushCurrent(cuda_ctx);
-
-        if (ctx->cuparser)
-            ctx->cvdl->cuvidDestroyVideoParser(ctx->cuparser);
-
-        if (ctx->cudecoder)
-            ctx->cvdl->cuvidDestroyDecoder(ctx->cudecoder);
-
-        ctx->cudl->cuCtxPopCurrent(&dummy);
-    }
-
-    ctx->cudl = NULL;
-
-    av_buffer_unref(&ctx->hwframe);
-    av_buffer_unref(&ctx->hwdevice);
-
-    av_freep(&ctx->key_frame);
-    av_freep(&ctx->cuparse_ext);
-
-    cuvid_free_functions(&ctx->cvdl);
-
-    return 0;
-}
-
-static int cuvid_test_capabilities(AVCodecContext *avctx,
-                                   const CUVIDPARSERPARAMS *cuparseinfo,
-                                   int probed_width,
-                                   int probed_height,
-                                   int bit_depth)
-{
-    CuvidContext *ctx = avctx->priv_data;
-    CUVIDDECODECAPS *caps;
-    int res8 = 0, res10 = 0, res12 = 0;
-
-    if (!ctx->cvdl->cuvidGetDecoderCaps) {
-        av_log(avctx, AV_LOG_WARNING, "Used Nvidia driver is too old to perform a capability check.\n");
-        av_log(avctx, AV_LOG_WARNING, "The minimum required version is "
-#if defined(_WIN32) || defined(__CYGWIN__)
-            "378.66"
-#else
-            "378.13"
-#endif
-            ". Continuing blind.\n");
-        ctx->caps8.bIsSupported = ctx->caps10.bIsSupported = 1;
-        // 12 bit was not supported before the capability check was introduced, so disable it.
-        ctx->caps12.bIsSupported = 0;
-        return 0;
-    }
-
-    ctx->caps8.eCodecType = ctx->caps10.eCodecType = ctx->caps12.eCodecType
-        = cuparseinfo->CodecType;
-    ctx->caps8.eChromaFormat = ctx->caps10.eChromaFormat = ctx->caps12.eChromaFormat
-        = cudaVideoChromaFormat_420;
-
-    ctx->caps8.nBitDepthMinus8 = 0;
-    ctx->caps10.nBitDepthMinus8 = 2;
-    ctx->caps12.nBitDepthMinus8 = 4;
-
-    res8 = CHECK_CU(ctx->cvdl->cuvidGetDecoderCaps(&ctx->caps8));
-    res10 = CHECK_CU(ctx->cvdl->cuvidGetDecoderCaps(&ctx->caps10));
-    res12 = CHECK_CU(ctx->cvdl->cuvidGetDecoderCaps(&ctx->caps12));
-
-    av_log(avctx, AV_LOG_VERBOSE, "CUVID capabilities for %s:\n", avctx->codec->name);
-    av_log(avctx, AV_LOG_VERBOSE, "8 bit: supported: %d, min_width: %d, max_width: %d, min_height: %d, max_height: %d\n",
-           ctx->caps8.bIsSupported, ctx->caps8.nMinWidth, ctx->caps8.nMaxWidth, ctx->caps8.nMinHeight, ctx->caps8.nMaxHeight);
-    av_log(avctx, AV_LOG_VERBOSE, "10 bit: supported: %d, min_width: %d, max_width: %d, min_height: %d, max_height: %d\n",
-           ctx->caps10.bIsSupported, ctx->caps10.nMinWidth, ctx->caps10.nMaxWidth, ctx->caps10.nMinHeight, ctx->caps10.nMaxHeight);
-    av_log(avctx, AV_LOG_VERBOSE, "12 bit: supported: %d, min_width: %d, max_width: %d, min_height: %d, max_height: %d\n",
-           ctx->caps12.bIsSupported, ctx->caps12.nMinWidth, ctx->caps12.nMaxWidth, ctx->caps12.nMinHeight, ctx->caps12.nMaxHeight);
-
-    switch (bit_depth) {
-    case 10:
-        caps = &ctx->caps10;
-        if (res10 < 0)
-            return res10;
-        break;
-    case 12:
-        caps = &ctx->caps12;
-        if (res12 < 0)
-            return res12;
-        break;
-    default:
-        caps = &ctx->caps8;
-        if (res8 < 0)
-            return res8;
-    }
-
-    if (!ctx->caps8.bIsSupported) {
-        av_log(avctx, AV_LOG_ERROR, "Codec %s is not supported.\n", avctx->codec->name);
-        return AVERROR(EINVAL);
-    }
-
-    if (!caps->bIsSupported) {
-        av_log(avctx, AV_LOG_ERROR, "Bit depth %d is not supported.\n", bit_depth);
-        return AVERROR(EINVAL);
-    }
-
-    if (probed_width > caps->nMaxWidth || probed_width < caps->nMinWidth) {
-        av_log(avctx, AV_LOG_ERROR, "Video width %d not within range from %d to %d\n",
-               probed_width, caps->nMinWidth, caps->nMaxWidth);
-        return AVERROR(EINVAL);
-    }
-
-    if (probed_height > caps->nMaxHeight || probed_height < caps->nMinHeight) {
-        av_log(avctx, AV_LOG_ERROR, "Video height %d not within range from %d to %d\n",
-               probed_height, caps->nMinHeight, caps->nMaxHeight);
-        return AVERROR(EINVAL);
-    }
-
-    if ((probed_width * probed_height) / 256 > caps->nMaxMBCount) {
-        av_log(avctx, AV_LOG_ERROR, "Video macroblock count %d exceeds maximum of %d\n",
-               (int)(probed_width * probed_height) / 256, caps->nMaxMBCount);
-        return AVERROR(EINVAL);
-    }
-
-    return 0;
-}
-
-static av_cold int cuvid_decode_init(AVCodecContext *avctx)
-{
-    CuvidContext *ctx = avctx->priv_data;
-    AVCUDADeviceContext *device_hwctx;
-    AVHWDeviceContext *device_ctx;
-    AVHWFramesContext *hwframe_ctx;
-    CUVIDSOURCEDATAPACKET seq_pkt;
-    CUcontext cuda_ctx = NULL;
-    CUcontext dummy;
-    uint8_t *extradata;
-    int extradata_size;
-    int ret = 0;
-
-    enum AVPixelFormat pix_fmts[3] = { AV_PIX_FMT_CUDA,
-                                       AV_PIX_FMT_NV12,
-                                       AV_PIX_FMT_NONE };
-
-    int probed_width = avctx->coded_width ? avctx->coded_width : 1280;
-    int probed_height = avctx->coded_height ? avctx->coded_height : 720;
-    int probed_bit_depth = 8;
-
-    const AVPixFmtDescriptor *probe_desc = av_pix_fmt_desc_get(avctx->pix_fmt);
-    if (probe_desc && probe_desc->nb_components)
-        probed_bit_depth = probe_desc->comp[0].depth;
-
-    ctx->pkt = avctx->internal->in_pkt;
-    // Accelerated transcoding scenarios with 'ffmpeg' require that the
-    // pix_fmt be set to AV_PIX_FMT_CUDA early. The sw_pix_fmt, and the
-    // pix_fmt for non-accelerated transcoding, do not need to be correct
-    // but need to be set to something. We arbitrarily pick NV12.
-    ret = ff_get_format(avctx, pix_fmts);
-    if (ret < 0) {
-        av_log(avctx, AV_LOG_ERROR, "ff_get_format failed: %d\n", ret);
-        return ret;
-    }
-    avctx->pix_fmt = ret;
-
-    if (ctx->resize_expr && sscanf(ctx->resize_expr, "%dx%d",
-                                   &ctx->resize.width, &ctx->resize.height) != 2) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid resize expressions\n");
-        ret = AVERROR(EINVAL);
-        goto error;
-    }
-
-    if (ctx->crop_expr && sscanf(ctx->crop_expr, "%dx%dx%dx%d",
-                                 &ctx->crop.top, &ctx->crop.bottom,
-                                 &ctx->crop.left, &ctx->crop.right) != 4) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid cropping expressions\n");
-        ret = AVERROR(EINVAL);
-        goto error;
-    }
-
-    ret = cuvid_load_functions(&ctx->cvdl, avctx);
-    if (ret < 0) {
-        av_log(avctx, AV_LOG_ERROR, "Failed loading nvcuvid.\n");
-        goto error;
-    }
-
-    ctx->frame_queue = av_fifo_alloc2(ctx->nb_surfaces, sizeof(CuvidParsedFrame), 0);
-    if (!ctx->frame_queue) {
-        ret = AVERROR(ENOMEM);
-        goto error;
-    }
-
-    if (avctx->hw_frames_ctx) {
-        ctx->hwframe = av_buffer_ref(avctx->hw_frames_ctx);
-        if (!ctx->hwframe) {
-            ret = AVERROR(ENOMEM);
-            goto error;
-        }
-
-        hwframe_ctx = (AVHWFramesContext*)ctx->hwframe->data;
-
-        ctx->hwdevice = av_buffer_ref(hwframe_ctx->device_ref);
-        if (!ctx->hwdevice) {
-            ret = AVERROR(ENOMEM);
-            goto error;
-        }
-    } else {
-        if (avctx->hw_device_ctx) {
-            ctx->hwdevice = av_buffer_ref(avctx->hw_device_ctx);
-            if (!ctx->hwdevice) {
-                ret = AVERROR(ENOMEM);
-                goto error;
-            }
-        } else {
-            ret = av_hwdevice_ctx_create(&ctx->hwdevice, AV_HWDEVICE_TYPE_CUDA, ctx->cu_gpu, NULL, 0);
-            if (ret < 0)
-                goto error;
-        }
-
-        ctx->hwframe = av_hwframe_ctx_alloc(ctx->hwdevice);
-        if (!ctx->hwframe) {
-            av_log(avctx, AV_LOG_ERROR, "av_hwframe_ctx_alloc failed\n");
-            ret = AVERROR(ENOMEM);
-            goto error;
-        }
-
-        hwframe_ctx = (AVHWFramesContext*)ctx->hwframe->data;
-    }
-
-    device_ctx = hwframe_ctx->device_ctx;
-    device_hwctx = device_ctx->hwctx;
-
-    cuda_ctx = device_hwctx->cuda_ctx;
-    ctx->cudl = device_hwctx->internal->cuda_dl;
-
-    memset(&ctx->cuparseinfo, 0, sizeof(ctx->cuparseinfo));
-    memset(&seq_pkt, 0, sizeof(seq_pkt));
-
-    switch (avctx->codec->id) {
-#if CONFIG_H264_CUVID_DECODER
-    case AV_CODEC_ID_H264:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_H264;
-        break;
-#endif
-#if CONFIG_HEVC_CUVID_DECODER
-    case AV_CODEC_ID_HEVC:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_HEVC;
-        break;
-#endif
-#if CONFIG_MJPEG_CUVID_DECODER
-    case AV_CODEC_ID_MJPEG:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_JPEG;
-        break;
-#endif
-#if CONFIG_MPEG1_CUVID_DECODER
-    case AV_CODEC_ID_MPEG1VIDEO:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_MPEG1;
-        break;
-#endif
-#if CONFIG_MPEG2_CUVID_DECODER
-    case AV_CODEC_ID_MPEG2VIDEO:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_MPEG2;
-        break;
-#endif
-#if CONFIG_MPEG4_CUVID_DECODER
-    case AV_CODEC_ID_MPEG4:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_MPEG4;
-        break;
-#endif
-#if CONFIG_VP8_CUVID_DECODER
-    case AV_CODEC_ID_VP8:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_VP8;
-        break;
-#endif
-#if CONFIG_VP9_CUVID_DECODER
-    case AV_CODEC_ID_VP9:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_VP9;
-        break;
-#endif
-#if CONFIG_VC1_CUVID_DECODER
-    case AV_CODEC_ID_VC1:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_VC1;
-        break;
-#endif
-#if CONFIG_AV1_CUVID_DECODER && defined(CUVID_HAS_AV1_SUPPORT)
-    case AV_CODEC_ID_AV1:
-        ctx->cuparseinfo.CodecType = cudaVideoCodec_AV1;
-        break;
-#endif
-    default:
-        av_log(avctx, AV_LOG_ERROR, "Invalid CUVID codec!\n");
-        return AVERROR_BUG;
-    }
-
-    if (ffcodec(avctx->codec)->bsfs) {
-        const AVCodecParameters *par = avctx->internal->bsf->par_out;
-        extradata = par->extradata;
-        extradata_size = par->extradata_size;
-    } else {
-        extradata = avctx->extradata;
-        extradata_size = avctx->extradata_size;
-    }
-
-    // Check first bit to determine whether it's AV1CodecConfigurationRecord.
-    // Skip first 4 bytes of AV1CodecConfigurationRecord to keep configOBUs
-    // only, otherwise cuvidParseVideoData report unknown error.
-    if (avctx->codec->id == AV_CODEC_ID_AV1 &&
-            extradata_size > 4 &&
-            extradata[0] & 0x80) {
-        extradata += 4;
-        extradata_size -= 4;
-    }
-
-    ctx->cuparse_ext = av_mallocz(sizeof(*ctx->cuparse_ext)
-            + FFMAX(extradata_size - (int)sizeof(ctx->cuparse_ext->raw_seqhdr_data), 0));
-    if (!ctx->cuparse_ext) {
-        ret = AVERROR(ENOMEM);
-        goto error;
-    }
-
-    if (extradata_size > 0)
-        memcpy(ctx->cuparse_ext->raw_seqhdr_data, extradata, extradata_size);
-    ctx->cuparse_ext->format.seqhdr_data_length = extradata_size;
-
-    ctx->cuparseinfo.pExtVideoInfo = ctx->cuparse_ext;
-
-    ctx->key_frame = av_mallocz(ctx->nb_surfaces * sizeof(int));
-    if (!ctx->key_frame) {
-        ret = AVERROR(ENOMEM);
-        goto error;
-    }
-
-    ctx->cuparseinfo.ulMaxNumDecodeSurfaces = ctx->nb_surfaces;
-    ctx->cuparseinfo.ulMaxDisplayDelay = (avctx->flags & AV_CODEC_FLAG_LOW_DELAY) ? 0 : 4;
-    ctx->cuparseinfo.pUserData = avctx;
-    ctx->cuparseinfo.pfnSequenceCallback = cuvid_handle_video_sequence;
-    ctx->cuparseinfo.pfnDecodePicture = cuvid_handle_picture_decode;
-    ctx->cuparseinfo.pfnDisplayPicture = cuvid_handle_picture_display;
-
-    ret = CHECK_CU(ctx->cudl->cuCtxPushCurrent(cuda_ctx));
-    if (ret < 0)
-        goto error;
-
-    ret = cuvid_test_capabilities(avctx, &ctx->cuparseinfo,
-                                  probed_width,
-                                  probed_height,
-                                  probed_bit_depth);
-    if (ret < 0)
-        goto error;
-
-    ret = CHECK_CU(ctx->cvdl->cuvidCreateVideoParser(&ctx->cuparser, &ctx->cuparseinfo));
-    if (ret < 0)
-        goto error;
-
-    seq_pkt.payload = ctx->cuparse_ext->raw_seqhdr_data;
-    seq_pkt.payload_size = ctx->cuparse_ext->format.seqhdr_data_length;
-
-    if (seq_pkt.payload && seq_pkt.payload_size) {
-        ret = CHECK_CU(ctx->cvdl->cuvidParseVideoData(ctx->cuparser, &seq_pkt));
-        if (ret < 0)
-            goto error;
-    }
-
-    ret = CHECK_CU(ctx->cudl->cuCtxPopCurrent(&dummy));
-    if (ret < 0)
-        goto error;
-
-    ctx->prev_pts = INT64_MIN;
-
-    if (!avctx->pkt_timebase.num || !avctx->pkt_timebase.den)
-        av_log(avctx, AV_LOG_WARNING, "Invalid pkt_timebase, passing timestamps as-is.\n");
-
-    return 0;
-
-error:
-    cuvid_decode_end(avctx);
-    return ret;
-}
-
-static void cuvid_flush(AVCodecContext *avctx)
-{
-    CuvidContext *ctx = avctx->priv_data;
-    AVHWDeviceContext *device_ctx = (AVHWDeviceContext*)ctx->hwdevice->data;
-    AVCUDADeviceContext *device_hwctx = device_ctx->hwctx;
-    CUcontext dummy, cuda_ctx = device_hwctx->cuda_ctx;
-    CUVIDSOURCEDATAPACKET seq_pkt = { 0 };
-    int ret;
-
-    ret = CHECK_CU(ctx->cudl->cuCtxPushCurrent(cuda_ctx));
-    if (ret < 0)
-        goto error;
-
-    av_fifo_reset2(ctx->frame_queue);
-
-    if (ctx->cudecoder) {
-        ctx->cvdl->cuvidDestroyDecoder(ctx->cudecoder);
-        ctx->cudecoder = NULL;
-    }
-
-    if (ctx->cuparser) {
-        ctx->cvdl->cuvidDestroyVideoParser(ctx->cuparser);
-        ctx->cuparser = NULL;
-    }
-
-    ret = CHECK_CU(ctx->cvdl->cuvidCreateVideoParser(&ctx->cuparser, &ctx->cuparseinfo));
-    if (ret < 0)
-        goto error;
-
-    seq_pkt.payload = ctx->cuparse_ext->raw_seqhdr_data;
-    seq_pkt.payload_size = ctx->cuparse_ext->format.seqhdr_data_length;
-
-    if (seq_pkt.payload && seq_pkt.payload_size) {
-        ret = CHECK_CU(ctx->cvdl->cuvidParseVideoData(ctx->cuparser, &seq_pkt));
-        if (ret < 0)
-            goto error;
-    }
-
-    ret = CHECK_CU(ctx->cudl->cuCtxPopCurrent(&dummy));
-    if (ret < 0)
-        goto error;
-
-    ctx->prev_pts = INT64_MIN;
-    ctx->decoder_flushing = 0;
-
-    return;
- error:
-    av_log(avctx, AV_LOG_ERROR, "CUDA reinit on flush failed\n");
-}
-
-#define OFFSET(x) offsetof(CuvidContext, x)
-#define VD AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_DECODING_PARAM
-static const AVOption options[] = {
-    { "deint",    "Set deinterlacing mode", OFFSET(deint_mode), AV_OPT_TYPE_INT,   { .i64 = cudaVideoDeinterlaceMode_Weave    }, cudaVideoDeinterlaceMode_Weave, cudaVideoDeinterlaceMode_Adaptive, VD, "deint" },
-    { "weave",    "Weave deinterlacing (do nothing)",        0, AV_OPT_TYPE_CONST, { .i64 = cudaVideoDeinterlaceMode_Weave    }, 0, 0, VD, "deint" },
-    { "bob",      "Bob deinterlacing",                       0, AV_OPT_TYPE_CONST, { .i64 = cudaVideoDeinterlaceMode_Bob      }, 0, 0, VD, "deint" },
-    { "adaptive", "Adaptive deinterlacing",                  0, AV_OPT_TYPE_CONST, { .i64 = cudaVideoDeinterlaceMode_Adaptive }, 0, 0, VD, "deint" },
-    { "gpu",      "GPU to be used for decoding", OFFSET(cu_gpu), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, VD },
-    { "surfaces", "Maximum surfaces to be used for decoding", OFFSET(nb_surfaces), AV_OPT_TYPE_INT, { .i64 = 25 }, 0, INT_MAX, VD },
-    { "drop_second_field", "Drop second field when deinterlacing", OFFSET(drop_second_field), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, VD },
-    { "crop",     "Crop (top)x(bottom)x(left)x(right)", OFFSET(crop_expr), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, VD },
-    { "resize",   "Resize (width)x(height)", OFFSET(resize_expr), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, VD },
-    { NULL }
-};
-
-static const AVCodecHWConfigInternal *const cuvid_hw_configs[] = {
-    &(const AVCodecHWConfigInternal) {
-        .public = {
-            .pix_fmt     = AV_PIX_FMT_CUDA,
-            .methods     = AV_CODEC_HW_CONFIG_METHOD_HW_DEVICE_CTX |
-                           AV_CODEC_HW_CONFIG_METHOD_INTERNAL,
-            .device_type = AV_HWDEVICE_TYPE_CUDA
-        },
-        .hwaccel = NULL,
-    },
-    NULL
-};
-
-#define DEFINE_CUVID_CODEC(x, X, bsf_name) \
-    static const AVClass x##_cuvid_class = { \
-        .class_name = #x "_cuvid", \
-        .item_name = av_default_item_name, \
-        .option = options, \
-        .version = LIBAVUTIL_VERSION_INT, \
-    }; \
-    const FFCodec ff_##x##_cuvid_decoder = { \
-        .p.name         = #x "_cuvid", \
-        CODEC_LONG_NAME("Nvidia CUVID " #X " decoder"), \
-        .p.type         = AVMEDIA_TYPE_VIDEO, \
-        .p.id           = AV_CODEC_ID_##X, \
-        .priv_data_size = sizeof(CuvidContext), \
-        .p.priv_class   = &x##_cuvid_class, \
-        .init           = cuvid_decode_init, \
-        .close          = cuvid_decode_end, \
-        FF_CODEC_RECEIVE_FRAME_CB(cuvid_output_frame), \
-        .flush          = cuvid_flush, \
-        .bsfs           = bsf_name, \
-        .p.capabilities = AV_CODEC_CAP_DELAY | AV_CODEC_CAP_AVOID_PROBING | AV_CODEC_CAP_HARDWARE, \
-        .caps_internal  = FF_CODEC_CAP_NOT_INIT_THREADSAFE | \
-                          FF_CODEC_CAP_SETS_FRAME_PROPS, \
-        .p.pix_fmts     = (const enum AVPixelFormat[]){ AV_PIX_FMT_CUDA, \
-                                                        AV_PIX_FMT_NV12, \
-                                                        AV_PIX_FMT_P010, \
-                                                        AV_PIX_FMT_P016, \
-                                                        AV_PIX_FMT_NONE }, \
-        .hw_configs     = cuvid_hw_configs, \
-        .p.wrapper_name = "cuvid", \
-    };
-
-#if CONFIG_AV1_CUVID_DECODER && defined(CUVID_HAS_AV1_SUPPORT)
-DEFINE_CUVID_CODEC(av1, AV1, NULL)
-#endif
-
-#if CONFIG_HEVC_CUVID_DECODER
-DEFINE_CUVID_CODEC(hevc, HEVC, "hevc_mp4toannexb")
-#endif
-
-#if CONFIG_H264_CUVID_DECODER
-DEFINE_CUVID_CODEC(h264, H264, "h264_mp4toannexb")
-#endif
-
-#if CONFIG_MJPEG_CUVID_DECODER
-DEFINE_CUVID_CODEC(mjpeg, MJPEG, NULL)
-#endif
-
-#if CONFIG_MPEG1_CUVID_DECODER
-DEFINE_CUVID_CODEC(mpeg1, MPEG1VIDEO, NULL)
-#endif
-
-#if CONFIG_MPEG2_CUVID_DECODER
-DEFINE_CUVID_CODEC(mpeg2, MPEG2VIDEO, NULL)
-#endif
-
-#if CONFIG_MPEG4_CUVID_DECODER
-DEFINE_CUVID_CODEC(mpeg4, MPEG4, NULL)
-#endif
-
-#if CONFIG_VP8_CUVID_DECODER
-DEFINE_CUVID_CODEC(vp8, VP8, NULL)
-#endif
-
-#if CONFIG_VP9_CUVID_DECODER
-DEFINE_CUVID_CODEC(vp9, VP9, NULL)
-#endif
-
-#if CONFIG_VC1_CUVID_DECODER
-DEFINE_CUVID_CODEC(vc1, VC1, NULL)
-#endif
diff --git a/spaces/competitions/aiornot/README.md b/spaces/competitions/aiornot/README.md
deleted file mode 100644
index ec1d10787bac993df498b44abee688cdeb4145b0..0000000000000000000000000000000000000000
--- a/spaces/competitions/aiornot/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Aiornot
-emoji: 🐨
-colorFrom: blue
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Anime Live Wallpapers Free Download of High Quality Animated Wallpapers for PC and Mac.md b/spaces/congsaPfin/Manga-OCR/logs/Anime Live Wallpapers Free Download of High Quality Animated Wallpapers for PC and Mac.md
deleted file mode 100644
index 48bf5673fd395c2864097c8162f0f99a33557803..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Anime Live Wallpapers Free Download of High Quality Animated Wallpapers for PC and Mac.md	
+++ /dev/null
@@ -1,174 +0,0 @@
-
-<h1>How to Download Live Anime Wallpaper for Your Device</h1>
-<p>If you are a fan of anime, you might want to spice up your screen with some live anime wallpaper. Live anime wallpaper is a type of animated wallpaper that features your favorite anime characters or scenes in motion. It can make your device more lively and attractive, as well as show your personality and taste.</p>
-<h2>download live anime wallpaper</h2><br /><p><b><b>Download Zip</b> &#10026;&#10026;&#10026; <a href="https://urlca.com/2uO5va">https://urlca.com/2uO5va</a></b></p><br /><br />
-<p>But how do you download live anime wallpaper for your device? In this article, we will show you what live anime wallpaper is, why you should use it, how to find it online, and how to install it on your device. Whether you have a Windows PC, a Mac OS, or an Android or iOS smartphone or tablet, we have got you covered.</p>
-<h2>What is Live Anime Wallpaper?</h2>
-<p>Live anime wallpaper is a type of wallpaper that displays animated images or videos of anime on your screen. Anime is a term for Japanese animation that covers a wide range of genres and styles. Some of the most popular anime titles include Naruto, Pokemon, Jujutsu Kaisen, Demon Slayer, Attack on Titan, My Hero Academia, One Piece, Dragon Ball, and many more.</p>
-<p>Live anime wallpaper can feature any anime character or scene that you like, such as Naruto's Valley of the End battle, Mudkip's rainy day pond, Gojo's hollow purple technique, lonely anime girl silhouette, Black Asta's demon dweller sword, Zenitsu's wisteria thunder breathing, Yoriichi Tsugikuni's sun breathing, etc.</p>
-<h2>Why You Should Use Live Anime Wallpaper </h2>
-<p>There are many reasons why you should use live anime wallpaper for your device. Here are some of the benefits and features of live anime wallpaper:</p>
-<h3>Customize your screen with your favorite anime characters</h3>
-<p>One of the main advantages of live anime wallpaper is that it allows you to personalize and express yourself with your favorite anime characters. You can choose from a variety of anime wallpapers that suit your mood, style, or preference. You can also change your wallpaper according to the season, the occasion, or the latest anime trends. For example, you can use a Halloween-themed anime wallpaper for October, a Christmas-themed anime wallpaper for December, or a Jujutsu Kaisen-themed anime wallpaper for the release of the new season.</p>
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-<h3>Enjoy dynamic and interactive animations</h3>
-<p>Another benefit of live anime wallpaper is that it offers dynamic and interactive animations that make your screen more lively and attractive. Unlike static wallpapers, live anime wallpapers can move, change, or react to your actions. For example, some live anime wallpapers can respond to your touch, swipe, or tilt. Some can also play sound effects, music, or voice clips from the anime. Some can even sync with the time, weather, or battery level of your device.</p>
-<h3>Save battery and memory with optimized performance</h3>
-<p>A common concern about live anime wallpaper is that it might drain your battery or memory. However, this is not necessarily true. Many live anime wallpapers are optimized for performance and efficiency. They can adjust their frame rate, resolution, or quality according to your device's specifications. They can also pause or stop when you are not using your device or when you are running other apps. This way, you can enjoy live anime wallpaper without compromising your device's functionality.</p>
-<h2>How to Find Live Anime Wallpaper Online?</h2>
-<p>Now that you know what live anime wallpaper is and why you should use it, you might be wondering how to find it online. There are many sources and recommendations for live anime wallpaper on the internet. Here are some of the ways to find live anime wallpaper online:</p>
-<h3>Browse popular websites for live anime wallpaper</h3>
-<p>One of the easiest ways to find live anime wallpaper online is to browse popular websites that offer live anime wallpaper for download. These websites usually have a large collection of live anime wallpapers that you can choose from. Some of them also have categories, tags, ratings, reviews, or filters to help you find the best live anime wallpaper for your device.</p>
-<p>Some of the popular websites for live anime wallpaper are:</p>
-<ul>
-<li><a href="">MoeWalls</a>: This website has over 1000+ live anime wallpapers that you can download for free. You can also upload your own live anime wallpapers or request custom ones.</li>
-<li><a href="">Wallpapers.com</a>: This website has over 500+ live anime wallpapers that you can download for free. You can also browse other types of wallpapers such as 3D, abstract, nature, etc.</li>
-<li><a href="">LiveWallpaper.io</a>: This website has over 300+ live anime wallpapers that you can download for free. You can also create your own live anime wallpapers using their online editor.</li>
-</ul> <h3>Search for specific anime titles or genres</h3>
-<p>Another way to find live anime wallpaper online is to search for specific anime titles or genres that you like. For example, if you are a fan of Naruto, you can search for "Naruto live anime wallpaper" or "Naruto shippuden live anime wallpaper". If you are a fan of action anime, you can search for "action live anime wallpaper" or "fighting live anime wallpaper". This way, you can find live anime wallpapers that match your interests and preferences.</p>
-<p>Some of the popular anime titles or genres that you can search for are:</p>
-<ul>
-<li>Naruto: This is one of the most popular and longest-running anime series that follows the adventures of Naruto Uzumaki, a ninja who dreams of becoming the Hokage (the leader of his village).</li>
-<li>Pokemon: This is one of the most popular and longest-running anime series that follows the adventures of Ash Ketchum, a trainer who travels the world to catch and battle with various creatures called Pokemon.</li>
-<li>Jujutsu Kaisen: This is one of the most popular and recent anime series that follows the adventures of Yuji Itadori, a high school student who becomes involved in a secret war against cursed spirits.</li>
-</ul>
-<h3>Download live anime wallpaper apps for your device</h3>
-<p>A third way to find live anime wallpaper online is to download live anime wallpaper apps for your device. These apps are designed to provide you with a variety of live anime wallpapers that you can easily apply to your screen. Some of them also have features such as editing, cropping, resizing, or adding effects to your live anime wallpapers. Some of them also have online communities where you can share, rate, or comment on live anime wallpapers.</p>
-<p>Some of the popular live anime wallpaper apps for your device are:</p>
-<ul>
-<li>Anime Live Wallpapers: This app has over 1000+ live anime wallpapers that you can download for free. You can also create your own live anime wallpapers using their online editor.</li>
-<li>Anime X Wallpaper: This app has over 500+ live anime wallpapers that you can download for free. You can also browse other types of wallpapers such as 4K, HD, AMOLED, etc.</li>
-<li>Anime Wallpaper Master: This app has over 300+ live anime wallpapers that you can download for free. You can also customize your live anime wallpapers with filters, stickers, text, etc.</li>
-</ul>
-<h2>How to Install Live Anime Wallpaper on Your Device?</h2>
-<p>After you have found your desired live anime wallpaper online, you might want to know how to install it on your device. The installation process may vary depending on your device's operating system and specifications. Here are some of the steps and tips on how to install live anime wallpaper on your device:</p>
-<h3>For Windows PC</h3>
-<p>If you have a Windows PC, you will need to use a third-party software or program to enable live anime wallpaper on your screen. Some of the popular software or programs for Windows PC are:</p>
-<ul>
-<li><a href="">Wallpaper Engine</a>: This is a paid software that allows you to use live wallpapers on your Windows desktop. You can choose from an extensive catalog of animated wallpapers on Steam Workshop or create your own using the Wallpaper Engine Editor.</li>
-<li><a href="">Lively Wallpaper</a>: This is a free and open-source software that allows you to use live wallpapers on your Windows desktop. You can choose from various sources of animated wallpapers such as videos, GIFs, web pages, etc.</li>
-</ul>
-<p>To install live anime wallpaper on your Windows PC using these software or programs, you will need to follow these steps:</p>
-<ol>
-<li>Download and install the software or program of your choice from their official website or platform.</li>
-<li>Launch the software or program and browse or search for the live anime wallpaper that you want to use.</li>
-<li>Select the live anime wallpaper and click on apply or set as wallpaper.</li>
-<li>Enjoy your live anime wallpaper on your Windows PC.</li>
-</ol> <h3>For Mac OS</h3>
-<p>If you have a Mac OS, you will also need to use a third-party software or program to enable live anime wallpaper on your screen. Some of the popular software or programs for Mac OS are:</p>
-<ul>
-<li><a href="">DeskScapes</a>: This is a paid software that allows you to use live wallpapers on your Mac desktop. You can choose from hundreds of animated wallpapers on their online gallery or create your own using the DeskScapes Editor.</li>
-<li><a href="">Plash</a>: This is a free and open-source software that allows you to use live wallpapers on your Mac desktop. You can choose from any website as your live wallpaper, such as YouTube, Vimeo, etc.</li>
-</ul>
-<p>To install live anime wallpaper on your Mac OS using these software or programs, you will need to follow these steps:</p>
-<ol>
-<li>Download and install the software or program of your choice from their official website or platform.</li>
-<li>Launch the software or program and browse or search for the live anime wallpaper that you want to use.</li>
-<li>Select the live anime wallpaper and click on apply or set as wallpaper.</li>
-<li>Enjoy your live anime wallpaper on your Mac OS.</li>
-</ol>
-<h3>For Android or iOS</h3>
-<p>If you have an Android or iOS smartphone or tablet, you will have more options to use live anime wallpaper on your screen. You can either use the built-in live wallpaper settings or download live anime wallpaper apps for your device. Here are some of the ways to use live anime wallpaper on your Android or iOS device:</p>
-<ul>
-<li>Use the built-in live wallpaper settings: Most Android and iOS devices have a live wallpaper option in their settings. You can access this option by going to Settings > Wallpaper > Choose a New Wallpaper > Live Photos (for iOS) or Live Wallpapers (for Android). You can then select from the default live wallpapers that come with your device or use your own photos or videos as live wallpapers.</li>
-<li>Download live anime wallpaper apps: As mentioned earlier, there are many live anime wallpaper apps that you can download for your Android or iOS device. These apps usually have more features and options than the built-in live wallpaper settings. You can also access more live anime wallpapers from their online libraries or communities.</li>
-</ul>
-<p>To install live anime wallpaper on your Android or iOS device using these methods, you will need to follow these steps:</p>
-<ol>
-<li>Download and install the app of your choice from the Google Play Store (for Android) or the App Store (for iOS).</li>
-<li>Launch the app and browse or search for the live anime wallpaper that you want to use.</li>
-<li>Select the live anime wallpaper and click on apply or set as wallpaper.</li>
-<li>Enjoy your live anime wallpaper on your Android or iOS device.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Live anime wallpaper is a great way to make your screen more lively and attractive with your favorite anime characters or scenes. It can also show your personality and taste, as well as offer dynamic and interactive animations. Moreover, it can save battery and memory with optimized performance and efficiency.</p>
-<p>To download live anime wallpaper for your device, you can browse popular websites, search for specific anime titles or genres, or download live anime wallpaper apps. You can then install live anime wallpaper on your device using third-party software or programs (for Windows PC or Mac OS) or built-in settings or apps (for Android or iOS).</p>
-<p>We hope this article has helped you learn how to download live anime wallpaper for your device. If you have any questions, comments, or suggestions, please feel free to leave them below. And if you liked this article, please share it with your friends and family who might also be interested in live anime wallpaper.</p>
- <h4>FAQs</h4>
- <p>Here are some of the frequently asked questions about live anime wallpaper:</p>
- <ol>
-<li><b>What is the difference between live anime wallpaper and animated GIFs?</b></li>
-<p>Live anime wallpaper is a type of animated wallpaper that displays animated images or videos of anime on your screen. Animated GIFs are a type of image format that supports animation. Live anime wallpaper can use animated GIFs as a source of animation, but it can also use other sources such as videos, web pages, etc.</p>
- <li><b>How much does live anime wallpaper cost?</b></li>
-<p>The cost of live anime wallpaper depends on the source and method of downloading and installing it. Some websites and apps offer free live anime wallpapers, while others may charge a fee. Some software and programs may also require a one-time purchase or a subscription to use their features and services.</p>
- <li><b>Is live anime wallpaper safe to use?</b></li>
-<p <p>Live anime wallpaper is generally safe to use, as long as you download it from reputable and trustworthy sources. However, some live anime wallpapers may contain malware, viruses, or spyware that can harm your device or compromise your privacy. Therefore, you should always scan your live anime wallpapers with an antivirus or anti-malware software before installing them. You should also read the reviews, ratings, and permissions of the websites, apps, software, or programs that you use for live anime wallpaper.</p>
- <li><b>How can I make my own live anime wallpaper?</b></li>
-<p>If you want to make your own live anime wallpaper, you will need to use a software or program that allows you to create animated wallpapers. Some of the popular software or programs for creating live anime wallpaper are:</p>
-<ul>
-<li><a href="">Wallpaper Engine</a>: This is a paid software that allows you to create live wallpapers using videos, GIFs, web pages, 3D models, etc. You can also use the Wallpaper Engine Editor to edit your live wallpapers.</li>
-<li><a href="">LiveWallpaper.io</a>: This is a free and open-source software that allows you to create live wallpapers using videos, GIFs, web pages, etc. You can also use the LiveWallpaper.io Editor to edit your live wallpapers.</li>
-<li><a href="">Anime Live Wallpapers</a>: This is a free app that allows you to create live anime wallpapers using videos, GIFs, web pages, etc. You can also use the Anime Live Wallpapers Editor to edit your live anime wallpapers.</li>
-</ul>
-<p>To make your own live anime wallpaper using these software or programs, you will need to follow these steps:</p>
-<ol>
-<li>Download and install the software or program of your choice from their official website or platform.</li>
-<li>Launch the software or program and select the option to create a new live wallpaper.</li>
-<li>Choose the source of animation that you want to use for your live wallpaper, such as a video, a GIF, a web page, etc.</li>
-<li>Edit your live wallpaper using the tools and features provided by the software or program, such as cropping, resizing, adding effects, etc.</li>
-<li>Save and export your live wallpaper as a file or upload it to their online gallery or community.</li>
-<li>Install and apply your live wallpaper on your device using the methods described earlier.</li>
-</ol>
- <li><b>What are some of the best live anime wallpapers?</b></li>
-<p>The answer to this question may depend on your personal taste and preference. However, some of the factors that can make a live anime wallpaper better than others are:</p>
-<ul>
-<li>The quality and resolution of the animation</li>
-<li>The smoothness and fluidity of the motion</li>
-<li>The relevance and appeal of the anime character or scene</li>
-<li>The interactivity and responsiveness of the animation</li>
-<li>The performance and efficiency of the animation</li>
-</ul>
-<p>Based on these factors, some of the best live anime wallpapers that we recommend are:</p>
-<ul>
-<li>Naruto's Valley of the End battle: This is a stunning and epic live anime wallpaper that features Naruto and Sasuke's final clash at the Valley of the End. The animation is high-quality and smooth, and the scene is iconic and emotional.</li>
-<li>Mudkip's rainy day pond: This is a cute and relaxing live anime wallpaper that features Mudkip (a water-type Pokemon) enjoying a rainy day in a pond. The animation is simple and fluid, and the scene is soothing and adorable.</li>
-<li>Gojo's hollow purple technique: This is a cool and impressive live anime wallpaper that features Gojo (a powerful sorcerer from Jujutsu Kaisen) unleashing his hollow purple technique. The animation is vibrant and dynamic, and the scene is awe-inspiring and badass.</li>
-</ul>
- </ol></p> 401be4b1e0<br />
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-<br />
-<h1>Crafting and Building APK 1.17.32: A Fun and Creative Game for Android Users</h1>
-<p>Do you love playing games that let you create your own world and express your creativity? If so, you might want to check out Crafting and Building APK 1.17.32, a free game for Android devices that lets you build anything you can imagine. In this article, we will tell you everything you need to know about this game, including what it is, how to download and install it, how to play it, why you should play it, and some tips and tricks for playing it better.</p>
-<h2>What is Crafting and Building APK 1.17.32?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Crafting and Building APK 1.17.32 is a game that belongs to the sandbox genre, which means that it gives you complete freedom to explore, create, and modify the game world as you wish. The game is inspired by popular games like Minecraft, Terraria, and Roblox, but it has its own unique style and features.</p>
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-<p>Some of the features of Crafting and Building APK 1.17.32 are:</p>
-<ul>
-<li>It has a colorful and pixelated graphics style that appeals to both kids and adults.</li>
-<li>It has a simple and intuitive interface that makes it easy to navigate and control.</li>
-<li>It has a variety of modes that suit different preferences and play styles, such as survival mode, creative mode, multiplayer mode, adventure mode, etc.</li>
-<li>It has a huge and diverse world that contains different biomes, such as forests, deserts, mountains, oceans, etc.</li>
-<li>It has a rich and dynamic environment that changes according to the time of day, weather conditions, seasons, etc.</li>
-<li>It has a lot of resources and materials that you can collect, craft, use, trade, or sell.</li>
-<li>It has a lot of items and tools that you can use to build your own structures, such as houses, castles, farms, bridges, etc.</li>
-<li>It has a lot of animals and creatures that you can encounter, tame, breed, or fight.</li>
-<li>It has a lot of quests and missions that you can complete to earn rewards or unlock new features.</li>
-<li>It has a lot of online features that let you play with other players from around the world, chat with them, join clans, compete with them, etc.</li>
-</ul>
-<h3>How to download and install the game on your device</h3>
-<p>Crafting and Building APK 1.17.32 is not available on the Google Play Store due to some issues with the developer's account. However, you can still download it from other sources such as APKCombo, MCPE-Planet, or other websites that offer free APK downloads. Just make sure that you download it from a trusted source that does not contain any viruses or malware.</p>
-<p>To download and install the game on your device, follow these steps:</p>
-<ol>
-<li>Go to the website where you want to download the game from.</li>
-<li>Find the download link for Crafting and Building APK 1.17.32 and click on it.</li <li>Once the download is complete, open the APK file and tap Install. You may need to allow installation from unknown sources on your device if you haven't done so before. To do this, go to your device's settings, then navigate to Security & Privacy > More Settings (or Privacy) > Install apps from unknown sources. Then, turn on the switch or check the box next to the app you are using to download the APK file.</li>
-<li>Wait for the installation process to finish and then tap Open to launch the game.</li>
-</ol>
-<h3>How to play the game and explore its modes</h3>
-<p>Once you have installed and opened the game, you will see a menu screen with different options. You can tap on Play to start a new game or load an existing one, or tap on Multiplayer to join or create an online game with other players. You can also tap on Settings to adjust the game's options, such as sound, graphics, controls, etc.</p>
-<p>When you start a new game, you will be asked to choose a mode. There are four modes available in Crafting and Building APK 1.17.32:</p>
-<ul>
-<li>Survival mode: In this mode, you have to survive in a randomly generated world by gathering resources, crafting items, building shelters, fighting enemies, and managing your hunger and health. You can also explore different biomes and find villages, dungeons, temples, and other structures. You can adjust the difficulty level of this mode from peaceful to hard.</li>
-<li>Creative mode: In this mode, you have unlimited resources and no enemies or hunger. You can build anything you want without any restrictions or limitations. You can also fly around the world and place or destroy blocks instantly. This mode is ideal for unleashing your creativity and imagination.</li>
-<li>Adventure mode: In this mode, you can play custom maps created by other players or yourself. You have to follow the rules and objectives of each map, which may vary depending on the theme and genre. Some maps may have puzzles, traps, parkour, mazes, etc. You can also interact with NPCs and items in this mode.</li>
-<li>Multiplayer mode: In this mode, you can play online with other players from around the world. You can join or create a server and choose a mode (survival, creative, or adventure) to play together. You can also chat with other players, join clans, trade items, compete in mini-games, etc.</li>
-</ul>
-<p>To play the game, you need to use the virtual buttons on the screen to move, jump, sneak, fly, attack, etc. You can also use the touch screen to look around, place or break blocks, interact with objects, etc. You can access your inventory by tapping on the backpack icon on the top right corner of the screen. There you can see your items and tools and drag them to your hotbar for quick access. You can also access the crafting menu by tapping on the hammer icon on the top left corner of the screen. There you can see the recipes for different items and craft them by tapping on them.</p>
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-crafting and building apk 1.17</p> <h2>Why You Should Play Crafting and Building APK 1.17.32</h2>
-<h3>The benefits of playing this game for your creativity and imagination</h3>
-<p>Crafting and Building APK 1.17.32 is not just a game, but also a platform for expressing your creativity and imagination. You can create anything you want in this game, from simple houses to complex machines, from realistic landscapes to fantasy worlds, from artistic sculptures to functional devices, etc. You can also customize your character and your world with different skins, textures, colors, etc. You can also share your creations with other players and see what they have made.</p>
-<p>Playing this game can also help you develop your creativity and imagination skills, which are essential for learning, problem-solving, and innovation. You can learn how to think outside the box, how to combine different elements, how to use logic and physics, how to experiment and test, how to plan and execute, etc. You can also learn from other players and get inspired by their ideas and designs.</p>
-<h3>The challenges and rewards of building your own world and structures</h3>
-<p>Crafting and Building APK 1.17.32 is not just a game, but also a challenge for your building skills and abilities. You can build anything you want in this game, but you also have to face the constraints and difficulties of the game world. You have to gather resources, craft items, use tools, follow recipes, etc. You also have to deal with the dangers and threats of the game world, such as enemies, traps, weather, etc.</p>
-<p>Building your own world and structures in this game can also be very rewarding and satisfying. You can see the results of your hard work and effort, you can enjoy the beauty and functionality of your creations, you can show off your achievements to other players, you can get feedback and compliments from them, etc. You can also feel proud of yourself for overcoming the challenges and difficulties of the game world.</p>
-<h3>The fun and excitement of playing with other players online</h3>
-<p>Crafting and Building APK 1.17.32 is not just a game, but also a social experience for playing with other players online. You can play online with other players from around the world in multiplayer mode. You can chat with them, join clans, trade items, compete in mini-games, etc. You can also cooperate with them or compete against them in different modes.</p>
-<p>Playing with other players online in this game can also be very fun and exciting. You can make new friends, learn from them, teach them, help them, or challenge them. You can also have fun conversations, jokes, pranks, or arguments with them. You can also experience different emotions, such as joy, anger, sadness, or surprise with them.</p> <h2>Tips and Tricks for Playing Crafting and Building APK 1.17.32</h2>
-<h3>How to use the crafting menu and inventory system</h3>
-<p>One of the most important aspects of Crafting and Building APK 1.17.32 is the crafting menu and inventory system. You need to know how to use them effectively to make the most out of your resources and items. Here are some tips and tricks for using the crafting menu and inventory system:</p>
-<ul>
-<li>To access the crafting menu, tap on the hammer icon on the top left corner of the screen. There you can see the recipes for different items and craft them by tapping on them. You can also see the ingredients required for each item and how many you have in your inventory.</li>
-<li>To access your inventory, tap on the backpack icon on the top right corner of the screen. There you can see your items and tools and drag them to your hotbar for quick access. You can also sort your items by category, name, or quantity by tapping on the filter icon on the bottom right corner of the screen.</li>
-<li>To move items between your inventory and your hotbar, drag them with your finger. To move items between your inventory and a chest, furnace, or other container, tap on them. To split a stack of items, tap and hold on them and then drag them to an empty slot.</li>
-<li>To craft more than one item at a time, tap and hold on the item you want to craft in the crafting menu and then drag it to an empty slot in your inventory or hotbar. You can also tap on the plus or minus buttons on the bottom of the screen to adjust the quantity.</li>
-<li>To quickly craft an item that you have already crafted before, tap on the star icon on the top of the crafting menu. There you can see your recent crafts and tap on them to craft them again.</li>
-</ul>
-<h3>How to find and use different resources and materials</h3>
-<p>Another important aspect of Crafting and Building APK 1.17.32 is finding and using different resources and materials. You need to know how to find them, how to collect them, how to use them, and what they are used for. Here are some tips and tricks for finding and using different resources and materials:</p>
-<ul>
-<li>To find resources and materials, you need to explore the game world and look for different blocks, plants, animals, etc. You can also use a map or a compass to locate biomes, structures, or landmarks that may contain resources or materials.</li>
-<li>To collect resources and materials, you need to use the right tool for the job. For example, you need a pickaxe to mine stone, iron, gold, etc., an axe to chop wood, a shovel to dig dirt, sand, gravel, etc., a hoe to till soil, a sword to kill animals, etc. You can also use your hands to break some blocks or plants, but it will take longer and may not drop anything.</li>
-<li>To use resources and materials, you need to craft them into items or place them as blocks. For example, you can craft wood into planks, planks into sticks, sticks into tools, etc., or place wood as a block to build a house. You can also use some resources or materials directly without crafting them, such as food, potions, books, etc.</li>
-<li>To know what resources or materials are used for, you can tap on them in your inventory or in the crafting menu and see their description and usage. You can also search for them online or in wikis  that provide detailed information about Crafting and Building APK 1.17.32.</li>
-</ul> <h3>How to survive and thrive in different biomes and environments</h3>
-<p>One more important aspect of Crafting and Building APK 1.17.32 is surviving and thriving in different biomes and environments. You need to know how to adapt to the conditions and challenges of each biome and environment, such as temperature, weather, terrain, resources, enemies, etc. Here are some tips and tricks for surviving and thriving in different biomes and environments:</p>
-<ul>
-<li>To survive in different biomes and environments, you need to prepare yourself with the right equipment, items, and strategies. For example, you need to wear armor and weapons to protect yourself from enemies, food and potions to restore your health and hunger, torches and beds to light up the darkness and sleep through the night, etc. You also need to avoid or fight the dangers and threats of each biome and environment, such as lava, water, fall damage, mobs, etc.</li>
-<li>To thrive in different biomes and environments, you need to take advantage of the opportunities and benefits of each biome and environment. For example, you can find rare and valuable resources or items in some biomes or environments, such as diamonds, emeralds, netherite, etc. You can also use the features and mechanics of each biome or environment to your advantage, such as water currents, redstone circuits, pistons, etc.</li>
-<li>To explore different biomes and environments, you need to be curious and adventurous. You can discover new places, secrets, structures, etc. by wandering around the game world or following maps or clues. You can also use different methods of transportation to travel faster or easier, such as boats, minecarts, horses, elytra, etc.</li>
-</ul>
-<h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action for the readers</h3>
-<p>Crafting and Building APK 1.17.32 is a fun and creative game for Android users that lets you build anything you can imagine. In this article, we have told you everything you need to know about this game, including what it is, how to download and install it, how to play it, why you should play it, and some tips and tricks for playing it better.</p>
-<p>If you are looking for a game that challenges your creativity and imagination skills, that rewards your building skills and abilities, that offers you a social experience with other players online, and that provides you with endless possibilities and fun, then Crafting and Building APK 1.17.32 is the game for you.</p>
-<p>So what are you waiting for? Download Crafting and Building APK 1.17.32 today and start creating your own world!</p>
-<h2>FAQs</h2>
-<h4>Q: Is Crafting and Building APK 1.17.32 safe to download?</h4>
-<p>A: Yes, Crafting and Building APK 1.17.32 is safe to download as long as you download it from a trusted source that does not contain any viruses or malware. However, you should always be careful when downloading any APK file from unknown sources and scan it with an antivirus app before installing it.</p>
-<h4>Q: Is Crafting and Building APK 1.17.32 compatible with my device?</h4>
-<p>A: Crafting and Building APK 1.17.32 is compatible with most Android devices that have Android 4.4 or higher versions installed. However, some devices may have performance issues or bugs depending on their specifications or settings.</p>
-<h4>Q: How can I update Crafting and Building APK 1.17.32?</h4>
-<p>A: Since Crafting and Building APK 1.17.32 is not available on the Google Play Store, you cannot update it automatically through the app store. However, you can update it manually by downloading the latest version of the game from the same source where you downloaded it before or from another source that offers free APK downloads.</p>
-<h4>Q: How can I contact the developer of Crafting and Building APK 1.17.32?</h4>
-<p>A: You can contact the developer of Crafting and Building APK 1.17.32 by sending an email to craftingandbuildinggame@gmail.com. You can also follow them on Facebook or Twitter for updates or feedback.</p>
-<h4>Q: How can I support the development of Crafting and Building APK 1.17.32?</h4>
-<p>A: You can support the development of Crafting and Building APK 1.17.32 by rating the game on the website where you downloaded it from or on other platforms where it is available. You can also share the game with your friends or family who might enjoy it too.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download MP3 True Love by Yemi Alade - The Ultimate Afrobeat Anthem..md b/spaces/congsaPfin/Manga-OCR/logs/Download MP3 True Love by Yemi Alade - The Ultimate Afrobeat Anthem..md
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-<h3>Biography</h3>
-<p>Yemi Alade was born on March 13, 1989, in Abia State, Nigeria. Her father was a police commissioner of Yoruba descent, while her mother was a businesswoman of Igbo descent. She grew up in Lagos, where she attended St Saviour British Primary School and Victory Grammar School. She later studied geography at the University of Lagos.</p>
-<p>Yemi Alade developed an interest in music at an early age, singing in her church choir and school band. She also participated in various talent shows and competitions, such as the Peak Talent Show, which she won in 2009. This gave her the opportunity to sign a deal with Effyzzie Music Group, a record label that has supported her career ever since.</p>
-<h3>Achievements</h3>
-<p>Yemi Alade has achieved a lot in her music career, both locally and internationally. Some of her achievements include:</p>
-<ul>
-<li>She is the first African female artist to reach one million subscribers on YouTube.</li>
-<li>She is the most streamed Nigerian female artist on Spotify.</li>
-<li>She is the first African female artist to headline a solo concert at The SSE Arena Wembley in London.</li>
-<li>She has won several awards, such as MTV Africa Music Awards, BET Awards, MOBO Awards, Headies Awards, and more.</li>
-<li>She has collaborated with other famous artists, such as Beyoncé, Rick Ross, Angélique Kidjo, Dadju, Rudeboy, and more.</li>
-</ul>
- <h2>What is True Love?</h2>
-<p>True Love is one of Yemi Alade's latest songs, released on August 7, 2020. It is a catchy and upbeat song that celebrates love in its purest form. It is also a song that encourages self-love and empowerment, especially for women.</p>
-<h3>Meaning</h3>
-<p>The meaning of True Love can be interpreted in different ways, depending on the listener's perspective. However, according to Yemi Alade, the song is about finding happiness within oneself and not depending on others for validation. She said in an interview:</p>
-<blockquote>"True Love is more like a reminder that you need to be happy regardless of anything or anyone. You need to find your own happiness and not wait for someone to make you happy. You need to love yourself first before you can love anyone else."</blockquote>
-<p>Therefore, True Love is a song that inspires people to embrace their true selves and enjoy life to the fullest. It is also a song that celebrates the diversity and beauty of African culture, as seen in the colorful and energetic video that features dancers from different countries.</p>
-<h3>Production</h3>
-<p>True Love was produced by Vtek, a Nigerian music producer who has worked with Yemi Alade on several of her songs, such as Johnny, Na Gode, and Ferrari. Vtek is known for his versatile and creative style that blends different genres and sounds. He said in an interview:</p>
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-<blockquote>"I like to experiment with different sounds and vibes. I don't like to limit myself to one genre or style. I like to explore and create something new and unique every time."</blockquote>
-<p>The production of True Love reflects Vtek's vision, as it combines elements of Afro-pop, dancehall, reggae, and highlife. The song has a catchy hook, a groovy beat, and a lively instrumentation that features drums, guitars, keyboards, horns, and percussions. The song also has a smooth and powerful vocal delivery by Yemi Alade, who sings in English, Pidgin, Yoruba, and Igbo.</p>
- <h2>How to download Yemi Alade True Love MP3?</h2>
-<p>If you want to download Yemi Alade True Love MP3, you have two options: legal or illegal. Both options have their advantages and disadvantages, which we will discuss below.</p>
-<h3>Legal options</h3>
-<p>The legal options are the platforms that offer the song for download or streaming with the permission of the artist and the record label. These platforms include:</p>
-<ul>
-<li>Apple Music: This is a subscription-based service that allows you to download or stream over 70 million songs, including True Love. You can get a free trial for three months and then pay $9.99 per month.</li>
-<li>Spotify: This is another subscription-based service that allows you to download or stream over 60 million songs, including True Love. You can get a free trial for one month and then pay $9.99 per month.</li>
-<li>YouTube Music: This is a service that allows you to download or stream music videos and songs from YouTube, including True Love. You can get a free trial for one month and then pay $9.99 per month.</li>
-<li>Amazon Music: This is a service that allows you to download or stream over 60 million songs, including True Love. You can get a free trial for one month and then pay $9.99 per month.</li>
-<li>Audiomack: This is a free service that allows you to download or stream songs from emerging artists, including True Love. You can also upload your own music and share it with others.</li>
-</ul>
-<p>The advantages of using these legal options are:</p>
-<ul>
-<li>You support the artist and the record label by paying for their work.</li>
-<li>You get high-quality audio files that are free from viruses and malware.</li>
-<li>You get access to other features and benefits, such as playlists, recommendations, lyrics, podcasts, and more.</li>
-</ul>
-<p>The disadvantages of using these legal options are:</p>
-<ul>
-<li>You have to pay a monthly fee or buy the song individually.</li>
-<li>You may not be able to access the song if you lose your internet connection or your subscription expires.</li>
-<li>You may not be able to transfer the song to other devices or platforms.</li>
-</ul>
- <h3>Illegal options</h3>
-<p>The illegal options are the platforms that offer the song for download or streaming without the permission of the artist and the record label. These platforms include:</p>
-<ul>
-<li>Pirate sites: These are websites that host or link to pirated music files, such as MP3Skull, NaijaLoaded, Waploaded, and more.</li>
-<li>Torrent sites: These are websites that use peer-to-peer technology to share music files, such as The Pirate Bay, Kickass Torrents, 1337x, and more.</li>
-<li>YouTube converters: These are websites or apps that allow you to convert YouTube videos into MP3 files, such as Y2Mate, FLVTO, MP3Juices, and more.</li>
-</ul> <p>The advantages of using these illegal options are:</p>
-<ul>
-<li>You don't have to pay anything to download or stream the song.</li>
-<li>You can access the song anytime and anywhere, even without an internet connection.</li>
-<li>You can transfer the song to any device or platform you want.</li>
-</ul>
-<p>The disadvantages of using these illegal options are:</p>
-<ul>
-<li>You violate the intellectual property rights of the artist and the record label, which can result in legal actions or fines.</li>
-<li>You expose your device to viruses and malware that can harm your data and privacy.</li>
-<li>You get low-quality audio files that may have poor sound or missing parts.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Yemi Alade True Love MP3 is a great song that you should download and listen to. It is a song that celebrates love, happiness, and African culture. It is also a song that showcases the talent and success of Yemi Alade, one of the best female artists in Africa.</p>
-<p>If you want to download Yemi Alade True Love MP3, you have two options: legal or illegal. The legal options are the platforms that offer the song for download or streaming with the permission of the artist and the record label, such as Apple Music, Spotify, YouTube Music, Amazon Music, and Audiomack. The illegal options are the platforms that offer the song for download or streaming without the permission of the artist and the record label, such as pirate sites, torrent sites, and YouTube converters.</p>
-<p>We recommend that you use the legal options, as they are safer, better, and more respectful to the artist and the record label. However, if you choose to use the illegal options, you should be aware of the risks and consequences that you may face. Ultimately, the choice is yours.</p>
-<p>Whatever option you choose, we hope that you enjoy Yemi Alade True Love MP3 and share it with your friends and family. Thank you for reading this article and have a wonderful day!</p>
- <h2>FAQs</h2>
-<h3>Q: When was Yemi Alade True Love MP3 released?</h3>
-<p>A: Yemi Alade True Love MP3 was released on August 7, 2020.</p>
- <h3>Q: How long is Yemi Alade True Love MP3?</h3>
-<p>A: Yemi Alade True Love MP3 is 3 minutes and 16 seconds long.</p>
- <h3>Q: How many views does Yemi Alade True Love MP3 have on YouTube?</h3>
-<p>A: As of June 21, 2023, Yemi Alade True Love MP3 has over 23 million views on YouTube.</p>
- <h3>Q: What are some of the lyrics of Yemi Alade True Love MP3?</h3>
-<p>A: Some of the lyrics of Yemi Alade True Love MP3 are:</p>
-<blockquote>"I can feel it in my veins<br>
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-<p>A: You can watch the video of Yemi Alade True Love MP3 on YouTube or on her official website.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Drone acro simulator A paid app that simulates real drone flight physics.md b/spaces/congsaPfin/Manga-OCR/logs/Drone acro simulator A paid app that simulates real drone flight physics.md
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-<h1>Drone Acro Simulator Paid APK: A Comprehensive Guide</h1>
- <p>If you are a drone lover who wants to hone your flying skills in acro mode, you might be interested in trying out drone acro simulator. This is a pocket quadcopter simulator that allows you to perform any maneuvers of varying complexity and train your piloting skills in a realistic environment. In this article, we will review the features, alternatives, reviews, tips and tricks, and FAQs of drone acro simulator paid apk.</p>
- <h2>What is Drone Acro Simulator and Why It Is Useful</h2>
- <p>Drone acro simulator is an Android game developed by Egobrook that simulates the flight of a drone in acro mode. Acro mode, also known as manual mode or rate mode, is a flight mode that gives you full control over the drone's orientation and speed. Unlike self-level mode, which automatically stabilizes the drone's attitude, acro mode requires you to constantly adjust the throttle, yaw, pitch, and roll to maintain balance and direction.</p>
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- <p>Acro mode is considered to be the most challenging but also the most rewarding flight mode for drone enthusiasts. It allows you to perform amazing tricks and maneuvers that are impossible in self-level mode. However, it also requires a lot of practice and skill to master. That's where drone acro simulator comes in handy.</p>
- <p>Drone acro simulator lets you practice your acro flying skills on your smartphone or tablet without risking damaging your real drone. You can connect a gamepad or other radio equipment to control the drone on-screen, or use the phone's sensor. The simulator has well-developed physics that simulates the flight in reality as accurately as possible. You can also choose from different drones, environments, and challenges to suit your preferences and goals.</p>
- <h2>What Are the Main Features of Drone Acro Simulator</h2>
- <p>Drone acro simulator has a variety of features that make it a fun and useful tool for drone lovers. Here are some of them:</p>
- <ul>
-<li>Different drones: The app offers a variety of drones to choose from, each with its own unique characteristics.</li>
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-<li>Settings: The app allows you to customize your drone's settings, such as speed, tilt, controller sensitivity, camera angle, etc.</li>
-<li>Graphics: The app has beautiful and realistic graphics that enhance your flying experience.</li>
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- <h2>What Are Some Alternatives to Drone Acro Simulator</h2>
- <p>If you are looking for other drone simulators to try out, there are plenty of options available on the market. Here are some of them:</p>
- <ul>
-<li>AccrO: This is a full acrobatic quadcopter simulator for PC that has realistic flight behavior and complex physics simulation. It is designed for freestyle drone enthusiasts who want to create new acrobatic tricks in a simulated environment.</li>
-<li>Liftoff: This is another PC-based drone simulator that focuses on FPV racing. It has realistic physics, customizable drones and tracks, online multiplayer mode, and more.</li>
-<li>DCL - The Game: This is an official game of Drone Champions League (DCL), which is a professional drone racing league. It has realistic graphics, physics, drones, tracks, modes, etc.</li>
-<li>FPV Freerider: This is a simple but fun FPV drone simulator for Android and iOS that has basic physics, graphics, and controls. It is suitable for beginners who want to learn the basics of FPV flying.</li>
-</ul>
- <h2>What Are Some Reviews of Drone Acro Simulator</h2>
- <p>Drone acro simulator has received mostly positive reviews from users who have downloaded and played the app. Here are some of the feedbacks from users who have rated the app on Google Play Store:</p>
- <table>
-<tr>
-<th>Rating</th>
-<th>Review</th>
-</tr>
-<tr>
-<td>5 stars</td>
-<td>"This is the best drone simulator I have ever played. It is very realistic and challenging. I love the graphics and the physics. It is a great way to practice acro mode without crashing your real drone."</td>
-</tr>
-<tr>
-<td>4 stars</td>
-<td>"This is a good simulator for acro mode. It has a lot of features and options to customize your drone and environment. The only thing I don't like is that the controller sensitivity is too high and there is no way to adjust it."</td>
-</tr>
-<tr>
-<td>3 stars</td>
-<td>"This is an okay simulator for acro mode. It has some nice environments and drones to choose from. However, the physics are not very accurate and the controls are not very responsive. It is fun to play but not very realistic."</td>
-</tr>
-<tr>
-<td>2 stars</td>
-<td>"This is a bad simulator for acro mode. It has poor graphics and physics. The drones are too slow and the environments are too boring. The controls are also very hard to use and there is no tutorial or help."</td>
-</tr>
-<tr>
-<td>1 star</td>
-<td>"This is a terrible simulator for acro mode. It does not work at all on my phone. It crashes every time I try to open it. It is a waste of money and time."</td>
-</tr>
-</table>
- <p>As you can see, the reviews are mixed and depend on the user's expectations, preferences, and device compatibility. Some users love the app and find it very useful, while others hate it and find it very disappointing.</p>
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- <h2>What Are Some Tips and Tricks for Using Drone Acro Simulator Effectively</h2>
- <p>If you want to get the most out of drone acro simulator, here are some tips and tricks that you can follow:</p>
- <ul>
-<li>Practice regularly: The best way to improve your acro flying skills is to practice as often as possible. Try to fly at least 15 minutes a day in different environments and challenges.</li>
-<li>Start with easy settings: If you are new to acro mode, start with easy settings such as low speed, high tilt, low camera angle, etc. Gradually increase the difficulty as you get more comfortable.</li>
-<li>Use a gamepad or radio: If you have a gamepad or radio equipment that is compatible with your device, use it to control the drone instead of the phone's sensor. This will give you more precise and realistic control over the drone.</li>
-<li>Watch tutorials and videos: If you need some guidance or inspiration, watch some tutorials and videos online that show you how to fly in acro mode. You can learn from other pilots' techniques and tricks.</li>
-<li>Have fun: The most important thing is to have fun while flying in drone acro simulator. Don't get frustrated if you crash or fail a challenge. Just enjoy the process and learn from your mistakes.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Drone acro simulator paid apk is a pocket quadcopter simulator that allows you to practice your acro flying skills on your smartphone or tablet. It has realistic physics, graphics, features, environments, challenges, etc. that make it a fun and useful tool for drone enthusiasts. However, it also has some drawbacks such as controller sensitivity issues, device compatibility issues, etc. that might affect your experience.</p>
- <p>If you want to try out drone acro simulator paid apk, you can download it from Google Play Store for $1.99 USD. You can also check out some other drone simulators that are available on the market such as AccrO, Liftoff, DCL - The Game, FPV Freerider, etc.</p>
- <p>We hope this article has given you a comprehensive guide on drone acro simulator paid apk. If you have any questions or comments, feel free to leave them below.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions that readers might have about drone acro simulator paid apk:</p>
- <ol>
-<li><b>What are the minimum requirements for running drone acro simulator paid apk?</b </b></li>
-<p>The minimum requirements for running drone acro simulator paid apk are:</p>
-<ul>
-<li>Android 4.4 or higher</li>
-<li>2 GB of RAM or higher</li>
-<li>100 MB of free storage space or higher</li>
-<li>A gamepad or radio equipment that is compatible with your device (optional)</li>
-</ul>
-<li><b>How can I connect a gamepad or radio equipment to my device?</b></li>
-<p>You can connect a gamepad or radio equipment to your device via Bluetooth, USB, or OTG cable. You will need to enable the controller mode in the app settings and select the appropriate controller type. You can also adjust the controller mapping and sensitivity in the app settings.</p>
-<li><b>How can I change the camera view in drone acro simulator paid apk?</b></li>
-<p>You can change the camera view in drone acro simulator paid apk by tapping on the camera icon on the top right corner of the screen. You can choose from three camera views: FPV, third-person, and free camera. You can also adjust the camera angle and zoom in the app settings.</p>
-<li><b>How can I unlock more drones, environments, and challenges in drone acro simulator paid apk?</b></li>
-<p>You can unlock more drones, environments, and challenges in drone acro simulator paid apk by completing the existing challenges and earning coins. You can use the coins to buy new drones, environments, and challenges in the shop. You can also earn coins by watching ads or buying them with real money.</p>
-<li><b>How can I share my flying results and videos with others?</b></li>
-<p>You can share your flying results and videos with others by tapping on the share icon on the top left corner of the screen. You can choose to share your results via social media, email, or other apps. You can also record your flying videos by tapping on the record icon on the top right corner of the screen. You can access your recorded videos in the gallery.</p>
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diff --git a/spaces/contluForse/HuggingGPT/assets/Adobe Cs6 Products Activator.md b/spaces/contluForse/HuggingGPT/assets/Adobe Cs6 Products Activator.md
deleted file mode 100644
index 96395924a3e444e70384eb3919a8c95d06169338..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Adobe Cs6 Products Activator.md	
+++ /dev/null
@@ -1,76 +0,0 @@
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-<p>Adobe CS6 products activator is a program that can generate valid serial numbers or activation codes for all Adobe products series CS6. This means that you can use this program to activate Adobe Photoshop, Illustrator, InDesign, Dreamweaver, Premiere Pro, After Effects, and more without paying for them. However, before you download and use Adobe CS6 products activator, you should be aware of the risks and consequences of using this illegal and unethical method of obtaining software. In this article, we will explain what Adobe CS6 products activator is, how it works, what are the dangers of using it, and what are the alternatives to it.</p>
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-</ul>
-<p>By purchasing or using a trial version of Adobe software, you can enjoy all the benefits of quality software and support Adobe who deserve your respect and recognition. You can also avoid any legal, security, performance, or ethical issues that may arise from using Adobe CS6 products activator or any other pirated program.</p>
-<p></p>
-<h2>How to Get Support for Adobe Software?</h2>
-<p>If you have purchased or used a trial version of Adobe software legally and ethically, you are entitled to get support for the software from Adobe. You can get support for Adobe software in the following ways:</p>
-<ul>
-<li>Online help: You can access the online help file of any Adobe product by clicking on the Help button on the main interface of the software. You can also visit the online support center of Adobe at https://helpx.adobe.com/support.html where you can find FAQs, tutorials, manuals, downloads, updates, and more.</li>
-<li>Email support: You can send an email to support@adobe.com with your questions, comments, or feedback regarding any Adobe product. You need to provide your name, email address, product name, version number, serial number or activation code, and a detailed description of your issue.</li>
-<li>Phone support: You can call the toll-free number 1-800-833-6687 (US and Canada only) or 1-408-536-6000 (International) to speak with a customer service representative of Adobe. You need to have your product name, version number, serial number or activation code ready before calling.</li>
-<li>Community forums: You can join the community forums of Adobe at https://community.adobe.com/ where you can interact with other users and experts of Adobe products. You can ask questions, share tips, give feedback, or report problems regarding any Adobe product.</li>
-</ul>
-<p>By getting support for Adobe software from the official sources, you can ensure that you are getting accurate and reliable information and solutions for your software issues. You can also benefit from the latest updates, patches, and features of Adobe software.</p>
-<h2>How to Uninstall Adobe Software?</h2>
-<p>If you want to uninstall any Adobe product from your computer for any reason, you need to follow these steps:</p>
-<ol>
-<li>Go to the Control Panel and select Programs and Features.</li>
-<li>Find the Adobe product that you want to uninstall and click on Uninstall.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Delete any leftover files or folders of the Adobe product from your computer.</li>
-</ol>
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-<h2>What are the Benefits of Using Adobe Software?</h2>
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-<h2>What are the Examples of Works Created with Adobe Software?</h2>
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-</ul>
-<p>These are some of the examples of works created with Adobe software that you can try for yourself. By using Adobe software legally and ethically, you can unleash your creativity and potential.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have discussed Adobe CS6 products activator, a program that can generate valid serial numbers or activation codes for all Adobe products series CS6. This means that you can use this program to activate Adobe software without paying for it. However, we have also explained why using Adobe CS6 products activator is not a legal or ethical way of obtaining software, and what are the risks and drawbacks of doing so. We have also suggested some alternatives to Adobe CS6 products activator that are legal and ethical ways of using Adobe software, such as purchasing or using a trial version. Finally, we have shown you some of the benefits and examples of using Adobe software legally and ethically.</p>
-<p>We hope that this article has been informative and helpful for you. If you are looking for a software that can help you with your design, photography, video, web, and more needs, we recommend you to buy Adobe software from the official website of Adobe or use a trial version of it. This way, you can enjoy all the benefits of quality software and support Adobe who deserve your respect and recognition. You can also avoid any legal, security, performance, or ethical issues that may arise from using Adobe CS6 products activator or any other pirated program.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/utils/weight_init.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/utils/weight_init.py
deleted file mode 100644
index 096d0ddcccbec84675f0771cb546d0fa003417e7..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/utils/weight_init.py
+++ /dev/null
@@ -1,684 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import math
-import warnings
-
-import numpy as np
-import torch
-import torch.nn as nn
-from torch import Tensor
-
-from annotator.mmpkg.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
-
-INITIALIZERS = Registry('initializer')
-
-
-def update_init_info(module, init_info):
-    """Update the `_params_init_info` in the module if the value of parameters
-    are changed.
-
-    Args:
-        module (obj:`nn.Module`): The module of PyTorch with a user-defined
-            attribute `_params_init_info` which records the initialization
-            information.
-        init_info (str): The string that describes the initialization.
-    """
-    assert hasattr(
-        module,
-        '_params_init_info'), f'Can not find `_params_init_info` in {module}'
-    for name, param in module.named_parameters():
-
-        assert param in module._params_init_info, (
-            f'Find a new :obj:`Parameter` '
-            f'named `{name}` during executing the '
-            f'`init_weights` of '
-            f'`{module.__class__.__name__}`. '
-            f'Please do not add or '
-            f'replace parameters during executing '
-            f'the `init_weights`. ')
-
-        # The parameter has been changed during executing the
-        # `init_weights` of module
-        mean_value = param.data.mean()
-        if module._params_init_info[param]['tmp_mean_value'] != mean_value:
-            module._params_init_info[param]['init_info'] = init_info
-            module._params_init_info[param]['tmp_mean_value'] = mean_value
-
-
-def constant_init(module, val, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.constant_(module.weight, val)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def xavier_init(module, gain=1, bias=0, distribution='normal'):
-    assert distribution in ['uniform', 'normal']
-    if hasattr(module, 'weight') and module.weight is not None:
-        if distribution == 'uniform':
-            nn.init.xavier_uniform_(module.weight, gain=gain)
-        else:
-            nn.init.xavier_normal_(module.weight, gain=gain)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def normal_init(module, mean=0, std=1, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.normal_(module.weight, mean, std)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def trunc_normal_init(module: nn.Module,
-                      mean: float = 0,
-                      std: float = 1,
-                      a: float = -2,
-                      b: float = 2,
-                      bias: float = 0) -> None:
-    if hasattr(module, 'weight') and module.weight is not None:
-        trunc_normal_(module.weight, mean, std, a, b)  # type: ignore
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)  # type: ignore
-
-
-def uniform_init(module, a=0, b=1, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.uniform_(module.weight, a, b)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def kaiming_init(module,
-                 a=0,
-                 mode='fan_out',
-                 nonlinearity='relu',
-                 bias=0,
-                 distribution='normal'):
-    assert distribution in ['uniform', 'normal']
-    if hasattr(module, 'weight') and module.weight is not None:
-        if distribution == 'uniform':
-            nn.init.kaiming_uniform_(
-                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
-        else:
-            nn.init.kaiming_normal_(
-                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def caffe2_xavier_init(module, bias=0):
-    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
-    # Acknowledgment to FAIR's internal code
-    kaiming_init(
-        module,
-        a=1,
-        mode='fan_in',
-        nonlinearity='leaky_relu',
-        bias=bias,
-        distribution='uniform')
-
-
-def bias_init_with_prob(prior_prob):
-    """initialize conv/fc bias value according to a given probability value."""
-    bias_init = float(-np.log((1 - prior_prob) / prior_prob))
-    return bias_init
-
-
-def _get_bases_name(m):
-    return [b.__name__ for b in m.__class__.__bases__]
-
-
-class BaseInit(object):
-
-    def __init__(self, *, bias=0, bias_prob=None, layer=None):
-        self.wholemodule = False
-        if not isinstance(bias, (int, float)):
-            raise TypeError(f'bias must be a number, but got a {type(bias)}')
-
-        if bias_prob is not None:
-            if not isinstance(bias_prob, float):
-                raise TypeError(f'bias_prob type must be float, \
-                    but got {type(bias_prob)}')
-
-        if layer is not None:
-            if not isinstance(layer, (str, list)):
-                raise TypeError(f'layer must be a str or a list of str, \
-                    but got a {type(layer)}')
-        else:
-            layer = []
-
-        if bias_prob is not None:
-            self.bias = bias_init_with_prob(bias_prob)
-        else:
-            self.bias = bias
-        self.layer = [layer] if isinstance(layer, str) else layer
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Constant')
-class ConstantInit(BaseInit):
-    """Initialize module parameters with constant values.
-
-    Args:
-        val (int | float): the value to fill the weights in the module with
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, val, **kwargs):
-        super().__init__(**kwargs)
-        self.val = val
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                constant_init(m, self.val, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    constant_init(m, self.val, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: val={self.val}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Xavier')
-class XavierInit(BaseInit):
-    r"""Initialize module parameters with values according to the method
-    described in `Understanding the difficulty of training deep feedforward
-    neural networks - Glorot, X. & Bengio, Y. (2010).
-    <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
-
-    Args:
-        gain (int | float): an optional scaling factor. Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        distribution (str): distribution either be ``'normal'``
-            or ``'uniform'``. Defaults to ``'normal'``.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, gain=1, distribution='normal', **kwargs):
-        super().__init__(**kwargs)
-        self.gain = gain
-        self.distribution = distribution
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                xavier_init(m, self.gain, self.bias, self.distribution)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    xavier_init(m, self.gain, self.bias, self.distribution)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: gain={self.gain}, ' \
-               f'distribution={self.distribution}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Normal')
-class NormalInit(BaseInit):
-    r"""Initialize module parameters with the values drawn from the normal
-    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
-
-    Args:
-        mean (int | float):the mean of the normal distribution. Defaults to 0.
-        std (int | float): the standard deviation of the normal distribution.
-            Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-
-    """
-
-    def __init__(self, mean=0, std=1, **kwargs):
-        super().__init__(**kwargs)
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                normal_init(m, self.mean, self.std, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    normal_init(m, self.mean, self.std, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: mean={self.mean},' \
-               f' std={self.std}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='TruncNormal')
-class TruncNormalInit(BaseInit):
-    r"""Initialize module parameters with the values drawn from the normal
-    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values
-    outside :math:`[a, b]`.
-
-    Args:
-        mean (float): the mean of the normal distribution. Defaults to 0.
-        std (float):  the standard deviation of the normal distribution.
-            Defaults to 1.
-        a (float): The minimum cutoff value.
-        b ( float): The maximum cutoff value.
-        bias (float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-
-    """
-
-    def __init__(self,
-                 mean: float = 0,
-                 std: float = 1,
-                 a: float = -2,
-                 b: float = 2,
-                 **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.mean = mean
-        self.std = std
-        self.a = a
-        self.b = b
-
-    def __call__(self, module: nn.Module) -> None:
-
-        def init(m):
-            if self.wholemodule:
-                trunc_normal_init(m, self.mean, self.std, self.a, self.b,
-                                  self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    trunc_normal_init(m, self.mean, self.std, self.a, self.b,
-                                      self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a}, b={self.b},' \
-               f' mean={self.mean}, std={self.std}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Uniform')
-class UniformInit(BaseInit):
-    r"""Initialize module parameters with values drawn from the uniform
-    distribution :math:`\mathcal{U}(a, b)`.
-
-    Args:
-        a (int | float): the lower bound of the uniform distribution.
-            Defaults to 0.
-        b (int | float): the upper bound of the uniform distribution.
-            Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, a=0, b=1, **kwargs):
-        super().__init__(**kwargs)
-        self.a = a
-        self.b = b
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                uniform_init(m, self.a, self.b, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    uniform_init(m, self.a, self.b, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a},' \
-               f' b={self.b}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Kaiming')
-class KaimingInit(BaseInit):
-    r"""Initialize module parameters with the values according to the method
-    described in `Delving deep into rectifiers: Surpassing human-level
-    performance on ImageNet classification - He, K. et al. (2015).
-    <https://www.cv-foundation.org/openaccess/content_iccv_2015/
-    papers/He_Delving_Deep_into_ICCV_2015_paper.pdf>`_
-
-    Args:
-        a (int | float): the negative slope of the rectifier used after this
-            layer (only used with ``'leaky_relu'``). Defaults to 0.
-        mode (str):  either ``'fan_in'`` or ``'fan_out'``. Choosing
-            ``'fan_in'`` preserves the magnitude of the variance of the weights
-            in the forward pass. Choosing ``'fan_out'`` preserves the
-            magnitudes in the backwards pass. Defaults to ``'fan_out'``.
-        nonlinearity (str): the non-linear function (`nn.functional` name),
-            recommended to use only with ``'relu'`` or ``'leaky_relu'`` .
-            Defaults to 'relu'.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        distribution (str): distribution either be ``'normal'`` or
-            ``'uniform'``. Defaults to ``'normal'``.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 a=0,
-                 mode='fan_out',
-                 nonlinearity='relu',
-                 distribution='normal',
-                 **kwargs):
-        super().__init__(**kwargs)
-        self.a = a
-        self.mode = mode
-        self.nonlinearity = nonlinearity
-        self.distribution = distribution
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                kaiming_init(m, self.a, self.mode, self.nonlinearity,
-                             self.bias, self.distribution)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    kaiming_init(m, self.a, self.mode, self.nonlinearity,
-                                 self.bias, self.distribution)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a}, mode={self.mode}, ' \
-               f'nonlinearity={self.nonlinearity}, ' \
-               f'distribution ={self.distribution}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Caffe2Xavier')
-class Caffe2XavierInit(KaimingInit):
-    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
-    # Acknowledgment to FAIR's internal code
-    def __init__(self, **kwargs):
-        super().__init__(
-            a=1,
-            mode='fan_in',
-            nonlinearity='leaky_relu',
-            distribution='uniform',
-            **kwargs)
-
-    def __call__(self, module):
-        super().__call__(module)
-
-
-@INITIALIZERS.register_module(name='Pretrained')
-class PretrainedInit(object):
-    """Initialize module by loading a pretrained model.
-
-    Args:
-        checkpoint (str): the checkpoint file of the pretrained model should
-            be load.
-        prefix (str, optional): the prefix of a sub-module in the pretrained
-            model. it is for loading a part of the pretrained model to
-            initialize. For example, if we would like to only load the
-            backbone of a detector model, we can set ``prefix='backbone.'``.
-            Defaults to None.
-        map_location (str): map tensors into proper locations.
-    """
-
-    def __init__(self, checkpoint, prefix=None, map_location=None):
-        self.checkpoint = checkpoint
-        self.prefix = prefix
-        self.map_location = map_location
-
-    def __call__(self, module):
-        from annotator.mmpkg.mmcv.runner import (_load_checkpoint_with_prefix, load_checkpoint,
-                                 load_state_dict)
-        logger = get_logger('mmcv')
-        if self.prefix is None:
-            print_log(f'load model from: {self.checkpoint}', logger=logger)
-            load_checkpoint(
-                module,
-                self.checkpoint,
-                map_location=self.map_location,
-                strict=False,
-                logger=logger)
-        else:
-            print_log(
-                f'load {self.prefix} in model from: {self.checkpoint}',
-                logger=logger)
-            state_dict = _load_checkpoint_with_prefix(
-                self.prefix, self.checkpoint, map_location=self.map_location)
-            load_state_dict(module, state_dict, strict=False, logger=logger)
-
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: load from {self.checkpoint}'
-        return info
-
-
-def _initialize(module, cfg, wholemodule=False):
-    func = build_from_cfg(cfg, INITIALIZERS)
-    # wholemodule flag is for override mode, there is no layer key in override
-    # and initializer will give init values for the whole module with the name
-    # in override.
-    func.wholemodule = wholemodule
-    func(module)
-
-
-def _initialize_override(module, override, cfg):
-    if not isinstance(override, (dict, list)):
-        raise TypeError(f'override must be a dict or a list of dict, \
-                but got {type(override)}')
-
-    override = [override] if isinstance(override, dict) else override
-
-    for override_ in override:
-
-        cp_override = copy.deepcopy(override_)
-        name = cp_override.pop('name', None)
-        if name is None:
-            raise ValueError('`override` must contain the key "name",'
-                             f'but got {cp_override}')
-        # if override only has name key, it means use args in init_cfg
-        if not cp_override:
-            cp_override.update(cfg)
-        # if override has name key and other args except type key, it will
-        # raise error
-        elif 'type' not in cp_override.keys():
-            raise ValueError(
-                f'`override` need "type" key, but got {cp_override}')
-
-        if hasattr(module, name):
-            _initialize(getattr(module, name), cp_override, wholemodule=True)
-        else:
-            raise RuntimeError(f'module did not have attribute {name}, '
-                               f'but init_cfg is {cp_override}.')
-
-
-def initialize(module, init_cfg):
-    """Initialize a module.
-
-    Args:
-        module (``torch.nn.Module``): the module will be initialized.
-        init_cfg (dict | list[dict]): initialization configuration dict to
-            define initializer. OpenMMLab has implemented 6 initializers
-            including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
-            ``Kaiming``, and ``Pretrained``.
-    Example:
-        >>> module = nn.Linear(2, 3, bias=True)
-        >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
-        >>> initialize(module, init_cfg)
-
-        >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
-        >>> # define key ``'layer'`` for initializing layer with different
-        >>> # configuration
-        >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
-                dict(type='Constant', layer='Linear', val=2)]
-        >>> initialize(module, init_cfg)
-
-        >>> # define key``'override'`` to initialize some specific part in
-        >>> # module
-        >>> class FooNet(nn.Module):
-        >>>     def __init__(self):
-        >>>         super().__init__()
-        >>>         self.feat = nn.Conv2d(3, 16, 3)
-        >>>         self.reg = nn.Conv2d(16, 10, 3)
-        >>>         self.cls = nn.Conv2d(16, 5, 3)
-        >>> model = FooNet()
-        >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
-        >>>     override=dict(type='Constant', name='reg', val=3, bias=4))
-        >>> initialize(model, init_cfg)
-
-        >>> model = ResNet(depth=50)
-        >>> # Initialize weights with the pretrained model.
-        >>> init_cfg = dict(type='Pretrained',
-                checkpoint='torchvision://resnet50')
-        >>> initialize(model, init_cfg)
-
-        >>> # Initialize weights of a sub-module with the specific part of
-        >>> # a pretrained model by using "prefix".
-        >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
-        >>>     'retinanet_r50_fpn_1x_coco/'\
-        >>>     'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
-        >>> init_cfg = dict(type='Pretrained',
-                checkpoint=url, prefix='backbone.')
-    """
-    if not isinstance(init_cfg, (dict, list)):
-        raise TypeError(f'init_cfg must be a dict or a list of dict, \
-                but got {type(init_cfg)}')
-
-    if isinstance(init_cfg, dict):
-        init_cfg = [init_cfg]
-
-    for cfg in init_cfg:
-        # should deeply copy the original config because cfg may be used by
-        # other modules, e.g., one init_cfg shared by multiple bottleneck
-        # blocks, the expected cfg will be changed after pop and will change
-        # the initialization behavior of other modules
-        cp_cfg = copy.deepcopy(cfg)
-        override = cp_cfg.pop('override', None)
-        _initialize(module, cp_cfg)
-
-        if override is not None:
-            cp_cfg.pop('layer', None)
-            _initialize_override(module, override, cp_cfg)
-        else:
-            # All attributes in module have same initialization.
-            pass
-
-
-def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float,
-                           b: float) -> Tensor:
-    # Method based on
-    # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    # Modified from
-    # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
-            'The distribution of values may be incorrect.',
-            stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        lower = norm_cdf((a - mean) / std)
-        upper = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [lower, upper], then translate
-        # to [2lower-1, 2upper-1].
-        tensor.uniform_(2 * lower - 1, 2 * upper - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor: Tensor,
-                  mean: float = 0.,
-                  std: float = 1.,
-                  a: float = -2.,
-                  b: float = 2.) -> Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    Modified from
-    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-
-    Args:
-        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
-        mean (float): the mean of the normal distribution.
-        std (float): the standard deviation of the normal distribution.
-        a (float): the minimum cutoff value.
-        b (float): the maximum cutoff value.
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py
deleted file mode 100644
index 687cdc58c0336c92b1e4f9a410ba67ebaab2bc7a..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ...dist_utils import master_only
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class DvcliveLoggerHook(LoggerHook):
-    """Class to log metrics with dvclive.
-
-    It requires `dvclive`_ to be installed.
-
-    Args:
-        path (str): Directory where dvclive will write TSV log files.
-        interval (int): Logging interval (every k iterations).
-            Default 10.
-        ignore_last (bool): Ignore the log of last iterations in each epoch
-            if less than `interval`.
-            Default: True.
-        reset_flag (bool): Whether to clear the output buffer after logging.
-            Default: True.
-        by_epoch (bool): Whether EpochBasedRunner is used.
-            Default: True.
-
-    .. _dvclive:
-        https://dvc.org/doc/dvclive
-    """
-
-    def __init__(self,
-                 path,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=True,
-                 by_epoch=True):
-
-        super(DvcliveLoggerHook, self).__init__(interval, ignore_last,
-                                                reset_flag, by_epoch)
-        self.path = path
-        self.import_dvclive()
-
-    def import_dvclive(self):
-        try:
-            import dvclive
-        except ImportError:
-            raise ImportError(
-                'Please run "pip install dvclive" to install dvclive')
-        self.dvclive = dvclive
-
-    @master_only
-    def before_run(self, runner):
-        self.dvclive.init(self.path)
-
-    @master_only
-    def log(self, runner):
-        tags = self.get_loggable_tags(runner)
-        if tags:
-            for k, v in tags.items():
-                self.dvclive.log(k, v, step=self.get_iter(runner))
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/log_buffer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/log_buffer.py
deleted file mode 100644
index d949e2941c5400088c7cd8a1dc893d8b233ae785..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/log_buffer.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from collections import OrderedDict
-
-import numpy as np
-
-
-class LogBuffer:
-
-    def __init__(self):
-        self.val_history = OrderedDict()
-        self.n_history = OrderedDict()
-        self.output = OrderedDict()
-        self.ready = False
-
-    def clear(self):
-        self.val_history.clear()
-        self.n_history.clear()
-        self.clear_output()
-
-    def clear_output(self):
-        self.output.clear()
-        self.ready = False
-
-    def update(self, vars, count=1):
-        assert isinstance(vars, dict)
-        for key, var in vars.items():
-            if key not in self.val_history:
-                self.val_history[key] = []
-                self.n_history[key] = []
-            self.val_history[key].append(var)
-            self.n_history[key].append(count)
-
-    def average(self, n=0):
-        """Average latest n values or all values."""
-        assert n >= 0
-        for key in self.val_history:
-            values = np.array(self.val_history[key][-n:])
-            nums = np.array(self.n_history[key][-n:])
-            avg = np.sum(values * nums) / np.sum(nums)
-            self.output[key] = avg
-        self.ready = True
diff --git a/spaces/cvlab/zero123-live/taming-transformers/taming/data/open_images_helper.py b/spaces/cvlab/zero123-live/taming-transformers/taming/data/open_images_helper.py
deleted file mode 100644
index 8feb7c6e705fc165d2983303192aaa88f579b243..0000000000000000000000000000000000000000
--- a/spaces/cvlab/zero123-live/taming-transformers/taming/data/open_images_helper.py
+++ /dev/null
@@ -1,379 +0,0 @@
-open_images_unify_categories_for_coco = {
-    '/m/03bt1vf': '/m/01g317',
-    '/m/04yx4': '/m/01g317',
-    '/m/05r655': '/m/01g317',
-    '/m/01bl7v': '/m/01g317',
-    '/m/0cnyhnx': '/m/01xq0k1',
-    '/m/01226z': '/m/018xm',
-    '/m/05ctyq': '/m/018xm',
-    '/m/058qzx': '/m/04ctx',
-    '/m/06pcq': '/m/0l515',
-    '/m/03m3pdh': '/m/02crq1',
-    '/m/046dlr': '/m/01x3z',
-    '/m/0h8mzrc': '/m/01x3z',
-}
-
-
-top_300_classes_plus_coco_compatibility = [
-    ('Man', 1060962),
-    ('Clothing', 986610),
-    ('Tree', 748162),
-    ('Woman', 611896),
-    ('Person', 610294),
-    ('Human face', 442948),
-    ('Girl', 175399),
-    ('Building', 162147),
-    ('Car', 159135),
-    ('Plant', 155704),
-    ('Human body', 137073),
-    ('Flower', 133128),
-    ('Window', 127485),
-    ('Human arm', 118380),
-    ('House', 114365),
-    ('Wheel', 111684),
-    ('Suit', 99054),
-    ('Human hair', 98089),
-    ('Human head', 92763),
-    ('Chair', 88624),
-    ('Boy', 79849),
-    ('Table', 73699),
-    ('Jeans', 57200),
-    ('Tire', 55725),
-    ('Skyscraper', 53321),
-    ('Food', 52400),
-    ('Footwear', 50335),
-    ('Dress', 50236),
-    ('Human leg', 47124),
-    ('Toy', 46636),
-    ('Tower', 45605),
-    ('Boat', 43486),
-    ('Land vehicle', 40541),
-    ('Bicycle wheel', 34646),
-    ('Palm tree', 33729),
-    ('Fashion accessory', 32914),
-    ('Glasses', 31940),
-    ('Bicycle', 31409),
-    ('Furniture', 30656),
-    ('Sculpture', 29643),
-    ('Bottle', 27558),
-    ('Dog', 26980),
-    ('Snack', 26796),
-    ('Human hand', 26664),
-    ('Bird', 25791),
-    ('Book', 25415),
-    ('Guitar', 24386),
-    ('Jacket', 23998),
-    ('Poster', 22192),
-    ('Dessert', 21284),
-    ('Baked goods', 20657),
-    ('Drink', 19754),
-    ('Flag', 18588),
-    ('Houseplant', 18205),
-    ('Tableware', 17613),
-    ('Airplane', 17218),
-    ('Door', 17195),
-    ('Sports uniform', 17068),
-    ('Shelf', 16865),
-    ('Drum', 16612),
-    ('Vehicle', 16542),
-    ('Microphone', 15269),
-    ('Street light', 14957),
-    ('Cat', 14879),
-    ('Fruit', 13684),
-    ('Fast food', 13536),
-    ('Animal', 12932),
-    ('Vegetable', 12534),
-    ('Train', 12358),
-    ('Horse', 11948),
-    ('Flowerpot', 11728),
-    ('Motorcycle', 11621),
-    ('Fish', 11517),
-    ('Desk', 11405),
-    ('Helmet', 10996),
-    ('Truck', 10915),
-    ('Bus', 10695),
-    ('Hat', 10532),
-    ('Auto part', 10488),
-    ('Musical instrument', 10303),
-    ('Sunglasses', 10207),
-    ('Picture frame', 10096),
-    ('Sports equipment', 10015),
-    ('Shorts', 9999),
-    ('Wine glass', 9632),
-    ('Duck', 9242),
-    ('Wine', 9032),
-    ('Rose', 8781),
-    ('Tie', 8693),
-    ('Butterfly', 8436),
-    ('Beer', 7978),
-    ('Cabinetry', 7956),
-    ('Laptop', 7907),
-    ('Insect', 7497),
-    ('Goggles', 7363),
-    ('Shirt', 7098),
-    ('Dairy Product', 7021),
-    ('Marine invertebrates', 7014),
-    ('Cattle', 7006),
-    ('Trousers', 6903),
-    ('Van', 6843),
-    ('Billboard', 6777),
-    ('Balloon', 6367),
-    ('Human nose', 6103),
-    ('Tent', 6073),
-    ('Camera', 6014),
-    ('Doll', 6002),
-    ('Coat', 5951),
-    ('Mobile phone', 5758),
-    ('Swimwear', 5729),
-    ('Strawberry', 5691),
-    ('Stairs', 5643),
-    ('Goose', 5599),
-    ('Umbrella', 5536),
-    ('Cake', 5508),
-    ('Sun hat', 5475),
-    ('Bench', 5310),
-    ('Bookcase', 5163),
-    ('Bee', 5140),
-    ('Computer monitor', 5078),
-    ('Hiking equipment', 4983),
-    ('Office building', 4981),
-    ('Coffee cup', 4748),
-    ('Curtain', 4685),
-    ('Plate', 4651),
-    ('Box', 4621),
-    ('Tomato', 4595),
-    ('Coffee table', 4529),
-    ('Office supplies', 4473),
-    ('Maple', 4416),
-    ('Muffin', 4365),
-    ('Cocktail', 4234),
-    ('Castle', 4197),
-    ('Couch', 4134),
-    ('Pumpkin', 3983),
-    ('Computer keyboard', 3960),
-    ('Human mouth', 3926),
-    ('Christmas tree', 3893),
-    ('Mushroom', 3883),
-    ('Swimming pool', 3809),
-    ('Pastry', 3799),
-    ('Lavender (Plant)', 3769),
-    ('Football helmet', 3732),
-    ('Bread', 3648),
-    ('Traffic sign', 3628),
-    ('Common sunflower', 3597),
-    ('Television', 3550),
-    ('Bed', 3525),
-    ('Cookie', 3485),
-    ('Fountain', 3484),
-    ('Paddle', 3447),
-    ('Bicycle helmet', 3429),
-    ('Porch', 3420),
-    ('Deer', 3387),
-    ('Fedora', 3339),
-    ('Canoe', 3338),
-    ('Carnivore', 3266),
-    ('Bowl', 3202),
-    ('Human eye', 3166),
-    ('Ball', 3118),
-    ('Pillow', 3077),
-    ('Salad', 3061),
-    ('Beetle', 3060),
-    ('Orange', 3050),
-    ('Drawer', 2958),
-    ('Platter', 2937),
-    ('Elephant', 2921),
-    ('Seafood', 2921),
-    ('Monkey', 2915),
-    ('Countertop', 2879),
-    ('Watercraft', 2831),
-    ('Helicopter', 2805),
-    ('Kitchen appliance', 2797),
-    ('Personal flotation device', 2781),
-    ('Swan', 2739),
-    ('Lamp', 2711),
-    ('Boot', 2695),
-    ('Bronze sculpture', 2693),
-    ('Chicken', 2677),
-    ('Taxi', 2643),
-    ('Juice', 2615),
-    ('Cowboy hat', 2604),
-    ('Apple', 2600),
-    ('Tin can', 2590),
-    ('Necklace', 2564),
-    ('Ice cream', 2560),
-    ('Human beard', 2539),
-    ('Coin', 2536),
-    ('Candle', 2515),
-    ('Cart', 2512),
-    ('High heels', 2441),
-    ('Weapon', 2433),
-    ('Handbag', 2406),
-    ('Penguin', 2396),
-    ('Rifle', 2352),
-    ('Violin', 2336),
-    ('Skull', 2304),
-    ('Lantern', 2285),
-    ('Scarf', 2269),
-    ('Saucer', 2225),
-    ('Sheep', 2215),
-    ('Vase', 2189),
-    ('Lily', 2180),
-    ('Mug', 2154),
-    ('Parrot', 2140),
-    ('Human ear', 2137),
-    ('Sandal', 2115),
-    ('Lizard', 2100),
-    ('Kitchen & dining room table', 2063),
-    ('Spider', 1977),
-    ('Coffee', 1974),
-    ('Goat', 1926),
-    ('Squirrel', 1922),
-    ('Cello', 1913),
-    ('Sushi', 1881),
-    ('Tortoise', 1876),
-    ('Pizza', 1870),
-    ('Studio couch', 1864),
-    ('Barrel', 1862),
-    ('Cosmetics', 1841),
-    ('Moths and butterflies', 1841),
-    ('Convenience store', 1817),
-    ('Watch', 1792),
-    ('Home appliance', 1786),
-    ('Harbor seal', 1780),
-    ('Luggage and bags', 1756),
-    ('Vehicle registration plate', 1754),
-    ('Shrimp', 1751),
-    ('Jellyfish', 1730),
-    ('French fries', 1723),
-    ('Egg (Food)', 1698),
-    ('Football', 1697),
-    ('Musical keyboard', 1683),
-    ('Falcon', 1674),
-    ('Candy', 1660),
-    ('Medical equipment', 1654),
-    ('Eagle', 1651),
-    ('Dinosaur', 1634),
-    ('Surfboard', 1630),
-    ('Tank', 1628),
-    ('Grape', 1624),
-    ('Lion', 1624),
-    ('Owl', 1622),
-    ('Ski', 1613),
-    ('Waste container', 1606),
-    ('Frog', 1591),
-    ('Sparrow', 1585),
-    ('Rabbit', 1581),
-    ('Pen', 1546),
-    ('Sea lion', 1537),
-    ('Spoon', 1521),
-    ('Sink', 1512),
-    ('Teddy bear', 1507),
-    ('Bull', 1495),
-    ('Sofa bed', 1490),
-    ('Dragonfly', 1479),
-    ('Brassiere', 1478),
-    ('Chest of drawers', 1472),
-    ('Aircraft', 1466),
-    ('Human foot', 1463),
-    ('Pig', 1455),
-    ('Fork', 1454),
-    ('Antelope', 1438),
-    ('Tripod', 1427),
-    ('Tool', 1424),
-    ('Cheese', 1422),
-    ('Lemon', 1397),
-    ('Hamburger', 1393),
-    ('Dolphin', 1390),
-    ('Mirror', 1390),
-    ('Marine mammal', 1387),
-    ('Giraffe', 1385),
-    ('Snake', 1368),
-    ('Gondola', 1364),
-    ('Wheelchair', 1360),
-    ('Piano', 1358),
-    ('Cupboard', 1348),
-    ('Banana', 1345),
-    ('Trumpet', 1335),
-    ('Lighthouse', 1333),
-    ('Invertebrate', 1317),
-    ('Carrot', 1268),
-    ('Sock', 1260),
-    ('Tiger', 1241),
-    ('Camel', 1224),
-    ('Parachute', 1224),
-    ('Bathroom accessory', 1223),
-    ('Earrings', 1221),
-    ('Headphones', 1218),
-    ('Skirt', 1198),
-    ('Skateboard', 1190),
-    ('Sandwich', 1148),
-    ('Saxophone', 1141),
-    ('Goldfish', 1136),
-    ('Stool', 1104),
-    ('Traffic light', 1097),
-    ('Shellfish', 1081),
-    ('Backpack', 1079),
-    ('Sea turtle', 1078),
-    ('Cucumber', 1075),
-    ('Tea', 1051),
-    ('Toilet', 1047),
-    ('Roller skates', 1040),
-    ('Mule', 1039),
-    ('Bust', 1031),
-    ('Broccoli', 1030),
-    ('Crab', 1020),
-    ('Oyster', 1019),
-    ('Cannon', 1012),
-    ('Zebra', 1012),
-    ('French horn', 1008),
-    ('Grapefruit', 998),
-    ('Whiteboard', 997),
-    ('Zucchini', 997),
-    ('Crocodile', 992),
-
-    ('Clock', 960),
-    ('Wall clock', 958),
-
-    ('Doughnut', 869),
-    ('Snail', 868),
-
-    ('Baseball glove', 859),
-
-    ('Panda', 830),
-    ('Tennis racket', 830),
-
-    ('Pear', 652),
-
-    ('Bagel', 617),
-    ('Oven', 616),
-    ('Ladybug', 615),
-    ('Shark', 615),
-    ('Polar bear', 614),
-    ('Ostrich', 609),
-
-    ('Hot dog', 473),
-    ('Microwave oven', 467),
-    ('Fire hydrant', 20),
-    ('Stop sign', 20),
-    ('Parking meter', 20),
-    ('Bear', 20),
-    ('Flying disc', 20),
-    ('Snowboard', 20),
-    ('Tennis ball', 20),
-    ('Kite', 20),
-    ('Baseball bat', 20),
-    ('Kitchen knife', 20),
-    ('Knife', 20),
-    ('Submarine sandwich', 20),
-    ('Computer mouse', 20),
-    ('Remote control', 20),
-    ('Toaster', 20),
-    ('Sink', 20),
-    ('Refrigerator', 20),
-    ('Alarm clock', 20),
-    ('Wall clock', 20),
-    ('Scissors', 20),
-    ('Hair dryer', 20),
-    ('Toothbrush', 20),
-    ('Suitcase', 20)
-]
diff --git a/spaces/declare-lab/tango/diffusers/examples/community/checkpoint_merger.py b/spaces/declare-lab/tango/diffusers/examples/community/checkpoint_merger.py
deleted file mode 100644
index 3e29ae50078b8db4264e8cbaf83f6670fb84217c..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/community/checkpoint_merger.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import glob
-import os
-from typing import Dict, List, Union
-
-import torch
-
-from diffusers.utils import is_safetensors_available
-
-
-if is_safetensors_available():
-    import safetensors.torch
-
-from huggingface_hub import snapshot_download
-
-from diffusers import DiffusionPipeline, __version__
-from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
-from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
-
-
-class CheckpointMergerPipeline(DiffusionPipeline):
-    """
-    A class that that supports merging diffusion models based on the discussion here:
-    https://github.com/huggingface/diffusers/issues/877
-
-    Example usage:-
-
-    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
-
-    merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
-
-    merged_pipe.to('cuda')
-
-    prompt = "An astronaut riding a unicycle on Mars"
-
-    results = merged_pipe(prompt)
-
-    ## For more details, see the docstring for the merge method.
-
-    """
-
-    def __init__(self):
-        self.register_to_config()
-        super().__init__()
-
-    def _compare_model_configs(self, dict0, dict1):
-        if dict0 == dict1:
-            return True
-        else:
-            config0, meta_keys0 = self._remove_meta_keys(dict0)
-            config1, meta_keys1 = self._remove_meta_keys(dict1)
-            if config0 == config1:
-                print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.")
-                return True
-        return False
-
-    def _remove_meta_keys(self, config_dict: Dict):
-        meta_keys = []
-        temp_dict = config_dict.copy()
-        for key in config_dict.keys():
-            if key.startswith("_"):
-                temp_dict.pop(key)
-                meta_keys.append(key)
-        return (temp_dict, meta_keys)
-
-    @torch.no_grad()
-    def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs):
-        """
-        Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed
-        in the argument 'pretrained_model_name_or_path_list' as a list.
-
-        Parameters:
-        -----------
-            pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.
-
-            **kwargs:
-                Supports all the default DiffusionPipeline.get_config_dict kwargs viz..
-
-                cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map.
-
-                alpha - The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
-                    would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
-
-                interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_diff" and None.
-                    Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_diff" is supported.
-
-                force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
-
-        """
-        # Default kwargs from DiffusionPipeline
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        resume_download = kwargs.pop("resume_download", False)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        device_map = kwargs.pop("device_map", None)
-
-        alpha = kwargs.pop("alpha", 0.5)
-        interp = kwargs.pop("interp", None)
-
-        print("Received list", pretrained_model_name_or_path_list)
-        print(f"Combining with alpha={alpha}, interpolation mode={interp}")
-
-        checkpoint_count = len(pretrained_model_name_or_path_list)
-        # Ignore result from model_index_json comparision of the two checkpoints
-        force = kwargs.pop("force", False)
-
-        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
-        if checkpoint_count > 3 or checkpoint_count < 2:
-            raise ValueError(
-                "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
-                " passed."
-            )
-
-        print("Received the right number of checkpoints")
-        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
-        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
-
-        # Validate that the checkpoints can be merged
-        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
-        config_dicts = []
-        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
-            config_dict = DiffusionPipeline.load_config(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                force_download=force_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-            )
-            config_dicts.append(config_dict)
-
-        comparison_result = True
-        for idx in range(1, len(config_dicts)):
-            comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx])
-            if not force and comparison_result is False:
-                raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.")
-                print(config_dicts[0], config_dicts[1])
-        print("Compatible model_index.json files found")
-        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
-        cached_folders = []
-        for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts):
-            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
-            allow_patterns = [os.path.join(k, "*") for k in folder_names]
-            allow_patterns += [
-                WEIGHTS_NAME,
-                SCHEDULER_CONFIG_NAME,
-                CONFIG_NAME,
-                ONNX_WEIGHTS_NAME,
-                DiffusionPipeline.config_name,
-            ]
-            requested_pipeline_class = config_dict.get("_class_name")
-            user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
-
-            cached_folder = (
-                pretrained_model_name_or_path
-                if os.path.isdir(pretrained_model_name_or_path)
-                else snapshot_download(
-                    pretrained_model_name_or_path,
-                    cache_dir=cache_dir,
-                    resume_download=resume_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    revision=revision,
-                    allow_patterns=allow_patterns,
-                    user_agent=user_agent,
-                )
-            )
-            print("Cached Folder", cached_folder)
-            cached_folders.append(cached_folder)
-
-        # Step 3:-
-        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
-        final_pipe = DiffusionPipeline.from_pretrained(
-            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
-        )
-        final_pipe.to(self.device)
-
-        checkpoint_path_2 = None
-        if len(cached_folders) > 2:
-            checkpoint_path_2 = os.path.join(cached_folders[2])
-
-        if interp == "sigmoid":
-            theta_func = CheckpointMergerPipeline.sigmoid
-        elif interp == "inv_sigmoid":
-            theta_func = CheckpointMergerPipeline.inv_sigmoid
-        elif interp == "add_diff":
-            theta_func = CheckpointMergerPipeline.add_difference
-        else:
-            theta_func = CheckpointMergerPipeline.weighted_sum
-
-        # Find each module's state dict.
-        for attr in final_pipe.config.keys():
-            if not attr.startswith("_"):
-                checkpoint_path_1 = os.path.join(cached_folders[1], attr)
-                if os.path.exists(checkpoint_path_1):
-                    files = [
-                        *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
-                        *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
-                    ]
-                    checkpoint_path_1 = files[0] if len(files) > 0 else None
-                if len(cached_folders) < 3:
-                    checkpoint_path_2 = None
-                else:
-                    checkpoint_path_2 = os.path.join(cached_folders[2], attr)
-                    if os.path.exists(checkpoint_path_2):
-                        files = [
-                            *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
-                            *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
-                        ]
-                        checkpoint_path_2 = files[0] if len(files) > 0 else None
-                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
-                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
-                if checkpoint_path_1 is None and checkpoint_path_2 is None:
-                    print(f"Skipping {attr}: not present in 2nd or 3d model")
-                    continue
-                try:
-                    module = getattr(final_pipe, attr)
-                    if isinstance(module, bool):  # ignore requires_safety_checker boolean
-                        continue
-                    theta_0 = getattr(module, "state_dict")
-                    theta_0 = theta_0()
-
-                    update_theta_0 = getattr(module, "load_state_dict")
-                    theta_1 = (
-                        safetensors.torch.load_file(checkpoint_path_1)
-                        if (is_safetensors_available() and checkpoint_path_1.endswith(".safetensors"))
-                        else torch.load(checkpoint_path_1, map_location="cpu")
-                    )
-                    theta_2 = None
-                    if checkpoint_path_2:
-                        theta_2 = (
-                            safetensors.torch.load_file(checkpoint_path_2)
-                            if (is_safetensors_available() and checkpoint_path_2.endswith(".safetensors"))
-                            else torch.load(checkpoint_path_2, map_location="cpu")
-                        )
-
-                    if not theta_0.keys() == theta_1.keys():
-                        print(f"Skipping {attr}: key mismatch")
-                        continue
-                    if theta_2 and not theta_1.keys() == theta_2.keys():
-                        print(f"Skipping {attr}:y mismatch")
-                except Exception as e:
-                    print(f"Skipping {attr} do to an unexpected error: {str(e)}")
-                    continue
-                print(f"MERGING {attr}")
-
-                for key in theta_0.keys():
-                    if theta_2:
-                        theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha)
-                    else:
-                        theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha)
-
-                del theta_1
-                del theta_2
-                update_theta_0(theta_0)
-
-                del theta_0
-        return final_pipe
-
-    @staticmethod
-    def weighted_sum(theta0, theta1, theta2, alpha):
-        return ((1 - alpha) * theta0) + (alpha * theta1)
-
-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    @staticmethod
-    def sigmoid(theta0, theta1, theta2, alpha):
-        alpha = alpha * alpha * (3 - (2 * alpha))
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    @staticmethod
-    def inv_sigmoid(theta0, theta1, theta2, alpha):
-        import math
-
-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    @staticmethod
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * (1.0 - alpha)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/README.md b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/README.md
deleted file mode 100644
index be4c5d942b2e313ebfac5acc22764de8bae48bf5..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/README.md
+++ /dev/null
@@ -1,176 +0,0 @@
-# Stable Diffusion
-
-## Overview
-
-Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team.
-
-The summary of the model is the following:
-
-*Stable Diffusion is a text-to-image model that will empower billions of people to create stunning art within seconds. It is a breakthrough in speed and quality meaning that it can run on consumer GPUs. You can see some of the amazing output that has been created by this model without pre or post-processing on this page. The model itself builds upon the work of the team at CompVis and Runway in their widely used latent diffusion model combined with insights from the conditional diffusion models by our lead generative AI developer Katherine Crowson, Dall-E 2 by Open AI, Imagen by Google Brain and many others. We are delighted that AI media generation is a cooperative field and hope it can continue this way to bring the gift of creativity to all.* 
-
-## Tips:
-
-- Stable Diffusion has the same architecture as [Latent Diffusion](https://arxiv.org/abs/2112.10752) but uses a frozen CLIP Text Encoder instead of training the text encoder jointly with the diffusion model.
-- An in-detail explanation of the Stable Diffusion model can be found under [Stable Diffusion with 🧨 Diffusers](https://huggingface.co/blog/stable_diffusion).
-- If you don't want to rely on the Hugging Face Hub and having to pass a authentication token, you can 
-download the weights with `git lfs install; git clone https://huggingface.co/runwayml/stable-diffusion-v1-5` and instead pass the local path to the cloned folder to `from_pretrained` as shown below.
-- Stable Diffusion can work with a variety of different samplers as is shown below.
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [pipeline_stable_diffusion_img2img](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
-| [pipeline_stable_diffusion_inpaint](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
-
-## Examples:
-
-### Using Stable Diffusion without being logged into the Hub.
-
-If you want to download the model weights using a single Python line, you need to be logged in via `huggingface-cli login`. 
-
-```python
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-```
-
-This however can make it difficult to build applications on top of `diffusers` as you will always have to pass the token around. A potential way to solve this issue is by downloading the weights to a local path `"./stable-diffusion-v1-5"`:
-
-```
-git lfs install
-git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
-```
-
-and simply passing the local path to `from_pretrained`:
-
-```python
-from diffusers import StableDiffusionPipeline
-
-pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
-```
-
-### Text-to-Image with default PLMS scheduler
-
-```python
-# make sure you're logged in with `huggingface-cli login`
-from diffusers import StableDiffusionPipeline
-
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-pipe = pipe.to("cuda")
-
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt).sample[0]  
-    
-image.save("astronaut_rides_horse.png")
-```
-
-### Text-to-Image with DDIM scheduler
-
-```python
-# make sure you're logged in with `huggingface-cli login`
-from diffusers import StableDiffusionPipeline, DDIMScheduler
-
-scheduler =  DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", 
-    scheduler=scheduler,
-).to("cuda")
-
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt).sample[0]  
-    
-image.save("astronaut_rides_horse.png")
-```
-
-### Text-to-Image with K-LMS scheduler
-
-```python
-# make sure you're logged in with `huggingface-cli login`
-from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
-
-lms = LMSDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", 
-    scheduler=lms,
-).to("cuda")
-
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt).sample[0]  
-    
-image.save("astronaut_rides_horse.png")
-```
-
-### CycleDiffusion using Stable Diffusion and DDIM scheduler
-
-```python
-import requests
-import torch
-from PIL import Image
-from io import BytesIO
-
-from diffusers import CycleDiffusionPipeline, DDIMScheduler
-
-
-# load the scheduler. CycleDiffusion only supports stochastic schedulers.
-
-# load the pipeline
-# make sure you're logged in with `huggingface-cli login`
-model_id_or_path = "CompVis/stable-diffusion-v1-4"
-scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
-pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
-
-# let's download an initial image
-url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
-response = requests.get(url)
-init_image = Image.open(BytesIO(response.content)).convert("RGB")
-init_image = init_image.resize((512, 512))
-init_image.save("horse.png")
-
-# let's specify a prompt
-source_prompt = "An astronaut riding a horse"
-prompt = "An astronaut riding an elephant"
-
-# call the pipeline
-image = pipe(
-    prompt=prompt,
-    source_prompt=source_prompt,
-    image=init_image,
-    num_inference_steps=100,
-    eta=0.1,
-    strength=0.8,
-    guidance_scale=2,
-    source_guidance_scale=1,
-).images[0]
-
-image.save("horse_to_elephant.png")
-
-# let's try another example
-# See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
-url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
-response = requests.get(url)
-init_image = Image.open(BytesIO(response.content)).convert("RGB")
-init_image = init_image.resize((512, 512))
-init_image.save("black.png")
-
-source_prompt = "A black colored car"
-prompt = "A blue colored car"
-
-# call the pipeline
-torch.manual_seed(0)
-image = pipe(
-    prompt=prompt,
-    source_prompt=source_prompt,
-    image=init_image,
-    num_inference_steps=100,
-    eta=0.1,
-    strength=0.85,
-    guidance_scale=3,
-    source_guidance_scale=1,
-).images[0]
-
-image.save("black_to_blue.png")
-```
diff --git a/spaces/declare-lab/tango/diffusers/tests/pipelines/karras_ve/test_karras_ve.py b/spaces/declare-lab/tango/diffusers/tests/pipelines/karras_ve/test_karras_ve.py
deleted file mode 100644
index 391e61a2b9c90c58049270a192884bd358621c52..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/tests/pipelines/karras_ve/test_karras_ve.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import KarrasVePipeline, KarrasVeScheduler, UNet2DModel
-from diffusers.utils.testing_utils import require_torch, slow, torch_device
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-class KarrasVePipelineFastTests(unittest.TestCase):
-    @property
-    def dummy_uncond_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=3,
-            out_channels=3,
-            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
-            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
-        )
-        return model
-
-    def test_inference(self):
-        unet = self.dummy_uncond_unet
-        scheduler = KarrasVeScheduler()
-
-        pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-
-@slow
-@require_torch
-class KarrasVePipelineIntegrationTests(unittest.TestCase):
-    def test_inference(self):
-        model_id = "google/ncsnpp-celebahq-256"
-        model = UNet2DModel.from_pretrained(model_id)
-        scheduler = KarrasVeScheduler()
-
-        pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_run_code.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_run_code.py
deleted file mode 100644
index 1e451cb141cbf6a9a952e8706cfdbed559235c64..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_run_code.py
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 17:46
-@Author  : alexanderwu
-@File    : test_run_code.py
-"""
-import pytest
-
-from metagpt.actions.run_code import RunCode
-
-
-@pytest.mark.asyncio
-async def test_run_text():
-    result, errs = await RunCode.run_text("result = 1 + 1")
-    assert result == 2
-    assert errs == ""
-
-    result, errs = await RunCode.run_text("result = 1 / 0")
-    assert result == ""
-    assert "ZeroDivisionError" in errs
-
-
-@pytest.mark.asyncio
-async def test_run_script():
-    # Successful command
-    out, err = await RunCode.run_script(".", command=["echo", "Hello World"])
-    assert out.strip() == "Hello World"
-    assert err == ""
-
-    # Unsuccessful command
-    out, err = await RunCode.run_script(".", command=["python", "-c", "print(1/0)"])
-    assert "ZeroDivisionError" in err
-
-
-@pytest.mark.asyncio
-async def test_run():
-    action = RunCode()
-    result = await action.run(mode="text", code="print('Hello, World')")
-    assert "PASS" in result
-
-    result = await action.run(
-        mode="script",
-        code="echo 'Hello World'",
-        code_file_name="",
-        test_code="",
-        test_file_name="",
-        command=["echo", "Hello World"],
-        working_directory=".",
-        additional_python_paths=[],
-    )
-    assert "PASS" in result
-
-
-@pytest.mark.asyncio
-async def test_run_failure():
-    action = RunCode()
-    result = await action.run(mode="text", code="result = 1 / 0")
-    assert "FAIL" in result
-
-    result = await action.run(
-        mode="script",
-        code='python -c "print(1/0)"',
-        code_file_name="",
-        test_code="",
-        test_file_name="",
-        command=["python", "-c", "print(1/0)"],
-        working_directory=".",
-        additional_python_paths=[],
-    )
-    assert "FAIL" in result
diff --git a/spaces/diacanFperku/AutoGPT/Crack [CRACKED] Techsmith Snagit 11.4.3 Portable.md b/spaces/diacanFperku/AutoGPT/Crack [CRACKED] Techsmith Snagit 11.4.3 Portable.md
deleted file mode 100644
index 1562b425f5d8ae970bd1fe3c137e643664d81e55..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Crack [CRACKED] Techsmith Snagit 11.4.3 Portable.md	
+++ /dev/null
@@ -1,98 +0,0 @@
-<br />
-<h1>CRACK Techsmith Snagit 11.4.3 Portable: How to Capture and Edit Screenshots and Videos</h1>
-
-<p>If you are looking for a powerful and versatile tool to capture and edit screenshots and videos on your computer, you may want to try CRACK Techsmith Snagit 11.4.3 Portable. This is a cracked version of Techsmith Snagit, a popular screen capture software that allows you to capture anything on your screen, add annotations, effects, and edits, and share your creations with ease. In this article, we will explain what CRACK Techsmith Snagit 11.4.3 Portable is, how to download and install it, and how to use it for various purposes.</p>
-<h2>CRACK Techsmith Snagit 11.4.3 Portable</h2><br /><p><b><b>Download Zip</b> &#187; <a href="https://gohhs.com/2uFUBJ">https://gohhs.com/2uFUBJ</a></b></p><br /><br />
-
-<h2>What is CRACK Techsmith Snagit 11.4.3 Portable?</h2>
-
-<p>CRACK Techsmith Snagit 11.4.3 Portable is a modified version of Techsmith Snagit 11.4.3, which is a screen capture software developed by TechSmith. Techsmith Snagit allows you to capture any part of your screen, whether it is a window, a region, a scrolling area, or a video. You can also capture audio from your microphone or system sound. You can then edit your captures with various tools, such as crop, resize, rotate, cut, trim, annotate, blur, highlight, and more. You can also apply filters, effects, transitions, and stamps to enhance your captures. You can then save your captures in various formats, such as PNG, JPEG, GIF, PDF, MP4, AVI, and more. You can also share your captures via email, FTP, cloud services, social media platforms, or other applications.</p>
-
-<p>CRACK Techsmith Snagit 11.4.3 Portable is a cracked version of Techsmith Snagit 11.4.3 that bypasses the activation process and allows you to use the full features of the software without paying for a license. It also does not require installation on your computer; you can run it from a USB drive or any other portable device. This makes it convenient and flexible for users who want to use the software on different computers or devices.</p>
-
-<h2>How to Download and Install CRACK Techsmith Snagit 11.4.3 Portable?</h2>
-
-<p>To download and install CRACK Techsmith Snagit 11.4.3 Portable on your computer, you need to follow these steps:</p>
-
-<ol>
-<li>Go to a reliable website that offers free and safe DLL downloads, such as DLL‑files.com.</li>
-<li>Type "CRACK Techsmith Snagit 11.4.3 Portable" in the search box and select the version or variant that matches your system and program requirements.</li>
-<li>Click on the "Download" button and save the ZIP file on your computer.</li>
-<li>Extract the ZIP file and copy the CRACK Techsmith Snagit 11.4.3 Portable folder to your USB drive or any other portable device.</li>
-<li>Run the SnagitPortable.exe file from the folder and enjoy the software.</li>
-</ol>
-
-<p>Note: You should always be careful and verify the authenticity and safety of the source and the file before downloading it. You can use antivirus software, online scanners, or other tools to scan and verify the file. You should also be aware of the legal and ethical implications of using a cracked software without paying for a license.</p>
-<p></p>
-
-<h2>How to Use CRACK Techsmith Snagit 11.4.3 Portable?</h2>
-
-<p>To use CRACK Techsmith Snagit 11.4.3 Portable for capturing and editing screenshots and videos on your computer, you need to follow these steps:</p>
-
-<ol>
-<li>Run the SnagitPortable.exe file from the folder on your USB drive or any other portable device.</li>
-<li>Select the capture mode that you want to use from the toolbar at the top of the screen. You can choose from All-in-One (capture anything on your screen), Image (capture a still image), Video (capture a video), Panoramic (capture a scrolling area), or Webpage (capture a full webpage).</li>
-<li>Select the capture area that you want to capture by clicking and dragging on your screen or by using predefined presets.</li>
-<li>Select the capture options that you want to use from the toolbar at the bottom of the screen. You can choose from Capture Cursor (include or exclude the mouse cursor in your capture), Delay (set a timer for your capture), Audio (record audio from your microphone or system sound), Webcam (record video from your webcam), Effects (apply effects such as border or shadow to your capture), Output (choose where to save or share your capture).</li>
-<li>Click on the Capture button or press the Print Screen key on your keyboard to start capturing.</li>
-<li>Edit your capture with various tools in the Snagit Editor window that opens after capturing. You can use tools such as Draw (add shapes, lines, arrows, text, etc.), Cut Out (remove unwanted parts of your capture), Resize (change the size of your capture), Rotate (change the orientation of your capture), Step Tool (add numbered steps to your capture), Blur (blur sensitive information in your capture), Highlighter (highlight important parts of your capture), Stamp (add pre-made icons or images to your capture), etc.</li>
-<li>Save or share your capture with various options in the Share menu at the top of the Snagit Editor window. You can save your capture in various formats such as PNG, JPEG, GIF, PDF, MP4, AVI, etc., or share it via email, FTP, cloud services such as Google Drive or Dropbox , social media platforms such as Facebook or Twitter , or other applications such as Microsoft Word or PowerPoint .</li>
-</ol>
-
-<p>CRACK Techsmith Snagit 11.4.3 Portable is a powerful and versatile tool that allows you to capture and edit screenshots and videos on your computer with ease and flexibility . By following these steps , you can use this tool for various purposes , such as creating tutorials , presentations , reports , documentation , etc.</p>
-<h2>Why You Should Use CRACK Techsmith Snagit 11.4.3 Portable for Your Screen Capture Needs</h2>
-
-<p>Screen capture is a useful and common task for many users who want to create tutorials, presentations, reports, documentation, or simply share their screen with others. However, not all screen capture tools are created equal. Some are too basic and limited, while others are too complex and expensive. If you are looking for a screen capture tool that is powerful, versatile, easy to use, and free, you may want to try CRACK Techsmith Snagit 11.4.3 Portable.</p>
-
-<p>CRACK Techsmith Snagit 11.4.3 Portable is a cracked version of Techsmith Snagit 11.4.3, which is one of the best screen capture software on the market. Techsmith Snagit allows you to capture anything on your screen, whether it is a window, a region, a scrolling area, or a video. You can also capture audio from your microphone or system sound. You can then edit your captures with various tools, such as crop, resize, rotate, cut, trim, annotate, blur, highlight, and more. You can also apply filters, effects, transitions, and stamps to enhance your captures. You can then save your captures in various formats, such as PNG, JPEG, GIF, PDF, MP4, AVI, and more. You can also share your captures via email, FTP, cloud services, social media platforms, or other applications.</p>
-
-<p>CRACK Techsmith Snagit 11.4.3 Portable is a cracked version of Techsmith Snagit 11.4.3 that bypasses the activation process and allows you to use the full features of the software without paying for a license. It also does not require installation on your computer; you can run it from a USB drive or any other portable device. This makes it convenient and flexible for users who want to use the software on different computers or devices.</p>
-
-<p>Some of the benefits of using CRACK Techsmith Snagit 11.4.3 Portable for your screen capture needs are:</p>
-
-<ul>
-<li>You can capture anything on your screen with high quality and speed.</li>
-<li>You can edit your captures with various tools and options to suit your needs.</li>
-<li>You can save your captures in various formats and share them with ease.</li>
-<li>You can use the software for free without paying for a license.</li>
-<li>You can use the software on any computer or device without installing it.</li>
-</ul>
-
-<p>CRACK Techsmith Snagit 11.4.3 Portable is a powerful and versatile tool that allows you to capture and edit screenshots and videos on your computer with ease and flexibility . By using this tool , you can create stunning and professional-looking captures for various purposes , such as creating tutorials , presentations , reports , documentation , etc.</p>
-</li>
-<li>Click on the Stop button or press the Print Screen key on your keyboard to stop recording .</li>
-<li>Edit your video with various tools in the Snagit Editor window that opens after recording . You can use tools such as Cut Out , Resize , Rotate , Step Tool , Blur , Highlighter , Stamp , etc .</li>
-<li>Save or share your video with various options in the Share menu at the top of the Snagit Editor window . You can save your video in various formats such as MP4 , AVI , etc . , or share it via email , FTP , cloud services , social media platforms , or other applications .</li>
-</ol>
-
-<p>Here is an example of a documentation video created with CRACK Techsmith Snagit 11 . 4 . 3 Portable :</p>
-
-<video src="https://www.youtube.com/watch?v=9ZfN87gSjvI" controls></video>
-
-<h3>How to Use CRACK Techsmith Snagit 11 . 4 . 3 Portable for Sharing Your Screen</h3>
-
-<p>If you want to share your screen with others for collaboration , feedback , or demonstration purposes , you can use CRACK Techsmith Snagit 11 . 4 . 3 Portable to capture and share your screen in real time or as a recorded file . Here are some steps on how to do that :</p>
-
-<ol>
-<li>Run the SnagitPortable.exe file from the folder on your USB drive or any other portable device .</li>
-<li>Select either Image or Video capture mode from the toolbar at the top of the screen depending on whether you want to capture a still image or a video of your screen .</li>
-<li>Select the capture area that you want to capture by clicking and dragging on your screen or by using predefined presets .</li>
-<li>Select the capture options that you want to use from the toolbar at the bottom of the screen . You can choose from Capture Cursor , Delay , Audio , Webcam , Effects , Output , etc .</li>
-<li>If you want to share your screen in real time , click on the Share button and select one of the available options such as Google Meet , Zoom , Skype , etc . You will need to have these applications installed and running on your computer and sign in with your account . You will also need to invite or join other participants for the screen sharing session .</li>
-<li>If you want to share your screen as a recorded file , click on the Capture button or press the Print Screen key on your keyboard to start capturing . You can then stop capturing when you are done and save or share your file with various options in the Share menu at the top of the Snagit Editor window . You can save your file in various formats such as PNG , JPEG , GIF , PDF , MP4 , AVI , etc . , or share it via email , FTP , cloud services , social media platforms , or other applications .</li>
-</ol>
-
-<p>Here is an example of a screen sharing session created with CRACK Techsmith Snagit 11 . 4 . 3 Portable :</p>
-
-<video src="https://www.youtube.com/watch?v=9ZfN87gSjvI" controls></video>
-
-<p>CRACK Techsmith Snagit 11 . 4 . 3 Portable is a cracked version of Techsmith Snagit 11 . 4 . 3 , which is a screen capture software that allows you to capture and edit screenshots and videos on your computer . You can use this software for various purposes such as creating tutorials, presentations, reports, documentation, or simply sharing your screen with others. By following these examples and steps, you can use this software effectively and efficiently for your needs.</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have discussed what CRACK Techsmith Snagit 11.4.3 Portable is, how to download and use it safely and legally, and how to use it for various purposes such as creating tutorials, presentations, reports, documentation, or sharing your screen. We have also shown you some examples and steps on how to use this software for different purposes. We hope that this article has been informative and helpful for you.</p>
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-<p>CRACK Techsmith Snagit 11.4.3 Portable is a powerful and versatile tool that allows you to capture and edit screenshots and videos on your computer with ease and flexibility. By using this tool, you can create stunning and professional-looking captures for various purposes, such as creating tutorials, presentations, reports, documentation, etc. However, you should also be careful and responsible while using a cracked software without paying for a license. You should respect the rights and interests of the program developer or owner and consider buying a legitimate license if you want to use the software for commercial or professional purposes.</p>
-
-<p>If you have any questions or feedback about this article or CRACK Techsmith Snagit 11.4.3 Portable, please feel free to leave a comment below or contact us via email. We would love to hear from you and help you with any issues or problems that you may have. Thank you for reading this article and happy capturing!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Eplan Electric P8 2.4.4.8366 Crack.epub.md b/spaces/diacanFperku/AutoGPT/Eplan Electric P8 2.4.4.8366 Crack.epub.md
deleted file mode 100644
index d7b3b4af32b304d2e2893d76122951b48a1129c7..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Eplan Electric P8 2.4.4.8366 Crack.epub.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-<h2>Eplan Electric P8 2.4.4.8366 Crack.epub</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot; <a href="https://gohhs.com/2uFVFa">https://gohhs.com/2uFVFa</a></b></p><br /><br />
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diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/utils/logging.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/utils/logging.py
deleted file mode 100644
index f08e85465e642305fb6fbeb39144aedabfe4564c..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/colbert/utils/logging.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import sys
-import ujson
-# import mlflow
-import traceback
-
-# from torch.utils.tensorboard import SummaryWriter
-from colbert.utils.utils import print_message, create_directory
-
-
-class Logger():
-    def __init__(self, rank, run):
-        self.rank = rank
-        self.is_main = self.rank in [-1, 0]
-        self.run = run
-        self.logs_path = os.path.join(self.run.path, "logs/")
-
-        if self.is_main:
-            # self._init_mlflow()
-            # self.initialized_tensorboard = False
-            create_directory(self.logs_path)
-
-    # def _init_mlflow(self):
-    #     mlflow.set_tracking_uri('file://' + os.path.join(self.run.experiments_root, "logs/mlruns/"))
-    #     mlflow.set_experiment('/'.join([self.run.experiment, self.run.script]))
-        
-    #     mlflow.set_tag('experiment', self.run.experiment)
-    #     mlflow.set_tag('name', self.run.name)
-    #     mlflow.set_tag('path', self.run.path)
-
-    # def _init_tensorboard(self):
-    #     root = os.path.join(self.run.experiments_root, "logs/tensorboard/")
-    #     logdir = '__'.join([self.run.experiment, self.run.script, self.run.name])
-    #     logdir = os.path.join(root, logdir)
-
-    #     self.writer = SummaryWriter(log_dir=logdir)
-    #     self.initialized_tensorboard = True
-
-    def _log_exception(self, etype, value, tb):
-        if not self.is_main:
-            return
-
-        output_path = os.path.join(self.logs_path, 'exception.txt')
-        trace = ''.join(traceback.format_exception(etype, value, tb)) + '\n'
-        print_message(trace, '\n\n')
-
-        self.log_new_artifact(output_path, trace)
-
-    def _log_all_artifacts(self):
-        if not self.is_main:
-            return
-
-        # mlflow.log_artifacts(self.logs_path)
-
-    def _log_args(self, args):
-        if not self.is_main:
-            return
-
-        # for key in vars(args):
-        #     value = getattr(args, key)
-        #     if type(value) in [int, float, str, bool]:
-        #         mlflow.log_param(key, value)
-
-        # with open(os.path.join(self.logs_path, 'args.json'), 'w') as output_metadata:
-        #     # TODO: Call provenance() on the values that support it
-        #     ujson.dump(args.input_arguments.__dict__, output_metadata, indent=4)
-        #     output_metadata.write('\n')
-
-        with open(os.path.join(self.logs_path, 'args.txt'), 'w') as output_metadata:
-            output_metadata.write(' '.join(sys.argv) + '\n')
-
-    def log_metric(self, name, value, step, log_to_mlflow=True):
-        if not self.is_main:
-            return
-
-        # if not self.initialized_tensorboard:
-        #     self._init_tensorboard()
-
-        # if log_to_mlflow:
-        #     mlflow.log_metric(name, value, step=step)
-        # self.writer.add_scalar(name, value, step)
-
-    def log_new_artifact(self, path, content):
-        with open(path, 'w') as f:
-            f.write(content)
-
-        # mlflow.log_artifact(path)
-
-    def warn(self, *args):
-        msg = print_message('[WARNING]', '\t', *args)
-
-        with open(os.path.join(self.logs_path, 'warnings.txt'), 'a') as output_metadata:
-            output_metadata.write(msg + '\n\n\n')
-
-    def info_all(self, *args):
-        print_message('[' + str(self.rank) + ']', '\t', *args)
-
-    def info(self, *args):
-        if self.is_main:
-            print_message(*args)
diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/start.bat b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/start.bat
deleted file mode 100644
index 418d21233dbf720b0dd09821904d9d6a31b123a2..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/start.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-set PYTHON=venv\python.exe
-start cmd /k "set PYTHON=%PYTHON%"
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Lixiang-Bert-Vits2/text/japanese.py b/spaces/digitalxingtong/Lixiang-Bert-Vits2/text/japanese.py
deleted file mode 100644
index ddedafa0c5b7986068dc6c91637a86febc3923a9..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Lixiang-Bert-Vits2/text/japanese.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
-import re
-import sys
-
-import pyopenjtalk
-
-from text import symbols
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def preprocess_jap(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = []
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            p = pyopenjtalk.g2p(sentence)
-            text += p.split(" ")
-
-        if i < len(marks):
-            text += [marks[i].replace(' ', '')]
-    return text
-
-def text_normalize(text):
-    # todo: jap text normalize
-    return text
-
-def g2p(norm_text):
-    phones = preprocess_jap(norm_text)
-    phones = [post_replace_ph(i) for i in phones]
-    # todo: implement tones and word2ph
-    tones = [0 for i in phones]
-    word2ph = [1 for i in phones]
-    return phones, tones, word2ph
-
-
-if __name__ == '__main__':
-    for line in open("../../../Downloads/transcript_utf8.txt").readlines():
-        text = line.split(":")[1]
-        phones, tones, word2ph = g2p(text)
-        for p in phones:
-            if p == "z":
-                print(text, phones)
-                sys.exit(0)
diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/grid_head.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/grid_head.py
deleted file mode 100644
index 83058cbdda934ebfc3a76088e1820848ac01b78b..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/grid_head.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init, normal_init
-
-from mmdet.models.builder import HEADS, build_loss
-
-
-@HEADS.register_module()
-class GridHead(nn.Module):
-
-    def __init__(self,
-                 grid_points=9,
-                 num_convs=8,
-                 roi_feat_size=14,
-                 in_channels=256,
-                 conv_kernel_size=3,
-                 point_feat_channels=64,
-                 deconv_kernel_size=4,
-                 class_agnostic=False,
-                 loss_grid=dict(
-                     type='CrossEntropyLoss', use_sigmoid=True,
-                     loss_weight=15),
-                 conv_cfg=None,
-                 norm_cfg=dict(type='GN', num_groups=36)):
-        super(GridHead, self).__init__()
-        self.grid_points = grid_points
-        self.num_convs = num_convs
-        self.roi_feat_size = roi_feat_size
-        self.in_channels = in_channels
-        self.conv_kernel_size = conv_kernel_size
-        self.point_feat_channels = point_feat_channels
-        self.conv_out_channels = self.point_feat_channels * self.grid_points
-        self.class_agnostic = class_agnostic
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN':
-            assert self.conv_out_channels % norm_cfg['num_groups'] == 0
-
-        assert self.grid_points >= 4
-        self.grid_size = int(np.sqrt(self.grid_points))
-        if self.grid_size * self.grid_size != self.grid_points:
-            raise ValueError('grid_points must be a square number')
-
-        # the predicted heatmap is half of whole_map_size
-        if not isinstance(self.roi_feat_size, int):
-            raise ValueError('Only square RoIs are supporeted in Grid R-CNN')
-        self.whole_map_size = self.roi_feat_size * 4
-
-        # compute point-wise sub-regions
-        self.sub_regions = self.calc_sub_regions()
-
-        self.convs = []
-        for i in range(self.num_convs):
-            in_channels = (
-                self.in_channels if i == 0 else self.conv_out_channels)
-            stride = 2 if i == 0 else 1
-            padding = (self.conv_kernel_size - 1) // 2
-            self.convs.append(
-                ConvModule(
-                    in_channels,
-                    self.conv_out_channels,
-                    self.conv_kernel_size,
-                    stride=stride,
-                    padding=padding,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    bias=True))
-        self.convs = nn.Sequential(*self.convs)
-
-        self.deconv1 = nn.ConvTranspose2d(
-            self.conv_out_channels,
-            self.conv_out_channels,
-            kernel_size=deconv_kernel_size,
-            stride=2,
-            padding=(deconv_kernel_size - 2) // 2,
-            groups=grid_points)
-        self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels)
-        self.deconv2 = nn.ConvTranspose2d(
-            self.conv_out_channels,
-            grid_points,
-            kernel_size=deconv_kernel_size,
-            stride=2,
-            padding=(deconv_kernel_size - 2) // 2,
-            groups=grid_points)
-
-        # find the 4-neighbor of each grid point
-        self.neighbor_points = []
-        grid_size = self.grid_size
-        for i in range(grid_size):  # i-th column
-            for j in range(grid_size):  # j-th row
-                neighbors = []
-                if i > 0:  # left: (i - 1, j)
-                    neighbors.append((i - 1) * grid_size + j)
-                if j > 0:  # up: (i, j - 1)
-                    neighbors.append(i * grid_size + j - 1)
-                if j < grid_size - 1:  # down: (i, j + 1)
-                    neighbors.append(i * grid_size + j + 1)
-                if i < grid_size - 1:  # right: (i + 1, j)
-                    neighbors.append((i + 1) * grid_size + j)
-                self.neighbor_points.append(tuple(neighbors))
-        # total edges in the grid
-        self.num_edges = sum([len(p) for p in self.neighbor_points])
-
-        self.forder_trans = nn.ModuleList()  # first-order feature transition
-        self.sorder_trans = nn.ModuleList()  # second-order feature transition
-        for neighbors in self.neighbor_points:
-            fo_trans = nn.ModuleList()
-            so_trans = nn.ModuleList()
-            for _ in range(len(neighbors)):
-                # each transition module consists of a 5x5 depth-wise conv and
-                # 1x1 conv.
-                fo_trans.append(
-                    nn.Sequential(
-                        nn.Conv2d(
-                            self.point_feat_channels,
-                            self.point_feat_channels,
-                            5,
-                            stride=1,
-                            padding=2,
-                            groups=self.point_feat_channels),
-                        nn.Conv2d(self.point_feat_channels,
-                                  self.point_feat_channels, 1)))
-                so_trans.append(
-                    nn.Sequential(
-                        nn.Conv2d(
-                            self.point_feat_channels,
-                            self.point_feat_channels,
-                            5,
-                            1,
-                            2,
-                            groups=self.point_feat_channels),
-                        nn.Conv2d(self.point_feat_channels,
-                                  self.point_feat_channels, 1)))
-            self.forder_trans.append(fo_trans)
-            self.sorder_trans.append(so_trans)
-
-        self.loss_grid = build_loss(loss_grid)
-
-    def init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-                # TODO: compare mode = "fan_in" or "fan_out"
-                kaiming_init(m)
-        for m in self.modules():
-            if isinstance(m, nn.ConvTranspose2d):
-                normal_init(m, std=0.001)
-        nn.init.constant_(self.deconv2.bias, -np.log(0.99 / 0.01))
-
-    def forward(self, x):
-        assert x.shape[-1] == x.shape[-2] == self.roi_feat_size
-        # RoI feature transformation, downsample 2x
-        x = self.convs(x)
-
-        c = self.point_feat_channels
-        # first-order fusion
-        x_fo = [None for _ in range(self.grid_points)]
-        for i, points in enumerate(self.neighbor_points):
-            x_fo[i] = x[:, i * c:(i + 1) * c]
-            for j, point_idx in enumerate(points):
-                x_fo[i] = x_fo[i] + self.forder_trans[i][j](
-                    x[:, point_idx * c:(point_idx + 1) * c])
-
-        # second-order fusion
-        x_so = [None for _ in range(self.grid_points)]
-        for i, points in enumerate(self.neighbor_points):
-            x_so[i] = x[:, i * c:(i + 1) * c]
-            for j, point_idx in enumerate(points):
-                x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx])
-
-        # predicted heatmap with fused features
-        x2 = torch.cat(x_so, dim=1)
-        x2 = self.deconv1(x2)
-        x2 = F.relu(self.norm1(x2), inplace=True)
-        heatmap = self.deconv2(x2)
-
-        # predicted heatmap with original features (applicable during training)
-        if self.training:
-            x1 = x
-            x1 = self.deconv1(x1)
-            x1 = F.relu(self.norm1(x1), inplace=True)
-            heatmap_unfused = self.deconv2(x1)
-        else:
-            heatmap_unfused = heatmap
-
-        return dict(fused=heatmap, unfused=heatmap_unfused)
-
-    def calc_sub_regions(self):
-        """Compute point specific representation regions.
-
-        See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details.
-        """
-        # to make it consistent with the original implementation, half_size
-        # is computed as 2 * quarter_size, which is smaller
-        half_size = self.whole_map_size // 4 * 2
-        sub_regions = []
-        for i in range(self.grid_points):
-            x_idx = i // self.grid_size
-            y_idx = i % self.grid_size
-            if x_idx == 0:
-                sub_x1 = 0
-            elif x_idx == self.grid_size - 1:
-                sub_x1 = half_size
-            else:
-                ratio = x_idx / (self.grid_size - 1) - 0.25
-                sub_x1 = max(int(ratio * self.whole_map_size), 0)
-
-            if y_idx == 0:
-                sub_y1 = 0
-            elif y_idx == self.grid_size - 1:
-                sub_y1 = half_size
-            else:
-                ratio = y_idx / (self.grid_size - 1) - 0.25
-                sub_y1 = max(int(ratio * self.whole_map_size), 0)
-            sub_regions.append(
-                (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size))
-        return sub_regions
-
-    def get_targets(self, sampling_results, rcnn_train_cfg):
-        # mix all samples (across images) together.
-        pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results],
-                               dim=0).cpu()
-        pos_gt_bboxes = torch.cat(
-            [res.pos_gt_bboxes for res in sampling_results], dim=0).cpu()
-        assert pos_bboxes.shape == pos_gt_bboxes.shape
-
-        # expand pos_bboxes to 2x of original size
-        x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
-        y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
-        x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
-        y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
-        pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
-        pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1)
-        pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1)
-
-        num_rois = pos_bboxes.shape[0]
-        map_size = self.whole_map_size
-        # this is not the final target shape
-        targets = torch.zeros((num_rois, self.grid_points, map_size, map_size),
-                              dtype=torch.float)
-
-        # pre-compute interpolation factors for all grid points.
-        # the first item is the factor of x-dim, and the second is y-dim.
-        # for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1)
-        factors = []
-        for j in range(self.grid_points):
-            x_idx = j // self.grid_size
-            y_idx = j % self.grid_size
-            factors.append((1 - x_idx / (self.grid_size - 1),
-                            1 - y_idx / (self.grid_size - 1)))
-
-        radius = rcnn_train_cfg.pos_radius
-        radius2 = radius**2
-        for i in range(num_rois):
-            # ignore small bboxes
-            if (pos_bbox_ws[i] <= self.grid_size
-                    or pos_bbox_hs[i] <= self.grid_size):
-                continue
-            # for each grid point, mark a small circle as positive
-            for j in range(self.grid_points):
-                factor_x, factor_y = factors[j]
-                gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + (
-                    1 - factor_x) * pos_gt_bboxes[i, 2]
-                gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + (
-                    1 - factor_y) * pos_gt_bboxes[i, 3]
-
-                cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] *
-                         map_size)
-                cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] *
-                         map_size)
-
-                for x in range(cx - radius, cx + radius + 1):
-                    for y in range(cy - radius, cy + radius + 1):
-                        if x >= 0 and x < map_size and y >= 0 and y < map_size:
-                            if (x - cx)**2 + (y - cy)**2 <= radius2:
-                                targets[i, j, y, x] = 1
-        # reduce the target heatmap size by a half
-        # proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688).
-        sub_targets = []
-        for i in range(self.grid_points):
-            sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i]
-            sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2])
-        sub_targets = torch.cat(sub_targets, dim=1)
-        sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device)
-        return sub_targets
-
-    def loss(self, grid_pred, grid_targets):
-        loss_fused = self.loss_grid(grid_pred['fused'], grid_targets)
-        loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets)
-        loss_grid = loss_fused + loss_unfused
-        return dict(loss_grid=loss_grid)
-
-    def get_bboxes(self, det_bboxes, grid_pred, img_metas):
-        # TODO: refactoring
-        assert det_bboxes.shape[0] == grid_pred.shape[0]
-        det_bboxes = det_bboxes.cpu()
-        cls_scores = det_bboxes[:, [4]]
-        det_bboxes = det_bboxes[:, :4]
-        grid_pred = grid_pred.sigmoid().cpu()
-
-        R, c, h, w = grid_pred.shape
-        half_size = self.whole_map_size // 4 * 2
-        assert h == w == half_size
-        assert c == self.grid_points
-
-        # find the point with max scores in the half-sized heatmap
-        grid_pred = grid_pred.view(R * c, h * w)
-        pred_scores, pred_position = grid_pred.max(dim=1)
-        xs = pred_position % w
-        ys = pred_position // w
-
-        # get the position in the whole heatmap instead of half-sized heatmap
-        for i in range(self.grid_points):
-            xs[i::self.grid_points] += self.sub_regions[i][0]
-            ys[i::self.grid_points] += self.sub_regions[i][1]
-
-        # reshape to (num_rois, grid_points)
-        pred_scores, xs, ys = tuple(
-            map(lambda x: x.view(R, c), [pred_scores, xs, ys]))
-
-        # get expanded pos_bboxes
-        widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1)
-        heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1)
-        x1 = (det_bboxes[:, 0, None] - widths / 2)
-        y1 = (det_bboxes[:, 1, None] - heights / 2)
-        # map the grid point to the absolute coordinates
-        abs_xs = (xs.float() + 0.5) / w * widths + x1
-        abs_ys = (ys.float() + 0.5) / h * heights + y1
-
-        # get the grid points indices that fall on the bbox boundaries
-        x1_inds = [i for i in range(self.grid_size)]
-        y1_inds = [i * self.grid_size for i in range(self.grid_size)]
-        x2_inds = [
-            self.grid_points - self.grid_size + i
-            for i in range(self.grid_size)
-        ]
-        y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)]
-
-        # voting of all grid points on some boundary
-        bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum(
-            dim=1, keepdim=True) / (
-                pred_scores[:, x1_inds].sum(dim=1, keepdim=True))
-        bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum(
-            dim=1, keepdim=True) / (
-                pred_scores[:, y1_inds].sum(dim=1, keepdim=True))
-        bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum(
-            dim=1, keepdim=True) / (
-                pred_scores[:, x2_inds].sum(dim=1, keepdim=True))
-        bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum(
-            dim=1, keepdim=True) / (
-                pred_scores[:, y2_inds].sum(dim=1, keepdim=True))
-
-        bbox_res = torch.cat(
-            [bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1)
-        bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1])
-        bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0])
-
-        return bbox_res
diff --git a/spaces/dorkai/ChatUIPro/app/api/chat-messages/route.ts b/spaces/dorkai/ChatUIPro/app/api/chat-messages/route.ts
deleted file mode 100644
index a046f95157428fadc424ac20d0ec6c8ec071aecc..0000000000000000000000000000000000000000
--- a/spaces/dorkai/ChatUIPro/app/api/chat-messages/route.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-import { type NextRequest } from 'next/server'
-import { getInfo, client } from '@/app/api/utils/common'
-import { OpenAIStream } from '@/app/api/utils/stream'
-
-export async function POST(request: NextRequest) {
-  const body = await request.json()
-  const {
-    inputs,
-    query,
-    conversation_id: conversationId,
-    response_mode: responseMode
-  } = body
-  const { user } = getInfo(request);
-  const res = await client.createChatMessage(inputs, query, user, responseMode, conversationId)
-  const stream = await OpenAIStream(res as any)
-  return new Response(stream as any)
-}
\ No newline at end of file
diff --git a/spaces/dorkai/text-generation-webui-main/.github/ISSUE_TEMPLATE/feature_request.md b/spaces/dorkai/text-generation-webui-main/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index b94974f865491731a1251e3e9736e01cbe81b06f..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-name: Feature request
-about: Suggest an improvement or new feature for the web UI
-title: ''
-labels: 'enhancement'
-assignees: ''
-
----
-
-**Description**
-
-A clear and concise description of what you want to be implemented.
-
-**Additional Context**
-
-If applicable, please provide any extra information, external links, or screenshots that could be useful.
diff --git a/spaces/dreamreyansan/hakurei-waifu-diffusion/README.md b/spaces/dreamreyansan/hakurei-waifu-diffusion/README.md
deleted file mode 100644
index 3bffed1427af15e7b8b7229d9e640a309a20b8be..0000000000000000000000000000000000000000
--- a/spaces/dreamreyansan/hakurei-waifu-diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hakurei Waifu Diffusion
-emoji: 📉
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ds520/bingo/src/components/external-link.tsx b/spaces/ds520/bingo/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}
diff --git a/spaces/dsxailab/Lama-Cleaner-lama-12/README.md b/spaces/dsxailab/Lama-Cleaner-lama-12/README.md
deleted file mode 100644
index 71bc2ec79ae0e1c7118773832337d75d66658f67..0000000000000000000000000000000000000000
--- a/spaces/dsxailab/Lama-Cleaner-lama-12/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Lama Cleaner Lama
-emoji: ⚡
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: dsxailab/Lama-Cleaner-lama-11
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/edugp/embedding-lenses/app.py b/spaces/edugp/embedding-lenses/app.py
deleted file mode 100644
index f074108d9b4045ea8d5efb4b840f4369e2485f08..0000000000000000000000000000000000000000
--- a/spaces/edugp/embedding-lenses/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from embedding_lenses.app import app
-
-
-app()
diff --git a/spaces/egumasa/engagement-analyzer-demo/resources/template_list.py b/spaces/egumasa/engagement-analyzer-demo/resources/template_list.py
deleted file mode 100644
index cbe2eef58d2ccd6c253dbc50b7ec37d1a392d8e7..0000000000000000000000000000000000000000
--- a/spaces/egumasa/engagement-analyzer-demo/resources/template_list.py
+++ /dev/null
@@ -1,48 +0,0 @@
-TPL_ENT = """
-<mark class="entity" style="background: {bg}; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
-    {text}
-    <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">{label}</span>
-</mark>
-"""
-
-TPL_SPANS = """
-<div class="spans" style="line-height: 4.5;">
-    {text}
-    {span_slices}
-    {span_starts}
-</div>
-"""
-
-TPL_SPAN = """
-<span style="font-weight: bold; display: inline-block; line-height: 3; padding-bottom: 12px;position: relative;">
-    {text}
-    {span_slices}
-    {span_starts}
-</span>
-"""
-
-TPL_SPAN_SLICE = """
-<span style="background: {bg}; top: {top_offset}px;  display: inline-block; height: 4px; left: -1px; width: calc(100% + 2px); position: absolute;">
-</span>
-"""
-
-TPL_SPAN_START = """
-<span style="background: {bg}; top: {top_offset}px; height: 4px; border-top-left-radius: 3px; border-bottom-left-radius: 3px; left: -1px; width: calc(100% + 2px); position: absolute;">
-    <span style="background: {bg}; z-index: 10; color: #000; top: -0.5em; padding: 2px 3px; position: absolute; font-size: 0.6em; font-weight: bold; line-height: 1; border-radius: 3px">
-       
-        {label}{kb_link}
-    </span>
-</span>
-
-"""
-
-TPL_SPAN_START_RTL = """
-<span style="background: {bg}; top: {top_offset}px; height: 4px; border-top-left-radius: 3px; border-bottom-left-radius: 3px; left: -1px; width: calc(100% + 2px); position: absolute;">
-    <span style="background: {bg}; z-index: 10; color: #000; top: -0.5em; padding: 2px 3px; position: absolute; font-size: 0.6em; font-weight: bold; line-height: 1; border-radius: 3px">
-        {label}{kb_link}
-    </span>
-</span>
-"""
-
-DEFAULT_TEXT = """Tickner said regardless of the result, the royal commission was a waste of money and he would proceed with a separate inquiry into the issue headed by Justice Jane Matthews. His attack came as the Aboriginal women involved in the case demanded a female minister examine the religious beliefs they claim are inherent in their fight against a bridge to the island near Goolwa in South Australia."""
-
diff --git a/spaces/ekenkel/dog-identifier/README.md b/spaces/ekenkel/dog-identifier/README.md
deleted file mode 100644
index 81732eee08c05adefccb4bfbce26a77e8317b9b9..0000000000000000000000000000000000000000
--- a/spaces/ekenkel/dog-identifier/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
----
-title: Dog Identifier
-emoji: 🦮
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# Dog Identifier
-
-
-
-[Open Demo](https://huggingface.co/spaces/ekenkel/dog-identifier)
-
----
-
-I created a model using fastai and pytorch that can recognize 146 breeds of dogs with 82% accuracy.
-
-I used the [Dog API](https://dog.ceo/dog-api/documentation/) to get the breeds, then collected 105 images for each using [Bing Search API](https://docs.microsoft.com/en-us/azure/cognitive-services/bing-web-search/). Due to memory restrictions, I could not increase the dataset.
-
-
-I randomly split the data:
-- 80% training set
-- 20% validation set
-
-I trained using the ConvNeXt-22k convolution neural network architecture with 11 epochs and a base learning rate of 1e-3.
-
-I previously attained 75% accuracy. I made improvements by resizing each image to the same dimension (460x460 px) and then doing augmentations on the batches (distortion, lighting, cropping). I also froze the pretrained model (ConvNeXt-22k) layers, and trained my newly added layers for 3 epochs. I saved time and GPU memory by using half-precision floating point where applicable during training.
-
-Before that, my base model used the ResNet architecture with 18 layers and 25 epochs, but was only able to get 63% accuracy.
-
diff --git a/spaces/epexVfeibi/Imagedeblurr/64 Bit Photograv 2.11 WORK Free 1538.md b/spaces/epexVfeibi/Imagedeblurr/64 Bit Photograv 2.11 WORK Free 1538.md
deleted file mode 100644
index fa386a91a6a0b59a8e9296072033ed90a0420850..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/64 Bit Photograv 2.11 WORK Free 1538.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-
-<p>photograv 2.11 is a powerful solution for every photo editor. it is a complete software suite that gives you everything you need for photo editing, conversion and printing. the software offers professional tools and utilities for retouching, image post-processing and graphic design.</p>
-<p>this software is easy to use, and provides the most advanced functionality youll need to achieve the best results. with photograv, you can edit a wide range of images, including still images and videos. the program is easy to use and has a user-friendly interface. you can also create and edit videos and merge them into one file. you can use the tool bar or keyboard shortcuts to quickly adjust your settings.</p>
-<h2>64 bit photograv 2.11 free 1538</h2><br /><p><b><b>Download</b> &mdash; <a href="https://jinyurl.com/2uEmQf">https://jinyurl.com/2uEmQf</a></b></p><br /><br />
-<p>photograv can open, rotate, resize and save multiple files. it also has various editing tools such as curves, levels and sliders to help you adjust your images. the program has an easy-to-use interface with clearly presented and easy-to-understand menus and options. there is also a fast and powerful batch processor that allows you to edit multiple files and save your work. you can also create and edit videos, add transitions, add music, add text and apply effects. you can even use this tool to create professional-looking projects and presentations. the program is also available as a standalone application for pc.</p>
-<p>photograv has a detailed help file that guides you through the program. the program has numerous menus, toolbars and options that help you work faster and more efficiently. photograv is a powerful image and video editor that provides great results. you can use the program to create and edit images, videos and presentations. the program has a powerful batch processor that allows you to work on several images simultaneously.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/eugenkalosha/Semmap/Dockerfile b/spaces/eugenkalosha/Semmap/Dockerfile
deleted file mode 100644
index 9f88183f989e33f3b33abb345288f6f8f5adaef2..0000000000000000000000000000000000000000
--- a/spaces/eugenkalosha/Semmap/Dockerfile
+++ /dev/null
@@ -1,16 +0,0 @@
-FROM python:3.10
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-RUN python3 -m pip install --no-cache-dir --upgrade pip
-RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]
-
-RUN mkdir /.cache
-RUN chmod 777 /.cache
-RUN mkdir .chroma
-RUN chmod 777 .chroma
\ No newline at end of file
diff --git a/spaces/evaluate-metric/ter/app.py b/spaces/evaluate-metric/ter/app.py
deleted file mode 100644
index 063222ebb9814a282c1b479b84e58adc8bd3bf0e..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/ter/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("ter")
-launch_gradio_widget(module)
diff --git a/spaces/evanpierce/3D_Photo_Inpainting2/download.sh b/spaces/evanpierce/3D_Photo_Inpainting2/download.sh
deleted file mode 100644
index a2ce8b2686dd62791510af335e306ca8de4ea28d..0000000000000000000000000000000000000000
--- a/spaces/evanpierce/3D_Photo_Inpainting2/download.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/sh
-fb_status=$(wget --spider -S https://filebox.ece.vt.edu/ 2>&1 | grep  "HTTP/1.1 200 OK")
-
-mkdir checkpoints
-
-echo "downloading from filebox ..."
-wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/color-model.pth
-wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/depth-model.pth
-wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/edge-model.pth
-wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/model.pt
-
-mv color-model.pth checkpoints/.
-mv depth-model.pth checkpoints/.
-mv edge-model.pth checkpoints/.
-mv model.pt MiDaS/.
-
-echo "cloning from BoostingMonocularDepth ..."
-git clone https://github.com/compphoto/BoostingMonocularDepth.git
-mkdir -p BoostingMonocularDepth/pix2pix/checkpoints/mergemodel/
-
-echo "downloading mergenet weights ..."
-wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/latest_net_G.pth
-mv latest_net_G.pth BoostingMonocularDepth/pix2pix/checkpoints/mergemodel/
-wget https://github.com/intel-isl/MiDaS/releases/download/v2/model-f46da743.pt
-mv model-f46da743.pt BoostingMonocularDepth/midas/model.pt
diff --git a/spaces/ewgewgewg/IndexingAlpha/README.md b/spaces/ewgewgewg/IndexingAlpha/README.md
deleted file mode 100644
index 6f408de52951a0860c68fd224b9e4420448a2c41..0000000000000000000000000000000000000000
--- a/spaces/ewgewgewg/IndexingAlpha/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: IndexingAlpha
-emoji: 🏃
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: gpl
----
-
-A basic tool made to create back-of-the-book indexes, using yake
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/exbert-project/exbert/client/src/ts/uiConfig.ts b/spaces/exbert-project/exbert/client/src/ts/uiConfig.ts
deleted file mode 100644
index cd8c88754109d0b378a8e3d6df346e1795aecac3..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/ts/uiConfig.ts
+++ /dev/null
@@ -1,282 +0,0 @@
-import * as tp from "./etc/types"
-import * as x_ from "./etc/_Tools"
-import * as _ from "lodash"
-import * as R from 'ramda'
-import { URLHandler } from "./etc/URLHandler";
-
-const falsey = val => (new Set(['false', 0, "no", false, null, ""])).has(val)
-const truthy = val => !falsey(val)
-const toNumber = x => +x;
-
-
-type InspectorOptions = "context" | "embeddings" | null
-
-// Must be optional params for initializations
-interface URLParameters {
-    sentence?: string
-    model?: string
-    modelKind?: string
-    layer?: number
-    heads?: number[]
-    threshold?: number
-    tokenInd?: number | 'null'
-    tokenSide?: tp.SideOptions
-    maskInds?: number[]
-    hideClsSep?: boolean
-}
-
-export class UIConfig {
-
-    private _conf: URLParameters = {}
-    private _headSet: Set<number>;
-    attType: "aa"
-    _nHeads: number | null;
-    _nLayers: number | null;
-    private _token: tp.TokenEvent;
-
-    constructor() {
-        this._nHeads = 12; 
-        this._nLayers = null;
-        this.attType = 'aa'
-        this.fromURL()
-        this.toURL(false)
-    }
-
-    toURL(updateHistory = false) {
-        URLHandler.updateUrl(this._conf, updateHistory)
-    }
-
-
-    fromURL() {
-        const params = URLHandler.parameters
-
-        this._conf = {
-            model: params['model'] || 'bert-base-cased',
-            modelKind: params['modelKind'] || tp.ModelKind.Bidirectional,
-            sentence: params['sentence'] || "The girl ran to a local pub to escape the din of her city.",
-            layer: params['layer'] || 1,
-            heads: this._initHeads(params['heads']),
-            threshold: params['threshold'] || 0.7,
-            tokenInd: params['tokenInd'] || null,
-            tokenSide: params['tokenSide'] || null,
-            maskInds: params['maskInds'] || [9],
-            hideClsSep: truthy(params['hideClsSep']) || true,
-        }
-
-        this._token = { side: this._conf.tokenSide, ind: this._conf.tokenInd }
-
-    }
-
-    private _initHeads(v: number[] | null) {
-        if (v == null || v.length < 1) {
-            this.selectAllHeads()
-        }
-        else {
-            console.log(this.headSet(new Set(v))._conf.heads);
-        }
-
-        return this.heads()
-    }
-
-    nHeads(): number
-    nHeads(val: number): this
-    nHeads(val?) {
-        if (val == null) return this._nHeads
-        this._nHeads = val
-        return this
-    }
-
-    nLayers(): number
-    nLayers(val: number): this
-    nLayers(val?) {
-        if (val == null) return this._nLayers
-        this._nLayers = val
-        return this
-    }
-
-    toggleSelectAllHeads() {
-        if (this.heads().length == 0) {
-            this.selectAllHeads()
-        }
-        else {
-            this.selectNoHeads()
-        }
-    }
-
-    selectAllHeads() {
-        this.headSet(new Set(_.range(0, this._nHeads)))
-    }
-
-    selectNoHeads() {
-        this.headSet(new Set([]))
-    }
-
-    toggleHead(head: number): tp.Toggled {
-        let out;
-        if (this.headSet().has(head)) {
-            this.headSet().delete(head);
-            out = tp.Toggled.REMOVED
-        }
-        else {
-            this.headSet().add(head);
-            out = tp.Toggled.ADDED
-        }
-
-        // Set through setter function to ensure url is updated
-        this.headSet(this.headSet()); // I hate mutable datastructures... This is confusing.
-
-        return out
-    }
-
-    toggleToken(e: tp.TokenEvent): this {
-        const picker = R.pick(['ind', 'side'])
-        const compareEvent = picker(e)
-        const compareToken = picker(this.token())
-
-        if (R.equals(compareToken, compareEvent)) {
-            this.rmToken();
-        }
-        else {
-            this.token(e);
-        }
-        return this;
-    }
-
-    token(): tp.TokenEvent;
-    token(val: tp.TokenEvent): this;
-    token(val?: tp.TokenEvent) {
-        if (val == null)
-            return this._token
-
-        this._token = val;
-        this._conf.tokenInd = val.ind;
-        this._conf.tokenSide = val.side;
-        this.toURL();
-
-        return this
-    }
-
-    hasToken() {
-        const conf = this._conf
-        const actuallyNull = ((conf.tokenInd == null) && (conf.tokenSide == null))
-        const strNull = (conf.tokenInd == "null")
-        return (!actuallyNull) && (!strNull)
-    }
-
-    rmToken() {
-        this.token({ ind: null, side: null });
-        return this
-    }
-
-    sentence(): string;
-    sentence(val: string): this;
-    sentence(val?) {
-        if (val == null)
-            return this._conf.sentence
-
-        this._conf.sentence = val
-        this.toURL(true)
-        return this
-    }
-
-    threshold(): number;
-    threshold(val: number): this;
-    threshold(val?) {
-        if (val == null) return this._conf.threshold;
-
-        this._conf.threshold = val;
-        this.toURL();
-        return this;
-    }
-
-    heads(): number[] {
-        return this._conf.heads
-    }
-
-    layer(): number
-    layer(val: number): this
-    layer(val?) {
-        if (val == null)
-            return this._conf.layer
-
-        this._conf.layer = val;
-        this.toURL();
-        return this
-    }
-
-    headSet(): Set<number>;
-    headSet(val: Set<number>): this;
-    headSet(val?) {
-        if (val == null) {
-            return this._headSet;
-        }
-
-        this._headSet = val;
-        this._conf.heads = x_.set2SortedArray(this._headSet)
-        this.toURL();
-        return this
-    }
-
-    maskInds(): number[];
-    maskInds(val: number[]): this;
-    maskInds(val?) {
-        if (val == null) return this._conf.maskInds;
-
-        this._conf.maskInds = val;
-        this.toURL();
-        return this;
-    }
-
-    hideClsSep(): boolean;
-    hideClsSep(val: boolean): this;
-    hideClsSep(val?) {
-        if (val == null) return this._conf.hideClsSep;
-
-        this._conf.hideClsSep = truthy(val);
-        this.toURL();
-        return this;
-    }
-
-    model(): string;
-    model(val: string): this;
-    model(val?) {
-        if (val == null) return this._conf.model
-        this._conf.model = val
-        this.toURL();
-        return this
-    }
-
-    modelKind(): string;
-    modelKind(val: string): this;
-    modelKind(val?) {
-        if (val == null) return this._conf.modelKind
-        this._conf.modelKind = val
-        this.toURL();
-        return this
-    }
-
-    /**
-     * Return the offset needed for the modelKind in the configuration
-     */
-    get offset() {
-        switch (this.modelKind()) {
-            case tp.ModelKind.Bidirectional: {
-                return 0
-            }
-            case tp.ModelKind.Autoregressive: {
-                return 0
-            }
-            default: {
-                return 0
-            }
-        }
-    }
-
-    get showNext() {
-        return this.modelKind() == tp.ModelKind.Autoregressive ? true : false
-    }
-
-    get matchHistogramDescription() {
-        return this.modelKind() == tp.ModelKind.Autoregressive ? "Next" : "Matched"
-    }
-}
diff --git a/spaces/faisalhr1997/wd14_tagging_online/app.py b/spaces/faisalhr1997/wd14_tagging_online/app.py
deleted file mode 100644
index 5127e973d92ec1518df2e31c32a1b13e141e1068..0000000000000000000000000000000000000000
--- a/spaces/faisalhr1997/wd14_tagging_online/app.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import os
-import re
-from typing import Mapping, Tuple, Dict
-
-import cv2
-import gradio as gr
-import numpy as np
-import pandas as pd
-from PIL import Image
-from huggingface_hub import hf_hub_download
-from onnxruntime import InferenceSession
-
-
-# noinspection PyUnresolvedReferences
-def make_square(img, target_size):
-    old_size = img.shape[:2]
-    desired_size = max(old_size)
-    desired_size = max(desired_size, target_size)
-
-    delta_w = desired_size - old_size[1]
-    delta_h = desired_size - old_size[0]
-    top, bottom = delta_h // 2, delta_h - (delta_h // 2)
-    left, right = delta_w // 2, delta_w - (delta_w // 2)
-
-    color = [255, 255, 255]
-    return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
-
-
-# noinspection PyUnresolvedReferences
-def smart_resize(img, size):
-    # Assumes the image has already gone through make_square
-    if img.shape[0] > size:
-        img = cv2.resize(img, (size, size), interpolation=cv2.INTER_AREA)
-    elif img.shape[0] < size:
-        img = cv2.resize(img, (size, size), interpolation=cv2.INTER_CUBIC)
-    else:  # just do nothing
-        pass
-
-    return img
-
-
-class WaifuDiffusionInterrogator:
-    def __init__(
-            self,
-            repo='SmilingWolf/wd-v1-4-vit-tagger',
-            model_path='model.onnx',
-            tags_path='selected_tags.csv',
-            mode: str = "auto"
-    ) -> None:
-        self.__repo = repo
-        self.__model_path = model_path
-        self.__tags_path = tags_path
-        self._provider_mode = mode
-
-        self.__initialized = False
-        self._model, self._tags = None, None
-
-    def _init(self) -> None:
-        if self.__initialized:
-            return
-
-        model_path = hf_hub_download(self.__repo, filename=self.__model_path)
-        tags_path = hf_hub_download(self.__repo, filename=self.__tags_path)
-
-        self._model = InferenceSession(str(model_path))
-        self._tags = pd.read_csv(tags_path)
-
-        self.__initialized = True
-
-    def _calculation(self, image: Image.Image) -> pd.DataFrame:
-        self._init()
-
-        # code for converting the image and running the model is taken from the link below
-        # thanks, SmilingWolf!
-        # https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py
-
-        # convert an image to fit the model
-        _, height, _, _ = self._model.get_inputs()[0].shape
-
-        # alpha to white
-        image = image.convert('RGBA')
-        new_image = Image.new('RGBA', image.size, 'WHITE')
-        new_image.paste(image, mask=image)
-        image = new_image.convert('RGB')
-        image = np.asarray(image)
-
-        # PIL RGB to OpenCV BGR
-        image = image[:, :, ::-1]
-
-        image = make_square(image, height)
-        image = smart_resize(image, height)
-        image = image.astype(np.float32)
-        image = np.expand_dims(image, 0)
-
-        # evaluate model
-        input_name = self._model.get_inputs()[0].name
-        label_name = self._model.get_outputs()[0].name
-        confidence = self._model.run([label_name], {input_name: image})[0]
-
-        full_tags = self._tags[['name', 'category']].copy()
-        full_tags['confidence'] = confidence[0]
-
-        return full_tags
-
-    def interrogate(self, image: Image) -> Tuple[Dict[str, float], Dict[str, float]]:
-        full_tags = self._calculation(image)
-
-        # first 4 items are for rating (general, sensitive, questionable, explicit)
-        ratings = dict(full_tags[full_tags['category'] == 9][['name', 'confidence']].values)
-
-        # rest are regular tags
-        tags = dict(full_tags[full_tags['category'] != 9][['name', 'confidence']].values)
-
-        return ratings, tags
-
-
-WAIFU_MODELS: Mapping[str, WaifuDiffusionInterrogator] = {
-    'wd14-vit': WaifuDiffusionInterrogator(),
-    'wd14-convnext': WaifuDiffusionInterrogator(
-        repo='SmilingWolf/wd-v1-4-convnext-tagger'
-    ),
-}
-RE_SPECIAL = re.compile(r'([\\()])')
-
-
-def image_to_wd14_tags(image: Image.Image, model_name: str, threshold: float,
-                       use_spaces: bool, use_escape: bool, include_ranks: bool, score_descend: bool) \
-        -> Tuple[Mapping[str, float], str, Mapping[str, float]]:
-    model = WAIFU_MODELS[model_name]
-    ratings, tags = model.interrogate(image)
-
-    filtered_tags = {
-        tag: score for tag, score in tags.items()
-        if score >= threshold
-    }
-
-    text_items = []
-    tags_pairs = filtered_tags.items()
-    if score_descend:
-        tags_pairs = sorted(tags_pairs, key=lambda x: (-x[1], x[0]))
-    for tag, score in tags_pairs:
-        tag_outformat = tag
-        if use_spaces:
-            tag_outformat = tag_outformat.replace('_', ' ')
-        if use_escape:
-            tag_outformat = re.sub(RE_SPECIAL, r'\\\1', tag_outformat)
-        if include_ranks:
-            tag_outformat = f"({tag_outformat}:{score:.3f})"
-        text_items.append(tag_outformat)
-    output_text = ', '.join(text_items)
-
-    return ratings, output_text, filtered_tags
-
-
-if __name__ == '__main__':
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                gr_input_image = gr.Image(type='pil', label='Original Image')
-                with gr.Row():
-                    gr_model = gr.Radio(list(WAIFU_MODELS.keys()), value='wd14-vit', label='Waifu Model')
-                    gr_threshold = gr.Slider(0.0, 1.0, 0.5, label='Tagging Confidence Threshold')
-                with gr.Row():
-                    gr_space = gr.Checkbox(value=False, label='Use Space Instead Of _')
-                    gr_escape = gr.Checkbox(value=True, label='Use Text Escape')
-                    gr_confidence = gr.Checkbox(value=False, label='Keep Confidences')
-                    gr_order = gr.Checkbox(value=True, label='Descend By Confidence')
-
-                gr_btn_submit = gr.Button(value='Tagging', variant='primary')
-
-            with gr.Column():
-                gr_ratings = gr.Label(label='Ratings')
-                with gr.Tabs():
-                    with gr.Tab("Tags"):
-                        gr_tags = gr.Label(label='Tags')
-                    with gr.Tab("Exported Text"):
-                        gr_output_text = gr.TextArea(label='Exported Text')
-
-        gr_btn_submit.click(
-            image_to_wd14_tags,
-            inputs=[gr_input_image, gr_model, gr_threshold, gr_space, gr_escape, gr_confidence, gr_order],
-            outputs=[gr_ratings, gr_output_text, gr_tags],
-        )
-    demo.queue(os.cpu_count()).launch()
diff --git a/spaces/falterWliame/Face_Mask_Detection/ACDSee Photo Manager 12.0.342 Keys Keygen.md b/spaces/falterWliame/Face_Mask_Detection/ACDSee Photo Manager 12.0.342 Keys Keygen.md
deleted file mode 100644
index 67aad6c7b79cfe67b35f935251e601a28bdbc3cf..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/ACDSee Photo Manager 12.0.342 Keys Keygen.md	
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deleted file mode 100644
index f05f53dbdc1c7fe5bd92da786fd8dd14d2eefdd5..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download Crack Dragon City Hack Tool 5.8v.md	
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diff --git a/spaces/fatiXbelha/sd/Dragon Robot Police Car Game Mod APK with Enhanced Graphics and Sound Effects.md b/spaces/fatiXbelha/sd/Dragon Robot Police Car Game Mod APK with Enhanced Graphics and Sound Effects.md
deleted file mode 100644
index cd9e44735423c25091a0aca12c839b130da4c8c2..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Dragon Robot Police Car Game Mod APK with Enhanced Graphics and Sound Effects.md	
+++ /dev/null
@@ -1,91 +0,0 @@
-
-<h1>Dragon Robot Police Car Game Mod APK: A Fun and Exciting Adventure Game</h1>
- <p>If you are looking for a game that combines the thrill of action, the fun of transformation, and the challenge of adventure, then you should try <strong>Dragon Robot Police Car Game</strong>. This game lets you control a powerful dragon robot that can transform into a police car and fight against evil enemies. You can explore different environments, complete missions, collect coins, and upgrade your robot. You can also enjoy the amazing graphics, sound effects, and animations that make this game more realistic and immersive.</p>
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- <h2>What is Dragon Robot Police Car Game?</h2>
- <p>Dragon Robot Police Car Game is an adventure game developed by Centaurus Games. It is available for Android devices and has more than 10 million downloads on Google Play Store. The game has a rating of 4.1 out of 5 stars and has received positive reviews from many users.</p>
- <h3>Features of Dragon Robot Police Car Game</h3>
- <p>Some of the features that make Dragon Robot Police Car Game a great game are:</p>
- <ul>
-<li><strong>Multiple modes:</strong> You can choose from different modes such as free roam, survival, and mission mode. Each mode has its own objectives and challenges that will test your skills and strategy.</li>
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-<li><strong>Multiple transformations:</strong> You can transform your dragon robot into a police car and vice versa. Each form has its own advantages and disadvantages that you need to use wisely.</li>
-<li><strong>Multiple upgrades:</strong> You can upgrade your dragon robot by collecting coins and buying new parts. You can improve your speed, strength, defense, and fire power.</li>
-</ul>
- <h4>How to play Dragon Robot Police Car Game</h4>
- <p>The gameplay of Dragon Robot Police Car Game is simple and intuitive. You can use the virtual joystick to move your robot or car around. You can use the buttons to transform, shoot, jump, or fly. You can also use the map to navigate and find your objectives. You can complete missions by following the instructions and destroying the enemies. You can also roam freely and explore the environment at your own pace.</p>
- <h2>Why download Dragon Robot Police Car Game Mod APK?</h2>
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-<li><strong>Unlimited money:</strong> You can get unlimited coins that you can use to buy new parts and upgrade your robot. You can also unlock new modes and environments that are otherwise locked in the original game.</li>
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-<li><strong>No ads:</strong> You can play the game without any interruptions or distractions from annoying ads. You can also save your data and battery life by not having to watch or download ads.</li>
-</ul>
- <h4>How to download and install Dragon Robot Police Car Game Mod APK</h4>
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- <ol>
-<li><strong>Download the APK file:</strong> You can download the APK file from a trusted source such as [this link]. Make sure you have enough storage space on your device and a stable internet connection.</li>
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-</ol>
- <h2>Conclusion</h2>
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- <p>Here are some frequently asked questions about Dragon Robot Police Car Game and its mod APK:</p>
- <ul>
-<li><strong>Q: Is Dragon Robot Police Car Game safe to play?</strong></li>
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diff --git a/spaces/fatimahhussain/workoutwizard/sample_utils/__init__.py b/spaces/fatimahhussain/workoutwizard/sample_utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fclong/summary/fengshen/examples/disco_project/disco.py b/spaces/fclong/summary/fengshen/examples/disco_project/disco.py
deleted file mode 100644
index 8c8b516f13311c9797ea27fa6410361a5dfa715a..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/disco_project/disco.py
+++ /dev/null
@@ -1,735 +0,0 @@
-import os
-import sys
-# sys.path.insert(0, f'{PROJECT_DIR}/guided-diffusion')   # 加在前面，不再读取库文件的东西。
-import subprocess
-import io
-import torch.nn as nn
-from torch.nn import functional as F
-import torch
-import torchvision.transforms.functional as TF
-import torchvision.transforms as T
-import math
-import requests
-import cv2
-from resize_right import resize
-from guided_diffusion.guided_diffusion.script_util import model_and_diffusion_defaults
-from types import SimpleNamespace
-from PIL import Image
-import argparse
-from guided_diffusion.guided_diffusion.unet import HFUNetModel
-from tqdm.notebook import tqdm
-from datetime import datetime
-from guided_diffusion.guided_diffusion.script_util import create_model_and_diffusion
-import clip
-from transformers import BertForSequenceClassification, BertTokenizer
-import gc
-import random
-
-
-# ======================== GLOBAL SETTING ========================
-PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
-
-useCPU = False  # @param {type:"boolean"}
-skip_augs = False  # @param{type: 'boolean'}
-perlin_init = False  # @param{type: 'boolean'}
-
-use_secondary_model = False
-diffusion_model = "custom"
-
-# Dimensions must by multiples of 64.
-side_x = 512
-side_y = 512
-
-diffusion_sampling_mode = 'ddim'  # @param ['plms','ddim']
-use_checkpoint = True  # @param {type: 'boolean'}
-ViTB32 = False  # @param{type:"boolean"}
-ViTB16 = False  # @param{type:"boolean"}
-ViTL14 = True  # @param{type:"boolean"}
-ViTL14_336px = False  # @param{type:"boolean"}
-RN101 = False  # @param{type:"boolean"}
-RN50 = False  # @param{type:"boolean"}
-RN50x4 = False  # @param{type:"boolean"}
-RN50x16 = False  # @param{type:"boolean"}
-RN50x64 = False  # @param{type:"boolean"}
-
-
-# @markdown #####**OpenCLIP settings:**
-ViTB32_laion2b_e16 = False  # @param{type:"boolean"}
-ViTB32_laion400m_e31 = False  # @param{type:"boolean"}
-ViTB32_laion400m_32 = False  # @param{type:"boolean"}
-ViTB32quickgelu_laion400m_e31 = False  # @param{type:"boolean"}
-ViTB32quickgelu_laion400m_e32 = False  # @param{type:"boolean"}
-ViTB16_laion400m_e31 = False  # @param{type:"boolean"}
-ViTB16_laion400m_e32 = False  # @param{type:"boolean"}
-RN50_yffcc15m = False  # @param{type:"boolean"}
-RN50_cc12m = False  # @param{type:"boolean"}
-RN50_quickgelu_yfcc15m = False  # @param{type:"boolean"}
-RN50_quickgelu_cc12m = False  # @param{type:"boolean"}
-RN101_yfcc15m = False  # @param{type:"boolean"}
-RN101_quickgelu_yfcc15m = False  # @param{type:"boolean"}
-
-# @markdown ####**Basic Settings:**
-
-# NOTE steps可以改这里，需要重新初始化模型，我懒得改接口了orz
-steps = 100  # @param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}
-tv_scale = 0  # @param{type: 'number'}
-range_scale = 150  # @param{type: 'number'}
-sat_scale = 0  # @param{type: 'number'}
-cutn_batches = 1  # @param{type: 'number'}  # NOTE 这里会对图片做数据增强，累计计算n次CLIP的梯度，以此作为guidance。
-skip_augs = False  # @param{type: 'boolean'}
-# @markdown ####**Saving:**
-
-intermediate_saves = 0  # @param{type: 'raw'}
-intermediates_in_subfolder = True  # @param{type: 'boolean'}
-
-# perlin_init = False  # @param{type: 'boolean'}
-perlin_mode = 'mixed'  # @param ['mixed', 'color', 'gray']
-set_seed = 'random_seed'  # @param{type: 'string'}
-eta = 0.8  # @param{type: 'number'}
-clamp_grad = True  # @param{type: 'boolean'}
-clamp_max = 0.05  # @param{type: 'number'}
-
-# EXTRA ADVANCED SETTINGS:
-randomize_class = True
-clip_denoised = False
-fuzzy_prompt = False
-rand_mag = 0.05
-
-# @markdown ---
-cut_overview = "[12]*400+[4]*600"  # @param {type: 'string'}
-cut_innercut = "[4]*400+[12]*600"  # @param {type: 'string'}
-cut_ic_pow = "[1]*1000"  # @param {type: 'string'}
-cut_icgray_p = "[0.2]*400+[0]*600"  # @param {type: 'string'}
-
-
-# @markdown ####**Transformation Settings:**
-use_vertical_symmetry = False  # @param {type:"boolean"}
-use_horizontal_symmetry = False  # @param {type:"boolean"}
-transformation_percent = [0.09]  # @param
-
-display_rate = 3  # @param{type: 'number'}
-n_batches = 1  # @param{type: 'number'}
-
-# @markdown If you're having issues with model downloads, check this to compare SHA's:
-check_model_SHA = False  # @param{type:"boolean"}
-interp_spline = 'Linear'  # Do not change, currently will not look good. param ['Linear','Quadratic','Cubic']{type:"string"}
-resume_run = False
-batch_size = 1
-
-
-def createPath(filepath):
-    os.makedirs(filepath, exist_ok=True)
-
-
-def wget(url, outputdir):
-    res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')
-    print(res)
-
-
-def alpha_sigma_to_t(alpha, sigma):
-    return torch.atan2(sigma, alpha) * 2 / math.pi
-
-
-def interp(t):
-    return 3 * t**2 - 2 * t ** 3
-
-
-def perlin(width, height, scale=10, device=None):
-    gx, gy = torch.randn(2, width + 1, height + 1, 1, 1, device=device)
-    xs = torch.linspace(0, 1, scale + 1)[:-1, None].to(device)
-    ys = torch.linspace(0, 1, scale + 1)[None, :-1].to(device)
-    wx = 1 - interp(xs)
-    wy = 1 - interp(ys)
-    dots = 0
-    dots += wx * wy * (gx[:-1, :-1] * xs + gy[:-1, :-1] * ys)
-    dots += (1 - wx) * wy * (-gx[1:, :-1] * (1 - xs) + gy[1:, :-1] * ys)
-    dots += wx * (1 - wy) * (gx[:-1, 1:] * xs - gy[:-1, 1:] * (1 - ys))
-    dots += (1 - wx) * (1 - wy) * (-gx[1:, 1:] * (1 - xs) - gy[1:, 1:] * (1 - ys))
-    return dots.permute(0, 2, 1, 3).contiguous().view(width * scale, height * scale)
-
-
-def perlin_ms(octaves, width, height, grayscale, device=None):
-    out_array = [0.5] if grayscale else [0.5, 0.5, 0.5]
-    # out_array = [0.0] if grayscale else [0.0, 0.0, 0.0]
-    for i in range(1 if grayscale else 3):
-        scale = 2 ** len(octaves)
-        oct_width = width
-        oct_height = height
-        for oct in octaves:
-            p = perlin(oct_width, oct_height, scale, device)
-            out_array[i] += p * oct
-            scale //= 2
-            oct_width *= 2
-            oct_height *= 2
-    return torch.cat(out_array)
-
-
-def fetch(url_or_path):
-    if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
-        r = requests.get(url_or_path)
-        r.raise_for_status()
-        fd = io.BytesIO()
-        fd.write(r.content)
-        fd.seek(0)
-        return fd
-    return open(url_or_path, 'rb')
-
-
-def read_image_workaround(path):
-    """OpenCV reads images as BGR, Pillow saves them as RGB. Work around
-    this incompatibility to avoid colour inversions."""
-    im_tmp = cv2.imread(path)
-    return cv2.cvtColor(im_tmp, cv2.COLOR_BGR2RGB)
-
-
-def parse_prompt(prompt):
-    if prompt.startswith('http://') or prompt.startswith('https://'):
-        vals = prompt.rsplit(':', 2)
-        vals = [vals[0] + ':' + vals[1], *vals[2:]]
-    else:
-        vals = prompt.rsplit(':', 1)
-    vals = vals + ['', '1'][len(vals):]
-    return vals[0], float(vals[1])
-
-
-def sinc(x):
-    return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
-
-
-def lanczos(x, a):
-    cond = torch.logical_and(-a < x, x < a)
-    out = torch.where(cond, sinc(x) * sinc(x / a), x.new_zeros([]))
-    return out / out.sum()
-
-
-def ramp(ratio, width):
-    n = math.ceil(width / ratio + 1)
-    out = torch.empty([n])
-    cur = 0
-    for i in range(out.shape[0]):
-        out[i] = cur
-        cur += ratio
-    return torch.cat([-out[1:].flip([0]), out])[1:-1]
-
-
-def resample(input, size, align_corners=True):
-    n, c, h, w = input.shape
-    dh, dw = size
-
-    input = input.reshape([n * c, 1, h, w])
-
-    if dh < h:
-        kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
-        pad_h = (kernel_h.shape[0] - 1) // 2
-        input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
-        input = F.conv2d(input, kernel_h[None, None, :, None])
-
-    if dw < w:
-        kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
-        pad_w = (kernel_w.shape[0] - 1) // 2
-        input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
-        input = F.conv2d(input, kernel_w[None, None, None, :])
-
-    input = input.reshape([n, c, h, w])
-    return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)
-
-
-class MakeCutouts(nn.Module):
-    def __init__(self, cut_size, cutn, skip_augs=False):
-        super().__init__()
-        self.cut_size = cut_size
-        self.cutn = cutn
-        self.skip_augs = skip_augs
-        self.augs = T.Compose([
-            T.RandomHorizontalFlip(p=0.5),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            T.RandomPerspective(distortion_scale=0.4, p=0.7),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            T.RandomGrayscale(p=0.15),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
-        ])
-
-    def forward(self, input):
-        input = T.Pad(input.shape[2] // 4, fill=0)(input)
-        sideY, sideX = input.shape[2:4]
-        max_size = min(sideX, sideY)
-
-        cutouts = []
-        for ch in range(self.cutn):
-            if ch > self.cutn - self.cutn // 4:
-                cutout = input.clone()
-            else:
-                size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size / max_size), 1.))
-                offsetx = torch.randint(0, abs(sideX - size + 1), ())
-                offsety = torch.randint(0, abs(sideY - size + 1), ())
-                cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
-
-            if not self.skip_augs:
-                cutout = self.augs(cutout)
-            cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
-            del cutout
-
-        cutouts = torch.cat(cutouts, dim=0)
-        return cutouts
-
-
-class MakeCutoutsDango(nn.Module):
-    def __init__(self, cut_size, args,
-                 Overview=4,
-                 InnerCrop=0, IC_Size_Pow=0.5, IC_Grey_P=0.2,
-                 ):
-        super().__init__()
-        self.padargs = {}
-        self.cutout_debug = False
-        self.cut_size = cut_size
-        self.Overview = Overview
-        self.InnerCrop = InnerCrop
-        self.IC_Size_Pow = IC_Size_Pow
-        self.IC_Grey_P = IC_Grey_P
-        self.augs = T.Compose([
-            T.RandomHorizontalFlip(p=0.5),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation=T.InterpolationMode.BILINEAR),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            T.RandomGrayscale(p=0.1),
-            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
-            T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
-        ])
-
-    def forward(self, input):
-        cutouts = []
-        gray = T.Grayscale(3)
-        sideY, sideX = input.shape[2:4]
-        max_size = min(sideX, sideY)
-        min_size = min(sideX, sideY, self.cut_size)
-        output_shape = [1, 3, self.cut_size, self.cut_size]
-        pad_input = F.pad(input, ((sideY - max_size) // 2, (sideY - max_size) // 2, (sideX - max_size) // 2, (sideX - max_size) // 2), **self.padargs)
-        cutout = resize(pad_input, out_shape=output_shape)
-
-        if self.Overview > 0:
-            if self.Overview <= 4:
-                if self.Overview >= 1:
-                    cutouts.append(cutout)
-                if self.Overview >= 2:
-                    cutouts.append(gray(cutout))
-                if self.Overview >= 3:
-                    cutouts.append(TF.hflip(cutout))
-                if self.Overview == 4:
-                    cutouts.append(gray(TF.hflip(cutout)))
-            else:
-                cutout = resize(pad_input, out_shape=output_shape)
-                for _ in range(self.Overview):
-                    cutouts.append(cutout)
-
-            if self.cutout_debug:
-                # if is_colab:
-                #     TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("/content/cutout_overview0.jpg",quality=99)
-                # else:
-                TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("cutout_overview0.jpg", quality=99)
-
-        if self.InnerCrop > 0:
-            for i in range(self.InnerCrop):
-                size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)
-                offsetx = torch.randint(0, sideX - size + 1, ())
-                offsety = torch.randint(0, sideY - size + 1, ())
-                cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
-                if i <= int(self.IC_Grey_P * self.InnerCrop):
-                    cutout = gray(cutout)
-                cutout = resize(cutout, out_shape=output_shape)
-                cutouts.append(cutout)
-            if self.cutout_debug:
-                # if is_colab:
-                #     TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("/content/cutout_InnerCrop.jpg",quality=99)
-                # else:
-                TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("cutout_InnerCrop.jpg", quality=99)
-        cutouts = torch.cat(cutouts)
-        if skip_augs is not True:
-            cutouts = self.augs(cutouts)
-        return cutouts
-
-
-def spherical_dist_loss(x, y):
-    x = F.normalize(x, dim=-1)
-    y = F.normalize(y, dim=-1)
-    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-
-
-def tv_loss(input):
-    """L2 total variation loss, as in Mahendran et al."""
-    input = F.pad(input, (0, 1, 0, 1), 'replicate')
-    x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
-    y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
-    return (x_diff**2 + y_diff**2).mean([1, 2, 3])
-
-
-def range_loss(input):
-    return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
-
-
-def symmetry_transformation_fn(x):
-    # NOTE 强制图像对称
-    use_horizontal_symmetry = False
-    if use_horizontal_symmetry:
-        [n, c, h, w] = x.size()
-        x = torch.concat((x[:, :, :, :w // 2], torch.flip(x[:, :, :, :w // 2], [-1])), -1)
-        print("horizontal symmetry applied")
-    if use_vertical_symmetry:
-        [n, c, h, w] = x.size()
-        x = torch.concat((x[:, :, :h // 2, :], torch.flip(x[:, :, :h // 2, :], [-2])), -2)
-        print("vertical symmetry applied")
-    return x
-
-
-# def split_prompts(prompts):
-#     prompt_series = pd.Series([np.nan for a in range(max_frames)])
-#     for i, prompt in prompts.items():
-#         prompt_series[i] = prompt
-#     # prompt_series = prompt_series.astype(str)
-#     prompt_series = prompt_series.ffill().bfill()
-#     return prompt_series
-
-
-"""
-other chaos settings
-"""
-# dir settings
-
-outDirPath = f'{PROJECT_DIR}/images_out'
-createPath(outDirPath)
-model_path = f'{PROJECT_DIR}/models'
-createPath(model_path)
-
-
-# GPU setup
-DEVICE = torch.device('cuda:0' if (torch.cuda.is_available() and not useCPU) else 'cpu')
-print('Using device:', DEVICE)
-device = DEVICE  # At least one of the modules expects this name..
-if not useCPU:
-    if torch.cuda.get_device_capability(DEVICE) == (8, 0):  # A100 fix thanks to Emad
-        print('Disabling CUDNN for A100 gpu', file=sys.stderr)
-        torch.backends.cudnn.enabled = False
-
-model_config = model_and_diffusion_defaults()
-model_config.update({
-    'attention_resolutions': '32, 16, 8',
-    'class_cond': False,
-    'diffusion_steps': 1000,  # No need to edit this, it is taken care of later.
-    'rescale_timesteps': True,
-    'timestep_respacing': 250,  # No need to edit this, it is taken care of later.
-    'image_size': 512,
-    'learn_sigma': True,
-    'noise_schedule': 'linear',
-    'num_channels': 256,
-    'num_head_channels': 64,
-    'num_res_blocks': 2,
-    'resblock_updown': True,
-    'use_checkpoint': use_checkpoint,
-    'use_fp16': not useCPU,
-    'use_scale_shift_norm': True,
-})
-
-model_default = model_config['image_size']
-normalize = T.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
-
-# Make folder for batch
-steps_per_checkpoint = steps + 10
-# Update Model Settings
-timestep_respacing = f'ddim{steps}'
-diffusion_steps = (1000 // steps) * steps if steps < 1000 else steps
-model_config.update({
-    'timestep_respacing': timestep_respacing,
-    'diffusion_steps': diffusion_steps,
-})
-
-
-start_frame = 0
-print('Starting Run:')
-if set_seed == 'random_seed':
-    random.seed()
-    seed = random.randint(0, 2**32)
-    # print(f'Using seed: {seed}')
-else:
-    seed = int(set_seed)
-
-args = {
-    # 'seed': seed,
-    'display_rate': display_rate,
-    'n_batches': n_batches,
-    'batch_size': batch_size,
-    'steps': steps,
-    'diffusion_sampling_mode': diffusion_sampling_mode,
-    # 'width_height': width_height,
-    'tv_scale': tv_scale,
-    'range_scale': range_scale,
-    'sat_scale': sat_scale,
-    'cutn_batches': cutn_batches,
-    # 'side_x': side_x,
-    # 'side_y': side_y,
-    'timestep_respacing': timestep_respacing,
-    'diffusion_steps': diffusion_steps,
-    'cut_overview': eval(cut_overview),
-    'cut_innercut': eval(cut_innercut),
-    'cut_ic_pow': eval(cut_ic_pow),
-    'cut_icgray_p': eval(cut_icgray_p),
-    'intermediate_saves': intermediate_saves,
-    'intermediates_in_subfolder': intermediates_in_subfolder,
-    'steps_per_checkpoint': steps_per_checkpoint,
-    'set_seed': set_seed,
-    'eta': eta,
-    'clamp_grad': clamp_grad,
-    'clamp_max': clamp_max,
-    'skip_augs': skip_augs,
-    'randomize_class': randomize_class,
-    'clip_denoised': clip_denoised,
-    'fuzzy_prompt': fuzzy_prompt,
-    'rand_mag': rand_mag,
-    'use_vertical_symmetry': use_vertical_symmetry,
-    'use_horizontal_symmetry': use_horizontal_symmetry,
-    'transformation_percent': transformation_percent,
-}
-args = SimpleNamespace(**args)
-
-# ======================== GLOBAL SETTING END ========================
-
-
-class Diffuser:
-    def __init__(self, cutom_path='IDEA-CCNL/Taiyi-Diffusion-532M-Nature'):
-        self.model_setup(cutom_path)
-
-    def model_setup(self, custom_path):
-        # LOADING MODEL
-        os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
-        print(f'Prepping model...model name: {custom_path}')
-        __, self.diffusion = create_model_and_diffusion(**model_config)
-        self.model = HFUNetModel.from_pretrained(custom_path)
-        # total = get_parameter_num(self.model)
-        # print("Number of parameter: %.2fM" % (total/1e6))
-        # print("Number of parameter: %.2fM" % (total/1024/1024))
-
-        self.model.requires_grad_(False).eval().to(device)
-        for name, param in self.model.named_parameters():
-            if 'qkv' in name or 'norm' in name or 'proj' in name:
-                param.requires_grad_()
-        if model_config['use_fp16']:
-            self.model.convert_to_fp16()
-        print(f'Diffusion_model Loaded {diffusion_model}')
-
-        # NOTE Directly Load The Text Encoder From Hugging Face
-        print('Prepping model...model name: CLIP')
-        self.taiyi_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese")
-        self.taiyi_transformer = BertForSequenceClassification.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese").eval().to(device)
-        self.clip_models = []
-        if ViTB32:
-            self.clip_models.append(clip.load('ViT-B/32', jit=False)[0].eval().requires_grad_(False).to(device))
-        if ViTB16:
-            self.clip_models.append(clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device))
-        if ViTL14:
-            self.clip_models.append(clip.load('ViT-L/14', jit=False)[0].eval().requires_grad_(False).to(device))
-        if ViTL14_336px:
-            self.clip_models.append(clip.load('ViT-L/14@336px', jit=False)[0].eval().requires_grad_(False).to(device))
-        print('CLIP Loaded')
-        # self.lpips_model = lpips.LPIPS(net='vgg').to(device)
-
-    def generate(self,
-                 input_text_prompts=['夕阳西下'],
-                 init_image=None,
-                 skip_steps=10,
-                 clip_guidance_scale=7500,
-                 init_scale=2000,
-                 st_dynamic_image=None,
-                 seed=None,
-                 side_x=512,
-                 side_y=512,
-                 ):
-
-        seed = seed
-        frame_num = 0
-        init_image = init_image
-        init_scale = init_scale
-        skip_steps = skip_steps
-        loss_values = []
-        # if seed is not None:
-        #     np.random.seed(seed)
-        #     random.seed(seed)
-        #     torch.manual_seed(seed)
-        #     torch.cuda.manual_seed_all(seed)
-        #     torch.backends.cudnn.deterministic = True
-        # target_embeds, weights = [], []
-        frame_prompt = input_text_prompts
-
-        print(f'Frame {frame_num} Prompt: {frame_prompt}')
-
-        model_stats = []
-        for clip_model in self.clip_models:
-            # cutn = 16
-            model_stat = {"clip_model": None, "target_embeds": [], "make_cutouts": None, "weights": []}
-            model_stat["clip_model"] = clip_model
-
-            for prompt in frame_prompt:
-                txt, weight = parse_prompt(prompt)
-                # txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
-                # NOTE use chinese CLIP
-                txt = self.taiyi_transformer(self.taiyi_tokenizer(txt, return_tensors='pt')['input_ids'].to(device)).logits
-                if args.fuzzy_prompt:
-                    for i in range(25):
-                        model_stat["target_embeds"].append((txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0, 1))
-                        model_stat["weights"].append(weight)
-                else:
-                    model_stat["target_embeds"].append(txt)
-                    model_stat["weights"].append(weight)
-
-            model_stat["target_embeds"] = torch.cat(model_stat["target_embeds"])
-            model_stat["weights"] = torch.tensor(model_stat["weights"], device=device)
-            if model_stat["weights"].sum().abs() < 1e-3:
-                raise RuntimeError('The weights must not sum to 0.')
-            model_stat["weights"] /= model_stat["weights"].sum().abs()
-            model_stats.append(model_stat)
-
-        init = None
-        if init_image is not None:
-            # init = Image.open(fetch(init_image)).convert('RGB')   # 传递的是加载好的图片。而非地址~
-            init = init_image
-            init = init.resize((side_x, side_y), Image.LANCZOS)
-            init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
-
-        cur_t = None
-
-        def cond_fn(x, t, y=None):
-            with torch.enable_grad():
-                x_is_NaN = False
-                x = x.detach().requires_grad_()
-                n = x.shape[0]
-
-                my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t
-                out = self.diffusion.p_mean_variance(self.model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
-                fac = self.diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
-                x_in = out['pred_xstart'] * fac + x * (1 - fac)
-                x_in_grad = torch.zeros_like(x_in)
-
-                for model_stat in model_stats:
-                    for i in range(args.cutn_batches):
-                        t_int = int(t.item()) + 1  # errors on last step without +1, need to find source
-                        # try:
-                        input_resolution = model_stat["clip_model"].visual.input_resolution
-                        # except:
-                        #     input_resolution = 224
-
-                        cuts = MakeCutoutsDango(input_resolution,
-                                                Overview=args.cut_overview[1000 - t_int],
-                                                InnerCrop=args.cut_innercut[1000 - t_int],
-                                                IC_Size_Pow=args.cut_ic_pow[1000 - t_int],
-                                                IC_Grey_P=args.cut_icgray_p[1000 - t_int],
-                                                args=args,
-                                                )
-                        clip_in = normalize(cuts(x_in.add(1).div(2)))
-                        image_embeds = model_stat["clip_model"].encode_image(clip_in).float()
-                        dists = spherical_dist_loss(image_embeds.unsqueeze(1), model_stat["target_embeds"].unsqueeze(0))
-                        dists = dists.view([args.cut_overview[1000 - t_int] + args.cut_innercut[1000 - t_int], n, -1])
-                        losses = dists.mul(model_stat["weights"]).sum(2).mean(0)
-                        loss_values.append(losses.sum().item())  # log loss, probably shouldn't do per cutn_batch
-                        x_in_grad += torch.autograd.grad(losses.sum() * clip_guidance_scale, x_in)[0] / cutn_batches
-                tv_losses = tv_loss(x_in)
-                range_losses = range_loss(out['pred_xstart'])
-                sat_losses = torch.abs(x_in - x_in.clamp(min=-1, max=1)).mean()
-                loss = tv_losses.sum() * tv_scale + range_losses.sum() * range_scale + sat_losses.sum() * sat_scale
-                if init is not None and init_scale:
-                    init_losses = self.lpips_model(x_in, init)
-                    loss = loss + init_losses.sum() * init_scale
-                x_in_grad += torch.autograd.grad(loss, x_in)[0]
-                if not torch.isnan(x_in_grad).any():
-                    grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]
-                else:
-                    x_is_NaN = True
-                    grad = torch.zeros_like(x)
-            if args.clamp_grad and not x_is_NaN:
-                magnitude = grad.square().mean().sqrt()
-                return grad * magnitude.clamp(max=args.clamp_max) / magnitude  # min=-0.02, min=-clamp_max,
-            return grad
-
-        if args.diffusion_sampling_mode == 'ddim':
-            sample_fn = self.diffusion.ddim_sample_loop_progressive
-        else:
-            sample_fn = self.diffusion.plms_sample_loop_progressive
-
-        for i in range(args.n_batches):
-            current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')
-
-            batchBar = tqdm(range(args.n_batches), desc="Batches")
-            batchBar.n = i
-            batchBar.refresh()
-            gc.collect()
-            torch.cuda.empty_cache()
-            cur_t = self.diffusion.num_timesteps - skip_steps - 1
-            # total_steps = cur_t
-
-            if args.diffusion_sampling_mode == 'ddim':
-                samples = sample_fn(
-                    self.model,
-                    (batch_size, 3, side_y, side_x),
-                    clip_denoised=clip_denoised,
-                    model_kwargs={},
-                    cond_fn=cond_fn,
-                    progress=True,
-                    skip_timesteps=skip_steps,
-                    init_image=init,
-                    randomize_class=randomize_class,
-                    eta=eta,
-                    transformation_fn=symmetry_transformation_fn,
-                    transformation_percent=args.transformation_percent
-                )
-            else:
-                samples = sample_fn(
-                    self.model,
-                    (batch_size, 3, side_y, side_x),
-                    clip_denoised=clip_denoised,
-                    model_kwargs={},
-                    cond_fn=cond_fn,
-                    progress=True,
-                    skip_timesteps=skip_steps,
-                    init_image=init,
-                    randomize_class=randomize_class,
-                    order=2,
-                )
-
-            for j, sample in enumerate(samples):
-                cur_t -= 1
-                intermediateStep = False
-                if args.steps_per_checkpoint is not None:
-                    if j % steps_per_checkpoint == 0 and j > 0:
-                        intermediateStep = True
-                elif j in args.intermediate_saves:
-                    intermediateStep = True
-                if j % args.display_rate == 0 or cur_t == -1 or intermediateStep:
-                    for k, image in enumerate(sample['pred_xstart']):
-                        # tqdm.write(f'Batch {i}, step {j}, output {k}:')
-                        # percent = math.ceil(j / total_steps * 100)
-                        if args.n_batches > 0:
-                            filename = f'{current_time}-{parse_prompt(prompt)[0]}.png'
-                        image = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))
-                        if j % args.display_rate == 0 or cur_t == -1:
-                            image.save(f'{outDirPath}/{filename}')
-                            if st_dynamic_image:
-                                st_dynamic_image.image(image, use_column_width=True)
-                            # self.current_image = image
-        return image
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="setting")
-    parser.add_argument('--prompt', type=str, required=True)
-    parser.add_argument('--text_scale', type=int, default=5000)
-    parser.add_argument('--model_path', type=str, default="IDEA-CCNL/Taiyi-Diffusion-532M-Nature")
-    parser.add_argument('--width', type=int, default=512)
-    parser.add_argument('--height', type=int, default=512)
-
-    user_args = parser.parse_args()
-
-    dd = Diffuser(user_args.model_path)
-    dd.generate([user_args.prompt],
-                clip_guidance_scale=user_args.text_scale,
-                side_x=user_args.width,
-                side_y=user_args.height,
-                )
diff --git a/spaces/fffiloni/AnimateDiff-Image-Init/download_bashscripts/3-RcnzCartoon.sh b/spaces/fffiloni/AnimateDiff-Image-Init/download_bashscripts/3-RcnzCartoon.sh
deleted file mode 100644
index 07f4f69d399e10b0a618501d7f72bcf7da571dd0..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/AnimateDiff-Image-Init/download_bashscripts/3-RcnzCartoon.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/bash
-wget https://civitai.com/api/download/models/71009 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/stream.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/stream.d.ts
deleted file mode 100644
index 711fd9ca593045e2e285113fc7a212ced84da948..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/stream.d.ts
+++ /dev/null
@@ -1,1340 +0,0 @@
-/**
- * A stream is an abstract interface for working with streaming data in Node.js.
- * The `stream` module provides an API for implementing the stream interface.
- *
- * There are many stream objects provided by Node.js. For instance, a `request to an HTTP server` and `process.stdout` are both stream instances.
- *
- * Streams can be readable, writable, or both. All streams are instances of `EventEmitter`.
- *
- * To access the `stream` module:
- *
- * ```js
- * const stream = require('stream');
- * ```
- *
- * The `stream` module is useful for creating new types of stream instances. It is
- * usually not necessary to use the `stream` module to consume streams.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/stream.js)
- */
-declare module 'stream' {
-    import { EventEmitter, Abortable } from 'node:events';
-    import { Blob as NodeBlob } from "node:buffer";
-    import * as streamPromises from 'node:stream/promises';
-    import * as streamConsumers from 'node:stream/consumers';
-    import * as streamWeb from 'node:stream/web';
-    class internal extends EventEmitter {
-        pipe<T extends NodeJS.WritableStream>(
-            destination: T,
-            options?: {
-                end?: boolean | undefined;
-            }
-        ): T;
-    }
-    namespace internal {
-        class Stream extends internal {
-            constructor(opts?: ReadableOptions);
-        }
-        interface StreamOptions<T extends Stream> extends Abortable {
-            emitClose?: boolean | undefined;
-            highWaterMark?: number | undefined;
-            objectMode?: boolean | undefined;
-            construct?(this: T, callback: (error?: Error | null) => void): void;
-            destroy?(this: T, error: Error | null, callback: (error: Error | null) => void): void;
-            autoDestroy?: boolean | undefined;
-        }
-        interface ReadableOptions extends StreamOptions<Readable> {
-            encoding?: BufferEncoding | undefined;
-            read?(this: Readable, size: number): void;
-        }
-        /**
-         * @since v0.9.4
-         */
-        class Readable extends Stream implements NodeJS.ReadableStream {
-            /**
-             * A utility method for creating Readable Streams out of iterators.
-             */
-            static from(iterable: Iterable<any> | AsyncIterable<any>, options?: ReadableOptions): Readable;
-            /**
-             * A utility method for creating a `Readable` from a web `ReadableStream`.
-             * @since v17.0.0
-             * @experimental
-             */
-            static fromWeb(readableStream: streamWeb.ReadableStream, options?: Pick<ReadableOptions, 'encoding' | 'highWaterMark' | 'objectMode' | 'signal'>): Readable;
-            /**
-             * Returns whether the stream has been read from or cancelled.
-             * @since v16.8.0
-             */
-            static isDisturbed(stream: Readable | NodeJS.ReadableStream): boolean;
-            /**
-             * A utility method for creating a web `ReadableStream` from a `Readable`.
-             * @since v17.0.0
-             * @experimental
-             */
-            static toWeb(streamReadable: Readable): streamWeb.ReadableStream;
-            /**
-             * Returns whether the stream was destroyed or errored before emitting `'end'`.
-             * @since v16.8.0
-             * @experimental
-             */
-            readonly readableAborted: boolean;
-            /**
-             * Is `true` if it is safe to call `readable.read()`, which means
-             * the stream has not been destroyed or emitted `'error'` or `'end'`.
-             * @since v11.4.0
-             */
-            readable: boolean;
-            /**
-             * Returns whether `'data'` has been emitted.
-             * @since v16.7.0, v14.18.0
-             * @experimental
-             */
-            readonly readableDidRead: boolean;
-            /**
-             * Getter for the property `encoding` of a given `Readable` stream. The `encoding`property can be set using the `readable.setEncoding()` method.
-             * @since v12.7.0
-             */
-            readonly readableEncoding: BufferEncoding | null;
-            /**
-             * Becomes `true` when `'end'` event is emitted.
-             * @since v12.9.0
-             */
-            readonly readableEnded: boolean;
-            /**
-             * This property reflects the current state of a `Readable` stream as described
-             * in the `Three states` section.
-             * @since v9.4.0
-             */
-            readonly readableFlowing: boolean | null;
-            /**
-             * Returns the value of `highWaterMark` passed when creating this `Readable`.
-             * @since v9.3.0
-             */
-            readonly readableHighWaterMark: number;
-            /**
-             * This property contains the number of bytes (or objects) in the queue
-             * ready to be read. The value provides introspection data regarding
-             * the status of the `highWaterMark`.
-             * @since v9.4.0
-             */
-            readonly readableLength: number;
-            /**
-             * Getter for the property `objectMode` of a given `Readable` stream.
-             * @since v12.3.0
-             */
-            readonly readableObjectMode: boolean;
-            /**
-             * Is `true` after `readable.destroy()` has been called.
-             * @since v8.0.0
-             */
-            destroyed: boolean;
-            /**
-             * Is true after 'close' has been emitted.
-             * @since v18.0.0
-             */
-            readonly closed: boolean;
-            /**
-             * Returns error if the stream has been destroyed with an error.
-             * @since v18.0.0
-             */
-            readonly errored: Error | null;
-            constructor(opts?: ReadableOptions);
-            _construct?(callback: (error?: Error | null) => void): void;
-            _read(size: number): void;
-            /**
-             * The `readable.read()` method reads data out of the internal buffer and
-             * returns it. If no data is available to be read, `null` is returned. By default,
-             * the data is returned as a `Buffer` object unless an encoding has been
-             * specified using the `readable.setEncoding()` method or the stream is operating
-             * in object mode.
-             *
-             * The optional `size` argument specifies a specific number of bytes to read. If`size` bytes are not available to be read, `null` will be returned _unless_the stream has ended, in which
-             * case all of the data remaining in the internal
-             * buffer will be returned.
-             *
-             * If the `size` argument is not specified, all of the data contained in the
-             * internal buffer will be returned.
-             *
-             * The `size` argument must be less than or equal to 1 GiB.
-             *
-             * The `readable.read()` method should only be called on `Readable` streams
-             * operating in paused mode. In flowing mode, `readable.read()` is called
-             * automatically until the internal buffer is fully drained.
-             *
-             * ```js
-             * const readable = getReadableStreamSomehow();
-             *
-             * // 'readable' may be triggered multiple times as data is buffered in
-             * readable.on('readable', () => {
-             *   let chunk;
-             *   console.log('Stream is readable (new data received in buffer)');
-             *   // Use a loop to make sure we read all currently available data
-             *   while (null !== (chunk = readable.read())) {
-             *     console.log(`Read ${chunk.length} bytes of data...`);
-             *   }
-             * });
-             *
-             * // 'end' will be triggered once when there is no more data available
-             * readable.on('end', () => {
-             *   console.log('Reached end of stream.');
-             * });
-             * ```
-             *
-             * Each call to `readable.read()` returns a chunk of data, or `null`. The chunks
-             * are not concatenated. A `while` loop is necessary to consume all data
-             * currently in the buffer. When reading a large file `.read()` may return `null`,
-             * having consumed all buffered content so far, but there is still more data to
-             * come not yet buffered. In this case a new `'readable'` event will be emitted
-             * when there is more data in the buffer. Finally the `'end'` event will be
-             * emitted when there is no more data to come.
-             *
-             * Therefore to read a file's whole contents from a `readable`, it is necessary
-             * to collect chunks across multiple `'readable'` events:
-             *
-             * ```js
-             * const chunks = [];
-             *
-             * readable.on('readable', () => {
-             *   let chunk;
-             *   while (null !== (chunk = readable.read())) {
-             *     chunks.push(chunk);
-             *   }
-             * });
-             *
-             * readable.on('end', () => {
-             *   const content = chunks.join('');
-             * });
-             * ```
-             *
-             * A `Readable` stream in object mode will always return a single item from
-             * a call to `readable.read(size)`, regardless of the value of the`size` argument.
-             *
-             * If the `readable.read()` method returns a chunk of data, a `'data'` event will
-             * also be emitted.
-             *
-             * Calling {@link read} after the `'end'` event has
-             * been emitted will return `null`. No runtime error will be raised.
-             * @since v0.9.4
-             * @param size Optional argument to specify how much data to read.
-             */
-            read(size?: number): any;
-            /**
-             * The `readable.setEncoding()` method sets the character encoding for
-             * data read from the `Readable` stream.
-             *
-             * By default, no encoding is assigned and stream data will be returned as`Buffer` objects. Setting an encoding causes the stream data
-             * to be returned as strings of the specified encoding rather than as `Buffer`objects. For instance, calling `readable.setEncoding('utf8')` will cause the
-             * output data to be interpreted as UTF-8 data, and passed as strings. Calling`readable.setEncoding('hex')` will cause the data to be encoded in hexadecimal
-             * string format.
-             *
-             * The `Readable` stream will properly handle multi-byte characters delivered
-             * through the stream that would otherwise become improperly decoded if simply
-             * pulled from the stream as `Buffer` objects.
-             *
-             * ```js
-             * const readable = getReadableStreamSomehow();
-             * readable.setEncoding('utf8');
-             * readable.on('data', (chunk) => {
-             *   assert.equal(typeof chunk, 'string');
-             *   console.log('Got %d characters of string data:', chunk.length);
-             * });
-             * ```
-             * @since v0.9.4
-             * @param encoding The encoding to use.
-             */
-            setEncoding(encoding: BufferEncoding): this;
-            /**
-             * The `readable.pause()` method will cause a stream in flowing mode to stop
-             * emitting `'data'` events, switching out of flowing mode. Any data that
-             * becomes available will remain in the internal buffer.
-             *
-             * ```js
-             * const readable = getReadableStreamSomehow();
-             * readable.on('data', (chunk) => {
-             *   console.log(`Received ${chunk.length} bytes of data.`);
-             *   readable.pause();
-             *   console.log('There will be no additional data for 1 second.');
-             *   setTimeout(() => {
-             *     console.log('Now data will start flowing again.');
-             *     readable.resume();
-             *   }, 1000);
-             * });
-             * ```
-             *
-             * The `readable.pause()` method has no effect if there is a `'readable'`event listener.
-             * @since v0.9.4
-             */
-            pause(): this;
-            /**
-             * The `readable.resume()` method causes an explicitly paused `Readable` stream to
-             * resume emitting `'data'` events, switching the stream into flowing mode.
-             *
-             * The `readable.resume()` method can be used to fully consume the data from a
-             * stream without actually processing any of that data:
-             *
-             * ```js
-             * getReadableStreamSomehow()
-             *   .resume()
-             *   .on('end', () => {
-             *     console.log('Reached the end, but did not read anything.');
-             *   });
-             * ```
-             *
-             * The `readable.resume()` method has no effect if there is a `'readable'`event listener.
-             * @since v0.9.4
-             */
-            resume(): this;
-            /**
-             * The `readable.isPaused()` method returns the current operating state of the`Readable`. This is used primarily by the mechanism that underlies the`readable.pipe()` method. In most
-             * typical cases, there will be no reason to
-             * use this method directly.
-             *
-             * ```js
-             * const readable = new stream.Readable();
-             *
-             * readable.isPaused(); // === false
-             * readable.pause();
-             * readable.isPaused(); // === true
-             * readable.resume();
-             * readable.isPaused(); // === false
-             * ```
-             * @since v0.11.14
-             */
-            isPaused(): boolean;
-            /**
-             * The `readable.unpipe()` method detaches a `Writable` stream previously attached
-             * using the {@link pipe} method.
-             *
-             * If the `destination` is not specified, then _all_ pipes are detached.
-             *
-             * If the `destination` is specified, but no pipe is set up for it, then
-             * the method does nothing.
-             *
-             * ```js
-             * const fs = require('fs');
-             * const readable = getReadableStreamSomehow();
-             * const writable = fs.createWriteStream('file.txt');
-             * // All the data from readable goes into 'file.txt',
-             * // but only for the first second.
-             * readable.pipe(writable);
-             * setTimeout(() => {
-             *   console.log('Stop writing to file.txt.');
-             *   readable.unpipe(writable);
-             *   console.log('Manually close the file stream.');
-             *   writable.end();
-             * }, 1000);
-             * ```
-             * @since v0.9.4
-             * @param destination Optional specific stream to unpipe
-             */
-            unpipe(destination?: NodeJS.WritableStream): this;
-            /**
-             * Passing `chunk` as `null` signals the end of the stream (EOF) and behaves the
-             * same as `readable.push(null)`, after which no more data can be written. The EOF
-             * signal is put at the end of the buffer and any buffered data will still be
-             * flushed.
-             *
-             * The `readable.unshift()` method pushes a chunk of data back into the internal
-             * buffer. This is useful in certain situations where a stream is being consumed by
-             * code that needs to "un-consume" some amount of data that it has optimistically
-             * pulled out of the source, so that the data can be passed on to some other party.
-             *
-             * The `stream.unshift(chunk)` method cannot be called after the `'end'` event
-             * has been emitted or a runtime error will be thrown.
-             *
-             * Developers using `stream.unshift()` often should consider switching to
-             * use of a `Transform` stream instead. See the `API for stream implementers` section for more information.
-             *
-             * ```js
-             * // Pull off a header delimited by \n\n.
-             * // Use unshift() if we get too much.
-             * // Call the callback with (error, header, stream).
-             * const { StringDecoder } = require('string_decoder');
-             * function parseHeader(stream, callback) {
-             *   stream.on('error', callback);
-             *   stream.on('readable', onReadable);
-             *   const decoder = new StringDecoder('utf8');
-             *   let header = '';
-             *   function onReadable() {
-             *     let chunk;
-             *     while (null !== (chunk = stream.read())) {
-             *       const str = decoder.write(chunk);
-             *       if (str.includes('\n\n')) {
-             *         // Found the header boundary.
-             *         const split = str.split(/\n\n/);
-             *         header += split.shift();
-             *         const remaining = split.join('\n\n');
-             *         const buf = Buffer.from(remaining, 'utf8');
-             *         stream.removeListener('error', callback);
-             *         // Remove the 'readable' listener before unshifting.
-             *         stream.removeListener('readable', onReadable);
-             *         if (buf.length)
-             *           stream.unshift(buf);
-             *         // Now the body of the message can be read from the stream.
-             *         callback(null, header, stream);
-             *         return;
-             *       }
-             *       // Still reading the header.
-             *       header += str;
-             *     }
-             *   }
-             * }
-             * ```
-             *
-             * Unlike {@link push}, `stream.unshift(chunk)` will not
-             * end the reading process by resetting the internal reading state of the stream.
-             * This can cause unexpected results if `readable.unshift()` is called during a
-             * read (i.e. from within a {@link _read} implementation on a
-             * custom stream). Following the call to `readable.unshift()` with an immediate {@link push} will reset the reading state appropriately,
-             * however it is best to simply avoid calling `readable.unshift()` while in the
-             * process of performing a read.
-             * @since v0.9.11
-             * @param chunk Chunk of data to unshift onto the read queue. For streams not operating in object mode, `chunk` must be a string, `Buffer`, `Uint8Array` or `null`. For object mode
-             * streams, `chunk` may be any JavaScript value.
-             * @param encoding Encoding of string chunks. Must be a valid `Buffer` encoding, such as `'utf8'` or `'ascii'`.
-             */
-            unshift(chunk: any, encoding?: BufferEncoding): void;
-            /**
-             * Prior to Node.js 0.10, streams did not implement the entire `stream` module API
-             * as it is currently defined. (See `Compatibility` for more information.)
-             *
-             * When using an older Node.js library that emits `'data'` events and has a {@link pause} method that is advisory only, the`readable.wrap()` method can be used to create a `Readable`
-             * stream that uses
-             * the old stream as its data source.
-             *
-             * It will rarely be necessary to use `readable.wrap()` but the method has been
-             * provided as a convenience for interacting with older Node.js applications and
-             * libraries.
-             *
-             * ```js
-             * const { OldReader } = require('./old-api-module.js');
-             * const { Readable } = require('stream');
-             * const oreader = new OldReader();
-             * const myReader = new Readable().wrap(oreader);
-             *
-             * myReader.on('readable', () => {
-             *   myReader.read(); // etc.
-             * });
-             * ```
-             * @since v0.9.4
-             * @param stream An "old style" readable stream
-             */
-            wrap(stream: NodeJS.ReadableStream): this;
-            push(chunk: any, encoding?: BufferEncoding): boolean;
-            _destroy(error: Error | null, callback: (error?: Error | null) => void): void;
-            /**
-             * Destroy the stream. Optionally emit an `'error'` event, and emit a `'close'`event (unless `emitClose` is set to `false`). After this call, the readable
-             * stream will release any internal resources and subsequent calls to `push()`will be ignored.
-             *
-             * Once `destroy()` has been called any further calls will be a no-op and no
-             * further errors except from `_destroy()` may be emitted as `'error'`.
-             *
-             * Implementors should not override this method, but instead implement `readable._destroy()`.
-             * @since v8.0.0
-             * @param error Error which will be passed as payload in `'error'` event
-             */
-            destroy(error?: Error): this;
-            /**
-             * Event emitter
-             * The defined events on documents including:
-             * 1. close
-             * 2. data
-             * 3. end
-             * 4. error
-             * 5. pause
-             * 6. readable
-             * 7. resume
-             */
-            addListener(event: 'close', listener: () => void): this;
-            addListener(event: 'data', listener: (chunk: any) => void): this;
-            addListener(event: 'end', listener: () => void): this;
-            addListener(event: 'error', listener: (err: Error) => void): this;
-            addListener(event: 'pause', listener: () => void): this;
-            addListener(event: 'readable', listener: () => void): this;
-            addListener(event: 'resume', listener: () => void): this;
-            addListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            emit(event: 'close'): boolean;
-            emit(event: 'data', chunk: any): boolean;
-            emit(event: 'end'): boolean;
-            emit(event: 'error', err: Error): boolean;
-            emit(event: 'pause'): boolean;
-            emit(event: 'readable'): boolean;
-            emit(event: 'resume'): boolean;
-            emit(event: string | symbol, ...args: any[]): boolean;
-            on(event: 'close', listener: () => void): this;
-            on(event: 'data', listener: (chunk: any) => void): this;
-            on(event: 'end', listener: () => void): this;
-            on(event: 'error', listener: (err: Error) => void): this;
-            on(event: 'pause', listener: () => void): this;
-            on(event: 'readable', listener: () => void): this;
-            on(event: 'resume', listener: () => void): this;
-            on(event: string | symbol, listener: (...args: any[]) => void): this;
-            once(event: 'close', listener: () => void): this;
-            once(event: 'data', listener: (chunk: any) => void): this;
-            once(event: 'end', listener: () => void): this;
-            once(event: 'error', listener: (err: Error) => void): this;
-            once(event: 'pause', listener: () => void): this;
-            once(event: 'readable', listener: () => void): this;
-            once(event: 'resume', listener: () => void): this;
-            once(event: string | symbol, listener: (...args: any[]) => void): this;
-            prependListener(event: 'close', listener: () => void): this;
-            prependListener(event: 'data', listener: (chunk: any) => void): this;
-            prependListener(event: 'end', listener: () => void): this;
-            prependListener(event: 'error', listener: (err: Error) => void): this;
-            prependListener(event: 'pause', listener: () => void): this;
-            prependListener(event: 'readable', listener: () => void): this;
-            prependListener(event: 'resume', listener: () => void): this;
-            prependListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            prependOnceListener(event: 'close', listener: () => void): this;
-            prependOnceListener(event: 'data', listener: (chunk: any) => void): this;
-            prependOnceListener(event: 'end', listener: () => void): this;
-            prependOnceListener(event: 'error', listener: (err: Error) => void): this;
-            prependOnceListener(event: 'pause', listener: () => void): this;
-            prependOnceListener(event: 'readable', listener: () => void): this;
-            prependOnceListener(event: 'resume', listener: () => void): this;
-            prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            removeListener(event: 'close', listener: () => void): this;
-            removeListener(event: 'data', listener: (chunk: any) => void): this;
-            removeListener(event: 'end', listener: () => void): this;
-            removeListener(event: 'error', listener: (err: Error) => void): this;
-            removeListener(event: 'pause', listener: () => void): this;
-            removeListener(event: 'readable', listener: () => void): this;
-            removeListener(event: 'resume', listener: () => void): this;
-            removeListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            [Symbol.asyncIterator](): AsyncIterableIterator<any>;
-        }
-        interface WritableOptions extends StreamOptions<Writable> {
-            decodeStrings?: boolean | undefined;
-            defaultEncoding?: BufferEncoding | undefined;
-            write?(this: Writable, chunk: any, encoding: BufferEncoding, callback: (error?: Error | null) => void): void;
-            writev?(
-                this: Writable,
-                chunks: Array<{
-                    chunk: any;
-                    encoding: BufferEncoding;
-                }>,
-                callback: (error?: Error | null) => void
-            ): void;
-            final?(this: Writable, callback: (error?: Error | null) => void): void;
-        }
-        /**
-         * @since v0.9.4
-         */
-        class Writable extends Stream implements NodeJS.WritableStream {
-            /**
-             * A utility method for creating a `Writable` from a web `WritableStream`.
-             * @since v17.0.0
-             * @experimental
-             */
-            static fromWeb(writableStream: streamWeb.WritableStream, options?: Pick<WritableOptions, 'decodeStrings' | 'highWaterMark' | 'objectMode' | 'signal'>): Writable;
-            /**
-             * A utility method for creating a web `WritableStream` from a `Writable`.
-             * @since v17.0.0
-             * @experimental
-             */
-            static toWeb(streamWritable: Writable): streamWeb.WritableStream;
-            /**
-             * Is `true` if it is safe to call `writable.write()`, which means
-             * the stream has not been destroyed, errored or ended.
-             * @since v11.4.0
-             */
-            readonly writable: boolean;
-            /**
-             * Is `true` after `writable.end()` has been called. This property
-             * does not indicate whether the data has been flushed, for this use `writable.writableFinished` instead.
-             * @since v12.9.0
-             */
-            readonly writableEnded: boolean;
-            /**
-             * Is set to `true` immediately before the `'finish'` event is emitted.
-             * @since v12.6.0
-             */
-            readonly writableFinished: boolean;
-            /**
-             * Return the value of `highWaterMark` passed when creating this `Writable`.
-             * @since v9.3.0
-             */
-            readonly writableHighWaterMark: number;
-            /**
-             * This property contains the number of bytes (or objects) in the queue
-             * ready to be written. The value provides introspection data regarding
-             * the status of the `highWaterMark`.
-             * @since v9.4.0
-             */
-            readonly writableLength: number;
-            /**
-             * Getter for the property `objectMode` of a given `Writable` stream.
-             * @since v12.3.0
-             */
-            readonly writableObjectMode: boolean;
-            /**
-             * Number of times `writable.uncork()` needs to be
-             * called in order to fully uncork the stream.
-             * @since v13.2.0, v12.16.0
-             */
-            readonly writableCorked: number;
-            /**
-             * Is `true` after `writable.destroy()` has been called.
-             * @since v8.0.0
-             */
-            destroyed: boolean;
-            /**
-             * Is true after 'close' has been emitted.
-             * @since v18.0.0
-             */
-            readonly closed: boolean;
-            /**
-             * Returns error if the stream has been destroyed with an error.
-             * @since v18.0.0
-             */
-            readonly errored: Error | null;
-            /**
-             * Is `true` if the stream's buffer has been full and stream will emit 'drain'.
-             * @since v15.2.0, v14.17.0
-             */
-            readonly writableNeedDrain: boolean;
-            constructor(opts?: WritableOptions);
-            _write(chunk: any, encoding: BufferEncoding, callback: (error?: Error | null) => void): void;
-            _writev?(
-                chunks: Array<{
-                    chunk: any;
-                    encoding: BufferEncoding;
-                }>,
-                callback: (error?: Error | null) => void
-            ): void;
-            _construct?(callback: (error?: Error | null) => void): void;
-            _destroy(error: Error | null, callback: (error?: Error | null) => void): void;
-            _final(callback: (error?: Error | null) => void): void;
-            /**
-             * The `writable.write()` method writes some data to the stream, and calls the
-             * supplied `callback` once the data has been fully handled. If an error
-             * occurs, the `callback` will be called with the error as its
-             * first argument. The `callback` is called asynchronously and before `'error'` is
-             * emitted.
-             *
-             * The return value is `true` if the internal buffer is less than the`highWaterMark` configured when the stream was created after admitting `chunk`.
-             * If `false` is returned, further attempts to write data to the stream should
-             * stop until the `'drain'` event is emitted.
-             *
-             * While a stream is not draining, calls to `write()` will buffer `chunk`, and
-             * return false. Once all currently buffered chunks are drained (accepted for
-             * delivery by the operating system), the `'drain'` event will be emitted.
-             * Once `write()` returns false, do not write more chunks
-             * until the `'drain'` event is emitted. While calling `write()` on a stream that
-             * is not draining is allowed, Node.js will buffer all written chunks until
-             * maximum memory usage occurs, at which point it will abort unconditionally.
-             * Even before it aborts, high memory usage will cause poor garbage collector
-             * performance and high RSS (which is not typically released back to the system,
-             * even after the memory is no longer required). Since TCP sockets may never
-             * drain if the remote peer does not read the data, writing a socket that is
-             * not draining may lead to a remotely exploitable vulnerability.
-             *
-             * Writing data while the stream is not draining is particularly
-             * problematic for a `Transform`, because the `Transform` streams are paused
-             * by default until they are piped or a `'data'` or `'readable'` event handler
-             * is added.
-             *
-             * If the data to be written can be generated or fetched on demand, it is
-             * recommended to encapsulate the logic into a `Readable` and use {@link pipe}. However, if calling `write()` is preferred, it is
-             * possible to respect backpressure and avoid memory issues using the `'drain'` event:
-             *
-             * ```js
-             * function write(data, cb) {
-             *   if (!stream.write(data)) {
-             *     stream.once('drain', cb);
-             *   } else {
-             *     process.nextTick(cb);
-             *   }
-             * }
-             *
-             * // Wait for cb to be called before doing any other write.
-             * write('hello', () => {
-             *   console.log('Write completed, do more writes now.');
-             * });
-             * ```
-             *
-             * A `Writable` stream in object mode will always ignore the `encoding` argument.
-             * @since v0.9.4
-             * @param chunk Optional data to write. For streams not operating in object mode, `chunk` must be a string, `Buffer` or `Uint8Array`. For object mode streams, `chunk` may be any
-             * JavaScript value other than `null`.
-             * @param [encoding='utf8'] The encoding, if `chunk` is a string.
-             * @param callback Callback for when this chunk of data is flushed.
-             * @return `false` if the stream wishes for the calling code to wait for the `'drain'` event to be emitted before continuing to write additional data; otherwise `true`.
-             */
-            write(chunk: any, callback?: (error: Error | null | undefined) => void): boolean;
-            write(chunk: any, encoding: BufferEncoding, callback?: (error: Error | null | undefined) => void): boolean;
-            /**
-             * The `writable.setDefaultEncoding()` method sets the default `encoding` for a `Writable` stream.
-             * @since v0.11.15
-             * @param encoding The new default encoding
-             */
-            setDefaultEncoding(encoding: BufferEncoding): this;
-            /**
-             * Calling the `writable.end()` method signals that no more data will be written
-             * to the `Writable`. The optional `chunk` and `encoding` arguments allow one
-             * final additional chunk of data to be written immediately before closing the
-             * stream.
-             *
-             * Calling the {@link write} method after calling {@link end} will raise an error.
-             *
-             * ```js
-             * // Write 'hello, ' and then end with 'world!'.
-             * const fs = require('fs');
-             * const file = fs.createWriteStream('example.txt');
-             * file.write('hello, ');
-             * file.end('world!');
-             * // Writing more now is not allowed!
-             * ```
-             * @since v0.9.4
-             * @param chunk Optional data to write. For streams not operating in object mode, `chunk` must be a string, `Buffer` or `Uint8Array`. For object mode streams, `chunk` may be any
-             * JavaScript value other than `null`.
-             * @param encoding The encoding if `chunk` is a string
-             * @param callback Callback for when the stream is finished.
-             */
-            end(cb?: () => void): this;
-            end(chunk: any, cb?: () => void): this;
-            end(chunk: any, encoding: BufferEncoding, cb?: () => void): this;
-            /**
-             * The `writable.cork()` method forces all written data to be buffered in memory.
-             * The buffered data will be flushed when either the {@link uncork} or {@link end} methods are called.
-             *
-             * The primary intent of `writable.cork()` is to accommodate a situation in which
-             * several small chunks are written to the stream in rapid succession. Instead of
-             * immediately forwarding them to the underlying destination, `writable.cork()`buffers all the chunks until `writable.uncork()` is called, which will pass them
-             * all to `writable._writev()`, if present. This prevents a head-of-line blocking
-             * situation where data is being buffered while waiting for the first small chunk
-             * to be processed. However, use of `writable.cork()` without implementing`writable._writev()` may have an adverse effect on throughput.
-             *
-             * See also: `writable.uncork()`, `writable._writev()`.
-             * @since v0.11.2
-             */
-            cork(): void;
-            /**
-             * The `writable.uncork()` method flushes all data buffered since {@link cork} was called.
-             *
-             * When using `writable.cork()` and `writable.uncork()` to manage the buffering
-             * of writes to a stream, defer calls to `writable.uncork()` using`process.nextTick()`. Doing so allows batching of all`writable.write()` calls that occur within a given Node.js event
-             * loop phase.
-             *
-             * ```js
-             * stream.cork();
-             * stream.write('some ');
-             * stream.write('data ');
-             * process.nextTick(() => stream.uncork());
-             * ```
-             *
-             * If the `writable.cork()` method is called multiple times on a stream, the
-             * same number of calls to `writable.uncork()` must be called to flush the buffered
-             * data.
-             *
-             * ```js
-             * stream.cork();
-             * stream.write('some ');
-             * stream.cork();
-             * stream.write('data ');
-             * process.nextTick(() => {
-             *   stream.uncork();
-             *   // The data will not be flushed until uncork() is called a second time.
-             *   stream.uncork();
-             * });
-             * ```
-             *
-             * See also: `writable.cork()`.
-             * @since v0.11.2
-             */
-            uncork(): void;
-            /**
-             * Destroy the stream. Optionally emit an `'error'` event, and emit a `'close'`event (unless `emitClose` is set to `false`). After this call, the writable
-             * stream has ended and subsequent calls to `write()` or `end()` will result in
-             * an `ERR_STREAM_DESTROYED` error.
-             * This is a destructive and immediate way to destroy a stream. Previous calls to`write()` may not have drained, and may trigger an `ERR_STREAM_DESTROYED` error.
-             * Use `end()` instead of destroy if data should flush before close, or wait for
-             * the `'drain'` event before destroying the stream.
-             *
-             * Once `destroy()` has been called any further calls will be a no-op and no
-             * further errors except from `_destroy()` may be emitted as `'error'`.
-             *
-             * Implementors should not override this method,
-             * but instead implement `writable._destroy()`.
-             * @since v8.0.0
-             * @param error Optional, an error to emit with `'error'` event.
-             */
-            destroy(error?: Error): this;
-            /**
-             * Event emitter
-             * The defined events on documents including:
-             * 1. close
-             * 2. drain
-             * 3. error
-             * 4. finish
-             * 5. pipe
-             * 6. unpipe
-             */
-            addListener(event: 'close', listener: () => void): this;
-            addListener(event: 'drain', listener: () => void): this;
-            addListener(event: 'error', listener: (err: Error) => void): this;
-            addListener(event: 'finish', listener: () => void): this;
-            addListener(event: 'pipe', listener: (src: Readable) => void): this;
-            addListener(event: 'unpipe', listener: (src: Readable) => void): this;
-            addListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            emit(event: 'close'): boolean;
-            emit(event: 'drain'): boolean;
-            emit(event: 'error', err: Error): boolean;
-            emit(event: 'finish'): boolean;
-            emit(event: 'pipe', src: Readable): boolean;
-            emit(event: 'unpipe', src: Readable): boolean;
-            emit(event: string | symbol, ...args: any[]): boolean;
-            on(event: 'close', listener: () => void): this;
-            on(event: 'drain', listener: () => void): this;
-            on(event: 'error', listener: (err: Error) => void): this;
-            on(event: 'finish', listener: () => void): this;
-            on(event: 'pipe', listener: (src: Readable) => void): this;
-            on(event: 'unpipe', listener: (src: Readable) => void): this;
-            on(event: string | symbol, listener: (...args: any[]) => void): this;
-            once(event: 'close', listener: () => void): this;
-            once(event: 'drain', listener: () => void): this;
-            once(event: 'error', listener: (err: Error) => void): this;
-            once(event: 'finish', listener: () => void): this;
-            once(event: 'pipe', listener: (src: Readable) => void): this;
-            once(event: 'unpipe', listener: (src: Readable) => void): this;
-            once(event: string | symbol, listener: (...args: any[]) => void): this;
-            prependListener(event: 'close', listener: () => void): this;
-            prependListener(event: 'drain', listener: () => void): this;
-            prependListener(event: 'error', listener: (err: Error) => void): this;
-            prependListener(event: 'finish', listener: () => void): this;
-            prependListener(event: 'pipe', listener: (src: Readable) => void): this;
-            prependListener(event: 'unpipe', listener: (src: Readable) => void): this;
-            prependListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            prependOnceListener(event: 'close', listener: () => void): this;
-            prependOnceListener(event: 'drain', listener: () => void): this;
-            prependOnceListener(event: 'error', listener: (err: Error) => void): this;
-            prependOnceListener(event: 'finish', listener: () => void): this;
-            prependOnceListener(event: 'pipe', listener: (src: Readable) => void): this;
-            prependOnceListener(event: 'unpipe', listener: (src: Readable) => void): this;
-            prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this;
-            removeListener(event: 'close', listener: () => void): this;
-            removeListener(event: 'drain', listener: () => void): this;
-            removeListener(event: 'error', listener: (err: Error) => void): this;
-            removeListener(event: 'finish', listener: () => void): this;
-            removeListener(event: 'pipe', listener: (src: Readable) => void): this;
-            removeListener(event: 'unpipe', listener: (src: Readable) => void): this;
-            removeListener(event: string | symbol, listener: (...args: any[]) => void): this;
-        }
-        interface DuplexOptions extends ReadableOptions, WritableOptions {
-            allowHalfOpen?: boolean | undefined;
-            readableObjectMode?: boolean | undefined;
-            writableObjectMode?: boolean | undefined;
-            readableHighWaterMark?: number | undefined;
-            writableHighWaterMark?: number | undefined;
-            writableCorked?: number | undefined;
-            construct?(this: Duplex, callback: (error?: Error | null) => void): void;
-            read?(this: Duplex, size: number): void;
-            write?(this: Duplex, chunk: any, encoding: BufferEncoding, callback: (error?: Error | null) => void): void;
-            writev?(
-                this: Duplex,
-                chunks: Array<{
-                    chunk: any;
-                    encoding: BufferEncoding;
-                }>,
-                callback: (error?: Error | null) => void
-            ): void;
-            final?(this: Duplex, callback: (error?: Error | null) => void): void;
-            destroy?(this: Duplex, error: Error | null, callback: (error: Error | null) => void): void;
-        }
-        /**
-         * Duplex streams are streams that implement both the `Readable` and `Writable` interfaces.
-         *
-         * Examples of `Duplex` streams include:
-         *
-         * * `TCP sockets`
-         * * `zlib streams`
-         * * `crypto streams`
-         * @since v0.9.4
-         */
-        class Duplex extends Readable implements Writable {
-            readonly writable: boolean;
-            readonly writableEnded: boolean;
-            readonly writableFinished: boolean;
-            readonly writableHighWaterMark: number;
-            readonly writableLength: number;
-            readonly writableObjectMode: boolean;
-            readonly writableCorked: number;
-            readonly writableNeedDrain: boolean;
-            readonly closed: boolean;
-            readonly errored: Error | null;
-            /**
-             * If `false` then the stream will automatically end the writable side when the
-             * readable side ends. Set initially by the `allowHalfOpen` constructor option,
-             * which defaults to `false`.
-             *
-             * This can be changed manually to change the half-open behavior of an existing`Duplex` stream instance, but must be changed before the `'end'` event is
-             * emitted.
-             * @since v0.9.4
-             */
-            allowHalfOpen: boolean;
-            constructor(opts?: DuplexOptions);
-            /**
-             * A utility method for creating duplex streams.
-             *
-             * - `Stream` converts writable stream into writable `Duplex` and readable stream
-             *   to `Duplex`.
-             * - `Blob` converts into readable `Duplex`.
-             * - `string` converts into readable `Duplex`.
-             * - `ArrayBuffer` converts into readable `Duplex`.
-             * - `AsyncIterable` converts into a readable `Duplex`. Cannot yield `null`.
-             * - `AsyncGeneratorFunction` converts into a readable/writable transform
-             *   `Duplex`. Must take a source `AsyncIterable` as first parameter. Cannot yield
-             *   `null`.
-             * - `AsyncFunction` converts into a writable `Duplex`. Must return
-             *   either `null` or `undefined`
-             * - `Object ({ writable, readable })` converts `readable` and
-             *   `writable` into `Stream` and then combines them into `Duplex` where the
-             *   `Duplex` will write to the `writable` and read from the `readable`.
-             * - `Promise` converts into readable `Duplex`. Value `null` is ignored.
-             *
-             * @since v16.8.0
-             */
-            static from(src: Stream | NodeBlob | ArrayBuffer | string | Iterable<any> | AsyncIterable<any> | AsyncGeneratorFunction | Promise<any> | Object): Duplex;
-            _write(chunk: any, encoding: BufferEncoding, callback: (error?: Error | null) => void): void;
-            _writev?(
-                chunks: Array<{
-                    chunk: any;
-                    encoding: BufferEncoding;
-                }>,
-                callback: (error?: Error | null) => void
-            ): void;
-            _destroy(error: Error | null, callback: (error: Error | null) => void): void;
-            _final(callback: (error?: Error | null) => void): void;
-            write(chunk: any, encoding?: BufferEncoding, cb?: (error: Error | null | undefined) => void): boolean;
-            write(chunk: any, cb?: (error: Error | null | undefined) => void): boolean;
-            setDefaultEncoding(encoding: BufferEncoding): this;
-            end(cb?: () => void): this;
-            end(chunk: any, cb?: () => void): this;
-            end(chunk: any, encoding?: BufferEncoding, cb?: () => void): this;
-            cork(): void;
-            uncork(): void;
-        }
-        type TransformCallback = (error?: Error | null, data?: any) => void;
-        interface TransformOptions extends DuplexOptions {
-            construct?(this: Transform, callback: (error?: Error | null) => void): void;
-            read?(this: Transform, size: number): void;
-            write?(this: Transform, chunk: any, encoding: BufferEncoding, callback: (error?: Error | null) => void): void;
-            writev?(
-                this: Transform,
-                chunks: Array<{
-                    chunk: any;
-                    encoding: BufferEncoding;
-                }>,
-                callback: (error?: Error | null) => void
-            ): void;
-            final?(this: Transform, callback: (error?: Error | null) => void): void;
-            destroy?(this: Transform, error: Error | null, callback: (error: Error | null) => void): void;
-            transform?(this: Transform, chunk: any, encoding: BufferEncoding, callback: TransformCallback): void;
-            flush?(this: Transform, callback: TransformCallback): void;
-        }
-        /**
-         * Transform streams are `Duplex` streams where the output is in some way
-         * related to the input. Like all `Duplex` streams, `Transform` streams
-         * implement both the `Readable` and `Writable` interfaces.
-         *
-         * Examples of `Transform` streams include:
-         *
-         * * `zlib streams`
-         * * `crypto streams`
-         * @since v0.9.4
-         */
-        class Transform extends Duplex {
-            constructor(opts?: TransformOptions);
-            _transform(chunk: any, encoding: BufferEncoding, callback: TransformCallback): void;
-            _flush(callback: TransformCallback): void;
-        }
-        /**
-         * The `stream.PassThrough` class is a trivial implementation of a `Transform` stream that simply passes the input bytes across to the output. Its purpose is
-         * primarily for examples and testing, but there are some use cases where`stream.PassThrough` is useful as a building block for novel sorts of streams.
-         */
-        class PassThrough extends Transform {}
-        /**
-         * Attaches an AbortSignal to a readable or writeable stream. This lets code
-         * control stream destruction using an `AbortController`.
-         *
-         * Calling `abort` on the `AbortController` corresponding to the passed`AbortSignal` will behave the same way as calling `.destroy(new AbortError())`on the stream.
-         *
-         * ```js
-         * const fs = require('fs');
-         *
-         * const controller = new AbortController();
-         * const read = addAbortSignal(
-         *   controller.signal,
-         *   fs.createReadStream(('object.json'))
-         * );
-         * // Later, abort the operation closing the stream
-         * controller.abort();
-         * ```
-         *
-         * Or using an `AbortSignal` with a readable stream as an async iterable:
-         *
-         * ```js
-         * const controller = new AbortController();
-         * setTimeout(() => controller.abort(), 10_000); // set a timeout
-         * const stream = addAbortSignal(
-         *   controller.signal,
-         *   fs.createReadStream(('object.json'))
-         * );
-         * (async () => {
-         *   try {
-         *     for await (const chunk of stream) {
-         *       await process(chunk);
-         *     }
-         *   } catch (e) {
-         *     if (e.name === 'AbortError') {
-         *       // The operation was cancelled
-         *     } else {
-         *       throw e;
-         *     }
-         *   }
-         * })();
-         * ```
-         * @since v15.4.0
-         * @param signal A signal representing possible cancellation
-         * @param stream a stream to attach a signal to
-         */
-        function addAbortSignal<T extends Stream>(signal: AbortSignal, stream: T): T;
-        interface FinishedOptions extends Abortable {
-            error?: boolean | undefined;
-            readable?: boolean | undefined;
-            writable?: boolean | undefined;
-        }
-        /**
-         * A function to get notified when a stream is no longer readable, writable
-         * or has experienced an error or a premature close event.
-         *
-         * ```js
-         * const { finished } = require('stream');
-         *
-         * const rs = fs.createReadStream('archive.tar');
-         *
-         * finished(rs, (err) => {
-         *   if (err) {
-         *     console.error('Stream failed.', err);
-         *   } else {
-         *     console.log('Stream is done reading.');
-         *   }
-         * });
-         *
-         * rs.resume(); // Drain the stream.
-         * ```
-         *
-         * Especially useful in error handling scenarios where a stream is destroyed
-         * prematurely (like an aborted HTTP request), and will not emit `'end'`or `'finish'`.
-         *
-         * The `finished` API provides promise version:
-         *
-         * ```js
-         * const { finished } = require('stream/promises');
-         *
-         * const rs = fs.createReadStream('archive.tar');
-         *
-         * async function run() {
-         *   await finished(rs);
-         *   console.log('Stream is done reading.');
-         * }
-         *
-         * run().catch(console.error);
-         * rs.resume(); // Drain the stream.
-         * ```
-         *
-         * `stream.finished()` leaves dangling event listeners (in particular`'error'`, `'end'`, `'finish'` and `'close'`) after `callback` has been
-         * invoked. The reason for this is so that unexpected `'error'` events (due to
-         * incorrect stream implementations) do not cause unexpected crashes.
-         * If this is unwanted behavior then the returned cleanup function needs to be
-         * invoked in the callback:
-         *
-         * ```js
-         * const cleanup = finished(rs, (err) => {
-         *   cleanup();
-         *   // ...
-         * });
-         * ```
-         * @since v10.0.0
-         * @param stream A readable and/or writable stream.
-         * @param callback A callback function that takes an optional error argument.
-         * @return A cleanup function which removes all registered listeners.
-         */
-        function finished(stream: NodeJS.ReadableStream | NodeJS.WritableStream | NodeJS.ReadWriteStream, options: FinishedOptions, callback: (err?: NodeJS.ErrnoException | null) => void): () => void;
-        function finished(stream: NodeJS.ReadableStream | NodeJS.WritableStream | NodeJS.ReadWriteStream, callback: (err?: NodeJS.ErrnoException | null) => void): () => void;
-        namespace finished {
-            function __promisify__(stream: NodeJS.ReadableStream | NodeJS.WritableStream | NodeJS.ReadWriteStream, options?: FinishedOptions): Promise<void>;
-        }
-        type PipelineSourceFunction<T> = () => Iterable<T> | AsyncIterable<T>;
-        type PipelineSource<T> = Iterable<T> | AsyncIterable<T> | NodeJS.ReadableStream | PipelineSourceFunction<T>;
-        type PipelineTransform<S extends PipelineTransformSource<any>, U> =
-            | NodeJS.ReadWriteStream
-            | ((source: S extends (...args: any[]) => Iterable<infer ST> | AsyncIterable<infer ST> ? AsyncIterable<ST> : S) => AsyncIterable<U>);
-        type PipelineTransformSource<T> = PipelineSource<T> | PipelineTransform<any, T>;
-        type PipelineDestinationIterableFunction<T> = (source: AsyncIterable<T>) => AsyncIterable<any>;
-        type PipelineDestinationPromiseFunction<T, P> = (source: AsyncIterable<T>) => Promise<P>;
-        type PipelineDestination<S extends PipelineTransformSource<any>, P> = S extends PipelineTransformSource<infer ST>
-            ? NodeJS.WritableStream | PipelineDestinationIterableFunction<ST> | PipelineDestinationPromiseFunction<ST, P>
-            : never;
-        type PipelineCallback<S extends PipelineDestination<any, any>> = S extends PipelineDestinationPromiseFunction<any, infer P>
-            ? (err: NodeJS.ErrnoException | null, value: P) => void
-            : (err: NodeJS.ErrnoException | null) => void;
-        type PipelinePromise<S extends PipelineDestination<any, any>> = S extends PipelineDestinationPromiseFunction<any, infer P> ? Promise<P> : Promise<void>;
-        interface PipelineOptions {
-            signal: AbortSignal;
-        }
-        /**
-         * A module method to pipe between streams and generators forwarding errors and
-         * properly cleaning up and provide a callback when the pipeline is complete.
-         *
-         * ```js
-         * const { pipeline } = require('stream');
-         * const fs = require('fs');
-         * const zlib = require('zlib');
-         *
-         * // Use the pipeline API to easily pipe a series of streams
-         * // together and get notified when the pipeline is fully done.
-         *
-         * // A pipeline to gzip a potentially huge tar file efficiently:
-         *
-         * pipeline(
-         *   fs.createReadStream('archive.tar'),
-         *   zlib.createGzip(),
-         *   fs.createWriteStream('archive.tar.gz'),
-         *   (err) => {
-         *     if (err) {
-         *       console.error('Pipeline failed.', err);
-         *     } else {
-         *       console.log('Pipeline succeeded.');
-         *     }
-         *   }
-         * );
-         * ```
-         *
-         * The `pipeline` API provides a promise version, which can also
-         * receive an options argument as the last parameter with a`signal` `AbortSignal` property. When the signal is aborted,`destroy` will be called on the underlying pipeline, with
-         * an`AbortError`.
-         *
-         * ```js
-         * const { pipeline } = require('stream/promises');
-         *
-         * async function run() {
-         *   await pipeline(
-         *     fs.createReadStream('archive.tar'),
-         *     zlib.createGzip(),
-         *     fs.createWriteStream('archive.tar.gz')
-         *   );
-         *   console.log('Pipeline succeeded.');
-         * }
-         *
-         * run().catch(console.error);
-         * ```
-         *
-         * To use an `AbortSignal`, pass it inside an options object,
-         * as the last argument:
-         *
-         * ```js
-         * const { pipeline } = require('stream/promises');
-         *
-         * async function run() {
-         *   const ac = new AbortController();
-         *   const signal = ac.signal;
-         *
-         *   setTimeout(() => ac.abort(), 1);
-         *   await pipeline(
-         *     fs.createReadStream('archive.tar'),
-         *     zlib.createGzip(),
-         *     fs.createWriteStream('archive.tar.gz'),
-         *     { signal },
-         *   );
-         * }
-         *
-         * run().catch(console.error); // AbortError
-         * ```
-         *
-         * The `pipeline` API also supports async generators:
-         *
-         * ```js
-         * const { pipeline } = require('stream/promises');
-         * const fs = require('fs');
-         *
-         * async function run() {
-         *   await pipeline(
-         *     fs.createReadStream('lowercase.txt'),
-         *     async function* (source, { signal }) {
-         *       source.setEncoding('utf8');  // Work with strings rather than `Buffer`s.
-         *       for await (const chunk of source) {
-         *         yield await processChunk(chunk, { signal });
-         *       }
-         *     },
-         *     fs.createWriteStream('uppercase.txt')
-         *   );
-         *   console.log('Pipeline succeeded.');
-         * }
-         *
-         * run().catch(console.error);
-         * ```
-         *
-         * Remember to handle the `signal` argument passed into the async generator.
-         * Especially in the case where the async generator is the source for the
-         * pipeline (i.e. first argument) or the pipeline will never complete.
-         *
-         * ```js
-         * const { pipeline } = require('stream/promises');
-         * const fs = require('fs');
-         *
-         * async function run() {
-         *   await pipeline(
-         *     async function* ({ signal }) {
-         *       await someLongRunningfn({ signal });
-         *       yield 'asd';
-         *     },
-         *     fs.createWriteStream('uppercase.txt')
-         *   );
-         *   console.log('Pipeline succeeded.');
-         * }
-         *
-         * run().catch(console.error);
-         * ```
-         *
-         * `stream.pipeline()` will call `stream.destroy(err)` on all streams except:
-         *
-         * * `Readable` streams which have emitted `'end'` or `'close'`.
-         * * `Writable` streams which have emitted `'finish'` or `'close'`.
-         *
-         * `stream.pipeline()` leaves dangling event listeners on the streams
-         * after the `callback` has been invoked. In the case of reuse of streams after
-         * failure, this can cause event listener leaks and swallowed errors. If the last
-         * stream is readable, dangling event listeners will be removed so that the last
-         * stream can be consumed later.
-         *
-         * `stream.pipeline()` closes all the streams when an error is raised.
-         * The `IncomingRequest` usage with `pipeline` could lead to an unexpected behavior
-         * once it would destroy the socket without sending the expected response.
-         * See the example below:
-         *
-         * ```js
-         * const fs = require('fs');
-         * const http = require('http');
-         * const { pipeline } = require('stream');
-         *
-         * const server = http.createServer((req, res) => {
-         *   const fileStream = fs.createReadStream('./fileNotExist.txt');
-         *   pipeline(fileStream, res, (err) => {
-         *     if (err) {
-         *       console.log(err); // No such file
-         *       // this message can't be sent once `pipeline` already destroyed the socket
-         *       return res.end('error!!!');
-         *     }
-         *   });
-         * });
-         * ```
-         * @since v10.0.0
-         * @param callback Called when the pipeline is fully done.
-         */
-        function pipeline<A extends PipelineSource<any>, B extends PipelineDestination<A, any>>(
-            source: A,
-            destination: B,
-            callback?: PipelineCallback<B>
-        ): B extends NodeJS.WritableStream ? B : NodeJS.WritableStream;
-        function pipeline<A extends PipelineSource<any>, T1 extends PipelineTransform<A, any>, B extends PipelineDestination<T1, any>>(
-            source: A,
-            transform1: T1,
-            destination: B,
-            callback?: PipelineCallback<B>
-        ): B extends NodeJS.WritableStream ? B : NodeJS.WritableStream;
-        function pipeline<A extends PipelineSource<any>, T1 extends PipelineTransform<A, any>, T2 extends PipelineTransform<T1, any>, B extends PipelineDestination<T2, any>>(
-            source: A,
-            transform1: T1,
-            transform2: T2,
-            destination: B,
-            callback?: PipelineCallback<B>
-        ): B extends NodeJS.WritableStream ? B : NodeJS.WritableStream;
-        function pipeline<
-            A extends PipelineSource<any>,
-            T1 extends PipelineTransform<A, any>,
-            T2 extends PipelineTransform<T1, any>,
-            T3 extends PipelineTransform<T2, any>,
-            B extends PipelineDestination<T3, any>
-        >(source: A, transform1: T1, transform2: T2, transform3: T3, destination: B, callback?: PipelineCallback<B>): B extends NodeJS.WritableStream ? B : NodeJS.WritableStream;
-        function pipeline<
-            A extends PipelineSource<any>,
-            T1 extends PipelineTransform<A, any>,
-            T2 extends PipelineTransform<T1, any>,
-            T3 extends PipelineTransform<T2, any>,
-            T4 extends PipelineTransform<T3, any>,
-            B extends PipelineDestination<T4, any>
-        >(source: A, transform1: T1, transform2: T2, transform3: T3, transform4: T4, destination: B, callback?: PipelineCallback<B>): B extends NodeJS.WritableStream ? B : NodeJS.WritableStream;
-        function pipeline(
-            streams: ReadonlyArray<NodeJS.ReadableStream | NodeJS.WritableStream | NodeJS.ReadWriteStream>,
-            callback?: (err: NodeJS.ErrnoException | null) => void
-        ): NodeJS.WritableStream;
-        function pipeline(
-            stream1: NodeJS.ReadableStream,
-            stream2: NodeJS.ReadWriteStream | NodeJS.WritableStream,
-            ...streams: Array<NodeJS.ReadWriteStream | NodeJS.WritableStream | ((err: NodeJS.ErrnoException | null) => void)>
-        ): NodeJS.WritableStream;
-        namespace pipeline {
-            function __promisify__<A extends PipelineSource<any>, B extends PipelineDestination<A, any>>(source: A, destination: B, options?: PipelineOptions): PipelinePromise<B>;
-            function __promisify__<A extends PipelineSource<any>, T1 extends PipelineTransform<A, any>, B extends PipelineDestination<T1, any>>(
-                source: A,
-                transform1: T1,
-                destination: B,
-                options?: PipelineOptions
-            ): PipelinePromise<B>;
-            function __promisify__<A extends PipelineSource<any>, T1 extends PipelineTransform<A, any>, T2 extends PipelineTransform<T1, any>, B extends PipelineDestination<T2, any>>(
-                source: A,
-                transform1: T1,
-                transform2: T2,
-                destination: B,
-                options?: PipelineOptions
-            ): PipelinePromise<B>;
-            function __promisify__<
-                A extends PipelineSource<any>,
-                T1 extends PipelineTransform<A, any>,
-                T2 extends PipelineTransform<T1, any>,
-                T3 extends PipelineTransform<T2, any>,
-                B extends PipelineDestination<T3, any>
-            >(source: A, transform1: T1, transform2: T2, transform3: T3, destination: B, options?: PipelineOptions): PipelinePromise<B>;
-            function __promisify__<
-                A extends PipelineSource<any>,
-                T1 extends PipelineTransform<A, any>,
-                T2 extends PipelineTransform<T1, any>,
-                T3 extends PipelineTransform<T2, any>,
-                T4 extends PipelineTransform<T3, any>,
-                B extends PipelineDestination<T4, any>
-            >(source: A, transform1: T1, transform2: T2, transform3: T3, transform4: T4, destination: B, options?: PipelineOptions): PipelinePromise<B>;
-            function __promisify__(streams: ReadonlyArray<NodeJS.ReadableStream | NodeJS.WritableStream | NodeJS.ReadWriteStream>, options?: PipelineOptions): Promise<void>;
-            function __promisify__(
-                stream1: NodeJS.ReadableStream,
-                stream2: NodeJS.ReadWriteStream | NodeJS.WritableStream,
-                ...streams: Array<NodeJS.ReadWriteStream | NodeJS.WritableStream | PipelineOptions>
-            ): Promise<void>;
-        }
-        interface Pipe {
-            close(): void;
-            hasRef(): boolean;
-            ref(): void;
-            unref(): void;
-        }
-
-        /**
-         * Returns whether the stream has encountered an error.
-         * @since v17.3.0
-         */
-        function isErrored(stream: Readable | Writable | NodeJS.ReadableStream | NodeJS.WritableStream): boolean;
-
-        /**
-         * Returns whether the stream is readable.
-         * @since v17.4.0
-         */
-        function isReadable(stream: Readable | NodeJS.ReadableStream): boolean;
-
-        const promises: typeof streamPromises;
-        const consumers: typeof streamConsumers;
-    }
-    export = internal;
-}
-declare module 'node:stream' {
-    import stream = require('stream');
-    export = stream;
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/call-bind/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/call-bind/index.js
deleted file mode 100644
index 6fa3e4af7e19fd9d15e61bf77d96f8ca4e7d5b74..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/call-bind/index.js
+++ /dev/null
@@ -1,47 +0,0 @@
-'use strict';
-
-var bind = require('function-bind');
-var GetIntrinsic = require('get-intrinsic');
-
-var $apply = GetIntrinsic('%Function.prototype.apply%');
-var $call = GetIntrinsic('%Function.prototype.call%');
-var $reflectApply = GetIntrinsic('%Reflect.apply%', true) || bind.call($call, $apply);
-
-var $gOPD = GetIntrinsic('%Object.getOwnPropertyDescriptor%', true);
-var $defineProperty = GetIntrinsic('%Object.defineProperty%', true);
-var $max = GetIntrinsic('%Math.max%');
-
-if ($defineProperty) {
-	try {
-		$defineProperty({}, 'a', { value: 1 });
-	} catch (e) {
-		// IE 8 has a broken defineProperty
-		$defineProperty = null;
-	}
-}
-
-module.exports = function callBind(originalFunction) {
-	var func = $reflectApply(bind, $call, arguments);
-	if ($gOPD && $defineProperty) {
-		var desc = $gOPD(func, 'length');
-		if (desc.configurable) {
-			// original length, plus the receiver, minus any additional arguments (after the receiver)
-			$defineProperty(
-				func,
-				'length',
-				{ value: 1 + $max(0, originalFunction.length - (arguments.length - 1)) }
-			);
-		}
-	}
-	return func;
-};
-
-var applyBind = function applyBind() {
-	return $reflectApply(bind, $apply, arguments);
-};
-
-if ($defineProperty) {
-	$defineProperty(module.exports, 'apply', { value: applyBind });
-} else {
-	module.exports.apply = applyBind;
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/on-finished/HISTORY.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/on-finished/HISTORY.md
deleted file mode 100644
index 1917595a714e11049ec9402d87aa625c68caa080..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/on-finished/HISTORY.md
+++ /dev/null
@@ -1,98 +0,0 @@
-2.4.1 / 2022-02-22
-==================
-
-  * Fix error on early async hooks implementations
-
-2.4.0 / 2022-02-21
-==================
-
-  * Prevent loss of async hooks context
-
-2.3.0 / 2015-05-26
-==================
-
-  * Add defined behavior for HTTP `CONNECT` requests
-  * Add defined behavior for HTTP `Upgrade` requests
-  * deps: ee-first@1.1.1
-
-2.2.1 / 2015-04-22
-==================
-
-  * Fix `isFinished(req)` when data buffered
-
-2.2.0 / 2014-12-22
-==================
-
-  * Add message object to callback arguments
-
-2.1.1 / 2014-10-22
-==================
-
-  * Fix handling of pipelined requests
-
-2.1.0 / 2014-08-16
-==================
-
-  * Check if `socket` is detached
-  * Return `undefined` for `isFinished` if state unknown
-
-2.0.0 / 2014-08-16
-==================
-
-  * Add `isFinished` function
-  * Move to `jshttp` organization
-  * Remove support for plain socket argument
-  * Rename to `on-finished`
-  * Support both `req` and `res` as arguments
-  * deps: ee-first@1.0.5
-
-1.2.2 / 2014-06-10
-==================
-
-  * Reduce listeners added to emitters
-    - avoids "event emitter leak" warnings when used multiple times on same request
-
-1.2.1 / 2014-06-08
-==================
-
-  * Fix returned value when already finished
-
-1.2.0 / 2014-06-05
-==================
-
-  * Call callback when called on already-finished socket
-
-1.1.4 / 2014-05-27
-==================
-
-  * Support node.js 0.8
-
-1.1.3 / 2014-04-30
-==================
-
-  * Make sure errors passed as instanceof `Error`
-
-1.1.2 / 2014-04-18
-==================
-
-  * Default the `socket` to passed-in object
-
-1.1.1 / 2014-01-16
-==================
-
-  * Rename module to `finished`
-
-1.1.0 / 2013-12-25
-==================
-
-  * Call callback when called on already-errored socket
-
-1.0.1 / 2013-12-20
-==================
-
-  * Actually pass the error to the callback
-
-1.0.0 / 2013-12-20
-==================
-
-  * Initial release
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/type-is/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/type-is/README.md
deleted file mode 100644
index b85ef8f78ff14113fe26b3a04bf82c3d07f88a73..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/type-is/README.md
+++ /dev/null
@@ -1,170 +0,0 @@
-# type-is
-
-[![NPM Version][npm-version-image]][npm-url]
-[![NPM Downloads][npm-downloads-image]][npm-url]
-[![Node.js Version][node-version-image]][node-version-url]
-[![Build Status][travis-image]][travis-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-Infer the content-type of a request.
-
-### Install
-
-This is a [Node.js](https://nodejs.org/en/) module available through the
-[npm registry](https://www.npmjs.com/). Installation is done using the
-[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally):
-
-```sh
-$ npm install type-is
-```
-
-## API
-
-```js
-var http = require('http')
-var typeis = require('type-is')
-
-http.createServer(function (req, res) {
-  var istext = typeis(req, ['text/*'])
-  res.end('you ' + (istext ? 'sent' : 'did not send') + ' me text')
-})
-```
-
-### typeis(request, types)
-
-Checks if the `request` is one of the `types`. If the request has no body,
-even if there is a `Content-Type` header, then `null` is returned. If the
-`Content-Type` header is invalid or does not matches any of the `types`, then
-`false` is returned. Otherwise, a string of the type that matched is returned.
-
-The `request` argument is expected to be a Node.js HTTP request. The `types`
-argument is an array of type strings.
-
-Each type in the `types` array can be one of the following:
-
-- A file extension name such as `json`. This name will be returned if matched.
-- A mime type such as `application/json`.
-- A mime type with a wildcard such as `*/*` or `*/json` or `application/*`.
-  The full mime type will be returned if matched.
-- A suffix such as `+json`. This can be combined with a wildcard such as
-  `*/vnd+json` or `application/*+json`. The full mime type will be returned
-  if matched.
-
-Some examples to illustrate the inputs and returned value:
-
-<!-- eslint-disable no-undef -->
-
-```js
-// req.headers.content-type = 'application/json'
-
-typeis(req, ['json']) // => 'json'
-typeis(req, ['html', 'json']) // => 'json'
-typeis(req, ['application/*']) // => 'application/json'
-typeis(req, ['application/json']) // => 'application/json'
-
-typeis(req, ['html']) // => false
-```
-
-### typeis.hasBody(request)
-
-Returns a Boolean if the given `request` has a body, regardless of the
-`Content-Type` header.
-
-Having a body has no relation to how large the body is (it may be 0 bytes).
-This is similar to how file existence works. If a body does exist, then this
-indicates that there is data to read from the Node.js request stream.
-
-<!-- eslint-disable no-undef -->
-
-```js
-if (typeis.hasBody(req)) {
-  // read the body, since there is one
-
-  req.on('data', function (chunk) {
-    // ...
-  })
-}
-```
-
-### typeis.is(mediaType, types)
-
-Checks if the `mediaType` is one of the `types`. If the `mediaType` is invalid
-or does not matches any of the `types`, then `false` is returned. Otherwise, a
-string of the type that matched is returned.
-
-The `mediaType` argument is expected to be a
-[media type](https://tools.ietf.org/html/rfc6838) string. The `types` argument
-is an array of type strings.
-
-Each type in the `types` array can be one of the following:
-
-- A file extension name such as `json`. This name will be returned if matched.
-- A mime type such as `application/json`.
-- A mime type with a wildcard such as `*/*` or `*/json` or `application/*`.
-  The full mime type will be returned if matched.
-- A suffix such as `+json`. This can be combined with a wildcard such as
-  `*/vnd+json` or `application/*+json`. The full mime type will be returned
-  if matched.
-
-Some examples to illustrate the inputs and returned value:
-
-<!-- eslint-disable no-undef -->
-
-```js
-var mediaType = 'application/json'
-
-typeis.is(mediaType, ['json']) // => 'json'
-typeis.is(mediaType, ['html', 'json']) // => 'json'
-typeis.is(mediaType, ['application/*']) // => 'application/json'
-typeis.is(mediaType, ['application/json']) // => 'application/json'
-
-typeis.is(mediaType, ['html']) // => false
-```
-
-## Examples
-
-### Example body parser
-
-```js
-var express = require('express')
-var typeis = require('type-is')
-
-var app = express()
-
-app.use(function bodyParser (req, res, next) {
-  if (!typeis.hasBody(req)) {
-    return next()
-  }
-
-  switch (typeis(req, ['urlencoded', 'json', 'multipart'])) {
-    case 'urlencoded':
-      // parse urlencoded body
-      throw new Error('implement urlencoded body parsing')
-    case 'json':
-      // parse json body
-      throw new Error('implement json body parsing')
-    case 'multipart':
-      // parse multipart body
-      throw new Error('implement multipart body parsing')
-    default:
-      // 415 error code
-      res.statusCode = 415
-      res.end()
-      break
-  }
-})
-```
-
-## License
-
-[MIT](LICENSE)
-
-[coveralls-image]: https://badgen.net/coveralls/c/github/jshttp/type-is/master
-[coveralls-url]: https://coveralls.io/r/jshttp/type-is?branch=master
-[node-version-image]: https://badgen.net/npm/node/type-is
-[node-version-url]: https://nodejs.org/en/download
-[npm-downloads-image]: https://badgen.net/npm/dm/type-is
-[npm-url]: https://npmjs.org/package/type-is
-[npm-version-image]: https://badgen.net/npm/v/type-is
-[travis-image]: https://badgen.net/travis/jshttp/type-is/master
-[travis-url]: https://travis-ci.org/jshttp/type-is
diff --git a/spaces/fffiloni/lama-video-watermark-remover/fetch_data/places_standard_train_prepare.sh b/spaces/fffiloni/lama-video-watermark-remover/fetch_data/places_standard_train_prepare.sh
deleted file mode 100644
index b5389e7096bade08526162733658e221808716fd..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/fetch_data/places_standard_train_prepare.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-mkdir -p places_standard_dataset/train
-
-# untar without folder structure
-tar -xvf train_large_places365standard.tar --transform='s/.*\///' -C places_standard_dataset/train
-
-# create location config places.yaml
-PWD=$(pwd)
-DATASET=${PWD}/places_standard_dataset
-PLACES=${PWD}/configs/training/location/places_standard.yaml
-
-touch $PLACES
-echo "# @package _group_" >> $PLACES
-echo "data_root_dir: ${DATASET}/" >> $PLACES
-echo "out_root_dir: ${PWD}/experiments/" >> $PLACES
-echo "tb_dir: ${PWD}/tb_logs/" >> $PLACES
-echo "pretrained_models: ${PWD}/" >> $PLACES
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_update/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_update/run.py
deleted file mode 100644
index 35576951d117a12a1bddc0ed096d9bfec28e93e7..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_update/run.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-    # Animal Generator
-    Once you select a species, the detail panel should be visible.
-    """
-    )
-
-    species = gr.Radio(label="Animal Class", choices=["Mammal", "Fish", "Bird"])
-    animal = gr.Dropdown(label="Animal", choices=[])
-
-    with gr.Column(visible=False) as details_col:
-        weight = gr.Slider(0, 20)
-        details = gr.Textbox(label="Extra Details")
-        generate_btn = gr.Button("Generate")
-        output = gr.Textbox(label="Output")
-
-    species_map = {
-        "Mammal": ["Elephant", "Giraffe", "Hamster"],
-        "Fish": ["Shark", "Salmon", "Tuna"],
-        "Bird": ["Chicken", "Eagle", "Hawk"],
-    }
-
-    def filter_species(species):
-        return gr.Dropdown.update(
-            choices=species_map[species], value=species_map[species][1]
-        ), gr.update(visible=True)
-
-    species.change(filter_species, species, [animal, details_col])
-
-    def filter_weight(animal):
-        if animal in ("Elephant", "Shark", "Giraffe"):
-            return gr.update(maximum=100)
-        else:
-            return gr.update(maximum=20)
-
-    animal.change(filter_weight, animal, weight)
-    weight.change(lambda w: gr.update(lines=int(w / 10) + 1), weight, details)
-
-    generate_btn.click(lambda x: x, details, output)
-
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/freddyaboulton/all_demos_3/demos/sst_or_tts/run.py b/spaces/freddyaboulton/all_demos_3/demos/sst_or_tts/run.py
deleted file mode 100644
index 55f86ce01829c1440cd9a3b6f855e9fd227d4314..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/all_demos_3/demos/sst_or_tts/run.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-
-title = "GPT-J-6B"
-
-tts_examples = [
-    "I love learning machine learning",
-    "How do you do?",
-]
-
-tts_demo = gr.Interface.load(
-    "huggingface/facebook/fastspeech2-en-ljspeech",
-    title=None,
-    examples=tts_examples,
-    description="Give me something to say!",
-)
-
-stt_demo = gr.Interface.load(
-    "huggingface/facebook/wav2vec2-base-960h",
-    title=None,
-    inputs="mic",
-    description="Let me try to guess what you're saying!",
-)
-
-demo = gr.TabbedInterface([tts_demo, stt_demo], ["Text-to-speech", "Speech-to-text"])
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_ade20k_full.py b/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_ade20k_full.py
deleted file mode 100644
index e354b65d072c2e30ec976dabe14f25a771c4a720..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_ade20k_full.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-import numpy as np
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-
-from . import openseg_classes
-
-ADE20K_847_CATEGORIES = openseg_classes.get_ade20k_847_categories_with_prompt_eng()
-
-# TODO(Ju): Do we need color for this?
-ADE20k_847_COLORS = [np.random.randint(256, size=3).tolist() for k in ADE20K_847_CATEGORIES]
-
-MetadataCatalog.get("openvocab_ade20k_full_sem_seg_train").set(
-    stuff_colors=ADE20k_847_COLORS[:],
-)
-
-MetadataCatalog.get("openvocab_ade20k_full_sem_seg_val").set(
-    stuff_colors=ADE20k_847_COLORS[:],
-)
-
-
-def _get_ade20k_847_meta():
-    # We only need class names
-    stuff_classes = [k["name"] for k in ADE20K_847_CATEGORIES]
-    assert len(stuff_classes) == 847, len(stuff_classes)
-
-    ret = {
-        "stuff_classes": stuff_classes,
-    }
-    return ret
-
-
-def register_all_ade20k_847(root):
-    root = os.path.join(root, "ADE20K_2021_17_01")
-    meta = _get_ade20k_847_meta()
-    for name, dirname in [("train", "training"), ("val", "validation")]:
-        image_dir = os.path.join(root, "images_detectron2", dirname)
-        gt_dir = os.path.join(root, "annotations_detectron2", dirname)
-        name = f"openvocab_ade20k_full_sem_seg_{name}"
-        DatasetCatalog.register(
-            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg")
-        )
-        MetadataCatalog.get(name).set(
-            stuff_classes=meta["stuff_classes"][:],
-            image_root=image_dir,
-            sem_seg_root=gt_dir,
-            evaluator_type="sem_seg",
-            ignore_label=65535,  # NOTE: gt is saved in 16-bit TIFF images
-            gt_ext="tif",
-        )
-
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_ade20k_847(_root)
\ No newline at end of file
diff --git a/spaces/fynn3003/image_to_text/README.md b/spaces/fynn3003/image_to_text/README.md
deleted file mode 100644
index bf7d755bec34c34b6be410cad9e490b135e59ec5..0000000000000000000000000000000000000000
--- a/spaces/fynn3003/image_to_text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image To Text
-emoji: 🏆
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/__init__.py
deleted file mode 100644
index dc5ac03eea6f5ba7968706f1863c8bc4f8aaaf6a..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import cv2
-import numpy as np
-import torch
-
-from einops import rearrange
-from .api import MiDaSInference
-
-
-class MidasDetector:
-    def __init__(self):
-        self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
-
-    def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1):
-        assert input_image.ndim == 3
-        image_depth = input_image
-        with torch.no_grad():
-            image_depth = torch.from_numpy(image_depth).float().cuda()
-            image_depth = image_depth / 127.5 - 1.0
-            image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
-            depth = self.model(image_depth)[0]
-
-            depth_pt = depth.clone()
-            depth_pt -= torch.min(depth_pt)
-            depth_pt /= torch.max(depth_pt)
-            depth_pt = depth_pt.cpu().numpy()
-            depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
-
-            depth_np = depth.cpu().numpy()
-            x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
-            y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
-            z = np.ones_like(x) * a
-            x[depth_pt < bg_th] = 0
-            y[depth_pt < bg_th] = 0
-            normal = np.stack([x, y, z], axis=2)
-            normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
-            normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
-
-            return depth_image, normal_image
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/datasets/ade20k.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/datasets/ade20k.py
deleted file mode 100644
index efc8b4bb20c981f3db6df7eb52b3dc0744c94cc0..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/datasets/ade20k.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# dataset settings
-dataset_type = 'ADE20KDataset'
-data_root = 'data/ade/ADEChallengeData2016'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (512, 512)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', reduce_zero_label=True),
-    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2048, 512),
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/training',
-        ann_dir='annotations/training',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline))
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/correlation.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/correlation.py
deleted file mode 100644
index 3d0b79c301b29915dfaf4d2b1846c59be73127d3..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/correlation.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import Tensor, nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['correlation_forward', 'correlation_backward'])
-
-
-class CorrelationFunction(Function):
-
-    @staticmethod
-    def forward(ctx,
-                input1,
-                input2,
-                kernel_size=1,
-                max_displacement=1,
-                stride=1,
-                padding=1,
-                dilation=1,
-                dilation_patch=1):
-
-        ctx.save_for_backward(input1, input2)
-
-        kH, kW = ctx.kernel_size = _pair(kernel_size)
-        patch_size = max_displacement * 2 + 1
-        ctx.patch_size = patch_size
-        dH, dW = ctx.stride = _pair(stride)
-        padH, padW = ctx.padding = _pair(padding)
-        dilationH, dilationW = ctx.dilation = _pair(dilation)
-        dilation_patchH, dilation_patchW = ctx.dilation_patch = _pair(
-            dilation_patch)
-
-        output_size = CorrelationFunction._output_size(ctx, input1)
-
-        output = input1.new_zeros(output_size)
-
-        ext_module.correlation_forward(
-            input1,
-            input2,
-            output,
-            kH=kH,
-            kW=kW,
-            patchH=patch_size,
-            patchW=patch_size,
-            padH=padH,
-            padW=padW,
-            dilationH=dilationH,
-            dilationW=dilationW,
-            dilation_patchH=dilation_patchH,
-            dilation_patchW=dilation_patchW,
-            dH=dH,
-            dW=dW)
-
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input1, input2 = ctx.saved_tensors
-
-        kH, kW = ctx.kernel_size
-        patch_size = ctx.patch_size
-        padH, padW = ctx.padding
-        dilationH, dilationW = ctx.dilation
-        dilation_patchH, dilation_patchW = ctx.dilation_patch
-        dH, dW = ctx.stride
-        grad_input1 = torch.zeros_like(input1)
-        grad_input2 = torch.zeros_like(input2)
-
-        ext_module.correlation_backward(
-            grad_output,
-            input1,
-            input2,
-            grad_input1,
-            grad_input2,
-            kH=kH,
-            kW=kW,
-            patchH=patch_size,
-            patchW=patch_size,
-            padH=padH,
-            padW=padW,
-            dilationH=dilationH,
-            dilationW=dilationW,
-            dilation_patchH=dilation_patchH,
-            dilation_patchW=dilation_patchW,
-            dH=dH,
-            dW=dW)
-        return grad_input1, grad_input2, None, None, None, None, None, None
-
-    @staticmethod
-    def _output_size(ctx, input1):
-        iH, iW = input1.size(2), input1.size(3)
-        batch_size = input1.size(0)
-        kH, kW = ctx.kernel_size
-        patch_size = ctx.patch_size
-        dH, dW = ctx.stride
-        padH, padW = ctx.padding
-        dilationH, dilationW = ctx.dilation
-        dilatedKH = (kH - 1) * dilationH + 1
-        dilatedKW = (kW - 1) * dilationW + 1
-
-        oH = int((iH + 2 * padH - dilatedKH) / dH + 1)
-        oW = int((iW + 2 * padW - dilatedKW) / dW + 1)
-
-        output_size = (batch_size, patch_size, patch_size, oH, oW)
-        return output_size
-
-
-class Correlation(nn.Module):
-    r"""Correlation operator
-
-    This correlation operator works for optical flow correlation computation.
-
-    There are two batched tensors with shape :math:`(N, C, H, W)`,
-    and the correlation output's shape is :math:`(N, max\_displacement \times
-    2 + 1, max\_displacement * 2 + 1, H_{out}, W_{out})`
-
-    where
-
-    .. math::
-        H_{out} = \left\lfloor\frac{H_{in}  + 2 \times padding -
-            dilation \times (kernel\_size - 1) - 1}
-            {stride} + 1\right\rfloor
-
-    .. math::
-        W_{out} = \left\lfloor\frac{W_{in}  + 2 \times padding - dilation
-            \times (kernel\_size - 1) - 1}
-            {stride} + 1\right\rfloor
-
-    the correlation item :math:`(N_i, dy, dx)` is formed by taking the sliding
-    window convolution between input1 and shifted input2,
-
-    .. math::
-        Corr(N_i, dx, dy) =
-        \sum_{c=0}^{C-1}
-        input1(N_i, c) \star
-        \mathcal{S}(input2(N_i, c), dy, dx)
-
-    where :math:`\star` is the valid 2d sliding window convolution operator,
-    and :math:`\mathcal{S}` means shifting the input features (auto-complete
-    zero marginal), and :math:`dx, dy` are shifting distance, :math:`dx, dy \in
-    [-max\_displacement \times dilation\_patch, max\_displacement \times
-    dilation\_patch]`.
-
-    Args:
-        kernel_size (int): The size of sliding window i.e. local neighborhood
-            representing the center points and involved in correlation
-            computation. Defaults to 1.
-        max_displacement (int): The radius for computing correlation volume,
-            but the actual working space can be dilated by dilation_patch.
-            Defaults to 1.
-        stride (int): The stride of the sliding blocks in the input spatial
-            dimensions. Defaults to 1.
-        padding (int): Zero padding added to all four sides of the input1.
-            Defaults to 0.
-        dilation (int): The spacing of local neighborhood that will involved
-            in correlation. Defaults to 1.
-        dilation_patch (int): The spacing between position need to compute
-            correlation.  Defaults to 1.
-    """
-
-    def __init__(self,
-                 kernel_size: int = 1,
-                 max_displacement: int = 1,
-                 stride: int = 1,
-                 padding: int = 0,
-                 dilation: int = 1,
-                 dilation_patch: int = 1) -> None:
-        super().__init__()
-        self.kernel_size = kernel_size
-        self.max_displacement = max_displacement
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.dilation_patch = dilation_patch
-
-    def forward(self, input1: Tensor, input2: Tensor) -> Tensor:
-        return CorrelationFunction.apply(input1, input2, self.kernel_size,
-                                         self.max_displacement, self.stride,
-                                         self.padding, self.dilation,
-                                         self.dilation_patch)
-
-    def __repr__(self) -> str:
-        s = self.__class__.__name__
-        s += f'(kernel_size={self.kernel_size}, '
-        s += f'max_displacement={self.max_displacement}, '
-        s += f'stride={self.stride}, '
-        s += f'padding={self.padding}, '
-        s += f'dilation={self.dilation}, '
-        s += f'dilation_patch={self.dilation_patch})'
-        return s
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv_custom/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv_custom/__init__.py
deleted file mode 100644
index 4b958738b9fd93bfcec239c550df1d9a44b8c536..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv_custom/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from .checkpoint import load_checkpoint
-
-__all__ = ['load_checkpoint']
\ No newline at end of file
diff --git a/spaces/giseldo/story_point_estimator_metrics/util.py b/spaces/giseldo/story_point_estimator_metrics/util.py
deleted file mode 100644
index 672d547f8189c54dc863ba5257f4239bc97afc37..0000000000000000000000000000000000000000
--- a/spaces/giseldo/story_point_estimator_metrics/util.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import re
-from string import punctuation
-
-def escape_tags_and_content(text):
-    """Escape tags and their content containing text, which is not written in natural language, such as code snippets"""
-
-    NO_TEXT_TAGS = "code", "noformat"
-    for tag in NO_TEXT_TAGS:
-        regex_matching_tag = re.compile("\{%s(.*?)\}(.*?)\{%s\}" % (tag, tag), re.DOTALL)
-        text = re.sub(regex_matching_tag, "", text)
-
-    return text
-
-def escape_tags(text):
-    """Escape markup tags, but retain their content"""
-
-    ESCAPE_TAGS = "color", "quote", "anchor", "panel"
-    for tag in  ESCAPE_TAGS:
-        text = re.sub("\{%s(.*?)\}" % tag, "", text)
-
-    return text
-
-def escape_strings(text):
-    """Escape line breaks, tabulators, slashes and JIRA heading markup symbols"""
-
-    ESCAPE_STRINGS = "\\r", "\\n", "\\t", "\\f", "\\v", "\"", "\\\\", "h1. ", "h2. ", "h3. ", "h4. ", "h5. ", "h6. "
-    for escape_string in ESCAPE_STRINGS:
-        text = text.replace(escape_string, " ")
-
-    return text
-
-def escape_links(text):
-    """Escape external and internal links, recognized by JIRA markup or leading 'http://' or 'https://' """
-
-    LINK_STARTERS = r"\#", r"\^", r"http\:\/\/", r"https\:\/\/", r"malto\:", r"file\:", r"\~"
-    for link_starter in LINK_STARTERS:
-        text = re.sub("\[(.*?\\|)?%s(.*?)\]" % link_starter, "", text)
-        text = re.sub(r"\bhttps?://\S+", "", text)
-
-    return text
-
-def escape_hex_character_codes(text):
-    """Escape characters outside the latin alphabet which are converted to hex code representation"""
-
-    return re.sub(r"\\x\w\w", "", text)
-
-def escape_punctuation_boundaries(text):
-    """Remove all punctuation marks from the beginning and end of words,
-    except for trailing period at the end of words"""
-
-    return " ".join([word.strip(punctuation.replace(".", "")).lstrip(".") for word in text.split()])
-
-def escape_odd_spaces(text):
-    """Replace several consequent spaces with one space
-    and remove spaces from string start and end"""
-
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-
-    return text
\ No newline at end of file
diff --git a/spaces/google/sdxl/README.md b/spaces/google/sdxl/README.md
deleted file mode 100644
index 5583208e664923cd7ed69eb20d9853fd0ee47db5..0000000000000000000000000000000000000000
--- a/spaces/google/sdxl/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stable Diffusion XL on TPUv5e
-emoji: 🏋️	#👕🌟
-colorFrom: red
-colorTo: black
-sdk: gradio
-sdk_version: 3.45.1
-app_file: app.py
-license: mit
-disable_embedding: true
-hf_oauth: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/7 Welcome To London 3 Full Movie in Telugu Free Download A Gripping Tale of Thriller and Romance.md b/spaces/gotiQspiryo/whisper-ui/examples/7 Welcome To London 3 Full Movie in Telugu Free Download A Gripping Tale of Thriller and Romance.md
deleted file mode 100644
index 66a0a2250dd24be4a2b5a9f90d378fea603707b5..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/7 Welcome To London 3 Full Movie in Telugu Free Download A Gripping Tale of Thriller and Romance.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>7 Welcome To London 3 full movie in telugu free download</h2><br /><p><b><b>Download Zip</b> &#127775; <a href="https://urlgoal.com/2uyM27">https://urlgoal.com/2uyM27</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Animate Cc 2018 Download Mac __FULL__.md b/spaces/gotiQspiryo/whisper-ui/examples/Adobe Animate Cc 2018 Download Mac __FULL__.md
deleted file mode 100644
index 40abcfd7f6a3b45adb4be0ea30d3686257cb5a0a..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Animate Cc 2018 Download Mac __FULL__.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>Click the Below Download Button to start the Adobe Animate CC 2018 Mac Free with Direct Download Link Pause and Resume.Adobe Animate CC 2018 Mac is Placed on Our High speed dedicated server with the High-speed download of Adobe Animate CC 2018 Latest For macOSX.</p>
-<p>I tried following some of the earlier links you provided, (2015.5, 2015, even 2014), but when I clink on the links to animate it says access denied. It does for the Creative Cloud desktop app as well, as I thought downloading a new version of the desktop app might help in some way. Any other ideas?</p>
-<h2>Adobe Animate Cc 2018 Download Mac</h2><br /><p><b><b>Download File</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://urlgoal.com/2uyNhP">https://urlgoal.com/2uyNhP</a></b></p><br /><br />
-<p>There is also a message that pops up and says C:\ProgramFiles\Adobe\AdobeAnimateCC2017\Common\Configuration\Publish is modified. Reinstall Animate CC to resolve issue. If that helps narrow down the problem... I've uninstalled all of Creative Cloud, the desktop app and all software, and reinstalled it. No software can be downloaded through the desktop app now. They all say "installation failed" immediately. I was able to redownload Photoshop 2018 through the links you provided, and it works perfectly fine. It's just Animate that is still giving me problems. And of course, the desktop app, which won't download anything.</p>
-<p>Adobe Animate CC 2018 is famous application for 2D and 3D designing including animations. Animated graphics can be used in multiple web portals. You will find a huge collection of designing and development tools in it. By using Adobe Animate CC you can easily create videos, Smart phones contents and 3D animated games by using Adobe Animate CC 2018 Portable. Here you can also download Adobe Character Animator CC 2018.</p>
-<p>You can create animations of natural objects like plants, blossoms and atmosphere using Deco Tool of Adobe Animate 2018. A high level written text Engine is also involved which can be used for styled writing in a quite easy way. While concluding we are right to say that Adobe Animate CC 2018 Portable is an awesome application to create outstanding 2D as well as 3D animated graphics.</p>
-<p>The reason for which this software was originally developed was to make games, movies and other content for mobile devices. With adobe animate, designing animations is now easier than ever. It has a friendly environment which makes it even easier to work with it. It can be used by an artist as well as a flash programmer. An artist can use it to express his native skills in an excellent manner.</p>
-<p>There is a timeline in the layout to work on your project just like any other adobe product. There is also a motion editor in it. You can animate the motion of your objects and characters exactly the way you want to. It also carries some really magnificent tools in it which can be used to develop high quality animation in lesser time.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/examples/constrained_decoding/tok.py b/spaces/gradio/HuBERT/examples/constrained_decoding/tok.py
deleted file mode 100644
index b1f888a8c0d1b8ec7174859476cc3222456e0d2c..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/constrained_decoding/tok.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-import sacremoses
-
-
-def main(args):
-    """Tokenizes, preserving tabs"""
-    mt = sacremoses.MosesTokenizer(lang=args.lang)
-
-    def tok(s):
-        return mt.tokenize(s, return_str=True)
-
-    for line in sys.stdin:
-        parts = list(map(tok, line.split("\t")))
-        print(*parts, sep="\t", flush=True)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--lang", "-l", default="en")
-    parser.add_argument("--penn", "-p", action="store_true")
-    parser.add_argument("--fields", "-f", help="fields to tokenize")
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/gradio/HuBERT/examples/multilingual/data_scripts/binarize.py b/spaces/gradio/HuBERT/examples/multilingual/data_scripts/binarize.py
deleted file mode 100644
index ee54c6aabf021ca526743f8f1f67b91889e1e335..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/multilingual/data_scripts/binarize.py
+++ /dev/null
@@ -1,200 +0,0 @@
-import shutil
-import os, sys
-from subprocess import check_call, check_output
-import glob
-import argparse
-import shutil
-import pathlib
-import itertools
-
-def call_output(cmd):
-    print(f"Executing: {cmd}")
-    ret = check_output(cmd, shell=True)
-    print(ret)
-    return ret
-
-def call(cmd):
-    print(cmd)
-    check_call(cmd, shell=True)
-
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or  not WORKDIR_ROOT.strip():
-    print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
-    sys.exit(-1)
-
-SPM_PATH = os.environ.get('SPM_PATH', None)
-
-if SPM_PATH is None or not SPM_PATH.strip():
-    print("Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting...")
-    sys.exit(-1)
-
-
-SPM_MODEL = f'{WORKDIR_ROOT}/sentence.bpe.model'
-SPM_VOCAB = f'{WORKDIR_ROOT}/dict_250k.txt'
-
-SPM_ENCODE = f'{SPM_PATH}'
-
-if not os.path.exists(SPM_MODEL):
-    call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/sentence.bpe.model -O {SPM_MODEL}")
-
-
-if not os.path.exists(SPM_VOCAB):
-    call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/dict_250k.txt -O {SPM_VOCAB}")
-
-
-
-def get_data_size(raw):
-    cmd = f'wc -l {raw}'
-    ret = call_output(cmd)
-    return int(ret.split()[0])
-
-def encode_spm(model, direction, prefix='', splits=['train', 'test', 'valid'], pairs_per_shard=None):
-    src, tgt = direction.split('-')
-
-    for split in splits:
-        src_raw, tgt_raw = f'{RAW_DIR}/{split}{prefix}.{direction}.{src}', f'{RAW_DIR}/{split}{prefix}.{direction}.{tgt}'
-        if os.path.exists(src_raw) and os.path.exists(tgt_raw):
-            cmd = f"""python {SPM_ENCODE} \
-            --model {model}\
-            --output_format=piece \
-            --inputs {src_raw} {tgt_raw}  \
-            --outputs {BPE_DIR}/{direction}{prefix}/{split}.bpe.{src} {BPE_DIR}/{direction}{prefix}/{split}.bpe.{tgt} """
-            print(cmd)
-            call(cmd)
-
-
-def binarize_(
-    bpe_dir,
-    databin_dir,
-    direction, spm_vocab=SPM_VOCAB, 
-    splits=['train', 'test', 'valid'],
-):
-    src, tgt = direction.split('-')
-
-    try:
-        shutil.rmtree(f'{databin_dir}', ignore_errors=True)
-        os.mkdir(f'{databin_dir}')
-    except OSError as error:
-        print(error)
-    cmds = [
-        "fairseq-preprocess",
-        f"--source-lang {src} --target-lang {tgt}",
-        f"--destdir {databin_dir}/",
-        f"--workers 8",
-    ]
-    if isinstance(spm_vocab, tuple):
-        src_vocab, tgt_vocab = spm_vocab
-        cmds.extend(
-            [
-                f"--srcdict {src_vocab}",
-                f"--tgtdict {tgt_vocab}",
-            ]
-        )
-    else:
-        cmds.extend(
-            [
-                f"--joined-dictionary",
-                f"--srcdict {spm_vocab}",
-            ]
-        )
-    input_options = []
-    if 'train' in splits and glob.glob(f"{bpe_dir}/train.bpe*"):
-        input_options.append(
-            f"--trainpref {bpe_dir}/train.bpe",
-        )        
-    if 'valid' in splits and glob.glob(f"{bpe_dir}/valid.bpe*"):
-        input_options.append(f"--validpref {bpe_dir}/valid.bpe")
-    if 'test' in splits and glob.glob(f"{bpe_dir}/test.bpe*"):
-        input_options.append(f"--testpref {bpe_dir}/test.bpe")   
-    if len(input_options) > 0:    
-        cmd = " ".join(cmds + input_options)
-        print(cmd)
-        call(cmd)
-
-
-def binarize(
-    databin_dir,
-    direction, spm_vocab=SPM_VOCAB, prefix='',
-    splits=['train', 'test', 'valid'],
-    pairs_per_shard=None,
-):
-    def move_databin_files(from_folder, to_folder):
-        for bin_file in glob.glob(f"{from_folder}/*.bin") \
-            +  glob.glob(f"{from_folder}/*.idx") \
-            +  glob.glob(f"{from_folder}/dict*"):
-            try:
-                shutil.move(bin_file, to_folder)
-            except OSError as error:
-                print(error)      
-    bpe_databin_dir = f"{BPE_DIR}/{direction}{prefix}_databin"
-    bpe_dir = f"{BPE_DIR}/{direction}{prefix}"
-    if pairs_per_shard is None:
-        binarize_(bpe_dir, bpe_databin_dir, direction, spm_vocab=spm_vocab, splits=splits)
-        move_databin_files(bpe_databin_dir, databin_dir)
-    else:
-        # binarize valid and test which will not be sharded
-        binarize_(
-            bpe_dir, bpe_databin_dir, direction,
-            spm_vocab=spm_vocab, splits=[s for s in splits if s != "train"])
-        for shard_bpe_dir in glob.glob(f"{bpe_dir}/shard*"):
-            path_strs = os.path.split(shard_bpe_dir)
-            shard_str = path_strs[-1]
-            shard_folder = f"{bpe_databin_dir}/{shard_str}"
-            databin_shard_folder = f"{databin_dir}/{shard_str}"
-            print(f'working from {shard_folder} to {databin_shard_folder}')
-            os.makedirs(databin_shard_folder, exist_ok=True)
-            binarize_(
-                shard_bpe_dir, shard_folder, direction,
-                spm_vocab=spm_vocab, splits=["train"])
-
-            for test_data in glob.glob(f"{bpe_databin_dir}/valid.*") + glob.glob(f"{bpe_databin_dir}/test.*"):
-                filename = os.path.split(test_data)[-1]
-                try:
-                    os.symlink(test_data, f"{databin_shard_folder}/{filename}")
-                except OSError as error:
-                    print(error)                
-            move_databin_files(shard_folder, databin_shard_folder)
-
-
-def load_langs(path):
-    with open(path) as fr:
-        langs = [l.strip() for l in fr]
-    return langs
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--data_root", default=f"{WORKDIR_ROOT}/ML50")
-    parser.add_argument("--raw-folder", default='raw')
-    parser.add_argument("--bpe-folder", default='bpe')    
-    parser.add_argument("--databin-folder", default='databin')    
-
-    args = parser.parse_args()
-
-    DATA_PATH = args.data_root #'/private/home/yuqtang/public_data/ML50'   
-    RAW_DIR = f'{DATA_PATH}/{args.raw_folder}'
-    BPE_DIR = f'{DATA_PATH}/{args.bpe_folder}'
-    DATABIN_DIR = f'{DATA_PATH}/{args.databin_folder}'
-    os.makedirs(BPE_DIR, exist_ok=True)
-
-    raw_files = itertools.chain(
-        glob.glob(f'{RAW_DIR}/train*'),
-        glob.glob(f'{RAW_DIR}/valid*'),
-        glob.glob(f'{RAW_DIR}/test*'),
-    )
-
-    directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files]
-
-    for direction in directions:
-        prefix = ""
-        splits = ['train', 'valid', 'test']
-        try:
-            shutil.rmtree(f'{BPE_DIR}/{direction}{prefix}', ignore_errors=True)
-            os.mkdir(f'{BPE_DIR}/{direction}{prefix}')
-            os.makedirs(DATABIN_DIR, exist_ok=True)
-        except OSError as error: 
-            print(error)     
-        spm_model, spm_vocab = SPM_MODEL, SPM_VOCAB
-        encode_spm(spm_model, direction=direction, splits=splits)
-        binarize(DATABIN_DIR, direction, spm_vocab=spm_vocab, splits=splits)
diff --git a/spaces/gradio/HuBERT/fairseq/data/numel_dataset.py b/spaces/gradio/HuBERT/fairseq/data/numel_dataset.py
deleted file mode 100644
index ac86dfd2f1d89055de909656d61d6aca85523f00..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/numel_dataset.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-from . import BaseWrapperDataset
-
-
-class NumelDataset(BaseWrapperDataset):
-    def __init__(self, dataset, reduce=False):
-        super().__init__(dataset)
-        self.reduce = reduce
-
-    def __getitem__(self, index):
-        item = self.dataset[index]
-        if torch.is_tensor(item):
-            return torch.numel(item)
-        else:
-            return np.size(item)
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def collater(self, samples):
-        if self.reduce:
-            return sum(samples)
-        else:
-            return torch.tensor(samples)
diff --git a/spaces/gradio/HuBERT/fairseq/hub_utils.py b/spaces/gradio/HuBERT/fairseq/hub_utils.py
deleted file mode 100644
index d74470d2ecba2825221a2efa2ce21a9b698340df..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/hub_utils.py
+++ /dev/null
@@ -1,303 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import copy
-import logging
-import os
-from typing import Any, Dict, Iterator, List
-
-import torch
-from fairseq import utils
-from fairseq.data import encoders
-from omegaconf import open_dict
-from torch import nn
-
-
-logger = logging.getLogger(__name__)
-
-
-def from_pretrained(
-    model_name_or_path,
-    checkpoint_file="model.pt",
-    data_name_or_path=".",
-    archive_map=None,
-    **kwargs
-):
-    from fairseq import checkpoint_utils, file_utils
-
-    if archive_map is not None:
-        if model_name_or_path in archive_map:
-            model_name_or_path = archive_map[model_name_or_path]
-        if data_name_or_path is not None and data_name_or_path in archive_map:
-            data_name_or_path = archive_map[data_name_or_path]
-
-        # allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
-        # for each model
-        if isinstance(model_name_or_path, dict):
-            for k, v in model_name_or_path.items():
-                if k == "checkpoint_file":
-                    checkpoint_file = v
-                elif (
-                    k != "path"
-                    # only set kwargs that don't already have overrides
-                    and k not in kwargs
-                ):
-                    kwargs[k] = v
-            model_name_or_path = model_name_or_path["path"]
-
-    model_path = file_utils.load_archive_file(model_name_or_path)
-
-    # convenience hack for loading data and BPE codes from model archive
-    if data_name_or_path.startswith("."):
-        kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path))
-    else:
-        kwargs["data"] = file_utils.load_archive_file(data_name_or_path)
-    for file, arg in {
-        "code": "bpe_codes",
-        "bpecodes": "bpe_codes",
-        "sentencepiece.bpe.model": "sentencepiece_model",
-        "merges.txt": "bpe_merges",
-        "vocab.json": "bpe_vocab",
-    }.items():
-        path = os.path.join(model_path, file)
-        if os.path.exists(path):
-            kwargs[arg] = path
-
-    if "user_dir" in kwargs:
-        utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"]))
-
-    models, args, task = checkpoint_utils.load_model_ensemble_and_task(
-        [os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
-        arg_overrides=kwargs,
-    )
-
-    return {
-        "args": args,
-        "task": task,
-        "models": models,
-    }
-
-
-class GeneratorHubInterface(nn.Module):
-    """
-    PyTorch Hub interface for generating sequences from a pre-trained
-    translation or language model.
-    """
-
-    def __init__(self, cfg, task, models):
-        super().__init__()
-        self.cfg = cfg
-        self.task = task
-        self.models = nn.ModuleList(models)
-        self.src_dict = task.source_dictionary
-        self.tgt_dict = task.target_dictionary
-
-        # optimize model for generation
-        for model in self.models:
-            model.prepare_for_inference_(cfg)
-
-        # Load alignment dictionary for unknown word replacement
-        # (None if no unknown word replacement, empty if no path to align dictionary)
-        self.align_dict = utils.load_align_dict(cfg.generation.replace_unk)
-
-        self.tokenizer = encoders.build_tokenizer(cfg.tokenizer)
-        self.bpe = encoders.build_bpe(cfg.bpe)
-
-        self.max_positions = utils.resolve_max_positions(
-            self.task.max_positions(), *[model.max_positions() for model in models]
-        )
-
-        # this is useful for determining the device
-        self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float))
-
-    @property
-    def device(self):
-        return self._float_tensor.device
-
-    def translate(
-        self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs
-    ) -> List[str]:
-        return self.sample(sentences, beam, verbose, **kwargs)
-
-    def sample(
-        self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs
-    ) -> List[str]:
-        if isinstance(sentences, str):
-            return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0]
-        tokenized_sentences = [self.encode(sentence) for sentence in sentences]
-        batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs)
-        return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos]
-
-    def score(self, sentences: List[str], **kwargs):
-        if isinstance(sentences, str):
-            return self.score([sentences], **kwargs)[0]
-        # NOTE: this doesn't support translation tasks currently
-        tokenized_sentences = [self.encode(sentence) for sentence in sentences]
-        return [
-            hypos[0]
-            for hypos in self.generate(
-                tokenized_sentences, score_reference=True, **kwargs
-            )
-        ]
-
-    def generate(
-        self,
-        tokenized_sentences: List[torch.LongTensor],
-        beam: int = 5,
-        verbose: bool = False,
-        skip_invalid_size_inputs=False,
-        inference_step_args=None,
-        prefix_allowed_tokens_fn=None,
-        **kwargs
-    ) -> List[List[Dict[str, torch.Tensor]]]:
-        if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
-            return self.generate(
-                tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs
-            )[0]
-
-        # build generator using current args as well as any kwargs
-        gen_args = copy.deepcopy(self.cfg.generation)
-        with open_dict(gen_args):
-            gen_args.beam = beam
-            for k, v in kwargs.items():
-                setattr(gen_args, k, v)
-        generator = self.task.build_generator(
-            self.models,
-            gen_args,
-            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
-        )
-
-        inference_step_args = inference_step_args or {}
-        results = []
-        for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
-            batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
-            translations = self.task.inference_step(
-                generator, self.models, batch, **inference_step_args
-            )
-            for id, hypos in zip(batch["id"].tolist(), translations):
-                results.append((id, hypos))
-
-        # sort output to match input order
-        outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])]
-
-        if verbose:
-
-            def getarg(name, default):
-                return getattr(gen_args, name, getattr(self.cfg, name, default))
-
-            for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs):
-                src_str_with_unk = self.string(source_tokens)
-                logger.info("S\t{}".format(src_str_with_unk))
-                for hypo in target_hypotheses:
-                    hypo_str = self.decode(hypo["tokens"])
-                    logger.info("H\t{}\t{}".format(hypo["score"], hypo_str))
-                    logger.info(
-                        "P\t{}".format(
-                            " ".join(
-                                map(
-                                    lambda x: "{:.4f}".format(x),
-                                    hypo["positional_scores"].tolist(),
-                                )
-                            )
-                        )
-                    )
-                    if hypo["alignment"] is not None and getarg(
-                        "print_alignment", False
-                    ):
-                        logger.info(
-                            "A\t{}".format(
-                                " ".join(
-                                    [
-                                        "{}-{}".format(src_idx, tgt_idx)
-                                        for src_idx, tgt_idx in hypo["alignment"]
-                                    ]
-                                )
-                            )
-                        )
-        return outputs
-
-    def encode(self, sentence: str) -> torch.LongTensor:
-        sentence = self.tokenize(sentence)
-        sentence = self.apply_bpe(sentence)
-        return self.binarize(sentence)
-
-    def decode(self, tokens: torch.LongTensor) -> str:
-        sentence = self.string(tokens)
-        sentence = self.remove_bpe(sentence)
-        return self.detokenize(sentence)
-
-    def tokenize(self, sentence: str) -> str:
-        if self.tokenizer is not None:
-            sentence = self.tokenizer.encode(sentence)
-        return sentence
-
-    def detokenize(self, sentence: str) -> str:
-        if self.tokenizer is not None:
-            sentence = self.tokenizer.decode(sentence)
-        return sentence
-
-    def apply_bpe(self, sentence: str) -> str:
-        if self.bpe is not None:
-            sentence = self.bpe.encode(sentence)
-        return sentence
-
-    def remove_bpe(self, sentence: str) -> str:
-        if self.bpe is not None:
-            sentence = self.bpe.decode(sentence)
-        return sentence
-
-    def binarize(self, sentence: str) -> torch.LongTensor:
-        return self.src_dict.encode_line(sentence, add_if_not_exist=False).long()
-
-    def string(self, tokens: torch.LongTensor) -> str:
-        return self.tgt_dict.string(tokens)
-
-    def _build_batches(
-        self, tokens: List[List[int]], skip_invalid_size_inputs: bool
-    ) -> Iterator[Dict[str, Any]]:
-        lengths = torch.LongTensor([t.numel() for t in tokens])
-        batch_iterator = self.task.get_batch_iterator(
-            dataset=self.task.build_dataset_for_inference(tokens, lengths),
-            max_tokens=self.cfg.dataset.max_tokens,
-            max_sentences=self.cfg.dataset.batch_size,
-            max_positions=self.max_positions,
-            ignore_invalid_inputs=skip_invalid_size_inputs,
-            disable_iterator_cache=True,
-        ).next_epoch_itr(shuffle=False)
-        return batch_iterator
-
-
-class BPEHubInterface(object):
-    """PyTorch Hub interface for Byte-Pair Encoding (BPE)."""
-
-    def __init__(self, bpe, **kwargs):
-        super().__init__()
-        args = argparse.Namespace(bpe=bpe, **kwargs)
-        self.bpe = encoders.build_bpe(args)
-        assert self.bpe is not None
-
-    def encode(self, sentence: str) -> str:
-        return self.bpe.encode(sentence)
-
-    def decode(self, sentence: str) -> str:
-        return self.bpe.decode(sentence)
-
-
-class TokenizerHubInterface(object):
-    """PyTorch Hub interface for tokenization."""
-
-    def __init__(self, tokenizer, **kwargs):
-        super().__init__()
-        args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
-        self.tokenizer = encoders.build_tokenizer(args)
-        assert self.tokenizer is not None
-
-    def encode(self, sentence: str) -> str:
-        return self.tokenizer.encode(sentence)
-
-    def decode(self, sentence: str) -> str:
-        return self.tokenizer.decode(sentence)
diff --git a/spaces/gradio/longformer/tvm/module.py b/spaces/gradio/longformer/tvm/module.py
deleted file mode 100644
index 191788627776a4475d65496511b7b675a203dd66..0000000000000000000000000000000000000000
--- a/spaces/gradio/longformer/tvm/module.py
+++ /dev/null
@@ -1,289 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you 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.
-"""Container of compiled functions of TVM."""
-# from __future__ import absolute_import as _abs
-
-# import struct
-# from collections import namedtuple
-
-from ._ffi.function import ModuleBase, _set_class_module
-from ._ffi.function import _init_api
-# from ._ffi.libinfo import find_include_path
-# from .contrib import cc as _cc, tar as _tar, util as _util
-
-# ProfileResult = namedtuple("ProfileResult", ["mean", "results"])
-
-class Module(ModuleBase):
-    """Module container of all TVM generated functions"""
-
-    def __repr__(self):
-        return "Module(%s, %x)" % (self.type_key, self.handle.value)
-
-    @property
-    def type_key(self):
-        """Get type key of the module."""
-        return _GetTypeKey(self)
-
-    def get_source(self, fmt=""):
-        """Get source code from module, if available.
-
-        Parameters
-        ----------
-        fmt : str, optional
-            The specified format.
-
-        Returns
-        -------
-        source : str
-            The result source code.
-        """
-        return _GetSource(self, fmt)
-
-    @property
-    def imported_modules(self):
-        """Get imported modules
-
-        Returns
-        ----------
-        modules : list of Module
-            The module
-        """
-        nmod = _ImportsSize(self)
-        return [_GetImport(self, i) for i in range(nmod)]
-
-    def save(self, file_name, fmt=""):
-        """Save the module to file.
-
-        This do not save the dependent device modules.
-        See also export_shared
-
-        Parameters
-        ----------
-        file_name : str
-            The name of the file.
-        fmt : str
-            The format of the file.
-
-        See Also
-        --------
-        Module.export_library : export the module to shared library.
-        """
-        _SaveToFile(self, file_name, fmt)
-
-    def export_library(self,
-                       file_name,
-                       fcompile=None,
-                       **kwargs):
-        """Export the module and its imported device code one library.
-
-        This function only works on host llvm modules.
-        It will pack all the imported modules
-
-        Parameters
-        ----------
-        file_name : str
-            The name of the shared library.
-
-        fcompile : function(target, file_list, kwargs), optional
-            Compilation function to use create dynamic library.
-            If fcompile has attribute object_format, will compile host library
-            to that format. Otherwise, will use default format "o".
-
-        kwargs : dict, optional
-            Additional arguments passed to fcompile
-        """
-        from pathlib import Path
-        if isinstance(file_name, Path):
-            file_name = str(file_name)
-
-        if self.type_key == "stackvm":
-            if not file_name.endswith(".stackvm"):
-                raise ValueError("Module[%s]: can only be saved as stackvm format."
-                                 "did you build with LLVM enabled?" % self.type_key)
-            self.save(file_name)
-            return
-
-        if not (self.type_key == "llvm" or self.type_key == "c"):
-            raise ValueError("Module[%s]: Only llvm and c support export shared" % self.type_key)
-        temp = _util.tempdir()
-        if fcompile is not None and hasattr(fcompile, "object_format"):
-            object_format = fcompile.object_format
-        else:
-            if self.type_key == "llvm":
-                object_format = "o"
-            else:
-                assert self.type_key == "c"
-                object_format = "cc"
-        path_obj = temp.relpath("lib." + object_format)
-        self.save(path_obj)
-        files = [path_obj]
-        is_system_lib = self.type_key == "llvm" and self.get_function("__tvm_is_system_module")()
-        if self.imported_modules:
-            path_cc = temp.relpath("devc.cc")
-            with open(path_cc, "w") as f:
-                f.write(_PackImportsToC(self, is_system_lib))
-            files.append(path_cc)
-        if not fcompile:
-            if file_name.endswith(".tar"):
-                fcompile = _tar.tar
-            else:
-                fcompile = _cc.create_shared
-        if self.type_key == "c":
-            kwargs.update({'options': ["-I" + path for path in find_include_path()]})
-        fcompile(file_name, files, **kwargs)
-
-    def time_evaluator(self, func_name, ctx, number=10, repeat=1, min_repeat_ms=0):
-        """Get an evaluator that measures time cost of running function.
-
-        Parameters
-        ----------
-        func_name: str
-            The name of the function in the module.
-
-        ctx: TVMContext
-            The context we should run this function on.
-
-        number: int
-            The number of times to run this function for taking average.
-            We call these runs as one `repeat` of measurement.
-
-        repeat: int, optional
-            The number of times to repeat the measurement.
-            In total, the function will be invoked (1 + number x repeat) times,
-            where the first one is warm up and will be discarded.
-            The returned result contains `repeat` costs,
-            each of which is an average of `number` costs.
-
-        min_repeat_ms: int, optional
-            The minimum duration of one `repeat` in milliseconds.
-            By default, one `repeat` contains `number` runs. If this parameter is set,
-            the parameters `number` will be dynamically adjusted to meet the
-            minimum duration requirement of one `repeat`.
-            i.e., When the run time of one `repeat` falls below this time, the `number` parameter
-            will be automatically increased.
-
-        Note
-        ----
-        The function will be invoked  (1 + number x repeat) times,
-        with the first call discarded in case there is lazy initialization.
-
-        Returns
-        -------
-        ftimer : Function
-            The function that takes same argument as func and returns a ProfileResult.
-            The ProfileResult reports `repeat` time costs in seconds.
-        """
-        try:
-            feval = _RPCTimeEvaluator(
-                self, func_name, ctx.device_type, ctx.device_id, number, repeat, min_repeat_ms)
-
-            def evaluator(*args):
-                """Internal wrapped evaluator."""
-                # Wrap feval so we can add more stats in future.
-                blob = feval(*args)
-                fmt = "@" + ("d" * repeat)
-                results = struct.unpack(fmt, blob)
-                mean = sum(results) / float(repeat)
-                return ProfileResult(mean=mean, results=results)
-
-            return evaluator
-        except NameError:
-            raise NameError("time_evaluate is only supported when RPC is enabled")
-
-
-def system_lib():
-    """Get system-wide library module singleton.
-
-    System lib is a global module that contains self register functions in startup.
-    Unlike normal dso modules which need to be loaded explicitly.
-    It is useful in environments where dynamic loading api like dlopen is banned.
-
-    To build system lib function, simply specify target option ```llvm --system-lib```
-    The system lib will be available as long as the result code is linked by the program.
-
-    The system lib is intended to be linked and loaded during the entire life-cyle of the program.
-    If you want dynamic loading features, use dso modules instead.
-
-    Returns
-    -------
-    module : Module
-        The system-wide library module.
-    """
-    return _GetSystemLib()
-
-
-def load(path, fmt=""):
-    """Load module from file.
-
-    Parameters
-    ----------
-    path : str
-        The path to the module file.
-
-    fmt : str, optional
-        The format of the file, if not specified
-        it will be inferred from suffix of the file.
-
-    Returns
-    -------
-    module : Module
-        The loaded module
-
-    Note
-    ----
-    This function will automatically call
-    cc.create_shared if the path is in format .o or .tar
-    """
-    # High level handling for .o and .tar file.
-    # We support this to be consistent with RPC module load.
-    if path.endswith(".o"):
-        _cc.create_shared(path + ".so", path)
-        path += ".so"
-    elif path.endswith(".tar"):
-        tar_temp = _util.tempdir(custom_path=path.replace('.tar', ''))
-        _tar.untar(path, tar_temp.temp_dir)
-        files = [tar_temp.relpath(x) for x in tar_temp.listdir()]
-        _cc.create_shared(path + ".so", files)
-        path += ".so"
-    # Redirect to the load API
-    return _LoadFromFile(path, fmt)
-
-
-def enabled(target):
-    """Whether module runtime is enabled for target
-
-    Parameters
-    ----------
-    target : str
-        The target device type.
-
-    Returns
-    -------
-    enabled : bool
-        Whether runtime is enabled.
-
-    Examples
-    --------
-    The following code checks if gpu is enabled.
-
-    >>> tvm.module.enabled("gpu")
-    """
-    return _Enabled(target)
-
-
-_init_api("tvm.module")
-_set_class_module(Module)
diff --git a/spaces/gradio/neon-tts-plugin-coqui_main/run.py b/spaces/gradio/neon-tts-plugin-coqui_main/run.py
deleted file mode 100644
index b5a78d625ffde42934edbbb31bbffe70081218c0..0000000000000000000000000000000000000000
--- a/spaces/gradio/neon-tts-plugin-coqui_main/run.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import tempfile
-import gradio as gr
-from neon_tts_plugin_coqui import CoquiTTS
-
-LANGUAGES = list(CoquiTTS.langs.keys())
-coquiTTS = CoquiTTS()
-
-def tts(text: str, language: str):
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        coquiTTS.get_tts(text, fp, speaker = {"language" : language})
-        return fp.name
-
-inputs = [gr.Textbox(label="Input", value=CoquiTTS.langs["en"]["sentence"], max_lines=3), 
-            gr.Radio(label="Language", choices=LANGUAGES, value="en")]
-outputs = gr.Audio(label="Output")
-
-demo = gr.Interface(fn=tts, inputs=inputs, outputs=outputs)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/gulabpatel/GFP_GAN/tests/test_gfpgan_arch.py b/spaces/gulabpatel/GFP_GAN/tests/test_gfpgan_arch.py
deleted file mode 100644
index cef14a435aa824a1b7c4baaf2d1fe0a2f6cc4441..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/GFP_GAN/tests/test_gfpgan_arch.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import torch
-
-from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1, StyleGAN2GeneratorSFT
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean, StyleGAN2GeneratorCSFT
-
-
-def test_stylegan2generatorsft():
-    """Test arch: StyleGAN2GeneratorSFT."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = StyleGAN2GeneratorSFT(
-            out_size=32,
-            num_style_feat=512,
-            num_mlp=8,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            lr_mlp=0.01,
-            narrow=1,
-            sft_half=False).cuda().eval()
-        style = torch.rand((1, 512), dtype=torch.float32).cuda()
-        condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
-        condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
-        condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
-        conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
-        output = net([style], conditions)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with return_latents ----------------------- #
-        output = net([style], conditions, return_latents=True)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 1
-        # check latent
-        assert output[1][0].shape == (8, 512)
-
-        # -------------------- with randomize_noise = False ----------------------- #
-        output = net([style], conditions, randomize_noise=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with truncation = 0.5 and mixing----------------------- #
-        output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-
-def test_gfpganv1():
-    """Test arch: GFPGANv1."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = GFPGANv1(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=True).cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
-
-        # -------------------- with different_w = True ----------------------- #
-        net = GFPGANv1(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=True,
-            narrow=1,
-            sft_half=True).cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
-
-
-def test_facialcomponentdiscriminator():
-    """Test arch: FacialComponentDiscriminator."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = FacialComponentDiscriminator().cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert len(output) == 2
-        assert output[0].shape == (1, 1, 8, 8)
-        assert output[1] is None
-
-        # -------------------- return intermediate features ----------------------- #
-        output = net(img, return_feats=True)
-        assert len(output) == 2
-        assert output[0].shape == (1, 1, 8, 8)
-        assert len(output[1]) == 2
-        assert output[1][0].shape == (1, 128, 16, 16)
-        assert output[1][1].shape == (1, 256, 8, 8)
-
-
-def test_stylegan2generatorcsft():
-    """Test arch: StyleGAN2GeneratorCSFT."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = StyleGAN2GeneratorCSFT(
-            out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=1, sft_half=False).cuda().eval()
-        style = torch.rand((1, 512), dtype=torch.float32).cuda()
-        condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
-        condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
-        condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
-        conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
-        output = net([style], conditions)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with return_latents ----------------------- #
-        output = net([style], conditions, return_latents=True)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 1
-        # check latent
-        assert output[1][0].shape == (8, 512)
-
-        # -------------------- with randomize_noise = False ----------------------- #
-        output = net([style], conditions, randomize_noise=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with truncation = 0.5 and mixing----------------------- #
-        output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-
-def test_gfpganv1clean():
-    """Test arch: GFPGANv1Clean."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = GFPGANv1Clean(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=True).cuda().eval()
-
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
-
-        # -------------------- with different_w = True ----------------------- #
-        net = GFPGANv1Clean(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=False,
-            different_w=True,
-            narrow=1,
-            sft_half=True).cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
diff --git a/spaces/guoyww/AnimateDiff/app.py b/spaces/guoyww/AnimateDiff/app.py
deleted file mode 100644
index f1a6d1e07a966e69f268753176c244aecc85ea40..0000000000000000000000000000000000000000
--- a/spaces/guoyww/AnimateDiff/app.py
+++ /dev/null
@@ -1,633 +0,0 @@
-
-
-# import os
-# import json
-# import torch
-# import random
-# import copy
-
-# import gradio as gr
-# from glob import glob
-# from omegaconf import OmegaConf
-# from datetime import datetime
-# from safetensors import safe_open
-
-# from diffusers import AutoencoderKL
-# from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler
-# from diffusers.utils.import_utils import is_xformers_available
-# from transformers import CLIPTextModel, CLIPTokenizer
-
-# from animatediff.models.unet import UNet3DConditionModel
-# from animatediff.pipelines.pipeline_animation import AnimationPipeline
-# from animatediff.utils.util import save_videos_grid
-# from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
-# from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora
-
-
-# sample_idx     = 0
-# scheduler_dict = {
-#     "Euler": EulerDiscreteScheduler,
-#     "PNDM": PNDMScheduler,
-#     "DDIM": DDIMScheduler,
-# }
-
-# css = """
-# .toolbutton {
-#     margin-buttom: 0em 0em 0em 0em;
-#     max-width: 2.5em;
-#     min-width: 2.5em !important;
-#     height: 2.5em;
-# }
-# """
-
-# class AnimateController:
-#     def __init__(self):
-        
-#         # config dirs
-#         self.basedir                = os.getcwd()
-#         self.stable_diffusion_dir   = os.path.join(self.basedir, "models", "StableDiffusion")
-#         self.motion_module_dir      = os.path.join(self.basedir, "models", "Motion_Module")
-#         self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
-#         self.savedir                = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
-#         self.savedir_sample         = os.path.join(self.savedir, "sample")
-#         os.makedirs(self.savedir, exist_ok=True)
-
-#         self.stable_diffusion_list   = []
-#         self.motion_module_list      = []
-#         self.personalized_model_list = []
-        
-#         self.refresh_stable_diffusion()
-#         self.refresh_motion_module()
-#         self.refresh_personalized_model()
-        
-#         # config models
-#         self.tokenizer             = None
-#         self.text_encoder          = None
-#         self.vae                   = None
-#         self.unet                  = None
-#         self.pipeline              = None
-#         self.lora_model_state_dict = {}
-        
-#         self.inference_config      = OmegaConf.load("configs/inference/inference.yaml")
-
-#     def refresh_stable_diffusion(self):
-#         self.stable_diffusion_list = glob(os.path.join(self.stable_diffusion_dir, "*/"))
-
-#     def refresh_motion_module(self):
-#         motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
-#         self.motion_module_list = [os.path.basename(p) for p in motion_module_list]
-
-#     def refresh_personalized_model(self):
-#         personalized_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
-#         self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]
-
-#     def update_stable_diffusion(self, stable_diffusion_dropdown):
-#         self.tokenizer = CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer")
-#         self.text_encoder = CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder").cuda()
-#         self.vae = AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae").cuda()
-#         self.unet = UNet3DConditionModel.from_pretrained_2d(stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
-#         return gr.Dropdown.update()
-
-#     def update_motion_module(self, motion_module_dropdown):
-#         if self.unet is None:
-#             gr.Info(f"Please select a pretrained model path.")
-#             return gr.Dropdown.update(value=None)
-#         else:
-#             motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)
-#             motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")
-#             missing, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
-#             assert len(unexpected) == 0
-#             return gr.Dropdown.update()
-
-#     def update_base_model(self, base_model_dropdown):
-#         if self.unet is None:
-#             gr.Info(f"Please select a pretrained model path.")
-#             return gr.Dropdown.update(value=None)
-#         else:
-#             base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
-#             base_model_state_dict = {}
-#             with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
-#                 for key in f.keys():
-#                     base_model_state_dict[key] = f.get_tensor(key)
-                    
-#             converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config)
-#             self.vae.load_state_dict(converted_vae_checkpoint)
-
-#             converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)
-#             self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
-
-#             self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
-#             return gr.Dropdown.update()
-
-#     def update_lora_model(self, lora_model_dropdown):
-#         lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)
-#         self.lora_model_state_dict = {}
-#         if lora_model_dropdown == "none": pass
-#         else:
-#             with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f:
-#                 for key in f.keys():
-#                     self.lora_model_state_dict[key] = f.get_tensor(key)
-#         return gr.Dropdown.update()
-
-#     def animate(
-#         self,
-#         stable_diffusion_dropdown,
-#         motion_module_dropdown,
-#         base_model_dropdown,
-#         lora_alpha_slider,
-#         prompt_textbox, 
-#         negative_prompt_textbox, 
-#         sampler_dropdown, 
-#         sample_step_slider, 
-#         width_slider, 
-#         length_slider, 
-#         height_slider, 
-#         cfg_scale_slider, 
-#         seed_textbox
-#     ):    
-#         if self.unet is None:
-#             raise gr.Error(f"Please select a pretrained model path.")
-#         if motion_module_dropdown == "": 
-#             raise gr.Error(f"Please select a motion module.")
-#         # if base_model_dropdown == "":
-#         #     raise gr.Error(f"Please select a base DreamBooth model.")
-
-#         if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
-
-#         pipeline = AnimationPipeline(
-#             vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
-#             scheduler=scheduler_dict[sampler_dropdown](**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
-#         ).to("cuda")
-        
-#         if self.lora_model_state_dict != {}:
-#             print(f"Lora alpha: {lora_alpha_slider}")
-#             pipeline = convert_lora(copy.deepcopy(pipeline), self.lora_model_state_dict, alpha=lora_alpha_slider)
-#         pipeline.to("cuda")
-
-#         torch.cuda.empty_cache()
-        
-#         seed_textbox = int(seed_textbox)
-#         if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(seed_textbox)
-#         else: torch.seed()
-#         seed = torch.initial_seed()
-        
-#         sample = pipeline(
-#             prompt_textbox,
-#             negative_prompt     = negative_prompt_textbox,
-#             num_inference_steps = sample_step_slider,
-#             guidance_scale      = cfg_scale_slider,
-#             width               = width_slider,
-#             height              = height_slider,
-#             video_length        = length_slider,
-#         ).videos
-
-#         save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4")
-#         save_videos_grid(sample, save_sample_path)
-    
-#         sample_config = {
-#             "prompt": prompt_textbox,
-#             "n_prompt": negative_prompt_textbox,
-#             "sampler": sampler_dropdown,
-#             "num_inference_steps": sample_step_slider,
-#             "guidance_scale": cfg_scale_slider,
-#             "width": width_slider,
-#             "height": height_slider,
-#             "video_length": length_slider,
-#             "seed": seed
-#         }
-#         json_str = json.dumps(sample_config, indent=4)
-#         with open(os.path.join(self.savedir, "logs.json"), "a") as f:
-#             f.write(json_str)
-#             f.write("\n\n")
-
-#         del pipeline
-#         torch.cuda.empty_cache()
-
-#         return gr.Video.update(value=save_sample_path)
-        
-
-# controller = AnimateController()
-
-
-# def ui():
-#     with gr.Blocks(css=css) as demo:
-#         gr.Markdown(
-#             """
-#             # [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)
-#             Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>
-#             [Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)
-#             """
-#         )
-#         with gr.Column(variant="panel"):
-#             gr.Markdown(
-#                 """
-#                 ### 1. Model checkpoints (select pretrained model path first).
-#                 """
-#             )
-#             with gr.Row():
-#                 stable_diffusion_dropdown = gr.Dropdown(
-#                     label="Pretrained Model Path",
-#                     choices=controller.stable_diffusion_list,
-#                     interactive=True,
-#                 )
-#                 stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown])
-                
-#                 stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
-#                 def update_stable_diffusion():
-#                     controller.refresh_stable_diffusion()
-#                     return gr.Dropdown.update(choices=controller.stable_diffusion_list)
-#                 stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown])
-
-#             with gr.Row():
-#                 motion_module_dropdown = gr.Dropdown(
-#                     label="Select motion module",
-#                     choices=controller.motion_module_list,
-#                     interactive=True,
-#                 )
-#                 motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
-                
-#                 motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
-#                 def update_motion_module():
-#                     controller.refresh_motion_module()
-#                     return gr.Dropdown.update(choices=controller.motion_module_list)
-#                 motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown])
-                
-#                 base_model_dropdown = gr.Dropdown(
-#                     label="Select base Dreambooth model (required)",
-#                     choices=controller.personalized_model_list,
-#                     interactive=True,
-#                 )
-#                 base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])
-                
-#                 lora_model_dropdown = gr.Dropdown(
-#                     label="Select LoRA model (optional)",
-#                     choices=["none"] + controller.personalized_model_list,
-#                     value="none",
-#                     interactive=True,
-#                 )
-#                 lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[lora_model_dropdown], outputs=[lora_model_dropdown])
-                
-#                 lora_alpha_slider = gr.Slider(label="LoRA alpha", value=0.7, minimum=0, maximum=2, interactive=True)
-                
-#                 personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
-#                 def update_personalized_model():
-#                     controller.refresh_personalized_model()
-#                     return [
-#                         gr.Dropdown.update(choices=controller.personalized_model_list),
-#                         gr.Dropdown.update(choices=["none"] + controller.personalized_model_list)
-#                     ]
-#                 personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown])
-
-#         with gr.Column(variant="panel"):
-#             gr.Markdown(
-#                 """
-#                 ### 2. Configs for AnimateDiff.
-#                 """
-#             )
-            
-#             prompt_textbox = gr.Textbox(label="Prompt", lines=2)
-#             negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2)
-                
-#             with gr.Row().style(equal_height=False):
-#                 with gr.Column():
-#                     with gr.Row():
-#                         sampler_dropdown   = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
-#                         sample_step_slider = gr.Slider(label="Sampling steps", value=25, minimum=10, maximum=100, step=1)
-                        
-#                     width_slider     = gr.Slider(label="Width",            value=512, minimum=256, maximum=1024, step=64)
-#                     height_slider    = gr.Slider(label="Height",           value=512, minimum=256, maximum=1024, step=64)
-#                     length_slider    = gr.Slider(label="Animation length", value=16,  minimum=8,   maximum=24,   step=1)
-#                     cfg_scale_slider = gr.Slider(label="CFG Scale",        value=7.5, minimum=0,   maximum=20)
-                    
-#                     with gr.Row():
-#                         seed_textbox = gr.Textbox(label="Seed", value=-1)
-#                         seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
-#                         seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox])
-            
-#                     generate_button = gr.Button(value="Generate", variant='primary')
-                    
-#                 result_video = gr.Video(label="Generated Animation", interactive=False)
-
-#             generate_button.click(
-#                 fn=controller.animate,
-#                 inputs=[
-#                     stable_diffusion_dropdown,
-#                     motion_module_dropdown,
-#                     base_model_dropdown,
-#                     lora_alpha_slider,
-#                     prompt_textbox, 
-#                     negative_prompt_textbox, 
-#                     sampler_dropdown, 
-#                     sample_step_slider, 
-#                     width_slider, 
-#                     length_slider, 
-#                     height_slider, 
-#                     cfg_scale_slider, 
-#                     seed_textbox,
-#                 ],
-#                 outputs=[result_video]
-#             )
-            
-#     return demo
-
-
-# if __name__ == "__main__":
-#     demo = ui()
-#     demo.queue(max_size=20)
-#     demo.launch()
-
-
-import os
-import torch
-import random
-
-import gradio as gr
-from glob import glob
-from omegaconf import OmegaConf
-from safetensors import safe_open
-
-from diffusers import AutoencoderKL
-from diffusers import EulerDiscreteScheduler, DDIMScheduler
-from diffusers.utils.import_utils import is_xformers_available
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from animatediff.models.unet import UNet3DConditionModel
-from animatediff.pipelines.pipeline_animation import AnimationPipeline
-from animatediff.utils.util import save_videos_grid
-from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
-
-
-pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5"
-inference_config_path = "configs/inference/inference.yaml"
-
-css = """
-.toolbutton {
-    margin-buttom: 0em 0em 0em 0em;
-    max-width: 2.5em;
-    min-width: 2.5em !important;
-    height: 2.5em;
-}
-"""
-
-examples = [
-    # 1-ToonYou
-    [
-        "toonyou_beta3.safetensors", 
-        "mm_sd_v14.ckpt", 
-        "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes",
-        "worst quality, low quality, nsfw, logo",
-        512, 512, "13204175718326964000"
-    ],
-    # 2-Lyriel
-    [
-        "lyriel_v16.safetensors", 
-        "mm_sd_v15.ckpt", 
-        "A forbidden castle high up in the mountains, pixel art, intricate details2, hdr, intricate details, hyperdetailed5, natural skin texture, hyperrealism, soft light, sharp, game art, key visual, surreal",
-        "3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular",
-        512, 512, "6681501646976930000"
-    ],
-    # 3-RCNZ
-    [
-        "rcnzCartoon3d_v10.safetensors", 
-        "mm_sd_v14.ckpt", 
-        "Jane Eyre with headphones, natural skin texture,4mm,k textures, soft cinematic light, adobe lightroom, photolab, hdr, intricate, elegant, highly detailed, sharp focus, cinematic look, soothing tones, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, faded",
-        "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
-        512, 512, "2416282124261060"
-    ],
-    # 4-MajicMix
-    [
-        "majicmixRealistic_v5Preview.safetensors", 
-        "mm_sd_v14.ckpt", 
-        "1girl, offshoulder, light smile, shiny skin best quality, masterpiece, photorealistic",
-        "bad hand, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles",
-        512, 512, "7132772652786303"
-    ],
-    # 5-RealisticVision
-    [
-        "realisticVisionV20_v20.safetensors", 
-        "mm_sd_v15.ckpt", 
-        "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
-        "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation",
-        512, 512, "1490157606650685400"
-    ]
-]
-
-# clean unrelated ckpts
-# ckpts = [
-#     "realisticVisionV40_v20Novae.safetensors",
-#     "majicmixRealistic_v5Preview.safetensors",
-#     "rcnzCartoon3d_v10.safetensors",
-#     "lyriel_v16.safetensors",
-#     "toonyou_beta3.safetensors"
-# ]
-
-# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
-#     for ckpt in ckpts:
-#         if path.endswith(ckpt): break
-#     else:
-#         print(f"### Cleaning {path} ...")
-#         os.system(f"rm -rf {path}")
-
-# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")
-
-# os.system(f"bash download_bashscripts/1-ToonYou.sh")
-# os.system(f"bash download_bashscripts/2-Lyriel.sh")
-# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
-# os.system(f"bash download_bashscripts/4-MajicMix.sh")
-# os.system(f"bash download_bashscripts/5-RealisticVision.sh")
-
-# clean Grdio cache
-print(f"### Cleaning cached examples ...")
-os.system(f"rm -rf gradio_cached_examples/")
-
-
-class AnimateController:
-    def __init__(self):
-        
-        # config dirs
-        self.basedir                = os.getcwd()
-        self.stable_diffusion_dir   = os.path.join(self.basedir, "models", "StableDiffusion")
-        self.motion_module_dir      = os.path.join(self.basedir, "models", "Motion_Module")
-        self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
-        self.savedir                = os.path.join(self.basedir, "samples")
-        os.makedirs(self.savedir, exist_ok=True)
-
-        self.base_model_list    = []
-        self.motion_module_list = []
-        
-        self.selected_base_model    = None
-        self.selected_motion_module = None
-        
-        self.refresh_motion_module()
-        self.refresh_personalized_model()
-        
-        # config models
-        self.inference_config      = OmegaConf.load(inference_config_path)
-
-        self.tokenizer             = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
-        self.text_encoder          = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
-        self.vae                   = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()
-        self.unet                  = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
-        
-        self.update_base_model(self.base_model_list[0])
-        self.update_motion_module(self.motion_module_list[0])
-        
-        
-    def refresh_motion_module(self):
-        motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
-        self.motion_module_list = [os.path.basename(p) for p in motion_module_list]
-
-    def refresh_personalized_model(self):
-        base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
-        self.base_model_list = [os.path.basename(p) for p in base_model_list]
-
-
-    def update_base_model(self, base_model_dropdown):
-        self.selected_base_model = base_model_dropdown
-        
-        base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
-        base_model_state_dict = {}
-        with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
-            for key in f.keys(): base_model_state_dict[key] = f.get_tensor(key)
-                
-        converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config)
-        self.vae.load_state_dict(converted_vae_checkpoint)
-
-        converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)
-        self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
-
-        self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
-        return gr.Dropdown.update()
-
-    def update_motion_module(self, motion_module_dropdown):
-        self.selected_motion_module = motion_module_dropdown
-        
-        motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)
-        motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")
-        _, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
-        assert len(unexpected) == 0
-        return gr.Dropdown.update()
-    
-    
-    def animate(
-        self,
-        base_model_dropdown,
-        motion_module_dropdown,
-        prompt_textbox,
-        negative_prompt_textbox,
-        width_slider,
-        height_slider,
-        seed_textbox,
-    ):
-        if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
-        if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown)
-        
-        if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
-
-        pipeline = AnimationPipeline(
-            vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
-            scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
-        ).to("cuda")
-        
-        if int(seed_textbox) > 0: seed = int(seed_textbox)
-        else: seed = random.randint(1, 1e16)
-        torch.manual_seed(int(seed))
-        
-        assert seed == torch.initial_seed()
-        print(f"### seed: {seed}")
-        
-        generator = torch.Generator(device="cuda")
-        generator.manual_seed(seed)
-        
-        sample = pipeline(
-            prompt_textbox,
-            negative_prompt     = negative_prompt_textbox,
-            num_inference_steps = 25,
-            guidance_scale      = 8.,
-            width               = width_slider,
-            height              = height_slider,
-            video_length        = 16,
-            generator           = generator,
-        ).videos
-
-        save_sample_path = os.path.join(self.savedir, f"sample.mp4")
-        save_videos_grid(sample, save_sample_path)
-    
-        json_config = {
-            "prompt": prompt_textbox,
-            "n_prompt": negative_prompt_textbox,
-            "width": width_slider,
-            "height": height_slider,
-            "seed": seed,
-            "base_model": base_model_dropdown,
-            "motion_module": motion_module_dropdown,
-        }
-        return gr.Video.update(value=save_sample_path), gr.Json.update(value=json_config)
-        
-
-controller = AnimateController()
-
-
-def ui():
-    with gr.Blocks(css=css) as demo:
-        gr.Markdown(
-            """
-            # AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
-            Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>
-            [Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)
-            """
-        )
-        gr.Markdown(
-            """
-            ### Quick Start
-            1. Select desired `Base DreamBooth Model`.
-            2. Select `Motion Module` from `mm_sd_v14.ckpt` and `mm_sd_v15.ckpt`. We recommend trying both of them for the best results.
-            3. Provide `Prompt` and `Negative Prompt` for each model. You are encouraged to refer to each model's webpage on CivitAI to learn how to write prompts for them. Below are the DreamBooth models in this demo. Click to visit their homepage.
-                - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775)
-                - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel)
-                - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009)
-                - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068)
-                - [`realisticVisionV20_v20.safetensors`](https://civitai.com/models/4201?modelVersionId=29460)
-            4. Click `Generate`, wait for ~1 min, and enjoy.
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                base_model_dropdown     = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list,    value=controller.base_model_list[0],    interactive=True )
-                motion_module_dropdown  = gr.Dropdown( label="Motion Module",  choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True )
-
-                base_model_dropdown.change(fn=controller.update_base_model,       inputs=[base_model_dropdown],    outputs=[base_model_dropdown])
-                motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
-
-                prompt_textbox          = gr.Textbox( label="Prompt",          lines=3 )
-                negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo")
-
-                with gr.Accordion("Advance", open=False):
-                    with gr.Row():
-                        width_slider  = gr.Slider(  label="Width",  value=512, minimum=256, maximum=1024, step=64 )
-                        height_slider = gr.Slider(  label="Height", value=512, minimum=256, maximum=1024, step=64 )
-                    with gr.Row():
-                        seed_textbox = gr.Textbox( label="Seed",  value=-1)
-                        seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
-                        seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox])
-
-                generate_button = gr.Button( value="Generate", variant='primary' )
-
-            with gr.Column():
-                result_video = gr.Video( label="Generated Animation", interactive=False )
-                json_config  = gr.Json( label="Config", value=None )
-
-            inputs  = [base_model_dropdown, motion_module_dropdown, prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox]
-            outputs = [result_video, json_config]
-            
-            generate_button.click( fn=controller.animate, inputs=inputs, outputs=outputs )
-                
-        gr.Examples( fn=controller.animate, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True )
-        
-    return demo
-
-
-if __name__ == "__main__":
-    demo = ui()
-    demo.queue(max_size=20)
-    demo.launch()
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/dnnlib/__init__.py b/spaces/gwang-kim/DATID-3D/eg3d/dnnlib/__init__.py
deleted file mode 100644
index dd91ed142e955581e83948455fb71cd837215f61..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/dnnlib/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-from .util import EasyDict, make_cache_dir_path
diff --git a/spaces/h2oai/wave-tour/examples/color_picker.py b/spaces/h2oai/wave-tour/examples/color_picker.py
deleted file mode 100644
index 3a1d9e37848f07fbcd7b4e242b5589213ed9ace8..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/color_picker.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Form / Color Picker
-# Use a color picker to allow a user to select a color.
-# #form #color_picker
-# ---
-from h2o_wave import main, app, Q, ui
-
-
-@app('/demo')
-async def serve(q: Q):
-    if q.args.show_inputs:
-        q.page['example'].items = [
-            ui.text(f'color={q.args.color}'),
-            ui.button(name='show_form', label='Back', primary=True),
-        ]
-    else:
-        q.page['example'] = ui.form_card(box='1 1 4 7', items=[
-            ui.color_picker(name='color', label='Pick a color', value='#F25F5C'),
-            ui.button(name='show_inputs', label='Submit', primary=True),
-        ])
-    await q.page.save()
diff --git a/spaces/hacknc23/hacknc23/fileReader.py b/spaces/hacknc23/hacknc23/fileReader.py
deleted file mode 100644
index 4c97a868013c508eb406d880a0890ca25f166c1b..0000000000000000000000000000000000000000
--- a/spaces/hacknc23/hacknc23/fileReader.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# import
-from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.document_loaders import TextLoader
-
-
-# load the document and split it into chunks
-
-relativePath = "data/test.txt"
-
-
-from langchain.document_loaders import PyPDFLoader
-
-
-
-def convertPDF(uploadFile):
-    loader = PyPDFLoader(uploadFile)
-
-    pages = loader.load()
-    print(pages)
-
diff --git a/spaces/hamacojr/CAT-Seg/open_clip/src/open_clip/transformer.py b/spaces/hamacojr/CAT-Seg/open_clip/src/open_clip/transformer.py
deleted file mode 100644
index 4905a11cb6776312989629d690569a975e59074f..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/CAT-Seg/open_clip/src/open_clip/transformer.py
+++ /dev/null
@@ -1,507 +0,0 @@
-from collections import OrderedDict
-import math
-from typing import Callable, Optional, Sequence
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint
-
-from .utils import to_2tuple
-
-
-class LayerNormFp32(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm (with cast back to input dtype)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
-    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class LayerScale(nn.Module):
-    def __init__(self, dim, init_values=1e-5, inplace=False):
-        super().__init__()
-        self.inplace = inplace
-        self.gamma = nn.Parameter(init_values * torch.ones(dim))
-
-    def forward(self, x):
-        return x.mul_(self.gamma) if self.inplace else x * self.gamma
-
-
-class PatchDropout(nn.Module):
-    """
-    https://arxiv.org/abs/2212.00794
-    """
-
-    def __init__(self, prob, exclude_first_token=True):
-        super().__init__()
-        assert 0 <= prob < 1.
-        self.prob = prob
-        self.exclude_first_token = exclude_first_token  # exclude CLS token
-
-    def forward(self, x):
-        if not self.training or self.prob == 0.:
-            return x
-
-        if self.exclude_first_token:
-            cls_tokens, x = x[:, :1], x[:, 1:]
-        else:
-            cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
-
-        batch = x.size()[0]
-        num_tokens = x.size()[1]
-
-        batch_indices = torch.arange(batch)
-        batch_indices = batch_indices[..., None]
-
-        keep_prob = 1 - self.prob
-        num_patches_keep = max(1, int(num_tokens * keep_prob))
-
-        rand = torch.randn(batch, num_tokens)
-        patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
-
-        x = x[batch_indices, patch_indices_keep]
-
-        if self.exclude_first_token:
-            x = torch.cat((cls_tokens, x), dim=1)
-
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=True,
-            scaled_cosine=False,
-            scale_heads=False,
-            logit_scale_max=math.log(1. / 0.01),
-            attn_drop=0.,
-            proj_drop=0.
-    ):
-        super().__init__()
-        self.scaled_cosine = scaled_cosine
-        self.scale_heads = scale_heads
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-        self.logit_scale_max = logit_scale_max
-
-        # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original
-        self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)
-        if qkv_bias:
-            self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))
-        else:
-            self.in_proj_bias = None
-
-        if self.scaled_cosine:
-            self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
-        else:
-            self.logit_scale = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        if self.scale_heads:
-            self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))
-        else:
-            self.head_scale = None
-        self.out_proj = nn.Linear(dim, dim)
-        self.out_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
-        L, N, C = x.shape
-        q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)
-        q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-        k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-        v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-
-        if self.logit_scale is not None:
-            attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))
-            logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
-            attn = attn.view(N, self.num_heads, L, L) * logit_scale
-            attn = attn.view(-1, L, L)
-        else:
-            q = q * self.scale
-            attn = torch.bmm(q, k.transpose(-1, -2))
-
-        if attn_mask is not None:
-            if attn_mask.dtype == torch.bool:
-                new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
-                new_attn_mask.masked_fill_(attn_mask, float("-inf"))
-                attn_mask = new_attn_mask
-            attn += attn_mask
-
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = torch.bmm(attn, v)
-        if self.head_scale is not None:
-            x = x.view(N, self.num_heads, L, C) * self.head_scale
-            x = x.view(-1, L, C)
-        x = x.transpose(0, 1).reshape(L, N, C)
-        x = self.out_proj(x)
-        x = self.out_drop(x)
-        return x
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            n_head: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-    ):
-        super().__init__()
-
-        self.ln_1 = norm_layer(d_model)
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-        self.ln_2 = norm_layer(d_model)
-        mlp_width = int(d_model * mlp_ratio)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, mlp_width)),
-            ("gelu", act_layer()),
-            ("c_proj", nn.Linear(mlp_width, d_model))
-        ]))
-        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-    def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask))
-        x = x + self.ls_2(self.mlp(self.ln_2(x)))
-        return x
-    
-    def forward_dense(self, x):
-        y = self.ln_1(x)
-        y = F.linear(y, self.attn.in_proj_weight, self.attn.in_proj_bias)
-        L, N, D = y.shape # L N 3D
-        
-        y = y.reshape(L, N, 3, D // 3).permute(2, 1, 0, 3).reshape(3 * N, L, D // 3)
-        y = F.linear(y, self.attn.out_proj.weight, self.attn.out_proj.bias)
-        
-        q, k, v = y.tensor_split(3, dim=0)
-        v = v.transpose(1, 0) + x # L N D
-
-        v = v + self.mlp(self.ln_2(v))
-
-        return v
-
-
-class CustomResidualAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            n_head: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            scale_cosine_attn: bool = False,
-            scale_heads: bool = False,
-            scale_attn: bool = False,
-            scale_fc: bool = False,
-    ):
-        super().__init__()
-
-        self.ln_1 = norm_layer(d_model)
-        self.attn = Attention(
-            d_model, n_head,
-            scaled_cosine=scale_cosine_attn,
-            scale_heads=scale_heads,
-        )
-        self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity()
-        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-        self.ln_2 = norm_layer(d_model)
-        mlp_width = int(d_model * mlp_ratio)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, mlp_width)),
-            ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()),
-            ("gelu", act_layer()),
-            ("c_proj", nn.Linear(mlp_width, d_model))
-        ]))
-        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)))
-        x = x + self.ls_2(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-            self,
-            width: int,
-            layers: int,
-            heads: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.grad_checkpointing = False
-
-        self.resblocks = nn.ModuleList([
-            ResidualAttentionBlock(
-                width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer)
-            for _ in range(layers)
-        ])
-
-    def get_cast_dtype(self) -> torch.dtype:
-        return self.resblocks[0].mlp.c_fc.weight.dtype
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, dense=False):
-        for i, r in enumerate(self.resblocks):
-            if self.grad_checkpointing and not torch.jit.is_scripting():
-                x = checkpoint(r, x, attn_mask)
-            else:
-                if dense and i == self.layers - 1:
-                    x = r.forward_dense(x)
-                else:
-                    x = r(x, attn_mask=attn_mask)
-        return x
-
-
-class VisionTransformer(nn.Module):
-    def __init__(
-            self,
-            image_size: int,
-            patch_size: int,
-            width: int,
-            layers: int,
-            heads: int,
-            mlp_ratio: float,
-            ls_init_value: float = None,
-            global_average_pool: bool = False,
-            output_dim: int = 512,
-            patch_dropout: float = 0.,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-    ):
-        super().__init__()
-        self.image_size = to_2tuple(image_size)
-        self.patch_size = to_2tuple(patch_size)
-        self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1])
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))
-
-        # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
-        self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
-
-        self.ln_pre = norm_layer(width)
-        self.transformer = Transformer(
-            width,
-            layers,
-            heads,
-            mlp_ratio,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-
-        self.global_average_pool = global_average_pool
-        self.ln_post = norm_layer(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-        self.init_parameters()
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        for param in self.parameters():
-            param.requires_grad = False
-
-        if unlocked_groups != 0:
-            groups = [
-                [
-                    self.conv1,
-                    self.class_embedding,
-                    self.positional_embedding,
-                    self.ln_pre,
-                ],
-                *self.transformer.resblocks[:-1],
-                [
-                    self.transformer.resblocks[-1],
-                    self.ln_post,
-                ],
-                self.proj,
-            ]
-
-            def _unlock(x):
-                if isinstance(x, Sequence):
-                    for g in x:
-                        _unlock(g)
-                else:
-                    if isinstance(x, torch.nn.Parameter):
-                        x.requires_grad = True
-                    else:
-                        for p in x.parameters():
-                            p.requires_grad = True
-
-            _unlock(groups[-unlocked_groups:])
-
-    def init_parameters(self):
-        # FIXME OpenAI CLIP did not define an init for the VisualTransformer
-        # TODO experiment if default PyTorch init, below, or alternate init is best.
-
-        # nn.init.normal_(self.class_embedding, std=self.scale)
-        # nn.init.normal_(self.positional_embedding, std=self.scale)
-        #
-        # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        # attn_std = self.transformer.width ** -0.5
-        # fc_std = (2 * self.transformer.width) ** -0.5
-        # for block in self.transformer.resblocks:
-        #     nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-        #     nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-        #     nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-        #     nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        #
-        # if self.text_projection is not None:
-        #     nn.init.normal_(self.text_projection, std=self.scale)
-        pass
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.grad_checkpointing = enable
-
-    def forward(self, x: torch.Tensor, dense=False):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat(
-            [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
-             x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-
-        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
-        x = self.patch_dropout(x)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x, dense=dense)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        if self.global_average_pool:
-            x = x.mean(dim=1)
-        elif dense:
-            x = x
-        else:
-            x = x[:, 0]
-
-        x = self.ln_post(x)
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-class TextTransformer(nn.Module):
-
-    def __init__(
-            self,
-            context_length: int = 77,
-            vocab_size: int = 49408,
-            width: int = 512,
-            heads: int = 8,
-            layers: int = 12,
-            ls_init_value: float = None,
-            output_dim: int = 512,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-    ):
-        super().__init__()
-        self.context_length = context_length
-        self.vocab_size = vocab_size
-        self.width = width
-        self.output_dim = output_dim
-
-        self.token_embedding = nn.Embedding(vocab_size, width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))
-        self.transformer = Transformer(
-            width=width,
-            layers=layers,
-            heads=heads,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-        self.ln_final = norm_layer(width)
-        self.text_projection = nn.Parameter(torch.empty(width, output_dim))
-
-        self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
-
-        self.init_parameters()
-
-    def init_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.grad_checkpointing = enable
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def forward(self, text):
-        cast_dtype = self.transformer.get_cast_dtype()
-
-        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.to(cast_dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x, attn_mask=self.attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x
diff --git a/spaces/hamacojr/SAM-CAT-Seg/cat_seg/data/dataset_mappers/mask_former_semantic_dataset_mapper.py b/spaces/hamacojr/SAM-CAT-Seg/cat_seg/data/dataset_mappers/mask_former_semantic_dataset_mapper.py
deleted file mode 100644
index 41c82f2c76cb6d74020ae0a6a3ba045469755f01..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/cat_seg/data/dataset_mappers/mask_former_semantic_dataset_mapper.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-
-import numpy as np
-import torch
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.data import MetadataCatalog
-from detectron2.data import detection_utils as utils
-from detectron2.data import transforms as T
-from detectron2.projects.point_rend import ColorAugSSDTransform
-from detectron2.structures import BitMasks, Instances
-
-__all__ = ["MaskFormerSemanticDatasetMapper"]
-
-
-class MaskFormerSemanticDatasetMapper:
-    """
-    A callable which takes a dataset dict in Detectron2 Dataset format,
-    and map it into a format used by MaskFormer for semantic segmentation.
-
-    The callable currently does the following:
-
-    1. Read the image from "file_name"
-    2. Applies geometric transforms to the image and annotation
-    3. Find and applies suitable cropping to the image and annotation
-    4. Prepare image and annotation to Tensors
-    """
-
-    @configurable
-    def __init__(
-        self,
-        is_train=True,
-        *,
-        augmentations,
-        image_format,
-        ignore_label,
-        size_divisibility,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            is_train: for training or inference
-            augmentations: a list of augmentations or deterministic transforms to apply
-            image_format: an image format supported by :func:`detection_utils.read_image`.
-            ignore_label: the label that is ignored to evaluation
-            size_divisibility: pad image size to be divisible by this value
-        """
-        self.is_train = is_train
-        self.tfm_gens = augmentations
-        self.img_format = image_format
-        self.ignore_label = ignore_label
-        self.size_divisibility = size_divisibility
-
-        logger = logging.getLogger(__name__)
-        mode = "training" if is_train else "inference"
-        logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
-
-    @classmethod
-    def from_config(cls, cfg, is_train=True):
-        # Build augmentation
-        augs = [
-            T.ResizeShortestEdge(
-                cfg.INPUT.MIN_SIZE_TRAIN,
-                cfg.INPUT.MAX_SIZE_TRAIN,
-                cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
-            )
-        ]
-        if cfg.INPUT.CROP.ENABLED:
-            augs.append(
-                T.RandomCrop_CategoryAreaConstraint(
-                    cfg.INPUT.CROP.TYPE,
-                    cfg.INPUT.CROP.SIZE,
-                    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
-                    cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
-                )
-            )
-        if cfg.INPUT.COLOR_AUG_SSD:
-            augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
-        augs.append(T.RandomFlip())
-
-        # Assume always applies to the training set.
-        dataset_names = cfg.DATASETS.TRAIN
-        meta = MetadataCatalog.get(dataset_names[0])
-        ignore_label = meta.ignore_label
-
-        ret = {
-            "is_train": is_train,
-            "augmentations": augs,
-            "image_format": cfg.INPUT.FORMAT,
-            "ignore_label": ignore_label,
-            "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
-        }
-        return ret
-
-    def __call__(self, dataset_dict):
-        """
-        Args:
-            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
-
-        Returns:
-            dict: a format that builtin models in detectron2 accept
-        """
-        assert self.is_train, "MaskFormerSemanticDatasetMapper should only be used for training!"
-
-        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
-        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
-        utils.check_image_size(dataset_dict, image)
-
-        if "sem_seg_file_name" in dataset_dict:
-            # PyTorch transformation not implemented for uint16, so converting it to double first
-            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
-        else:
-            sem_seg_gt = None
-
-        if sem_seg_gt is None:
-            raise ValueError(
-                "Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.".format(
-                    dataset_dict["file_name"]
-                )
-            )
-
-        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
-        aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
-        image = aug_input.image
-        sem_seg_gt = aug_input.sem_seg
-
-        # Pad image and segmentation label here!
-        image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
-        if sem_seg_gt is not None:
-            sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
-        # import ipdb; ipdb.set_trace()
-        if self.size_divisibility > 0:
-            image_size = (image.shape[-2], image.shape[-1])
-            # The ori_size is not the real original size, but size before padding
-            dataset_dict['ori_size'] = image_size
-            padding_size = [
-                0,
-                self.size_divisibility - image_size[1], # w: (left, right)
-                0,
-                self.size_divisibility - image_size[0], # h: 0,(top, bottom)
-            ]
-            image = F.pad(image, padding_size, value=128).contiguous()
-            if sem_seg_gt is not None:
-                sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
-
-        image_shape = (image.shape[-2], image.shape[-1])  # h, w
-
-        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
-        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
-        # Therefore it's important to use torch.Tensor.
-        dataset_dict["image"] = image
-        # print('#########################################################################################')
-        if sem_seg_gt is not None:
-            dataset_dict["sem_seg"] = sem_seg_gt.long()
-
-        if "annotations" in dataset_dict:
-            raise ValueError("Semantic segmentation dataset should not have 'annotations'.")
-
-        # Prepare per-category binary masks
-        if sem_seg_gt is not None:
-            sem_seg_gt = sem_seg_gt.numpy()
-            instances = Instances(image_shape)
-            classes = np.unique(sem_seg_gt)
-            # remove ignored region
-            classes = classes[classes != self.ignore_label]
-            instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
-
-            masks = []
-            for class_id in classes:
-                masks.append(sem_seg_gt == class_id)
-
-            if len(masks) == 0:
-                # Some image does not have annotation (all ignored)
-                instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))
-            else:
-                masks = BitMasks(
-                    torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
-                )
-                instances.gt_masks = masks.tensor
-
-            dataset_dict["instances"] = instances
-
-        return dataset_dict
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/config/defaults.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/config/defaults.py
deleted file mode 100644
index b9ad62f5f01606438082e012ba5a4a68381c3b3c..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/config/defaults.py
+++ /dev/null
@@ -1,598 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .config import CfgNode as CN
-
-# -----------------------------------------------------------------------------
-# Convention about Training / Test specific parameters
-# -----------------------------------------------------------------------------
-# Whenever an argument can be either used for training or for testing, the
-# corresponding name will be post-fixed by a _TRAIN for a training parameter,
-# or _TEST for a test-specific parameter.
-# For example, the number of images during training will be
-# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
-# IMAGES_PER_BATCH_TEST
-
-# -----------------------------------------------------------------------------
-# Config definition
-# -----------------------------------------------------------------------------
-
-_C = CN()
-
-# The version number, to upgrade from old configs to new ones if any
-# changes happen. It's recommended to keep a VERSION in your config file.
-_C.VERSION = 2
-
-_C.MODEL = CN()
-_C.MODEL.LOAD_PROPOSALS = False
-_C.MODEL.MASK_ON = False
-_C.MODEL.KEYPOINT_ON = False
-_C.MODEL.DEVICE = "cuda"
-_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
-
-# Path (possibly with schema like catalog:// or detectron2://) to a checkpoint file
-# to be loaded to the model. You can find available models in the model zoo.
-_C.MODEL.WEIGHTS = ""
-
-# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
-# To train on images of different number of channels, just set different mean & std.
-# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
-_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
-# When using pre-trained models in Detectron1 or any MSRA models,
-# std has been absorbed into its conv1 weights, so the std needs to be set 1.
-# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
-_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
-
-
-# -----------------------------------------------------------------------------
-# INPUT
-# -----------------------------------------------------------------------------
-_C.INPUT = CN()
-# Size of the smallest side of the image during training
-_C.INPUT.MIN_SIZE_TRAIN = (800,)
-# Sample size of smallest side by choice or random selection from range give by
-# INPUT.MIN_SIZE_TRAIN
-_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
-# Maximum size of the side of the image during training
-_C.INPUT.MAX_SIZE_TRAIN = 1333
-# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
-_C.INPUT.MIN_SIZE_TEST = 800
-# Maximum size of the side of the image during testing
-_C.INPUT.MAX_SIZE_TEST = 1333
-
-# `True` if cropping is used for data augmentation during training
-_C.INPUT.CROP = CN({"ENABLED": False})
-# Cropping type:
-# - "relative" crop (H * CROP.SIZE[0], W * CROP.SIZE[1]) part of an input of size (H, W)
-# - "relative_range" uniformly sample relative crop size from between [CROP.SIZE[0], [CROP.SIZE[1]].
-#   and  [1, 1] and use it as in "relative" scenario.
-# - "absolute" crop part of an input with absolute size: (CROP.SIZE[0], CROP.SIZE[1]).
-_C.INPUT.CROP.TYPE = "relative_range"
-# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
-# pixels if CROP.TYPE is "absolute"
-_C.INPUT.CROP.SIZE = [0.9, 0.9]
-
-
-# Whether the model needs RGB, YUV, HSV etc.
-# Should be one of the modes defined here, as we use PIL to read the image:
-# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
-# with BGR being the one exception. One can set image format to BGR, we will
-# internally use RGB for conversion and flip the channels over
-_C.INPUT.FORMAT = "BGR"
-# The ground truth mask format that the model will use.
-# Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
-_C.INPUT.MASK_FORMAT = "polygon"  # alternative: "bitmask"
-
-
-# -----------------------------------------------------------------------------
-# Dataset
-# -----------------------------------------------------------------------------
-_C.DATASETS = CN()
-# List of the dataset names for training. Must be registered in DatasetCatalog
-_C.DATASETS.TRAIN = ()
-# List of the pre-computed proposal files for training, which must be consistent
-# with data listed in DATASETS.TRAIN.
-_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
-# Number of top scoring precomputed proposals to keep for training
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
-# List of the dataset names for testing. Must be registered in DatasetCatalog
-_C.DATASETS.TEST = ()
-# List of the pre-computed proposal files for test, which must be consistent
-# with data listed in DATASETS.TEST.
-_C.DATASETS.PROPOSAL_FILES_TEST = ()
-# Number of top scoring precomputed proposals to keep for test
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
-
-# -----------------------------------------------------------------------------
-# DataLoader
-# -----------------------------------------------------------------------------
-_C.DATALOADER = CN()
-# Number of data loading threads
-_C.DATALOADER.NUM_WORKERS = 4
-# If True, each batch should contain only images for which the aspect ratio
-# is compatible. This groups portrait images together, and landscape images
-# are not batched with portrait images.
-_C.DATALOADER.ASPECT_RATIO_GROUPING = True
-# Options: TrainingSampler, RepeatFactorTrainingSampler
-_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
-# Repeat threshold for RepeatFactorTrainingSampler
-_C.DATALOADER.REPEAT_THRESHOLD = 0.0
-# if True, the dataloader will filter out images that have no associated
-# annotations at train time.
-_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
-
-# ---------------------------------------------------------------------------- #
-# Backbone options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.BACKBONE = CN()
-
-_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
-# Freeze the first several stages so they are not trained.
-# There are 5 stages in ResNet. The first is a convolution, and the following
-# stages are each group of residual blocks.
-_C.MODEL.BACKBONE.FREEZE_AT = 2
-
-
-# ---------------------------------------------------------------------------- #
-# FPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.FPN = CN()
-# Names of the input feature maps to be used by FPN
-# They must have contiguous power of 2 strides
-# e.g., ["res2", "res3", "res4", "res5"]
-_C.MODEL.FPN.IN_FEATURES = []
-_C.MODEL.FPN.OUT_CHANNELS = 256
-
-# Options: "" (no norm), "GN"
-_C.MODEL.FPN.NORM = ""
-
-# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
-_C.MODEL.FPN.FUSE_TYPE = "sum"
-
-
-# ---------------------------------------------------------------------------- #
-# Proposal generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.PROPOSAL_GENERATOR = CN()
-# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
-_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
-# Proposal height and width both need to be greater than MIN_SIZE
-# (a the scale used during training or inference)
-_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
-
-
-# ---------------------------------------------------------------------------- #
-# Anchor generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ANCHOR_GENERATOR = CN()
-# The generator can be any name in the ANCHOR_GENERATOR registry
-_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
-# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
-# Format: list[list[float]]. SIZES[i] specifies the list of sizes
-# to use for IN_FEATURES[i]; len(SIZES) == len(IN_FEATURES) must be true,
-# or len(SIZES) == 1 is true and size list SIZES[0] is used for all
-# IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
-# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
-# ratios are generated by an anchor generator.
-# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
-# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
-# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
-# for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
-# Anchor angles.
-# list[list[float]], the angle in degrees, for each input feature map.
-# ANGLES[i] specifies the list of angles for IN_FEATURES[i].
-_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
-# Relative offset between the center of the first anchor and the top-left corner of the image
-# Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
-# The value is not expected to affect model accuracy.
-_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
-
-# ---------------------------------------------------------------------------- #
-# RPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RPN = CN()
-_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead"  # used by RPN_HEAD_REGISTRY
-
-# Names of the input feature maps to be used by RPN
-# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
-_C.MODEL.RPN.IN_FEATURES = ["res4"]
-# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
-# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
-_C.MODEL.RPN.BOUNDARY_THRESH = -1
-# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
-# Minimum overlap required between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
-# ==> positive RPN example: 1)
-# Maximum overlap allowed between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
-# ==> negative RPN example: 0)
-# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
-# are ignored (-1)
-_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
-_C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
-# Total number of RPN examples per image
-_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
-# Target fraction of foreground (positive) examples per RPN minibatch
-_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
-# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
-_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
-_C.MODEL.RPN.LOSS_WEIGHT = 1.0
-# Number of top scoring RPN proposals to keep before applying NMS
-# When FPN is used, this is *per FPN level* (not total)
-_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
-_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
-# Number of top scoring RPN proposals to keep after applying NMS
-# When FPN is used, this limit is applied per level and then again to the union
-# of proposals from all levels
-# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
-# It means per-batch topk in Detectron1, but per-image topk here.
-# See "modeling/rpn/rpn_outputs.py" for details.
-_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
-_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
-# NMS threshold used on RPN proposals
-_C.MODEL.RPN.NMS_THRESH = 0.7
-
-# ---------------------------------------------------------------------------- #
-# ROI HEADS options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_HEADS = CN()
-_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
-# Number of foreground classes
-_C.MODEL.ROI_HEADS.NUM_CLASSES = 80
-# Names of the input feature maps to be used by ROI heads
-# Currently all heads (box, mask, ...) use the same input feature map list
-# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
-_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
-# IOU overlap ratios [IOU_THRESHOLD]
-# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
-# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
-_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
-_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
-# RoI minibatch size *per image* (number of regions of interest [ROIs])
-# Total number of RoIs per training minibatch =
-#   ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
-# E.g., a common configuration is: 512 * 16 = 8192
-_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
-# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
-_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
-
-# Only used on test mode
-
-# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
-# balance obtaining high recall with not having too many low precision
-# detections that will slow down inference post processing steps (like NMS)
-# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
-# inference.
-_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
-# Overlap threshold used for non-maximum suppression (suppress boxes with
-# IoU >= this threshold)
-_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
-# If True, augment proposals with ground-truth boxes before sampling proposals to
-# train ROI heads.
-_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
-
-# ---------------------------------------------------------------------------- #
-# Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_HEAD = CN()
-# C4 don't use head name option
-# Options for non-C4 models: FastRCNNConvFCHead,
-_C.MODEL.ROI_BOX_HEAD.NAME = ""
-# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
-# These are empirically chosen to approximately lead to unit variance targets
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
-_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
-# Hidden layer dimension for FC layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
-_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
-# Channel dimension for Conv layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_BOX_HEAD.NORM = ""
-# Whether to use class agnostic for bbox regression
-_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
-# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
-_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
-
-# ---------------------------------------------------------------------------- #
-# Cascaded Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
-# The number of cascade stages is implicitly defined by the length of the following two configs.
-_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
-    (10.0, 10.0, 5.0, 5.0),
-    (20.0, 20.0, 10.0, 10.0),
-    (30.0, 30.0, 15.0, 15.0),
-)
-_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
-
-
-# ---------------------------------------------------------------------------- #
-# Mask Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_MASK_HEAD = CN()
-_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0  # The number of convs in the mask head
-_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_MASK_HEAD.NORM = ""
-# Whether to use class agnostic for mask prediction
-_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-
-# ---------------------------------------------------------------------------- #
-# Keypoint Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_KEYPOINT_HEAD = CN()
-_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
-_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17  # 17 is the number of keypoints in COCO.
-
-# Images with too few (or no) keypoints are excluded from training.
-_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
-# Normalize by the total number of visible keypoints in the minibatch if True.
-# Otherwise, normalize by the total number of keypoints that could ever exist
-# in the minibatch.
-# The keypoint softmax loss is only calculated on visible keypoints.
-# Since the number of visible keypoints can vary significantly between
-# minibatches, this has the effect of up-weighting the importance of
-# minibatches with few visible keypoints. (Imagine the extreme case of
-# only one visible keypoint versus N: in the case of N, each one
-# contributes 1/N to the gradient compared to the single keypoint
-# determining the gradient direction). Instead, we can normalize the
-# loss by the total number of keypoints, if it were the case that all
-# keypoints were visible in a full minibatch. (Returning to the example,
-# this means that the one visible keypoint contributes as much as each
-# of the N keypoints.)
-_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True
-# Multi-task loss weight to use for keypoints
-# Recommended values:
-#   - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True
-#   - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False
-_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-# ---------------------------------------------------------------------------- #
-# Semantic Segmentation Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.SEM_SEG_HEAD = CN()
-_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead"
-_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"]
-# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for
-# the correposnding pixel.
-_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
-# Number of classes in the semantic segmentation head
-_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
-# Number of channels in the 3x3 convs inside semantic-FPN heads.
-_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128
-# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride.
-_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
-# Normalization method for the convolution layers. Options: "" (no norm), "GN".
-_C.MODEL.SEM_SEG_HEAD.NORM = "GN"
-_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0
-
-_C.MODEL.PANOPTIC_FPN = CN()
-# Scaling of all losses from instance detection / segmentation head.
-_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0
-
-# options when combining instance & semantic segmentation outputs
-_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True})
-_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5
-_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096
-_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5
-
-
-# ---------------------------------------------------------------------------- #
-# RetinaNet Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RETINANET = CN()
-
-# This is the number of foreground classes.
-_C.MODEL.RETINANET.NUM_CLASSES = 80
-
-_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
-
-# Convolutions to use in the cls and bbox tower
-# NOTE: this doesn't include the last conv for logits
-_C.MODEL.RETINANET.NUM_CONVS = 4
-
-# IoU overlap ratio [bg, fg] for labeling anchors.
-# Anchors with < bg are labeled negative (0)
-# Anchors  with >= bg and < fg are ignored (-1)
-# Anchors with >= fg are labeled positive (1)
-_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5]
-_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1]
-
-# Prior prob for rare case (i.e. foreground) at the beginning of training.
-# This is used to set the bias for the logits layer of the classifier subnet.
-# This improves training stability in the case of heavy class imbalance.
-_C.MODEL.RETINANET.PRIOR_PROB = 0.01
-
-# Inference cls score threshold, only anchors with score > INFERENCE_TH are
-# considered for inference (to improve speed)
-_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05
-_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000
-_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5
-
-# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets
-_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-
-# Loss parameters
-_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0
-_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
-_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1
-
-
-# ---------------------------------------------------------------------------- #
-# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
-# Note that parts of a resnet may be used for both the backbone and the head
-# These options apply to both
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RESNETS = CN()
-
-_C.MODEL.RESNETS.DEPTH = 50
-_C.MODEL.RESNETS.OUT_FEATURES = ["res4"]  # res4 for C4 backbone, res2..5 for FPN backbone
-
-# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
-_C.MODEL.RESNETS.NUM_GROUPS = 1
-
-# Options: FrozenBN, GN, "SyncBN", "BN"
-_C.MODEL.RESNETS.NORM = "FrozenBN"
-
-# Baseline width of each group.
-# Scaling this parameters will scale the width of all bottleneck layers.
-_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
-
-# Place the stride 2 conv on the 1x1 filter
-# Use True only for the original MSRA ResNet; use False for C2 and Torch models
-_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
-
-# Apply dilation in stage "res5"
-_C.MODEL.RESNETS.RES5_DILATION = 1
-
-# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet
-# For R18 and R34, this needs to be set to 64
-_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
-_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
-
-# Apply Deformable Convolution in stages
-# Specify if apply deform_conv on Res2, Res3, Res4, Res5
-_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False]
-# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168);
-# Use False for DeformableV1.
-_C.MODEL.RESNETS.DEFORM_MODULATED = False
-# Number of groups in deformable conv.
-_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1
-
-
-# ---------------------------------------------------------------------------- #
-# Solver
-# ---------------------------------------------------------------------------- #
-_C.SOLVER = CN()
-
-# See detectron2/solver/build.py for LR scheduler options
-_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
-
-_C.SOLVER.MAX_ITER = 40000
-
-_C.SOLVER.BASE_LR = 0.001
-
-_C.SOLVER.MOMENTUM = 0.9
-
-_C.SOLVER.NESTEROV = False
-
-_C.SOLVER.WEIGHT_DECAY = 0.0001
-# The weight decay that's applied to parameters of normalization layers
-# (typically the affine transformation)
-_C.SOLVER.WEIGHT_DECAY_NORM = 0.0
-
-_C.SOLVER.GAMMA = 0.1
-# The iteration number to decrease learning rate by GAMMA.
-_C.SOLVER.STEPS = (30000,)
-
-_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
-_C.SOLVER.WARMUP_ITERS = 1000
-_C.SOLVER.WARMUP_METHOD = "linear"
-
-# Save a checkpoint after every this number of iterations
-_C.SOLVER.CHECKPOINT_PERIOD = 5000
-
-# Number of images per batch across all machines.
-# If we have 16 GPUs and IMS_PER_BATCH = 32,
-# each GPU will see 2 images per batch.
-_C.SOLVER.IMS_PER_BATCH = 16
-
-# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for
-# biases. This is not useful (at least for recent models). You should avoid
-# changing these and they exist only to reproduce Detectron v1 training if
-# desired.
-_C.SOLVER.BIAS_LR_FACTOR = 1.0
-_C.SOLVER.WEIGHT_DECAY_BIAS = _C.SOLVER.WEIGHT_DECAY
-
-# Gradient clipping
-_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False})
-# Type of gradient clipping, currently 2 values are supported:
-# - "value": the absolute values of elements of each gradients are clipped
-# - "norm": the norm of the gradient for each parameter is clipped thus
-#   affecting all elements in the parameter
-_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value"
-# Maximum absolute value used for clipping gradients
-_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0
-# Floating point number p for L-p norm to be used with the "norm"
-# gradient clipping type; for L-inf, please specify .inf
-_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0
-
-# ---------------------------------------------------------------------------- #
-# Specific test options
-# ---------------------------------------------------------------------------- #
-_C.TEST = CN()
-# For end-to-end tests to verify the expected accuracy.
-# Each item is [task, metric, value, tolerance]
-# e.g.: [['bbox', 'AP', 38.5, 0.2]]
-_C.TEST.EXPECTED_RESULTS = []
-# The period (in terms of steps) to evaluate the model during training.
-# Set to 0 to disable.
-_C.TEST.EVAL_PERIOD = 0
-# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval
-# When empty it will use the defaults in COCO.
-# Otherwise it should have the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
-_C.TEST.KEYPOINT_OKS_SIGMAS = []
-# Maximum number of detections to return per image during inference (100 is
-# based on the limit established for the COCO dataset).
-_C.TEST.DETECTIONS_PER_IMAGE = 100
-
-_C.TEST.AUG = CN({"ENABLED": False})
-_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
-_C.TEST.AUG.MAX_SIZE = 4000
-_C.TEST.AUG.FLIP = True
-
-_C.TEST.PRECISE_BN = CN({"ENABLED": False})
-_C.TEST.PRECISE_BN.NUM_ITER = 200
-
-# ---------------------------------------------------------------------------- #
-# Misc options
-# ---------------------------------------------------------------------------- #
-# Directory where output files are written
-_C.OUTPUT_DIR = "./output"
-# Set seed to negative to fully randomize everything.
-# Set seed to positive to use a fixed seed. Note that a fixed seed increases
-# reproducibility but does not guarantee fully deterministic behavior.
-# Disabling all parallelism further increases reproducibility.
-_C.SEED = -1
-# Benchmark different cudnn algorithms.
-# If input images have very different sizes, this option will have large overhead
-# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
-# If input images have the same or similar sizes, benchmark is often helpful.
-_C.CUDNN_BENCHMARK = False
-# The period (in terms of steps) for minibatch visualization at train time.
-# Set to 0 to disable.
-_C.VIS_PERIOD = 0
-
-# global config is for quick hack purposes.
-# You can set them in command line or config files,
-# and access it with:
-#
-# from detectron2.config import global_cfg
-# print(global_cfg.HACK)
-#
-# Do not commit any configs into it.
-_C.GLOBAL = CN()
-_C.GLOBAL.HACK = 1.0
diff --git a/spaces/hebert2099/MusicGen/CODE_OF_CONDUCT.md b/spaces/hebert2099/MusicGen/CODE_OF_CONDUCT.md
deleted file mode 100644
index 83f431e8feeb7e80d571f39c9f6c1b96857b5f85..0000000000000000000000000000000000000000
--- a/spaces/hebert2099/MusicGen/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <opensource-conduct@fb.com>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
diff --git a/spaces/hebert2099/MusicGen/tests/common_utils/__init__.py b/spaces/hebert2099/MusicGen/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/hebert2099/MusicGen/tests/common_utils/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .temp_utils import TempDirMixin
-from .wav_utils import get_batch_white_noise, get_white_noise, save_wav
diff --git a/spaces/heiyuan/ChatGPT/run_Linux.sh b/spaces/heiyuan/ChatGPT/run_Linux.sh
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/heiyuan/ChatGPT/run_Linux.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Dream Theater Breaking The Fourth Wall 720p 13.md b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Dream Theater Breaking The Fourth Wall 720p 13.md
deleted file mode 100644
index a72f808f5cdda135d906f417f670c9e28f7bec8a..0000000000000000000000000000000000000000
--- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Dream Theater Breaking The Fourth Wall 720p 13.md	
+++ /dev/null
@@ -1,69 +0,0 @@
-## Dream Theater Breaking The Fourth Wall 720p 13
-
-
-
-**Dream Theater Breaking The Fourth Wall 720p 13 >> [https://ditzcosupo.blogspot.com/?d=2twsi2](https://ditzcosupo.blogspot.com/?d=2twsi2)**
-
-
-
- ``` 
-
-# Dream Theater Breaking The Fourth Wall 720p 13: A Review of the Epic Live Album and Video
-
-  
-
-Dream Theater is one of the most influential and innovative progressive metal bands in the world. Their music combines complex compositions, virtuosic performances, and diverse influences from classical, jazz, rock, and metal genres. They have released 15 studio albums, 8 live albums, and several videos and DVDs over their 35-year career.
-
-  
-
-One of their most recent live releases is *Breaking The Fourth Wall*, a live album and video recorded at the Boston Opera House on March 25, 2014. The concert was filmed and directed by Pierre and François Lamoureux, and features guest performances from the Berklee College of Music Orchestra and Choir. The album and video were released on September 29, 2014.
-
-  
-
-In this article, we will review *Breaking The Fourth Wall*, focusing on the 13th track of the second DVD, *Space Dye Vest*. This track is one of the most emotional and personal songs in Dream Theater's catalog, and showcases their ability to create powerful and captivating music.
-
-  
-
-## What is Space Dye Vest?
-
-  
-
-*Space Dye Vest* is a song written by former keyboardist Kevin Moore, who left the band after the recording of their third studio album, *Awake*, in 1994. The song was inspired by a photo of a woman wearing a space dye vest that Moore saw in a magazine. He became obsessed with the image and the woman, and wrote the lyrics as a reflection of his feelings of isolation, loneliness, and unrequited love.
-
-  
-
-The song features samples from various sources, such as a news report about the Waco siege, a dialogue from the movie *The Dead Poet's Society*, and a phone message from Moore's ex-girlfriend. The music is mostly based on piano and synth sounds, creating a dark and melancholic atmosphere. The song also features a guitar solo by John Petrucci, which adds some intensity and contrast to the song.
-
-  
-
-*Space Dye Vest* is considered by many fans as one of Dream Theater's best songs, and one of the most emotional ones. It is also one of the rare songs that Moore sang lead vocals on, along with James LaBrie.
-
-  
-
-## How did Dream Theater perform Space Dye Vest live?
-
-  
-
-Dream Theater rarely performed *Space Dye Vest* live during Moore's tenure in the band. They only played it twice in 1994, during their *Waking Up The World* tour. After Moore's departure, they did not play it again until 2014, when they decided to include it in their setlist for their *Along For The Ride* tour.
-
-  
-
-The reason for this long hiatus was that *Space Dye Vest* was a very personal song for Moore, and the band felt that it would be disrespectful to play it without him. However, in 2014, they decided to honor Moore's contribution to the band by playing it again, with Jordan Rudess on keyboards and LaBrie on vocals.
-
-  
-
-The live version of *Space Dye Vest* on *Breaking The Fourth Wall* is very faithful to the original studio version. The only difference is that Rudess added some new synth sounds to enhance the song's mood. The performance was very well received by the fans and critics alike, who praised the band's respect for Moore's legacy and their ability to deliver an emotional rendition of the song.
-
-  
-
-## Why should you watch Dream Theater Breaking The Fourth Wall 720p 13?
-
-  
-
-If you are a fan of Dream Theater or progressive metal in general, you should definitely watch *Dream Theater Breaking The Fourth Wall 720p 13*. This video will show you one of the most memorable moments of Dream Theater's live career, as they play one of their most beloved songs for the first time in 20 years.
-
-  
-
-You will also enjoy watching the band's amazing musicianship and chemistry on stage, as they perform a diverse setlist that spans their
-
- dfd1c89656
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/documentation/extending_nnunet.md b/spaces/ho11laqe/nnUNet_calvingfront_detection/documentation/extending_nnunet.md
deleted file mode 100644
index ae416a51a4a39946575ba436d70255ea93f9a74e..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/documentation/extending_nnunet.md
+++ /dev/null
@@ -1,119 +0,0 @@
-
-# Extending/Changing nnU-Net
-
-To use nnU-Net as a framework and make changes to its components, please make sure to install it with the `git clone` 
-and `pip install -e .` commands so that a local copy of the code is created.
-Changing components of nnU-Net needs to be done in different places, depending on whether these components belong to 
-the inferred, blueprint or empirical parameters. We cover some of the most common use cases below. They should give 
-you a good indication of where to start.
-
-Generally it is recommended to look into the code where the thing you would like to change is currently implemented 
-and then derive a strategy on how to change it. If you have any questions, feel free to open an issue on GitHub and 
-we will help you as much as we can.
-
-## Changes to blueprint parameters
-This section gives guidance on how to implement changes to loss function, training schedule, learning rates, optimizer, 
-some architecture parameters, data augmentation etc. All these parameters are part of the **nnU-Net trainer class**, 
-which we have already seen in the sections above. The default trainer class for 2D, 3D low resolution and 3D full 
-resolution U-Net is nnUNetTrainerV2, the default for the 3D full resolution U-Net from the cascade is 
-nnUNetTrainerV2CascadeFullRes. Trainer classes in nnU-Net inherit form each other, nnUNetTrainerV2CascadeFullRes for 
-example has nnUNetTrainerV2 as parent class and only overrides cascade-specific code.
-
-Due to the inheritance of trainer classes, changes can be integrated into nnU-Net quite easily and with minimal effort. 
-Simply create a new trainer class (with some custom name), change the functionality you need to change and then specify 
-this class (via its name) during training - done.
-
-This process requires the new class to be located in a subfolder of nnunet.training.network_training! Do not save it 
-somewhere else or nnU-Net will not be able to find it! Also don't use the same name twice! nnU-Net always picks the 
-first trainer that matches the requested name.
-
-Don't worry about overwriting results of another trainer class. nnU-Net always generates output folders that are named 
-after the trainer class used to generate the results. 
-
-Due to the variety of possible changes to the blueprint parameters of nnU-Net, we here only present a summary of where 
-to look for what kind of modification. During method development we have already created a large number of nnU-Net 
-blueprint variations which should give a good indication of where to start:
-
-| Type of modification    | Examples                                                                                                                                                                                                                                                                                                                                              |
-|-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| loss function           | nnunet.training.network_training.loss_function.*                                                                                                                                                                                                                                                                                                      |
-| data augmentation       | nnunet.training.network_training.data_augmentation.*                                                                                                                                                                                                                                                                                                  |
-| Optimizer, lr, momentum | nnunet.training.network_training.optimizer_and_lr.*                                                                                                                                                                                                                                                                                                   |
-| (Batch)Normalization    | nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_BN.py<br>nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_FRN.py<br>nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_GN.py<br>nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_NoNormalization_lr1en3.py |
-| Nonlinearity            | nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_ReLU.py<br>nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_Mish.py                                                                                                                                                                                    |
-| Architecture            | nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_3ConvPerStage.py<br>nnunet.training.network_training.architectural_variants.nnUNetTrainerV2_ResencUNet                                                                                                                                                                        |
-| ...                     | (see nnunet.training.network_training and subfolders)                                                                                                                                                                                                                                                                                                 |
-
-## Changes to Inferred Parameters
-The inferred parameters are determined based on the dataset fingerprint, a low dimensional representation of the properties 
-of the training cases. It captures, for example, the image shapes, voxel spacings and intensity information from 
-the training cases. The datset fingerprint is created by the DatasetAnalyzer (which is located in nnunet.preprocessing) 
-while running `nnUNet_plan_and_preprocess`. 
-
-`nnUNet_plan_and_preprocess` uses so called ExperimentPlanners for running the adaptation process. Default ExperimentPlanner 
-classes are ExperimentPlanner2D_v21 for the 2D U-Net and ExperimentPlanner3D_v21 for the 3D full resolution U-Net and the 
-U-Net cascade. Just like nnUNetTrainers, the ExperimentPlanners inherit from each other, resulting in minimal programming 
-effort to incorporate changes. Just like with the trainers, simply give your custom ExperimentPlanners a unique name and 
-save them in some subfolder of nnunet.experiment_planning. You can then specify your class names when running 
-`nnUNet_plan_and_preprocess` and nnU-Net will find them automatically. When inheriting form ExperimentPlanners, you **MUST** 
-overwrite the class variables `self.data_identifier` and `self.plans_fname` (just like for example 
-[here](../nnunet/experiment_planning/alternative_experiment_planning/normalization/experiment_planner_3DUNet_CT2.py)). 
-If you omit this step the planner will overwrite the plans file and the preprocessed data of the planner it inherits from.
-
-To train with your custom configuration, simply specify the correct plans identifier with `-p` when you call the 
-`nnUNet_train` command. The plans file also contains the data_identifier specified in your ExperimentPlanner, so the 
-trainer class will automatically know what data should be used.
-
-Possible adaptations to the inferred parameters could include a different way of prioritizing batch size vs patch size 
-(currently, nnU-Net prioritizies patch size), a different handling of the spacing information for architecture template 
-instantiation, changing the definition of target spacing, or using different strategies for finding the 3d low 
-resolution U-Net configuration.
-
-The folders located in nnunet.experiment_planning contain several example ExperimentPlanner that modify various aspects 
-of the inferred parameters. You can use them as inspiration for your own.
-
-If you wish to run a different preprocessing, you most likely will have to implement your own Preprocessor class. 
-The preprocessor class that is used by some ExperimentPlanner is specified in its preprocessor_name class variable. The 
-default is `self.preprocessor_name = "GenericPreprocessor"` for 3D and `PreprocessorFor2D` for 2D (the 2D preprocessor 
-ignores the target spacing for the first axis to ensure that images are only resampled in the axes that will make up the training samples). 
-GenericPreprocessor (and all custom Preprocessors you implement) must be located in nnunet.preprocessing. The 
-preprocessor_name is saved in the plans file (by ExperimentPlanner), so that the 
-nnUNetTrainer knows which preprocessor must be used during inference to match the preprocessing of the training data. 
-
-Modifications to the preprocessing pipeline could be the addition of bias field correction to MRI images, a different CT
-preprocessing scheme or a different way of resampling segmentations and image data for anisotropic cases. 
-An example is provided [here](../nnunet/preprocessing/preprocessing.py).
-
-When implementing a custom preprocessor, you should also create a custom ExperimentPlanner that uses it (via self.preprocessor_name). 
-This experiment planner must also use a matching data_identifier and plans_fname to ensure no other data is overwritten.
-
-## Use a different network architecture
-Changing the network architecture in nnU-Net is easy, but not self-explanatory. Any new segmentation network you implement 
-needs to understand what nnU-Net requests from it (wrt how many downsampling operations are done, whether deep supervision 
-is used, what the convolutional kernel sizes are supposed to be). It needs to be able to dynamiccaly change its topology, 
-just like our implementation of the [Generic_UNet](../nnunet/network_architecture/generic_UNet.py). Furthermore, it must be
-able to generate a value that can be used to estimate memory consumption. What we have implemented for Generic_UNet effectively
-counts the number of voxels found in all feature maps that are present in a given configuration. Although this estimation 
-disregards the number of parameters we have found it to work quite well. Unless you implement an architecture with 
-unreasonably high number of parameters, the large majority of the VRAM used during training will be occupied by feature 
-maps, so parameters can be (mostly) disregarded. For implementing your own network, it is key to understand that the 
-number we are computing here cannot be interpreted directly as memory consumption (other factors than the feature maps 
-of the convolutions also play a role, such as instance normalization. This is furthermore very hard to predict because 
-there are also several different algorithms for running the convolutions, each with its own memory requirement. We train 
-models with cudnn.benchmark=True, so it is impossible to predict which algorithm is used). 
-So instead, to approch this problem in the most straightforward way, we manually identify the largest configuration we 
-can fit in the GPU of choice (manually define the dowmsampling, patch size etc) and use this value (-10% or so to be save) 
-as **reference** in the ExperimentPlanner that uses this architecture. 
-
-To illustrate this process, we have implemented a U-Net with a residual encoder 
-(see FabiansUNet in [generic_modular_residual_UNet.py](../nnunet/network_architecture/generic_modular_residual_UNet.py)). 
-This UNet has a class variable called use_this_for_3D_configuration. This value was found with the code located in 
-find_3d_configuration (same python file). The corresponding ExperimentPlanner 
-[ExperimentPlanner3DFabiansResUNet_v21](../nnunet/experiment_planning/alternative_experiment_planning/experiment_planner_residual_3DUNet_v21.py)
-compares this value to values generated for the currently configured network topology (which are also computed by 
-FabiansUNet.compute_approx_vram_consumption) to ensure that the GPU memory target is met.
-
-## Tutorials
-We have created tutorials on how to [manually edit plans files](tutorials/edit_plans_files.md), 
-[change the target spacing](tutorials/custom_spacing.md) and 
-[changing the normalization scheme for preprocessing](tutorials/custom_preprocessing.md).
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/__init__.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/__init__.py
deleted file mode 100644
index 72b8078b9dddddf22182fec2555d8d118ea72622..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from __future__ import absolute_import
-from . import *
\ No newline at end of file
diff --git a/spaces/housexu123/bingo-2.0/tests/kblob.ts b/spaces/housexu123/bingo-2.0/tests/kblob.ts
deleted file mode 100644
index 9e15b41c1c94a690beb61b23cdb42fc78767ccd2..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/tests/kblob.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import FormData from 'form-data'
-
-import { fetch } from '@/lib/isomorphic'
-
-const formData = new FormData()
-
-const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}}
-
-formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-
-
-fetch('https://bing.vcanbb.top/images/kblob',
-  {
-    method: 'POST',
-    body: formData.getBuffer(),
-    headers: {
-      "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-      "sec-ch-ua-mobile": "?0",
-      "sec-ch-ua-platform": "\"Windows\"",
-      "Referer": "https://bing.vcanbb.top/web/index.html",
-      "Referrer-Policy": "origin-when-cross-origin",
-      ...formData.getHeaders()
-    }
-
-  }
-).then(res => res.text())
-.then(res => console.log('res', res))
diff --git a/spaces/hrnph/rvc-models/README.md b/spaces/hrnph/rvc-models/README.md
deleted file mode 100644
index 70213f355334a9b836bef13bc31ee25877d9af9b..0000000000000000000000000000000000000000
--- a/spaces/hrnph/rvc-models/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: mocci24/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huggingface/text-data-filtering/filtering.py b/spaces/huggingface/text-data-filtering/filtering.py
deleted file mode 100644
index eb2f4358b0a520098dd41a678a51250b4be88176..0000000000000000000000000000000000000000
--- a/spaces/huggingface/text-data-filtering/filtering.py
+++ /dev/null
@@ -1,957 +0,0 @@
-import re
-
-import numpy as np
-
-import fasttext
-
-import sentencepiece
-import kenlm
-
-import pathlib
-
-from languages_id import langs_id
-from parameters_filtering import parameters_filtering
-from normalization import normalization
-from stopwords import stopwords
-from flagged_words import flagged_words
-
-
-class LoadParameters:
-    @staticmethod
-    def load_parameters(lang_dataset_id):
-        if lang_dataset_id in parameters_filtering:
-            param = parameters_filtering[lang_dataset_id]
-        else:
-            param = parameters_filtering["default"]
-        return param
-
-    @staticmethod
-    def load_stopwords(lang_dataset_id):
-        stopwords_lang_id = langs_id.loc[
-            langs_id["dataset_id"] == lang_dataset_id, "stopwords_id"
-        ].iloc[0]
-        if stopwords_lang_id:
-            stopwords_lang = set(stopwords[stopwords_lang_id])
-        else:
-            stopwords_lang = None
-        return stopwords_lang
-
-    @staticmethod
-    def load_flagged_words(lang_dataset_id):
-        flagged_words_lang_id = langs_id.loc[
-            langs_id["dataset_id"] == lang_dataset_id, "flagged_words_id"
-        ].iloc[0]
-        if flagged_words_lang_id:
-            flagged_words_lang = set(flagged_words[flagged_words_lang_id])
-        else:
-            flagged_words_lang = None
-        return flagged_words_lang
-
-    @staticmethod
-    def load_model_lang_id(lang_dataset_id, path_fasttext_model):
-        fasttext_lang_id = langs_id.loc[
-            langs_id["dataset_id"] == lang_dataset_id, "fasttext_id"
-        ].iloc[0]
-        if fasttext_lang_id:
-            model_lang_id = fasttext.load_model(path_fasttext_model)
-        else:
-            model_lang_id = None
-        return model_lang_id
-
-    @staticmethod
-    def load_sentencepiece_model(lang_dataset_id, path_sentencepiece_model):
-        sentencepiece_lang_id = langs_id.loc[
-            langs_id["dataset_id"] == lang_dataset_id, "sentencepiece_id"
-        ].iloc[0]
-        if sentencepiece_lang_id:
-            sentencepiece_model = sentencepiece.SentencePieceProcessor()
-            sentencepiece_model.load(path_sentencepiece_model)
-        else:
-            sentencepiece_model = None
-        return sentencepiece_model
-
-    @staticmethod
-    def load_kenlm_model(lang_dataset_id, path_kenlm_model):
-        kenlm_lang_id = langs_id.loc[
-            langs_id["dataset_id"] == lang_dataset_id, "kenlm_id"
-        ].iloc[0]
-        if kenlm_lang_id:
-            kenlm_model = kenlm.Model(path_kenlm_model)
-        else:
-            kenlm_model = None
-        return kenlm_model
-
-
-class ModifyingDocuments:
-    @staticmethod
-    def remove_empty_el_from_list(list_):
-        return [el for el in list_ if el]
-
-    @staticmethod
-    def remove_non_printing_characters(document, non_printing_characters_re):
-        return non_printing_characters_re.sub("", document)
-
-    @staticmethod
-    def uniform_whitespace(
-        document,
-        whitespace=[
-            " ",
-            " ",
-            " ",
-            " ",
-            " ",
-            "　",
-            " ",
-            " ",
-            " ",
-            " ",
-            "",
-            "„",
-        ],
-    ):
-        """There are different whitespace characters."""
-        whitespace = set(whitespace)
-        document = "".join(
-            [char if char not in whitespace else " " for char in document]
-        )
-        return document
-
-    @staticmethod
-    def replace_digits_with_zeros(document, digits_re):
-        return digits_re.sub("0", document)
-
-    @staticmethod
-    def replace_unicode_punctuation(document, unicode_punctuation):
-        return "".join(unicode_punctuation.get(c, c) for c in document)
-
-    @staticmethod
-    def normalization(
-        document,
-        remove_non_printing_characters,
-        strip,
-        lower_case,
-        uniform_whitespace,
-        replace_digits_with_zeros,
-        replace_unicode_punctuation,
-        non_printing_characters_re=normalization["non_printing_characters_re"],
-        digits_re=normalization["digits_re"],
-        unicode_punctuation=normalization["unicode_punctuation"],
-    ):
-        if remove_non_printing_characters:
-            document = ModifyingDocuments.remove_non_printing_characters(
-                document, non_printing_characters_re
-            )
-        if strip:
-            document = document.strip()
-        if not document:
-            return document
-        if lower_case:
-            document = document.lower()
-        if uniform_whitespace:
-            document = ModifyingDocuments.uniform_whitespace(document)
-        if replace_digits_with_zeros:
-            document = ModifyingDocuments.replace_digits_with_zeros(document, digits_re)
-        if replace_unicode_punctuation:
-            document = ModifyingDocuments.replace_unicode_punctuation(
-                document, unicode_punctuation
-            )
-        return document
-
-    @staticmethod
-    def tokenization(document, sentencepiece_model, join_on_whitespace):
-        document_tokenized = sentencepiece_model.encode_as_pieces(document)
-        if join_on_whitespace:
-            document_tokenized = " ".join(document_tokenized)
-        return document_tokenized
-
-    @staticmethod
-    def split_on_whitespace(
-        document,
-        new_line=False,
-        tab=False,
-    ):
-        """This method also removes concatenated spaces."""
-        sep = [" "] + new_line * ["\n"] + tab * ["\t"]
-        sep = "|".join(sep)
-        split_document = re.split(sep, document)
-        split_document = ModifyingDocuments.remove_empty_el_from_list(split_document)
-        return split_document
-
-    @staticmethod
-    def strip(document, strip_characters):
-        """Way faster than document.strip(strip_characters)
-        since strip_characters is now a set instead of a str,
-        and it contains a lot of elements (all the emojis)."""
-        if not document:
-            return document
-        beg_ind = 0
-        end_ind = len(document)
-        for i in range(len(document)):
-            if document[i] in strip_characters:
-                beg_ind += 1
-            else:
-                break
-        for i in range(1, len(document) + 1):
-            if document[-i] in strip_characters:
-                end_ind -= 1
-            else:
-                break
-        document_stripped = document[beg_ind:end_ind]
-        return document_stripped
-
-    @staticmethod
-    def get_words_from_document(
-        document, sentencepiece_model_tok, lower_case, strip_characters
-    ):
-        """Get words from a document. Non reversible since the document
-        is split on multiple characters, words are stripped of
-        special characters and characters are converted to lower case.
-        Useful to compute ratios, like the stopwords ratio."""
-        if sentencepiece_model_tok:
-            document_normalized = ModifyingDocuments.normalization(
-                document=document,
-                remove_non_printing_characters=True,
-                strip=True,
-                lower_case=True,
-                uniform_whitespace=True,
-                replace_digits_with_zeros=True,
-                replace_unicode_punctuation=True,
-            )
-            words = ModifyingDocuments.tokenization(
-                document_normalized, sentencepiece_model_tok, join_on_whitespace=False
-            )
-        else:
-            words = ModifyingDocuments.split_on_whitespace(
-                document, new_line=True, tab=True
-            )
-        if lower_case:
-            words = [word.lower() for word in words]
-        if strip_characters:
-            words = [ModifyingDocuments.strip(word, strip_characters) for word in words]
-            words = ModifyingDocuments.remove_empty_el_from_list(words)
-        return words
-
-    @staticmethod
-    def words_augmentation(words, group_size, join_char):
-        """Augment words, especially for Chinese (without a space between words)
-        and Vietnamese (with a space between syllables)."""
-        augmentation = [
-            join_char.join(words[i : i + group_size])
-            for i in range(len(words) - group_size + 1)
-        ]
-        return augmentation
-
-    @staticmethod
-    def split_on_newline_tab_whitespace(document):
-        """First split on "\n", then on "\t", then on " "."""
-        sentences = document.split("\n")
-        sentences = [sentence.split("\t") for sentence in sentences]
-        sentences = [
-            [
-                ModifyingDocuments.split_on_whitespace(subsentence)
-                for subsentence in sentence
-            ]
-            for sentence in sentences
-        ]
-        return sentences
-
-    @staticmethod
-    def merge_on_whitespace_tab_newline(sentences):
-        """Invert the method split_on_newline_tab_whitespace.
-        Removes concatenated separators."""
-        sentences = [
-            [" ".join(subsentence) for subsentence in sentence if subsentence]
-            for sentence in sentences
-        ]
-        sentences = ["\t".join(sentence) for sentence in sentences if sentence]
-        if not sentences:
-            return ""
-        document = "\n".join(sentences)
-        return document
-
-    @staticmethod
-    def should_keep_word_with_incorrect_substrings(
-        word, strip_characters, incorrect_word_substrings
-    ):
-        word = ModifyingDocuments.strip(word, strip_characters)
-        should_keep = all(
-            [(i_substr not in word) for i_substr in incorrect_word_substrings]
-        )
-        return should_keep
-
-    @staticmethod
-    def remove_words_with_incorrect_substrings(
-        document,
-        strip_characters,
-        incorrect_word_substrings,
-    ):
-        sentences = ModifyingDocuments.split_on_newline_tab_whitespace(document)
-        sentences = [
-            [
-                [
-                    word
-                    for word in subsentence
-                    if ModifyingDocuments.should_keep_word_with_incorrect_substrings(
-                        word, strip_characters, incorrect_word_substrings
-                    )
-                ]
-                for subsentence in sentence
-            ]
-            for sentence in sentences
-        ]
-        document = ModifyingDocuments.merge_on_whitespace_tab_newline(sentences)
-        return document
-
-    @staticmethod
-    def should_keep_long_word(word, strip_characters, length_word_max_cutoff):
-        """If the word is too long but it contains only one
-        special character, it might be a concatenation of one word,
-        a punctuation, and another word, with no space between them.
-        In this case, we give the word a pass."""
-        if len(word) <= length_word_max_cutoff:
-            return True
-        word = ModifyingDocuments.strip(word, strip_characters)
-        if not word:  # The word consisted only of strip characters
-            return False
-        if len(word) <= length_word_max_cutoff:
-            return True
-        return False
-
-    def remove_long_words(
-        document,
-        strip_characters,
-        length_word_max_cutoff,
-    ):
-        sentences = ModifyingDocuments.split_on_newline_tab_whitespace(document)
-        sentences = [
-            [
-                [
-                    word
-                    for word in subsentence
-                    if ModifyingDocuments.should_keep_long_word(
-                        word,
-                        strip_characters,
-                        length_word_max_cutoff,
-                    )
-                ]
-                for subsentence in sentence
-            ]
-            for sentence in sentences
-        ]
-        document = ModifyingDocuments.merge_on_whitespace_tab_newline(sentences)
-        return document
-
-    @staticmethod
-    def modifying_documents(
-        document,
-        cond_uniform_whitespace,
-        cond_replace_unicode_punctuation,
-        cond_remove_words_with_incorrect_substrings,
-        strip_characters,
-        incorrect_word_substrings,
-        cond_remove_long_words,
-        length_word_max_cutoff,
-    ):
-        document = ModifyingDocuments.normalization(
-            document=document,
-            remove_non_printing_characters=False,
-            strip=True,
-            lower_case=False,
-            uniform_whitespace=cond_uniform_whitespace,
-            replace_digits_with_zeros=False,
-            replace_unicode_punctuation=cond_replace_unicode_punctuation,
-        )
-        if cond_remove_words_with_incorrect_substrings:
-            document = ModifyingDocuments.remove_words_with_incorrect_substrings(
-                document,
-                strip_characters,
-                incorrect_word_substrings,
-            )
-        if cond_remove_long_words:
-            document = ModifyingDocuments.remove_long_words(
-                document,
-                strip_characters,
-                length_word_max_cutoff,
-            )
-        return document
-
-
-class FunctionDatasetModifyingDocuments:
-    def __init__(self, lang_dataset_id):
-        self.lang_dataset_id = lang_dataset_id
-        self.param = LoadParameters.load_parameters(lang_dataset_id)
-
-    def __call__(self, example):
-        example["text"] = ModifyingDocuments.modifying_documents(
-            document=example["text"],
-            cond_uniform_whitespace=self.param["cond_uniform_whitespace"],
-            cond_replace_unicode_punctuation=self.param[
-                "cond_replace_unicode_punctuation"
-            ],
-            cond_remove_words_with_incorrect_substrings=self.param[
-                "cond_remove_words_with_incorrect_substrings"
-            ],
-            strip_characters=self.param["strip_characters"],
-            incorrect_word_substrings=self.param["incorrect_word_substrings"],
-            cond_remove_long_words=self.param["cond_remove_long_words"],
-            length_word_max_cutoff=self.param["length_word_max_cutoff"],
-        )
-        return example
-
-    def __reduce__(self):
-        return (self.__class__, (self.lang_dataset_id,))
-
-
-class Filtering:
-    @staticmethod
-    def check_number_words(
-        document,
-        sentencepiece_model_tok,
-        strip_characters,
-        number_words_min_cutoff,
-        number_words_max_cutoff,
-    ):
-        words = ModifyingDocuments.get_words_from_document(
-            document,
-            sentencepiece_model_tok,
-            lower_case=False,
-            strip_characters=strip_characters,
-        )
-        cond = (len(words) >= number_words_min_cutoff) and (
-            len(words) <= number_words_max_cutoff
-        )
-        return cond
-
-    @staticmethod
-    def compute_character_repetition_ratio(document, character_repetition_length):
-        def get_freq_character_ngrams(document, n):
-            character_ngrams = [
-                document[i : i + n] for i in range(len(document) - n + 1)
-            ]
-            freq_character_ngrams = {}
-            for character_ngram in character_ngrams:
-                freq_character_ngrams[character_ngram] = (
-                    freq_character_ngrams.get(character_ngram, 0) + 1
-                )
-            return freq_character_ngrams
-
-        freq_character_ngrams = get_freq_character_ngrams(
-            document, character_repetition_length
-        )
-        if len(freq_character_ngrams) == 0:
-            return 0
-        freq_character_ngrams = list(freq_character_ngrams.values())
-        freq_character_ngrams = sorted(freq_character_ngrams, reverse=True)
-        val_less_than_one = len([el for el in freq_character_ngrams if el > 1])
-        num_rep_character_ngrams = min(
-            int(np.sqrt(len(freq_character_ngrams))),
-            len(freq_character_ngrams) - val_less_than_one,
-        )
-        character_repetition_ratio = sum(
-            freq_character_ngrams[:num_rep_character_ngrams]
-        ) / sum(freq_character_ngrams)
-        return character_repetition_ratio
-
-    @staticmethod
-    def check_character_repetition_removal(
-        document,
-        character_repetition_length,
-        character_repetition_max_cutoff,
-    ):
-        character_repetition_ratio = Filtering.compute_character_repetition_ratio(
-            document, character_repetition_length
-        )
-        cond = character_repetition_ratio <= character_repetition_max_cutoff
-        return cond
-
-    @staticmethod
-    def compute_word_repetition_ratio(
-        document, sentencepiece_model_tok, strip_characters, word_repetition_length
-    ):
-        def get_freq_word_ngrams(
-            document, sentencepiece_model_tok, strip_characters, n
-        ):
-            words = ModifyingDocuments.get_words_from_document(
-                document,
-                sentencepiece_model_tok,
-                lower_case=True,
-                strip_characters=strip_characters,
-            )
-            word_ngrams = [
-                " ".join(words[i : i + n]) for i in range(len(words) - n + 1)
-            ]
-            freq_word_ngrams = {}
-            for word_ngram in word_ngrams:
-                freq_word_ngrams[word_ngram] = freq_word_ngrams.get(word_ngram, 0) + 1
-            return freq_word_ngrams
-
-        freq_word_ngrams = get_freq_word_ngrams(
-            document, sentencepiece_model_tok, strip_characters, word_repetition_length
-        )
-        if len(freq_word_ngrams) == 0:
-            return 0
-        freq_word_ngrams = list(freq_word_ngrams.values())
-        word_repetition_ratio = sum(
-            freq for freq in freq_word_ngrams if freq > 1
-        ) / sum(freq_word_ngrams)
-        return word_repetition_ratio
-
-    @staticmethod
-    def check_word_repetition_removal(
-        document,
-        sentencepiece_model_tok,
-        strip_characters,
-        word_repetition_length,
-        word_repetition_max_cutoff,
-    ):
-        word_repetition_ratio = Filtering.compute_word_repetition_ratio(
-            document, sentencepiece_model_tok, strip_characters, word_repetition_length
-        )
-        cond = word_repetition_ratio <= word_repetition_max_cutoff
-        return cond
-
-    @staticmethod
-    def compute_special_characters_ratio(document, special_characters):
-        if len(document) == 0:
-            return 0
-        special_characters_ratio = len(
-            [char for char in document if char in special_characters]
-        ) / len(document)
-        return special_characters_ratio
-
-    @staticmethod
-    def check_special_characters(
-        document,
-        special_characters,
-        special_characters_max_cutoff,
-    ):
-        special_characters_ratio = Filtering.compute_special_characters_ratio(
-            document, special_characters
-        )
-        cond = special_characters_ratio <= special_characters_max_cutoff
-        return cond
-
-    @staticmethod
-    def compute_stopwords_ratio(
-        document,
-        sentencepiece_model_tok,
-        strip_characters,
-        cond_words_augmentation,
-        words_augmentation_group_sizes,
-        words_augmentation_join_char,
-        stopwords,
-    ):
-        words = ModifyingDocuments.get_words_from_document(
-            document,
-            sentencepiece_model_tok,
-            lower_case=True,
-            strip_characters=strip_characters,
-        )
-        if not words:
-            return 0
-        augmentation = []
-        if cond_words_augmentation:
-            augmentation = [
-                ModifyingDocuments.words_augmentation(
-                    words, group_size, words_augmentation_join_char
-                )
-                for group_size in words_augmentation_group_sizes
-            ]
-            augmentation = [word for augm in augmentation for word in augm]
-        stopwords_ratio = len(
-            [word for word in words + augmentation if word in stopwords]
-        ) / len(words)
-        if stopwords_ratio > 1.0:
-            stopwords_ratio = 1.0
-        return stopwords_ratio
-
-    @staticmethod
-    def check_stopwords(
-        document,
-        sentencepiece_model_tok,
-        strip_characters,
-        cond_words_augmentation,
-        words_augmentation_group_sizes,
-        words_augmentation_join_char,
-        stopwords,
-        stopwords_min_cutoff,
-    ):
-        cond = True
-        if stopwords:
-            stopwords_ratio = Filtering.compute_stopwords_ratio(
-                document,
-                sentencepiece_model_tok,
-                strip_characters,
-                cond_words_augmentation,
-                words_augmentation_group_sizes,
-                words_augmentation_join_char,
-                stopwords,
-            )
-            cond = stopwords_ratio >= stopwords_min_cutoff
-        return cond
-
-    @staticmethod
-    def compute_flagged_words_ratio(
-        document,
-        sentencepiece_model_tok,
-        strip_characters,
-        cond_words_augmentation,
-        words_augmentation_group_sizes,
-        words_augmentation_join_char,
-        flagged_words,
-    ):
-        words = ModifyingDocuments.get_words_from_document(
-            document,
-            sentencepiece_model_tok,
-            lower_case=True,
-            strip_characters=strip_characters,
-        )
-        if not words:
-            return 0
-        augmentation = []
-        if cond_words_augmentation:
-            augmentation = [
-                ModifyingDocuments.words_augmentation(
-                    words, group_size, words_augmentation_join_char
-                )
-                for group_size in words_augmentation_group_sizes
-            ]
-            augmentation = [word for augm in augmentation for word in augm]
-        flagged_words_ratio = len(
-            [word for word in words + augmentation if word in flagged_words]
-        ) / len(words)
-        if flagged_words_ratio > 1.0:
-            flagged_words_ratio = 1.0
-        return flagged_words_ratio
-
-    @staticmethod
-    def check_flagged_words(
-        document,
-        sentencepiece_model_tok,
-        strip_characters,
-        cond_words_augmentation,
-        words_augmentation_group_sizes,
-        words_augmentation_join_char,
-        flagged_words,
-        flagged_words_max_cutoff,
-    ):
-        cond = True
-        if flagged_words:
-            flagged_words_ratio = Filtering.compute_flagged_words_ratio(
-                document,
-                sentencepiece_model_tok,
-                strip_characters,
-                cond_words_augmentation,
-                words_augmentation_group_sizes,
-                words_augmentation_join_char,
-                flagged_words,
-            )
-            cond = flagged_words_ratio <= flagged_words_max_cutoff
-        return cond
-
-    @staticmethod
-    def compute_lang_id_pred_score(document, model_lang_id):
-        document = document.lower().replace("\n", " ")
-        pred = model_lang_id.predict(document)
-        lang_pred_fasttext_id = pred[0][0].replace("__label__", "")
-        score_pred = pred[1][0]
-        lang_pred_dataset_id = langs_id.loc[
-            langs_id["fasttext_id"] == lang_pred_fasttext_id, "dataset_id"
-        ]
-        if len(lang_pred_dataset_id) > 0:
-            lang_pred_dataset_id = lang_pred_dataset_id.iloc[0]
-        else:
-            lang_pred_dataset_id = "unknown"
-        return lang_pred_dataset_id, score_pred
-
-    @staticmethod
-    def check_lang_id(
-        document,
-        lang_dataset_id,
-        model_lang_id,
-        lang_id_min_cutoff,
-    ):
-        cond = True
-        if model_lang_id:
-            lang_pred_dataset_id, score_pred = Filtering.compute_lang_id_pred_score(
-                document, model_lang_id
-            )
-            cond = (lang_pred_dataset_id == lang_dataset_id) and (
-                score_pred >= lang_id_min_cutoff
-            )
-        return cond
-
-    @staticmethod
-    def compute_perplexity_score(document, sentencepiece_model, kenlm_model):
-        document = ModifyingDocuments.normalization(
-            document=document,
-            remove_non_printing_characters=True,
-            strip=True,
-            lower_case=False,
-            uniform_whitespace=True,
-            replace_digits_with_zeros=True,
-            replace_unicode_punctuation=True,
-        )
-        document = ModifyingDocuments.tokenization(
-            document, sentencepiece_model, join_on_whitespace=True
-        )
-        doc_log_score, doc_length = 0, 0
-        for line in document.split("\n"):
-            log_score = kenlm_model.score(line)
-            length = len(line.split()) + 1
-            doc_log_score += log_score
-            doc_length += length
-        pp_score = 10.0 ** (-doc_log_score / doc_length)
-        pp_score = round(pp_score, 1)
-        return pp_score
-
-    @staticmethod
-    def check_perplexity(
-        document,
-        sentencepiece_model,
-        kenlm_model,
-        perplexity_max_cutoff,
-    ):
-        cond = True
-        if kenlm_model:
-            score = Filtering.compute_perplexity_score(
-                document, sentencepiece_model, kenlm_model
-            )
-            cond = score <= perplexity_max_cutoff
-        return cond
-
-    @staticmethod
-    def filtering(
-        document,
-        cond_check_number_words,
-        sentencepiece_model_tok,
-        strip_characters,
-        number_words_min_cutoff,
-        number_words_max_cutoff,
-        cond_check_character_repetition_removal,
-        character_repetition_length,
-        character_repetition_max_cutoff,
-        cond_check_word_repetition_removal,
-        word_repetition_length,
-        word_repetition_max_cutoff,
-        cond_check_special_characters,
-        special_characters,
-        special_characters_max_cutoff,
-        cond_words_augmentation,
-        words_augmentation_group_sizes,
-        words_augmentation_join_char,
-        cond_check_stopwords,
-        stopwords,
-        stopwords_min_cutoff,
-        cond_check_flagged_words,
-        flagged_words,
-        flagged_words_max_cutoff,
-        cond_check_lang_id,
-        lang_dataset_id,
-        model_lang_id,
-        lang_id_min_cutoff,
-        cond_check_perplexity,
-        sentencepiece_model,
-        kenlm_model,
-        perplexity_max_cutoff,
-    ):
-        if cond_check_number_words:
-            if not Filtering.check_number_words(
-                document,
-                sentencepiece_model_tok,
-                strip_characters,
-                number_words_min_cutoff,
-                number_words_max_cutoff,
-            ):
-                return False
-        if cond_check_character_repetition_removal:
-            if not Filtering.check_character_repetition_removal(
-                document,
-                character_repetition_length,
-                character_repetition_max_cutoff,
-            ):
-                return False
-        if cond_check_word_repetition_removal:
-            if not Filtering.check_word_repetition_removal(
-                document,
-                sentencepiece_model_tok,
-                strip_characters,
-                word_repetition_length,
-                word_repetition_max_cutoff,
-            ):
-                return False
-        if cond_check_special_characters:
-            if not Filtering.check_special_characters(
-                document,
-                special_characters,
-                special_characters_max_cutoff,
-            ):
-                return False
-        if cond_check_stopwords:
-            if not Filtering.check_stopwords(
-                document,
-                sentencepiece_model_tok,
-                strip_characters,
-                cond_words_augmentation,
-                words_augmentation_group_sizes,
-                words_augmentation_join_char,
-                stopwords,
-                stopwords_min_cutoff,
-            ):
-                return False
-        if cond_check_flagged_words:
-            if not Filtering.check_flagged_words(
-                document,
-                sentencepiece_model_tok,
-                strip_characters,
-                cond_words_augmentation,
-                words_augmentation_group_sizes,
-                words_augmentation_join_char,
-                flagged_words,
-                flagged_words_max_cutoff,
-            ):
-                return False
-        if cond_check_lang_id:
-            if not Filtering.check_lang_id(
-                document,
-                lang_dataset_id,
-                model_lang_id,
-                lang_id_min_cutoff,
-            ):
-                return False
-        if cond_check_perplexity:
-            if not Filtering.check_perplexity(
-                document,
-                sentencepiece_model,
-                kenlm_model,
-                perplexity_max_cutoff,
-            ):
-                return False
-        return True
-
-
-class FunctionDatasetFiltering:
-    def __init__(
-        self,
-        lang_dataset_id,
-        path_fasttext_model,
-        path_sentencepiece_model,
-        path_kenlm_model,
-    ):
-        self.lang_dataset_id = lang_dataset_id
-        self.path_fasttext_model = path_fasttext_model
-        self.path_sentencepiece_model = path_sentencepiece_model
-        self.path_kenlm_model = path_kenlm_model
-
-        self.param = LoadParameters.load_parameters(lang_dataset_id)
-        self.stopwords = LoadParameters.load_stopwords(lang_dataset_id)
-        self.flagged_words = LoadParameters.load_flagged_words(lang_dataset_id)
-        self.model_lang_id = LoadParameters.load_model_lang_id(
-            lang_dataset_id, path_fasttext_model
-        )
-        self.sentencepiece_model = LoadParameters.load_sentencepiece_model(
-            lang_dataset_id, path_sentencepiece_model
-        )
-        self.sentencepiece_model_tok = (
-            self.sentencepiece_model if self.param["tokenization"] else None
-        )
-        self.kenlm_model = LoadParameters.load_kenlm_model(
-            lang_dataset_id, path_kenlm_model
-        )
-
-    def __call__(self, example):
-        keep_example = Filtering.filtering(
-            document=example["text"],
-            cond_check_number_words=self.param["cond_check_number_words"],
-            sentencepiece_model_tok=self.sentencepiece_model_tok,
-            strip_characters=self.param["strip_characters"],
-            number_words_min_cutoff=self.param["number_words_min_cutoff"],
-            number_words_max_cutoff=self.param["number_words_max_cutoff"],
-            cond_check_character_repetition_removal=self.param[
-                "cond_check_character_repetition_removal"
-            ],
-            character_repetition_length=self.param["character_repetition_length"],
-            character_repetition_max_cutoff=self.param[
-                "character_repetition_max_cutoff"
-            ],
-            cond_check_word_repetition_removal=self.param[
-                "cond_check_word_repetition_removal"
-            ],
-            word_repetition_length=self.param["word_repetition_length"],
-            word_repetition_max_cutoff=self.param["word_repetition_max_cutoff"],
-            cond_check_special_characters=self.param["cond_check_special_characters"],
-            special_characters=self.param["special_characters"],
-            special_characters_max_cutoff=self.param["special_characters_max_cutoff"],
-            cond_words_augmentation=self.param["cond_words_augmentation"],
-            words_augmentation_group_sizes=self.param["words_augmentation_group_sizes"],
-            words_augmentation_join_char=self.param["words_augmentation_join_char"],
-            cond_check_stopwords=self.param["cond_check_stopwords"],
-            stopwords=self.stopwords,
-            stopwords_min_cutoff=self.param["stopwords_min_cutoff"],
-            cond_check_flagged_words=self.param["cond_check_flagged_words"],
-            flagged_words=self.flagged_words,
-            flagged_words_max_cutoff=self.param["flagged_words_max_cutoff"],
-            cond_check_lang_id=self.param["cond_check_lang_id"],
-            lang_dataset_id=self.lang_dataset_id,
-            model_lang_id=self.model_lang_id,
-            lang_id_min_cutoff=self.param["lang_id_min_cutoff"],
-            cond_check_perplexity=self.param["cond_check_perplexity"],
-            sentencepiece_model=self.sentencepiece_model,
-            kenlm_model=self.kenlm_model,
-            perplexity_max_cutoff=self.param["perplexity_max_cutoff"],
-        )
-        return keep_example
-
-    def __reduce__(self):
-        return (
-            self.__class__,
-            (
-                self.lang_dataset_id,
-                self.path_fasttext_model,
-                self.path_sentencepiece_model,
-                self.path_kenlm_model,
-            ),
-        )
-
-
-class DatasetFiltering:
-    def __init__(
-        self,
-        dataset,
-        lang_dataset_id,
-        path_fasttext_model,
-        path_sentencepiece_model,
-        path_kenlm_model,
-        num_proc,
-        path_dir_save_dataset,
-    ):
-        self.ds = dataset
-        self.lang_dataset_id = lang_dataset_id
-        self.path_fasttext_model = path_fasttext_model
-        self.path_sentencepiece_model = path_sentencepiece_model
-        self.path_kenlm_model = path_kenlm_model
-        self.num_proc = num_proc
-        self.path_dir_save_dataset = path_dir_save_dataset
-
-    def modifying_documents(self):
-        func_dataset_modifying_documents = FunctionDatasetModifyingDocuments(
-            self.lang_dataset_id
-        )
-        self.ds = self.ds.map(func_dataset_modifying_documents, num_proc=self.num_proc)
-
-    def filtering(self):
-        func_dataset_filtering = FunctionDatasetFiltering(
-            self.lang_dataset_id,
-            self.path_fasttext_model,
-            self.path_sentencepiece_model,
-            self.path_kenlm_model,
-        )
-        self.ds = self.ds.filter(func_dataset_filtering, num_proc=self.num_proc)
-
-    def save_dataset(self):
-        pathlib.Path(self.path_dir_save_dataset).mkdir(parents=True, exist_ok=True)
-        path_dir_save_dataset = pathlib.PurePath(
-            self.path_dir_save_dataset, self.lang_dataset_id
-        )
-        pathlib.Path(path_dir_save_dataset).mkdir(parents=True, exist_ok=True)
-        self.ds.save_to_disk(path_dir_save_dataset)
diff --git a/spaces/hugo-guo/bingo-hugo/README.md b/spaces/hugo-guo/bingo-hugo/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/hugo-guo/bingo-hugo/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/hushell/pmf_with_gis/models/clip/__init__.py b/spaces/hushell/pmf_with_gis/models/clip/__init__.py
deleted file mode 100644
index dcc5619538c0f7c782508bdbd9587259d805e0d9..0000000000000000000000000000000000000000
--- a/spaces/hushell/pmf_with_gis/models/clip/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .clip import *
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_s.py b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_s.py
deleted file mode 100644
index f6ce7010d9c297ed0832dcb5639d552078cea95c..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_s.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "vit_s_dp005_mask_0"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.3
-config.fp16 = True
-config.weight_decay = 0.1
-config.batch_size = 384
-config.optimizer = "adamw"
-config.lr = 0.001
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace42M"
-config.num_classes = 2059906
-config.num_image = 42474557
-config.num_epoch = 40
-config.warmup_epoch = config.num_epoch // 10
-config.val_targets = []
diff --git a/spaces/igashov/DiffLinker/src/noise.py b/spaces/igashov/DiffLinker/src/noise.py
deleted file mode 100644
index c51dca027d2ee44fa6970aed74541cd252b78338..0000000000000000000000000000000000000000
--- a/spaces/igashov/DiffLinker/src/noise.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-import numpy as np
-
-
-def clip_noise_schedule(alphas2, clip_value=0.001):
-    """
-    For a noise schedule given by alpha^2, this clips alpha_t / alpha_t-1. This may help improve stability during
-    sampling.
-    """
-    alphas2 = np.concatenate([np.ones(1), alphas2], axis=0)
-
-    alphas_step = (alphas2[1:] / alphas2[:-1])
-
-    alphas_step = np.clip(alphas_step, a_min=clip_value, a_max=1.)
-    alphas2 = np.cumprod(alphas_step, axis=0)
-
-    return alphas2
-
-
-def polynomial_schedule(timesteps: int, s=1e-4, power=3.):
-    """
-    A noise schedule based on a simple polynomial equation: 1 - x^power.
-    """
-    steps = timesteps + 1
-    x = np.linspace(0, steps, steps)
-    alphas2 = (1 - np.power(x / steps, power)) ** 2
-
-    alphas2 = clip_noise_schedule(alphas2, clip_value=0.001)
-
-    precision = 1 - 2 * s
-
-    alphas2 = precision * alphas2 + s
-
-    return alphas2
-
-
-def cosine_beta_schedule(timesteps, s=0.008, raise_to_power: float = 1):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 2
-    x = np.linspace(0, steps, steps)
-    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
-    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
-    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    betas = np.clip(betas, a_min=0, a_max=0.999)
-    alphas = 1. - betas
-    alphas_cumprod = np.cumprod(alphas, axis=0)
-
-    if raise_to_power != 1:
-        alphas_cumprod = np.power(alphas_cumprod, raise_to_power)
-
-    return alphas_cumprod
-
-
-class PositiveLinear(torch.nn.Module):
-    """Linear layer with weights forced to be positive."""
-
-    def __init__(self, in_features: int, out_features: int, bias: bool = True,
-                 weight_init_offset: int = -2):
-        super(PositiveLinear, self).__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.weight = torch.nn.Parameter(
-            torch.empty((out_features, in_features)))
-        if bias:
-            self.bias = torch.nn.Parameter(torch.empty(out_features))
-        else:
-            self.register_parameter('bias', None)
-        self.weight_init_offset = weight_init_offset
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-
-        with torch.no_grad():
-            self.weight.add_(self.weight_init_offset)
-
-        if self.bias is not None:
-            fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
-            bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
-            torch.nn.init.uniform_(self.bias, -bound, bound)
-
-    def forward(self, x):
-        positive_weight = F.softplus(self.weight)
-        return F.linear(x, positive_weight, self.bias)
-
-
-class PredefinedNoiseSchedule(torch.nn.Module):
-    """
-    Predefined noise schedule. Essentially creates a lookup array for predefined (non-learned) noise schedules.
-    """
-
-    def __init__(self, noise_schedule, timesteps, precision):
-        super(PredefinedNoiseSchedule, self).__init__()
-        self.timesteps = timesteps
-
-        if noise_schedule == 'cosine':
-            alphas2 = cosine_beta_schedule(timesteps)
-        elif 'polynomial' in noise_schedule:
-            splits = noise_schedule.split('_')
-            assert len(splits) == 2
-            power = float(splits[1])
-            alphas2 = polynomial_schedule(timesteps, s=precision, power=power)
-        else:
-            raise ValueError(noise_schedule)
-
-        # print('alphas2', alphas2)
-
-        sigmas2 = 1 - alphas2
-
-        log_alphas2 = np.log(alphas2)
-        log_sigmas2 = np.log(sigmas2)
-
-        log_alphas2_to_sigmas2 = log_alphas2 - log_sigmas2
-
-        # print('gamma', -log_alphas2_to_sigmas2)
-
-        self.gamma = torch.nn.Parameter(
-            torch.from_numpy(-log_alphas2_to_sigmas2).float(),
-            requires_grad=False)
-
-    def forward(self, t):
-        t_int = torch.round(t * self.timesteps).long()
-        return self.gamma[t_int]
-
-
-class GammaNetwork(torch.nn.Module):
-    """The gamma network models a monotonic increasing function. Construction as in the VDM paper."""
-
-    def __init__(self):
-        super().__init__()
-
-        self.l1 = PositiveLinear(1, 1)
-        self.l2 = PositiveLinear(1, 1024)
-        self.l3 = PositiveLinear(1024, 1)
-
-        self.gamma_0 = torch.nn.Parameter(torch.tensor([-5.]))
-        self.gamma_1 = torch.nn.Parameter(torch.tensor([10.]))
-        self.show_schedule()
-
-    def show_schedule(self, num_steps=50):
-        t = torch.linspace(0, 1, num_steps).view(num_steps, 1)
-        gamma = self.forward(t)
-        print('Gamma schedule:')
-        print(gamma.detach().cpu().numpy().reshape(num_steps))
-
-    def gamma_tilde(self, t):
-        l1_t = self.l1(t)
-        return l1_t + self.l3(torch.sigmoid(self.l2(l1_t)))
-
-    def forward(self, t):
-        zeros, ones = torch.zeros_like(t), torch.ones_like(t)
-        # Not super efficient.
-        gamma_tilde_0 = self.gamma_tilde(zeros)
-        gamma_tilde_1 = self.gamma_tilde(ones)
-        gamma_tilde_t = self.gamma_tilde(t)
-
-        # Normalize to [0, 1]
-        normalized_gamma = (gamma_tilde_t - gamma_tilde_0) / (
-                gamma_tilde_1 - gamma_tilde_0)
-
-        # Rescale to [gamma_0, gamma_1]
-        gamma = self.gamma_0 + (self.gamma_1 - self.gamma_0) * normalized_gamma
-
-        return gamma
diff --git a/spaces/ilan541/OncUponTim/README.md b/spaces/ilan541/OncUponTim/README.md
deleted file mode 100644
index 07822fad7d68aca0fff9ed8cdb95892f41c41e2e..0000000000000000000000000000000000000000
--- a/spaces/ilan541/OncUponTim/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: OncUponTim
-emoji: 💻
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/utilities.py b/spaces/imseldrith/DeepFakeAI/DeepFakeAI/utilities.py
deleted file mode 100644
index dd33cf157f684dc1bad324bca4d9326b8e3f82f2..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/utilities.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import glob
-import mimetypes
-import os
-import platform
-import shutil
-import ssl
-import subprocess
-import tempfile
-import urllib
-from pathlib import Path
-from typing import List, Optional
-
-import onnxruntime
-from tqdm import tqdm
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-
-TEMP_DIRECTORY_PATH = os.path.join(tempfile.gettempdir(), 'DeepFakeAI')
-TEMP_OUTPUT_NAME = 'temp.mp4'
-
-# monkey patch ssl
-if platform.system().lower() == 'darwin':
-	ssl._create_default_https_context = ssl._create_unverified_context
-
-
-def run_ffmpeg(args : List[str]) -> bool:
-	commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
-	commands.extend(args)
-	try:
-		subprocess.check_output(commands, stderr = subprocess.STDOUT)
-		return True
-	except subprocess.CalledProcessError:
-		return False
-
-
-def detect_fps(target_path : str) -> Optional[float]:
-	commands = [ 'ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'default=noprint_wrappers = 1:nokey = 1', target_path ]
-	output = subprocess.check_output(commands).decode().strip().split('/')
-	try:
-		numerator, denominator = map(int, output)
-		return numerator / denominator
-	except (ValueError, ZeroDivisionError):
-		return None
-
-
-def extract_frames(target_path : str, fps : float) -> bool:
-	temp_directory_path = get_temp_directory_path(target_path)
-	temp_frame_quality = round(31 - (DeepFakeAI.globals.temp_frame_quality * 0.31))
-	trim_frame_start = DeepFakeAI.globals.trim_frame_start
-	trim_frame_end = DeepFakeAI.globals.trim_frame_end
-	commands = [ '-hwaccel', 'auto', '-i', target_path, '-q:v', str(temp_frame_quality), '-pix_fmt', 'rgb24', ]
-	if trim_frame_start is not None and trim_frame_end is not None:
-		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
-	elif trim_frame_start is not None:
-		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',fps=' + str(fps) ])
-	elif trim_frame_end is not None:
-		commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
-	else:
-		commands.extend([ '-vf', 'fps=' + str(fps) ])
-	commands.extend([os.path.join(temp_directory_path, '%04d.' + DeepFakeAI.globals.temp_frame_format)])
-	return run_ffmpeg(commands)
-
-
-def create_video(target_path : str, fps : float) -> bool:
-	temp_output_path = get_temp_output_path(target_path)
-	temp_directory_path = get_temp_directory_path(target_path)
-	output_video_quality = round(51 - (DeepFakeAI.globals.output_video_quality * 0.5))
-	commands = [ '-hwaccel', 'auto', '-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.' + DeepFakeAI.globals.temp_frame_format), '-c:v', DeepFakeAI.globals.output_video_encoder ]
-	if DeepFakeAI.globals.output_video_encoder in [ 'libx264', 'libx265', 'libvpx' ]:
-		commands.extend([ '-crf', str(output_video_quality) ])
-	if DeepFakeAI.globals.output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
-		commands.extend([ '-cq', str(output_video_quality) ])
-	commands.extend([ '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625', '-y', temp_output_path ])
-	return run_ffmpeg(commands)
-
-
-def restore_audio(target_path : str, output_path : str) -> None:
-	fps = detect_fps(target_path)
-	trim_frame_start = DeepFakeAI.globals.trim_frame_start
-	trim_frame_end = DeepFakeAI.globals.trim_frame_end
-	temp_output_path = get_temp_output_path(target_path)
-	commands = [ '-hwaccel', 'auto', '-i', temp_output_path, '-i', target_path ]
-	if trim_frame_start is None and trim_frame_end is None:
-		commands.extend([ '-c:a', 'copy' ])
-	else:
-		if trim_frame_start is not None:
-			start_time = trim_frame_start / fps
-			commands.extend([ '-ss', str(start_time) ])
-		else:
-			commands.extend([ '-ss', '0' ])
-		if trim_frame_end is not None:
-			end_time = trim_frame_end / fps
-			commands.extend([ '-to', str(end_time) ])
-		commands.extend([ '-c:a', 'aac' ])
-	commands.extend([ '-map', '0:v:0', '-map', '1:a:0', '-y', output_path ])
-	done = run_ffmpeg(commands)
-	if not done:
-		move_temp(target_path, output_path)
-
-
-def get_temp_frame_paths(target_path : str) -> List[str]:
-	temp_directory_path = get_temp_directory_path(target_path)
-	return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.' + DeepFakeAI.globals.temp_frame_format)))
-
-
-def get_temp_directory_path(target_path : str) -> str:
-	target_name, _ = os.path.splitext(os.path.basename(target_path))
-	return os.path.join(TEMP_DIRECTORY_PATH, target_name)
-
-
-def get_temp_output_path(target_path : str) -> str:
-	temp_directory_path = get_temp_directory_path(target_path)
-	return os.path.join(temp_directory_path, TEMP_OUTPUT_NAME)
-
-
-def normalize_output_path(source_path : str, target_path : str, output_path : str) -> Optional[str]:
-	if source_path and target_path and output_path:
-		source_name, _ = os.path.splitext(os.path.basename(source_path))
-		target_name, target_extension = os.path.splitext(os.path.basename(target_path))
-		if os.path.isdir(output_path):
-			return os.path.join(output_path, source_name + '-' + target_name + target_extension)
-	return output_path
-
-
-def create_temp(target_path : str) -> None:
-	temp_directory_path = get_temp_directory_path(target_path)
-	Path(temp_directory_path).mkdir(parents = True, exist_ok = True)
-
-
-def move_temp(target_path : str, output_path : str) -> None:
-	temp_output_path = get_temp_output_path(target_path)
-	if os.path.isfile(temp_output_path):
-		if os.path.isfile(output_path):
-			os.remove(output_path)
-		shutil.move(temp_output_path, output_path)
-
-
-def clear_temp(target_path : str) -> None:
-	temp_directory_path = get_temp_directory_path(target_path)
-	parent_directory_path = os.path.dirname(temp_directory_path)
-	if not DeepFakeAI.globals.keep_temp and os.path.isdir(temp_directory_path):
-		shutil.rmtree(temp_directory_path)
-	if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
-		os.rmdir(parent_directory_path)
-
-
-def is_image(image_path : str) -> bool:
-	if image_path and os.path.isfile(image_path):
-		mimetype, _ = mimetypes.guess_type(image_path)
-		return bool(mimetype and mimetype.startswith('image/'))
-	return False
-
-
-def is_video(video_path : str) -> bool:
-	if video_path and os.path.isfile(video_path):
-		mimetype, _ = mimetypes.guess_type(video_path)
-		return bool(mimetype and mimetype.startswith('video/'))
-	return False
-
-
-def conditional_download(download_directory_path : str, urls : List[str]) -> None:
-	if not os.path.exists(download_directory_path):
-		os.makedirs(download_directory_path)
-	for url in urls:
-		download_file_path = os.path.join(download_directory_path, os.path.basename(url))
-		if not os.path.exists(download_file_path):
-			request = urllib.request.urlopen(url) # type: ignore[attr-defined]
-			total = int(request.headers.get('Content-Length', 0))
-			with tqdm(total = total, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024) as progress:
-				urllib.request.urlretrieve(url, download_file_path, reporthook = lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
-
-
-def resolve_relative_path(path : str) -> str:
-	return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
-
-
-def list_module_names(path : str) -> Optional[List[str]]:
-	if os.path.exists(path):
-		files = os.listdir(path)
-		return [Path(file).stem for file in files if not Path(file).stem.startswith('__')]
-	return None
-
-
-def encode_execution_providers(execution_providers : List[str]) -> List[str]:
-	return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers : List[str]) -> List[str]:
-	return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers())) if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Aatma Movie Download In Hindi 720p Hd Kickass VERIFIED.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Aatma Movie Download In Hindi 720p Hd Kickass VERIFIED.md
deleted file mode 100644
index f87e871c453d155c3fe08d023d6887df6a857a5a..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Aatma Movie Download In Hindi 720p Hd Kickass VERIFIED.md	
+++ /dev/null
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Descarga Instala Y Activa AutoCad 2020 [64 Bits][Espanol E Ingles]Licencias De Por Vida.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Descarga Instala Y Activa AutoCad 2020 [64 Bits][Espanol E Ingles]Licencias De Por Vida.md
deleted file mode 100644
index 7df3f385083e89b1916ba63b1deeb9d3902cc092..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Descarga Instala Y Activa AutoCad 2020 [64 Bits][Espanol E Ingles]Licencias De Por Vida.md	
+++ /dev/null
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-<p>Para descargar AutoCad 2020 [64 bits][Espanol e Ingles], puedes hacerlo desde la página oficial de Autodesk, la empresa desarrolladora del software. Allí podrás elegir el idioma que prefieras y el tipo de licencia que quieras: individual, multiusuario o educativa. También podrás elegir entre una versión de prueba gratuita por 30 días o una versión completa de pago.</p>
-<p>Para descargar AutoCad 2020 [64 bits][Espanol e Ingles], sigue estos pasos:</p>
-<ol>
-<li>Entra en la página oficial de Autodesk: https://www.autodesk.com/products/autocad/overview</li>
-<li>Haz clic en el botón "Descargar versión de prueba gratuita" o "Comprar" según tu opción.</li>
-<li>Selecciona el idioma que quieras: español o inglés.</li>
-<li>Selecciona el tipo de licencia que quieras: individual, multiusuario o educativa.</li>
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-<li>Espera a que se descargue el archivo de instalación en tu PC.</li>
-</ol>
-<h2>Cómo instalar AutoCad 2020 [64 bits][Espanol e Ingles]</h2>
-<p>Para instalar AutoCad 2020 [64 bits][Espanol e Ingles], necesitas tener un PC que cumpla con los requisitos mínimos del sistema: Windows 10 (64 bits), procesador de 2.5 GHz o superior, memoria RAM de 8 GB o superior, espacio en disco duro de 6 GB o superior, tarjeta gráfica compatible con DirectX 11 o superior y resolución de pantalla de 1920 x 1080 o superior.</p>
-<p></p>
-<p>Para instalar AutoCad 2020 [64 bits][Espanol e Ingles], sigue estos pasos:</p>
-<ol>
-<li>Abre el archivo de instalación que descargaste en el paso anterior.</li>
-<li>Acepta los términos y condiciones del contrato de licencia y haz clic en "Instalar".</li>
-<li>Espera a que se complete el proceso de instalación.</li>
-<li>Reinicia tu PC si se te solicita.</li>
-</ol>
-<h2>Cómo activar AutoCad 2020 [64 bits][Espanol e Ingles]||Licencias de por Vida</h2>
-<p>Para activar AutoCad 2020 [64 bits][Espanol e Ingles]||Licencias de por Vida, necesitas tener una clave de producto válida que hayas obtenido al comprar la versión completa del software o al solicitar la versión educativa. La clave de producto es un código alfanumérico de 25 caracteres que te permite activar el programa y usarlo sin limitaciones ni restricciones.</p>
-<p>Para activar AutoCad 2020 [64 bits][Espanol e Ingles]||Licencias de por Vida, sigue estos pasos:</p>
-<ol>
-<li>Abre el programa AutoCad 2020 en tu PC.</li>
-<li>Haz clic en el botón "Activar" que aparece en la pantalla inicial.</li>
-<li>Introduce tu clave de producto en el campo correspondiente y haz clic en "Siguiente".</li>
-<li>Sigue las instrucciones que aparecen en pantalla para completar el proceso de activación.</li>
-<li>Disfruta de AutoCad 2020 con licencias de por vida.</li>
-</ol>
-<h2>Conclusión</h2>
-<p>AutoCad 2020 es un software de diseño asistido por computadora (CAD) que te permite crear y editar planos, dibujos, modelos 3D y otros proyectos de ingeniería, arquitectura y diseño. Para usarlo correctamente, necesitas descargarlo, instalarlo y activarlo en tu PC. Además, necesitas una licencia válida que te permita usar el programa sin limitaciones ni restricciones. En este artículo, te hemos mostrado cómo hacer todo esto paso a paso, para que puedas usar AutoCad 2020 con licencias de por vida.</p> 3cee63e6c2<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Dhoom Movie Tamil Dubbed In 720p ((NEW)).md b/spaces/inplisQlawa/anything-midjourney-v4-1/Dhoom Movie Tamil Dubbed In 720p ((NEW)).md
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index c3caa6ebc7a35a74a81bfabd2e1a17a3195d2dfc..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Dhoom Movie Tamil Dubbed In 720p ((NEW)).md	
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deleted file mode 100644
index 34eceacd3d47b5c79126c6d2bebe94c50b8f65d8..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Microsoft Visio 2020 Crack Product Key Free Download.md	
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deleted file mode 100644
index 56b2f536bd24b4f09000ad02185e6074248908fd..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Age De Glace 2 Torrent French.md	
+++ /dev/null
@@ -1,10 +0,0 @@
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diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/settings.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/settings.py
deleted file mode 100644
index 8aed504edb3246e60d4508bab6c92c3512f3ee80..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/settings.py
+++ /dev/null
@@ -1,272 +0,0 @@
-from math import ceil
-import os
-import json
-import deforum_helpers.args as deforum_args
-from .args import mask_fill_choices, DeforumArgs, DeforumAnimArgs
-from .deprecation_utils import handle_deprecated_settings
-import logging
-
-def get_keys_to_exclude(setting_type):
-    if setting_type == 'general':
-        return ["n_batch", "restore_faces", "seed_enable_extras", "save_samples", "display_samples", "show_sample_per_step", "filename_format", "from_img2img_instead_of_link", "scale", "subseed", "subseed_strength", "C", "f", "init_latent", "init_sample", "init_c", "noise_mask", "seed_internal"]
-    else: #video
-        return ["mp4_path", "image_path", "output_format","render_steps","path_name_modifier"]
-
-def load_args(args_dict,anim_args_dict, parseq_args_dict, loop_args_dict, controlnet_args_dict, custom_settings_file, root):
-    print(f"reading custom settings from {custom_settings_file}")
-    if not os.path.isfile(custom_settings_file):
-        print('The custom settings file does not exist. The in-notebook settings will be used instead')
-    else:
-        with open(custom_settings_file, "r") as f:
-            jdata = json.loads(f.read())
-            handle_deprecated_settings(jdata)
-            root.animation_prompts = jdata["prompts"]
-            if "animation_prompts_positive" in jdata:
-                root.animation_prompts_positive = jdata["animation_prompts_positive"]
-            if "animation_prompts_negative" in jdata:
-                root.animation_prompts_negative = jdata["animation_prompts_negative"]
-            for i, k in enumerate(args_dict):
-                if k in jdata:
-                    args_dict[k] = jdata[k]
-                else:
-                    print(f"key {k} doesn't exist in the custom settings data! using the default value of {args_dict[k]}")
-            for i, k in enumerate(anim_args_dict):
-                if k in jdata:
-                    anim_args_dict[k] = jdata[k]
-                else:
-                    print(f"key {k} doesn't exist in the custom settings data! using the default value of {anim_args_dict[k]}")
-            for i, k in enumerate(parseq_args_dict):
-                if k in jdata:
-                    parseq_args_dict[k] = jdata[k]
-                else:
-                    print(f"key {k} doesn't exist in the custom settings data! using the default value of {parseq_args_dict[k]}")                    
-            for i, k in enumerate(loop_args_dict):
-                if k in jdata:
-                    loop_args_dict[k] = jdata[k]
-                else:
-                    print(f"key {k} doesn't exist in the custom settings data! using the default value of {loop_args_dict[k]}")                    
-            print(args_dict)
-            print(anim_args_dict)
-            print(parseq_args_dict)
-            print(loop_args_dict)
-
-# In gradio gui settings save/ load funs:
-def save_settings(*args, **kwargs):
-    settings_path = args[0].strip()
-    data = {deforum_args.settings_component_names[i]: args[i+1] for i in range(0, len(deforum_args.settings_component_names))}
-    from deforum_helpers.args import pack_args, pack_anim_args, pack_parseq_args, pack_loop_args, pack_controlnet_args
-    args_dict = pack_args(data)
-    anim_args_dict = pack_anim_args(data)
-    parseq_dict = pack_parseq_args(data)
-    args_dict["prompts"] = json.loads(data['animation_prompts'])
-    args_dict["animation_prompts_positive"] = data['animation_prompts_positive']
-    args_dict["animation_prompts_negative"] = data['animation_prompts_negative']
-    loop_dict = pack_loop_args(data)
-    controlnet_dict = pack_controlnet_args(data)
-    
-    combined = {**args_dict, **anim_args_dict, **parseq_dict, **loop_dict, **controlnet_dict}
-    exclude_keys = get_keys_to_exclude('general') + ['controlnet_input_video_chosen_file', 'controlnet_input_video_mask_chosen_file']
-    filtered_combined = {k: v for k, v in combined.items() if k not in exclude_keys}
-    
-    print(f"saving custom settings to {settings_path}")
-    with open(settings_path, "w") as f:
-        f.write(json.dumps(filtered_combined, ensure_ascii=False, indent=4))
-    
-    return [""]
-
-def save_video_settings(*args, **kwargs):
-    video_settings_path = args[0].strip()
-    data = {deforum_args.video_args_names[i]: args[i+1] for i in range(0, len(deforum_args.video_args_names))}
-    from deforum_helpers.args import pack_video_args
-    video_args_dict = pack_video_args(data)
-    exclude_keys = get_keys_to_exclude('video')
-    filtered_data = video_args_dict if exclude_keys is None else {k: v for k, v in video_args_dict.items() if k not in exclude_keys}
-    print(f"saving video settings to {video_settings_path}")
-    with open(video_settings_path, "w") as f:
-        f.write(json.dumps(filtered_data, ensure_ascii=False, indent=4))
-    return [""]
-
-def load_settings(*args, **kwargs):
-    settings_path = args[0].strip()
-    data = {deforum_args.settings_component_names[i]: args[i+1] for i in range(0, len(deforum_args.settings_component_names))}
-    print(f"reading custom settings from {settings_path}")
-    jdata = {}
-    if not os.path.isfile(settings_path):
-        print('The custom settings file does not exist. The values will be unchanged.')
-        return [data[name] for name in deforum_args.settings_component_names] + [""]
-    else:
-        with open(settings_path, "r") as f:
-            jdata = json.loads(f.read())
-            handle_deprecated_settings(jdata)
-    ret = []
-    if 'animation_prompts' in jdata:
-        jdata['prompts'] = jdata['animation_prompts']#compatibility with old versions
-    if 'animation_prompts_positive' in jdata:
-        data["animation_prompts_positive"] = jdata['animation_prompts_positive']
-    if 'animation_prompts_negative' in jdata:
-        data["animation_prompts_negative"] = jdata['animation_prompts_negative']
-    for key in data:
-        if key == 'sampler':
-            sampler_val = jdata[key]
-            if type(sampler_val) == int:
-                from modules.sd_samplers import samplers_for_img2img
-                ret.append(samplers_for_img2img[sampler_val].name)
-            else:
-                ret.append(sampler_val)
-        
-        elif key == 'fill':
-            if key in jdata:
-                fill_val = jdata[key]
-                if type(fill_val) == int:                    
-                    ret.append(mask_fill_choices[fill_val])
-                else:
-                    ret.append(fill_val)
-            else:
-                fill_default = DeforumArgs()['fill']
-                logging.debug(f"Fill not found in load file, using default value: {fill_default}")
-                ret.append(mask_fill_choices[fill_default])
-        
-        elif key == 'reroll_blank_frames':
-            if key in jdata:
-                reroll_blank_frames_val = jdata[key]
-                ret.append(reroll_blank_frames_val)
-            else:
-                reroll_blank_frames_default = DeforumArgs()['reroll_blank_frames']
-                logging.debug(f"Reroll blank frames not found in load file, using default value: {reroll_blank_frames_default}")
-                ret.append(reroll_blank_frames_default)
-        
-        elif key == 'noise_type':
-            if key in jdata:
-                noise_type_val = jdata[key]
-                ret.append(noise_type_val)
-            else:
-                noise_type_default = DeforumAnimArgs()['noise_type']
-                logging.debug(f"Noise type not found in load file, using default value: {noise_type_default}")
-                ret.append(noise_type_default)
-            
-        elif key in jdata:
-            ret.append(jdata[key])
-        else:
-            if key == 'animation_prompts':
-                ret.append(json.dumps(jdata['prompts'], ensure_ascii=False, indent=4))
-            elif key == 'animation_prompts_positive' and 'animation_prompts_positive' in jdata:
-                ret.append(jdata['animation_prompts_positive'])
-            elif key == 'animation_prompts_negative' and 'animation_prompts_negative' in jdata:
-                ret.append(jdata['animation_prompts_negative'])
-            else:
-                ret.append(data[key])
-
-    #stuff
-    ret.append("")
-
-    return ret
-
-def load_video_settings(*args, **kwargs):
-    video_settings_path = args[0].strip()
-    data = {deforum_args.video_args_names[i]: args[i+1] for i in range(0, len(deforum_args.video_args_names))}
-    print(f"reading custom video settings from {video_settings_path}")
-    jdata = {}
-    if not os.path.isfile(video_settings_path):
-        print('The custom video settings file does not exist. The values will be unchanged.')
-        return [data[name] for name in deforum_args.video_args_names] + [""]
-    else:
-        with open(video_settings_path, "r") as f:
-            jdata = json.loads(f.read())
-            handle_deprecated_settings(jdata)
-    ret = []
-
-    for key in data:
-        if key == 'add_soundtrack':
-            add_soundtrack_val = jdata[key]
-            if type(add_soundtrack_val) == bool:
-                ret.append('File' if add_soundtrack_val else 'None')
-            else:
-                ret.append(add_soundtrack_val)
-        elif key in jdata:
-            ret.append(jdata[key])
-        else:
-            ret.append(data[key])
-    
-    #stuff
-    ret.append("")
-    
-    return ret
-
-import tqdm
-from modules.shared import state, progress_print_out, opts, cmd_opts
-class DeforumTQDM:
-    def __init__(self, args, anim_args, parseq_args):
-        self._tqdm = None
-        self._args = args
-        self._anim_args = anim_args
-        self._parseq_args = parseq_args
-
-    def reset(self):
-        from .animation_key_frames import DeformAnimKeys
-        from .parseq_adapter import ParseqAnimKeys
-        deforum_total = 0
-        # FIXME: get only amount of steps
-        use_parseq = self._parseq_args.parseq_manifest != None and self._parseq_args.parseq_manifest.strip()
-        keys = DeformAnimKeys(self._anim_args) if not use_parseq else ParseqAnimKeys(self._parseq_args, self._anim_args)        
-        
-        start_frame = 0
-        if self._anim_args.resume_from_timestring:
-            for tmp in os.listdir(self._args.outdir):
-                filename = tmp.split("_")
-                # don't use saved depth maps to count number of frames
-                if self._anim_args.resume_timestring in filename and "depth" not in filename:
-                    start_frame += 1
-            start_frame = start_frame - 1
-        using_vid_init = self._anim_args.animation_mode == 'Video Input'
-        turbo_steps = 1 if using_vid_init else int(self._anim_args.diffusion_cadence)
-        if self._anim_args.resume_from_timestring:
-            last_frame = start_frame-1
-            if turbo_steps > 1:
-                last_frame -= last_frame%turbo_steps
-            if turbo_steps > 1:
-                turbo_next_frame_idx = last_frame
-                turbo_prev_frame_idx = turbo_next_frame_idx
-                start_frame = last_frame+turbo_steps
-        frame_idx = start_frame
-        had_first = False
-        while frame_idx < self._anim_args.max_frames:
-            strength = keys.strength_schedule_series[frame_idx]
-            if not had_first and self._args.use_init and self._args.init_image != None and self._args.init_image != '':
-                deforum_total += int(ceil(self._args.steps * (1-strength)))
-                had_first = True
-            elif not had_first:
-                deforum_total += self._args.steps
-                had_first = True
-            else:
-                deforum_total += int(ceil(self._args.steps * (1-strength)))
-
-            if turbo_steps > 1:
-                frame_idx += turbo_steps
-            else:
-                frame_idx += 1
-        
-        self._tqdm = tqdm.tqdm(
-            desc="Deforum progress",
-            total=deforum_total,
-            position=1,
-            file=progress_print_out
-        )
-
-    def update(self):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.update()
-
-    def updateTotal(self, new_total):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.total=new_total
-
-    def clear(self):
-        if self._tqdm is not None:
-            self._tqdm.close()
-            self._tqdm = None
diff --git a/spaces/jackyccl/segment-anything/groundingdino/util/slio.py b/spaces/jackyccl/segment-anything/groundingdino/util/slio.py
deleted file mode 100644
index 72c1f0f7b82cdc931d381feef64fe15815ba657e..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/util/slio.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# ==========================================================
-# Modified from mmcv
-# ==========================================================
-
-import json
-import pickle
-from abc import ABCMeta, abstractmethod
-from pathlib import Path
-
-import yaml
-
-try:
-    from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
-    from yaml import Loader, Dumper
-
-
-# ===========================
-# Rigister handler
-# ===========================
-
-
-class BaseFileHandler(metaclass=ABCMeta):
-    @abstractmethod
-    def load_from_fileobj(self, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_str(self, obj, **kwargs):
-        pass
-
-    def load_from_path(self, filepath, mode="r", **kwargs):
-        with open(filepath, mode) as f:
-            return self.load_from_fileobj(f, **kwargs)
-
-    def dump_to_path(self, obj, filepath, mode="w", **kwargs):
-        with open(filepath, mode) as f:
-            self.dump_to_fileobj(obj, f, **kwargs)
-
-
-class JsonHandler(BaseFileHandler):
-    def load_from_fileobj(self, file):
-        return json.load(file)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        json.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        return json.dumps(obj, **kwargs)
-
-
-class PickleHandler(BaseFileHandler):
-    def load_from_fileobj(self, file, **kwargs):
-        return pickle.load(file, **kwargs)
-
-    def load_from_path(self, filepath, **kwargs):
-        return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault("protocol", 2)
-        return pickle.dumps(obj, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault("protocol", 2)
-        pickle.dump(obj, file, **kwargs)
-
-    def dump_to_path(self, obj, filepath, **kwargs):
-        super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
-
-
-class YamlHandler(BaseFileHandler):
-    def load_from_fileobj(self, file, **kwargs):
-        kwargs.setdefault("Loader", Loader)
-        return yaml.load(file, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault("Dumper", Dumper)
-        yaml.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault("Dumper", Dumper)
-        return yaml.dump(obj, **kwargs)
-
-
-file_handlers = {
-    "json": JsonHandler(),
-    "yaml": YamlHandler(),
-    "yml": YamlHandler(),
-    "pickle": PickleHandler(),
-    "pkl": PickleHandler(),
-}
-
-# ===========================
-# load and dump
-# ===========================
-
-
-def is_str(x):
-    """Whether the input is an string instance.
-
-    Note: This method is deprecated since python 2 is no longer supported.
-    """
-    return isinstance(x, str)
-
-
-def slload(file, file_format=None, **kwargs):
-    """Load data from json/yaml/pickle files.
-
-    This method provides a unified api for loading data from serialized files.
-
-    Args:
-        file (str or :obj:`Path` or file-like object): Filename or a file-like
-            object.
-        file_format (str, optional): If not specified, the file format will be
-            inferred from the file extension, otherwise use the specified one.
-            Currently supported formats include "json", "yaml/yml" and
-            "pickle/pkl".
-
-    Returns:
-        The content from the file.
-    """
-    if isinstance(file, Path):
-        file = str(file)
-    if file_format is None and is_str(file):
-        file_format = file.split(".")[-1]
-    if file_format not in file_handlers:
-        raise TypeError(f"Unsupported format: {file_format}")
-
-    handler = file_handlers[file_format]
-    if is_str(file):
-        obj = handler.load_from_path(file, **kwargs)
-    elif hasattr(file, "read"):
-        obj = handler.load_from_fileobj(file, **kwargs)
-    else:
-        raise TypeError('"file" must be a filepath str or a file-object')
-    return obj
-
-
-def sldump(obj, file=None, file_format=None, **kwargs):
-    """Dump data to json/yaml/pickle strings or files.
-
-    This method provides a unified api for dumping data as strings or to files,
-    and also supports custom arguments for each file format.
-
-    Args:
-        obj (any): The python object to be dumped.
-        file (str or :obj:`Path` or file-like object, optional): If not
-            specified, then the object is dump to a str, otherwise to a file
-            specified by the filename or file-like object.
-        file_format (str, optional): Same as :func:`load`.
-
-    Returns:
-        bool: True for success, False otherwise.
-    """
-    if isinstance(file, Path):
-        file = str(file)
-    if file_format is None:
-        if is_str(file):
-            file_format = file.split(".")[-1]
-        elif file is None:
-            raise ValueError("file_format must be specified since file is None")
-    if file_format not in file_handlers:
-        raise TypeError(f"Unsupported format: {file_format}")
-
-    handler = file_handlers[file_format]
-    if file is None:
-        return handler.dump_to_str(obj, **kwargs)
-    elif is_str(file):
-        handler.dump_to_path(obj, file, **kwargs)
-    elif hasattr(file, "write"):
-        handler.dump_to_fileobj(obj, file, **kwargs)
-    else:
-        raise TypeError('"file" must be a filename str or a file-object')
diff --git a/spaces/jervinjosh68/vit-age-classifier/README.md b/spaces/jervinjosh68/vit-age-classifier/README.md
deleted file mode 100644
index 5a76dea5321ccfa2556b2232369d4a3996ba67b6..0000000000000000000000000000000000000000
--- a/spaces/jervinjosh68/vit-age-classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Vit Age Classifier
-emoji: 🏢
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/jharrison27/StoryWritingTransformers/README.md b/spaces/jharrison27/StoryWritingTransformers/README.md
deleted file mode 100644
index d72ce2f10a6bd422b3fa0ac7f48e2e17f543f646..0000000000000000000000000000000000000000
--- a/spaces/jharrison27/StoryWritingTransformers/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: StoryWritingTransformers
-emoji: 📃
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jhwen/bingo/src/components/chat.tsx b/spaces/jhwen/bingo/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Util/test_rfc1751.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Util/test_rfc1751.py
deleted file mode 100644
index af0aa2baf30bfa1e145c447bb5f29025e9c9cd46..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Util/test_rfc1751.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import unittest
-
-import binascii
-from Crypto.Util.RFC1751 import key_to_english, english_to_key
-
-
-class RFC1751_Tests(unittest.TestCase):
-
-    def test1(self):
-        data = [
-                ('EB33F77EE73D4053', 'TIDE ITCH SLOW REIN RULE MOT'),
-                ('CCAC2AED591056BE4F90FD441C534766', 'RASH BUSH MILK LOOK BAD BRIM AVID GAFF BAIT ROT POD LOVE'),
-                ('EFF81F9BFBC65350920CDD7416DE8009', 'TROD MUTE TAIL WARM CHAR KONG HAAG CITY BORE O TEAL AWL')
-                ]
-
-        for key_hex, words in data:
-            key_bin = binascii.a2b_hex(key_hex)
-
-            w2 = key_to_english(key_bin)
-            self.assertEqual(w2, words)
-
-            k2 = english_to_key(words)
-            self.assertEqual(k2, key_bin)
-
-    def test_error_key_to_english(self):
-
-        self.assertRaises(ValueError, key_to_english, b'0' * 7)
-
-
-def get_tests(config={}):
-    from Crypto.SelfTest.st_common import list_test_cases
-    tests = list_test_cases(RFC1751_Tests)
-    return tests
-
-
-if __name__ == '__main__':
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/jordyvl/ece/local_app.py b/spaces/jordyvl/ece/local_app.py
deleted file mode 100644
index 1ac565472b5a313f385e8aea6a57afd244f88c46..0000000000000000000000000000000000000000
--- a/spaces/jordyvl/ece/local_app.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import evaluate
-import json
-import sys
-from pathlib import Path
-import gradio as gr
-
-import numpy as np
-import pandas as pd
-import ast
-
-# from ece import ECE  # loads local instead
-
-
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-
-"""
-import seaborn as sns
-sns.set_style('white')
-sns.set_context("paper", font_scale=1)
-"""
-# plt.rcParams['figure.figsize'] = [10, 7]
-plt.rcParams["figure.dpi"] = 300
-plt.switch_backend(
-    "agg"
-)  # ; https://stackoverflow.com/questions/14694408/runtimeerror-main-thread-is-not-in-main-loop
-
-sliders = [
-    gr.Slider(0, 100, value=10, label="n_bins"),
-    gr.Slider(
-        0, 100, value=None, label="bin_range", visible=False
-    ),  # DEV: need to have a double slider
-    gr.Dropdown(choices=["equal-range", "equal-mass"], value="equal-range", label="scheme"),
-    gr.Dropdown(choices=["upper-edge", "center"], value="upper-edge", label="proxy"),
-    gr.Dropdown(choices=[1, 2, np.inf], value=1, label="p"),
-]
-
-slider_defaults = [slider.value for slider in sliders]
-
-# example data
-df = dict()
-df["predictions"] = [[0.6, 0.2, 0.2], [0, 0.95, 0.05], [0.7, 0.1, 0.2]]
-df["references"] = [0, 1, 2]
-
-component = gr.inputs.Dataframe(
-    headers=["predictions", "references"], col_count=2, datatype="number", type="pandas"
-)
-
-component.value = [
-    [[0.6, 0.2, 0.2], 0],
-    [[0.7, 0.1, 0.2], 2],
-    [[0, 0.95, 0.05], 1],
-]
-sample_data = [[component] + slider_defaults]  ##json.dumps(df)
-
-
-local_path = Path(sys.path[0])
-metric = evaluate.load("jordyvl/ece")
-# ECE()
-# module = evaluate.load("jordyvl/ece")
-# launch_gradio_widget(module)
-
-"""l
-Switch inputs and compute_fn
-"""
-
-
-def default_plot():
-    fig = plt.figure()
-    ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
-    ax2 = plt.subplot2grid((3, 1), (2, 0))
-    ranged = np.linspace(0, 1, 10)
-    ax1.plot(
-        ranged,
-        ranged,
-        color="darkgreen",
-        ls="dotted",
-        label="Perfect",
-    )
-
-    # Bin differences
-    ax1.set_ylabel("Conditional Expectation")
-    ax1.set_ylim([0, 1.05])  # respective to bin range
-    ax1.set_title("Reliability Diagram")
-    ax1.set_xlim([-0.05, 1.05])  # respective to bin range
-
-    # Bin frequencies
-    ax2.set_xlabel("Confidence")
-    ax2.set_ylabel("Count")
-    ax2.legend(loc="upper left")  # , ncol=2
-    ax2.set_xlim([-0.05, 1.05])  # respective to bin range
-
-    return fig, ax1, ax2
-
-
-def reliability_plot(results):
-    # DEV: might still need to write tests in case of equal mass binning
-    # DEV: nicer would be to plot like a polygon
-    # see: https://github.com/markus93/fit-on-the-test/blob/main/Experiments_Synthetic/binnings.py
-
-    def over_under_confidence(results):
-        colors = []
-        for j, bin in enumerate(results["y_bar"]):
-            perfect = results["y_bar"][j]
-            empirical = results["p_bar"][j]
-
-            bin_color = (
-                "limegreen"
-                if np.allclose(perfect, empirical)
-                else "dodgerblue"
-                if empirical < perfect
-                else "orangered"
-            )
-            colors.append(bin_color)
-        return colors
-
-    fig, ax1, ax2 = default_plot()
-
-    # Bin differences
-    bins_with_left_edge = np.insert(results["y_bar"], 0, 0, axis=0)
-    B, bins, patches = ax1.hist(
-        results["y_bar"],
-        weights=np.nan_to_num(results["p_bar"][:-1], copy=True, nan=0),
-        bins=bins_with_left_edge,
-    )
-    colors = over_under_confidence(results)
-    for b in range(len(B)):
-        patches[b].set_facecolor(colors[b])  # color based on over/underconfidence
-
-    ax1handles = [
-        mpatches.Patch(color="orangered", label="Overconfident"),
-        mpatches.Patch(color="limegreen", label="Perfect", linestyle="dotted"),
-        mpatches.Patch(color="dodgerblue", label="Underconfident"),
-    ]
-
-    # Bin frequencies
-    anindices = np.where(~np.isnan(results["p_bar"][:-1]))[0]
-    n_bins = len(results["y_bar"])
-    bin_freqs = np.zeros(n_bins)
-    bin_freqs[anindices] = results["bin_freq"]
-    B, newbins, patches = ax2.hist(
-        results["y_bar"], weights=bin_freqs, color="midnightblue", bins=bins_with_left_edge
-    )
-
-    acc_plt = ax2.axvline(x=results["accuracy"], ls="solid", lw=3, c="black", label="Accuracy")
-    conf_plt = ax2.axvline(
-        x=results["p_bar_cont"], ls="dotted", lw=3, c="#444", label="Avg. confidence"
-    )
-
-    ax1.legend(loc="lower right", handles=ax1handles)
-    ax2.legend(handles=[acc_plt, conf_plt])
-    ax1.set_xticks(bins_with_left_edge)
-    ax2.set_xticks(bins_with_left_edge)
-    plt.tight_layout()
-    return fig
-
-
-def compute_and_plot(data, n_bins, bin_range, scheme, proxy, p):
-    # DEV: check on invalid datatypes with better warnings
-
-    if isinstance(data, pd.DataFrame):
-        data.dropna(inplace=True)
-
-    predictions = [
-        ast.literal_eval(prediction) if not isinstance(prediction, list) else prediction
-        for prediction in data["predictions"]
-    ]
-    references = [reference for reference in data["references"]]
-
-    results = metric._compute(
-        predictions,
-        references,
-        n_bins=n_bins,
-        scheme=scheme,
-        proxy=proxy,
-        p=p,
-        detail=True,
-    )
-    print(results)
-    plot = reliability_plot(results)
-    return results["ECE"], plot
-
-
-outputs = [gr.outputs.Textbox(label="ECE"), gr.Plot(label="Reliability diagram")]
-# outputs[1].value = default_plot().__dict__ #Does not work; yet needs to be JSON encoded
-
-iface = gr.Interface(
-    fn=compute_and_plot,
-    inputs=[component] + sliders,
-    outputs=outputs,
-    description=metric.info.description,
-    article=evaluate.utils.parse_readme(local_path / "README.md"),
-    title=f"Metric: {metric.name}",
-    # examples=sample_data; # ValueError: Examples argument must either be a directory or a nested list, where each sublist represents a set of inputs.
-).launch()
\ No newline at end of file
diff --git a/spaces/josedolot/HybridNet_Demo2/encoders/timm_mobilenetv3.py b/spaces/josedolot/HybridNet_Demo2/encoders/timm_mobilenetv3.py
deleted file mode 100644
index a4ab6ecfc0aa7f6570be453a3356d8196aa591bf..0000000000000000000000000000000000000000
--- a/spaces/josedolot/HybridNet_Demo2/encoders/timm_mobilenetv3.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import timm
-import numpy as np
-import torch.nn as nn
-
-from ._base import EncoderMixin
-
-
-def _make_divisible(x, divisible_by=8):
-    return int(np.ceil(x * 1. / divisible_by) * divisible_by)
-
-
-class MobileNetV3Encoder(nn.Module, EncoderMixin):
-    def __init__(self, model_name, width_mult, depth=5, **kwargs):
-        super().__init__()
-        if "large" not in model_name and "small" not in model_name:
-            raise ValueError(
-                'MobileNetV3 wrong model name {}'.format(model_name)
-            )
-
-        self._mode = "small" if "small" in model_name else "large"
-        self._depth = depth
-        self._out_channels = self._get_channels(self._mode, width_mult)
-        self._in_channels = 3
-
-        # minimal models replace hardswish with relu
-        self.model = timm.create_model(
-            model_name=model_name,
-            scriptable=True,   # torch.jit scriptable
-            exportable=True,   # onnx export
-            features_only=True,
-        )
-
-    def _get_channels(self, mode, width_mult):
-        if mode == "small":
-            channels = [16, 16, 24, 48, 576]  
-        else:
-            channels = [16, 24, 40, 112, 960]
-        channels = [3,] + [_make_divisible(x * width_mult) for x in channels]
-        return tuple(channels)
-
-    def get_stages(self):
-        if self._mode == 'small':
-            return [
-                nn.Identity(),
-                nn.Sequential(
-                    self.model.conv_stem, 
-                    self.model.bn1, 
-                    self.model.act1,
-                ),
-                self.model.blocks[0],
-                self.model.blocks[1],
-                self.model.blocks[2:4],
-                self.model.blocks[4:],
-            ]
-        elif self._mode == 'large':
-            return [
-                nn.Identity(),
-                nn.Sequential(
-                    self.model.conv_stem,
-                    self.model.bn1,
-                    self.model.act1,
-                    self.model.blocks[0],
-                ),
-                self.model.blocks[1],
-                self.model.blocks[2],
-                self.model.blocks[3:5],
-                self.model.blocks[5:],
-            ]
-        else:
-            ValueError('MobileNetV3 mode should be small or large, got {}'.format(self._mode))
-
-    def forward(self, x):
-        stages = self.get_stages()
-
-        features = []
-        for i in range(self._depth + 1):
-            x = stages[i](x)
-            features.append(x)
-
-        return features
-
-    def load_state_dict(self, state_dict, **kwargs):
-        state_dict.pop('conv_head.weight', None)
-        state_dict.pop('conv_head.bias', None)
-        state_dict.pop('classifier.weight', None)
-        state_dict.pop('classifier.bias', None)
-        self.model.load_state_dict(state_dict, **kwargs)
-
-
-mobilenetv3_weights = {
-    'tf_mobilenetv3_large_075': {
-        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth'
-    },
-    'tf_mobilenetv3_large_100': {
-        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth'
-    },
-    'tf_mobilenetv3_large_minimal_100': {
-        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth'
-    },
-    'tf_mobilenetv3_small_075': {
-        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth'
-    },
-    'tf_mobilenetv3_small_100': {
-        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth'
-    },
-    'tf_mobilenetv3_small_minimal_100': {
-        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth'
-    },
-
-
-}
-
-pretrained_settings = {}
-for model_name, sources in mobilenetv3_weights.items():
-    pretrained_settings[model_name] = {}
-    for source_name, source_url in sources.items():
-        pretrained_settings[model_name][source_name] = {
-            "url": source_url,
-            'input_range': [0, 1],
-            'mean': [0.485, 0.456, 0.406],
-            'std': [0.229, 0.224, 0.225],
-            'input_space': 'RGB',
-        }
-
-
-timm_mobilenetv3_encoders = {
-    'timm-mobilenetv3_large_075': {
-        'encoder': MobileNetV3Encoder,
-        'pretrained_settings': pretrained_settings['tf_mobilenetv3_large_075'],
-        'params': {
-            'model_name': 'tf_mobilenetv3_large_075',
-            'width_mult': 0.75
-        }
-    },
-    'timm-mobilenetv3_large_100': {
-        'encoder': MobileNetV3Encoder,
-        'pretrained_settings': pretrained_settings['tf_mobilenetv3_large_100'],
-        'params': {
-            'model_name': 'tf_mobilenetv3_large_100',
-            'width_mult': 1.0
-        }
-    },
-    'timm-mobilenetv3_large_minimal_100': {
-        'encoder': MobileNetV3Encoder,
-        'pretrained_settings': pretrained_settings['tf_mobilenetv3_large_minimal_100'],
-        'params': {
-            'model_name': 'tf_mobilenetv3_large_minimal_100',
-            'width_mult': 1.0
-        }
-    },
-    'timm-mobilenetv3_small_075': {
-        'encoder': MobileNetV3Encoder,
-        'pretrained_settings': pretrained_settings['tf_mobilenetv3_small_075'],
-        'params': {
-            'model_name': 'tf_mobilenetv3_small_075',
-            'width_mult': 0.75
-        }
-    },
-    'timm-mobilenetv3_small_100': {
-        'encoder': MobileNetV3Encoder,
-        'pretrained_settings': pretrained_settings['tf_mobilenetv3_small_100'],
-        'params': {
-            'model_name': 'tf_mobilenetv3_small_100',
-            'width_mult': 1.0
-        }
-    },
-    'timm-mobilenetv3_small_minimal_100': {
-        'encoder': MobileNetV3Encoder,
-        'pretrained_settings': pretrained_settings['tf_mobilenetv3_small_minimal_100'],
-        'params': {
-            'model_name': 'tf_mobilenetv3_small_minimal_100',
-            'width_mult': 1.0
-        }
-    },
-}
diff --git a/spaces/josedolot/HybridNet_Demo2/utils/sync_batchnorm/batchnorm_reimpl.py b/spaces/josedolot/HybridNet_Demo2/utils/sync_batchnorm/batchnorm_reimpl.py
deleted file mode 100644
index 18145c3353e13d482c492ae46df91a537669fca0..0000000000000000000000000000000000000000
--- a/spaces/josedolot/HybridNet_Demo2/utils/sync_batchnorm/batchnorm_reimpl.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File   : batchnorm_reimpl.py
-# Author : acgtyrant
-# Date   : 11/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-
-__all__ = ['BatchNorm2dReimpl']
-
-
-class BatchNorm2dReimpl(nn.Module):
-    """
-    A re-implementation of batch normalization, used for testing the numerical
-    stability.
-
-    Author: acgtyrant
-    See also:
-    https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14
-    """
-    def __init__(self, num_features, eps=1e-5, momentum=0.1):
-        super().__init__()
-
-        self.num_features = num_features
-        self.eps = eps
-        self.momentum = momentum
-        self.weight = nn.Parameter(torch.empty(num_features))
-        self.bias = nn.Parameter(torch.empty(num_features))
-        self.register_buffer('running_mean', torch.zeros(num_features))
-        self.register_buffer('running_var', torch.ones(num_features))
-        self.reset_parameters()
-
-    def reset_running_stats(self):
-        self.running_mean.zero_()
-        self.running_var.fill_(1)
-
-    def reset_parameters(self):
-        self.reset_running_stats()
-        init.uniform_(self.weight)
-        init.zeros_(self.bias)
-
-    def forward(self, input_):
-        batchsize, channels, height, width = input_.size()
-        numel = batchsize * height * width
-        input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel)
-        sum_ = input_.sum(1)
-        sum_of_square = input_.pow(2).sum(1)
-        mean = sum_ / numel
-        sumvar = sum_of_square - sum_ * mean
-
-        self.running_mean = (
-                (1 - self.momentum) * self.running_mean
-                + self.momentum * mean.detach()
-        )
-        unbias_var = sumvar / (numel - 1)
-        self.running_var = (
-                (1 - self.momentum) * self.running_var
-                + self.momentum * unbias_var.detach()
-        )
-
-        bias_var = sumvar / numel
-        inv_std = 1 / (bias_var + self.eps).pow(0.5)
-        output = (
-                (input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) *
-                self.weight.unsqueeze(1) + self.bias.unsqueeze(1))
-
-        return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous()
-
diff --git a/spaces/joshen/gpt-academic/config.py b/spaces/joshen/gpt-academic/config.py
deleted file mode 100644
index d986750a3912abb41e5b1ee3edd79509e6843bd8..0000000000000000000000000000000000000000
--- a/spaces/joshen/gpt-academic/config.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# [step 1]>> 例如： API_KEY = "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" （此key无效）
-API_KEY = "sk-此处填API密钥"
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
-USE_PROXY = False
-if USE_PROXY:
-    # 填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
-    # 例如    "socks5h://localhost:11284"
-    # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
-    # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-
-    # 代理网络的地址，打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
-    proxies = { 
-        #          [协议]://  [地址]  :[端口]
-        "http":  "socks5h://localhost:11284", 
-        "https": "socks5h://localhost:11284", 
-    }
-else:
-    proxies = None
-
-
-# [step 3]>> 以下配置可以优化体验，但大部分场合下并不需要修改
-# 对话窗的高度
-CHATBOT_HEIGHT = 1115
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 25
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = -1
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 2
-
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo"
-
-# OpenAI的API_URL
-API_URL = "https://api.openai.com/v1/chat/completions"
-
-# 设置并行使用的线程数
-CONCURRENT_COUNT = 100
-
-# 设置用户名和密码（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-AUTHENTICATION = [] # [("username", "password"), ("username2", "password2"), ...]
diff --git a/spaces/joshuasundance/langchain-streamlit-demo/.github/ISSUE_TEMPLATE/bug_report.md b/spaces/joshuasundance/langchain-streamlit-demo/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index f3d5c415e090be510b646e88a7a523cdbab93d14..0000000000000000000000000000000000000000
--- a/spaces/joshuasundance/langchain-streamlit-demo/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: bug
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
-
-**Smartphone (please complete the following information):**
- - Device: [e.g. iPhone6]
- - OS: [e.g. iOS8.1]
- - Browser [e.g. stock browser, safari]
- - Version [e.g. 22]
-
-**Additional context**
-Add any other context about the problem here.
diff --git a/spaces/jpoptum/Daredevil-Text_generation/app.py b/spaces/jpoptum/Daredevil-Text_generation/app.py
deleted file mode 100644
index 059de4aa21931445b1b7f0337dfe260c7ff7ef60..0000000000000000000000000000000000000000
--- a/spaces/jpoptum/Daredevil-Text_generation/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import streamlit as st
-from transformers import pipeline, set_seed
-
-# Define pipeline for text generation
-generator = pipeline('text-generation', model='gpt2')
-
-# Set seed for reproducibility
-set_seed(42)
-
-# Set up Streamlit app
-st.title('Text Generation with Hugging Face')
-st.write('Type a prompt below and watch the model generate text!')
-
-# Get user input
-prompt = st.text_input('Enter your prompt here:')
-
-# Generate text
-if prompt:
-    text = generator(prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
-    st.write(text)
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/decoding_test.py b/spaces/juancopi81/youtube-music-transcribe/t5x/decoding_test.py
deleted file mode 100644
index f874504e048aa5bffe9958dffa7e3d41730aa749..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/decoding_test.py
+++ /dev/null
@@ -1,943 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tests for t5x.decoding."""
-
-import functools
-from typing import Mapping, Tuple
-from unittest import mock
-
-from absl.testing import absltest
-from absl.testing import parameterized
-import jax
-from jax._src import api
-from jax.experimental import host_callback as hcb
-import jax.numpy as jnp
-import numpy as np
-from t5x import decoding
-
-EOS_ID = 1
-NEG_INF = decoding.NEG_INF
-
-
-class DecodeTest(parameterized.TestCase):
-
-  def test_temperature_sample_uneven_prefix(self):
-
-    def token_to_logits(ids, cache):
-      del ids
-      del cache
-      # Always sample id 2 for batch element 0 and id 3 for element 1.
-      logits = np.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]],
-                        dtype=np.float32)
-      return logits, {}
-
-    inputs = np.array([[0, 5, 7, 1, 0, 0], [0, 6, 1, 0, 0, 0]])
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        jax.random.PRNGKey(0),
-        topk=0,
-        initial_index=np.array([3, 2]))
-    expected = np.array([[5, 7, 1, 2, 2, 2], [6, 1, 3, 3, 3, 3]])
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_no_prefix(self):
-    batch, max_decode_len = 2, 3
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Always sample id 2 for batch element 0 and id 3 for element 1.
-      logits = np.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]],
-                        dtype=np.float32)
-      return logits, {}
-
-    inputs = np.zeros((batch, max_decode_len), dtype=np.int32)
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {}, token_to_logits, EOS_ID, jax.random.PRNGKey(0), topk=0)
-
-    expected = [[2, 2, 2], [3, 3, 3]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_prefix(self):
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Always sample id 2 for batch element 0 and id 3 for element 1.
-      logits = np.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]],
-                        dtype=np.float32)
-      return logits, {}
-
-    # batch element 0 has length 3 prefix and element 1 has length 2.
-    inputs = np.array([[0, 5, 6, 7, 0], [0, 8, 9, 0, 0]], dtype=np.int32)
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {}, token_to_logits, EOS_ID, jax.random.PRNGKey(0), topk=0)
-
-    expected = [[5, 6, 7, 2, 2], [8, 9, 3, 3, 3]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_with_zero_temperature(self):
-    batch, max_decode_len = 2, 3
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Use very large logits that are close to one another.
-      logits = np.array(
-          [[1700.47, 1700.48, 1700.51, 1700.45], [3.2, 4.8, -5.3, 5.6]],
-          dtype=np.float32)
-      return logits, {}
-
-    inputs = np.zeros((batch, max_decode_len), dtype=np.int32)
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        jax.random.PRNGKey(0),
-        topk=4,
-        temperature=0.0)
-
-    expected = [[2, 2, 2], [3, 3, 3]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_prefix_ending_with_eos(self):
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Always sample id 2 for batch element 0 and id 3 for element 1.
-      logits = np.array([[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]],
-                        dtype=np.float32)
-      return logits, {}
-
-    # batch element 0 has length 4 prefix (including the initial dummy token and
-    # the last eos) and element 1 has length 3.
-    inputs = np.array([[0, 5, 6, 1, 0], [0, 8, 1, 0, 0]], dtype=np.int32)
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {}, token_to_logits, EOS_ID, jax.random.PRNGKey(0), topk=1)
-
-    expected = [[5, 6, 1, 2, 2], [8, 1, 3, 3, 3]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_with_state_callback(self):
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # A distribution with roughly all probability mass in sample id 3
-      logits = np.array([[-1e7, -1e7, -1e7, 0], [-1e7, -1e7, -1e7, 0]],
-                        dtype=np.float32)
-      return logits, {}
-
-    def state_callback_fn(state):
-      i, sequences, cache, cur_token, ended, rng, log_prob = state
-
-      def callback_fn(current_index_and_sequences):
-        """Add EOS token after first time token id 3 has been sampled."""
-        current_index, sequences = current_index_and_sequences
-        sequences = np.array(sequences)
-        for i in range(len(current_index)):
-          if sequences[i, current_index[i]] == 3:
-            sequences[i, current_index[i] + 1] = EOS_ID
-        return sequences
-
-      sequences = hcb.call(
-          callback_fn, (i, sequences),
-          result_shape=api.ShapeDtypeStruct(sequences.shape, sequences.dtype))
-      return i, sequences, cache, cur_token, ended, rng, log_prob
-
-    inputs = np.array([[0, 5, 6, 7, 0], [0, 8, 9, 0, 0]], dtype=np.int32)
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        jax.random.PRNGKey(0),
-        topk=0,
-        temperature=0.0,
-        state_callback_fn=state_callback_fn)
-
-    expected = [[5, 6, 7, 3, EOS_ID], [8, 9, 3, EOS_ID, 0]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_with_logit_callback(self):
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # uniform distribution over targets from model
-      logits = np.array([[-1e7, -1e7, -1e7, -1e7], [-1e7, -1e7, -1e7, -1e7]],
-                        dtype=np.float32)
-      return logits, {}
-
-    def logit_callback_fn(logits, state):
-      del state  # unused
-      # Rewrite logits to always sample id 2 for batch element 0 and
-      # id 3 for element 1.
-      logits[0, 2] = 0
-      logits[1, 3] = 0
-      return logits
-
-    # batch element 0 has length 3 prefix and element 1 has length 2.
-    inputs = np.array([[0, 5, 6, 7, 0], [0, 8, 9, 0, 0]], dtype=np.int32)
-    sampled_sequences, _ = decoding._temperature_sample_single_trial(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        jax.random.PRNGKey(0),
-        topk=0,
-        temperature=0.0,
-        logit_callback_fn=logit_callback_fn)
-
-    expected = [[5, 6, 7, 2, 2], [8, 9, 3, 3, 3]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_temperature_sample_prefix_ending_with_eos_early_stop(self):
-    batch, max_decode_len = 2, 7
-    rng0 = jax.random.PRNGKey(0)
-
-    ret = [np.array([2, 3]) for _ in range(max_decode_len)]
-    # Sequence 1 outputs EOS=1 when i = 3 where `i` is the while loop counter of
-    # `decoding._temperature_sample_single_trial`.
-    ret[3] = np.array([2, 1])
-    # Sequence 0 outputs EOS=1 when i = 4.
-    ret[4] = np.array([1, 3])
-    ret = jax.numpy.array(ret)
-
-    def mocked_categorical(rng_input, logits):  # pylint: disable=unused-argument
-      """Ignores logit and returns only based on the rng_input."""
-      rng = rng0
-      k = 0
-      # Mimic the rng split done in `decoding.sample_loop_body_fn`.
-      for j in range(max_decode_len):
-        rng1, rng = jax.random.split(rng)
-        # We want to sift out `j` for which rng1 == rng_input
-        # rngs are a pair of ints. So sum the bool and divide by 2.
-        k += j * (rng1 == rng_input).sum() // 2
-      # `k` at this point is equal to the while loop variable `i` of the caller.
-      return ret[k]
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # These values are not used in this test because random.categorical is
-      # directly mocked.
-      dummy_logits = np.zeros((batch, 4), dtype=np.float32)
-      return dummy_logits, {}
-
-    inputs = np.array([[0, 5, 1, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0]],
-                      dtype=np.int32)
-    with mock.patch.object(jax.random, 'categorical', new=mocked_categorical):
-      sampled_sequences, _ = decoding._temperature_sample_single_trial(
-          inputs, {}, token_to_logits, EOS_ID, rng0, topk=0)
-
-    expected = [[5, 1, 2, 2, 1, 0, 0], [8, 3, 3, 1, 0, 0, 0]]
-    np.testing.assert_array_equal(expected, sampled_sequences)
-
-  def test_greedy_decoding_topk_sample_log_probs(self):
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Sample [2, 3] with probability [0.6, 0.4].
-      logits = np.array([[-1e7, -1e7, -0.510825624, -0.916290732]],
-                        dtype=np.float32)
-      return logits, {}
-
-    inputs = np.array([[0, 2, 2, 2, 0]], dtype=np.int32)
-    sampled_sequences, sampled_log_probs = decoding._temperature_sample_single_trial(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        jax.random.PRNGKey(0),
-        topk=1,
-        rescale_log_probs=True)
-
-    expected_sequence = [[2, 2, 2, 2, 2]]
-    expected_log_probs = [0.0]
-    np.testing.assert_array_equal(expected_sequence, sampled_sequences)
-    np.testing.assert_array_almost_equal(expected_log_probs, sampled_log_probs)
-
-    inputs = np.array([[0, 2, 2, 3, 0]], dtype=np.int32)
-    sampled_sequences, sampled_log_probs = decoding._temperature_sample_single_trial(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        jax.random.PRNGKey(0),
-        topk=1,
-        rescale_log_probs=False)
-
-    expected_sequence = [[2, 2, 3, 2, 2]]
-    expected_log_probs = [-1.02165125]
-    np.testing.assert_array_equal(expected_sequence, sampled_sequences)
-    np.testing.assert_array_almost_equal(expected_log_probs, sampled_log_probs)
-
-  def test_temperature_sample_log_prob(self):
-    batch, max_decode_len = 2, 7
-    rng0 = jax.random.PRNGKey(0)
-
-    ret = [np.array([2, 3]) for _ in range(max_decode_len)]
-    # Sequence 1 outputs EOS=1 when i = 3 where `i` is the while loop counter of
-    # `decoding._temperature_sample_single_trial`.
-    ret[3] = np.array([2, 1])
-    # Sequence 0 outputs EOS=1 when i = 4.
-    ret[4] = np.array([1, 3])
-    ret = jax.numpy.array(ret)
-
-    # TODO(hwchung): refactor this.
-    def mocked_categorical(rng_input, logits):  # pylint: disable=unused-argument
-      """Ignores logit and returns only based on the rng_input."""
-      rng = rng0
-      k = 0
-      # Mimic the rng split done in `decoding.sample_loop_body_fn`.
-      for j in range(max_decode_len):
-        rng1, rng = jax.random.split(rng)
-        # We want to sift out `j` for which rng1 == rng_input
-        # rngs are a pair of ints. So sum the bool and divide by 2.
-        k += j * (rng1 == rng_input).sum() // 2
-      # `k` at this point is equal to the while loop variable `i` of the caller.
-      return ret[k]
-
-    logits = np.random.randn(batch, 4)
-    token_to_logits = lambda ids, cache: (logits, {})
-    inputs = np.array([[0, 5, 1, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0]],
-                      dtype=np.int32)
-    with mock.patch.object(jax.random, 'categorical', new=mocked_categorical):
-      sampled_sequences, log_prob = decoding._temperature_sample_single_trial(
-          inputs, {}, token_to_logits, EOS_ID, rng0, topk=0)
-
-    log_probs = jax.nn.log_softmax(logits)
-    expected = [[5, 1, 2, 2, 1, 0, 0], [8, 3, 3, 1, 0, 0, 0]]
-    expected_log_prob = [
-        log_probs[0, 2] + log_probs[0, 2] + log_probs[0, 1],
-        log_probs[1, 3] + log_probs[1, 3] + log_probs[1, 1]
-    ]
-    expected_log_prob = np.array(expected_log_prob)
-    np.testing.assert_array_equal(expected, sampled_sequences)
-    np.testing.assert_allclose(expected_log_prob, log_prob, atol=1e-5)
-
-  def test_temperature_sample_num_decodes(self):
-    num_decodes = 3
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.array([[0, 5, 1, 0], [0, 8, 7, 0]], dtype=np.int32)
-
-    with mock.patch.object(decoding,
-                           '_temperature_sample_single_trial') as mocked:
-      # expanded_decodes: [batch * num_decodes, max_decode_len]
-      expanded_decodes = np.array([[5, 1, 4, 4], [5, 1, 5, 5], [5, 1, 3, 3],
-                                   [8, 7, 5, 5], [8, 7, 3, 3], [8, 7, 4, 4]])
-      # expanded_log_prob: [batch * num_decodes]
-      expanded_log_prob = np.array([-2.3, -1.3, -3.6, -0.5, -2.5, -1.9])
-      mocked.return_value = expanded_decodes, expanded_log_prob
-
-      decodes, scores = decoding.temperature_sample(
-          inputs, {}, mock.Mock(), EOS_ID, rng0, num_decodes=num_decodes)
-
-      expanded_inputs = jnp.array([[0, 5, 1, 0], [0, 5, 1, 0], [0, 5, 1, 0],
-                                   [0, 8, 7, 0], [0, 8, 7, 0], [0, 8, 7, 0]])
-      # Test that the actual decode function is called with the expanded values.
-      np.testing.assert_array_equal(mocked.call_args[0][0], expanded_inputs)
-
-    np.testing.assert_array_equal(decodes,
-                                  [[[5, 1, 3, 3], [5, 1, 4, 4], [5, 1, 5, 5]],
-                                   [[8, 7, 3, 3], [8, 7, 4, 4], [8, 7, 5, 5]]])
-    np.testing.assert_allclose(scores, [[-3.6, -2.3, -1.3], [-2.5, -1.9, -0.5]])
-
-  def test_temperature_sample_num_decodes_with_initial_index(self):
-    num_decodes = 3
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.array([[0, 5, 1, 0], [0, 8, 7, 0]], dtype=np.int32)
-    initial_index = np.array([1, 2], dtype=np.int32)
-
-    with mock.patch.object(decoding,
-                           '_temperature_sample_single_trial') as mocked:
-      with mock.patch.object(decoding, 'cache_map') as mocked_cache_map:
-        # expanded_decodes: [batch * num_decodes, max_decode_len]
-        expanded_decodes = np.array([[5, 1, 4, 4], [5, 1, 5, 5], [5, 1, 3, 3],
-                                     [8, 7, 5, 5], [8, 7, 3, 3], [8, 7, 4, 4]])
-        # expanded_log_prob: [batch * num_decodes]
-        expanded_log_prob = np.array([-2.3, -1.3, -3.6, -0.5, -2.5, -1.9])
-        mocked.return_value = expanded_decodes, expanded_log_prob
-
-        decodes, scores = decoding.temperature_sample(
-            inputs, {},
-            mock.Mock(),
-            EOS_ID,
-            rng0,
-            num_decodes=num_decodes,
-            initial_index=initial_index)
-
-        expanded_inputs = jnp.array([[0, 5, 1, 0], [0, 5, 1, 0], [0, 5, 1, 0],
-                                     [0, 8, 7, 0], [0, 8, 7, 0], [0, 8, 7, 0]])
-        expanded_initial_index = np.array([1, 1, 1, 2, 2, 2], dtype=np.int32)
-        # Test that the actual decode function is called with the expanded
-        # values.
-        np.testing.assert_array_equal(mocked.call_args[0][0], expanded_inputs)
-        np.testing.assert_array_equal(mocked.call_args[1]['initial_index'],
-                                      expanded_initial_index)
-        # Test that the function was applied to the index in the cache map
-        self.assertTrue(mocked_cache_map.call_args[1]['apply_to_index'])
-
-    np.testing.assert_array_equal(decodes,
-                                  [[[5, 1, 3, 3], [5, 1, 4, 4], [5, 1, 5, 5]],
-                                   [[8, 7, 3, 3], [8, 7, 4, 4], [8, 7, 5, 5]]])
-    np.testing.assert_allclose(scores, [[-3.6, -2.3, -1.3], [-2.5, -1.9, -0.5]])
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='no_initial_index',
-          initial_index=None,
-          expected_calls=6,
-      ),
-      dict(
-          testcase_name='initial_index',
-          initial_index=np.array([1, 2], dtype=np.int32),
-          expected_calls=4,
-      ),
-      dict(
-          testcase_name='lower_initial_index',
-          initial_index=np.array([1, 1], dtype=np.int32),
-          expected_calls=5,  # we decode 4 tokens out of the prompt
-      ),
-  )
-  def test_temperature_sample_max_decode_steps_with_initial_index(
-      self, initial_index, expected_calls):
-    max_decode_steps = 4
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.array([[0, 2, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0]],
-                      dtype=np.int32)
-
-    token_to_logits = mock.Mock()
-    token_to_logits.return_value = (np.array(
-        [[-1e7, -1e7, -1e7, 0], [-1e7, -1e7, -1e7, 0]], dtype=np.float32), {})
-
-    # to unroll while loop
-    with jax.disable_jit():
-      decodes, scores = decoding.temperature_sample(
-          inputs, {},
-          token_to_logits,
-          EOS_ID,
-          rng0,
-          initial_index=initial_index,
-          topk=4,
-          max_decode_steps=max_decode_steps)
-
-    self.assertLen(token_to_logits.call_args_list, expected_calls)
-
-    expected_output = np.array([[2, 3, 3, 3, 3, 0, 0, 0],
-                                [2, 2, 3, 3, 3, 3, 0, 0]])
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-    np.testing.assert_array_equal(scores, [[0.], [0.]])
-
-  def test_temperature_sample_max_decode_steps_endpad(self):
-    max_decode_steps = 4
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.array([[0, 2, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 0],
-                       [0, 2, 2, 2, 0, 0, 0, 0]],
-                      dtype=np.int32)
-    initial_index = np.array([1, 6, 0])
-
-    token_to_logits = mock.Mock()
-    token_to_logits.return_value = (np.array(
-        [[-1e7, -1e7, -1e7, 0], [-1e7, -1e7, -1e7, 0], [-1e7, -1e7, -1e7, 0]],
-        dtype=np.float32), {})
-
-    # to unroll while loop
-    with jax.disable_jit():
-      decodes, scores = decoding.temperature_sample(
-          inputs, {},
-          token_to_logits,
-          EOS_ID,
-          rng0,
-          initial_index=initial_index,
-          topk=4,
-          max_decode_steps=max_decode_steps)
-
-    # `inputs[2]` starts from index 0. So it requires 3 calls to
-    # `token_to_logits` to exit the prompt (these generated tokens are
-    # overridden) and 4 more calls to fill the rest. `inputs[0]` only need 4
-    # calls. In the last 3 calls, it generates but MUST NOT populate the
-    # sequences because it is already ended.
-    self.assertLen(token_to_logits.call_args_list, 7)
-    expected_output = np.array(
-        [[2, 3, 3, 3, 3, 0, 0, 0], [2, 2, 2, 2, 2, 2, 3, 3],
-         [2, 2, 2, 3, 3, 3, 3, 0]],
-        dtype=np.int32)
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-    np.testing.assert_allclose(scores, [[0.], [0.], [0.]])
-
-  def test_temperature_sample_max_decode_steps_docstring_ex4(self):
-    max_decode_steps = 2
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.array([[0, 2, 0, 0, 0, 0, 0, 0], [0, 3, 4, 0, 0, 0, 0, 0]],
-                      dtype=np.int32)
-    initial_index = np.array([1, 2])
-
-    token_to_logits = mock.Mock()
-    token_to_logits.return_value = (np.array(
-        [[-1e7, -1e7, 0, -1e7], [-1e7, -1e7, -1e7, 0]], dtype=np.float32), {})
-
-    # to unroll while loop
-    with jax.disable_jit():
-      decodes, _ = decoding.temperature_sample(
-          inputs, {},
-          token_to_logits,
-          EOS_ID,
-          rng0,
-          initial_index=initial_index,
-          topk=4,
-          max_decode_steps=max_decode_steps)
-    self.assertLen(token_to_logits.call_args_list, 2)
-    expected_output = np.array(
-        [[2, 2, 2, 0, 0, 0, 0, 0], [3, 4, 3, 3, 0, 0, 0, 0]], dtype=np.int32)
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-
-  def test_temperature_sample_max_decode_steps_hard_limit(self):
-    max_decode_steps = 10
-    max_decode_steps_hard_limit = 4
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.array([[0, 2, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0]],
-                      dtype=np.int32)
-
-    token_to_logits = mock.Mock()
-    token_to_logits.return_value = (np.array(
-        [[-1e7, -1e7, -1e7, 0], [-1e7, -1e7, -1e7, 0]], dtype=np.float32), {})
-
-    # to unroll while loop
-    with jax.disable_jit():
-      decodes, scores = decoding.temperature_sample(
-          inputs, {},
-          token_to_logits,
-          EOS_ID,
-          rng0,
-          topk=4,
-          max_decode_steps=max_decode_steps,
-          max_decode_steps_hard_limit=max_decode_steps_hard_limit)
-
-    expected_output = np.array([[2, 3, 3, 3, 3, 0, 0, 0],
-                                [2, 2, 3, 3, 3, 3, 0, 0]])
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-    np.testing.assert_array_equal(scores, [[0.], [0.]])
-
-  def test_temperature_sample_topp(self):
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.zeros((1, 20), dtype=np.int32)
-
-    token_to_logits = mock.Mock()
-
-    # logits correspond to (0.3, 0, 0.1, 0.6)
-    token_to_logits.return_value = (np.array([[-1.2, -1e7, -2.3, -0.51]],
-                                             dtype=np.float32), {})
-
-    decodes, scores = decoding.temperature_sample(
-        inputs, {}, token_to_logits, EOS_ID, rng0, topp=0.55,
-        topk=0)  # anything under 0.6 will trigger deterministic decoding.
-
-    expected_output = np.array([[3] * 20])
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-    np.testing.assert_array_equal(scores, [[0.]])
-
-    # temperature is applied first, so the distribution becomes
-    # (0.27, 0, 0.069, 0.65), so if topp is 0.63, it should become greedy.
-    decodes, scores = decoding.temperature_sample(
-        inputs, {},
-        token_to_logits,
-        EOS_ID,
-        rng0,
-        temperature=0.8,
-        topp=0.63,
-        topk=0)
-
-    expected_output = np.array([[3] * 20])
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-    np.testing.assert_array_equal(scores, [[0.]])
-
-  def test_dynamic_topp_max_decode_steps(self):
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.zeros((1, 20), dtype=np.int32)
-
-    token_to_logits = mock.Mock()
-
-    # logits correspond to (0.3, 0, 0.1, 0.6)
-    token_to_logits.return_value = (np.array([[-1.2, -1e7, -2.3, -0.51]],
-                                             dtype=np.float32), {})
-
-    def dynamic_decode_fn(inputs, temperature, topp, max_decode_steps):
-      return decoding.temperature_sample(
-          inputs, {},
-          token_to_logits,
-          EOS_ID,
-          rng0,
-          temperature=temperature,
-          topp=topp,
-          topk=0,
-          max_decode_steps=max_decode_steps)
-
-    dynamic_decode_fn_jit = jax.jit(dynamic_decode_fn)
-
-    decodes, scores = dynamic_decode_fn_jit(inputs, 0.8, 0.63, 10)
-
-    expected_output = np.array([[3] * 10 + [0] * 10])
-    expected_output = jnp.expand_dims(expected_output, 1)
-
-    np.testing.assert_array_equal(decodes, expected_output)
-    np.testing.assert_array_equal(scores, [[0.]])
-
-  def test_topp_log_probs(self):
-    rng0 = jax.random.PRNGKey(0)
-    inputs = np.zeros((1, 1), dtype=np.int32)
-
-    token_to_logits = mock.Mock()
-
-    # logits correspond to (0.3, 0, 0.1, 0.6)
-    token_to_logits.return_value = (np.array([[-1.2, NEG_INF, -2.3, -0.51]],
-                                             dtype=np.float32), {})
-
-    with jax.disable_jit():
-      # this lets us see logits after topp and topk are applied
-      with mock.patch.object(jax.random, 'categorical') as mocked:
-        mocked.return_value = jnp.array([0], dtype=jnp.int32)
-        decodes, _ = decoding.temperature_sample(
-            inputs, {},
-            token_to_logits,
-            EOS_ID,
-            rng0,
-            temperature=1.4,
-            topp=0.7,
-            topk=0)
-
-    self.assertLen(token_to_logits.call_args_list, 1)
-    np.testing.assert_array_equal(decodes, jnp.asarray([[[0]]]))
-
-    np.testing.assert_array_almost_equal(
-        mocked.call_args_list[0][0][1],
-        jnp.asarray([[-0.85714293, NEG_INF, NEG_INF, -0.36428571]]))
-
-  def test_add_beam_dim(self):
-    x = np.array([[0, 5, 1, 0], [0, 8, 6, 9]], dtype=np.int32)
-    y = decoding.add_beam_dim(x, beam_size=3)
-    self.assertEqual(y.shape, (2, 3, 4))
-    np.testing.assert_array_equal([[[0, 5, 1, 0], [0, 5, 1, 0], [0, 5, 1, 0]],
-                                   [[0, 8, 6, 9], [0, 8, 6, 9], [0, 8, 6, 9]]],
-                                  y)
-
-  def test_flat_batch_beam_expand(self):
-    x = np.array([[0, 5, 1, 0], [0, 8, 6, 9]], dtype=np.int32)
-    np.testing.assert_array_equal(
-        [[0, 5, 1, 0], [0, 5, 1, 0], [0, 8, 6, 9], [0, 8, 6, 9]],
-        decoding.flat_batch_beam_expand(x, beam_size=2))
-
-  def test_top_k_two_stage(self):
-
-    def _test_top_k(batch_size, k):
-      # Pick sufficiently large seq_len.
-      seq_len = 2047 * k * batch_size
-      seq = np.arange(seq_len)
-      np.random.shuffle(seq)
-      x = jnp.reshape(seq, (batch_size, int(seq_len / batch_size))).astype(
-          jnp.float32)
-      np.testing.assert_almost_equal(
-          decoding.top_k_two_stage(x, k), jax.lax.top_k(x, k), decimal=5)
-
-    # Test small batch cases (batch={1,8}, k=16).
-    _test_top_k(1, 16)
-    _test_top_k(8, 16)
-    # Test large batch cases (batch={9,32}, k=11).
-    _test_top_k(9, 11)
-    _test_top_k(32, 11)
-
-  def test_cache_map(self):
-    cache = {
-        'layers_0': {
-            'cached_key': jnp.ones([3, 6]),
-            'cached_values': jnp.ones([3, 6]),
-            'cache_index': jnp.ones([
-                3,
-            ]),
-        },
-        'layers_1': {
-            'self_attention': {
-                'cached_key': jnp.ones([2, 7]),
-                'cached_values': jnp.ones([5, 8]),
-                'cache_index': jnp.array(1),
-            },
-            'encoder_decoder_attention': {
-                'cached_key': jnp.ones([10, 12, 2]),
-                'cached_values': jnp.ones([4, 7, 2]),
-                'cache_index': jnp.ones([4, 5, 6]),
-            }
-        },
-    }
-
-    fn = functools.partial(jnp.add, 4)
-
-    gold_cache = {
-        'layers_0': {
-            'cached_key': fn(jnp.ones([3, 6])),
-            'cached_values': fn(jnp.ones([3, 6])),
-            'cache_index': jnp.ones([
-                3,
-            ]),
-        },
-        'layers_1': {
-            'self_attention': {
-                'cached_key': fn(jnp.ones([2, 7])),
-                'cached_values': fn(jnp.ones([5, 8])),
-                'cache_index': jnp.array(1),
-            },
-            'encoder_decoder_attention': {
-                'cached_key': fn(jnp.ones([10, 12, 2])),
-                'cached_values': fn(jnp.ones([4, 7, 2])),
-                'cache_index': jnp.ones([4, 5, 6]),
-            }
-        }
-    }
-
-    jax.tree_multimap(np.testing.assert_array_equal,
-                      decoding.cache_map(fn, cache), gold_cache)
-
-  def test_cache_map_with_index(self):
-    cache = {
-        'layers_0': {
-            'cached_key': jnp.ones([3, 6]),
-            'cached_values': jnp.ones([3, 6]),
-            'cache_index': jnp.ones([
-                3,
-            ]),
-        },
-        'layers_1': {
-            'relpos_bias': {
-                'cached_bias': jnp.ones([1, 5, 3]),
-            },
-            'self_attention': {
-                'cached_key': jnp.ones([2, 7]),
-                'cached_values': jnp.ones([5, 8]),
-                'cache_index': jnp.array(1),
-            },
-            'encoder_decoder_attention': {
-                'cached_key': jnp.ones([10, 12, 2]),
-                'cached_values': jnp.ones([4, 7, 2]),
-                'cache_index': jnp.ones([4, 5, 6]),
-            }
-        },
-        'position_embedder': {
-            'position_embedder_index': jnp.array(-1),
-        },
-    }
-
-    fn = functools.partial(jnp.add, 8)
-
-    gold_cache = {
-        'layers_0': {
-            'cached_key': fn(jnp.ones([3, 6])),
-            'cached_values': fn(jnp.ones([3, 6])),
-            'cache_index': fn(jnp.ones([
-                3,
-            ])),
-        },
-        'layers_1': {
-            'relpos_bias': {
-                'cached_bias': jnp.ones([1, 5, 3]),
-            },
-            'self_attention': {
-                'cached_key': fn(jnp.ones([2, 7])),
-                'cached_values': fn(jnp.ones([5, 8])),
-                'cache_index': fn(jnp.array(1)),
-            },
-            'encoder_decoder_attention': {
-                'cached_key': fn(jnp.ones([10, 12, 2])),
-                'cached_values': fn(jnp.ones([4, 7, 2])),
-                'cache_index': fn(jnp.ones([4, 5, 6])),
-            }
-        },
-        'position_embedder': {
-            'position_embedder_index': jnp.array(-1),
-        },
-    }
-
-    jax.tree_multimap(np.testing.assert_array_equal,
-                      decoding.cache_map(fn, cache, apply_to_index=True),
-                      gold_cache)
-
-  def test_beam_search(self):
-    # Toy problem, we have 4 states, A, B, START, END, (plus PAD).
-    # Scores are given by a first-order Markov model.
-    batch_size = 2
-    beam_size = 2
-    # PAD doesn't matter for this test, but part of the contract for beam_search
-    # is giving the PAD token id 0.
-    states = ['PAD', 'A', 'B', 'START-', '-END']
-    num_states = len(states)
-    decode_length = 7
-
-    # Edge potentials (written inside edges for diagonals):
-    #            1      -1     1      -1
-    #         A ---- A ---- A ---- A ---- A
-    #       0   \  -1  \  1   \  -1  \  1   0
-    # START      X      X      X      X       END
-    #       0   /  -1  /  1   /  -1  /  1   0
-    #         B ---- B ---- B ---- B ---- B
-    #            1      -1     1      -1
-
-    # put the above edge potentials in a 3-tensor
-    ab_edge_potentials = np.asarray([[[1, -1], [-1, 1]], [[-1, 1], [1, -1]],
-                                     [[1, -1], [-1, 1]], [[-1, 1], [1, -1]]])
-    # now we have to add on the START, END states
-    # and PAD at 0
-    edge_potentials = np.ones([6, 5, 5]) * NEG_INF
-    edge_potentials[1:5, 1:3, 1:3] = ab_edge_potentials
-    # START can go to either A or B for free at t0
-    edge_potentials[0, 3, 1] = 0
-    edge_potentials[0, 3, 2] = 0
-    # either A or B can go to END for free at t5
-    edge_potentials[5, 1, 4] = 0
-    edge_potentials[5, 2, 4] = 0
-    # PAD can go to anything for free (doesn't matter for this test)
-    edge_potentials[:, 0, :] = 0
-
-    edge_potentials = jnp.asarray(edge_potentials)
-
-    # at time 0, we start with state=START=3
-    logits0 = jnp.asarray([NEG_INF, NEG_INF, NEG_INF, 0, NEG_INF])
-
-    # add dummy flattened batch x beam dim for broadcasting
-    logits0 = jnp.expand_dims(logits0, axis=0)
-    edge_potentials = jnp.expand_dims(edge_potentials, axis=0)
-
-    def tokens_to_logits(
-        token_indices: jnp.ndarray, state_cache: Mapping[str, jnp.ndarray]
-    ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:
-      cur_iter = state_cache['cur_iter']
-      # grab edge potentials for the current timestep
-      cur_edge_potentials = jnp.take_along_axis(
-          edge_potentials,
-          jnp.reshape(
-              jnp.maximum(0, cur_iter[:, 0].astype(jnp.int32) - 1),
-              (batch_size * beam_size, 1, 1, 1)),
-          axis=1)
-      cur_edge_potentials = jnp.squeeze(cur_edge_potentials, axis=1)
-      # get "logits" from edge potentials for requested tokens (except at t0)
-      cur_logits = jnp.matmul(
-          jnp.reshape(
-              jax.nn.one_hot(token_indices, num_states, axis=1),
-              (batch_size * beam_size, 1, num_states)), cur_edge_potentials)
-      cur_logits = jnp.squeeze(cur_logits, axis=1)
-      # use our START-only logits for t0, otherwise use the edge potentials
-      logits_for_tokens = jnp.where(cur_iter == 0, logits0, cur_logits)
-      # update state in the cache
-      new_cache = state_cache.copy()
-      new_cache['cur_iter'] = cur_iter + 1
-      return logits_for_tokens, new_cache
-
-    init_cache = {}
-    init_cache['cur_iter'] = jnp.zeros((batch_size, 1))
-
-    top_scoring, _ = decoding.beam_search(
-        inputs=np.zeros([batch_size, decode_length]),
-        cache=init_cache,
-        tokens_to_logits=tokens_to_logits,
-        eos_id=4,
-        num_decodes=beam_size,
-        alpha=0.0,
-        max_decode_len=decode_length)
-
-    # The two top scoring sequences should be a tie between
-    # START-AABBA-END
-    # and
-    # START-BBAAB-END
-    # (and greedy beam search will find both these with just two beams)
-
-    top_scoring_strings = [
-        ''.join(states[tok]
-                for tok in top_scoring[0, i, :])
-        for i in range(beam_size)
-    ]
-
-    expected = ['START-AABBA-END', 'START-BBAAB-END']
-    np.testing.assert_array_equal(expected, top_scoring_strings)
-
-  def test_beam_search_force_decode_prefix(self):
-    beam_size = 2
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Use id 2 then 3 for batch element 0 and id 3 then 2 for element 1.
-      logits = np.repeat(
-          np.expand_dims(
-              np.array([[-1e7, -1e10, -0.1, -0.9, -1e4, -1e4, -1e4, -1e4],
-                        [-1e7, -1e10, -0.9, -0.1, -1e4, -1e4, -1e4, -1e4]],
-                       dtype=np.float32),
-              axis=1), [beam_size],
-          axis=1)
-      logits = decoding.flatten_beam_dim(logits)
-      return logits, {}
-
-    # batch element 0 has length 1 and element 1 has length 2.
-    inputs = np.array([[0, 7, 0, 0, 0], [0, 4, 5, 0, 0]], dtype=np.int32)
-    rolled_inputs = np.array([[7, 0, 0, 0, 0], [4, 5, 0, 0, 0]], dtype=np.int32)
-    beam_search_sequences, decoding_scores = decoding.beam_search(
-        inputs, {}, token_to_logits, EOS_ID, num_decodes=beam_size, alpha=0)
-
-    # Prefixes are forced depending on inputs.
-    # Beam search sequences and corresponding scores are in reverse order.
-    self.assertTrue(np.all(np.diff(decoding_scores) >= 0))
-    expected = np.array([[[7, 3, 2, 2, 2], [7, 2, 2, 2, 2]],
-                         [[4, 5, 2, 3, 3], [4, 5, 3, 3, 3]]])
-    np.testing.assert_array_equal(expected, beam_search_sequences)
-
-    expected_scores = []
-    batch_logits = np.array([[-1e7, -1e10, -0.1, -0.9, -1e4, -1e4, -1e4, -1e4],
-                             [-1e7, -1e10, -0.9, -0.1, -1e4, -1e4, -1e4, -1e4]],
-                            dtype=np.float32)
-    for batch, logits, prompt in zip(expected, batch_logits, rolled_inputs):
-      beam_expected_scores = []
-      for beam in batch:
-        log_probs = jax.nn.log_softmax(logits)
-        # Add them directly since they are static.
-        beam_scores = []
-        for token, prompt_token in zip(beam, prompt):
-          if prompt_token != 0:
-            beam_scores.append(0)
-          else:
-            beam_scores.append(log_probs[token])
-        beam_expected_scores.append(sum(beam_scores))
-      expected_scores.append(beam_expected_scores)
-    np.testing.assert_allclose(expected_scores, decoding_scores, atol=1e-5)
-
-  def test_beam_search_force_decode_no_prefix(self):
-    beam_size = 2
-
-    def token_to_logits(ids, cache):  # pylint: disable=unused-argument
-      # Use id 2 then 3 for batch element 0 and id 3 then 2 for element 1.
-      logits = np.repeat(
-          np.expand_dims(
-              np.array([[-1e7, -1e10, -0.1, -0.9], [-1e7, -1e10, -0.9, -0.1]],
-                       dtype=np.float32),
-              axis=1), [beam_size],
-          axis=1)
-      logits = decoding.flatten_beam_dim(logits)
-      return logits, {}
-
-    # No prefix is passed.
-    inputs = np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], dtype=np.int32)
-    beam_search_sequences, decoding_scores = decoding.beam_search(
-        inputs, {}, token_to_logits, EOS_ID, num_decodes=beam_size)
-
-    # Prefixes are forced depending on inputs.
-    # Beam search sequences and corresponding scores are in reverse order.
-    self.assertTrue(np.all(np.diff(decoding_scores) >= 0))
-    expected = np.array([[[3, 2, 2, 2, 2], [2, 2, 2, 2, 2]],
-                         [[2, 3, 3, 3, 3], [3, 3, 3, 3, 3]]])
-    np.testing.assert_array_equal(expected, beam_search_sequences)
-
-
-if __name__ == '__main__':
-  absltest.main()
diff --git a/spaces/justest/gpt4free/.github/ISSUE_TEMPLATE/feature_request.md b/spaces/justest/gpt4free/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index bbcbbe7d61558adde3cbfd0c7a63a67c27ed6d30..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.
diff --git a/spaces/juuxn/SimpleRVC/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/juuxn/SimpleRVC/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/juuxn/SimpleRVC/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
-    def compute_f0(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length]
-        """
-        pass
-
-    def compute_f0_uv(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
-        """
-        pass
diff --git a/spaces/jyseo/3DFuse/voxnerf/pipelines.py b/spaces/jyseo/3DFuse/voxnerf/pipelines.py
deleted file mode 100644
index 9c2d7e5f311fa53146a6a34c2766d649775dc82c..0000000000000000000000000000000000000000
--- a/spaces/jyseo/3DFuse/voxnerf/pipelines.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import numpy as np
-import torch
-import imageio
-
-from my.utils.tqdm import tqdm
-from my.utils.event import EventStorage, read_stats, get_event_storage
-from my.utils.heartbeat import HeartBeat, get_heartbeat
-from my.utils.debug import EarlyLoopBreak
-
-from .utils import PSNR, Scrambler, every, at
-from .data import load_blender
-from .render import (
-    as_torch_tsrs, scene_box_filter, render_ray_bundle, render_one_view, rays_from_img
-)
-from .vis import vis, stitch_vis
-
-
-device_glb = torch.device("cuda")
-
-
-def all_train_rays(scene):
-    imgs, K, poses = load_blender("train", scene)
-    num_imgs = len(imgs)
-    ro, rd, rgbs = [], [], []
-    for i in tqdm(range(num_imgs)):
-        img, pose = imgs[i], poses[i]
-        H, W = img.shape[:2]
-        _ro, _rd = rays_from_img(H, W, K, pose)
-        ro.append(_ro)
-        rd.append(_rd)
-        rgbs.append(img.reshape(-1, 3))
-
-    ro, rd, rgbs = [
-        np.concatenate(xs, axis=0) for xs in (ro, rd, rgbs)
-    ]
-    return ro, rd, rgbs
-
-
-class OneTestView():
-    def __init__(self, scene):
-        imgs, K, poses = load_blender("test", scene)
-        self.imgs, self.K, self.poses = imgs, K, poses
-        self.i = 0
-
-    def render(self, model):
-        i = self.i
-        img, K, pose = self.imgs[i], self.K, self.poses[i]
-        with torch.no_grad():
-            aabb = model.aabb.T.cpu().numpy()
-            H, W = img.shape[:2]
-            rgbs, depth = render_one_view(model, aabb, H, W, K, pose)
-            psnr = PSNR.psnr(img, rgbs)
-
-        self.i = (self.i + 1) % len(self.imgs)
-
-        return img, rgbs, depth, psnr
-
-
-def train(
-    model, n_epoch=2, bs=4096, lr=0.02, scene="lego"
-):
-    fuse = EarlyLoopBreak(500)
-
-    aabb = model.aabb.T.numpy()
-    model = model.to(device_glb)
-    optim = torch.optim.Adam(model.parameters(), lr=lr)
-
-    test_view = OneTestView(scene)
-    all_ro, all_rd, all_rgbs = all_train_rays(scene)
-    print(n_epoch, len(all_ro), bs)
-    with tqdm(total=(n_epoch * len(all_ro) // bs)) as pbar, \
-            HeartBeat(pbar) as hbeat, EventStorage() as metric:
-
-        ro, rd, t_min, t_max, intsct_inds = scene_box_filter(all_ro, all_rd, aabb)
-        rgbs = all_rgbs[intsct_inds]
-        print(len(ro))
-        for epc in range(n_epoch):
-            n = len(ro)
-            scrambler = Scrambler(n) 
-            ro, rd, t_min, t_max, rgbs = scrambler.apply(ro, rd, t_min, t_max, rgbs)
-
-            num_batch = int(np.ceil(n / bs))
-            for i in range(num_batch):
-                if fuse.on_break():
-                    break
-                s = i * bs
-                e = min(n, s + bs)
-
-                optim.zero_grad()
-                _ro, _rd, _t_min, _t_max, _rgbs = as_torch_tsrs(
-                    model.device, ro[s:e], rd[s:e], t_min[s:e], t_max[s:e], rgbs[s:e]
-                )
-                pred, _, _ = render_ray_bundle(model, _ro, _rd, _t_min, _t_max)
-                loss = ((pred - _rgbs) ** 2).mean()
-                loss.backward()
-                optim.step()
-
-                pbar.update()
-
-                psnr = PSNR.psnr_from_mse(loss.item())
-                metric.put_scalars(psnr=psnr, d_scale=model.d_scale.item())
-
-                if every(pbar, step=50):
-                    pbar.set_description(f"TRAIN: psnr {psnr:.2f}")
-
-                if every(pbar, percent=1):
-                    gimg, rimg, depth, psnr = test_view.render(model)
-                    pane = vis(
-                        gimg, rimg, depth,
-                        msg=f"psnr: {psnr:.2f}", return_buffer=True
-                    )
-                    metric.put_artifact(
-                        "vis", ".png", lambda fn: imageio.imwrite(fn, pane)
-                    )
-
-                if at(pbar, percent=30):
-                    model.make_alpha_mask()
-
-                if every(pbar, percent=35):
-                    target_xyz = (model.grid_size * 1.328).int().tolist()
-                    model.resample(target_xyz)
-                    optim = torch.optim.Adam(model.parameters(), lr=lr)
-                    print(f"resamp the voxel to {model.grid_size}")
-
-                curr_lr = update_lr(pbar, optim, lr)
-                metric.put_scalars(lr=curr_lr)
-
-                metric.step()
-                hbeat.beat()
-
-        metric.put_artifact(
-            "ckpt", ".pt", lambda fn: torch.save(model.state_dict(), fn)
-        )
-        # metric.step(flush=True)  # no need to flush since the test routine directly takes the model
-
-        metric.put_artifact(
-            "train_seq", ".mp4",
-            lambda fn: stitch_vis(fn, read_stats(metric.output_dir, "vis")[1])
-        )
-
-        with EventStorage("test"):
-            final_psnr = test(model, scene)
-        metric.put("test_psnr", final_psnr)
-
-        metric.step()
-
-        hbeat.done()
-
-
-def update_lr(pbar, optimizer, init_lr):
-    i, N = pbar.n, pbar.total
-    factor = 0.1 ** (1 / N)
-    lr = init_lr * (factor ** i)
-    for param_group in optimizer.param_groups:
-        param_group['lr'] = lr
-    return lr
-
-
-def last_ckpt():
-    ts, ckpts = read_stats("./", "ckpt")
-    if len(ckpts) > 0:
-        fname = ckpts[-1]
-        last = torch.load(fname, map_location="cpu")
-        print(f"loaded ckpt from iter {ts[-1]}")
-        return last
-
-
-def __evaluate_ckpt(model, scene):
-    # this is for external script that needs to evaluate an checkpoint
-    # currently not used
-    metric = get_event_storage()
-
-    state = last_ckpt()
-    if state is not None:
-        model.load_state_dict(state)
-    model.to(device_glb)
-
-    with EventStorage("test"):
-        final_psnr = test(model, scene)
-    metric.put("test_psnr", final_psnr)
-
-
-def test(model, scene):
-    fuse = EarlyLoopBreak(5)
-    metric = get_event_storage()
-    hbeat = get_heartbeat()
-
-    aabb = model.aabb.T.cpu().numpy()
-    model = model.to(device_glb)
-
-    imgs, K, poses = load_blender("test", scene)
-    num_imgs = len(imgs)
-
-    stats = []
-
-    for i in (pbar := tqdm(range(num_imgs))):
-        if fuse.on_break():
-            break
-
-        img, pose = imgs[i], poses[i]
-        H, W = img.shape[:2]
-        rgbs, depth = render_one_view(model, aabb, H, W, K, pose)
-        psnr = PSNR.psnr(img, rgbs)
-
-        stats.append(psnr)
-        metric.put_scalars(psnr=psnr)
-        pbar.set_description(f"TEST: mean psnr {np.mean(stats):.2f}")
-
-        plot = vis(img, rgbs, depth, msg=f"PSNR: {psnr:.2f}", return_buffer=True)
-        metric.put_artifact("test_vis", ".png", lambda fn: imageio.imwrite(fn, plot))
-        metric.step()
-        hbeat.beat()
-
-    metric.put_artifact(
-        "test_seq", ".mp4",
-        lambda fn: stitch_vis(fn, read_stats(metric.output_dir, "test_vis")[1])
-    )
-
-    final_psnr = np.mean(stats)
-    metric.put("final_psnr", final_psnr)
-    metric.step()
-
-    return final_psnr
diff --git a/spaces/k1ngtai/MMS/uroman/lib/JSON/backportPP/Compat5005.pm b/spaces/k1ngtai/MMS/uroman/lib/JSON/backportPP/Compat5005.pm
deleted file mode 100644
index 139990edff0a28474e53f882d4c4efeb2ad7d701..0000000000000000000000000000000000000000
--- a/spaces/k1ngtai/MMS/uroman/lib/JSON/backportPP/Compat5005.pm
+++ /dev/null
@@ -1,131 +0,0 @@
-package # This is JSON::backportPP
-    JSON::backportPP5005;
-
-use 5.005;
-use strict;
-
-my @properties;
-
-$JSON::PP5005::VERSION = '1.10';
-
-BEGIN {
-
-    sub utf8::is_utf8 {
-        0; # It is considered that UTF8 flag off for Perl 5.005.
-    }
-
-    sub utf8::upgrade {
-    }
-
-    sub utf8::downgrade {
-        1; # must always return true.
-    }
-
-    sub utf8::encode  {
-    }
-
-    sub utf8::decode {
-    }
-
-    *JSON::PP::JSON_PP_encode_ascii      = \&_encode_ascii;
-    *JSON::PP::JSON_PP_encode_latin1     = \&_encode_latin1;
-    *JSON::PP::JSON_PP_decode_surrogates = \&_decode_surrogates;
-    *JSON::PP::JSON_PP_decode_unicode    = \&_decode_unicode;
-
-    # missing in B module.
-    sub B::SVp_IOK () { 0x01000000; }
-    sub B::SVp_NOK () { 0x02000000; }
-    sub B::SVp_POK () { 0x04000000; }
-
-    $INC{'bytes.pm'} = 1; # dummy
-}
-
-
-
-sub _encode_ascii {
-    join('', map { $_ <= 127 ? chr($_) : sprintf('\u%04x', $_) } unpack('C*', $_[0]) );
-}
-
-
-sub _encode_latin1 {
-    join('', map { chr($_) } unpack('C*', $_[0]) );
-}
-
-
-sub _decode_surrogates { # from http://homepage1.nifty.com/nomenclator/unicode/ucs_utf.htm
-    my $uni = 0x10000 + (hex($_[0]) - 0xD800) * 0x400 + (hex($_[1]) - 0xDC00); # from perlunicode
-    my $bit = unpack('B32', pack('N', $uni));
-
-    if ( $bit =~ /^00000000000(...)(......)(......)(......)$/ ) {
-        my ($w, $x, $y, $z) = ($1, $2, $3, $4);
-        return pack('B*', sprintf('11110%s10%s10%s10%s', $w, $x, $y, $z));
-    }
-    else {
-        Carp::croak("Invalid surrogate pair");
-    }
-}
-
-
-sub _decode_unicode {
-    my ($u) = @_;
-    my ($utf8bit);
-
-    if ( $u =~ /^00([89a-f][0-9a-f])$/i ) { # 0x80-0xff
-         return pack( 'H2', $1 );
-    }
-
-    my $bit = unpack("B*", pack("H*", $u));
-
-    if ( $bit =~ /^00000(.....)(......)$/ ) {
-        $utf8bit = sprintf('110%s10%s', $1, $2);
-    }
-    elsif ( $bit =~ /^(....)(......)(......)$/ ) {
-        $utf8bit = sprintf('1110%s10%s10%s', $1, $2, $3);
-    }
-    else {
-        Carp::croak("Invalid escaped unicode");
-    }
-
-    return pack('B*', $utf8bit);
-}
-
-
-sub JSON::PP::incr_text {
-    $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new;
-
-    if ( $_[0]->{_incr_parser}->{incr_parsing} ) {
-        Carp::croak("incr_text can not be called when the incremental parser already started parsing");
-    }
-
-    $_[0]->{_incr_parser}->{incr_text} = $_[1] if ( @_ > 1 );
-    $_[0]->{_incr_parser}->{incr_text};
-}
-
-
-1;
-__END__
-
-=pod
-
-=head1 NAME
-
-JSON::PP5005 - Helper module in using JSON::PP in Perl 5.005
-
-=head1 DESCRIPTION
-
-JSON::PP calls internally.
-
-=head1 AUTHOR
-
-Makamaka Hannyaharamitu, E<lt>makamaka[at]cpan.orgE<gt>
-
-
-=head1 COPYRIGHT AND LICENSE
-
-Copyright 2007-2012 by Makamaka Hannyaharamitu
-
-This library is free software; you can redistribute it and/or modify
-it under the same terms as Perl itself. 
-
-=cut
-
diff --git a/spaces/keithhon/tortoise-tts-webui/README.md b/spaces/keithhon/tortoise-tts-webui/README.md
deleted file mode 100644
index 55a78a7696c1133c233b99795c4368fd044601be..0000000000000000000000000000000000000000
--- a/spaces/keithhon/tortoise-tts-webui/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Tortoise Tts Webui
-emoji: 📊
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/Bark-Voice-Cloning/bark/hubert/customtokenizer.py b/spaces/kevinwang676/Bark-Voice-Cloning/bark/hubert/customtokenizer.py
deleted file mode 100644
index d0cbdbf30285c9b707aa5e11eb63dff0902bbb96..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bark-Voice-Cloning/bark/hubert/customtokenizer.py
+++ /dev/null
@@ -1,195 +0,0 @@
-"""
-Custom tokenizer model.
-Author: https://www.github.com/gitmylo/
-License: MIT
-"""
-
-import json
-import os.path
-from zipfile import ZipFile
-
-import numpy
-import torch
-from torch import nn, optim
-from torch.serialization import MAP_LOCATION
-from tqdm.auto import tqdm
-
-
-class CustomTokenizer(nn.Module):
-    def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0):
-        super(CustomTokenizer, self).__init__()
-        next_size = input_size
-        if version == 0:
-            self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
-            next_size = hidden_size
-        if version == 1:
-            self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
-            self.intermediate = nn.Linear(hidden_size, 4096)
-            next_size = 4096
-
-        self.fc = nn.Linear(next_size, output_size)
-        self.softmax = nn.LogSoftmax(dim=1)
-        self.optimizer: optim.Optimizer = None
-        self.lossfunc = nn.CrossEntropyLoss()
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.version = version
-
-    def forward(self, x):
-        x, _ = self.lstm(x)
-        if self.version == 1:
-            x = self.intermediate(x)
-        x = self.fc(x)
-        x = self.softmax(x)
-        return x
-
-    @torch.no_grad()
-    def get_token(self, x):
-        """
-        Used to get the token for the first
-        :param x: An array with shape (N, input_size) where N is a whole number greater or equal to 1, and input_size is the input size used when creating the model.
-        :return: An array with shape (N,) where N is the same as N from the input. Every number in the array is a whole number in range 0...output_size - 1 where output_size is the output size used when creating the model.
-        """
-        return torch.argmax(self(x), dim=1)
-
-    def prepare_training(self):
-        self.optimizer = optim.Adam(self.parameters(), 0.001)
-
-    def train_step(self, x_train, y_train, log_loss=False):
-        # y_train = y_train[:-1]
-        # y_train = y_train[1:]
-
-        optimizer = self.optimizer
-        lossfunc = self.lossfunc
-        # Zero the gradients
-        self.zero_grad()
-
-        # Forward pass
-        y_pred = self(x_train)
-
-        y_train_len = len(y_train)
-        y_pred_len = y_pred.shape[0]
-
-        if y_train_len > y_pred_len:
-            diff = y_train_len - y_pred_len
-            y_train = y_train[diff:]
-        elif y_train_len < y_pred_len:
-            diff = y_pred_len - y_train_len
-            y_pred = y_pred[:-diff, :]
-
-        y_train_hot = torch.zeros(len(y_train), self.output_size)
-        y_train_hot[range(len(y_train)), y_train] = 1
-        y_train_hot = y_train_hot.to('cuda')
-
-        # Calculate the loss
-        loss = lossfunc(y_pred, y_train_hot)
-
-        # Print loss
-        if log_loss:
-            print('Loss', loss.item())
-
-        # Backward pass
-        loss.backward()
-
-        # Update the weights
-        optimizer.step()
-
-    def save(self, path):
-        info_path = '.'.join(os.path.basename(path).split('.')[:-1]) + '/.info'
-        torch.save(self.state_dict(), path)
-        data_from_model = Data(self.input_size, self.hidden_size, self.output_size, self.version)
-        with ZipFile(path, 'a') as model_zip:
-            model_zip.writestr(info_path, data_from_model.save())
-            model_zip.close()
-
-    @staticmethod
-    def load_from_checkpoint(path, map_location: MAP_LOCATION = None):
-        old = True
-        with ZipFile(path) as model_zip:
-            filesMatch = [file for file in model_zip.namelist() if file.endswith('/.info')]
-            file = filesMatch[0] if filesMatch else None
-            if file:
-                old = False
-                print(f"Loading Custom Hubert Tokenizer {path}")
-                data_from_model = Data.load(model_zip.read(file).decode('utf-8'))
-            model_zip.close()
-        if old:
-            model = CustomTokenizer()
-        else:
-            model = CustomTokenizer(data_from_model.hidden_size, data_from_model.input_size, data_from_model.output_size, data_from_model.version)
-        model.load_state_dict(torch.load(path))
-        if map_location:
-            model = model.to(map_location)
-        return model
-
-
-
-class Data:
-    input_size: int
-    hidden_size: int
-    output_size: int
-    version: int
-
-    def __init__(self, input_size=768, hidden_size=1024, output_size=10000, version=0):
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.version = version
-
-    @staticmethod
-    def load(string):
-        data = json.loads(string)
-        return Data(data['input_size'], data['hidden_size'], data['output_size'], data['version'])
-
-    def save(self):
-        data = {
-            'input_size': self.input_size,
-            'hidden_size': self.hidden_size,
-            'output_size': self.output_size,
-            'version': self.version,
-        }
-        return json.dumps(data)
-
-
-def auto_train(data_path, save_path='model.pth', load_model: str | None = None, save_epochs=1, max_epochs=14):
-    data_x, data_y = [], []
-
-    if load_model and os.path.isfile(load_model):
-        print('Loading model from', load_model)
-        model_training = CustomTokenizer.load_from_checkpoint(load_model, 'cuda')
-    else:
-        print('Creating new model.')
-        model_training = CustomTokenizer(version=1).to('cuda')  # Settings for the model to run without lstm
-    save_path = os.path.join(data_path, save_path)
-    base_save_path = '.'.join(save_path.split('.')[:-1])
-
-    sem_string = '_semantic.npy'
-    feat_string = '_semantic_features.npy'
-
-    ready = os.path.join(data_path, 'ready')
-    for input_file in os.listdir(ready):
-        full_path = os.path.join(ready, input_file)
-        if input_file.endswith(sem_string):
-            data_y.append(numpy.load(full_path))
-        elif input_file.endswith(feat_string):
-            data_x.append(numpy.load(full_path))
-    model_training.prepare_training()
-
-    epoch = 1
-    with tqdm(total=((len(data_x) * len(data_y)) / 50) * save_epochs) as pbar1:
-        while epoch <= max_epochs:
-            for i in range(save_epochs):
-                j = 0
-                for x, y in zip(data_x, data_y):
-                    model_training.train_step(torch.tensor(x).to('cuda'), torch.tensor(y).to('cuda'), j % 50 == 0)  # Print loss every 50 steps
-                    j += 1
-                    pbar1.update()
-
-            save_p = save_path
-            save_p_2 = f'{base_save_path}_epoch_{epoch}.pth'
-            model_training.save(save_p)
-            model_training.save(save_p_2)
-            print(f'Epoch {epoch} completed')
-            epoch += 1
-        print(f'Done training for {max_epochs} epochs!')
\ No newline at end of file
diff --git a/spaces/kevinwang676/Bert-VITS2/monotonic_align/setup.py b/spaces/kevinwang676/Bert-VITS2/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bert-VITS2/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/kevinwang676/Bert-VITS2/transcribe_genshin.py b/spaces/kevinwang676/Bert-VITS2/transcribe_genshin.py
deleted file mode 100644
index acc98814af6189d129ab85946525bec55419a33f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bert-VITS2/transcribe_genshin.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# coding=gbk
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-
-import soundfile
-from scipy.io import wavfile
-from tqdm import tqdm
-
-global speaker_annos
-speaker_annos = []
-
-def process(item):  
-    spkdir, wav_name, args = item
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and '.wav' in wav_path:
-        os.makedirs(os.path.join(args.out_dir, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, sr=args.sr)
-        soundfile.write(
-            os.path.join(args.out_dir, speaker, wav_name),
-            wav,
-            sr
-        )
-
-def process_text(item):
-    spkdir, wav_name, args = item
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    global speaker_annos
-    tr_name = wav_name.replace('.wav', '')
-    with open(args.out_dir+'/'+speaker+'/'+tr_name+'.lab', "r", encoding="utf-8") as file:
-             text = file.read()
-    text = text.replace("{NICKNAME}",'������')
-    text = text.replace("{M#��}{F#��}",'��')
-    text = text.replace("{M#��}{F#��}",'��')
-    substring = "{M#����}{F#���}"  
-    if substring in text:
-        if tr_name.endswith("a"):
-           text = text.replace("{M#����}{F#���}",'����')
-        if tr_name.endswith("b"):
-           text = text.replace("{M#����}{F#���}",'���')
-    text = text.replace("#",'')   
-    text = "ZH|" + text + "\n" #
-    speaker_annos.append(args.out_dir+'/'+speaker+'/'+wav_name+ "|" + speaker + "|" + text)
-
-
-
-if __name__ == "__main__":
-    parent_dir = "./genshin_dataset/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr", type=int, default=44100, help="sampling rate")
-    parser.add_argument("--in_dir", type=str, default="./genshin_dataset", help="path to source dir")
-    parser.add_argument("--out_dir", type=str, default="./genshin_dataset", help="path to target dir")
-    args = parser.parse_args()
-    # processs = 8
-    processs = cpu_count()-2 if cpu_count() >4 else 1
-    pool = Pool(processes=processs)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
-                pass
-            for i in os.listdir(spk_dir):
-               if i.endswith("wav"):
-                  pro=(spk_dir, i, args)
-                  process_text(pro)
-    if len(speaker_annos) == 0:
-        print("transcribe error!!!")
-    with open("./filelists/short_character_anno.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-    print("transcript file finished.")
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/facerender/sync_batchnorm/comm.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/facerender/sync_batchnorm/comm.py
deleted file mode 100644
index 922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/facerender/sync_batchnorm/comm.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : comm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import queue
-import collections
-import threading
-
-__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
-
-
-class FutureResult(object):
-    """A thread-safe future implementation. Used only as one-to-one pipe."""
-
-    def __init__(self):
-        self._result = None
-        self._lock = threading.Lock()
-        self._cond = threading.Condition(self._lock)
-
-    def put(self, result):
-        with self._lock:
-            assert self._result is None, 'Previous result has\'t been fetched.'
-            self._result = result
-            self._cond.notify()
-
-    def get(self):
-        with self._lock:
-            if self._result is None:
-                self._cond.wait()
-
-            res = self._result
-            self._result = None
-            return res
-
-
-_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
-_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])
-
-
-class SlavePipe(_SlavePipeBase):
-    """Pipe for master-slave communication."""
-
-    def run_slave(self, msg):
-        self.queue.put((self.identifier, msg))
-        ret = self.result.get()
-        self.queue.put(True)
-        return ret
-
-
-class SyncMaster(object):
-    """An abstract `SyncMaster` object.
-
-    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
-    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
-    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
-    and passed to a registered callback.
-    - After receiving the messages, the master device should gather the information and determine to message passed
-    back to each slave devices.
-    """
-
-    def __init__(self, master_callback):
-        """
-
-        Args:
-            master_callback: a callback to be invoked after having collected messages from slave devices.
-        """
-        self._master_callback = master_callback
-        self._queue = queue.Queue()
-        self._registry = collections.OrderedDict()
-        self._activated = False
-
-    def __getstate__(self):
-        return {'master_callback': self._master_callback}
-
-    def __setstate__(self, state):
-        self.__init__(state['master_callback'])
-
-    def register_slave(self, identifier):
-        """
-        Register an slave device.
-
-        Args:
-            identifier: an identifier, usually is the device id.
-
-        Returns: a `SlavePipe` object which can be used to communicate with the master device.
-
-        """
-        if self._activated:
-            assert self._queue.empty(), 'Queue is not clean before next initialization.'
-            self._activated = False
-            self._registry.clear()
-        future = FutureResult()
-        self._registry[identifier] = _MasterRegistry(future)
-        return SlavePipe(identifier, self._queue, future)
-
-    def run_master(self, master_msg):
-        """
-        Main entry for the master device in each forward pass.
-        The messages were first collected from each devices (including the master device), and then
-        an callback will be invoked to compute the message to be sent back to each devices
-        (including the master device).
-
-        Args:
-            master_msg: the message that the master want to send to itself. This will be placed as the first
-            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
-
-        Returns: the message to be sent back to the master device.
-
-        """
-        self._activated = True
-
-        intermediates = [(0, master_msg)]
-        for i in range(self.nr_slaves):
-            intermediates.append(self._queue.get())
-
-        results = self._master_callback(intermediates)
-        assert results[0][0] == 0, 'The first result should belongs to the master.'
-
-        for i, res in results:
-            if i == 0:
-                continue
-            self._registry[i].result.put(res)
-
-        for i in range(self.nr_slaves):
-            assert self._queue.get() is True
-
-        return results[0][1]
-
-    @property
-    def nr_slaves(self):
-        return len(self._registry)
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/utils/croper.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/utils/croper.py
deleted file mode 100644
index 3d9a0ac58f97afdc95d40f2a400272b11fe38093..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/utils/croper.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import scipy
-import numpy as np
-from PIL import Image
-import torch
-from tqdm import tqdm
-from itertools import cycle
-
-from src.face3d.extract_kp_videos_safe import KeypointExtractor
-from facexlib.alignment import landmark_98_to_68
-
-import numpy as np
-from PIL import Image
-
-class Preprocesser:
-    def __init__(self, device='cuda'):
-        self.predictor = KeypointExtractor(device)
-
-    def get_landmark(self, img_np):
-        """get landmark with dlib
-        :return: np.array shape=(68, 2)
-        """
-        with torch.no_grad():
-            dets = self.predictor.det_net.detect_faces(img_np, 0.97)
-
-        if len(dets) == 0:
-            return None
-        det = dets[0]
-
-        img = img_np[int(det[1]):int(det[3]), int(det[0]):int(det[2]), :]
-        lm = landmark_98_to_68(self.predictor.detector.get_landmarks(img)) # [0]
-
-        #### keypoints to the original location
-        lm[:,0] += int(det[0])
-        lm[:,1] += int(det[1])
-
-        return lm
-
-    def align_face(self, img, lm, output_size=1024):
-        """
-        :param filepath: str
-        :return: PIL Image
-        """
-        lm_chin = lm[0: 17]  # left-right
-        lm_eyebrow_left = lm[17: 22]  # left-right
-        lm_eyebrow_right = lm[22: 27]  # left-right
-        lm_nose = lm[27: 31]  # top-down
-        lm_nostrils = lm[31: 36]  # top-down
-        lm_eye_left = lm[36: 42]  # left-clockwise
-        lm_eye_right = lm[42: 48]  # left-clockwise
-        lm_mouth_outer = lm[48: 60]  # left-clockwise
-        lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-        # Calculate auxiliary vectors.
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        eye_avg = (eye_left + eye_right) * 0.5
-        eye_to_eye = eye_right - eye_left
-        mouth_left = lm_mouth_outer[0]
-        mouth_right = lm_mouth_outer[6]
-        mouth_avg = (mouth_left + mouth_right) * 0.5
-        eye_to_mouth = mouth_avg - eye_avg
-
-        # Choose oriented crop rectangle.
-        x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]  # Addition of binocular difference and double mouth difference
-        x /= np.hypot(*x)   # hypot函数计算直角三角形的斜边长，用斜边长对三角形两条直边做归一化
-        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)    # 双眼差和眼嘴差，选较大的作为基准尺度
-        y = np.flipud(x) * [-1, 1]
-        c = eye_avg + eye_to_mouth * 0.1
-        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])   # 定义四边形，以面部基准位置为中心上下左右平移得到四个顶点
-        qsize = np.hypot(*x) * 2    # 定义四边形的大小（边长），为基准尺度的2倍
-
-        # Shrink.
-        # 如果计算出的四边形太大了，就按比例缩小它
-        shrink = int(np.floor(qsize / output_size * 0.5))
-        if shrink > 1:
-            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-            img = img.resize(rsize, Image.ANTIALIAS)
-            quad /= shrink
-            qsize /= shrink
-        else:
-            rsize = (int(np.rint(float(img.size[0]))), int(np.rint(float(img.size[1]))))
-
-        # Crop.
-        border = max(int(np.rint(qsize * 0.1)), 3)
-        crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-                int(np.ceil(max(quad[:, 1]))))
-        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-                min(crop[3] + border, img.size[1]))
-        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-            # img = img.crop(crop)
-            quad -= crop[0:2]
-
-        # Pad.
-        pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-               int(np.ceil(max(quad[:, 1]))))
-        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-               max(pad[3] - img.size[1] + border, 0))
-        # if enable_padding and max(pad) > border - 4:
-        #     pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        #     img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        #     h, w, _ = img.shape
-        #     y, x, _ = np.ogrid[:h, :w, :1]
-        #     mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-        #                       1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        #     blur = qsize * 0.02
-        #     img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        #     img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        #     img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        #     quad += pad[:2]
-
-        # Transform.
-        quad = (quad + 0.5).flatten()
-        lx = max(min(quad[0], quad[2]), 0)
-        ly = max(min(quad[1], quad[7]), 0)
-        rx = min(max(quad[4], quad[6]), img.size[0])
-        ry = min(max(quad[3], quad[5]), img.size[0])
-
-        # Save aligned image.
-        return rsize, crop, [lx, ly, rx, ry]
-    
-    def crop(self, img_np_list, still=False, xsize=512):    # first frame for all video
-        img_np = img_np_list[0]
-        lm = self.get_landmark(img_np)
-
-        if lm is None:
-            raise 'can not detect the landmark from source image'
-        rsize, crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize)
-        clx, cly, crx, cry = crop
-        lx, ly, rx, ry = quad
-        lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        for _i in range(len(img_np_list)):
-            _inp = img_np_list[_i]
-            _inp = cv2.resize(_inp, (rsize[0], rsize[1]))
-            _inp = _inp[cly:cry, clx:crx]
-            if not still:
-                _inp = _inp[ly:ry, lx:rx]
-            img_np_list[_i] = _inp
-        return img_np_list, crop, quad
-
diff --git a/spaces/kevinwang676/M4Singer/usr/diff/diffusion.py b/spaces/kevinwang676/M4Singer/usr/diff/diffusion.py
deleted file mode 100644
index c30976ab258feff830c2fa1a2d70876cb1d76eda..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/M4Singer/usr/diff/diffusion.py
+++ /dev/null
@@ -1,334 +0,0 @@
-import math
-import random
-from functools import partial
-from inspect import isfunction
-from pathlib import Path
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from tqdm import tqdm
-from einops import rearrange
-
-from modules.fastspeech.fs2 import FastSpeech2
-from modules.diffsinger_midi.fs2 import FastSpeech2MIDI
-from utils.hparams import hparams
-
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def cycle(dl):
-    while True:
-        for data in dl:
-            yield data
-
-
-def num_to_groups(num, divisor):
-    groups = num // divisor
-    remainder = num % divisor
-    arr = [divisor] * groups
-    if remainder > 0:
-        arr.append(remainder)
-    return arr
-
-
-class Residual(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-
-    def forward(self, x, *args, **kwargs):
-        return self.fn(x, *args, **kwargs) + x
-
-
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        device = x.device
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
-        emb = x[:, None] * emb[None, :]
-        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
-        return emb
-
-
-class Mish(nn.Module):
-    def forward(self, x):
-        return x * torch.tanh(F.softplus(x))
-
-
-class Upsample(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class Downsample(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class Rezero(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-        self.g = nn.Parameter(torch.zeros(1))
-
-    def forward(self, x):
-        return self.fn(x) * self.g
-
-
-# building block modules
-
-class Block(nn.Module):
-    def __init__(self, dim, dim_out, groups=8):
-        super().__init__()
-        self.block = nn.Sequential(
-            nn.Conv2d(dim, dim_out, 3, padding=1),
-            nn.GroupNorm(groups, dim_out),
-            Mish()
-        )
-
-    def forward(self, x):
-        return self.block(x)
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, dim, dim_out, *, time_emb_dim, groups=8):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            Mish(),
-            nn.Linear(time_emb_dim, dim_out)
-        )
-
-        self.block1 = Block(dim, dim_out)
-        self.block2 = Block(dim_out, dim_out)
-        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
-
-    def forward(self, x, time_emb):
-        h = self.block1(x)
-        h += self.mlp(time_emb)[:, :, None, None]
-        h = self.block2(h)
-        return h + self.res_conv(x)
-
-
-class LinearAttention(nn.Module):
-    def __init__(self, dim, heads=4, dim_head=32):
-        super().__init__()
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
-        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3)
-        k = k.softmax(dim=-1)
-        context = torch.einsum('bhdn,bhen->bhde', k, v)
-        out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
-        return self.to_out(out)
-
-
-# gaussian diffusion trainer class
-
-def extract(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
-
-def cosine_beta_schedule(timesteps, s=0.008):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 1
-    x = np.linspace(0, steps, steps)
-    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
-    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
-    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return np.clip(betas, a_min=0, a_max=0.999)
-
-
-class GaussianDiffusion(nn.Module):
-    def __init__(self, phone_encoder, out_dims, denoise_fn,
-                 timesteps=1000, loss_type='l1', betas=None, spec_min=None, spec_max=None):
-        super().__init__()
-        self.denoise_fn = denoise_fn
-        if hparams.get('use_midi') is not None and hparams['use_midi']:
-            self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims)
-        else:
-            self.fs2 = FastSpeech2(phone_encoder, out_dims)
-        self.fs2.decoder = None
-        self.mel_bins = out_dims
-
-        if exists(betas):
-            betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
-        else:
-            betas = cosine_beta_schedule(timesteps)
-
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.loss_type = loss_type
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']])
-        self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']])
-
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, cond, clip_denoised: bool):
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-        x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (
-                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
-    def p_losses(self, x_start, t, cond, noise=None, nonpadding=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        x_recon = self.denoise_fn(x_noisy, t, cond)
-
-        if self.loss_type == 'l1':
-            if nonpadding is not None:
-                loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean()
-            else:
-                # print('are you sure w/o nonpadding?')
-                loss = (noise - x_recon).abs().mean()
-
-        elif self.loss_type == 'l2':
-            loss = F.mse_loss(noise, x_recon)
-        else:
-            raise NotImplementedError()
-
-        return loss
-
-    def forward(self, txt_tokens, mel2ph=None, spk_embed=None,
-                ref_mels=None, f0=None, uv=None, energy=None, infer=False):
-        b, *_, device = *txt_tokens.shape, txt_tokens.device
-        ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy,
-                       skip_decoder=True, infer=infer)
-        cond = ret['decoder_inp'].transpose(1, 2)
-        if not infer:
-            t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
-            x = ref_mels
-            x = self.norm_spec(x)
-            x = x.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
-            nonpadding = (mel2ph != 0).float()
-            ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding)
-        else:
-            t = self.num_timesteps
-            shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
-            x = torch.randn(shape, device=device)
-            for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
-                x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-            x = x[:, 0].transpose(1, 2)
-            ret['mel_out'] = self.denorm_spec(x)
-
-        return ret
-
-    def norm_spec(self, x):
-        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
-
-    def denorm_spec(self, x):
-        return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
-
-    def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph):
-        return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph)
-
-    def out2mel(self, x):
-        return x
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/losses.py b/spaces/kevinwang676/SadTalker/src/face3d/models/losses.py
deleted file mode 100644
index 09d6a85870af1ef2b857e4a3fdd4b2f7fc991317..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/models/losses.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from kornia.geometry import warp_affine
-import torch.nn.functional as F
-
-def resize_n_crop(image, M, dsize=112):
-    # image: (b, c, h, w)
-    # M   :  (b, 2, 3)
-    return warp_affine(image, M, dsize=(dsize, dsize), align_corners=True)
-
-### perceptual level loss
-class PerceptualLoss(nn.Module):
-    def __init__(self, recog_net, input_size=112):
-        super(PerceptualLoss, self).__init__()
-        self.recog_net = recog_net
-        self.preprocess = lambda x: 2 * x - 1
-        self.input_size=input_size
-    def forward(imageA, imageB, M):
-        """
-        1 - cosine distance
-        Parameters:
-            imageA       --torch.tensor (B, 3, H, W), range (0, 1) , RGB order
-            imageB       --same as imageA
-        """
-
-        imageA = self.preprocess(resize_n_crop(imageA, M, self.input_size))
-        imageB = self.preprocess(resize_n_crop(imageB, M, self.input_size))
-
-        # freeze bn
-        self.recog_net.eval()
-        
-        id_featureA = F.normalize(self.recog_net(imageA), dim=-1, p=2)
-        id_featureB = F.normalize(self.recog_net(imageB), dim=-1, p=2)  
-        cosine_d = torch.sum(id_featureA * id_featureB, dim=-1)
-        # assert torch.sum((cosine_d > 1).float()) == 0
-        return torch.sum(1 - cosine_d) / cosine_d.shape[0]        
-
-def perceptual_loss(id_featureA, id_featureB):
-    cosine_d = torch.sum(id_featureA * id_featureB, dim=-1)
-        # assert torch.sum((cosine_d > 1).float()) == 0
-    return torch.sum(1 - cosine_d) / cosine_d.shape[0]  
-
-### image level loss
-def photo_loss(imageA, imageB, mask, eps=1e-6):
-    """
-    l2 norm (with sqrt, to ensure backward stabililty, use eps, otherwise Nan may occur)
-    Parameters:
-        imageA       --torch.tensor (B, 3, H, W), range (0, 1), RGB order 
-        imageB       --same as imageA
-    """
-    loss = torch.sqrt(eps + torch.sum((imageA - imageB) ** 2, dim=1, keepdims=True)) * mask
-    loss = torch.sum(loss) / torch.max(torch.sum(mask), torch.tensor(1.0).to(mask.device))
-    return loss
-
-def landmark_loss(predict_lm, gt_lm, weight=None):
-    """
-    weighted mse loss
-    Parameters:
-        predict_lm    --torch.tensor (B, 68, 2)
-        gt_lm         --torch.tensor (B, 68, 2)
-        weight        --numpy.array (1, 68)
-    """
-    if not weight:
-        weight = np.ones([68])
-        weight[28:31] = 20
-        weight[-8:] = 20
-        weight = np.expand_dims(weight, 0)
-        weight = torch.tensor(weight).to(predict_lm.device)
-    loss = torch.sum((predict_lm - gt_lm)**2, dim=-1) * weight
-    loss = torch.sum(loss) / (predict_lm.shape[0] * predict_lm.shape[1])
-    return loss
-
-
-### regulization
-def reg_loss(coeffs_dict, opt=None):
-    """
-    l2 norm without the sqrt, from yu's implementation (mse)
-    tf.nn.l2_loss https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss
-    Parameters:
-        coeffs_dict     -- a  dict of torch.tensors , keys: id, exp, tex, angle, gamma, trans
-
-    """
-    # coefficient regularization to ensure plausible 3d faces
-    if opt:
-        w_id, w_exp, w_tex = opt.w_id, opt.w_exp, opt.w_tex
-    else:
-        w_id, w_exp, w_tex = 1, 1, 1, 1
-    creg_loss = w_id * torch.sum(coeffs_dict['id'] ** 2) +  \
-           w_exp * torch.sum(coeffs_dict['exp'] ** 2) + \
-           w_tex * torch.sum(coeffs_dict['tex'] ** 2)
-    creg_loss = creg_loss / coeffs_dict['id'].shape[0]
-
-    # gamma regularization to ensure a nearly-monochromatic light
-    gamma = coeffs_dict['gamma'].reshape([-1, 3, 9])
-    gamma_mean = torch.mean(gamma, dim=1, keepdims=True)
-    gamma_loss = torch.mean((gamma - gamma_mean) ** 2)
-
-    return creg_loss, gamma_loss
-
-def reflectance_loss(texture, mask):
-    """
-    minimize texture variance (mse), albedo regularization to ensure an uniform skin albedo
-    Parameters:
-        texture       --torch.tensor, (B, N, 3)
-        mask          --torch.tensor, (N), 1 or 0
-
-    """
-    mask = mask.reshape([1, mask.shape[0], 1])
-    texture_mean = torch.sum(mask * texture, dim=1, keepdims=True) / torch.sum(mask)
-    loss = torch.sum(((texture - texture_mean) * mask)**2) / (texture.shape[0] * torch.sum(mask))
-    return loss
-
diff --git a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py b/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
deleted file mode 100644
index eb4e0d31f1aedf4590628d394e1606920fefb5c9..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r18"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/kinensake/quanquan/gramformer/demo.py b/spaces/kinensake/quanquan/gramformer/demo.py
deleted file mode 100644
index dd06b39fa6252ce6e7601d10ececa3871c373c0b..0000000000000000000000000000000000000000
--- a/spaces/kinensake/quanquan/gramformer/demo.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from gramformer import Gramformer
-import torch
-
-def set_seed(seed):
-  torch.manual_seed(seed)
-  if torch.cuda.is_available():
-    torch.cuda.manual_seed_all(seed)
-
-set_seed(1212)
-
-
-gf = Gramformer(models = 1, use_gpu=False) # 1=corrector, 2=detector
-
-influent_sentences = [
-    "Matt like fish",
-    "the collection of letters was original used by the ancient Romans",
-    "We enjoys horror movies",
-    "Anna and Mike is going skiing",
-    "I walk to the store and I bought milk",
-    "We all eat the fish and then made dessert",
-    "I will eat fish for dinner and drank milk",
-    "what be the reason for everyone leave the company",
-]   
-
-for influent_sentence in influent_sentences:
-    corrected_sentences = gf.correct(influent_sentence, max_candidates=1)
-    print("[Input] ", influent_sentence)
-    for corrected_sentence in corrected_sentences:
-      print("[Correction] ",corrected_sentence)
-    print("-" *100)
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/datasets/pascal_voc12.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/datasets/pascal_voc12.py
deleted file mode 100644
index ba1d42d0c5781f56dc177d860d856bb34adce555..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/datasets/pascal_voc12.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# dataset settings
-dataset_type = 'PascalVOCDataset'
-data_root = 'data/VOCdevkit/VOC2012'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (512, 512)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2048, 512),
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClass',
-        split='ImageSets/Segmentation/train.txt',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClass',
-        split='ImageSets/Segmentation/val.txt',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClass',
-        split='ImageSets/Segmentation/val.txt',
-        pipeline=test_pipeline))
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh b/spaces/koajoel/PolyFormer/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh
deleted file mode 100644
index 0428d8bef9d426ac3e664cd281ce0b688f5f580f..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-#
-source_lang=kk_KZ
-target_lang=en_XX
-MODEL=criss_checkpoints/criss.3rd.pt
-SPM=criss_checkpoints/sentence.bpe.model
-SPLIT=test
-LANG_DICT=criss_checkpoints/lang_dict.txt
-ENCODER_ANALYSIS=sentence_retrieval/encoder_analysis.py
-SAVE_ENCODER=save_encoder.py
-ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL
-
-
-
-DATA_DIR=data_tmp
-INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba
-ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang}
-mkdir -p $ENCODER_SAVE_DIR/${target_lang}
-mkdir -p $ENCODER_SAVE_DIR/${source_lang}
-
-# Save encoder outputs for source sentences
-python $SAVE_ENCODER \
-  ${INPUT_DIR} \
-  --path ${MODEL} \
-  --task translation_multi_simple_epoch \
-  --lang-dict ${LANG_DICT} \
-  --gen-subset ${SPLIT} \
-  --bpe 'sentencepiece' \
-  --lang-pairs ${source_lang}-${target_lang} \
-  -s ${source_lang} -t ${target_lang} \
-  --sentencepiece-model ${SPM} \
-  --remove-bpe 'sentencepiece' \
-  --beam 1 \
-  --lang-tok-style mbart \
-  --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang}
-
-# Save encoder outputs for target sentences
-python $SAVE_ENCODER \
-  ${INPUT_DIR} \
-  --path ${MODEL} \
-  --lang-dict ${LANG_DICT} \
-  --task translation_multi_simple_epoch \
-  --gen-subset ${SPLIT} \
-  --bpe 'sentencepiece' \
-  --lang-pairs ${target_lang}-${source_lang} \
-  -t ${source_lang} -s ${target_lang} \
-  --sentencepiece-model ${SPM} \
-  --remove-bpe 'sentencepiece' \
-  --beam 1 \
-  --lang-tok-style mbart \
-  --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang}
-
-# Analyze sentence retrieval accuracy
-python $ENCODER_ANALYSIS --langs "${source_lang},${target_lang}" ${ENCODER_SAVE_DIR}
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/__init__.py
deleted file mode 100644
index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/kouenYoung/anime-tts/text/__init__.py b/spaces/kouenYoung/anime-tts/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/kouenYoung/anime-tts/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/krrishD/google_pegasus-cnn_dailymail/app.py b/spaces/krrishD/google_pegasus-cnn_dailymail/app.py
deleted file mode 100644
index b5f48bfa206d3246606e2052fb95bca3140b8e2f..0000000000000000000000000000000000000000
--- a/spaces/krrishD/google_pegasus-cnn_dailymail/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipe = pipeline("summarization", model="google/pegasus-cnn_dailymail")
-
-def main(in_text):
-  print(in_text)
-  answer = pipe(in_text, min_length=5, max_length=20)
-  print(answer)
-  return answer[0]["summary_text"]
-
-with gr.Blocks() as demo:
-  gr.Markdown("""# Summarization Engine!""")
-  with gr.Row():
-    with gr.Column():
-      text1 = gr.Textbox(
-            label="Input Text",
-            lines=1,
-        )
-      output = gr.Textbox(label="Output Text")
-      b1 = gr.Button("Summarize!")
-      b1.click(main, inputs=[text1], outputs=output)
-  gr.Markdown("""#### powered by [Tassle](https://bit.ly/3LXMklV)""")
-
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/detoxify/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/detoxify/__init__.py
deleted file mode 100644
index debe990e23c711768dbaa8fb5783f189c108c98d..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/detoxify/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from .detoxify import (
-    Detoxify,
-    multilingual_toxic_xlm_r,
-    toxic_albert,
-    toxic_bert,
-    unbiased_albert,
-    unbiased_toxic_roberta,
-)
-
-__all__ = [
-    "Detoxify",
-    "toxic_bert",
-    "toxic_albert",
-    "unbiased_toxic_roberta",
-    "unbiased_albert",
-    "multilingual_toxic_xlm_r",
-]
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/B_A_S_E_.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/B_A_S_E_.py
deleted file mode 100644
index f468a963a1e2a8d503b57f4d7aeff12b8770cc67..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/B_A_S_E_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_B_A_S_E_(BaseTTXConverter):
-    pass
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_r_o_p.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_r_o_p.py
deleted file mode 100644
index aead9d72062e878d5e497f263a4f08eddbb048f6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_r_o_p.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6prop.html
-class table__p_r_o_p(BaseTTXConverter):
-    pass
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/common/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/common/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markupsafe/_native.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markupsafe/_native.py
deleted file mode 100644
index 8117b2716d110074d9a81365c59343e81396b7f5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markupsafe/_native.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import typing as t
-
-from . import Markup
-
-
-def escape(s: t.Any) -> Markup:
-    """Replace the characters ``&``, ``<``, ``>``, ``'``, and ``"`` in
-    the string with HTML-safe sequences. Use this if you need to display
-    text that might contain such characters in HTML.
-
-    If the object has an ``__html__`` method, it is called and the
-    return value is assumed to already be safe for HTML.
-
-    :param s: An object to be converted to a string and escaped.
-    :return: A :class:`Markup` string with the escaped text.
-    """
-    if hasattr(s, "__html__"):
-        return Markup(s.__html__())
-
-    return Markup(
-        str(s)
-        .replace("&", "&amp;")
-        .replace(">", "&gt;")
-        .replace("<", "&lt;")
-        .replace("'", "&#39;")
-        .replace('"', "&#34;")
-    )
-
-
-def escape_silent(s: t.Optional[t.Any]) -> Markup:
-    """Like :func:`escape` but treats ``None`` as the empty string.
-    Useful with optional values, as otherwise you get the string
-    ``'None'`` when the value is ``None``.
-
-    >>> escape(None)
-    Markup('None')
-    >>> escape_silent(None)
-    Markup('')
-    """
-    if s is None:
-        return Markup()
-
-    return escape(s)
-
-
-def soft_str(s: t.Any) -> str:
-    """Convert an object to a string if it isn't already. This preserves
-    a :class:`Markup` string rather than converting it back to a basic
-    string, so it will still be marked as safe and won't be escaped
-    again.
-
-    >>> value = escape("<User 1>")
-    >>> value
-    Markup('&lt;User 1&gt;')
-    >>> escape(str(value))
-    Markup('&amp;lt;User 1&amp;gt;')
-    >>> escape(soft_str(value))
-    Markup('&lt;User 1&gt;')
-    """
-    if not isinstance(s, str):
-        return str(s)
-
-    return s
diff --git a/spaces/ladapetrushenko/construction_prediction/construction_prediction/construction_calculator.py b/spaces/ladapetrushenko/construction_prediction/construction_prediction/construction_calculator.py
deleted file mode 100644
index 45679b217fa4b3ccf0f3bac7afb5e5e551714881..0000000000000000000000000000000000000000
--- a/spaces/ladapetrushenko/construction_prediction/construction_prediction/construction_calculator.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from construction_prediction.constants import load_morph
-
-
-morph = load_morph()
-
-
-def filter_results(model, word, target_pos, collocate_pos, topn, restrict_vocab):
-    collocates = []
-    target_word = '_'.join((word, target_pos))
-    for coll, similarity in model.similar_by_word(target_word, topn=topn, restrict_vocab=restrict_vocab):
-        try:
-            coll_word, pos = coll.split('_')
-            if pos == collocate_pos:
-                collocates.append((coll_word, similarity))
-            if len(collocates) == topn:
-                break
-        except ValueError:
-            continue
-    return collocates
-
-
-def get_collocates_for_word_type(model, word, target_pos, topn, restrict_vocab):
-    collocate_pos = 'NOUN' if target_pos == 'ADJ' else 'ADJ'
-
-    collocates = filter_results(model, word, target_pos, collocate_pos, topn * 100, restrict_vocab)
-    output = ''
-    for collocate_with_score in collocates[:topn]:
-        collocate = collocate_with_score[0]
-        similarity_score = round(collocate_with_score[1], 3)
-        noun = word if target_pos == 'NOUN' else collocate
-        adj = word if target_pos == 'ADJ' else collocate
-        try:
-            # Чтобы была конструкция, в которой один элемент склоняется
-            adj = morph.parse(adj)[0].inflect({morph.parse(noun)[0].tag.gender}).word
-            # Чтобы исключить результаты типа 'человечный человек'
-            if not adj[:3] == noun[:3]:
-                # if noun == 'день' and not 'днев' in adj and not 'недел' in adj:
-                output += f'\t{adj} {noun}: {similarity_score}\n\n'
-        except AttributeError:
-            continue
-    return output
diff --git a/spaces/lcipolina/Print_Gallery/glide_text2im/clip/__init__.py b/spaces/lcipolina/Print_Gallery/glide_text2im/clip/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/leilevy/bingo/src/pages/api/image.ts b/spaces/leilevy/bingo/src/pages/api/image.ts
deleted file mode 100644
index 26fdb31076a9c71e70d1725a630844b27f5a3221..0000000000000000000000000000000000000000
--- a/spaces/leilevy/bingo/src/pages/api/image.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, 'image')
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/lfolle/DeepNAPSI/backend.py b/spaces/lfolle/DeepNAPSI/backend.py
deleted file mode 100644
index 49fc03652b5fceb0185e9956f461df1414f35e2b..0000000000000000000000000000000000000000
--- a/spaces/lfolle/DeepNAPSI/backend.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch
-import cv2
-import numpy as np
-from huggingface_hub import hf_hub_download
-from nail_detection.main import get_nails
-
-from DummyModel import load_dummy_model
-from Model import Model
-
-
-class Infer():
-    def __init__(self, DEBUG):
-        # self.model = load_dummy_model(DEBUG)
-        self.model = Model(DEBUG)
-
-    def predict(self, data):
-        nails = get_nails(cv2.cvtColor(data, cv2.COLOR_RGB2BGR))
-        predictions = []
-        if nails is None:
-            for _ in range(5):
-                predictions.append(np.zeros((64, 64, 3)))
-                predictions.append(-1)
-            predictions.append("-1")
-        else:
-            model_prediction, uncertainty = self.model(nails)
-            model_prediction = model_prediction[0]
-            napsi_predictions = torch.argmax(model_prediction, 1)
-            napsi_sum = int(napsi_predictions.sum().detach().cpu())
-            for napsi_prediction, nail in zip(napsi_predictions, nails):
-                predictions.append(nail)
-                predictions.append(int(napsi_prediction.detach().cpu()))
-        predictions.append(napsi_sum)
-        return predictions
diff --git a/spaces/limcheekin/CodeLlama-13B-oasst-sft-v10-GGUF/start_server.sh b/spaces/limcheekin/CodeLlama-13B-oasst-sft-v10-GGUF/start_server.sh
deleted file mode 100644
index 9ec315638ea647912c58381a9409f1bea74d0180..0000000000000000000000000000000000000000
--- a/spaces/limcheekin/CodeLlama-13B-oasst-sft-v10-GGUF/start_server.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/sh
-
-# For mlock support
-ulimit -l unlimited
-
-python3 -B main.py
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Google Sketchup 14 Free Download With Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Google Sketchup 14 Free Download With Crack.md
deleted file mode 100644
index 824f3709fa4fcc4e72248abb9abd5da5686068a1..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Google Sketchup 14 Free Download With Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Google Sketchup 14 Free Download With Crack</h2><br /><p><b><b>Download File</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://bytlly.com/2uGww6">https://bytlly.com/2uGww6</a></b></p><br /><br />
-<br />
-Guys in this video i will show you how to download and install new sketchup 2014 in windows.Don't forget ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Internet Download Manager (IDM) 6.25 Build 28 Crack Serial Key [VERIFIED].md b/spaces/lincquiQcaudo/Top-20-Diffusion/Internet Download Manager (IDM) 6.25 Build 28 Crack Serial Key [VERIFIED].md
deleted file mode 100644
index 63c0a2c41a6b34363cb348bb4638ed5ff227da5c..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Internet Download Manager (IDM) 6.25 Build 28 Crack Serial Key [VERIFIED].md	
+++ /dev/null
@@ -1,40 +0,0 @@
-
-```html
-<h1>How to Download and Install Internet Download Manager (IDM) 6.25 Build 28 with Crack Serial Key</h1>
-<p>Internet Download Manager (IDM) is a popular tool that allows you to download files from the internet faster and more efficiently. It also supports resume and schedule downloads, as well as various protocols and browsers. However, IDM is not a free software and you need to purchase a license to use it. If you want to use IDM without paying, you can try using a crack serial key that can activate the full version of IDM.</p>
-<p>In this article, we will show you how to download and install IDM 6.25 Build 28 with crack serial key. Please note that this method is illegal and may harm your computer or expose you to malware. We do not recommend or endorse using cracked software. Use it at your own risk.</p>
-<h2>Internet Download Manager (IDM) 6.25 Build 28 Crack Serial Key</h2><br /><p><b><b>DOWNLOAD</b> &#8250;&#8250;&#8250; <a href="https://bytlly.com/2uGymQ">https://bytlly.com/2uGymQ</a></b></p><br /><br />
-<h2>Step 1: Download IDM 6.25 Build 28</h2>
-<p>You can download the setup file of IDM 6.25 Build 28 from the official website or from any other trusted source. The file size is about 6 MB. Save the file to your preferred location on your computer.</p>
-<h2>Step 2: Install IDM 6.25 Build 28</h2>
-<p>Run the setup file and follow the instructions to install IDM on your computer. You may need to restart your computer after the installation is complete.</p>
-<h2>Step 3: Download Crack Serial Key</h2>
-<p>You can search for a crack serial key for IDM 6.25 Build 28 on the internet or use the one provided below. The crack serial key is a text file that contains a series of alphanumeric characters that can activate IDM.</p>
-<p>Crack Serial Key: <code>629U7-XLT5H-6SCGJ-2CENZ</code></p>
-<h2>Step 4: Apply Crack Serial Key</h2>
-<p>Open IDM and go to Registration > Registration. Enter your name, email address and the crack serial key in the respective fields. Click OK to register IDM.</p>
-<h2>Step 5: Enjoy IDM 6.25 Build 28 with Crack Serial Key</h2>
-<p>You have successfully installed and activated IDM 6.25 Build 28 with crack serial key. You can now use IDM to download files from the internet faster and more efficiently.</p>
-```
-
-```html
-<h2>Step 6: Update IDM 6.25 Build 28</h2>
-<p>It is important to keep IDM updated to the latest version to fix any bugs and improve its performance. However, updating IDM may cause the crack serial key to become invalid and IDM to stop working. To avoid this, you need to disable the automatic update feature of IDM.</p>
-<p></p>
-<p>Open IDM and go to Downloads > Options. Under the General tab, uncheck the box that says "Check for updates automatically". Click OK to save the changes.</p>
-<h2>Step 7: Troubleshoot IDM 6.25 Build 28</h2>
-<p>If you encounter any problems while using IDM 6.25 Build 28 with crack serial key, such as IDM not working, IDM not showing download bar, IDM not integrating with browsers, etc., you can try some of the following solutions:</p>
-<ul>
-<li>Make sure you have installed IDM correctly and applied the crack serial key correctly.</li>
-<li>Make sure you have disabled the automatic update feature of IDM.</li>
-<li>Make sure you have enabled the browser integration feature of IDM. Open IDM and go to Downloads > Options. Under the File Types tab, check the box that says "Automatically start downloading of URLs placed to clipboard". Under the General tab, check the boxes that correspond to your browsers. Click OK to save the changes.</li>
-<li>Make sure you have cleared your browser cache and cookies. You can do this by going to your browser settings and deleting the browsing history.</li>
-<li>Make sure you have restarted your computer after installing or updating IDM.</li>
-<li>Make sure you have a stable internet connection and enough disk space on your computer.</li>
-<li>If none of the above solutions work, you can try uninstalling and reinstalling IDM 6.25 Build 28 with crack serial key.</li>
-</ul>
-<h2>Conclusion</h2>
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-```</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/LEGO DC Super-Villains Justice League Dark Directx 9 LINK Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/LEGO DC Super-Villains Justice League Dark Directx 9 LINK Download.md
deleted file mode 100644
index 917e849afa10ee29453f71c12b9b71d49bf31fd7..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/LEGO DC Super-Villains Justice League Dark Directx 9 LINK Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>LEGO DC Super-Villains Justice League Dark Directx 9 Download</h2><br /><p><b><b>Download File</b> &#10040;&#10040;&#10040; <a href="https://bytlly.com/2uGw3L">https://bytlly.com/2uGw3L</a></b></p><br /><br />
-
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diff --git a/spaces/littlegoldfish/simple_chatbot/README.md b/spaces/littlegoldfish/simple_chatbot/README.md
deleted file mode 100644
index 8075608436e91ff70ff82eb0062d4648a4634055..0000000000000000000000000000000000000000
--- a/spaces/littlegoldfish/simple_chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Simple Chatbot
-emoji: 🏃
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lizhen30/LangChainGo/openai_chat_agent.py b/spaces/lizhen30/LangChainGo/openai_chat_agent.py
deleted file mode 100644
index 8495efb22694fdba917dde5380b96c6cf13daaf7..0000000000000000000000000000000000000000
--- a/spaces/lizhen30/LangChainGo/openai_chat_agent.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from langchain.agents import load_tools
-from langchain.agents import initialize_agent
-from langchain.agents import AgentType
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import OpenAI
-
-# First, let's load the language model we're going to use to control the agent.
-chat = ChatOpenAI(temperature=0)
-
-# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
-llm = OpenAI(temperature=0)
-tools = load_tools(["serpapi", "llm-math"], llm=llm)
-
-
-# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
-agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
-
-# Now let's test it out!
-agent.run("作为一名优秀的投资理财市，您结合2023年100名优秀专业投资师的报告，给出投资建议。")
\ No newline at end of file
diff --git a/spaces/ljjggr/bingo/src/lib/isomorphic/browser.ts b/spaces/ljjggr/bingo/src/lib/isomorphic/browser.ts
deleted file mode 100644
index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000
--- a/spaces/ljjggr/bingo/src/lib/isomorphic/browser.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-'use client'
-
-const debug = console.info.bind(console)
-
-class WebSocketAlias extends WebSocket {
-  constructor(address: string | URL, ...args: any) {
-    super(address)
-  }
-}
-
-export default { fetch, WebSocket: WebSocketAlias, debug }
diff --git a/spaces/lkeab/transfiner/configs/common/data/coco_keypoint.py b/spaces/lkeab/transfiner/configs/common/data/coco_keypoint.py
deleted file mode 100644
index b4ceb066faf696954244205dc75376b767071217..0000000000000000000000000000000000000000
--- a/spaces/lkeab/transfiner/configs/common/data/coco_keypoint.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from detectron2.data.detection_utils import create_keypoint_hflip_indices
-
-from .coco import dataloader
-
-dataloader.train.dataset.min_keypoints = 1
-dataloader.train.dataset.names = "keypoints_coco_2017_train"
-dataloader.test.dataset.names = "keypoints_coco_2017_val"
-
-dataloader.train.mapper.update(
-    use_instance_mask=False,
-    use_keypoint=True,
-    keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names),
-)
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/vencoder/hubert/hubert_model_onnx.py b/spaces/lllqqq/so-vits-svc-models-pcr/vencoder/hubert/hubert_model_onnx.py
deleted file mode 100644
index d18f3c2a0fc29592a573a9780308d38f059640b9..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/vencoder/hubert/hubert_model_onnx.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-    def forward(self, x):
-        return self.units(x)
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/m3hrdadfi/gpt2-persian-qa/regexes/currency.py b/spaces/m3hrdadfi/gpt2-persian-qa/regexes/currency.py
deleted file mode 100644
index b342670839b2aaed4a9258ee4724b3d2ed497819..0000000000000000000000000000000000000000
--- a/spaces/m3hrdadfi/gpt2-persian-qa/regexes/currency.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import re
-
-CURRENCIES = {
-    "$": "USD",
-    "zł": "PLN",
-    "£": "GBP",
-    "¥": "JPY",
-    "฿": "THB",
-    "₡": "CRC",
-    "₦": "NGN",
-    "₩": "KRW",
-    "₪": "ILS",
-    "₫": "VND",
-    "€": "EUR",
-    "₱": "PHP",
-    "₲": "PYG",
-    "₴": "UAH",
-    "₹": "INR",
-    "﷼": "IRR",
-}
-CURRENCY_REGEX = re.compile(
-    "({})+".format("|".join(re.escape(c) for c in CURRENCIES.keys()))
-)
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/iterator/iterator_facade.h b/spaces/ma-xu/LIVE/thrust/thrust/iterator/iterator_facade.h
deleted file mode 100644
index 86757d7122ed457720b9cee966b24245d7cbdba7..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/iterator/iterator_facade.h
+++ /dev/null
@@ -1,543 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file thrust/iterator/iterator_facade.h
- *  \brief A class which exposes a public interface for iterators
- */
-
-/*
- * (C) Copyright David Abrahams 2002.
- * (C) Copyright Jeremy Siek    2002.
- * (C) Copyright Thomas Witt    2002.
- * 
- * Distributed under the Boost Software License, Version 1.0.
- * (See accompanying NOTICE file for the complete license)
- *
- * For more information, see http://www.boost.org
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/iterator/detail/iterator_facade_category.h>
-#include <thrust/iterator/detail/distance_from_result.h>
-
-namespace thrust
-{
-
-/*! \addtogroup iterators
- *  \{
- */
-
-/*! \addtogroup fancyiterator Fancy Iterators
- *  \ingroup iterators
- *  \{
- */
-
-
-// This forward declaration is required for the friend declaration
-// in iterator_core_access
-template<typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference> class iterator_facade;
-
-
-/*! \p iterator_core_access is the class which user iterator types derived from \p thrust::iterator_adaptor
- *  or \p thrust::iterator_facade must befriend to allow it to access their private interface.
- */
-class iterator_core_access
-{
-    /*! \cond
-     */
-
-    // declare our friends
-    template<typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference> friend class iterator_facade;
-
-    // iterator comparisons are our friends
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend bool
-    operator ==(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-                iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend bool
-    operator !=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-                iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend bool
-    operator <(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-               iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend bool
-    operator >(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-               iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend bool
-    operator <=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-                iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend bool
-    operator >=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-                iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    // iterator difference is our friend
-    template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-              typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-    inline __host__ __device__
-    friend
-      typename thrust::detail::distance_from_result<
-        iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1>,
-        iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2>
-      >::type
-    operator-(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-              iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
-
-    template<typename Facade>
-    __host__ __device__
-    static typename Facade::reference dereference(Facade const& f)
-    {
-      return f.dereference();
-    }
-
-    template<typename Facade>
-    __host__ __device__
-    static void increment(Facade& f)
-    {
-      f.increment();
-    }
-
-    template<typename Facade>
-    __host__ __device__
-    static void decrement(Facade& f)
-    {
-      f.decrement();
-    }
-
-    template <class Facade1, class Facade2>
-    __host__ __device__
-    static bool equal(Facade1 const& f1, Facade2 const& f2)
-    {
-      return f1.equal(f2);
-    }
-
-    // XXX TODO: Investigate whether we need both of these cases
-    //template <class Facade1, class Facade2>
-    //__host__ __device__
-    //static bool equal(Facade1 const& f1, Facade2 const& f2, mpl::true_)
-    //{
-    //  return f1.equal(f2);
-    //}
-
-    //template <class Facade1, class Facade2>
-    //__host__ __device__
-    //static bool equal(Facade1 const& f1, Facade2 const& f2, mpl::false_)
-    //{
-    //  return f2.equal(f1);
-    //}
-
-    template <class Facade>
-    __host__ __device__
-    static void advance(Facade& f, typename Facade::difference_type n)
-    {
-      f.advance(n);
-    }
-
-    // Facade2 is convertible to Facade1,
-    // so return Facade1's difference_type
-    template <class Facade1, class Facade2>
-    __host__ __device__
-    static typename Facade1::difference_type
-      distance_from(Facade1 const& f1, Facade2 const& f2, thrust::detail::true_type)
-    {
-      return -f1.distance_to(f2);
-    }
-
-    // Facade2 is not convertible to Facade1,
-    // so return Facade2's difference_type
-    template <class Facade1, class Facade2>
-    __host__ __device__
-    static typename Facade2::difference_type
-      distance_from(Facade1 const& f1, Facade2 const& f2, thrust::detail::false_type)
-    {
-      return f2.distance_to(f1);
-    }
-    
-    template <class Facade1, class Facade2>
-    __host__ __device__
-    static typename thrust::detail::distance_from_result<Facade1,Facade2>::type
-      distance_from(Facade1 const& f1, Facade2 const& f2)
-    {
-      // dispatch the implementation of this method upon whether or not
-      // Facade2 is convertible to Facade1
-      return distance_from(f1, f2,
-        typename thrust::detail::is_convertible<Facade2,Facade1>::type());
-    }
-
-    //
-    // Curiously Recurring Template interface.
-    //
-    template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
-    __host__ __device__
-    static Derived& derived(iterator_facade<Derived,Value,System,Traversal,Reference,Difference>& facade)
-    {
-      return *static_cast<Derived*>(&facade);
-    }
-
-    template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
-    __host__ __device__
-    static Derived const& derived(iterator_facade<Derived,Value,System,Traversal,Reference,Difference> const& facade)
-    {
-      return *static_cast<Derived const*>(&facade);
-    }
-
-    /*! \endcond
-     */
-}; // end iterator_core_access
-
-
-/*! \p iterator_facade is a template which allows the programmer to define a novel iterator with a standards-conforming interface
- *  which Thrust can use to reason about algorithm acceleration opportunities.
- *
- *  Because most of a standard iterator's interface is defined in terms of a small set of core primitives, \p iterator_facade
- *  defines the non-primitive portion mechanically. In principle a novel iterator could explicitly provide the entire interface in
- *  an ad hoc fashion but doing so might be tedious and prone to subtle errors.
- *
- *  Often \p iterator_facade is too primitive a tool to use for defining novel iterators. In these cases, \p iterator_adaptor
- *  or a specific fancy iterator should be used instead.
- *
- *  \p iterator_facade's functionality is derived from and generally equivalent to \p boost::iterator_facade.
- *  The exception is Thrust's addition of the template parameter \p System, which is necessary to allow Thrust
- *  to dispatch an algorithm to one of several parallel backend systems. An additional exception is Thrust's omission
- *  of the \c operator-> member function.
- *
- *  Interested users may refer to <tt>boost::iterator_facade</tt>'s documentation for usage examples.
- *
- *  \note \p iterator_facade's arithmetic operator free functions exist with the usual meanings but are omitted here for brevity.
- */
-template<typename Derived,
-         typename Value,
-         typename System,
-         typename Traversal,
-         typename Reference,
-         typename Difference = std::ptrdiff_t>
-  class iterator_facade
-{
-  private:
-    /*! \cond
-     */
-
-    //
-    // Curiously Recurring Template interface.
-    //
-    __host__ __device__
-    Derived& derived()
-    {
-      return *static_cast<Derived*>(this);
-    }
-
-    __host__ __device__
-    Derived const& derived() const
-    {
-      return *static_cast<Derived const*>(this);
-    }
-    /*! \endcond
-     */
-
-  public:
-    /*! The type of element pointed to by \p iterator_facade.
-     */
-    typedef typename thrust::detail::remove_const<Value>::type value_type;
-
-    /*! The return type of \p iterator_facade::operator*().
-     */
-    typedef Reference                                          reference;
-
-    /*! The return type of \p iterator_facade's non-existent \c operator->()
-     *  member function. Unlike \c boost::iterator_facade, \p iterator_facade
-     *  disallows access to the \p value_type's members through expressions of the
-     *  form <tt>iter->member</tt>. \p pointer is defined to \c void to indicate
-     *  that these expressions are not allowed. This limitation may be relaxed in a
-     *  future version of Thrust.
-     */
-    typedef void                                               pointer;
-
-    /*! The type of expressions of the form <tt>x - y</tt> where <tt>x</tt> and <tt>y</tt>
-     *  are of type \p iterator_facade.
-     */
-    typedef Difference                                         difference_type;
-
-    /*! The type of iterator category of \p iterator_facade.
-     */
-    typedef typename thrust::detail::iterator_facade_category<
-      System, Traversal, Value, Reference
-    >::type                                                    iterator_category;
-
-    /*! \p operator*() dereferences this \p iterator_facade.
-     *  \return A reference to the element pointed to by this \p iterator_facade.
-     */
-    __host__ __device__
-    reference operator*() const
-    {
-      return iterator_core_access::dereference(this->derived());
-    }
-
-    // XXX unimplemented for now, consider implementing it later
-    //pointer operator->() const
-    //{
-    //  return;
-    //}
-
-    // XXX investigate whether or not we need to go to the lengths
-    //     boost does to determine the return type
-
-    /*! \p operator[] performs indexed dereference.
-     *  \return A reference to the element \p n distance away from this \p iterator_facade.
-     */
-    __host__ __device__
-    reference operator[](difference_type n) const
-    {
-      return *(this->derived() + n);
-    }
-
-    /*! \p operator++ pre-increments this \p iterator_facade to refer to the element in the next position.
-     *  \return <tt>*this</tt>
-     */
-    __host__ __device__
-    Derived& operator++()
-    {
-      iterator_core_access::increment(this->derived());
-      return this->derived();
-    }
-
-    /*! \p operator++ post-increments this \p iterator_facade and returns a new \p iterator_facade referring to the element in the next position.
-     *  \return A copy of <tt>*this</tt> before increment.
-     */
-    __host__ __device__
-    Derived  operator++(int)
-    {
-      Derived tmp(this->derived());
-      ++*this;
-      return tmp;
-    }
-
-    /*! \p operator-- pre-decrements this \p iterator_facade to refer to the element in the previous position.
-     *  \return <tt>*this</tt>
-     */
-    __host__ __device__
-    Derived& operator--()
-    {
-      iterator_core_access::decrement(this->derived());
-      return this->derived();
-    }
-
-    /*! \p operator-- post-decrements this \p iterator_facade and returns a new \p iterator_facade referring to the element in the previous position.
-     *  \return A copy of <tt>*this</tt> before decrement.
-     */
-    __host__ __device__
-    Derived  operator--(int)
-    {
-      Derived tmp(this->derived());
-      --*this;
-      return tmp;
-    }
-
-    /*! \p operator+= increments this \p iterator_facade to refer to an element a given distance after its current position.
-     *  \param n The quantity to increment.
-     *  \return <tt>*this</tt>
-     */
-    __host__ __device__
-    Derived& operator+=(difference_type n)
-    {
-      iterator_core_access::advance(this->derived(), n);
-      return this->derived();
-    }
-
-    /*! \p operator-= decrements this \p iterator_facade to refer to an element a given distance before its current postition.
-     *  \param n The quantity to decrement.
-     *  \return <tt>*this</tt>
-     */
-    __host__ __device__
-    Derived& operator-=(difference_type n)
-    {
-      iterator_core_access::advance(this->derived(), -n);
-      return this->derived();
-    }
-
-    /*! \p operator- subtracts a given quantity from this \p iterator_facade and returns a new \p iterator_facade referring to the element at the given position before this \p iterator_facade.
-     *  \param n The quantity to decrement
-     *  \return An \p iterator_facade pointing \p n elements before this \p iterator_facade.
-     */
-    __host__ __device__
-    Derived  operator-(difference_type n) const
-    {
-      Derived result(this->derived());
-      return result -= n;
-    }
-}; // end iterator_facade
-
-/*! \cond
- */
-
-// Comparison operators
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-// XXX it might be nice to implement this at some point
-//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
-bool
-operator ==(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-            iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return iterator_core_access
-    ::equal(*static_cast<Derived1 const*>(&lhs),
-            *static_cast<Derived2 const*>(&rhs));
-}
-
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-// XXX it might be nice to implement this at some point
-//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
-bool
-operator !=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-            iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return !iterator_core_access
-    ::equal(*static_cast<Derived1 const*>(&lhs),
-            *static_cast<Derived2 const*>(&rhs));
-}
-
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-// XXX it might be nice to implement this at some point
-//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
-bool
-operator <(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-           iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return 0 > iterator_core_access
-    ::distance_from(*static_cast<Derived1 const*>(&lhs),
-                    *static_cast<Derived2 const*>(&rhs));
-}
-
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-// XXX it might be nice to implement this at some point
-//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
-bool
-operator >(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-           iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return 0 < iterator_core_access
-    ::distance_from(*static_cast<Derived1 const*>(&lhs),
-                    *static_cast<Derived2 const*>(&rhs));
-}
-
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-// XXX it might be nice to implement this at some point
-//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
-bool
-operator <=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-            iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return 0 >= iterator_core_access
-    ::distance_from(*static_cast<Derived1 const*>(&lhs),
-                    *static_cast<Derived2 const*>(&rhs));
-}
-
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-// XXX it might be nice to implement this at some point
-//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
-bool
-operator >=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-            iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return 0 <= iterator_core_access
-    ::distance_from(*static_cast<Derived1 const*>(&lhs),
-                    *static_cast<Derived2 const*>(&rhs));
-}
-
-// Iterator difference
-template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
-          typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
-inline __host__ __device__
-
-// divine the type this operator returns
-typename thrust::detail::distance_from_result<
-  iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1>,
-  iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2>
->::type
-
-operator-(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
-          iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
-{
-  return iterator_core_access
-    ::distance_from(*static_cast<Derived1 const*>(&lhs),
-                    *static_cast<Derived2 const*>(&rhs));
-}
-
-// Iterator addition
-template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
-inline __host__ __device__
-Derived operator+ (iterator_facade<Derived,Value,System,Traversal,Reference,Difference> const& i,
-                   typename Derived::difference_type n)
-{
-  Derived tmp(static_cast<Derived const&>(i));
-  return tmp += n;
-}
-
-template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
-inline __host__ __device__
-Derived operator+ (typename Derived::difference_type n,
-                   iterator_facade<Derived,Value,System,Traversal,Reference,Difference> const& i)
-{
-  Derived tmp(static_cast<Derived const&>(i));
-  return tmp += n;
-}
-
-/*! \endcond
- */
-
-/*! \} // end fancyiterators
- */
-
-/*! \} // end iterators
- */
-
-} // end thrust
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/extrema.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/extrema.h
deleted file mode 100644
index a3ee8188971687249b7052ef4f062f5adf972768..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/extrema.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file extrema.h
- *  \brief Generic device implementations of extrema functions.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/pair.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template <typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-ForwardIterator max_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last);
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-ForwardIterator max_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            BinaryPredicate comp);
-
-
-template <typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-ForwardIterator min_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last);
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-ForwardIterator min_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            BinaryPredicate comp);
-
-
-template <typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(thrust::execution_policy<DerivedPolicy> &exec,
-                                                             ForwardIterator first, 
-                                                             ForwardIterator last);
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(thrust::execution_policy<DerivedPolicy> &exec,
-                                                             ForwardIterator first, 
-                                                             ForwardIterator last,
-                                                             BinaryPredicate comp);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/extrema.inl>
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/merge.h b/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/merge.h
deleted file mode 100644
index 44608959ced1eff1a79da4dd8eef81979370ee29..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/merge.h
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-template<typename ExecutionPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename StrictWeakOrdering>
-OutputIterator merge(execution_policy<ExecutionPolicy> &exec,
-                     InputIterator1 first1,
-                     InputIterator1 last1,
-                     InputIterator2 first2,
-                     InputIterator2 last2,
-                     OutputIterator result,
-                     StrictWeakOrdering comp);
-
-template <typename ExecutionPolicy,
-          typename InputIterator1,
-          typename InputIterator2,
-          typename InputIterator3,
-          typename InputIterator4,
-          typename OutputIterator1,
-          typename OutputIterator2,
-          typename StrictWeakOrdering>
-thrust::pair<OutputIterator1,OutputIterator2>
-  merge_by_key(execution_policy<ExecutionPolicy> &exec,
-               InputIterator1 keys_first1,
-               InputIterator1 keys_last1,
-               InputIterator2 keys_first2,
-               InputIterator2 keys_last2,
-               InputIterator3 values_first3,
-               InputIterator4 values_first4,
-               OutputIterator1 keys_result,
-               OutputIterator2 values_result,
-               StrictWeakOrdering comp);
-
-} // end detail
-} // end tbb
-} // end system
-} // end thrust
-
-#include <thrust/system/tbb/detail/merge.inl>
-
diff --git a/spaces/maksimluzik/ml-learning/app.py b/spaces/maksimluzik/ml-learning/app.py
deleted file mode 100644
index 2b507cd83a19e2ad0195fe1fab4857e7342ee76f..0000000000000000000000000000000000000000
--- a/spaces/maksimluzik/ml-learning/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import gradio as gr
-
-# AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified).
-
-__all__ = ['is_car', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
-
-# Cell
-from fastai.vision.all import *
-import gradio as gr
-
-def is_cat(x): return x[0].isupper()
-
-# Cell
-learn = load_learner('model.pkl')
-
-# Cell
-categories = ('Car', 'Motorcycle')
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
-
-# Cell
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['car.jpg', 'motorcycle.jpg', 'trash.jpg', 'tribike-cycle.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
-
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
\ No newline at end of file
diff --git a/spaces/maliozer/microsoft-biogpt/app.py b/spaces/maliozer/microsoft-biogpt/app.py
deleted file mode 100644
index 96949beea2f428b8d0cd04efe53c69f2a47613ca..0000000000000000000000000000000000000000
--- a/spaces/maliozer/microsoft-biogpt/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch
-import gradio as gr
-from transformers import BioGptTokenizer, BioGptForCausalLM
-
-model_names = [
-    "BioGPT",
-    "BioGPT-Large",
-    "BioGPT-QA-PubMedQA-BioGPT",
-    "BioGPT-QA-PubMEDQA-BioGPT-Large",
-    "BioGPT-RE-BC5CDR",
-    "BioGPT-RE-DDI",
-    "BioGPT-RE-DTI",
-    "BioGPT-DC-HoC"
-]
-
-def load_model(model_name="BioGPT"):
-  model_name_map = {
-      "BioGPT":"microsoft/biogpt",
-      "BioGPT-QA-PubMedQA-BioGPT":"microsoft/BioGPT-Large-PubMedQA"
-  }
-
-  tokenizer = BioGptTokenizer.from_pretrained(model_name_map[model_name])
-  model = BioGptForCausalLM.from_pretrained(model_name_map[model_name])
-  return tokenizer, model
-
-
-def get_beam_output(sentence, selected_model, min_len=100,max_len=512, n_beams=1):
-    tokenizer, model = load_model(selected_model)
-    inputs = tokenizer(sentence, return_tensors="pt")
-    with torch.no_grad():
-        beam_output = model.generate(**inputs,
-                                    min_length=min_len,
-                                    max_length=max_len,
-                                    num_beams=n_beams,
-                                    early_stopping=True
-                                    )
-    output=tokenizer.decode(beam_output[0], skip_special_tokens=True)
-    return output
-
-inputs = [
-    gr.inputs.Textbox(label="prompt", lines=5, default="Bicalutamide"),
-    gr.Dropdown(model_names, value="BioGPT", label="selected_model"),
-    gr.inputs.Slider(1, 500, 1, default=100, label="min_len"),
-    gr.inputs.Slider(1, 2048, 1, default=1024, label="max_len"),
-    gr.inputs.Slider(1, 10, 1, default=5, label="num_beams")
-    ]
-
-outputs = gr.outputs.Textbox(label="output")
-
-examples = [
-    ["Bicalutamide", "BioGPT", 25, 100, 5],
-    ["Janus kinase 3 (JAK-3)", "BioGPT", 25, 100, 5],
-    ["Apricitabine", "BioGPT", 25, 100, 5],
-]
-
-iface = gr.Interface(
-  fn=get_beam_output, 
-  inputs=inputs,
-  outputs=outputs,
-  examples=examples,
-  title="BioGPT: generative pre-trained transformer for biomedical text generation and mining"
-)
-iface.launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/data/image_folder.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/data/image_folder.py
deleted file mode 100644
index 8b1b9563ff3c3e18b8547c39cf708d583c68c29b..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/data/image_folder.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import torch.utils.data as data
-from PIL import Image
-import os
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff'
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def make_dataset(dir):
-    images = []
-    assert os.path.isdir(dir), '%s is not a valid directory' % dir
-
-    for root, _, fnames in sorted(os.walk(dir)):
-        for fname in fnames:
-            if is_image_file(fname):
-                path = os.path.join(root, fname)
-                images.append(path)
-
-    return images
-
-
-def default_loader(path):
-    return Image.open(path).convert('RGB')
-
-
-class ImageFolder(data.Dataset):
-
-    def __init__(self, root, transform=None, return_paths=False,
-                 loader=default_loader):
-        imgs = make_dataset(root)
-        if len(imgs) == 0:
-            raise(RuntimeError("Found 0 images in: " + root + "\n"
-                               "Supported image extensions are: " +
-                               ",".join(IMG_EXTENSIONS)))
-
-        self.root = root
-        self.imgs = imgs
-        self.transform = transform
-        self.return_paths = return_paths
-        self.loader = loader
-
-    def __getitem__(self, index):
-        path = self.imgs[index]
-        img = self.loader(path)
-        if self.transform is not None:
-            img = self.transform(img)
-        if self.return_paths:
-            return img, path
-        else:
-            return img
-
-    def __len__(self):
-        return len(self.imgs)
diff --git a/spaces/manjuvallayil/te-reo/constants.py b/spaces/manjuvallayil/te-reo/constants.py
deleted file mode 100644
index 9fa107376df3d62374119ce3fa6bd89aaf7027e5..0000000000000000000000000000000000000000
--- a/spaces/manjuvallayil/te-reo/constants.py
+++ /dev/null
@@ -1,196 +0,0 @@
-DEFAULT_USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'
-
-DEFAULT_CLIENT_SERVICE_URLS = (
-    'translate.google.com',
-)
-
-DEFAULT_FALLBACK_SERVICE_URLS = (
-    'translate.googleapis.com',
-)
-
-DEFAULT_SERVICE_URLS = ('translate.google.ac', 'translate.google.ad', 'translate.google.ae',
-                        'translate.google.al', 'translate.google.am', 'translate.google.as',
-                        'translate.google.at', 'translate.google.az', 'translate.google.ba',
-                        'translate.google.be', 'translate.google.bf', 'translate.google.bg',
-                        'translate.google.bi', 'translate.google.bj', 'translate.google.bs',
-                        'translate.google.bt', 'translate.google.by', 'translate.google.ca',
-                        'translate.google.cat', 'translate.google.cc', 'translate.google.cd',
-                        'translate.google.cf', 'translate.google.cg', 'translate.google.ch',
-                        'translate.google.ci', 'translate.google.cl', 'translate.google.cm',
-                        'translate.google.cn', 'translate.google.co.ao', 'translate.google.co.bw',
-                        'translate.google.co.ck', 'translate.google.co.cr', 'translate.google.co.id',
-                        'translate.google.co.il', 'translate.google.co.in', 'translate.google.co.jp',
-                        'translate.google.co.ke', 'translate.google.co.kr', 'translate.google.co.ls',
-                        'translate.google.co.ma', 'translate.google.co.mz', 'translate.google.co.nz',
-                        'translate.google.co.th', 'translate.google.co.tz', 'translate.google.co.ug',
-                        'translate.google.co.uk', 'translate.google.co.uz', 'translate.google.co.ve',
-                        'translate.google.co.vi', 'translate.google.co.za', 'translate.google.co.zm',
-                        'translate.google.co.zw', 'translate.google.com.af', 'translate.google.com.ag',
-                        'translate.google.com.ai', 'translate.google.com.ar', 'translate.google.com.au',
-                        'translate.google.com.bd', 'translate.google.com.bh', 'translate.google.com.bn',
-                        'translate.google.com.bo', 'translate.google.com.br', 'translate.google.com.bz',
-                        'translate.google.com.co', 'translate.google.com.cu', 'translate.google.com.cy',
-                        'translate.google.com.do', 'translate.google.com.ec', 'translate.google.com.eg',
-                        'translate.google.com.et', 'translate.google.com.fj', 'translate.google.com.gh',
-                        'translate.google.com.gi', 'translate.google.com.gt', 'translate.google.com.hk',
-                        'translate.google.com.jm', 'translate.google.com.kh', 'translate.google.com.kw',
-                        'translate.google.com.lb', 'translate.google.com.ly', 'translate.google.com.mm',
-                        'translate.google.com.mt', 'translate.google.com.mx', 'translate.google.com.my',
-                        'translate.google.com.na', 'translate.google.com.ng', 'translate.google.com.ni',
-                        'translate.google.com.np', 'translate.google.com.om', 'translate.google.com.pa',
-                        'translate.google.com.pe', 'translate.google.com.pg', 'translate.google.com.ph',
-                        'translate.google.com.pk', 'translate.google.com.pr', 'translate.google.com.py',
-                        'translate.google.com.qa', 'translate.google.com.sa', 'translate.google.com.sb',
-                        'translate.google.com.sg', 'translate.google.com.sl', 'translate.google.com.sv',
-                        'translate.google.com.tj', 'translate.google.com.tr', 'translate.google.com.tw',
-                        'translate.google.com.ua', 'translate.google.com.uy', 'translate.google.com.vc',
-                        'translate.google.com.vn', 'translate.google.com', 'translate.google.cv',
-                        'translate.google.cz', 'translate.google.de', 'translate.google.dj',
-                        'translate.google.dk', 'translate.google.dm', 'translate.google.dz',
-                        'translate.google.ee', 'translate.google.es', 'translate.google.eu',
-                        'translate.google.fi', 'translate.google.fm', 'translate.google.fr',
-                        'translate.google.ga', 'translate.google.ge', 'translate.google.gf',
-                        'translate.google.gg', 'translate.google.gl', 'translate.google.gm',
-                        'translate.google.gp', 'translate.google.gr', 'translate.google.gy',
-                        'translate.google.hn', 'translate.google.hr', 'translate.google.ht',
-                        'translate.google.hu', 'translate.google.ie', 'translate.google.im',
-                        'translate.google.io', 'translate.google.iq', 'translate.google.is',
-                        'translate.google.it', 'translate.google.je', 'translate.google.jo',
-                        'translate.google.kg', 'translate.google.ki', 'translate.google.kz',
-                        'translate.google.la', 'translate.google.li', 'translate.google.lk',
-                        'translate.google.lt', 'translate.google.lu', 'translate.google.lv',
-                        'translate.google.md', 'translate.google.me', 'translate.google.mg',
-                        'translate.google.mk', 'translate.google.ml', 'translate.google.mn',
-                        'translate.google.ms', 'translate.google.mu', 'translate.google.mv',
-                        'translate.google.mw', 'translate.google.ne', 'translate.google.nf',
-                        'translate.google.nl', 'translate.google.no', 'translate.google.nr',
-                        'translate.google.nu', 'translate.google.pl', 'translate.google.pn',
-                        'translate.google.ps', 'translate.google.pt', 'translate.google.ro',
-                        'translate.google.rs', 'translate.google.ru', 'translate.google.rw',
-                        'translate.google.sc', 'translate.google.se', 'translate.google.sh',
-                        'translate.google.si', 'translate.google.sk', 'translate.google.sm',
-                        'translate.google.sn', 'translate.google.so', 'translate.google.sr',
-                        'translate.google.st', 'translate.google.td', 'translate.google.tg',
-                        'translate.google.tk', 'translate.google.tl', 'translate.google.tm',
-                        'translate.google.tn', 'translate.google.to', 'translate.google.tt',
-                        'translate.google.us', 'translate.google.vg', 'translate.google.vu',
-                        'translate.google.ws')
-
-SPECIAL_CASES = {
-    'ee': 'et',
-}
-
-LANGUAGES = {
-    'af': 'afrikaans',
-    'sq': 'albanian',
-    'am': 'amharic',
-    'ar': 'arabic',
-    'hy': 'armenian',
-    'az': 'azerbaijani',
-    'eu': 'basque',
-    'be': 'belarusian',
-    'bn': 'bengali',
-    'bs': 'bosnian',
-    'bg': 'bulgarian',
-    'ca': 'catalan',
-    'ceb': 'cebuano',
-    'ny': 'chichewa',
-    'zh-cn': 'chinese (simplified)',
-    'zh-tw': 'chinese (traditional)',
-    'co': 'corsican',
-    'hr': 'croatian',
-    'cs': 'czech',
-    'da': 'danish',
-    'nl': 'dutch',
-    'en': 'english',
-    'eo': 'esperanto',
-    'et': 'estonian',
-    'tl': 'filipino',
-    'fi': 'finnish',
-    'fr': 'french',
-    'fy': 'frisian',
-    'gl': 'galician',
-    'ka': 'georgian',
-    'de': 'german',
-    'el': 'greek',
-    'gu': 'gujarati',
-    'ht': 'haitian creole',
-    'ha': 'hausa',
-    'haw': 'hawaiian',
-    'iw': 'hebrew',
-    'he': 'hebrew',
-    'hi': 'hindi',
-    'hmn': 'hmong',
-    'hu': 'hungarian',
-    'is': 'icelandic',
-    'ig': 'igbo',
-    'id': 'indonesian',
-    'ga': 'irish',
-    'it': 'italian',
-    'ja': 'japanese',
-    'jw': 'javanese',
-    'kn': 'kannada',
-    'kk': 'kazakh',
-    'km': 'khmer',
-    'ko': 'korean',
-    'ku': 'kurdish (kurmanji)',
-    'ky': 'kyrgyz',
-    'lo': 'lao',
-    'la': 'latin',
-    'lv': 'latvian',
-    'lt': 'lithuanian',
-    'lb': 'luxembourgish',
-    'mk': 'macedonian',
-    'mg': 'malagasy',
-    'ms': 'malay',
-    'ml': 'malayalam',
-    'mt': 'maltese',
-    'mi': 'maori',
-    'mr': 'marathi',
-    'mn': 'mongolian',
-    'my': 'myanmar (burmese)',
-    'ne': 'nepali',
-    'no': 'norwegian',
-    'or': 'odia',
-    'ps': 'pashto',
-    'fa': 'persian',
-    'pl': 'polish',
-    'pt': 'portuguese',
-    'pa': 'punjabi',
-    'ro': 'romanian',
-    'ru': 'russian',
-    'sm': 'samoan',
-    'gd': 'scots gaelic',
-    'sr': 'serbian',
-    'st': 'sesotho',
-    'sn': 'shona',
-    'sd': 'sindhi',
-    'si': 'sinhala',
-    'sk': 'slovak',
-    'sl': 'slovenian',
-    'so': 'somali',
-    'es': 'spanish',
-    'su': 'sundanese',
-    'sw': 'swahili',
-    'sv': 'swedish',
-    'tg': 'tajik',
-    'ta': 'tamil',
-    'te': 'telugu',
-    'th': 'thai',
-    'tr': 'turkish',
-    'uk': 'ukrainian',
-    'ur': 'urdu',
-    'ug': 'uyghur',
-    'uz': 'uzbek',
-    'vi': 'vietnamese',
-    'cy': 'welsh',
-    'xh': 'xhosa',
-    'yi': 'yiddish',
-    'yo': 'yoruba',
-    'zu': 'zulu',
-}
-
-LANGCODES = dict(map(reversed, LANGUAGES.items()))
-DEFAULT_RAISE_EXCEPTION = False
-DUMMY_DATA = [[["", None, None, 0]], None, "en", None,
-              None, None, 1, None, [["en"], None, [1], ["en"]]]
diff --git a/spaces/marccgrau/whisper-asr-diarization/Dockerfile b/spaces/marccgrau/whisper-asr-diarization/Dockerfile
deleted file mode 100644
index caf68ca58f5ca0e726799de757afc67f91a11de7..0000000000000000000000000000000000000000
--- a/spaces/marccgrau/whisper-asr-diarization/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM python:3.10-slim
-
-WORKDIR /app
-ADD . /app
-
-COPY requirements.txt requirements.txt
-RUN apt-get update && apt-get install git -y
-RUN apt-get install build-essential -y
-RUN apt-get install -y ffmpeg
-RUN pip3 install -r requirements.txt
-
-EXPOSE 5000
-
-CMD ["python3", "app.py"]
diff --git a/spaces/marker22/Bark-Voice-Cloning/training/train.py b/spaces/marker22/Bark-Voice-Cloning/training/train.py
deleted file mode 100644
index be0cccc6145b46d026831cb71f198d2292fae931..0000000000000000000000000000000000000000
--- a/spaces/marker22/Bark-Voice-Cloning/training/train.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-import fnmatch
-import shutil
-
-import numpy
-import torchaudio
-import gradio
-
-from bark.hubert.pre_kmeans_hubert import CustomHubert
-from bark.hubert.customtokenizer import auto_train
-from tqdm.auto import tqdm
-
-
-def training_prepare_files(path, model,progress=gradio.Progress(track_tqdm=True)):
-
-    semanticsfolder = "./training/data/output"
-    wavfolder = "./training/data/output_wav"
-    ready = os.path.join(path, 'ready')
-
-    testfiles = fnmatch.filter(os.listdir(ready), '*.npy')
-    if(len(testfiles) < 1):
-        # prepare and copy for training
-        hubert_model = CustomHubert(checkpoint_path=model)
-
-        wavfiles = fnmatch.filter(os.listdir(wavfolder), '*.wav')
-        for i, f in tqdm(enumerate(wavfiles), total=len(wavfiles)):
-            semaname = '.'.join(f.split('.')[:-1])  # Cut off the extension
-            semaname = f'{semaname}.npy'
-            semafilename =  os.path.join(semanticsfolder, semaname)
-            if not os.path.isfile(semafilename):
-                print(f'Skipping {f} no semantics pair found!')
-                continue
-
-            print('Processing', f)
-            wav, sr = torchaudio.load(os.path.join(wavfolder, f))
-            if wav.shape[0] == 2:  # Stereo to mono if needed
-                wav = wav.mean(0, keepdim=True)
-            output = hubert_model.forward(wav, input_sample_hz=sr)
-            out_array = output.cpu().numpy()
-            fname = f'{i}_semantic_features.npy'
-            numpy.save(os.path.join(ready, fname), out_array)
-            fname = f'{i}_semantic.npy'
-            shutil.copy(semafilename, os.path.join(ready, fname))
-
-def train(path, save_every, max_epochs):
-    auto_train(path, save_epochs=save_every)
-
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/main_primary.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/main_primary.py
deleted file mode 100644
index cece9e41f8a8d1834052b74386c9d1f4a69eb297..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/main_primary.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import tensorflow as tf
-from deep_heatmaps_model_primary_net import DeepHeatmapsModel
-import os
-
-
-data_dir = '/Users/arik/Dropbox/a_mac_thesis/face_heatmap_networks/conventional_landmark_detection_dataset/'
-pre_train_path = 'saved_models/0.01/model/deep_heatmaps-50000'
-output_dir = os.getcwd()
-
-flags = tf.app.flags
-
-# mode and logging parameters
-flags.DEFINE_string('mode', 'TRAIN', "'TRAIN' or 'TEST'")
-flags.DEFINE_integer('print_every', 100, "print losses to screen + log every X steps")
-flags.DEFINE_integer('save_every', 20000, "save model every X steps")
-flags.DEFINE_integer('sample_every', 5000, "sample heatmaps + landmark predictions every X steps")
-flags.DEFINE_integer('sample_grid', 9, 'number of training images in sample')
-flags.DEFINE_bool('sample_to_log', True, 'samples will be saved to tensorboard log')
-flags.DEFINE_integer('valid_size', 9, 'number of validation images to run')
-flags.DEFINE_integer('log_valid_every', 10, 'evaluate on valid set every X epochs')
-flags.DEFINE_integer('debug_data_size', 20, 'subset data size to test in debug mode')
-flags.DEFINE_bool('debug', False, 'run in debug mode - use subset of the data')
-
-# define paths
-flags.DEFINE_string('output_dir', output_dir, "directory for saving models, logs and samples")
-flags.DEFINE_string('save_model_path', 'model', "directory for saving the model")
-flags.DEFINE_string('save_sample_path', 'sample', "directory for saving the sampled images")
-flags.DEFINE_string('save_log_path', 'logs', "directory for saving the log file")
-flags.DEFINE_string('img_path', data_dir, "data directory")
-flags.DEFINE_string('test_model_path', 'model/deep_heatmaps-50000', "saved model to test")
-flags.DEFINE_string('test_data', 'full', 'test set to use: full/common/challenging/test/art')
-flags.DEFINE_string('valid_data', 'full', 'validation set to use: full/common/challenging/test/art')
-flags.DEFINE_string('train_crop_dir', 'crop_gt_margin_0.25', "directory of train images cropped to bb (+margin)")
-flags.DEFINE_string('img_dir_ns', 'crop_gt_margin_0.25_ns', "dir of train imgs cropped to bb + style transfer")
-flags.DEFINE_string('epoch_data_dir', 'epoch_data', "directory containing pre-augmented data for each epoch")
-flags.DEFINE_bool('use_epoch_data', False, "use pre-augmented data")
-
-# pretrain parameters (for fine-tuning / resume training)
-flags.DEFINE_string('pre_train_path', pre_train_path, 'pretrained model path')
-flags.DEFINE_bool('load_pretrain', False, "load pretrained weight?")
-
-# input data parameters
-flags.DEFINE_integer('image_size', 256, "image size")
-flags.DEFINE_integer('c_dim', 3, "color channels")
-flags.DEFINE_integer('num_landmarks', 68, "number of face landmarks")
-flags.DEFINE_float('sigma', 1.5, "std for heatmap generation gaussian")
-flags.DEFINE_integer('scale', 1, 'scale for image normalization 255/1/0')
-flags.DEFINE_float('margin', 0.25, 'margin for face crops - % of bb size')
-flags.DEFINE_string('bb_type', 'gt', "bb to use -  'gt':for ground truth / 'init':for face detector output")
-flags.DEFINE_bool('approx_maps', True, 'use heatmap approximation - major speed up')
-flags.DEFINE_float('win_mult', 3.33335, 'gaussian filter size for approx maps: 2 * sigma * win_mult + 1')
-
-# optimization parameters
-flags.DEFINE_integer('train_iter', 100000, 'maximum training iterations')
-flags.DEFINE_integer('batch_size', 10, "batch_size")
-flags.DEFINE_float('learning_rate', 1e-4, "initial learning rate")
-flags.DEFINE_bool('adam_optimizer', True, "use adam optimizer (if False momentum optimizer is used)")
-flags.DEFINE_float('momentum', 0.95, "optimizer momentum (if adam_optimizer==False)")
-flags.DEFINE_integer('step', 100000, 'step for lr decay')
-flags.DEFINE_float('gamma', 0.1, 'exponential base for lr decay')
-flags.DEFINE_float('reg', 1e-5, 'scalar multiplier for weight decay (0 to disable)')
-flags.DEFINE_string('weight_initializer', 'xavier', 'weight initializer: random_normal / xavier')
-flags.DEFINE_float('weight_initializer_std', 0.01, 'std for random_normal weight initializer')
-flags.DEFINE_float('bias_initializer', 0.0, 'constant value for bias initializer')
-
-# augmentation parameters
-flags.DEFINE_bool('augment_basic', True, "use basic augmentation?")
-flags.DEFINE_integer('basic_start', 0,  'min epoch to start basic augmentation')
-flags.DEFINE_bool('augment_texture', False, "use artistic texture augmentation?")
-flags.DEFINE_float('p_texture', 0., 'initial probability of artistic texture augmentation')
-flags.DEFINE_bool('augment_geom', False, "use artistic geometric augmentation?")
-flags.DEFINE_float('p_geom', 0., 'initial probability of artistic geometric augmentation')
-flags.DEFINE_integer('artistic_step', -1, 'step for increasing probability of artistic augmentation in epochs')
-flags.DEFINE_integer('artistic_start', 0, 'min epoch to start artistic augmentation')
-
-
-FLAGS = flags.FLAGS
-
-if not os.path.exists(FLAGS.output_dir):
-    os.mkdir(FLAGS.output_dir)
-
-
-def main(_):
-
-    save_model_path = os.path.join(FLAGS.output_dir, FLAGS.save_model_path)
-    save_sample_path = os.path.join(FLAGS.output_dir, FLAGS.save_sample_path)
-    save_log_path = os.path.join(FLAGS.output_dir, FLAGS.save_log_path)
-
-    # create directories if not exist
-    if not os.path.exists(save_model_path):
-        os.mkdir(save_model_path)
-    if not os.path.exists(save_log_path):
-        os.mkdir(save_log_path)
-    if not os.path.exists(save_sample_path) and (not FLAGS.sample_to_log or FLAGS.mode != 'TRAIN'):
-        os.mkdir(save_sample_path)
-
-    model = DeepHeatmapsModel(
-        mode=FLAGS.mode, train_iter=FLAGS.train_iter, batch_size=FLAGS.batch_size, learning_rate=FLAGS.learning_rate,
-        adam_optimizer=FLAGS.adam_optimizer, momentum=FLAGS.momentum, step=FLAGS.step, gamma=FLAGS.gamma, reg=FLAGS.reg,
-        weight_initializer=FLAGS.weight_initializer, weight_initializer_std=FLAGS.weight_initializer_std,
-        bias_initializer=FLAGS.bias_initializer, image_size=FLAGS.image_size, c_dim=FLAGS.c_dim,
-        num_landmarks=FLAGS.num_landmarks, sigma=FLAGS.sigma, scale=FLAGS.scale, margin=FLAGS.margin,
-        bb_type=FLAGS.bb_type, approx_maps=FLAGS.approx_maps, win_mult=FLAGS.win_mult, augment_basic=FLAGS.augment_basic,
-        basic_start=FLAGS.basic_start, augment_texture=FLAGS.augment_texture, p_texture=FLAGS.p_texture,
-        augment_geom=FLAGS.augment_geom, p_geom=FLAGS.p_geom, artistic_step=FLAGS.artistic_step,
-        artistic_start=FLAGS.artistic_start, output_dir=FLAGS.output_dir, save_model_path=save_model_path,
-        save_sample_path=save_sample_path, save_log_path=save_log_path, test_model_path=FLAGS.test_model_path,
-        pre_train_path=FLAGS.pre_train_path, load_pretrain=FLAGS.load_pretrain, img_path=FLAGS.img_path,
-        test_data=FLAGS.test_data, valid_data=FLAGS.valid_data, valid_size=FLAGS.valid_size,
-        log_valid_every=FLAGS.log_valid_every, train_crop_dir=FLAGS.train_crop_dir, img_dir_ns=FLAGS.img_dir_ns,
-        print_every=FLAGS.print_every, save_every=FLAGS.save_every, sample_every=FLAGS.sample_every,
-        sample_grid=FLAGS.sample_grid, sample_to_log=FLAGS.sample_to_log, debug_data_size=FLAGS.debug_data_size,
-        debug=FLAGS.debug, use_epoch_data=FLAGS.use_epoch_data, epoch_data_dir=FLAGS.epoch_data_dir)
-
-    if FLAGS.mode == 'TRAIN':
-        model.train()
-    else:
-        model.eval()
-
-if __name__ == '__main__':
-    tf.app.run()
diff --git a/spaces/merve/data-leak/public/third_party/mobilenet@1.0.0.js b/spaces/merve/data-leak/public/third_party/mobilenet@1.0.0.js
deleted file mode 100644
index d50ffe68663e1aabfc07faec02e8a3cb41b5dfe5..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/third_party/mobilenet@1.0.0.js
+++ /dev/null
@@ -1,2 +0,0 @@
-// @tensorflow/tfjs-models Copyright 2019 Google
-!function(e,a){"object"==typeof exports&&"undefined"!=typeof module?a(exports,require("@tensorflow/tfjs")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs"],a):a((e=e||self).mobilenet={},e.tf)}(this,function(e,a){"use strict";function r(e,a,r,o){return new(r||(r=Promise))(function(i,t){function n(e){try{l(o.next(e))}catch(e){t(e)}}function s(e){try{l(o.throw(e))}catch(e){t(e)}}function l(e){e.done?i(e.value):new r(function(a){a(e.value)}).then(n,s)}l((o=o.apply(e,a||[])).next())})}function o(e,a){var r,o,i,t,n={label:0,sent:function(){if(1&i[0])throw i[1];return i[1]},trys:[],ops:[]};return t={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(t[Symbol.iterator]=function(){return this}),t;function s(t){return function(s){return function(t){if(r)throw new TypeError("Generator is already executing.");for(;n;)try{if(r=1,o&&(i=2&t[0]?o.return:t[0]?o.throw||((i=o.return)&&i.call(o),0):o.next)&&!(i=i.call(o,t[1])).done)return i;switch(o=0,i&&(t=[2&t[0],i.value]),t[0]){case 0:case 1:i=t;break;case 4:return n.label++,{value:t[1],done:!1};case 5:n.label++,o=t[1],t=[0];continue;case 7:t=n.ops.pop(),n.trys.pop();continue;default:if(!(i=(i=n.trys).length>0&&i[i.length-1])&&(6===t[0]||2===t[0])){n=0;continue}if(3===t[0]&&(!i||t[1]>i[0]&&t[1]<i[3])){n.label=t[1];break}if(6===t[0]&&n.label<i[1]){n.label=i[1],i=t;break}if(i&&n.label<i[2]){n.label=i[2],n.ops.push(t);break}i[2]&&n.ops.pop(),n.trys.pop();continue}t=a.call(e,n)}catch(e){t=[6,e],o=0}finally{r=i=0}if(5&t[0])throw t[1];return{value:t[0]?t[1]:void 0,done:!0}}([t,s])}}}var i={0:"tench, Tinca tinca",1:"goldfish, Carassius auratus",2:"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",3:"tiger shark, Galeocerdo cuvieri",4:"hammerhead, hammerhead shark",5:"electric ray, crampfish, numbfish, torpedo",6:"stingray",7:"cock",8:"hen",9:"ostrich, Struthio camelus",10:"brambling, Fringilla montifringilla",11:"goldfinch, Carduelis carduelis",12:"house finch, linnet, Carpodacus mexicanus",13:"junco, snowbird",14:"indigo bunting, indigo finch, indigo bird, Passerina cyanea",15:"robin, American robin, Turdus migratorius",16:"bulbul",17:"jay",18:"magpie",19:"chickadee",20:"water ouzel, dipper",21:"kite",22:"bald eagle, American eagle, Haliaeetus leucocephalus",23:"vulture",24:"great grey owl, great gray owl, Strix nebulosa",25:"European fire salamander, Salamandra salamandra",26:"common newt, Triturus vulgaris",27:"eft",28:"spotted salamander, Ambystoma maculatum",29:"axolotl, mud puppy, Ambystoma mexicanum",30:"bullfrog, Rana catesbeiana",31:"tree frog, tree-frog",32:"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",33:"loggerhead, loggerhead turtle, Caretta caretta",34:"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",35:"mud turtle",36:"terrapin",37:"box turtle, box tortoise",38:"banded gecko",39:"common iguana, iguana, Iguana iguana",40:"American chameleon, anole, Anolis carolinensis",41:"whiptail, whiptail lizard",42:"agama",43:"frilled lizard, Chlamydosaurus kingi",44:"alligator lizard",45:"Gila monster, Heloderma suspectum",46:"green lizard, Lacerta viridis",47:"African chameleon, Chamaeleo chamaeleon",48:"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",49:"African crocodile, Nile crocodile, Crocodylus niloticus",50:"American alligator, Alligator mississipiensis",51:"triceratops",52:"thunder snake, worm snake, Carphophis amoenus",53:"ringneck snake, ring-necked snake, ring snake",54:"hognose snake, puff adder, sand viper",55:"green snake, grass snake",56:"king snake, kingsnake",57:"garter snake, grass snake",58:"water snake",59:"vine snake",60:"night snake, Hypsiglena torquata",61:"boa constrictor, Constrictor constrictor",62:"rock python, rock snake, Python sebae",63:"Indian cobra, Naja naja",64:"green mamba",65:"sea snake",66:"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",67:"diamondback, diamondback rattlesnake, Crotalus adamanteus",68:"sidewinder, horned rattlesnake, Crotalus cerastes",69:"trilobite",70:"harvestman, daddy longlegs, Phalangium opilio",71:"scorpion",72:"black and gold garden spider, Argiope aurantia",73:"barn spider, Araneus cavaticus",74:"garden spider, Aranea diademata",75:"black widow, Latrodectus mactans",76:"tarantula",77:"wolf spider, hunting spider",78:"tick",79:"centipede",80:"black grouse",81:"ptarmigan",82:"ruffed grouse, partridge, Bonasa umbellus",83:"prairie chicken, prairie grouse, prairie fowl",84:"peacock",85:"quail",86:"partridge",87:"African grey, African gray, Psittacus erithacus",88:"macaw",89:"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",90:"lorikeet",91:"coucal",92:"bee eater",93:"hornbill",94:"hummingbird",95:"jacamar",96:"toucan",97:"drake",98:"red-breasted merganser, Mergus serrator",99:"goose",100:"black swan, Cygnus atratus",101:"tusker",102:"echidna, spiny anteater, anteater",103:"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",104:"wallaby, brush kangaroo",105:"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",106:"wombat",107:"jelly fish",108:"sea anemone, anemone",109:"brain coral",110:"flatworm, platyhelminth",111:"nematode, nematode worm, roundworm",112:"conch",113:"snail",114:"slug",115:"sea slug, nudibranch",116:"chiton, coat-of-mail shell, sea cradle, polyplacophore",117:"chambered nautilus, pearly nautilus, nautilus",118:"Dungeness crab, Cancer magister",119:"rock crab, Cancer irroratus",120:"fiddler crab",121:"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",122:"American lobster, Northern lobster, Maine lobster, Homarus americanus",123:"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",124:"crayfish, crawfish, crawdad, crawdaddy",125:"hermit crab",126:"isopod",127:"white stork, Ciconia ciconia",128:"black stork, Ciconia nigra",129:"spoonbill",130:"flamingo",131:"little blue heron, Egretta caerulea",132:"American egret, great white heron, Egretta albus",133:"bittern",134:"crane",135:"limpkin, Aramus pictus",136:"European gallinule, Porphyrio porphyrio",137:"American coot, marsh hen, mud hen, water hen, Fulica americana",138:"bustard",139:"ruddy turnstone, Arenaria interpres",140:"red-backed sandpiper, dunlin, Erolia alpina",141:"redshank, Tringa totanus",142:"dowitcher",143:"oystercatcher, oyster catcher",144:"pelican",145:"king penguin, Aptenodytes patagonica",146:"albatross, mollymawk",147:"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",148:"killer whale, killer, orca, grampus, sea wolf, Orcinus orca",149:"dugong, Dugong dugon",150:"sea lion",151:"Chihuahua",152:"Japanese spaniel",153:"Maltese dog, Maltese terrier, Maltese",154:"Pekinese, Pekingese, Peke",155:"Shih-Tzu",156:"Blenheim spaniel",157:"papillon",158:"toy terrier",159:"Rhodesian ridgeback",160:"Afghan hound, Afghan",161:"basset, basset hound",162:"beagle",163:"bloodhound, sleuthhound",164:"bluetick",165:"black-and-tan coonhound",166:"Walker hound, Walker foxhound",167:"English foxhound",168:"redbone",169:"borzoi, Russian wolfhound",170:"Irish wolfhound",171:"Italian greyhound",172:"whippet",173:"Ibizan hound, Ibizan Podenco",174:"Norwegian elkhound, elkhound",175:"otterhound, otter hound",176:"Saluki, gazelle hound",177:"Scottish deerhound, deerhound",178:"Weimaraner",179:"Staffordshire bullterrier, Staffordshire bull terrier",180:"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",181:"Bedlington terrier",182:"Border terrier",183:"Kerry blue terrier",184:"Irish terrier",185:"Norfolk terrier",186:"Norwich terrier",187:"Yorkshire terrier",188:"wire-haired fox terrier",189:"Lakeland terrier",190:"Sealyham terrier, Sealyham",191:"Airedale, Airedale terrier",192:"cairn, cairn terrier",193:"Australian terrier",194:"Dandie Dinmont, Dandie Dinmont terrier",195:"Boston bull, Boston terrier",196:"miniature schnauzer",197:"giant schnauzer",198:"standard schnauzer",199:"Scotch terrier, Scottish terrier, Scottie",200:"Tibetan terrier, chrysanthemum dog",201:"silky terrier, Sydney silky",202:"soft-coated wheaten terrier",203:"West Highland white terrier",204:"Lhasa, Lhasa apso",205:"flat-coated retriever",206:"curly-coated retriever",207:"golden retriever",208:"Labrador retriever",209:"Chesapeake Bay retriever",210:"German short-haired pointer",211:"vizsla, Hungarian pointer",212:"English setter",213:"Irish setter, red setter",214:"Gordon setter",215:"Brittany spaniel",216:"clumber, clumber spaniel",217:"English springer, English springer spaniel",218:"Welsh springer spaniel",219:"cocker spaniel, English cocker spaniel, cocker",220:"Sussex spaniel",221:"Irish water spaniel",222:"kuvasz",223:"schipperke",224:"groenendael",225:"malinois",226:"briard",227:"kelpie",228:"komondor",229:"Old English sheepdog, bobtail",230:"Shetland sheepdog, Shetland sheep dog, Shetland",231:"collie",232:"Border collie",233:"Bouvier des Flandres, Bouviers des Flandres",234:"Rottweiler",235:"German shepherd, German shepherd dog, German police dog, alsatian",236:"Doberman, Doberman pinscher",237:"miniature pinscher",238:"Greater Swiss Mountain dog",239:"Bernese mountain dog",240:"Appenzeller",241:"EntleBucher",242:"boxer",243:"bull mastiff",244:"Tibetan mastiff",245:"French bulldog",246:"Great Dane",247:"Saint Bernard, St Bernard",248:"Eskimo dog, husky",249:"malamute, malemute, Alaskan malamute",250:"Siberian husky",251:"dalmatian, coach dog, carriage dog",252:"affenpinscher, monkey pinscher, monkey dog",253:"basenji",254:"pug, pug-dog",255:"Leonberg",256:"Newfoundland, Newfoundland dog",257:"Great Pyrenees",258:"Samoyed, Samoyede",259:"Pomeranian",260:"chow, chow chow",261:"keeshond",262:"Brabancon griffon",263:"Pembroke, Pembroke Welsh corgi",264:"Cardigan, Cardigan Welsh corgi",265:"toy poodle",266:"miniature poodle",267:"standard poodle",268:"Mexican hairless",269:"timber wolf, grey wolf, gray wolf, Canis lupus",270:"white wolf, Arctic wolf, Canis lupus tundrarum",271:"red wolf, maned wolf, Canis rufus, Canis niger",272:"coyote, prairie wolf, brush wolf, Canis latrans",273:"dingo, warrigal, warragal, Canis dingo",274:"dhole, Cuon alpinus",275:"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",276:"hyena, hyaena",277:"red fox, Vulpes vulpes",278:"kit fox, Vulpes macrotis",279:"Arctic fox, white fox, Alopex lagopus",280:"grey fox, gray fox, Urocyon cinereoargenteus",281:"tabby, tabby cat",282:"tiger cat",283:"Persian cat",284:"Siamese cat, Siamese",285:"Egyptian cat",286:"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",287:"lynx, catamount",288:"leopard, Panthera pardus",289:"snow leopard, ounce, Panthera uncia",290:"jaguar, panther, Panthera onca, Felis onca",291:"lion, king of beasts, Panthera leo",292:"tiger, Panthera tigris",293:"cheetah, chetah, Acinonyx jubatus",294:"brown bear, bruin, Ursus arctos",295:"American black bear, black bear, Ursus americanus, Euarctos americanus",296:"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",297:"sloth bear, Melursus ursinus, Ursus ursinus",298:"mongoose",299:"meerkat, mierkat",300:"tiger beetle",301:"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",302:"ground beetle, carabid beetle",303:"long-horned beetle, longicorn, longicorn beetle",304:"leaf beetle, chrysomelid",305:"dung beetle",306:"rhinoceros beetle",307:"weevil",308:"fly",309:"bee",310:"ant, emmet, pismire",311:"grasshopper, hopper",312:"cricket",313:"walking stick, walkingstick, stick insect",314:"cockroach, roach",315:"mantis, mantid",316:"cicada, cicala",317:"leafhopper",318:"lacewing, lacewing fly",319:"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",320:"damselfly",321:"admiral",322:"ringlet, ringlet butterfly",323:"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",324:"cabbage butterfly",325:"sulphur butterfly, sulfur butterfly",326:"lycaenid, lycaenid butterfly",327:"starfish, sea star",328:"sea urchin",329:"sea cucumber, holothurian",330:"wood rabbit, cottontail, cottontail rabbit",331:"hare",332:"Angora, Angora rabbit",333:"hamster",334:"porcupine, hedgehog",335:"fox squirrel, eastern fox squirrel, Sciurus niger",336:"marmot",337:"beaver",338:"guinea pig, Cavia cobaya",339:"sorrel",340:"zebra",341:"hog, pig, grunter, squealer, Sus scrofa",342:"wild boar, boar, Sus scrofa",343:"warthog",344:"hippopotamus, hippo, river horse, Hippopotamus amphibius",345:"ox",346:"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",347:"bison",348:"ram, tup",349:"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",350:"ibex, Capra ibex",351:"hartebeest",352:"impala, Aepyceros melampus",353:"gazelle",354:"Arabian camel, dromedary, Camelus dromedarius",355:"llama",356:"weasel",357:"mink",358:"polecat, fitch, foulmart, foumart, Mustela putorius",359:"black-footed ferret, ferret, Mustela nigripes",360:"otter",361:"skunk, polecat, wood pussy",362:"badger",363:"armadillo",364:"three-toed sloth, ai, Bradypus tridactylus",365:"orangutan, orang, orangutang, Pongo pygmaeus",366:"gorilla, Gorilla gorilla",367:"chimpanzee, chimp, Pan troglodytes",368:"gibbon, Hylobates lar",369:"siamang, Hylobates syndactylus, Symphalangus syndactylus",370:"guenon, guenon monkey",371:"patas, hussar monkey, Erythrocebus patas",372:"baboon",373:"macaque",374:"langur",375:"colobus, colobus monkey",376:"proboscis monkey, Nasalis larvatus",377:"marmoset",378:"capuchin, ringtail, Cebus capucinus",379:"howler monkey, howler",380:"titi, titi monkey",381:"spider monkey, Ateles geoffroyi",382:"squirrel monkey, Saimiri sciureus",383:"Madagascar cat, ring-tailed lemur, Lemur catta",384:"indri, indris, Indri indri, Indri brevicaudatus",385:"Indian elephant, Elephas maximus",386:"African elephant, Loxodonta africana",387:"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",388:"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",389:"barracouta, snoek",390:"eel",391:"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",392:"rock beauty, Holocanthus tricolor",393:"anemone fish",394:"sturgeon",395:"gar, garfish, garpike, billfish, Lepisosteus osseus",396:"lionfish",397:"puffer, pufferfish, blowfish, globefish",398:"abacus",399:"abaya",400:"academic gown, academic robe, judge's robe",401:"accordion, piano accordion, squeeze box",402:"acoustic guitar",403:"aircraft carrier, carrier, flattop, attack aircraft carrier",404:"airliner",405:"airship, dirigible",406:"altar",407:"ambulance",408:"amphibian, amphibious vehicle",409:"analog clock",410:"apiary, bee house",411:"apron",412:"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",413:"assault rifle, assault gun",414:"backpack, back pack, knapsack, packsack, rucksack, haversack",415:"bakery, bakeshop, bakehouse",416:"balance beam, beam",417:"balloon",418:"ballpoint, ballpoint pen, ballpen, Biro",419:"Band Aid",420:"banjo",421:"bannister, banister, balustrade, balusters, handrail",422:"barbell",423:"barber chair",424:"barbershop",425:"barn",426:"barometer",427:"barrel, cask",428:"barrow, garden cart, lawn cart, wheelbarrow",429:"baseball",430:"basketball",431:"bassinet",432:"bassoon",433:"bathing cap, swimming cap",434:"bath towel",435:"bathtub, bathing tub, bath, tub",436:"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",437:"beacon, lighthouse, beacon light, pharos",438:"beaker",439:"bearskin, busby, shako",440:"beer bottle",441:"beer glass",442:"bell cote, bell cot",443:"bib",444:"bicycle-built-for-two, tandem bicycle, tandem",445:"bikini, two-piece",446:"binder, ring-binder",447:"binoculars, field glasses, opera glasses",448:"birdhouse",449:"boathouse",450:"bobsled, bobsleigh, bob",451:"bolo tie, bolo, bola tie, bola",452:"bonnet, poke bonnet",453:"bookcase",454:"bookshop, bookstore, bookstall",455:"bottlecap",456:"bow",457:"bow tie, bow-tie, bowtie",458:"brass, memorial tablet, plaque",459:"brassiere, bra, bandeau",460:"breakwater, groin, groyne, mole, bulwark, seawall, jetty",461:"breastplate, aegis, egis",462:"broom",463:"bucket, pail",464:"buckle",465:"bulletproof vest",466:"bullet train, bullet",467:"butcher shop, meat market",468:"cab, hack, taxi, taxicab",469:"caldron, cauldron",470:"candle, taper, wax light",471:"cannon",472:"canoe",473:"can opener, tin opener",474:"cardigan",475:"car mirror",476:"carousel, carrousel, merry-go-round, roundabout, whirligig",477:"carpenter's kit, tool kit",478:"carton",479:"car wheel",480:"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",481:"cassette",482:"cassette player",483:"castle",484:"catamaran",485:"CD player",486:"cello, violoncello",487:"cellular telephone, cellular phone, cellphone, cell, mobile phone",488:"chain",489:"chainlink fence",490:"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",491:"chain saw, chainsaw",492:"chest",493:"chiffonier, commode",494:"chime, bell, gong",495:"china cabinet, china closet",496:"Christmas stocking",497:"church, church building",498:"cinema, movie theater, movie theatre, movie house, picture palace",499:"cleaver, meat cleaver, chopper",500:"cliff dwelling",501:"cloak",502:"clog, geta, patten, sabot",503:"cocktail shaker",504:"coffee mug",505:"coffeepot",506:"coil, spiral, volute, whorl, helix",507:"combination lock",508:"computer keyboard, keypad",509:"confectionery, confectionary, candy store",510:"container ship, containership, container vessel",511:"convertible",512:"corkscrew, bottle screw",513:"cornet, horn, trumpet, trump",514:"cowboy boot",515:"cowboy hat, ten-gallon hat",516:"cradle",517:"crane",518:"crash helmet",519:"crate",520:"crib, cot",521:"Crock Pot",522:"croquet ball",523:"crutch",524:"cuirass",525:"dam, dike, dyke",526:"desk",527:"desktop computer",528:"dial telephone, dial phone",529:"diaper, nappy, napkin",530:"digital clock",531:"digital watch",532:"dining table, board",533:"dishrag, dishcloth",534:"dishwasher, dish washer, dishwashing machine",535:"disk brake, disc brake",536:"dock, dockage, docking facility",537:"dogsled, dog sled, dog sleigh",538:"dome",539:"doormat, welcome mat",540:"drilling platform, offshore rig",541:"drum, membranophone, tympan",542:"drumstick",543:"dumbbell",544:"Dutch oven",545:"electric fan, blower",546:"electric guitar",547:"electric locomotive",548:"entertainment center",549:"envelope",550:"espresso maker",551:"face powder",552:"feather boa, boa",553:"file, file cabinet, filing cabinet",554:"fireboat",555:"fire engine, fire truck",556:"fire screen, fireguard",557:"flagpole, flagstaff",558:"flute, transverse flute",559:"folding chair",560:"football helmet",561:"forklift",562:"fountain",563:"fountain pen",564:"four-poster",565:"freight car",566:"French horn, horn",567:"frying pan, frypan, skillet",568:"fur coat",569:"garbage truck, dustcart",570:"gasmask, respirator, gas helmet",571:"gas pump, gasoline pump, petrol pump, island dispenser",572:"goblet",573:"go-kart",574:"golf ball",575:"golfcart, golf cart",576:"gondola",577:"gong, tam-tam",578:"gown",579:"grand piano, grand",580:"greenhouse, nursery, glasshouse",581:"grille, radiator grille",582:"grocery store, grocery, food market, market",583:"guillotine",584:"hair slide",585:"hair spray",586:"half track",587:"hammer",588:"hamper",589:"hand blower, blow dryer, blow drier, hair dryer, hair drier",590:"hand-held computer, hand-held microcomputer",591:"handkerchief, hankie, hanky, hankey",592:"hard disc, hard disk, fixed disk",593:"harmonica, mouth organ, harp, mouth harp",594:"harp",595:"harvester, reaper",596:"hatchet",597:"holster",598:"home theater, home theatre",599:"honeycomb",600:"hook, claw",601:"hoopskirt, crinoline",602:"horizontal bar, high bar",603:"horse cart, horse-cart",604:"hourglass",605:"iPod",606:"iron, smoothing iron",607:"jack-o'-lantern",608:"jean, blue jean, denim",609:"jeep, landrover",610:"jersey, T-shirt, tee shirt",611:"jigsaw puzzle",612:"jinrikisha, ricksha, rickshaw",613:"joystick",614:"kimono",615:"knee pad",616:"knot",617:"lab coat, laboratory coat",618:"ladle",619:"lampshade, lamp shade",620:"laptop, laptop computer",621:"lawn mower, mower",622:"lens cap, lens cover",623:"letter opener, paper knife, paperknife",624:"library",625:"lifeboat",626:"lighter, light, igniter, ignitor",627:"limousine, limo",628:"liner, ocean liner",629:"lipstick, lip rouge",630:"Loafer",631:"lotion",632:"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",633:"loupe, jeweler's loupe",634:"lumbermill, sawmill",635:"magnetic compass",636:"mailbag, postbag",637:"mailbox, letter box",638:"maillot",639:"maillot, tank suit",640:"manhole cover",641:"maraca",642:"marimba, xylophone",643:"mask",644:"matchstick",645:"maypole",646:"maze, labyrinth",647:"measuring cup",648:"medicine chest, medicine cabinet",649:"megalith, megalithic structure",650:"microphone, mike",651:"microwave, microwave oven",652:"military uniform",653:"milk can",654:"minibus",655:"miniskirt, mini",656:"minivan",657:"missile",658:"mitten",659:"mixing bowl",660:"mobile home, manufactured home",661:"Model T",662:"modem",663:"monastery",664:"monitor",665:"moped",666:"mortar",667:"mortarboard",668:"mosque",669:"mosquito net",670:"motor scooter, scooter",671:"mountain bike, all-terrain bike, off-roader",672:"mountain tent",673:"mouse, computer mouse",674:"mousetrap",675:"moving van",676:"muzzle",677:"nail",678:"neck brace",679:"necklace",680:"nipple",681:"notebook, notebook computer",682:"obelisk",683:"oboe, hautboy, hautbois",684:"ocarina, sweet potato",685:"odometer, hodometer, mileometer, milometer",686:"oil filter",687:"organ, pipe organ",688:"oscilloscope, scope, cathode-ray oscilloscope, CRO",689:"overskirt",690:"oxcart",691:"oxygen mask",692:"packet",693:"paddle, boat paddle",694:"paddlewheel, paddle wheel",695:"padlock",696:"paintbrush",697:"pajama, pyjama, pj's, jammies",698:"palace",699:"panpipe, pandean pipe, syrinx",700:"paper towel",701:"parachute, chute",702:"parallel bars, bars",703:"park bench",704:"parking meter",705:"passenger car, coach, carriage",706:"patio, terrace",707:"pay-phone, pay-station",708:"pedestal, plinth, footstall",709:"pencil box, pencil case",710:"pencil sharpener",711:"perfume, essence",712:"Petri dish",713:"photocopier",714:"pick, plectrum, plectron",715:"pickelhaube",716:"picket fence, paling",717:"pickup, pickup truck",718:"pier",719:"piggy bank, penny bank",720:"pill bottle",721:"pillow",722:"ping-pong ball",723:"pinwheel",724:"pirate, pirate ship",725:"pitcher, ewer",726:"plane, carpenter's plane, woodworking plane",727:"planetarium",728:"plastic bag",729:"plate rack",730:"plow, plough",731:"plunger, plumber's helper",732:"Polaroid camera, Polaroid Land camera",733:"pole",734:"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",735:"poncho",736:"pool table, billiard table, snooker table",737:"pop bottle, soda bottle",738:"pot, flowerpot",739:"potter's wheel",740:"power drill",741:"prayer rug, prayer mat",742:"printer",743:"prison, prison house",744:"projectile, missile",745:"projector",746:"puck, hockey puck",747:"punching bag, punch bag, punching ball, punchball",748:"purse",749:"quill, quill pen",750:"quilt, comforter, comfort, puff",751:"racer, race car, racing car",752:"racket, racquet",753:"radiator",754:"radio, wireless",755:"radio telescope, radio reflector",756:"rain barrel",757:"recreational vehicle, RV, R.V.",758:"reel",759:"reflex camera",760:"refrigerator, icebox",761:"remote control, remote",762:"restaurant, eating house, eating place, eatery",763:"revolver, six-gun, six-shooter",764:"rifle",765:"rocking chair, rocker",766:"rotisserie",767:"rubber eraser, rubber, pencil eraser",768:"rugby ball",769:"rule, ruler",770:"running shoe",771:"safe",772:"safety pin",773:"saltshaker, salt shaker",774:"sandal",775:"sarong",776:"sax, saxophone",777:"scabbard",778:"scale, weighing machine",779:"school bus",780:"schooner",781:"scoreboard",782:"screen, CRT screen",783:"screw",784:"screwdriver",785:"seat belt, seatbelt",786:"sewing machine",787:"shield, buckler",788:"shoe shop, shoe-shop, shoe store",789:"shoji",790:"shopping basket",791:"shopping cart",792:"shovel",793:"shower cap",794:"shower curtain",795:"ski",796:"ski mask",797:"sleeping bag",798:"slide rule, slipstick",799:"sliding door",800:"slot, one-armed bandit",801:"snorkel",802:"snowmobile",803:"snowplow, snowplough",804:"soap dispenser",805:"soccer ball",806:"sock",807:"solar dish, solar collector, solar furnace",808:"sombrero",809:"soup bowl",810:"space bar",811:"space heater",812:"space shuttle",813:"spatula",814:"speedboat",815:"spider web, spider's web",816:"spindle",817:"sports car, sport car",818:"spotlight, spot",819:"stage",820:"steam locomotive",821:"steel arch bridge",822:"steel drum",823:"stethoscope",824:"stole",825:"stone wall",826:"stopwatch, stop watch",827:"stove",828:"strainer",829:"streetcar, tram, tramcar, trolley, trolley car",830:"stretcher",831:"studio couch, day bed",832:"stupa, tope",833:"submarine, pigboat, sub, U-boat",834:"suit, suit of clothes",835:"sundial",836:"sunglass",837:"sunglasses, dark glasses, shades",838:"sunscreen, sunblock, sun blocker",839:"suspension bridge",840:"swab, swob, mop",841:"sweatshirt",842:"swimming trunks, bathing trunks",843:"swing",844:"switch, electric switch, electrical switch",845:"syringe",846:"table lamp",847:"tank, army tank, armored combat vehicle, armoured combat vehicle",848:"tape player",849:"teapot",850:"teddy, teddy bear",851:"television, television system",852:"tennis ball",853:"thatch, thatched roof",854:"theater curtain, theatre curtain",855:"thimble",856:"thresher, thrasher, threshing machine",857:"throne",858:"tile roof",859:"toaster",860:"tobacco shop, tobacconist shop, tobacconist",861:"toilet seat",862:"torch",863:"totem pole",864:"tow truck, tow car, wrecker",865:"toyshop",866:"tractor",867:"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",868:"tray",869:"trench coat",870:"tricycle, trike, velocipede",871:"trimaran",872:"tripod",873:"triumphal arch",874:"trolleybus, trolley coach, trackless trolley",875:"trombone",876:"tub, vat",877:"turnstile",878:"typewriter keyboard",879:"umbrella",880:"unicycle, monocycle",881:"upright, upright piano",882:"vacuum, vacuum cleaner",883:"vase",884:"vault",885:"velvet",886:"vending machine",887:"vestment",888:"viaduct",889:"violin, fiddle",890:"volleyball",891:"waffle iron",892:"wall clock",893:"wallet, billfold, notecase, pocketbook",894:"wardrobe, closet, press",895:"warplane, military plane",896:"washbasin, handbasin, washbowl, lavabo, wash-hand basin",897:"washer, automatic washer, washing machine",898:"water bottle",899:"water jug",900:"water tower",901:"whiskey jug",902:"whistle",903:"wig",904:"window screen",905:"window shade",906:"Windsor tie",907:"wine bottle",908:"wing",909:"wok",910:"wooden spoon",911:"wool, woolen, woollen",912:"worm fence, snake fence, snake-rail fence, Virginia fence",913:"wreck",914:"yawl",915:"yurt",916:"web site, website, internet site, site",917:"comic book",918:"crossword puzzle, crossword",919:"street sign",920:"traffic light, traffic signal, stoplight",921:"book jacket, dust cover, dust jacket, dust wrapper",922:"menu",923:"plate",924:"guacamole",925:"consomme",926:"hot pot, hotpot",927:"trifle",928:"ice cream, icecream",929:"ice lolly, lolly, lollipop, popsicle",930:"French loaf",931:"bagel, beigel",932:"pretzel",933:"cheeseburger",934:"hotdog, hot dog, red hot",935:"mashed potato",936:"head cabbage",937:"broccoli",938:"cauliflower",939:"zucchini, courgette",940:"spaghetti squash",941:"acorn squash",942:"butternut squash",943:"cucumber, cuke",944:"artichoke, globe artichoke",945:"bell pepper",946:"cardoon",947:"mushroom",948:"Granny Smith",949:"strawberry",950:"orange",951:"lemon",952:"fig",953:"pineapple, ananas",954:"banana",955:"jackfruit, jak, jack",956:"custard apple",957:"pomegranate",958:"hay",959:"carbonara",960:"chocolate sauce, chocolate syrup",961:"dough",962:"meat loaf, meatloaf",963:"pizza, pizza pie",964:"potpie",965:"burrito",966:"red wine",967:"espresso",968:"cup",969:"eggnog",970:"alp",971:"bubble",972:"cliff, drop, drop-off",973:"coral reef",974:"geyser",975:"lakeside, lakeshore",976:"promontory, headland, head, foreland",977:"sandbar, sand bar",978:"seashore, coast, seacoast, sea-coast",979:"valley, vale",980:"volcano",981:"ballplayer, baseball player",982:"groom, bridegroom",983:"scuba diver",984:"rapeseed",985:"daisy",986:"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",987:"corn",988:"acorn",989:"hip, rose hip, rosehip",990:"buckeye, horse chestnut, conker",991:"coral fungus",992:"agaric",993:"gyromitra",994:"stinkhorn, carrion fungus",995:"earthstar",996:"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",997:"bolete",998:"ear, spike, capitulum",999:"toilet tissue, toilet paper, bathroom tissue"},t={"1.00":"module_apply_default/MobilenetV1/Logits/global_pool","2.00":"module_apply_default/MobilenetV2/Logits/AvgPool"},n={"1.00":{.25:"https://tfhub.dev/google/imagenet/mobilenet_v1_025_224/classification/1","0.50":"https://tfhub.dev/google/imagenet/mobilenet_v1_050_224/classification/1",.75:"https://tfhub.dev/google/imagenet/mobilenet_v1_075_224/classification/1","1.00":"https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/1"},"2.00":{"0.50":"https://tfhub.dev/google/imagenet/mobilenet_v2_050_224/classification/2",.75:"https://tfhub.dev/google/imagenet/mobilenet_v2_075_224/classification/2","1.00":"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/2"}};var s=function(){function e(e,r){this.version=e,this.alpha=r,this.normalizationOffset=a.scalar(127.5)}return e.prototype.load=function(){return r(this,void 0,void 0,function(){var e,r,i,t=this;return o(this,function(o){switch(o.label){case 0:return e=n[this.version][this.alpha],r=this,[4,a.loadGraphModel(e,{fromTFHub:!0})];case 1:return r.model=o.sent(),[4,(i=a.tidy(function(){return t.model.predict(a.zeros([1,224,224,3]))})).data()];case 2:return o.sent(),i.dispose(),[2]}})})},e.prototype.infer=function(e,r){var o=this;return void 0===r&&(r=!1),a.tidy(function(){e instanceof a.Tensor||(e=a.browser.fromPixels(e));var i=e.toFloat().sub(o.normalizationOffset).div(o.normalizationOffset),n=i;if(224!==e.shape[0]||224!==e.shape[1]){n=a.image.resizeBilinear(i,[224,224],!0)}var s,l=n.reshape([-1,224,224,3]);if(r){var c=t[o.version];s=o.model.execute(l,c).squeeze([1,2])}else{s=o.model.predict(l).slice([0,1],[-1,1e3])}return s})},e.prototype.classify=function(e,a){return void 0===a&&(a=3),r(this,void 0,void 0,function(){var t,n;return o(this,function(s){switch(s.label){case 0:return[4,function(e,a){return r(this,void 0,void 0,function(){var r,t,n,s,l,c,u;return o(this,function(o){switch(o.label){case 0:return[4,(r=e.softmax()).data()];case 1:for(t=o.sent(),r.dispose(),n=[],u=0;u<t.length;u++)n.push({value:t[u],index:u});for(n.sort(function(e,a){return a.value-e.value}),s=new Float32Array(a),l=new Int32Array(a),u=0;u<a;u++)s[u]=n[u].value,l[u]=n[u].index;for(c=[],u=0;u<l.length;u++)c.push({className:i[l[u]],probability:s[u]});return[2,c]}})})}(t=this.infer(e),a)];case 1:return n=s.sent(),t.dispose(),[2,n]}})})},e}();e.load=function(e,i){return void 0===e&&(e=1),void 0===i&&(i=1),r(this,void 0,void 0,function(){var r,t,l;return o(this,function(o){switch(o.label){case 0:if(null==a)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(r=e.toFixed(2),t=i.toFixed(2),!(r in n))throw new Error("Invalid version of MobileNet. Valid versions are: "+Object.keys(n));if(!(t in n[r]))throw new Error("MobileNet constructed with invalid alpha "+i+". Valid multipliers for this version are: "+Object.keys(n[r])+".");return[4,(l=new s(r,t)).load()];case 1:return o.sent(),[2,l]}})})},e.MobileNet=s,Object.defineProperty(e,"__esModule",{value:!0})});
\ No newline at end of file
diff --git a/spaces/merve/dataset-worldviews/public/anonymization/make-sliders.js b/spaces/merve/dataset-worldviews/public/anonymization/make-sliders.js
deleted file mode 100644
index 72f6dfd7c96d6c74cfb35db5854f06b668bf3d46..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/anonymization/make-sliders.js
+++ /dev/null
@@ -1,139 +0,0 @@
-window.makeSliders = function(){
-  var rv = {
-    population: 144,
-    headsProb: .5,
-  }
-
-  rv.updateHeadsProb = (headsProb) => {
-    rv.headsProb = headsProb
-    updateSliderPos()
-
-
-    estimates.updateEstimates()
-    estimates.render()
-  }
-
-  rv.updatePopulation = (population) => {
-    rv.population = population
-    updateSliderPos()
-
-
-    var scale = d3.clamp(0, 13 / Math.sqrt(population), 1)
-    sel.studentGroup.st({
-      transformOrigin: 'top',
-      transformOrigin: c.width/2 + 'px ' + 160 + 'px',
-      transform: `scale(${scale})`
-    })
-
-    estimates.updateEstimates()
-    estimates.render()
-
-    sel.student.classed('inactive',(d, i) => i >= population)
-  }
-
-  rv.updatePopulationSlider = (val) => {
-    rv.updatePopulation(val)
-  }
-
-  rv.updateNoiseSlider = (val) => {
-    rv.updateHeadsProb(val)
-  }
-
-  var updateSliderPos = (function(){
-    var width = d3.clamp(50, window.innerWidth/2 - 40, 145)
-    var height = 30
-    var color = '#007276'
-
-    var sliderVals = {
-      population: {
-        key: 'population',
-        textFn: d => rv.population + ' students' ,
-        r: [144, 756],
-        v: 144,
-        stepFn: d => rv.updatePopulation(Math.round(d.v/2)*2),
-      },
-      headsProb: {
-        key: 'headsProb',
-        textFn: d => d3.format('.1%')(rv.headsProb) + ' chance of heads',
-        r: [.2, .5],
-        v: .5,
-        stepFn: d => rv.updateHeadsProb(d.v),
-      }
-    }
-    var sliders = [sliderVals.headsProb, sliderVals.population, sliderVals.headsProb]
-    sliders.forEach(d => {
-      d.s = d3.scaleLinear().domain(d.r).range([0, width])
-    })
-
-    var sliderSel = d3.selectAll('.slide-container-population,.slide-container-heads-prob').html('')
-      .data(sliders)
-      .classed('slider', true)
-      .st({
-        display: 'inline-block',
-        width: width,
-        paddingRight: (d, i) => i == 1 ? 40 : 0,
-        marginTop: 20,
-      })
-
-    var textSel = sliderSel.append('div.slider-label-container')
-      .st({marginBottom: -5})
-
-    var svgSel = sliderSel.append('svg').at({width, height})
-      .on('click', function(d){
-        d.v = d.s.invert(d3.mouse(this)[0])
-        d.stepFn(d)
-      })
-      .st({
-        cursor: 'pointer'
-      })
-      .append('g').translate(height/2, 1)
-    svgSel.append('rect').at({width, height, y: -height/2, fill: 'rgba(0,0,0,0)'})
-
-    svgSel.append('path').at({
-      d: `M 0 -.5 H ${width}`, 
-      stroke: color,
-      strokeWidth: 1
-    })
-
-    var leftPathSel = svgSel.append('path').at({
-      d: `M 0 -.5 H ${width}`, 
-      stroke: color,
-      strokeWidth: 3
-    })
-
-
-    var drag = d3.drag()
-      .on('drag', function(d){
-        var x = d3.mouse(this)[0]
-        d.v = d3.clamp(d3.min(d.r), d.s.invert(x), d3.max(d.r))
-        d.stepFn(d)
-      })
-
-    var rectSel = svgSel.append('rect')
-      .at({
-        width: height/2 - 1,
-        height: height/2 - 1,
-        stroke: color, 
-        strokeWidth: 3,
-        fill: '#fff',
-      })
-      .translate([-height/4, -height/4])
-      .call(drag)
-
-    return isDrag => {
-      rectSel.at({x: d => Math.round(d.s(rv[d.key]))})
-      textSel.text(d => d.textFn(d))
-
-      leftPathSel.at({d: d => `M 0 -.5 H ${d.s(rv[d.key])}`})
-    }
-  })()
-  updateSliderPos()
-
-
-  return rv
-}
-
-
-
-
-if (window.init) window.init()
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/source/private-and-fair/rotated-accuracy.js b/spaces/merve/fill-in-the-blank/source/private-and-fair/rotated-accuracy.js
deleted file mode 100644
index 26219db5eeedb299541f14e192a6105b017a78e2..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/private-and-fair/rotated-accuracy.js
+++ /dev/null
@@ -1,362 +0,0 @@
-!(async function(){
-  var isLock = false
-
-  var csvstr = await (await fetch('rotated-accuracy.csv')).text()
-  var allData = d3.csvParse(csvstr)
-    .filter(d => {
-      d.slug = [d.dataset_size, d.aVal, d.minority_percent].join(' ')
-
-      d.accuracy_orig = (+d.accuracy_test_data_1     + +d.accuracy_test_data_7)/2000
-      d.accuracy_rot  = (+d.accuracy_test_data_1_rot + +d.accuracy_test_data_7_rot)/2000
-      d.accuracy_dif = d.accuracy_orig - d.accuracy_rot
-
-      return d.accuracy_orig > 0 && d.accuracy_rot > 0
-    })
-
-  var data = d3.nestBy(allData, d => d.slug)
-  data.forEach(slug => {
-    slug.accuracy_orig = d3.median(slug, d => d.accuracy_orig)
-    slug.accuracy_rot = d3.median(slug, d => d.accuracy_rot)
-    slug.accuracy_dif = slug.accuracy_orig - slug.accuracy_rot
-
-    slug.dataset_size = +slug[0].dataset_size
-    slug.aVal = +slug[0].aVal
-    slug.minority_percent = +slug[0].minority_percent
-  })
-
-  // d3.nestBy(data, d => d.length).forEach(d => {
-  //   console.log(d.key, d.length)
-  // })
-
-  var byMetrics = 'dataset_size aVal minority_percent'
-    .split(' ')
-    .map(metricStr => {
-      var byMetric = d3.nestBy(data, d => d[metricStr])
-      byMetric.forEach(d => d.key = +d.key)
-      byMetric = _.sortBy(byMetric, d => d.key)
-      byMetric.forEach((d, i) => {
-        d.metricIndex = i
-        d.forEach(e => e['metric_' + metricStr] = d)
-      })
-
-      byMetric.forEach((d, i) => {
-        if (metricStr == 'dataset_size') d.label = i % 2 == 0 ? '' : d3.format(',')(d.key)
-        if (metricStr == 'aVal') d.label = ''
-        if (metricStr == 'minority_percent') d.label = i % 2 ? '' : d3.format('.0%')(d.key)
-      })
-
-      byMetric.active = byMetric[5]
-      byMetric.metricStr = metricStr
-      byMetric.label = {dataset_size: 'Training Points', aVal: 'Less Privacy', minority_percent: 'Percent Rotated In Training Data'}[metricStr]
-
-      return byMetric
-    })
-
-
-  // Heat map
-  !(function(){
-    var sel = d3.select('.rotated-accuracy-heatmap').html('')
-      .st({width: 1100, position: 'relative', left: (850 - 1100)/2})
-      .at({role: 'graphics-document', 'aria-label': `Faceted MNIST models by the percent of rotated digits in training data. Heatmaps show how privacy and training data change accuracy on rotated and original digits.`})
-
-    sel.append('div.chart-title').text('Percentage of training data rotated 90° →')
-
-    sel.appendMany('div', byMetrics[2])//.filter((d, i) => i % 2 == 0))
-      .st({display: 'inline-block'})
-      .each(drawHeatmap)
-  })()
-  function drawHeatmap(sizeData, chartIndex){
-
-    var s = 8
-    var n = 11
-
-    var c = d3.conventions({
-      sel: d3.select(this),
-      width: s*n,
-      height: s*n,
-      margin: {left: 5, right: 5, top: 30, bottom: 50},
-    })
-
-    c.svg.append('rect').at({width: c.width, height: c.height, fillOpacity: 0})
-
-    c.svg.append('text.chart-title')
-      .text(d3.format('.0%')(sizeData.key)).at({dy: -4, textAnchor: 'middle', x: c.width/2})
-      .st({fontWeight: 300})
-
-    var linearScale = d3.scaleLinear().domain([0, .5]).clamp(1)
-    var colorScale = d => d3.interpolatePlasma(linearScale(d))
-    
-    var pad = .5
-    var dataSel = c.svg
-      .on('mouseleave', () => isLock = false)
-      .append('g').translate([.5, .5])
-      .appendMany('g.accuracy-rect', sizeData)
-      .translate(d => [
-        s*d.metric_dataset_size.metricIndex, 
-        s*(n - d.metric_aVal.metricIndex)
-      ])
-      .call(d3.attachTooltip)
-      .on('mouseover', (d, i, node, isClickOverride) => {
-        updateTooltip(d)
-
-        if (isLock && !isClickOverride) return
-
-        byMetrics[0].setActiveCol(d.metric_dataset_size)
-        byMetrics[1].setActiveCol(d.metric_aVal)
-        byMetrics[2].setActiveCol(d.metric_minority_percent)
-
-        return d
-      })
-      .on('click', clickCb)
-      .st({cursor: 'pointer'})
-
-
-
-    dataSel.append('rect')
-      .at({
-        width: s - pad, 
-        height: s - pad,
-        fillOpacity: .1
-      })
-    
-    // dataSel.append('rect')      
-    //   .at({
-    //     width:  d => Math.max(1, (s - pad)*(d.accuracy_orig - .5)*2), 
-    //     height: d => Math.max(1, (s - pad)*(d.accuracy_rot - .5)*2),
-    //   })
-    sizeData.forEach(d => {
-      d.y_orig = Math.max(0, (s - pad)*(d.accuracy_orig - .5)*2)
-      d.y_rot  = Math.max(0, (s - pad)*(d.accuracy_rot - .5)*2)
-    })
-
-    dataSel.append('rect')
-      .at({
-        height: d => d.y_orig,
-        y: d => s - d.y_orig,
-        width: s/2,
-        x: s/2,
-        fill: 'purple',
-      })
-    dataSel.append('rect')
-      .at({
-        height: d => d.y_rot,
-        y: d => s - d.y_rot,
-        width: s/2,
-        fill: 'orange',
-      })
-
-    sizeData.updateActiveRect = function(match){
-      dataSel
-        .classed('active', d => match == d)
-        .filter(d => match == d)
-        .raise()
-    }
-
-    if (chartIndex == 0){
-      c.svg.append('g.x.axis').translate([10, c.height])
-      c.svg.append('g.y.axis').translate([0, 5])
-
-      util.addAxisLabel(c, 'Training Points →', 'Less Privacy →', 30, -15)
-    }
-
-    if (chartIndex == 8){
-      c.svg.appendMany('g.axis', ['Original Digit Accuracy', 'Rotated Digit Accuracy'])
-        .translate((d, i) => [c.width - 230*i - 230 -50, c.height + 30])
-        .append('text.axis-label').text(d => d)
-        .st({fontSize: 14})
-        .parent()
-        .appendMany('rect', (d, i) => d3.range(.2, 1.2, .2).map((v, j) => ({i, v, j})))
-        .at({
-          width: s/2,
-          y: d => s - d.v*s - s,
-          height: d => d.v*s,
-          fill: d => ['purple', 'orange'][d.i],
-          x: d => d.j*s*.75 - 35
-        })
-    }
-  }
-
-  // Metric barbell charts
-  !(function(){
-    var sel = d3.select('.rotated-accuracy').html('')
-      .at({role: 'graphics-document', 'aria-label': `Barbell charts showing up privacy / data / percent underrepresented data all trade-off in complex ways.`})
-
-    sel.appendMany('div', byMetrics)
-      .st({display: 'inline-block', width: 300, marginRight: 10, marginBottom: 50, marginTop: 10})
-      .each(drawMetricBarbell)
-  })()
-  function drawMetricBarbell(byMetric, byMetricIndex){
-    var sel = d3.select(this)
-
-    var c = d3.conventions({
-      sel,
-      height: 220,
-      width: 220,
-      margin: {bottom: 10, top: 5},
-      layers: 's',
-    })
-    c.svg.append('rect').at({width: c.width, height: c.height, fillOpacity: 0})
-
-    c.y.domain([.5, 1]).interpolate(d3.interpolateRound)
-    c.x.domain([0, byMetric.length - 1]).clamp(1).interpolate(d3.interpolateRound)
-
-    c.xAxis
-      .tickValues(d3.range(byMetric.length))
-      .tickFormat(i => byMetric[i].label)
-    c.yAxis.ticks(5).tickFormat(d => d3.format('.0%')(d))
-
-    d3.drawAxis(c)
-    util.addAxisLabel(c, byMetric.label + ' →', byMetricIndex ? '' : 'Accuracy')
-    util.ggPlotBg(c, false)
-
-    c.svg.select('.x').raise()
-    c.svg.selectAll('.axis').st({pointerEvents: 'none'})
-
-    c.svg.append('defs').append('linearGradient#purple-to-orange')
-      .at({x1: '0%', x2: '0%', y1: '0%', y2: '100%'})
-      .append('stop').at({offset: '0%', 'stop-color': 'purple'}).parent()
-      .append('stop').at({offset: '100%', 'stop-color': 'orange'})
-
-    c.svg.append('defs').append('linearGradient#orange-to-purple')
-      .at({x1: '0%', x2: '0%', y2: '0%', y1: '100%'})
-      .append('stop').at({offset: '0%', 'stop-color': 'purple'}).parent()
-      .append('stop').at({offset: '100%', 'stop-color': 'orange'})
-
-    var colSel = c.svg.appendMany('g', byMetric)
-      .translate(d => c.x(d.metricIndex) + .5, 0)
-      .st({pointerEvents: 'none'})
-
-    var pathSel = colSel.append('path')
-      .at({stroke: 'url(#purple-to-orange)', strokeWidth: 1})
-
-    var rectSel = colSel.append('rect')
-      .at({width: 1, x: -.5})
-
-    var origCircleSel = colSel.append('circle')
-      .at({r: 3, fill: 'purple', stroke: '#000', strokeWidth: .5})
-
-    var rotCircleSel = colSel.append('circle')
-      .at({r: 3, fill: 'orange', stroke: '#000', strokeWidth: .5})
-
-    function clampY(d){
-      return d3.clamp(0, c.y(d), c.height + 3)
-    }
-
-    byMetric.updateActiveCol = function(){
-      var findObj = {}
-      byMetrics
-        .filter(d => d != byMetric)
-        .forEach(d => {
-          findObj[d.metricStr] = d.active.key
-        })
-
-      byMetric.forEach(col => {
-        col.active = _.find(col, findObj)
-      })
-
-      origCircleSel.at({cy: d => clampY(d.active.accuracy_orig)})
-      rotCircleSel.at({cy: d => clampY(d.active.accuracy_rot)})
-
-      // pathSel.at({
-      //   d: d => 'M 0 ' + clampY(d.active.accuracy_orig) + ' L 1 ' + clampY(d.active.accuracy_rot)
-      // })
-
-      rectSel.at({
-        y: d => Math.min(clampY(d.active.accuracy_orig), clampY(d.active.accuracy_rot)),
-        height: d => Math.abs(clampY(d.active.accuracy_orig) - clampY(d.active.accuracy_rot)),
-        fill: d => d.active.accuracy_orig > d.active.accuracy_rot ? 'url(#purple-to-orange)' : 'url(#orange-to-purple)'
-      })
-    }
-    byMetric.updateActiveCol()
-
-
-    c.svg
-      .call(d3.attachTooltip)
-      .st({cursor: 'pointer'})
-      .on('mousemove', function(d, i, node, isClickOverride){
-        var [mx] = d3.mouse(this)
-        var metricIndex = Math.round(c.x.invert(mx))
-
-        var prevActive = byMetric.active
-        byMetric.active = byMetric[metricIndex]
-        updateTooltip()
-        byMetric.active = prevActive
-
-        if (isLock && !isClickOverride) return
-        byMetric.setActiveCol(byMetric[metricIndex])
-
-        return byMetric[metricIndex]
-      })
-      .on('click', clickCb)
-      .on('mouseexit', () => isLock = false)
-
-
-    byMetric.setActiveCol = function(col){
-      if (col) byMetric.active = col
-
-      c.svg.selectAll('.x .tick')
-        .classed('active', i => i == byMetric.active.metricIndex)
-
-      colSel.classed('active', d => d == byMetric.active)
-
-      if (col) renderActiveCol()
-    }
-    byMetric.setActiveCol()
-  }
-
-  function renderActiveCol(){
-    byMetrics.forEach(d => {
-      if (d.updateActiveCol) d.updateActiveCol()
-    })
-
-    var findObj = {}
-    byMetrics.forEach(d => findObj[d.metricStr] = d.active.key)
-    var match = _.find(data, findObj)
-
-    byMetrics[2].forEach(d => {
-      if (d.updateActiveRect) d.updateActiveRect(match)
-    })
-  }
-
-  function updateTooltip(d){
-    if (!d){
-      var findObj = {}
-      byMetrics.forEach(d => findObj[d.metricStr] = d.active.key)
-      d = _.find(data, findObj)
-    }
-
-    var epsilon = Math.round(d[0].epsilon*100)/100
-    ttSel.html(`
-      <div>
-        <b>${d3.format('.0%')(d.accuracy_orig)}</b> 
-        accuracy on 
-        <span style='padding: 2px; background: purple; color: #fff'>
-          original digits
-        </span>
-      <div>
-      <div>
-        <b>${d3.format('.0%')(d.accuracy_rot)}</b> 
-        accuracy on 
-        <span style='padding: 2px; background: orange; color: #000'>
-          rotated digits
-        </span>
-      <br>
-      <br>
-      <div>Training points: ${d3.format(',')(d.dataset_size)}</div>
-      <div>Privacy: ${epsilon} ε</div>
-      <div>Rotated in training data: ${d3.format('.0%')(d.minority_percent)} </div>
-
-    `).st({width: 230})
-
-    ttSel.classed('tooltip-footnote', 0)
-  }
-
-  function clickCb(d, i, node){
-    var mFn = d3.select(this).on('mouseover') || d3.select(this).on('mousemove')
-
-    var e = mFn.call(this, d, i, node, true)
-    isLock = e == isLock ? null : e
-  }
-
-
-})()
diff --git a/spaces/merve/hidden-bias/source/_posts/2019-10-01-anonymization.html b/spaces/merve/hidden-bias/source/_posts/2019-10-01-anonymization.html
deleted file mode 100644
index b2cedac240d8f4c3dfbbd820f4057a92074f090e..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/_posts/2019-10-01-anonymization.html
+++ /dev/null
@@ -1,188 +0,0 @@
-<!--
-@license
-Copyright 2020 Google. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
----
-template: post.html
-title: How randomized response can help collect sensitive information responsibly
-shorttitle: Collecting Sensitive Information
-summary: Giant datasets are revealing new patterns in <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070532/'>cancer</a>, <a href='https://opportunityinsights.org/national_trends/'>income inequality</a> and other important areas. However, the widespread availability of fast computers that can cross reference public data is making it harder to collect private information without inadvertently violating people's privacy. Modern randomization techniques can help preserve anonymity. 
-socialsummary: The availability of giant datasets and faster computers is making it harder to collect and study private information without inadvertently violating people's privacy.
-shareimg: https://pair.withgoogle.com/explorables/images/anonymization.png
-permalink: /anonymization/
-date: 2020-09-01
----
-<link rel="stylesheet" href="style.css">
-<link rel="stylesheet" href="style-graph-scroll.css">
-
-<div id='container' class='container-1'>
-<div id='graph'></div>
-<div id='sections'>
-<div>
-
-<h3>Anonymous Data</h3>
-
-<p>Let's pretend we're analysts at a small college, looking at anonymous survey data about plagiarism.
-
-<p>We've gotten responses from the entire student body, reporting if they've ever <span class='highlight purple'>plagiarized</span> or <span class='highlight grey'>not</span>. To encourage them to respond honestly, names were not collected. 
-<p>
-
-<p class='note'>The data here has been randomly generated</p>
-</div>
-
-
-<div>
-<p>On the survey students also report several bits of information about themselves, like their age...  
-</div>
-
-
-<div>
-<p>...and what state they're from. 
-
-<p>This additional information is critical to finding potential patterns in the data—why have so many first-years from New Hampshire plagiarized?  
-</div>
-
-
-<div>
-<h3>Revealed Information</h3>
-<p>But granular information comes with a cost. 
-
-<p>One student has a <span class='highlight box square orange'>unique</span> age/home state combination. By searching another student database for a 19-year old from Vermont we can identify one of the plagiarists from supposedly anonymous survey data.
-</div>
-
-
-<div>
-<p>Increasing granularity exacerbates the problem. If the students reported slightly more about their ages by including what season they were born in, we'd be able to <span class='highlight box square orange'>identify</span> about a sixth of them. 
-
-<p>This isn't just a hypothetical:  A <a href="https://cpg.doc.ic.ac.uk/individual-risk/">birthday / gender / zip code combination</a> uniquely identifies 83% of the people in the United States. 
-
-<p>With the spread of large datasets, it is increasingly difficult to release detailed information without inadvertently revealing someone's identity. A week of a person's location data could <a href='https://www.nytimes.com/interactive/2018/12/10/business/location-data-privacy-apps.html'>reveal</a> a home and work address—possibly enough to find a name using public records.
-</div>
-
-
-<div>
-<h3>Randomization</h3>
-<p>One solution is to randomize responses so each student has plausible deniability. This lets us buy privacy at the cost of some uncertainty in our estimation of plagiarism rates.
-
-<p><b>Step 1:</b> Each student flips a coin and looks at it without showing anyone.
-</div>
-
-
-<div>
-<p><b>Step 2:</b> Students who flip heads <span class='highlight purple-box box'>report plagiarism</span>, even if they haven't plagiarized. 
-
-<p>Students that flipped tails report the truth, secure with the knowledge that even if their response is linked back to their name, they can claim they flipped heads.
-</div>
-
-
-<div>
-<p>With a little bit of math, we can approximate the rate of plagiarism from these randomized responses. We'll skip the algebra, but doubling the reported non-plagiarism rate gives a good estimate of the actual non-plagiarism rate.    
-
-<p class='rand-text'></p>
-
-<div class='button-outer'>
-<div class='button-container flip-coins-once'>
-Flip coins
-</div>
-</div>
-
-</div>
-
-
-<div>  
-<h3>How far off can we be?</h3>
-
-<p>If we simulate this coin flipping lots of times, we can see the distribution of errors. 
-
-<p>The estimates are close most of the time, but errors can be quite large.  
-
-<div class='button-outer'>
-<div class='button-container flip-coins'>
-Flip coins 200 times
-</div>
-</div>
-
-</div>
-
-
-<div>    
-<p>Reducing the random noise (by reducing the number of students who flip heads) increases the accuracy of our estimate, but risks leaking information about students.  
-
-<p>If the coin is heavily weighted towards tails, identified students can't credibly claim they reported plagiarizing because they flipped heads.  
-
-<div class="slider-outer">
-<div class="slide-container-heads-prob"></div>
-<div class='pointer'><div></div></div>
-</div>
-
-</div>
-
-
-<div>    
-<p>One surprising way out of this accuracy-privacy tradeoff: carefully collect information from even more people. 
-
-<p>If we got students from other schools to fill out this survey, we could accurately measure plagiarism while protecting everyone's privacy. With enough students, we could even start comparing plagiarism across different age groups again—safely this time.     
- 
-<div class="slider-outer">
-<div class="slide-container-population"></div>
-&nbsp;
-<div class="slide-container-heads-prob"></div>
-</div>
-</div>
-
-
-
-</div>
-</div>
-
-<h3>Conclusion</h3>
-
-<p>Aggregate statistics about private information are valuable, but can be risky to collect. We want researchers to be able to study things like the connection between demographics and health outcomes without revealing our entire medical history to our neighbors. The coin flipping technique in this article, called <a href='https://en.wikipedia.org/wiki/Randomized_response'>randomized response</a>, makes it possible to safely study private information.  
-
-<p>You might wonder if coin flipping is the only way to do this. It's not—<a href='https://desfontain.es/privacy/differential-privacy-in-more-detail.html'>differential privacy</a> can add targeted bits of random noise to a dataset and guarantee privacy. More flexible than randomized response, the 2020 Census will use it to <a href='https://www.youtube.com/watch?v=pT19VwBAqKA'>protect respondents' privacy</a>. In addition to randomizing responses, differential privacy also limits the impact any one response can have on the released data.
-
-
-<h3>Credits</h3>
-
-<p>Adam Pearce and Ellen Jiang // September 2020
-
-<p>Thanks to Carey Radebaugh, Fernanda Viégas, Emily Reif, Hal Abelson, Jess Holbrook, Kristen Olson, Mahima Pushkarna, Martin Wattenberg, Michael Terry, Miguel Guevara, Rebecca Salois, Yannick Assogba, Zan Armstrong and our other colleagues at Google for their help with this piece.
-
-</div>
-
-
-<h3>More Explorables</h3>
-
-<p id='recirc'></p>
-
-<div id='end'></div>
-
-<script src='../third_party/seedrandom.min.js'></script>
-<script src='../third_party/d3_.js'></script>
-<script src='../third_party/swoopy-drag.js'></script>
-<script src='../third_party/misc.js'></script>
-<script src='annotations.js'></script>
-
-
-<script src='make-axii.js'></script>
-<script src='make-students.js'></script>
-<script src='make-sel.js'></script>
-<script src='make-estimates.js'></script>
-<script src='make-sliders.js'></script>
-<script src='make-slides.js'></script>
-<script src='make-gs.js'></script>
-<script src='init.js'></script>
-
-<script src='../third_party/recirc.js'></script>
diff --git a/spaces/mfkeles/Track-Anything/inpainter/model/modules/flow_comp.py b/spaces/mfkeles/Track-Anything/inpainter/model/modules/flow_comp.py
deleted file mode 100644
index d3abf2f72a6162e2b420c572c55081c557638c59..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/inpainter/model/modules/flow_comp.py
+++ /dev/null
@@ -1,450 +0,0 @@
-import numpy as np
-
-import torch.nn as nn
-import torch.nn.functional as F
-import torch
-
-from mmcv.cnn import ConvModule
-from mmengine.runner import load_checkpoint
-
-
-class FlowCompletionLoss(nn.Module):
-    """Flow completion loss"""
-    def __init__(self):
-        super().__init__()
-        self.fix_spynet = SPyNet()
-        for p in self.fix_spynet.parameters():
-            p.requires_grad = False
-
-        self.l1_criterion = nn.L1Loss()
-
-    def forward(self, pred_flows, gt_local_frames):
-        b, l_t, c, h, w = gt_local_frames.size()
-
-        with torch.no_grad():
-            # compute gt forward and backward flows
-            gt_local_frames = F.interpolate(gt_local_frames.view(-1, c, h, w),
-                                            scale_factor=1 / 4,
-                                            mode='bilinear',
-                                            align_corners=True,
-                                            recompute_scale_factor=True)
-            gt_local_frames = gt_local_frames.view(b, l_t, c, h // 4, w // 4)
-            gtlf_1 = gt_local_frames[:, :-1, :, :, :].reshape(
-                -1, c, h // 4, w // 4)
-            gtlf_2 = gt_local_frames[:, 1:, :, :, :].reshape(
-                -1, c, h // 4, w // 4)
-            gt_flows_forward = self.fix_spynet(gtlf_1, gtlf_2)
-            gt_flows_backward = self.fix_spynet(gtlf_2, gtlf_1)
-
-        # calculate loss for flow completion
-        forward_flow_loss = self.l1_criterion(
-            pred_flows[0].view(-1, 2, h // 4, w // 4), gt_flows_forward)
-        backward_flow_loss = self.l1_criterion(
-            pred_flows[1].view(-1, 2, h // 4, w // 4), gt_flows_backward)
-        flow_loss = forward_flow_loss + backward_flow_loss
-
-        return flow_loss
-
-
-class SPyNet(nn.Module):
-    """SPyNet network structure.
-    The difference to the SPyNet in [tof.py] is that
-        1. more SPyNetBasicModule is used in this version, and
-        2. no batch normalization is used in this version.
-    Paper:
-        Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017
-    Args:
-        pretrained (str): path for pre-trained SPyNet. Default: None.
-    """
-    def __init__(
-        self,
-        use_pretrain=True,
-        pretrained='https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth'
-    ):
-        super().__init__()
-
-        self.basic_module = nn.ModuleList(
-            [SPyNetBasicModule() for _ in range(6)])
-
-        if use_pretrain:
-            if isinstance(pretrained, str):
-                print("load pretrained SPyNet...")
-                load_checkpoint(self, pretrained, strict=True)
-            elif pretrained is not None:
-                raise TypeError('[pretrained] should be str or None, '
-                                f'but got {type(pretrained)}.')
-
-        self.register_buffer(
-            'mean',
-            torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
-        self.register_buffer(
-            'std',
-            torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
-
-    def compute_flow(self, ref, supp):
-        """Compute flow from ref to supp.
-        Note that in this function, the images are already resized to a
-        multiple of 32.
-        Args:
-            ref (Tensor): Reference image with shape of (n, 3, h, w).
-            supp (Tensor): Supporting image with shape of (n, 3, h, w).
-        Returns:
-            Tensor: Estimated optical flow: (n, 2, h, w).
-        """
-        n, _, h, w = ref.size()
-
-        # normalize the input images
-        ref = [(ref - self.mean) / self.std]
-        supp = [(supp - self.mean) / self.std]
-
-        # generate downsampled frames
-        for level in range(5):
-            ref.append(
-                F.avg_pool2d(input=ref[-1],
-                             kernel_size=2,
-                             stride=2,
-                             count_include_pad=False))
-            supp.append(
-                F.avg_pool2d(input=supp[-1],
-                             kernel_size=2,
-                             stride=2,
-                             count_include_pad=False))
-        ref = ref[::-1]
-        supp = supp[::-1]
-
-        # flow computation
-        flow = ref[0].new_zeros(n, 2, h // 32, w // 32)
-        for level in range(len(ref)):
-            if level == 0:
-                flow_up = flow
-            else:
-                flow_up = F.interpolate(input=flow,
-                                        scale_factor=2,
-                                        mode='bilinear',
-                                        align_corners=True) * 2.0
-
-            # add the residue to the upsampled flow
-            flow = flow_up + self.basic_module[level](torch.cat([
-                ref[level],
-                flow_warp(supp[level],
-                          flow_up.permute(0, 2, 3, 1).contiguous(),
-                          padding_mode='border'), flow_up
-            ], 1))
-
-        return flow
-
-    def forward(self, ref, supp):
-        """Forward function of SPyNet.
-        This function computes the optical flow from ref to supp.
-        Args:
-            ref (Tensor): Reference image with shape of (n, 3, h, w).
-            supp (Tensor): Supporting image with shape of (n, 3, h, w).
-        Returns:
-            Tensor: Estimated optical flow: (n, 2, h, w).
-        """
-
-        # upsize to a multiple of 32
-        h, w = ref.shape[2:4]
-        w_up = w if (w % 32) == 0 else 32 * (w // 32 + 1)
-        h_up = h if (h % 32) == 0 else 32 * (h // 32 + 1)
-        ref = F.interpolate(input=ref,
-                            size=(h_up, w_up),
-                            mode='bilinear',
-                            align_corners=False)
-        supp = F.interpolate(input=supp,
-                             size=(h_up, w_up),
-                             mode='bilinear',
-                             align_corners=False)
-
-        # compute flow, and resize back to the original resolution
-        flow = F.interpolate(input=self.compute_flow(ref, supp),
-                             size=(h, w),
-                             mode='bilinear',
-                             align_corners=False)
-
-        # adjust the flow values
-        flow[:, 0, :, :] *= float(w) / float(w_up)
-        flow[:, 1, :, :] *= float(h) / float(h_up)
-
-        return flow
-
-
-class SPyNetBasicModule(nn.Module):
-    """Basic Module for SPyNet.
-    Paper:
-        Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017
-    """
-    def __init__(self):
-        super().__init__()
-
-        self.basic_module = nn.Sequential(
-            ConvModule(in_channels=8,
-                       out_channels=32,
-                       kernel_size=7,
-                       stride=1,
-                       padding=3,
-                       norm_cfg=None,
-                       act_cfg=dict(type='ReLU')),
-            ConvModule(in_channels=32,
-                       out_channels=64,
-                       kernel_size=7,
-                       stride=1,
-                       padding=3,
-                       norm_cfg=None,
-                       act_cfg=dict(type='ReLU')),
-            ConvModule(in_channels=64,
-                       out_channels=32,
-                       kernel_size=7,
-                       stride=1,
-                       padding=3,
-                       norm_cfg=None,
-                       act_cfg=dict(type='ReLU')),
-            ConvModule(in_channels=32,
-                       out_channels=16,
-                       kernel_size=7,
-                       stride=1,
-                       padding=3,
-                       norm_cfg=None,
-                       act_cfg=dict(type='ReLU')),
-            ConvModule(in_channels=16,
-                       out_channels=2,
-                       kernel_size=7,
-                       stride=1,
-                       padding=3,
-                       norm_cfg=None,
-                       act_cfg=None))
-
-    def forward(self, tensor_input):
-        """
-        Args:
-            tensor_input (Tensor): Input tensor with shape (b, 8, h, w).
-                8 channels contain:
-                [reference image (3), neighbor image (3), initial flow (2)].
-        Returns:
-            Tensor: Refined flow with shape (b, 2, h, w)
-        """
-        return self.basic_module(tensor_input)
-
-
-# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
-def make_colorwheel():
-    """
-    Generates a color wheel for optical flow visualization as presented in:
-        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
-        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
-
-    Code follows the original C++ source code of Daniel Scharstein.
-    Code follows the the Matlab source code of Deqing Sun.
-
-    Returns:
-        np.ndarray: Color wheel
-    """
-
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-    colorwheel = np.zeros((ncols, 3))
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)
-    col = col + RY
-    # YG
-    colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)
-    colorwheel[col:col + YG, 1] = 255
-    col = col + YG
-    # GC
-    colorwheel[col:col + GC, 1] = 255
-    colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)
-    col = col + GC
-    # CB
-    colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)
-    colorwheel[col:col + CB, 2] = 255
-    col = col + CB
-    # BM
-    colorwheel[col:col + BM, 2] = 255
-    colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)
-    col = col + BM
-    # MR
-    colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)
-    colorwheel[col:col + MR, 0] = 255
-    return colorwheel
-
-
-def flow_uv_to_colors(u, v, convert_to_bgr=False):
-    """
-    Applies the flow color wheel to (possibly clipped) flow components u and v.
-
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-
-    Args:
-        u (np.ndarray): Input horizontal flow of shape [H,W]
-        v (np.ndarray): Input vertical flow of shape [H,W]
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
-    colorwheel = make_colorwheel()  # shape [55x3]
-    ncols = colorwheel.shape[0]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    a = np.arctan2(-v, -u) / np.pi
-    fk = (a + 1) / 2 * (ncols - 1)
-    k0 = np.floor(fk).astype(np.int32)
-    k1 = k0 + 1
-    k1[k1 == ncols] = 0
-    f = fk - k0
-    for i in range(colorwheel.shape[1]):
-        tmp = colorwheel[:, i]
-        col0 = tmp[k0] / 255.0
-        col1 = tmp[k1] / 255.0
-        col = (1 - f) * col0 + f * col1
-        idx = (rad <= 1)
-        col[idx] = 1 - rad[idx] * (1 - col[idx])
-        col[~idx] = col[~idx] * 0.75  # out of range
-        # Note the 2-i => BGR instead of RGB
-        ch_idx = 2 - i if convert_to_bgr else i
-        flow_image[:, :, ch_idx] = np.floor(255 * col)
-    return flow_image
-
-
-def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
-    """
-    Expects a two dimensional flow image of shape.
-
-    Args:
-        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
-        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
-    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
-    if clip_flow is not None:
-        flow_uv = np.clip(flow_uv, 0, clip_flow)
-    u = flow_uv[:, :, 0]
-    v = flow_uv[:, :, 1]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    rad_max = np.max(rad)
-    epsilon = 1e-5
-    u = u / (rad_max + epsilon)
-    v = v / (rad_max + epsilon)
-    return flow_uv_to_colors(u, v, convert_to_bgr)
-
-
-def flow_warp(x,
-              flow,
-              interpolation='bilinear',
-              padding_mode='zeros',
-              align_corners=True):
-    """Warp an image or a feature map with optical flow.
-    Args:
-        x (Tensor): Tensor with size (n, c, h, w).
-        flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is
-            a two-channel, denoting the width and height relative offsets.
-            Note that the values are not normalized to [-1, 1].
-        interpolation (str): Interpolation mode: 'nearest' or 'bilinear'.
-            Default: 'bilinear'.
-        padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'.
-            Default: 'zeros'.
-        align_corners (bool): Whether align corners. Default: True.
-    Returns:
-        Tensor: Warped image or feature map.
-    """
-    if x.size()[-2:] != flow.size()[1:3]:
-        raise ValueError(f'The spatial sizes of input ({x.size()[-2:]}) and '
-                         f'flow ({flow.size()[1:3]}) are not the same.')
-    _, _, h, w = x.size()
-    # create mesh grid
-    grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))
-    grid = torch.stack((grid_x, grid_y), 2).type_as(x)  # (w, h, 2)
-    grid.requires_grad = False
-
-    grid_flow = grid + flow
-    # scale grid_flow to [-1,1]
-    grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0
-    grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0
-    grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3)
-    output = F.grid_sample(x,
-                           grid_flow,
-                           mode=interpolation,
-                           padding_mode=padding_mode,
-                           align_corners=align_corners)
-    return output
-
-
-def initial_mask_flow(mask):
-    """
-    mask 1 indicates valid pixel 0 indicates unknown pixel
-    """
-    B, T, C, H, W = mask.shape
-
-    # calculate relative position
-    grid_y, grid_x = torch.meshgrid(torch.arange(0, H), torch.arange(0, W))
-
-    grid_y, grid_x = grid_y.type_as(mask), grid_x.type_as(mask)
-    abs_relative_pos_y = H - torch.abs(grid_y[None, :, :] - grid_y[:, None, :])
-    relative_pos_y = H - (grid_y[None, :, :] - grid_y[:, None, :])
-
-    abs_relative_pos_x = W - torch.abs(grid_x[:, None, :] - grid_x[:, :, None])
-    relative_pos_x = W - (grid_x[:, None, :] - grid_x[:, :, None])
-
-    # calculate the nearest indices
-    pos_up = mask.unsqueeze(3).repeat(
-        1, 1, 1, H, 1, 1).flip(4) * abs_relative_pos_y[None, None, None] * (
-            relative_pos_y <= H)[None, None, None]
-    nearest_indice_up = pos_up.max(dim=4)[1]
-
-    pos_down = mask.unsqueeze(3).repeat(1, 1, 1, H, 1, 1) * abs_relative_pos_y[
-        None, None, None] * (relative_pos_y <= H)[None, None, None]
-    nearest_indice_down = (pos_down).max(dim=4)[1]
-
-    pos_left = mask.unsqueeze(4).repeat(
-        1, 1, 1, 1, W, 1).flip(5) * abs_relative_pos_x[None, None, None] * (
-            relative_pos_x <= W)[None, None, None]
-    nearest_indice_left = (pos_left).max(dim=5)[1]
-
-    pos_right = mask.unsqueeze(4).repeat(
-        1, 1, 1, 1, W, 1) * abs_relative_pos_x[None, None, None] * (
-            relative_pos_x <= W)[None, None, None]
-    nearest_indice_right = (pos_right).max(dim=5)[1]
-
-    # NOTE: IMPORTANT !!! depending on how to use this offset
-    initial_offset_up = -(nearest_indice_up - grid_y[None, None, None]).flip(3)
-    initial_offset_down = nearest_indice_down - grid_y[None, None, None]
-
-    initial_offset_left = -(nearest_indice_left -
-                            grid_x[None, None, None]).flip(4)
-    initial_offset_right = nearest_indice_right - grid_x[None, None, None]
-
-    # nearest_indice_x = (mask.unsqueeze(1).repeat(1, img_width, 1) * relative_pos_x).max(dim=2)[1]
-    # initial_offset_x = nearest_indice_x - grid_x
-
-    # handle the boundary cases
-    final_offset_down = (initial_offset_down < 0) * initial_offset_up + (
-        initial_offset_down > 0) * initial_offset_down
-    final_offset_up = (initial_offset_up > 0) * initial_offset_down + (
-        initial_offset_up < 0) * initial_offset_up
-    final_offset_right = (initial_offset_right < 0) * initial_offset_left + (
-        initial_offset_right > 0) * initial_offset_right
-    final_offset_left = (initial_offset_left > 0) * initial_offset_right + (
-        initial_offset_left < 0) * initial_offset_left
-    zero_offset = torch.zeros_like(final_offset_down)
-    # out = torch.cat([final_offset_left, zero_offset, final_offset_right, zero_offset, zero_offset, final_offset_up, zero_offset, final_offset_down], dim=2)
-    out = torch.cat([
-        zero_offset, final_offset_left, zero_offset, final_offset_right,
-        final_offset_up, zero_offset, final_offset_down, zero_offset
-    ],
-                    dim=2)
-
-    return out
diff --git a/spaces/microsoft/unispeech-speaker-verification/app.py b/spaces/microsoft/unispeech-speaker-verification/app.py
deleted file mode 100644
index ee518d25efc0249fff1aec08e677c7225c642484..0000000000000000000000000000000000000000
--- a/spaces/microsoft/unispeech-speaker-verification/app.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import gradio as gr
-import torch
-from torchaudio.sox_effects import apply_effects_file
-from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-STYLE = """
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous">
-"""
-OUTPUT_OK = (
-    STYLE
-    + """
-    <div class="container">
-        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
-        <div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
-        <div class="row"><h1 style="text-align: center">similar</h1></div>
-        <div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div>
-        <div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
-    </div>
-"""
-)
-OUTPUT_FAIL = (
-    STYLE
-    + """
-    <div class="container">
-        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
-        <div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div>
-        <div class="row"><h1 style="text-align: center">similar</h1></div>
-        <div class="row"><h1 class="text-danger" style="text-align: center">You shall not pass!</h1></div>
-        <div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
-    </div>
-"""
-)
-
-EFFECTS = [
-    ["remix", "-"],
-    ["channels", "1"],
-    ["rate", "16000"],
-    ["gain", "-1.0"],
-    ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
-    ["trim", "0", "10"],
-]
-
-THRESHOLD = 0.85
-
-model_name = "microsoft/unispeech-sat-base-plus-sv"
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
-cosine_sim = torch.nn.CosineSimilarity(dim=-1)
-
-
-def similarity_fn(path1, path2):
-    if not (path1 and path2):
-        return '<b style="color:red">ERROR: Please record audio for *both* speakers!</b>'
-
-    wav1, _ = apply_effects_file(path1, EFFECTS)
-    wav2, _ = apply_effects_file(path2, EFFECTS)
-    print(wav1.shape, wav2.shape)
-
-    input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
-    input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
-
-    with torch.no_grad():
-        emb1 = model(input1).embeddings
-        emb2 = model(input2).embeddings
-    emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()
-    emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()
-    similarity = cosine_sim(emb1, emb2).numpy()[0]
-
-    if similarity >= THRESHOLD:
-        output = OUTPUT_OK.format(similarity * 100)
-    else:
-        output = OUTPUT_FAIL.format(similarity * 100)
-
-    return output
-
-
-inputs = [
-    gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
-    gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"),
-]
-output = gr.outputs.HTML(label="")
-
-
-description = (
-    "This demo will compare two speech samples and determine if they are from the same speaker. "
-    "Try it with your own voice!"
-)
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://huggingface.co/microsoft/unispeech-sat-large-sv' target='_blank'>🎙️ Learn more about UniSpeech-SAT</a> | "
-    "<a href='https://arxiv.org/abs/2110.05752' target='_blank'>📚 UniSpeech-SAT paper</a> | "
-    "<a href='https://www.danielpovey.com/files/2018_icassp_xvectors.pdf' target='_blank'>📚 X-Vector paper</a>"
-    "</p>"
-)
-examples = [
-    ["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"],
-    ["samples/cate_blanch.mp3", "samples/cate_blanch_3.mp3"],
-    ["samples/cate_blanch_2.mp3", "samples/cate_blanch_3.mp3"],
-    ["samples/heath_ledger.mp3", "samples/heath_ledger_2.mp3"],
-    ["samples/heath_ledger.mp3", "samples/heath_ledger_3.mp3"],
-    ["samples/heath_ledger_2.mp3", "samples/heath_ledger_3.mp3"],
-    ["samples/russel_crowe.mp3", "samples/russel_crowe_2.mp3"],
-    ["samples/cate_blanch.mp3", "samples/kirsten_dunst.wav"],
-    ["samples/russel_crowe.mp3", "samples/kirsten_dunst.wav"],
-    ["samples/russel_crowe_2.mp3", "samples/kirsten_dunst.wav"],
-    ["samples/leonardo_dicaprio.mp3", "samples/denzel_washington.mp3"],
-    ["samples/heath_ledger.mp3", "samples/denzel_washington.mp3"],
-    ["samples/heath_ledger_2.mp3", "samples/denzel_washington.mp3"],
-    ["samples/leonardo_dicaprio.mp3", "samples/russel_crowe.mp3"],
-    ["samples/leonardo_dicaprio.mp3", "samples/russel_crowe_2.mp3"],
-    ["samples/naomi_watts.mp3", "samples/denzel_washington.mp3"],
-    ["samples/naomi_watts.mp3", "samples/leonardo_dicaprio.mp3"],
-    ["samples/naomi_watts.mp3", "samples/cate_blanch_2.mp3"],
-    ["samples/naomi_watts.mp3", "samples/kirsten_dunst.wav"],
-]
-
-interface = gr.Interface(
-    fn=similarity_fn,
-    inputs=inputs,
-    outputs=output,
-    title="Voice Authentication with UniSpeech-SAT + X-Vectors",
-    description=description,
-    article=article,
-    layout="horizontal",
-    theme="huggingface",
-    allow_flagging=False,
-    live=False,
-    examples=examples,
-)
-interface.launch(enable_queue=True)
diff --git a/spaces/ml6team/logo-generator/dalle/models/tokenizer.py b/spaces/ml6team/logo-generator/dalle/models/tokenizer.py
deleted file mode 100644
index ffa3e7e3c5a5a6e22a9ac0fca1df7ccbb38c2846..0000000000000000000000000000000000000000
--- a/spaces/ml6team/logo-generator/dalle/models/tokenizer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Minimal DALL-E
-# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------
-
-import os
-from functools import partial
-from tokenizers import CharBPETokenizer
-
-
-def build_tokenizer(path: str,
-                    context_length: int = 64,
-                    *args,
-                    **kwargs):
-    from_file = partial(CharBPETokenizer.from_file,
-                        vocab_filename=os.path.join(path, 'bpe-16k-vocab.json'),
-                        merges_filename=os.path.join(path, 'bpe-16k-merges.txt'),
-                        unk_token='[UNK]')
-    tokenizer = from_file(*args, **kwargs)
-    tokenizer.add_special_tokens(['[PAD]'])
-    tokenizer.enable_padding(length=context_length,
-                             pad_id=tokenizer.token_to_id('[PAD]'))
-    tokenizer.enable_truncation(max_length=context_length)
-    print(f'{path} successfully restored..')
-    return tokenizer
diff --git a/spaces/mshukor/UnIVAL/run_scripts/averaging/fusing/scaling_best/unival_vqa_initavg_caprefsnlivqa.sh b/spaces/mshukor/UnIVAL/run_scripts/averaging/fusing/scaling_best/unival_vqa_initavg_caprefsnlivqa.sh
deleted file mode 100644
index 897cacf44f2d30817fdfcb4ad033d6d330ec2654..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/averaging/fusing/scaling_best/unival_vqa_initavg_caprefsnlivqa.sh
+++ /dev/null
@@ -1,233 +0,0 @@
-
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-num_workers=0
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-
-exp_name=unival_vqa_initavg_caprefsnlivqa
-
-
-image_dir=${base_data_dir}
-data_dir=${base_data_dir}/ofa/vqa_data
-# data=${data_dir}/vqa_train.tsv,${data_dir}/vqa_val.tsv
-# Note: If you have shuffled the data in advance, please uncomment the line below.
-data=${data_dir}/vqa_train_1.tsv,${data_dir}/vqa_train_2.tsv,${data_dir}/vqa_train_3.tsv,${data_dir}/vqa_train_4.tsv,${data_dir}/vqa_train_5.tsv,${data_dir}/vqa_train_6.tsv,${data_dir}/vqa_train_7.tsv,${data_dir}/vqa_train_8.tsv,${data_dir}/vqa_train_9.tsv,${data_dir}/vqa_train_10.tsv,${data_dir}/vqa_val.tsv
-ans2label_file=${base_data_dir}/ofa/vqa_data/trainval_ans2label.pkl
-
-
-selected_cols=0,5,2,3,4
-
-
-
-save_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs
-save_dir=${save_base_log_dir}/ofa/checkpoints/vqa/${exp_name}
-
-# save_dir=${base_log_dir}/ofa/checkpoints/vqa/${exp_name}
-log_dir=${save_dir}
-
-mkdir -p $log_dir $save_dir
-
-restore_file=/lus/scratch/NAT/gda2204/SHARED/logs/ofa/pretrained_models/average_models/avg_caprefsnlivqa.pt
-
-lr=1e-4
-
-
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-
-
-task=vqa_gen
-arch=unival_base
-
-
-criterion=adjust_label_smoothed_cross_entropy
-label_smoothing=0.1
-batch_size=16
-update_freq=1
-resnet_drop_path_rate=0.0
-encoder_drop_path_rate=0.1
-decoder_drop_path_rate=0.1
-dropout=0.1
-attention_dropout=0.0
-max_src_length=80
-max_object_length=30
-max_tgt_length=30
-num_bins=1000
-# patch_image_size=480
-
-uses_ema="--uses-ema"
-store_ema="--store-ema"
-ema_fp32="--ema-fp32"
-ema_decay=0.9999
-ema_start_update=0
-
-# Specify the inference type in validation after each fine-tuning epoch
-# As mentioned in the readme, you can choose from allcand or beamsearch evaluation, default to allcand
-val_inference_type=beamsearch
-
-# Specify whether to activate unconstrained VQA finetuning, which does not use a pre-defined candidate answer set
-# If --unconstrained-training is acitvated, --ans2label-file will **not be used even if it is specified**
-# Meanwhile, --val-inference-type must be set to **beamsearch**
-# By default, we follow the constrained finetuning as we mentioned in OFA paper, the candidate answer set shall be specified by --ans2label-file
-# For more details about this option, please refer to issue #123 and PR #124
-unconstrained_training_flag=""
-# unconstrained_training_flag="--unconstrained-training"
-
-
-
-
-
-save_interval_updates=0
-
-###
-image_encoder_name=timm_resnet #vit_base_patch16_224
-patch_image_size=480
-resnet_type=resnet101
-
-resnet_model_path=${base_log_dir}/pretrained_models/resnet101-5d3b4d8f.pth
-
-# video
-video_encoder_name=all_resnext101
-patch_frame_size=384
-video_model_path=${base_log_dir}/pretrained_models/3dcnn/resnext-101-kinetics.pth #${base_log_dir}/pretrained_models/TimeSformer_divST_8x32_224_K600.pyth
-num_frames=4
-
-
-sample_patch_num='--sample-patch-num=784' # ''
-
-eval_args='--eval-args={"beam":5,"unnormalized":true,"temperature":1.0,"stop_on_max_len":true}'
-
-validate_interval_updates=2000
-save_interval_updates=0
-
-
-for max_epoch in {20,}; do
-  echo "max_epoch "${max_epoch}
-  for warmup_ratio in {0.04,}; do
-    echo "warmup_updates "${warmup_updates}  
-    for lr in {$lr,}; do
-      echo "lr "${lr}
-      for patch_image_size in {$patch_image_size,}; do
-        echo "patch_image_size "${patch_image_size}
-
-        log_file=${log_dir}/${max_epoch}"_"${warmup_ratio}"_"${lr}"_"${patch_image_size}"_rank"${RANK}".log"
-        save_path=${save_dir}/${max_epoch}"_"${warmup_ratio}"_"${lr}"_"${patch_image_size}
-        mkdir -p $save_path
-
-        python3 -m torch.distributed.launch \
-            --nnodes=${NUM_NODES} \
-            --nproc_per_node=${GPUS_PER_NODE} \
-            --master_port=${MASTER_PORT} \
-            --node_rank=${RANK} \
-            --master_addr=${MASTER_ADDR} \
-            --use_env ${ofa_dir}/train.py \
-            ${data} \
-            --selected-cols=${selected_cols} \
-            --bpe-dir=${bpe_dir} \
-            --user-dir=${user_dir} \
-            --restore-file=${restore_file} \
-            --save-dir=${save_path} \
-            --task=${task} \
-            --arch=${arch} \
-            --criterion=${criterion} \
-            --label-smoothing=${label_smoothing} \
-            --batch-size=${batch_size} \
-            --update-freq=${update_freq} \
-            --encoder-normalize-before \
-            --decoder-normalize-before \
-            --share-decoder-input-output-embed \
-            --share-all-embeddings \
-            --layernorm-embedding \
-            --patch-layernorm-embedding \
-            --code-layernorm-embedding \
-            --resnet-drop-path-rate=${resnet_drop_path_rate} \
-            --encoder-drop-path-rate=${encoder_drop_path_rate} \
-            --decoder-drop-path-rate=${decoder_drop_path_rate} \
-            --dropout=${dropout} \
-            --attention-dropout=${attention_dropout} \
-            --weight-decay=0.01 \
-            --optimizer=adam \
-            --adam-betas="(0.9,0.999)" \
-            --adam-eps=1e-08 \
-            --clip-norm=1.0 \
-            --lr-scheduler=polynomial_decay \
-            --lr=${lr} \
-            --max-epoch=${max_epoch} \
-            --warmup-ratio=${warmup_ratio} \
-            --log-format=simple \
-            --log-interval=10 \
-            --fixed-validation-seed=7 \
-            --keep-best-checkpoints=1 \
-            --no-epoch-checkpoints \
-            --save-interval=1 --validate-interval=1 \
-            --save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \
-            --best-checkpoint-metric=vqa_score --maximize-best-checkpoint-metric \
-            --max-src-length=${max_src_length} \
-            --max-object-length=${max_object_length} \
-            --max-tgt-length=${max_tgt_length} \
-            --find-unused-parameters \
-            --freeze-encoder-embedding \
-            --freeze-decoder-embedding \
-            ${unconstrained_training_flag} \
-            --ans2label-file=${ans2label_file} \
-            --valid-batch-size=20 \
-            --add-type-embedding \
-            --scale-attn \
-            --scale-fc \
-            --scale-heads \
-            --disable-entangle \
-            --num-bins=${num_bins} \
-            --patch-image-size=${patch_image_size} \
-            --prompt-type=prev_output \
-            --fp16 \
-            --fp16-scale-window=512 \
-            ${uses_ema} \
-            ${store_ema} \
-            ${ema_fp32} \
-            --ema-decay=${ema_decay} \
-            --ema-start-update=${ema_start_update} \
-            --val-inference-type=${val_inference_type} \
-            --num-workers=0 \
-            --image-encoder-name=${image_encoder_name} \
-            --image-dir=${image_dir} \
-            --video-encoder-name=${video_encoder_name} \
-            --video-model-path=${video_model_path} \
-            --patch-frame-size=${patch_frame_size} \
-            ${sample_patch_num} \
-            ${eval_args} \
-            --no-epoch-checkpoints \
-            --resnet-type=${resnet_type} \
-            --resnet-model-path=${resnet_model_path} \
-            --reset-dataloader --reset-meters --reset-optimizer 
-
-      done
-    done
-  done
-done
diff --git a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/chat.py b/spaces/msmilauer/AutoGPT-duplicated2/autogpt/chat.py
deleted file mode 100644
index 1f6bca96eb216c667656b50f131006b83c681065..0000000000000000000000000000000000000000
--- a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/chat.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import time
-
-from openai.error import RateLimitError
-
-from autogpt import token_counter
-from autogpt.config import Config
-from autogpt.llm_utils import create_chat_completion
-from autogpt.logs import logger
-
-cfg = Config()
-
-
-def create_chat_message(role, content):
-    """
-    Create a chat message with the given role and content.
-
-    Args:
-    role (str): The role of the message sender, e.g., "system", "user", or "assistant".
-    content (str): The content of the message.
-
-    Returns:
-    dict: A dictionary containing the role and content of the message.
-    """
-    return {"role": role, "content": content}
-
-
-def generate_context(prompt, relevant_memory, full_message_history, model):
-    current_context = [
-        create_chat_message("system", prompt),
-        create_chat_message(
-            "system", f"The current time and date is {time.strftime('%c')}"
-        ),
-        create_chat_message(
-            "system",
-            f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
-        ),
-    ]
-
-    # Add messages from the full message history until we reach the token limit
-    next_message_to_add_index = len(full_message_history) - 1
-    insertion_index = len(current_context)
-    # Count the currently used tokens
-    current_tokens_used = token_counter.count_message_tokens(current_context, model)
-    return (
-        next_message_to_add_index,
-        current_tokens_used,
-        insertion_index,
-        current_context,
-    )
-
-
-# TODO: Change debug from hardcode to argument
-def chat_with_ai(
-    prompt, user_input, full_message_history, permanent_memory, token_limit
-):
-    """Interact with the OpenAI API, sending the prompt, user input, message history,
-    and permanent memory."""
-    while True:
-        try:
-            """
-            Interact with the OpenAI API, sending the prompt, user input,
-                message history, and permanent memory.
-
-            Args:
-                prompt (str): The prompt explaining the rules to the AI.
-                user_input (str): The input from the user.
-                full_message_history (list): The list of all messages sent between the
-                    user and the AI.
-                permanent_memory (Obj): The memory object containing the permanent
-                  memory.
-                token_limit (int): The maximum number of tokens allowed in the API call.
-
-            Returns:
-            str: The AI's response.
-            """
-            model = cfg.fast_llm_model  # TODO: Change model from hardcode to argument
-            # Reserve 1000 tokens for the response
-
-            logger.debug(f"Token limit: {token_limit}")
-            send_token_limit = token_limit - 1000
-
-            relevant_memory = (
-                ""
-                if len(full_message_history) == 0
-                else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
-            )
-
-            logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
-
-            (
-                next_message_to_add_index,
-                current_tokens_used,
-                insertion_index,
-                current_context,
-            ) = generate_context(prompt, relevant_memory, full_message_history, model)
-
-            while current_tokens_used > 2500:
-                # remove memories until we are under 2500 tokens
-                relevant_memory = relevant_memory[:-1]
-                (
-                    next_message_to_add_index,
-                    current_tokens_used,
-                    insertion_index,
-                    current_context,
-                ) = generate_context(
-                    prompt, relevant_memory, full_message_history, model
-                )
-
-            current_tokens_used += token_counter.count_message_tokens(
-                [create_chat_message("user", user_input)], model
-            )  # Account for user input (appended later)
-
-            while next_message_to_add_index >= 0:
-                # print (f"CURRENT TOKENS USED: {current_tokens_used}")
-                message_to_add = full_message_history[next_message_to_add_index]
-
-                tokens_to_add = token_counter.count_message_tokens(
-                    [message_to_add], model
-                )
-                if current_tokens_used + tokens_to_add > send_token_limit:
-                    break
-
-                # Add the most recent message to the start of the current context,
-                #  after the two system prompts.
-                current_context.insert(
-                    insertion_index, full_message_history[next_message_to_add_index]
-                )
-
-                # Count the currently used tokens
-                current_tokens_used += tokens_to_add
-
-                # Move to the next most recent message in the full message history
-                next_message_to_add_index -= 1
-
-            # Append user input, the length of this is accounted for above
-            current_context.extend([create_chat_message("user", user_input)])
-
-            # Calculate remaining tokens
-            tokens_remaining = token_limit - current_tokens_used
-            # assert tokens_remaining >= 0, "Tokens remaining is negative.
-            # This should never happen, please submit a bug report at
-            #  https://www.github.com/Torantulino/Auto-GPT"
-
-            # Debug print the current context
-            logger.debug(f"Token limit: {token_limit}")
-            logger.debug(f"Send Token Count: {current_tokens_used}")
-            logger.debug(f"Tokens remaining for response: {tokens_remaining}")
-            logger.debug("------------ CONTEXT SENT TO AI ---------------")
-            for message in current_context:
-                # Skip printing the prompt
-                if message["role"] == "system" and message["content"] == prompt:
-                    continue
-                logger.debug(f"{message['role'].capitalize()}: {message['content']}")
-                logger.debug("")
-            logger.debug("----------- END OF CONTEXT ----------------")
-
-            # TODO: use a model defined elsewhere, so that model can contain
-            # temperature and other settings we care about
-            assistant_reply = create_chat_completion(
-                model=model,
-                messages=current_context,
-                max_tokens=tokens_remaining,
-            )
-
-            # Update full message history
-            full_message_history.append(create_chat_message("user", user_input))
-            full_message_history.append(
-                create_chat_message("assistant", assistant_reply)
-            )
-
-            return assistant_reply
-        except RateLimitError:
-            # TODO: When we switch to langchain, this is built in
-            print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
-            time.sleep(10)
diff --git a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/commands/write_tests.py b/spaces/msmilauer/AutoGPT-duplicated2/autogpt/commands/write_tests.py
deleted file mode 100644
index 35a086536c9d05d520a84b15ead49f775eacdcc9..0000000000000000000000000000000000000000
--- a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/commands/write_tests.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""A module that contains a function to generate test cases for the submitted code."""
-from __future__ import annotations
-
-import json
-
-from autogpt.llm_utils import call_ai_function
-
-
-def write_tests(code: str, focus: list[str]) -> str:
-    """
-    A function that takes in code and focus topics and returns a response from create
-      chat completion api call.
-
-    Parameters:
-        focus (list): A list of suggestions around what needs to be improved.
-        code (str): Code for test cases to be generated against.
-    Returns:
-        A result string from create chat completion. Test cases for the submitted code
-          in response.
-    """
-
-    function_string = (
-        "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
-    )
-    args = [code, json.dumps(focus)]
-    description_string = (
-        "Generates test cases for the existing code, focusing on"
-        " specific areas if required."
-    )
-
-    return call_ai_function(function_string, args, description_string)
diff --git a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro.css b/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro.css
deleted file mode 100644
index 99e4fb71a71141a6609c67ef440cb239df8a734b..0000000000000000000000000000000000000000
--- a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro.css
+++ /dev/null
@@ -1,39 +0,0 @@
-@font-face {
-    font-family: 'Source Sans Pro';
-    src: url('./source-sans-pro-regular.eot');
-    src: url('./source-sans-pro-regular.eot?#iefix') format('embedded-opentype'),
-         url('./source-sans-pro-regular.woff') format('woff'),
-         url('./source-sans-pro-regular.ttf') format('truetype');
-    font-weight: normal;
-    font-style: normal;
-}
-
-@font-face {
-    font-family: 'Source Sans Pro';
-    src: url('./source-sans-pro-italic.eot');
-    src: url('./source-sans-pro-italic.eot?#iefix') format('embedded-opentype'),
-         url('./source-sans-pro-italic.woff') format('woff'),
-         url('./source-sans-pro-italic.ttf') format('truetype');
-    font-weight: normal;
-    font-style: italic;
-}
-
-@font-face {
-    font-family: 'Source Sans Pro';
-    src: url('./source-sans-pro-semibold.eot');
-    src: url('./source-sans-pro-semibold.eot?#iefix') format('embedded-opentype'),
-         url('./source-sans-pro-semibold.woff') format('woff'),
-         url('./source-sans-pro-semibold.ttf') format('truetype');
-    font-weight: 600;
-    font-style: normal;
-}
-
-@font-face {
-    font-family: 'Source Sans Pro';
-    src: url('./source-sans-pro-semibolditalic.eot');
-    src: url('./source-sans-pro-semibolditalic.eot?#iefix') format('embedded-opentype'),
-         url('./source-sans-pro-semibolditalic.woff') format('woff'),
-         url('./source-sans-pro-semibolditalic.ttf') format('truetype');
-    font-weight: 600;
-    font-style: italic;
-}
diff --git a/spaces/mygenaisagar/MyGenAIsagarBot/README.md b/spaces/mygenaisagar/MyGenAIsagarBot/README.md
deleted file mode 100644
index dc7b96f71583a422e955df03ae5aedc253493227..0000000000000000000000000000000000000000
--- a/spaces/mygenaisagar/MyGenAIsagarBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIsagarBot
-emoji: 💻
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nanglo123/GTSRB-Deployment/app.py b/spaces/nanglo123/GTSRB-Deployment/app.py
deleted file mode 100644
index 112a31043196aafa7eb48a3a59aaa3f5dfb3e42c..0000000000000000000000000000000000000000
--- a/spaces/nanglo123/GTSRB-Deployment/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-### 1. Imports and class names setup ### 
-import gradio as gr
-import os
-import torch
-
-
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-from create_model import create_CNN
-import torchvision
-from torchvision import transforms
-# Setup class names
-with open("class_names.txt", "r") as f: # reading them in from class_names.txt
-    class_names = [food_name.strip() for food_name in  f.readlines()]
-    
-### 2. Model and transforms preparation ###    
-
-# Create model
-model = create_CNN()
-
-# Load saved weights
-model.load_state_dict(
-    torch.load(
-        f="model_adam_epoch25.pth",
-        map_location=torch.device("cpu"),  # load to CPU because gpu not guaranteed
-    )
-)
-
-### 3. Predict function ###
-
-# Create predict function
-def predict(img) -> Tuple[Dict, float]:
-    """Transforms and performs a prediction on img and returns prediction and time taken.
-    """
-    # Start the timer
-    start_time = timer()
-    t = transforms.Compose([
-        transforms.Resize(size=(32,32)),
-        transforms.ToTensor()
-        ])
-
-    # Transform the target image and add a batch dimension
-    img = t(img).unsqueeze(0)
-    
-    # Put model into evaluation mode and turn on inference mode
-    model.eval()
-    with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.softmax(model(img), dim=1)
-    
-    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-    
-    # Calculate the prediction time
-    pred_time = round(timer() - start_time, 5)
-    
-    # Return the prediction dictionary and prediction time 
-    return pred_labels_and_probs, pred_time
-
-### 4. Gradio app ###
-
-# Create title, description and article strings
-title = "GTSRB - German Traffic Sign Recognition by Tenzin Nanglo"
-description = "CNN created for the GTSRB Dataset, achieved 98% test accuracy"
-article = "Created a 3 block CNN consisting of two convolutional blocks and one classifier block. The model was trained on 25 epochs and a batch size of 32 using an Adam Optimizer with a learning rate of .001. The design of the CNN architecture was inspired by other projects conducted on this dataset. There were no additional preprocessing procedures done on the data besides resizing them into 32x32 images."
-
-# Create examples list from "examples/" directory
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create Gradio interface 
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Label(num_top_classes=5, label="Predictions"),
-        gr.Number(label="Prediction time (s)"),
-    ],
-    examples=example_list,
-    title=title,
-    description=description,
-    article=article,
-)
-
-# Launch the app!
-demo.launch()
diff --git a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/datasets/simmim_modis_dataset.py b/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/datasets/simmim_modis_dataset.py
deleted file mode 100644
index ff69735ab802df3819efbc6d830e5e62374d14b2..0000000000000000000000000000000000000000
--- a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/datasets/simmim_modis_dataset.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from ..utils import SimmimMaskGenerator
-
-import os
-import numpy as np
-
-from torch.utils.data import Dataset
-
-
-class MODISDataset(Dataset):
-    """
-    MODIS MOD09GA pre-training dataset
-    """
-    IMAGE_PATH = os.path.join("images")
-
-    def __init__(
-        self,
-        config,
-        data_paths: list,
-        split: str,
-        img_size: tuple = (192, 192),
-        transform=None,
-    ):
-
-        self.config = config
-
-        self.img_size = img_size
-
-        self.transform = transform
-
-        self.split = split
-
-        self.data_paths = data_paths
-
-        self.img_list = []
-
-        for data_path in data_paths:
-
-            img_path = os.path.join(data_path, self.IMAGE_PATH)
-
-            self.img_list.extend(self.get_filenames(img_path))
-
-        n_items = len(self.img_list)
-
-        print(f'> Found {n_items} patches for this dataset ({split})')
-
-        if config.MODEL.TYPE in ['swin', 'swinv2']:
-
-            model_patch_size = config.MODEL.SWINV2.PATCH_SIZE
-
-        else:
-
-            raise NotImplementedError
-
-        self.mask_generator = SimmimMaskGenerator(
-            input_size=config.DATA.IMG_SIZE,
-            mask_patch_size=config.DATA.MASK_PATCH_SIZE,
-            model_patch_size=model_patch_size,
-            mask_ratio=config.DATA.MASK_RATIO,
-        )
-
-    def __len__(self):
-
-        return len(self.img_list)
-
-    def __getitem__(self, idx, transpose=True):
-
-        # load image
-        img = np.load(self.img_list[idx])
-
-        img = np.clip(img, 0, 1.0)
-
-        # perform transformations
-        img = self.transform(img)
-
-        mask = self.mask_generator()
-
-        return img, mask
-
-    def get_filenames(self, path):
-        """
-        Returns a list of absolute paths to images inside given `path`
-        """
-
-        files_list = []
-
-        for filename in sorted(os.listdir(path)):
-
-            files_list.append(os.path.join(path, filename))
-
-        return files_list
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Free Download Khatu Shyam Ji Ringtonel BETTER.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Free Download Khatu Shyam Ji Ringtonel BETTER.md
deleted file mode 100644
index 6cb06e4d91898e1973d00b26c262d66a766a020e..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Free Download Khatu Shyam Ji Ringtonel BETTER.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<h1>Free Download Khatu Shyam Ji Ringtone: How to Get the Divine Sound of Baba Shyam on Your Phone</h1>
-
-<p>Khatu Shyam Ji is a popular Hindu deity who is worshipped as the incarnation of Barbarika, the grandson of Bhima and son of Ghatotkacha. He is also known as Sheesh Ke Daani, Morvinandan, Lakhdatar, Haare Ka Sahara, and Shyam Baba. He is revered for his supreme sacrifice in the Mahabharata war, where he offered his head to Lord Krishna before the battle began. He was granted a boon by Krishna that he would be able to witness the war from his head and that he would be worshipped in Kaliyuga as Shyam.</p>
-
-<p>Khatu Shyam Ji's temple is located in the village of Khatu in Sikar district of Rajasthan. It is one of the most visited pilgrim sites in the state and attracts devotees from all over India and abroad. The temple is believed to house the miraculous head of Barbarika that was rediscovered by a cowherd in the 17th century. The temple is also known for its beautiful architecture, paintings, and festivals.</p>
-<h2>Free Download Khatu Shyam Ji Ringtonel</h2><br /><p><b><b>Download</b> &#10001; <a href="https://urlcod.com/2uIcfA">https://urlcod.com/2uIcfA</a></b></p><br /><br />
-
-<p>Many devotees of Khatu Shyam Ji wish to have his divine sound on their phones as a ringtone. This way, they can feel his presence and blessings every time their phone rings. There are many websites and apps that offer free download of Khatu Shyam Ji ringtone in various formats and languages. Some of them are:</p>
-
-<ul>
-<li><a href="https://www.khatu.co.in/ringtone.php">Khatu.co.in</a>: This is the official website of Khatu Shyam Ji temple and it offers free download of various ringtones related to Baba Shyam. You can choose from Hindi, Rajasthani, Gujarati, Punjabi, and English ringtones. You can also listen to the ringtones online before downloading them.</li>
-<li><a href="https://www.bhajanradio.com/khatu-shyam-ji-bhajan-ringtone/">Bhajanradio.com</a>: This website provides free download of Khatu Shyam Ji bhajan ringtones in mp3 format. You can find ringtones of famous singers like Jaya Kishori Ji, Sanjay Mittal, Nandu Ji, Mukesh Bagda, and others. You can also listen to the bhajans online before downloading them.</li>
-<li><a href="https://www.zedge.net/find/ringtones/khatu%20shyam%20ji">Zedge.net</a>: This is a popular website for downloading ringtones, wallpapers, and other mobile content. You can search for Khatu Shyam Ji ringtones and download them for free. You can also preview the ringtones online before downloading them.</li>
-</ul>
-
-<p>These are some of the ways to get free download of Khatu Shyam Ji ringtone on your phone. You can also create your own ringtone by recording or editing your favorite bhajan or mantra of Baba Shyam. You can use apps like Ringtone Maker, MP3 Cutter, or Audacity to do so. You can then set the ringtone as your default or assign it to specific contacts.</p>
-
-<p>Khatu Shyam Ji ringtone is a great way to express your devotion and faith in Baba Shyam. It can also bring you peace, happiness, and prosperity in your life. So, download your favorite ringtone today and enjoy the divine sound of Khatu Shyam Ji on your phone.</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nosdigitalmedia/dutch-youth-comment-classifier/src/gibberish_detection/GibberishDetector.py b/spaces/nosdigitalmedia/dutch-youth-comment-classifier/src/gibberish_detection/GibberishDetector.py
deleted file mode 100644
index 4a969916b86a42c138e84992c0b89bcf8f92e914..0000000000000000000000000000000000000000
--- a/spaces/nosdigitalmedia/dutch-youth-comment-classifier/src/gibberish_detection/GibberishDetector.py
+++ /dev/null
@@ -1,25 +0,0 @@
-class GibberishDetector:
-    """
-        Detects whether the comment is actual text or a random sequence of characters.
-        The model has been trained to learn character sequence probabilities from a set of approximately 4 million
-        approved comments.
-    """
-
-    model = None
-    skip_sequences = ['duh', 'ah']  # tokens that are allowed even tough the model might see these as gibberish
-
-    def __init__(self, model):
-        self.model = model
-
-    def predict(self, text):
-        return not self.contains_gibberish(text)
-
-    def contains_gibberish(self, text):
-
-        # Skip over tokens that often are misjudged by the model
-        for skip in self.skip_sequences:
-            if skip in text:
-                return False
-
-        # Pass text through model
-        return self.model.is_gibberish(text)
diff --git a/spaces/oguzakif/video-object-remover/SiamMask/make.sh b/spaces/oguzakif/video-object-remover/SiamMask/make.sh
deleted file mode 100644
index 4183c7e9a0f7c1cf645925b9a401c4d96f6a1fd4..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/SiamMask/make.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/usr/bin/env bash
-
-cd utils/pyvotkit
-python setup.py build_ext --inplace
-cd ../../
-
-cd utils/pysot/utils/
-python setup.py build_ext --inplace
-cd ../../../
diff --git a/spaces/oliver2023/chatgpt-on-wechat/common/const.py b/spaces/oliver2023/chatgpt-on-wechat/common/const.py
deleted file mode 100644
index 8336da39bd738df2702d1342a050eeda758ea07c..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/common/const.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# bot_type
-OPEN_AI = "openAI"
-CHATGPT = "chatGPT"
-BAIDU = "baidu"
-CHATGPTONAZURE = "chatGPTOnAzure"
\ No newline at end of file
diff --git a/spaces/osanchik/PicFinder/setup.py b/spaces/osanchik/PicFinder/setup.py
deleted file mode 100644
index 8636cdceddaf3a981f84ff8c12ae937f76f5b923..0000000000000000000000000000000000000000
--- a/spaces/osanchik/PicFinder/setup.py
+++ /dev/null
@@ -1,27 +0,0 @@
-
-import os
-import requests, zipfile, io
-
-IMAGE_DIR = 'flickr30k_images/flickr30k_images'
-
-def downlad_images() :
-
-    zip_file = 'data/flickr.zip'
-
-    #TODO : zip_file_url?
-    zip_file_url = 'https://drive.google.com/open?id=14QhofCbby053kWbVeWEBHCxOROQS-bjN&authuser=0'
-
-    try :
-        if not os.path.exists(IMAGE_DIR) :
-            if not os.path.exists(zip_file) :
-                r = requests.get(zip_file_url)
-                z = zipfile.ZipFile(io.BytesIO(r.content))
-                z.extractall(".")
-
-            else :
-                with zipfile.ZipFile(zip_file, 'r') as zip_ref:
-                    zip_ref.extractall(".")
-    except :
-        print("Problems with image file download")
-
-    return
\ No newline at end of file
diff --git a/spaces/owaiskha9654/Custom_Yolov7/models/export.py b/spaces/owaiskha9654/Custom_Yolov7/models/export.py
deleted file mode 100644
index dc12559416b4510311f289dc1f93a11473bbd8f7..0000000000000000000000000000000000000000
--- a/spaces/owaiskha9654/Custom_Yolov7/models/export.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import argparse
-import sys
-import time
-
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-
-import torch
-import torch.nn as nn
-
-import models
-from models.experimental import attempt_load
-from utils.activations import Hardswish, SiLU
-from utils.general import set_logging, check_img_size
-from utils.torch_utils import select_device
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolor-csp-c.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes')
-    parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
-    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    opt = parser.parse_args()
-    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
-    print(opt)
-    set_logging()
-    t = time.time()
-
-    # Load PyTorch model
-    device = select_device(opt.device)
-    model = attempt_load(opt.weights, map_location=device)  # load FP32 model
-    labels = model.names
-
-    # Checks
-    gs = int(max(model.stride))  # grid size (max stride)
-    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
-
-    # Input
-    img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device)  # image size(1,3,320,192) iDetection
-
-    # Update model
-    for k, m in model.named_modules():
-        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-        if isinstance(m, models.common.Conv):  # assign export-friendly activations
-            if isinstance(m.act, nn.Hardswish):
-                m.act = Hardswish()
-            elif isinstance(m.act, nn.SiLU):
-                m.act = SiLU()
-        # elif isinstance(m, models.yolo.Detect):
-        #     m.forward = m.forward_export  # assign forward (optional)
-    model.model[-1].export = not opt.grid  # set Detect() layer grid export
-    y = model(img)  # dry run
-
-    # TorchScript export
-    try:
-        print('\nStarting TorchScript export with torch %s...' % torch.__version__)
-        f = opt.weights.replace('.pt', '.torchscript.pt')  # filename
-        ts = torch.jit.trace(model, img, strict=False)
-        ts.save(f)
-        print('TorchScript export success, saved as %s' % f)
-    except Exception as e:
-        print('TorchScript export failure: %s' % e)
-
-    # ONNX export
-    try:
-        import onnx
-
-        print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
-        f = opt.weights.replace('.pt', '.onnx')  # filename
-        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
-                          output_names=['classes', 'boxes'] if y is None else ['output'],
-                          dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # size(1,3,640,640)
-                                        'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
-
-        # Checks
-        onnx_model = onnx.load(f)  # load onnx model
-        onnx.checker.check_model(onnx_model)  # check onnx model
-        # print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
-        print('ONNX export success, saved as %s' % f)
-    except Exception as e:
-        print('ONNX export failure: %s' % e)
-
-    # CoreML export
-    try:
-        import coremltools as ct
-
-        print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
-        # convert model from torchscript and apply pixel scaling as per detect.py
-        model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
-        f = opt.weights.replace('.pt', '.mlmodel')  # filename
-        model.save(f)
-        print('CoreML export success, saved as %s' % f)
-    except Exception as e:
-        print('CoreML export failure: %s' % e)
-
-    # Finish
-    print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/using-diffusers/pipeline_overview.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/using-diffusers/pipeline_overview.md
deleted file mode 100644
index da39e738325fcf074a66215f1ecc27c8972ba8f5..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/using-diffusers/pipeline_overview.md
+++ /dev/null
@@ -1,17 +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.
--->
-
-# Overview
-
-파이프라인은 독립적으로 훈련된 모델과 스케줄러를 함께 모아서 추론을 위해 diffusion 시스템을 빠르고 쉽게 사용할 수 있는 방법을 제공하는 end-to-end 클래스입니다. 모델과 스케줄러의 특정 조합은 특수한 기능과 함께 [`StableDiffusionPipeline`] 또는 [`StableDiffusionControlNetPipeline`]과 같은 특정 파이프라인 유형을 정의합니다. 모든 파이프라인 유형은 기본 [`DiffusionPipeline`] 클래스에서 상속됩니다. 어느 체크포인트를 전달하면, 파이프라인 유형을 자동으로 감지하고 필요한 구성 요소들을 불러옵니다.
-
-이 섹션에서는 unconditional 이미지 생성, text-to-image 생성의 다양한 테크닉과 변화를 파이프라인에서 지원하는 작업들을 소개합니다. 프롬프트에 있는 특정 단어가 출력에 영향을 미치는 것을 조정하기 위해 재현성을 위한 시드 설정과 프롬프트에 가중치를 부여하는 것으로 생성 프로세스를 더 잘 제어하는 방법에 대해 배울 수 있습니다. 마지막으로 음성에서부터 이미지 생성과 같은 커스텀 작업을 위한 커뮤니티 파이프라인을 만드는 방법을 알 수 있습니다.
diff --git a/spaces/patgpt4/MusicGen/tests/models/test_musicgen.py b/spaces/patgpt4/MusicGen/tests/models/test_musicgen.py
deleted file mode 100644
index d43cf73763f6c690ab0b277227ac225b286fa143..0000000000000000000000000000000000000000
--- a/spaces/patgpt4/MusicGen/tests/models/test_musicgen.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pytest
-import torch
-
-from audiocraft.models import MusicGen
-
-
-class TestSEANetModel:
-    def get_musicgen(self):
-        mg = MusicGen.get_pretrained(name='debug', device='cpu')
-        mg.set_generation_params(duration=2.0, extend_stride=2.)
-        return mg
-
-    def test_base(self):
-        mg = self.get_musicgen()
-        assert mg.frame_rate == 25
-        assert mg.sample_rate == 32000
-        assert mg.audio_channels == 1
-
-    def test_generate_unconditional(self):
-        mg = self.get_musicgen()
-        wav = mg.generate_unconditional(3)
-        assert list(wav.shape) == [3, 1, 64000]
-
-    def test_generate_continuation(self):
-        mg = self.get_musicgen()
-        prompt = torch.randn(3, 1, 32000)
-        wav = mg.generate_continuation(prompt, 32000)
-        assert list(wav.shape) == [3, 1, 64000]
-
-        prompt = torch.randn(2, 1, 32000)
-        wav = mg.generate_continuation(
-            prompt, 32000, ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 64000]
-
-        prompt = torch.randn(2, 1, 32000)
-        with pytest.raises(AssertionError):
-            wav = mg.generate_continuation(
-                prompt, 32000, ['youpi', 'lapin dort', 'one too many'])
-
-    def test_generate(self):
-        mg = self.get_musicgen()
-        wav = mg.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 64000]
-
-    def test_generate_long(self):
-        mg = self.get_musicgen()
-        mg.max_duration = 3.
-        mg.set_generation_params(duration=4., extend_stride=2.)
-        wav = mg.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 32000 * 4]
diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/general.py b/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/general.py
deleted file mode 100644
index f08c01467d392150bf53c3bd7362153b8d9621aa..0000000000000000000000000000000000000000
--- a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/general.py
+++ /dev/null
@@ -1,1019 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-General utils
-"""
-
-import contextlib
-import glob
-import inspect
-import logging
-import math
-import os
-import platform
-import random
-import re
-import shutil
-import signal
-import time
-import urllib
-from datetime import datetime
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from subprocess import check_output
-from typing import Optional
-from zipfile import ZipFile
-
-import cv2
-import numpy as np
-import pandas as pd
-import pkg_resources as pkg
-import torch
-import torchvision
-import yaml
-
-# from utils.downloads import gsutil_getsize
-# from utils.metrics import box_iou, fitness
-
-# Settings
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-DATASETS_DIR = ROOT.parent / 'datasets'  # YOLOv5 datasets directory
-NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
-AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
-VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true'  # global verbose mode
-FONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf
-
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
-np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
-pd.options.display.max_columns = 10
-cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
-os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
-os.environ['OMP_NUM_THREADS'] = str(NUM_THREADS)  # OpenMP max threads (PyTorch and SciPy)
-
-def box_iou(box1, box2):
-    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Arguments:
-        box1 (Tensor[N, 4])
-        box2 (Tensor[M, 4])
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-
-    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
-    (a1, a2), (b1, b2) = box1[:, None].chunk(2, 2), box2.chunk(2, 1)
-    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
-
-    # IoU = inter / (area1 + area2 - inter)
-    return inter / (box_area(box1.T)[:, None] + box_area(box2.T) - inter)
-
-def fitness(x):
-    # Model fitness as a weighted combination of metrics
-    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
-    return (x[:, :4] * w).sum(1)
-
-def gsutil_getsize(url=''):
-    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
-    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
-    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
-
-
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
-    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
-    from utils.general import LOGGER
-
-    file = Path(file)
-    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
-    try:  # url1
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
-        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
-    except Exception as e:  # url2
-        file.unlink(missing_ok=True)  # remove partial downloads
-        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
-        os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
-    finally:
-        if not file.exists() or file.stat().st_size < min_bytes:  # check
-            file.unlink(missing_ok=True)  # remove partial downloads
-            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
-        LOGGER.info('')
-
-def is_kaggle():
-    # Is environment a Kaggle Notebook?
-    try:
-        assert os.environ.get('PWD') == '/kaggle/working'
-        assert os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'
-        return True
-    except AssertionError:
-        return False
-
-
-def is_writeable(dir, test=False):
-    # Return True if directory has write permissions, test opening a file with write permissions if test=True
-    if test:  # method 1
-        file = Path(dir) / 'tmp.txt'
-        try:
-            with open(file, 'w'):  # open file with write permissions
-                pass
-            file.unlink()  # remove file
-            return True
-        except OSError:
-            return False
-    else:  # method 2
-        return os.access(dir, os.R_OK)  # possible issues on Windows
-
-
-def set_logging(name=None, verbose=VERBOSE):
-    # Sets level and returns logger
-    if is_kaggle():
-        for h in logging.root.handlers:
-            logging.root.removeHandler(h)  # remove all handlers associated with the root logger object
-    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
-    level = logging.INFO if (verbose and rank in (-1, 0)) else logging.WARNING
-    log = logging.getLogger(name)
-    log.setLevel(level)
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(message)s"))
-    handler.setLevel(level)
-    log.addHandler(handler)
-
-
-set_logging()  # run before defining LOGGER
-LOGGER = logging.getLogger("yolov5")  # define globally (used in train.py, val.py, detect.py, etc.)
-
-
-def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
-    # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
-    env = os.getenv(env_var)
-    if env:
-        path = Path(env)  # use environment variable
-    else:
-        cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'}  # 3 OS dirs
-        path = Path.home() / cfg.get(platform.system(), '')  # OS-specific config dir
-        path = (path if is_writeable(path) else Path('/tmp')) / dir  # GCP and AWS lambda fix, only /tmp is writeable
-    path.mkdir(exist_ok=True)  # make if required
-    return path
-
-
-CONFIG_DIR = user_config_dir()  # Ultralytics settings dir
-
-
-class Profile(contextlib.ContextDecorator):
-    # Usage: @Profile() decorator or 'with Profile():' context manager
-    def __enter__(self):
-        self.start = time.time()
-
-    def __exit__(self, type, value, traceback):
-        print(f'Profile results: {time.time() - self.start:.5f}s')
-
-
-class Timeout(contextlib.ContextDecorator):
-    # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
-    def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
-        self.seconds = int(seconds)
-        self.timeout_message = timeout_msg
-        self.suppress = bool(suppress_timeout_errors)
-
-    def _timeout_handler(self, signum, frame):
-        raise TimeoutError(self.timeout_message)
-
-    def __enter__(self):
-        if platform.system() != 'Windows':  # not supported on Windows
-            signal.signal(signal.SIGALRM, self._timeout_handler)  # Set handler for SIGALRM
-            signal.alarm(self.seconds)  # start countdown for SIGALRM to be raised
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if platform.system() != 'Windows':
-            signal.alarm(0)  # Cancel SIGALRM if it's scheduled
-            if self.suppress and exc_type is TimeoutError:  # Suppress TimeoutError
-                return True
-
-
-class WorkingDirectory(contextlib.ContextDecorator):
-    # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
-    def __init__(self, new_dir):
-        self.dir = new_dir  # new dir
-        self.cwd = Path.cwd().resolve()  # current dir
-
-    def __enter__(self):
-        os.chdir(self.dir)
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        os.chdir(self.cwd)
-
-
-def try_except(func):
-    # try-except function. Usage: @try_except decorator
-    def handler(*args, **kwargs):
-        try:
-            func(*args, **kwargs)
-        except Exception as e:
-            print(e)
-
-    return handler
-
-
-def methods(instance):
-    # Get class/instance methods
-    return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
-
-
-def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
-    # Print function arguments (optional args dict)
-    x = inspect.currentframe().f_back  # previous frame
-    file, _, fcn, _, _ = inspect.getframeinfo(x)
-    if args is None:  # get args automatically
-        args, _, _, frm = inspect.getargvalues(x)
-        args = {k: v for k, v in frm.items() if k in args}
-    s = (f'{Path(file).stem}: ' if show_file else '') + (f'{fcn}: ' if show_fcn else '')
-    LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
-
-
-def init_seeds(seed=0):
-    # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
-    # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible
-    import torch.backends.cudnn as cudnn
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)
-
-
-def intersect_dicts(da, db, exclude=()):
-    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
-    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
-
-
-def get_latest_run(search_dir='.'):
-    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
-    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
-    return max(last_list, key=os.path.getctime) if last_list else ''
-
-
-def is_docker():
-    # Is environment a Docker container?
-    return Path('/workspace').exists()  # or Path('/.dockerenv').exists()
-
-
-def is_colab():
-    # Is environment a Google Colab instance?
-    try:
-        import google.colab
-        return True
-    except ImportError:
-        return False
-
-
-def is_pip():
-    # Is file in a pip package?
-    return 'site-packages' in Path(__file__).resolve().parts
-
-
-def is_ascii(s=''):
-    # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
-    s = str(s)  # convert list, tuple, None, etc. to str
-    return len(s.encode().decode('ascii', 'ignore')) == len(s)
-
-
-def is_chinese(s='人工智能'):
-    # Is string composed of any Chinese characters?
-    return True if re.search('[\u4e00-\u9fff]', str(s)) else False
-
-
-def emojis(str=''):
-    # Return platform-dependent emoji-safe version of string
-    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-def file_age(path=__file__):
-    # Return days since last file update
-    dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime))  # delta
-    return dt.days  # + dt.seconds / 86400  # fractional days
-
-
-def file_update_date(path=__file__):
-    # Return human-readable file modification date, i.e. '2021-3-26'
-    t = datetime.fromtimestamp(Path(path).stat().st_mtime)
-    return f'{t.year}-{t.month}-{t.day}'
-
-
-def file_size(path):
-    # Return file/dir size (MB)
-    mb = 1 << 20  # bytes to MiB (1024 ** 2)
-    path = Path(path)
-    if path.is_file():
-        return path.stat().st_size / mb
-    elif path.is_dir():
-        return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb
-    else:
-        return 0.0
-
-
-def check_online():
-    # Check internet connectivity
-    import socket
-    try:
-        socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
-        return True
-    except OSError:
-        return False
-
-
-def git_describe(path=ROOT):  # path must be a directory
-    # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
-    try:
-        assert (Path(path) / '.git').is_dir()
-        return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
-    except Exception:
-        return ''
-
-
-@try_except
-@WorkingDirectory(ROOT)
-def check_git_status():
-    # Recommend 'git pull' if code is out of date
-    msg = ', for updates see https://github.com/ultralytics/yolov5'
-    s = colorstr('github: ')  # string
-    assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg
-    assert not is_docker(), s + 'skipping check (Docker image)' + msg
-    assert check_online(), s + 'skipping check (offline)' + msg
-
-    cmd = 'git fetch && git config --get remote.origin.url'
-    url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git')  # git fetch
-    branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
-    n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True))  # commits behind
-    if n > 0:
-        s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
-    else:
-        s += f'up to date with {url} ✅'
-    LOGGER.info(emojis(s))  # emoji-safe
-
-
-def check_python(minimum='3.7.0'):
-    # Check current python version vs. required python version
-    check_version(platform.python_version(), minimum, name='Python ', hard=True)
-
-
-def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):
-    # Check version vs. required version
-    current, minimum = (pkg.parse_version(x) for x in (current, minimum))
-    result = (current == minimum) if pinned else (current >= minimum)  # bool
-    s = f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed'  # string
-    if hard:
-        assert result, s  # assert min requirements met
-    if verbose and not result:
-        LOGGER.warning(s)
-    return result
-
-
-@try_except
-def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=()):
-    # Check installed dependencies meet requirements (pass *.txt file or list of packages)
-    prefix = colorstr('red', 'bold', 'requirements:')
-    check_python()  # check python version
-    if isinstance(requirements, (str, Path)):  # requirements.txt file
-        file = Path(requirements)
-        assert file.exists(), f"{prefix} {file.resolve()} not found, check failed."
-        with file.open() as f:
-            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
-    else:  # list or tuple of packages
-        requirements = [x for x in requirements if x not in exclude]
-
-    n = 0  # number of packages updates
-    for i, r in enumerate(requirements):
-        try:
-            pkg.require(r)
-        except Exception:  # DistributionNotFound or VersionConflict if requirements not met
-            s = f"{prefix} {r} not found and is required by YOLOv5"
-            if install and AUTOINSTALL:  # check environment variable
-                LOGGER.info(f"{s}, attempting auto-update...")
-                try:
-                    assert check_online(), f"'pip install {r}' skipped (offline)"
-                    LOGGER.info(check_output(f"pip install '{r}' {cmds[i] if cmds else ''}", shell=True).decode())
-                    n += 1
-                except Exception as e:
-                    LOGGER.warning(f'{prefix} {e}')
-            else:
-                LOGGER.info(f'{s}. Please install and rerun your command.')
-
-    if n:  # if packages updated
-        source = file.resolve() if 'file' in locals() else requirements
-        s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
-            f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-        LOGGER.info(emojis(s))
-
-
-def check_img_size(imgsz, s=32, floor=0):
-    # Verify image size is a multiple of stride s in each dimension
-    if isinstance(imgsz, int):  # integer i.e. img_size=640
-        new_size = max(make_divisible(imgsz, int(s)), floor)
-    else:  # list i.e. img_size=[640, 480]
-        imgsz = list(imgsz)  # convert to list if tuple
-        new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
-    if new_size != imgsz:
-        LOGGER.warning(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
-    return new_size
-
-
-def check_imshow():
-    # Check if environment supports image displays
-    try:
-        assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
-        assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
-        cv2.imshow('test', np.zeros((1, 1, 3)))
-        cv2.waitKey(1)
-        cv2.destroyAllWindows()
-        cv2.waitKey(1)
-        return True
-    except Exception as e:
-        LOGGER.warning(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
-        return False
-
-
-def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
-    # Check file(s) for acceptable suffix
-    if file and suffix:
-        if isinstance(suffix, str):
-            suffix = [suffix]
-        for f in file if isinstance(file, (list, tuple)) else [file]:
-            s = Path(f).suffix.lower()  # file suffix
-            if len(s):
-                assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
-
-
-def check_yaml(file, suffix=('.yaml', '.yml')):
-    # Search/download YAML file (if necessary) and return path, checking suffix
-    return check_file(file, suffix)
-
-
-def check_file(file, suffix=''):
-    # Search/download file (if necessary) and return path
-    check_suffix(file, suffix)  # optional
-    file = str(file)  # convert to str()
-    if Path(file).is_file() or file == '':  # exists
-        return file
-    elif file.startswith(('http:/', 'https:/')):  # download
-        url = str(Path(file)).replace(':/', '://')  # Pathlib turns :// -> :/
-        file = Path(urllib.parse.unquote(file).split('?')[0]).name  # '%2F' to '/', split https://url.com/file.txt?auth
-        if Path(file).is_file():
-            LOGGER.info(f'Found {url} locally at {file}')  # file already exists
-        else:
-            LOGGER.info(f'Downloading {url} to {file}...')
-            torch.hub.download_url_to_file(url, file)
-            assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check
-        return file
-    else:  # search
-        files = []
-        for d in 'data', 'models', 'utils':  # search directories
-            files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True))  # find file
-        assert len(files), f'File not found: {file}'  # assert file was found
-        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
-        return files[0]  # return file
-
-
-def check_font(font=FONT, progress=False):
-    # Download font to CONFIG_DIR if necessary
-    font = Path(font)
-    file = CONFIG_DIR / font.name
-    if not font.exists() and not file.exists():
-        url = "https://ultralytics.com/assets/" + font.name
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=progress)
-
-
-def check_dataset(data, autodownload=True):
-    # Download and/or unzip dataset if not found locally
-    # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
-
-    # Download (optional)
-    extract_dir = ''
-    if isinstance(data, (str, Path)) and str(data).endswith('.zip'):  # i.e. gs://bucket/dir/coco128.zip
-        download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1)
-        data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
-        extract_dir, autodownload = data.parent, False
-
-    # Read yaml (optional)
-    if isinstance(data, (str, Path)):
-        with open(data, errors='ignore') as f:
-            data = yaml.safe_load(f)  # dictionary
-
-    # Resolve paths
-    path = Path(extract_dir or data.get('path') or '')  # optional 'path' default to '.'
-    if not path.is_absolute():
-        path = (ROOT / path).resolve()
-    for k in 'train', 'val', 'test':
-        if data.get(k):  # prepend path
-            data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
-
-    # Parse yaml
-    assert 'nc' in data, "Dataset 'nc' key missing."
-    if 'names' not in data:
-        data['names'] = [f'class{i}' for i in range(data['nc'])]  # assign class names if missing
-    train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
-    if val:
-        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
-        if not all(x.exists() for x in val):
-            LOGGER.info(emojis('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]))
-            if s and autodownload:  # download script
-                t = time.time()
-                root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'
-                if s.startswith('http') and s.endswith('.zip'):  # URL
-                    f = Path(s).name  # filename
-                    LOGGER.info(f'Downloading {s} to {f}...')
-                    torch.hub.download_url_to_file(s, f)
-                    Path(root).mkdir(parents=True, exist_ok=True)  # create root
-                    ZipFile(f).extractall(path=root)  # unzip
-                    Path(f).unlink()  # remove zip
-                    r = None  # success
-                elif s.startswith('bash '):  # bash script
-                    LOGGER.info(f'Running {s} ...')
-                    r = os.system(s)
-                else:  # python script
-                    r = exec(s, {'yaml': data})  # return None
-                dt = f'({round(time.time() - t, 1)}s)'
-                s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
-                LOGGER.info(emojis(f"Dataset download {s}"))
-            else:
-                raise Exception(emojis('Dataset not found ❌'))
-
-    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts
-    return data  # dictionary
-
-
-def url2file(url):
-    # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
-    url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/
-    file = Path(urllib.parse.unquote(url)).name.split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth
-    return file
-
-
-def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3):
-    # Multi-threaded file download and unzip function, used in data.yaml for autodownload
-    def download_one(url, dir):
-        # Download 1 file
-        success = True
-        f = dir / Path(url).name  # filename
-        if Path(url).is_file():  # exists in current path
-            Path(url).rename(f)  # move to dir
-        elif not f.exists():
-            LOGGER.info(f'Downloading {url} to {f}...')
-            for i in range(retry + 1):
-                if curl:
-                    s = 'sS' if threads > 1 else ''  # silent
-                    r = os.system(f"curl -{s}L '{url}' -o '{f}' --retry 9 -C -")  # curl download
-                    success = r == 0
-                else:
-                    torch.hub.download_url_to_file(url, f, progress=threads == 1)  # torch download
-                    success = f.is_file()
-                if success:
-                    break
-                elif i < retry:
-                    LOGGER.warning(f'Download failure, retrying {i + 1}/{retry} {url}...')
-                else:
-                    LOGGER.warning(f'Failed to download {url}...')
-
-        if unzip and success and f.suffix in ('.zip', '.gz'):
-            LOGGER.info(f'Unzipping {f}...')
-            if f.suffix == '.zip':
-                ZipFile(f).extractall(path=dir)  # unzip
-            elif f.suffix == '.gz':
-                os.system(f'tar xfz {f} --directory {f.parent}')  # unzip
-            if delete:
-                f.unlink()  # remove zip
-
-    dir = Path(dir)
-    dir.mkdir(parents=True, exist_ok=True)  # make directory
-    if threads > 1:
-        pool = ThreadPool(threads)
-        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multi-threaded
-        pool.close()
-        pool.join()
-    else:
-        for u in [url] if isinstance(url, (str, Path)) else url:
-            download_one(u, dir)
-
-
-def make_divisible(x, divisor):
-    # Returns nearest x divisible by divisor
-    if isinstance(divisor, torch.Tensor):
-        divisor = int(divisor.max())  # to int
-    return math.ceil(x / divisor) * divisor
-
-
-def clean_str(s):
-    # Cleans a string by replacing special characters with underscore _
-    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
-
-
-def one_cycle(y1=0.0, y2=1.0, steps=100):
-    # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
-    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
-
-
-def colorstr(*input):
-    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
-    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
-    colors = {
-        'black': '\033[30m',  # basic colors
-        'red': '\033[31m',
-        'green': '\033[32m',
-        'yellow': '\033[33m',
-        'blue': '\033[34m',
-        'magenta': '\033[35m',
-        'cyan': '\033[36m',
-        'white': '\033[37m',
-        'bright_black': '\033[90m',  # bright colors
-        'bright_red': '\033[91m',
-        'bright_green': '\033[92m',
-        'bright_yellow': '\033[93m',
-        'bright_blue': '\033[94m',
-        'bright_magenta': '\033[95m',
-        'bright_cyan': '\033[96m',
-        'bright_white': '\033[97m',
-        'end': '\033[0m',  # misc
-        'bold': '\033[1m',
-        'underline': '\033[4m'}
-    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
-
-
-def labels_to_class_weights(labels, nc=80):
-    # Get class weights (inverse frequency) from training labels
-    if labels[0] is None:  # no labels loaded
-        return torch.Tensor()
-
-    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
-    classes = labels[:, 0].astype(int)  # labels = [class xywh]
-    weights = np.bincount(classes, minlength=nc)  # occurrences per class
-
-    # Prepend gridpoint count (for uCE training)
-    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
-    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
-
-    weights[weights == 0] = 1  # replace empty bins with 1
-    weights = 1 / weights  # number of targets per class
-    weights /= weights.sum()  # normalize
-    return torch.from_numpy(weights)
-
-
-def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
-    # Produces image weights based on class_weights and image contents
-    class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
-    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
-    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
-    return image_weights
-
-
-def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
-    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
-    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
-    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
-    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
-    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
-    x = [
-        1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
-        35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-        64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-    return x
-
-
-def xyxy2xywh(x):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-    y[:, 2] = x[:, 2] - x[:, 0]  # width
-    y[:, 3] = x[:, 3] - x[:, 1]  # height
-    return y
-
-
-def xywh2xyxy(x):
-    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
-    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
-    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
-    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
-    return y
-
-
-def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
-    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
-    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
-    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
-    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
-    return y
-
-
-def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
-    if clip:
-        clip_coords(x, (h - eps, w - eps))  # warning: inplace clip
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center
-    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center
-    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width
-    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height
-    return y
-
-
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
-    # Convert normalized segments into pixel segments, shape (n,2)
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * x[:, 0] + padw  # top left x
-    y[:, 1] = h * x[:, 1] + padh  # top left y
-    return y
-
-
-def segment2box(segment, width=640, height=640):
-    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
-    x, y = segment.T  # segment xy
-    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
-    x, y, = x[inside], y[inside]
-    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy
-
-
-def segments2boxes(segments):
-    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
-    boxes = []
-    for s in segments:
-        x, y = s.T  # segment xy
-        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
-    return xyxy2xywh(np.array(boxes))  # cls, xywh
-
-
-def resample_segments(segments, n=1000):
-    # Up-sample an (n,2) segment
-    for i, s in enumerate(segments):
-        x = np.linspace(0, len(s) - 1, n)
-        xp = np.arange(len(s))
-        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
-    return segments
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    coords[:, [0, 2]] -= pad[0]  # x padding
-    coords[:, [1, 3]] -= pad[1]  # y padding
-    coords[:, :4] /= gain
-    clip_coords(coords, img0_shape)
-    return coords
-
-
-def clip_coords(boxes, shape):
-    # Clip bounding xyxy bounding boxes to image shape (height, width)
-    if isinstance(boxes, torch.Tensor):  # faster individually
-        boxes[:, 0].clamp_(0, shape[1])  # x1
-        boxes[:, 1].clamp_(0, shape[0])  # y1
-        boxes[:, 2].clamp_(0, shape[1])  # x2
-        boxes[:, 3].clamp_(0, shape[0])  # y2
-    else:  # np.array (faster grouped)
-        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2
-        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2
-
-
-def non_max_suppression(prediction,
-                        conf_thres=0.25,
-                        iou_thres=0.45,
-                        classes=None,
-                        agnostic=False,
-                        multi_label=False,
-                        labels=(),
-                        max_det=300):
-    """Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
-    """
-
-    bs = prediction.shape[0]  # batch size
-    nc = prediction.shape[2] - 5  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Checks
-    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
-    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
-
-    # Settings
-    # min_wh = 2  # (pixels) minimum box width and height
-    max_wh = 7680  # (pixels) maximum box width and height
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    time_limit = 0.1 + 0.03 * bs  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * bs
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            lb = labels[xi]
-            v = torch.zeros((len(lb), nc + 5), device=x.device)
-            v[:, :4] = lb[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_label:
-            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 5:].max(1, keepdim=True)
-            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Apply finite constraint
-        # if not torch.isfinite(x).all():
-        #     x = x[torch.isfinite(x).all(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if i.shape[0] > max_det:  # limit detections
-            i = i[:max_det]
-        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
-    # Strip optimizer from 'f' to finalize training, optionally save as 's'
-    x = torch.load(f, map_location=torch.device('cpu'))
-    if x.get('ema'):
-        x['model'] = x['ema']  # replace model with ema
-    for k in 'optimizer', 'best_fitness', 'wandb_id', 'ema', 'updates':  # keys
-        x[k] = None
-    x['epoch'] = -1
-    x['model'].half()  # to FP16
-    for p in x['model'].parameters():
-        p.requires_grad = False
-    torch.save(x, s or f)
-    mb = os.path.getsize(s or f) / 1E6  # filesize
-    LOGGER.info(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
-
-
-def print_mutation(results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')):
-    evolve_csv = save_dir / 'evolve.csv'
-    evolve_yaml = save_dir / 'hyp_evolve.yaml'
-    keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
-            'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys())  # [results + hyps]
-    keys = tuple(x.strip() for x in keys)
-    vals = results + tuple(hyp.values())
-    n = len(keys)
-
-    # Download (optional)
-    if bucket:
-        url = f'gs://{bucket}/evolve.csv'
-        if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
-            os.system(f'gsutil cp {url} {save_dir}')  # download evolve.csv if larger than local
-
-    # Log to evolve.csv
-    s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n')  # add header
-    with open(evolve_csv, 'a') as f:
-        f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
-
-    # Save yaml
-    with open(evolve_yaml, 'w') as f:
-        data = pd.read_csv(evolve_csv)
-        data = data.rename(columns=lambda x: x.strip())  # strip keys
-        i = np.argmax(fitness(data.values[:, :4]))  #
-        generations = len(data)
-        f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' +
-                f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) +
-                '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
-        yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
-
-    # Print to screen
-    LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix +
-                ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}'
-                                                                                         for x in vals) + '\n\n')
-
-    if bucket:
-        os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}')  # upload
-
-
-def apply_classifier(x, model, img, im0):
-    # Apply a second stage classifier to YOLO outputs
-    # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
-    im0 = [im0] if isinstance(im0, np.ndarray) else im0
-    for i, d in enumerate(x):  # per image
-        if d is not None and len(d):
-            d = d.clone()
-
-            # Reshape and pad cutouts
-            b = xyxy2xywh(d[:, :4])  # boxes
-            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
-            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
-            d[:, :4] = xywh2xyxy(b).long()
-
-            # Rescale boxes from img_size to im0 size
-            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
-
-            # Classes
-            pred_cls1 = d[:, 5].long()
-            ims = []
-            for j, a in enumerate(d):  # per item
-                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
-                im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('example%i.jpg' % j, cutout)
-
-                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
-                im /= 255  # 0 - 255 to 0.0 - 1.0
-                ims.append(im)
-
-            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
-            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
-
-    return x
-
-
-def increment_path(path, exist_ok=False, sep='', mkdir=False):
-    # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
-    path = Path(path)  # os-agnostic
-    if path.exists() and not exist_ok:
-        path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
-        dirs = glob.glob(f"{path}{sep}*")  # similar paths
-        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
-        i = [int(m.groups()[0]) for m in matches if m]  # indices
-        n = max(i) + 1 if i else 2  # increment number
-        path = Path(f"{path}{sep}{n}{suffix}")  # increment path
-    if mkdir:
-        path.mkdir(parents=True, exist_ok=True)  # make directory
-    return path
-
-
-# OpenCV Chinese-friendly functions ------------------------------------------------------------------------------------
-imshow_ = cv2.imshow  # copy to avoid recursion errors
-
-
-def imread(path, flags=cv2.IMREAD_COLOR):
-    return cv2.imdecode(np.fromfile(path, np.uint8), flags)
-
-
-def imwrite(path, im):
-    try:
-        cv2.imencode(Path(path).suffix, im)[1].tofile(path)
-        return True
-    except Exception:
-        return False
-
-
-def imshow(path, im):
-    imshow_(path.encode('unicode_escape').decode(), im)
-
-
-cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine
-
-# Variables ------------------------------------------------------------------------------------------------------------
-NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns  # terminal window size for tqdm
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/help.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/help.py
deleted file mode 100644
index 62066318b74dcc5c32bcd24b9493fb34d1ce52d7..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/help.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from optparse import Values
-from typing import List
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.exceptions import CommandError
-
-
-class HelpCommand(Command):
-    """Show help for commands"""
-
-    usage = """
-      %prog <command>"""
-    ignore_require_venv = True
-
-    def run(self, options: Values, args: List[str]) -> int:
-        from pip._internal.commands import (
-            commands_dict,
-            create_command,
-            get_similar_commands,
-        )
-
-        try:
-            # 'pip help' with no args is handled by pip.__init__.parseopt()
-            cmd_name = args[0]  # the command we need help for
-        except IndexError:
-            return SUCCESS
-
-        if cmd_name not in commands_dict:
-            guess = get_similar_commands(cmd_name)
-
-            msg = [f'unknown command "{cmd_name}"']
-            if guess:
-                msg.append(f'maybe you meant "{guess}"')
-
-            raise CommandError(" - ".join(msg))
-
-        command = create_command(cmd_name)
-        command.parser.print_help()
-
-        return SUCCESS
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/url.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/url.py
deleted file mode 100644
index a960b2f3c5f3d11fc9ae43638da9877d635e8d91..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/url.py
+++ /dev/null
@@ -1,435 +0,0 @@
-from __future__ import absolute_import
-
-import re
-from collections import namedtuple
-
-from ..exceptions import LocationParseError
-from ..packages import six
-
-url_attrs = ["scheme", "auth", "host", "port", "path", "query", "fragment"]
-
-# We only want to normalize urls with an HTTP(S) scheme.
-# urllib3 infers URLs without a scheme (None) to be http.
-NORMALIZABLE_SCHEMES = ("http", "https", None)
-
-# Almost all of these patterns were derived from the
-# 'rfc3986' module: https://github.com/python-hyper/rfc3986
-PERCENT_RE = re.compile(r"%[a-fA-F0-9]{2}")
-SCHEME_RE = re.compile(r"^(?:[a-zA-Z][a-zA-Z0-9+-]*:|/)")
-URI_RE = re.compile(
-    r"^(?:([a-zA-Z][a-zA-Z0-9+.-]*):)?"
-    r"(?://([^\\/?#]*))?"
-    r"([^?#]*)"
-    r"(?:\?([^#]*))?"
-    r"(?:#(.*))?$",
-    re.UNICODE | re.DOTALL,
-)
-
-IPV4_PAT = r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}"
-HEX_PAT = "[0-9A-Fa-f]{1,4}"
-LS32_PAT = "(?:{hex}:{hex}|{ipv4})".format(hex=HEX_PAT, ipv4=IPV4_PAT)
-_subs = {"hex": HEX_PAT, "ls32": LS32_PAT}
-_variations = [
-    #                            6( h16 ":" ) ls32
-    "(?:%(hex)s:){6}%(ls32)s",
-    #                       "::" 5( h16 ":" ) ls32
-    "::(?:%(hex)s:){5}%(ls32)s",
-    # [               h16 ] "::" 4( h16 ":" ) ls32
-    "(?:%(hex)s)?::(?:%(hex)s:){4}%(ls32)s",
-    # [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32
-    "(?:(?:%(hex)s:)?%(hex)s)?::(?:%(hex)s:){3}%(ls32)s",
-    # [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32
-    "(?:(?:%(hex)s:){0,2}%(hex)s)?::(?:%(hex)s:){2}%(ls32)s",
-    # [ *3( h16 ":" ) h16 ] "::"    h16 ":"   ls32
-    "(?:(?:%(hex)s:){0,3}%(hex)s)?::%(hex)s:%(ls32)s",
-    # [ *4( h16 ":" ) h16 ] "::"              ls32
-    "(?:(?:%(hex)s:){0,4}%(hex)s)?::%(ls32)s",
-    # [ *5( h16 ":" ) h16 ] "::"              h16
-    "(?:(?:%(hex)s:){0,5}%(hex)s)?::%(hex)s",
-    # [ *6( h16 ":" ) h16 ] "::"
-    "(?:(?:%(hex)s:){0,6}%(hex)s)?::",
-]
-
-UNRESERVED_PAT = r"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._\-~"
-IPV6_PAT = "(?:" + "|".join([x % _subs for x in _variations]) + ")"
-ZONE_ID_PAT = "(?:%25|%)(?:[" + UNRESERVED_PAT + "]|%[a-fA-F0-9]{2})+"
-IPV6_ADDRZ_PAT = r"\[" + IPV6_PAT + r"(?:" + ZONE_ID_PAT + r")?\]"
-REG_NAME_PAT = r"(?:[^\[\]%:/?#]|%[a-fA-F0-9]{2})*"
-TARGET_RE = re.compile(r"^(/[^?#]*)(?:\?([^#]*))?(?:#.*)?$")
-
-IPV4_RE = re.compile("^" + IPV4_PAT + "$")
-IPV6_RE = re.compile("^" + IPV6_PAT + "$")
-IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT + "$")
-BRACELESS_IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT[2:-2] + "$")
-ZONE_ID_RE = re.compile("(" + ZONE_ID_PAT + r")\]$")
-
-_HOST_PORT_PAT = ("^(%s|%s|%s)(?::0*?(|0|[1-9][0-9]{0,4}))?$") % (
-    REG_NAME_PAT,
-    IPV4_PAT,
-    IPV6_ADDRZ_PAT,
-)
-_HOST_PORT_RE = re.compile(_HOST_PORT_PAT, re.UNICODE | re.DOTALL)
-
-UNRESERVED_CHARS = set(
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._-~"
-)
-SUB_DELIM_CHARS = set("!$&'()*+,;=")
-USERINFO_CHARS = UNRESERVED_CHARS | SUB_DELIM_CHARS | {":"}
-PATH_CHARS = USERINFO_CHARS | {"@", "/"}
-QUERY_CHARS = FRAGMENT_CHARS = PATH_CHARS | {"?"}
-
-
-class Url(namedtuple("Url", url_attrs)):
-    """
-    Data structure for representing an HTTP URL. Used as a return value for
-    :func:`parse_url`. Both the scheme and host are normalized as they are
-    both case-insensitive according to RFC 3986.
-    """
-
-    __slots__ = ()
-
-    def __new__(
-        cls,
-        scheme=None,
-        auth=None,
-        host=None,
-        port=None,
-        path=None,
-        query=None,
-        fragment=None,
-    ):
-        if path and not path.startswith("/"):
-            path = "/" + path
-        if scheme is not None:
-            scheme = scheme.lower()
-        return super(Url, cls).__new__(
-            cls, scheme, auth, host, port, path, query, fragment
-        )
-
-    @property
-    def hostname(self):
-        """For backwards-compatibility with urlparse. We're nice like that."""
-        return self.host
-
-    @property
-    def request_uri(self):
-        """Absolute path including the query string."""
-        uri = self.path or "/"
-
-        if self.query is not None:
-            uri += "?" + self.query
-
-        return uri
-
-    @property
-    def netloc(self):
-        """Network location including host and port"""
-        if self.port:
-            return "%s:%d" % (self.host, self.port)
-        return self.host
-
-    @property
-    def url(self):
-        """
-        Convert self into a url
-
-        This function should more or less round-trip with :func:`.parse_url`. The
-        returned url may not be exactly the same as the url inputted to
-        :func:`.parse_url`, but it should be equivalent by the RFC (e.g., urls
-        with a blank port will have : removed).
-
-        Example: ::
-
-            >>> U = parse_url('http://google.com/mail/')
-            >>> U.url
-            'http://google.com/mail/'
-            >>> Url('http', 'username:password', 'host.com', 80,
-            ... '/path', 'query', 'fragment').url
-            'http://username:password@host.com:80/path?query#fragment'
-        """
-        scheme, auth, host, port, path, query, fragment = self
-        url = u""
-
-        # We use "is not None" we want things to happen with empty strings (or 0 port)
-        if scheme is not None:
-            url += scheme + u"://"
-        if auth is not None:
-            url += auth + u"@"
-        if host is not None:
-            url += host
-        if port is not None:
-            url += u":" + str(port)
-        if path is not None:
-            url += path
-        if query is not None:
-            url += u"?" + query
-        if fragment is not None:
-            url += u"#" + fragment
-
-        return url
-
-    def __str__(self):
-        return self.url
-
-
-def split_first(s, delims):
-    """
-    .. deprecated:: 1.25
-
-    Given a string and an iterable of delimiters, split on the first found
-    delimiter. Return two split parts and the matched delimiter.
-
-    If not found, then the first part is the full input string.
-
-    Example::
-
-        >>> split_first('foo/bar?baz', '?/=')
-        ('foo', 'bar?baz', '/')
-        >>> split_first('foo/bar?baz', '123')
-        ('foo/bar?baz', '', None)
-
-    Scales linearly with number of delims. Not ideal for large number of delims.
-    """
-    min_idx = None
-    min_delim = None
-    for d in delims:
-        idx = s.find(d)
-        if idx < 0:
-            continue
-
-        if min_idx is None or idx < min_idx:
-            min_idx = idx
-            min_delim = d
-
-    if min_idx is None or min_idx < 0:
-        return s, "", None
-
-    return s[:min_idx], s[min_idx + 1 :], min_delim
-
-
-def _encode_invalid_chars(component, allowed_chars, encoding="utf-8"):
-    """Percent-encodes a URI component without reapplying
-    onto an already percent-encoded component.
-    """
-    if component is None:
-        return component
-
-    component = six.ensure_text(component)
-
-    # Normalize existing percent-encoded bytes.
-    # Try to see if the component we're encoding is already percent-encoded
-    # so we can skip all '%' characters but still encode all others.
-    component, percent_encodings = PERCENT_RE.subn(
-        lambda match: match.group(0).upper(), component
-    )
-
-    uri_bytes = component.encode("utf-8", "surrogatepass")
-    is_percent_encoded = percent_encodings == uri_bytes.count(b"%")
-    encoded_component = bytearray()
-
-    for i in range(0, len(uri_bytes)):
-        # Will return a single character bytestring on both Python 2 & 3
-        byte = uri_bytes[i : i + 1]
-        byte_ord = ord(byte)
-        if (is_percent_encoded and byte == b"%") or (
-            byte_ord < 128 and byte.decode() in allowed_chars
-        ):
-            encoded_component += byte
-            continue
-        encoded_component.extend(b"%" + (hex(byte_ord)[2:].encode().zfill(2).upper()))
-
-    return encoded_component.decode(encoding)
-
-
-def _remove_path_dot_segments(path):
-    # See http://tools.ietf.org/html/rfc3986#section-5.2.4 for pseudo-code
-    segments = path.split("/")  # Turn the path into a list of segments
-    output = []  # Initialize the variable to use to store output
-
-    for segment in segments:
-        # '.' is the current directory, so ignore it, it is superfluous
-        if segment == ".":
-            continue
-        # Anything other than '..', should be appended to the output
-        elif segment != "..":
-            output.append(segment)
-        # In this case segment == '..', if we can, we should pop the last
-        # element
-        elif output:
-            output.pop()
-
-    # If the path starts with '/' and the output is empty or the first string
-    # is non-empty
-    if path.startswith("/") and (not output or output[0]):
-        output.insert(0, "")
-
-    # If the path starts with '/.' or '/..' ensure we add one more empty
-    # string to add a trailing '/'
-    if path.endswith(("/.", "/..")):
-        output.append("")
-
-    return "/".join(output)
-
-
-def _normalize_host(host, scheme):
-    if host:
-        if isinstance(host, six.binary_type):
-            host = six.ensure_str(host)
-
-        if scheme in NORMALIZABLE_SCHEMES:
-            is_ipv6 = IPV6_ADDRZ_RE.match(host)
-            if is_ipv6:
-                # IPv6 hosts of the form 'a::b%zone' are encoded in a URL as
-                # such per RFC 6874: 'a::b%25zone'. Unquote the ZoneID
-                # separator as necessary to return a valid RFC 4007 scoped IP.
-                match = ZONE_ID_RE.search(host)
-                if match:
-                    start, end = match.span(1)
-                    zone_id = host[start:end]
-
-                    if zone_id.startswith("%25") and zone_id != "%25":
-                        zone_id = zone_id[3:]
-                    else:
-                        zone_id = zone_id[1:]
-                    zone_id = "%" + _encode_invalid_chars(zone_id, UNRESERVED_CHARS)
-                    return host[:start].lower() + zone_id + host[end:]
-                else:
-                    return host.lower()
-            elif not IPV4_RE.match(host):
-                return six.ensure_str(
-                    b".".join([_idna_encode(label) for label in host.split(".")])
-                )
-    return host
-
-
-def _idna_encode(name):
-    if name and any(ord(x) >= 128 for x in name):
-        try:
-            from pip._vendor import idna
-        except ImportError:
-            six.raise_from(
-                LocationParseError("Unable to parse URL without the 'idna' module"),
-                None,
-            )
-        try:
-            return idna.encode(name.lower(), strict=True, std3_rules=True)
-        except idna.IDNAError:
-            six.raise_from(
-                LocationParseError(u"Name '%s' is not a valid IDNA label" % name), None
-            )
-    return name.lower().encode("ascii")
-
-
-def _encode_target(target):
-    """Percent-encodes a request target so that there are no invalid characters"""
-    path, query = TARGET_RE.match(target).groups()
-    target = _encode_invalid_chars(path, PATH_CHARS)
-    query = _encode_invalid_chars(query, QUERY_CHARS)
-    if query is not None:
-        target += "?" + query
-    return target
-
-
-def parse_url(url):
-    """
-    Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
-    performed to parse incomplete urls. Fields not provided will be None.
-    This parser is RFC 3986 and RFC 6874 compliant.
-
-    The parser logic and helper functions are based heavily on
-    work done in the ``rfc3986`` module.
-
-    :param str url: URL to parse into a :class:`.Url` namedtuple.
-
-    Partly backwards-compatible with :mod:`urlparse`.
-
-    Example::
-
-        >>> parse_url('http://google.com/mail/')
-        Url(scheme='http', host='google.com', port=None, path='/mail/', ...)
-        >>> parse_url('google.com:80')
-        Url(scheme=None, host='google.com', port=80, path=None, ...)
-        >>> parse_url('/foo?bar')
-        Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)
-    """
-    if not url:
-        # Empty
-        return Url()
-
-    source_url = url
-    if not SCHEME_RE.search(url):
-        url = "//" + url
-
-    try:
-        scheme, authority, path, query, fragment = URI_RE.match(url).groups()
-        normalize_uri = scheme is None or scheme.lower() in NORMALIZABLE_SCHEMES
-
-        if scheme:
-            scheme = scheme.lower()
-
-        if authority:
-            auth, _, host_port = authority.rpartition("@")
-            auth = auth or None
-            host, port = _HOST_PORT_RE.match(host_port).groups()
-            if auth and normalize_uri:
-                auth = _encode_invalid_chars(auth, USERINFO_CHARS)
-            if port == "":
-                port = None
-        else:
-            auth, host, port = None, None, None
-
-        if port is not None:
-            port = int(port)
-            if not (0 <= port <= 65535):
-                raise LocationParseError(url)
-
-        host = _normalize_host(host, scheme)
-
-        if normalize_uri and path:
-            path = _remove_path_dot_segments(path)
-            path = _encode_invalid_chars(path, PATH_CHARS)
-        if normalize_uri and query:
-            query = _encode_invalid_chars(query, QUERY_CHARS)
-        if normalize_uri and fragment:
-            fragment = _encode_invalid_chars(fragment, FRAGMENT_CHARS)
-
-    except (ValueError, AttributeError):
-        return six.raise_from(LocationParseError(source_url), None)
-
-    # For the sake of backwards compatibility we put empty
-    # string values for path if there are any defined values
-    # beyond the path in the URL.
-    # TODO: Remove this when we break backwards compatibility.
-    if not path:
-        if query is not None or fragment is not None:
-            path = ""
-        else:
-            path = None
-
-    # Ensure that each part of the URL is a `str` for
-    # backwards compatibility.
-    if isinstance(url, six.text_type):
-        ensure_func = six.ensure_text
-    else:
-        ensure_func = six.ensure_str
-
-    def ensure_type(x):
-        return x if x is None else ensure_func(x)
-
-    return Url(
-        scheme=ensure_type(scheme),
-        auth=ensure_type(auth),
-        host=ensure_type(host),
-        port=port,
-        path=ensure_type(path),
-        query=ensure_type(query),
-        fragment=ensure_type(fragment),
-    )
-
-
-def get_host(url):
-    """
-    Deprecated. Use :func:`parse_url` instead.
-    """
-    p = parse_url(url)
-    return p.scheme or "http", p.hostname, p.port
diff --git a/spaces/plzdontcry/dakubettergpt/src/components/Menu/ChatFolder.tsx b/spaces/plzdontcry/dakubettergpt/src/components/Menu/ChatFolder.tsx
deleted file mode 100644
index 42b144c29428bd3850e15dfe376172952c221113..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/components/Menu/ChatFolder.tsx
+++ /dev/null
@@ -1,315 +0,0 @@
-import React, { useEffect, useRef, useState } from 'react';
-import useStore from '@store/store';
-
-import DownChevronArrow from '@icon/DownChevronArrow';
-import FolderIcon from '@icon/FolderIcon';
-import {
-  ChatHistoryInterface,
-  ChatInterface,
-  FolderCollection,
-} from '@type/chat';
-
-import ChatHistory from './ChatHistory';
-import NewChat from './NewChat';
-import EditIcon from '@icon/EditIcon';
-import DeleteIcon from '@icon/DeleteIcon';
-import CrossIcon from '@icon/CrossIcon';
-import TickIcon from '@icon/TickIcon';
-import ColorPaletteIcon from '@icon/ColorPaletteIcon';
-import RefreshIcon from '@icon/RefreshIcon';
-
-import { folderColorOptions } from '@constants/color';
-
-import useHideOnOutsideClick from '@hooks/useHideOnOutsideClick';
-
-const ChatFolder = ({
-  folderChats,
-  folderId,
-}: {
-  folderChats: ChatHistoryInterface[];
-  folderId: string;
-}) => {
-  const folderName = useStore((state) => state.folders[folderId]?.name);
-  const isExpanded = useStore((state) => state.folders[folderId]?.expanded);
-  const color = useStore((state) => state.folders[folderId]?.color);
-
-  const setChats = useStore((state) => state.setChats);
-  const setFolders = useStore((state) => state.setFolders);
-
-  const inputRef = useRef<HTMLInputElement>(null);
-  const folderRef = useRef<HTMLDivElement>(null);
-  const gradientRef = useRef<HTMLDivElement>(null);
-
-  const [_folderName, _setFolderName] = useState<string>(folderName);
-  const [isEdit, setIsEdit] = useState<boolean>(false);
-  const [isDelete, setIsDelete] = useState<boolean>(false);
-  const [isHover, setIsHover] = useState<boolean>(false);
-
-  const [showPalette, setShowPalette, paletteRef] = useHideOnOutsideClick();
-
-  const editTitle = () => {
-    const updatedFolders: FolderCollection = JSON.parse(
-      JSON.stringify(useStore.getState().folders)
-    );
-    updatedFolders[folderId].name = _folderName;
-    setFolders(updatedFolders);
-    setIsEdit(false);
-  };
-
-  const deleteFolder = () => {
-    const updatedChats: ChatInterface[] = JSON.parse(
-      JSON.stringify(useStore.getState().chats)
-    );
-    updatedChats.forEach((chat) => {
-      if (chat.folder === folderId) delete chat.folder;
-    });
-    setChats(updatedChats);
-
-    const updatedFolders: FolderCollection = JSON.parse(
-      JSON.stringify(useStore.getState().folders)
-    );
-    delete updatedFolders[folderId];
-    setFolders(updatedFolders);
-
-    setIsDelete(false);
-  };
-
-  const updateColor = (_color?: string) => {
-    const updatedFolders: FolderCollection = JSON.parse(
-      JSON.stringify(useStore.getState().folders)
-    );
-    if (_color) updatedFolders[folderId].color = _color;
-    else delete updatedFolders[folderId].color;
-    setFolders(updatedFolders);
-    setShowPalette(false);
-  };
-
-  const handleKeyDown = (e: React.KeyboardEvent<HTMLInputElement>) => {
-    if (e.key === 'Enter') {
-      e.preventDefault();
-      editTitle();
-    }
-  };
-
-  const handleTick = (e: React.MouseEvent<HTMLButtonElement>) => {
-    e.stopPropagation();
-
-    if (isEdit) editTitle();
-    else if (isDelete) deleteFolder();
-  };
-
-  const handleCross = () => {
-    setIsDelete(false);
-    setIsEdit(false);
-  };
-
-  const handleDrop = (e: React.DragEvent<HTMLDivElement>) => {
-    if (e.dataTransfer) {
-      e.stopPropagation();
-      setIsHover(false);
-
-      // expand folder on drop
-      const updatedFolders: FolderCollection = JSON.parse(
-        JSON.stringify(useStore.getState().folders)
-      );
-      updatedFolders[folderId].expanded = true;
-      setFolders(updatedFolders);
-
-      // update chat folderId to new folderId
-      const chatIndex = Number(e.dataTransfer.getData('chatIndex'));
-      const updatedChats: ChatInterface[] = JSON.parse(
-        JSON.stringify(useStore.getState().chats)
-      );
-      updatedChats[chatIndex].folder = folderId;
-      setChats(updatedChats);
-    }
-  };
-
-  const handleDragOver = (e: React.DragEvent<HTMLDivElement>) => {
-    e.preventDefault();
-    e.stopPropagation();
-    setIsHover(true);
-  };
-
-  const handleDragLeave = () => {
-    setIsHover(false);
-  };
-
-  const toggleExpanded = () => {
-    const updatedFolders: FolderCollection = JSON.parse(
-      JSON.stringify(useStore.getState().folders)
-    );
-    updatedFolders[folderId].expanded = !updatedFolders[folderId].expanded;
-    setFolders(updatedFolders);
-  };
-
-  useEffect(() => {
-    if (inputRef && inputRef.current) inputRef.current.focus();
-  }, [isEdit]);
-
-  return (
-    <div
-      className={`w-full transition-colors group/folder ${
-        isHover ? 'bg-gray-800/40' : ''
-      }`}
-      onDrop={handleDrop}
-      onDragOver={handleDragOver}
-      onDragLeave={handleDragLeave}
-    >
-      <div
-        style={{ background: color || '' }}
-        className={`${
-          color ? '' : 'hover:bg-gray-850'
-        } transition-colors flex py-2 pl-2 pr-1 items-center gap-3 relative rounded-md break-all cursor-pointer parent-sibling`}
-        onClick={toggleExpanded}
-        ref={folderRef}
-        onMouseEnter={() => {
-          if (color && folderRef.current)
-            folderRef.current.style.background = `${color}dd`;
-          if (gradientRef.current) gradientRef.current.style.width = '0px';
-        }}
-        onMouseLeave={() => {
-          if (color && folderRef.current)
-            folderRef.current.style.background = color;
-          if (gradientRef.current) gradientRef.current.style.width = '1rem';
-        }}
-      >
-        <FolderIcon className='h-4 w-4' />
-        <div className='flex-1 text-ellipsis max-h-5 overflow-hidden break-all relative'>
-          {isEdit ? (
-            <input
-              type='text'
-              className='focus:outline-blue-600 text-sm border-none bg-transparent p-0 m-0 w-full'
-              value={_folderName}
-              onChange={(e) => {
-                _setFolderName(e.target.value);
-              }}
-              onClick={(e) => e.stopPropagation()}
-              onKeyDown={handleKeyDown}
-              ref={inputRef}
-            />
-          ) : (
-            _folderName
-          )}
-          {isEdit || (
-            <div
-              ref={gradientRef}
-              className='absolute inset-y-0 right-0 w-4 z-10 transition-all'
-              style={{
-                background:
-                  color &&
-                  `linear-gradient(to left, ${
-                    color || 'var(--color-900)'
-                  }, rgb(32 33 35 / 0))`,
-              }}
-            />
-          )}
-        </div>
-        <div
-          className='flex text-gray-300'
-          onClick={(e) => e.stopPropagation()}
-        >
-          {isDelete || isEdit ? (
-            <>
-              <button
-                className='p-1 hover:text-white'
-                onClick={handleTick}
-                aria-label='confirm'
-              >
-                <TickIcon />
-              </button>
-              <button
-                className='p-1 hover:text-white'
-                onClick={handleCross}
-                aria-label='cancel'
-              >
-                <CrossIcon />
-              </button>
-            </>
-          ) : (
-            <>
-              <div
-                className='relative md:hidden group-hover/folder:md:inline'
-                ref={paletteRef}
-              >
-                <button
-                  className='p-1 hover:text-white'
-                  onClick={() => {
-                    setShowPalette((prev) => !prev);
-                  }}
-                  aria-label='folder color'
-                >
-                  <ColorPaletteIcon />
-                </button>
-                {showPalette && (
-                  <div className='absolute left-0 bottom-0 translate-y-full p-2 z-20 bg-gray-900 rounded border border-gray-600 flex flex-col gap-2 items-center'>
-                    <>
-                      {folderColorOptions.map((c) => (
-                        <button
-                          key={c}
-                          style={{ background: c }}
-                          className={`hover:scale-90 transition-transform h-4 w-4 rounded-full`}
-                          onClick={() => {
-                            updateColor(c);
-                          }}
-                          aria-label={c}
-                        />
-                      ))}
-                      <button
-                        onClick={() => {
-                          updateColor();
-                        }}
-                        aria-label='default color'
-                      >
-                        <RefreshIcon />
-                      </button>
-                    </>
-                  </div>
-                )}
-              </div>
-
-              <button
-                className='p-1 hover:text-white md:hidden group-hover/folder:md:inline'
-                onClick={() => setIsEdit(true)}
-                aria-label='edit folder title'
-              >
-                <EditIcon />
-              </button>
-              <button
-                className='p-1 hover:text-white md:hidden group-hover/folder:md:inline'
-                onClick={() => setIsDelete(true)}
-                aria-label='delete folder'
-              >
-                <DeleteIcon />
-              </button>
-              <button
-                className='p-1 hover:text-white'
-                onClick={toggleExpanded}
-                aria-label='expand folder'
-              >
-                <DownChevronArrow
-                  className={`${
-                    isExpanded ? 'rotate-180' : ''
-                  } transition-transform`}
-                />
-              </button>
-            </>
-          )}
-        </div>
-      </div>
-      <div className='ml-3 pl-1 border-l-2 border-gray-700 flex flex-col gap-1 parent'>
-        {isExpanded && <NewChat folder={folderId} />}
-        {isExpanded &&
-          folderChats.map((chat) => (
-            <ChatHistory
-              title={chat.title}
-              chatIndex={chat.index}
-              key={`${chat.title}-${chat.index}`}
-            />
-          ))}
-      </div>
-    </div>
-  );
-};
-
-export default ChatFolder;
diff --git a/spaces/portal/Xenova-Semantic-Image-Search/style.css b/spaces/portal/Xenova-Semantic-Image-Search/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/portal/Xenova-Semantic-Image-Search/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/prerna9811/Chord/portaudio/cmake_support/FindJack.cmake b/spaces/prerna9811/Chord/portaudio/cmake_support/FindJack.cmake
deleted file mode 100644
index 96e0b501b6c6c053a7a96f80b744ac3c1c8c68b2..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/cmake_support/FindJack.cmake
+++ /dev/null
@@ -1,41 +0,0 @@
-# - Try to find jack
-# Once done this will define
-#  JACK_FOUND - System has jack
-#  JACK_INCLUDE_DIRS - The jack include directories
-#  JACK_LIBRARIES - The libraries needed to use jack
-#  JACK_DEFINITIONS - Compiler switches required for using jack
-
-if (JACK_LIBRARIES AND JACK_INCLUDE_DIRS)
-
-	# in cache already
-	set(JACK_FOUND TRUE)
-
-else (JACK_LIBRARIES AND JACK_INCLUDE_DIRS)
-
-	set(JACK_DEFINITIONS "")
-
-	# Look for pkg-config and use it (if available) to find package
-	find_package(PkgConfig QUIET)
-	if (PKG_CONFIG_FOUND)
-		pkg_search_module(JACK QUIET jack)
-	endif (PKG_CONFIG_FOUND)
-
-	if (NOT JACK_FOUND)
-
-		find_path(JACK_INCLUDE_DIR jack/jack.h HINTS ${JACK_INCLUDEDIR} ${JACK_INCLUDE_DIRS} PATH_SUFFIXES jack)
-		find_library(JACK_LIBRARY NAMES jack HINTS ${JACK_LIBDIR} ${JACK_LIBRARY_DIRS})
-
-		set(JACK_LIBRARIES    ${JACK_LIBRARY})
-		set(JACK_INCLUDE_DIRS ${JACK_INCLUDE_DIR})
-
-		include(FindPackageHandleStandardArgs)
-
-		# Set JACK_FOUND if the library and include paths were found
-		find_package_handle_standard_args(jack DEFAULT_MSG JACK_LIBRARY JACK_INCLUDE_DIR)
-
-		# Don't show include/library paths in cmake GUI
-		mark_as_advanced(JACK_INCLUDE_DIR JACK_LIBRARY)
-
-	endif (NOT JACK_FOUND)
-
-endif (JACK_LIBRARIES AND JACK_INCLUDE_DIRS)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/altair/jupyter/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/altair/jupyter/__init__.py
deleted file mode 100644
index 651ab11e4cf8de15370bbf02efd36315c1d27e82..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/altair/jupyter/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-try:
-    import anywidget  # noqa: F401
-except ImportError:
-    # When anywidget isn't available, create stand-in JupyterChart class
-    # that raises an informative import error on construction. This
-    # way we can make JupyterChart available in the altair namespace
-    # when anywidget is not installed
-    class JupyterChart:
-        def __init__(self, *args, **kwargs):
-            raise ImportError(
-                "The Altair JupyterChart requires the anywidget \n"
-                "Python package which may be installed using pip with\n"
-                "    pip install anywidget\n"
-                "or using conda with\n"
-                "    conda install -c conda-forge anywidget\n"
-                "Afterwards, you will need to restart your Python kernel."
-            )
-
-else:
-    from .jupyter_chart import JupyterChart  # noqa: F401
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/qu2cu/qu2cu.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/qu2cu/qu2cu.c
deleted file mode 100644
index a2b22b4273cdac93665592d46a18dae769067d44..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/qu2cu/qu2cu.c
+++ /dev/null
@@ -1,16235 +0,0 @@
-/* Generated by Cython 3.0.3 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "define_macros": [
-            [
-                "CYTHON_TRACE_NOGIL",
-                "1"
-            ]
-        ],
-        "name": "fontTools.qu2cu.qu2cu",
-        "sources": [
-            "Lib/fontTools/qu2cu/qu2cu.py"
-        ]
-    },
-    "module_name": "fontTools.qu2cu.qu2cu"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#if defined(CYTHON_LIMITED_API) && 0
-  #ifndef Py_LIMITED_API
-    #if CYTHON_LIMITED_API+0 > 0x03030000
-      #define Py_LIMITED_API CYTHON_LIMITED_API
-    #else
-      #define Py_LIMITED_API 0x03030000
-    #endif
-  #endif
-#endif
-
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.7+ or Python 3.3+.
-#else
-#if CYTHON_LIMITED_API
-#define __PYX_EXTRA_ABI_MODULE_NAME "limited"
-#else
-#define __PYX_EXTRA_ABI_MODULE_NAME ""
-#endif
-#define CYTHON_ABI "3_0_3" __PYX_EXTRA_ABI_MODULE_NAME
-#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
-#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030003F0
-#define CYTHON_FUTURE_DIVISION 1
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #define HAVE_LONG_LONG
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#define __PYX_LIMITED_VERSION_HEX PY_VERSION_HEX
-#if defined(GRAALVM_PYTHON)
-  /* For very preliminary testing purposes. Most variables are set the same as PyPy.
-     The existence of this section does not imply that anything works or is even tested */
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PYPY_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #ifndef CYTHON_USE_TYPE_SPECS
-    #define CYTHON_USE_TYPE_SPECS 0
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #if PY_VERSION_HEX < 0x03090000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00)
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(CYTHON_LIMITED_API)
-  #ifdef Py_LIMITED_API
-    #undef __PYX_LIMITED_VERSION_HEX
-    #define __PYX_LIMITED_VERSION_HEX Py_LIMITED_API
-  #endif
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 1
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_CLINE_IN_TRACEBACK
-  #define CYTHON_CLINE_IN_TRACEBACK 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 1
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #endif
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 1
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 0
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PY_NOGIL)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #ifndef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_TYPE_SPECS
-    #define CYTHON_USE_TYPE_SPECS 0
-  #endif
-  #ifndef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #ifndef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_GIL
-    #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6)
-  #endif
-  #ifndef CYTHON_METH_FASTCALL
-    #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1)
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL 1
-  #endif
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_MODULE_STATE
-    #define CYTHON_USE_MODULE_STATE 0
-  #endif
-  #if PY_VERSION_HEX < 0x030400a1
-    #undef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 0
-  #elif !defined(CYTHON_USE_TP_FINALIZE)
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #if PY_VERSION_HEX < 0x030600B1
-    #undef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS 0
-  #elif !defined(CYTHON_USE_DICT_VERSIONS)
-    #define CYTHON_USE_DICT_VERSIONS  (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #if PY_VERSION_HEX < 0x030700A3
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK 1
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL  (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(maybe_unused)
-        #define CYTHON_UNUSED [[maybe_unused]]
-      #endif
-    #endif
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-  #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_USE_CPP_STD_MOVE
-  #if defined(__cplusplus) && (\
-    __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1600))
-    #define CYTHON_USE_CPP_STD_MOVE 1
-  #else
-    #define CYTHON_USE_CPP_STD_MOVE 0
-  #endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-            typedef unsigned char     uint8_t;
-            typedef unsigned short    uint16_t;
-            typedef unsigned int      uint32_t;
-        #else
-            typedef unsigned __int8   uint8_t;
-            typedef unsigned __int16  uint16_t;
-            typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-    #if _MSC_VER < 1300
-        #ifdef _WIN64
-            typedef unsigned long long  __pyx_uintptr_t;
-        #else
-            typedef unsigned int        __pyx_uintptr_t;
-        #endif
-    #else
-        #ifdef _WIN64
-            typedef unsigned __int64    __pyx_uintptr_t;
-        #else
-            typedef unsigned __int32    __pyx_uintptr_t;
-        #endif
-    #endif
-#else
-    #include <stdint.h>
-    typedef uintptr_t  __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(fallthrough) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(fallthrough)
-        #define CYTHON_FALLTHROUGH [[fallthrough]]
-      #endif
-    #endif
-    #ifndef CYTHON_FALLTHROUGH
-      #if __has_cpp_attribute(clang::fallthrough)
-        #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-      #elif __has_cpp_attribute(gnu::fallthrough)
-        #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-      #endif
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-#ifdef __cplusplus
-  template <typename T>
-  struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);};
-  #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL<type>::value)
-#else
-  #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0)
-#endif
-#if CYTHON_COMPILING_IN_PYPY == 1
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000)
-#else
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000)
-#endif
-#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer))
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_DefaultClassType PyClass_Type
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if CYTHON_COMPILING_IN_LIMITED_API
-    static CYTHON_INLINE PyObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *exception_table = NULL;
-        PyObject *types_module=NULL, *code_type=NULL, *result=NULL;
-        #if __PYX_LIMITED_VERSION_HEX < 0x030B0000
-        PyObject *version_info; // borrowed
-        #endif
-        PyObject *py_minor_version = NULL;
-        long minor_version = 0;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        #if __PYX_LIMITED_VERSION_HEX >= 0x030B0000
-        minor_version = 11; // we don't yet need to distinguish between versions > 11
-        #else
-        if (!(version_info = PySys_GetObject("version_info"))) goto end;
-        if (!(py_minor_version = PySequence_GetItem(version_info, 1))) goto end;
-        minor_version = PyLong_AsLong(py_minor_version);
-        if (minor_version == -1 && PyErr_Occurred()) goto end;
-        #endif
-        if (!(types_module = PyImport_ImportModule("types"))) goto end;
-        if (!(code_type = PyObject_GetAttrString(types_module, "CodeType"))) goto end;
-        if (minor_version <= 7) {
-            (void)p;
-            result = PyObject_CallFunction(code_type, "iiiiiOOOOOOiOO", a, k, l, s, f, code,
-                          c, n, v, fn, name, fline, lnos, fv, cell);
-        } else if (minor_version <= 10) {
-            result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOiOO", a,p, k, l, s, f, code,
-                          c, n, v, fn, name, fline, lnos, fv, cell);
-        } else {
-            if (!(exception_table = PyBytes_FromStringAndSize(NULL, 0))) goto end;
-            result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOOiOO", a,p, k, l, s, f, code,
-                          c, n, v, fn, name, name, fline, lnos, exception_table, fv, cell);
-        }
-    end:
-        Py_XDECREF(code_type);
-        Py_XDECREF(exception_table);
-        Py_XDECREF(types_module);
-        Py_XDECREF(py_minor_version);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return result;
-    }
-    #ifndef CO_OPTIMIZED
-    #define CO_OPTIMIZED 0x0001
-    #endif
-    #ifndef CO_NEWLOCALS
-    #define CO_NEWLOCALS 0x0002
-    #endif
-    #ifndef CO_VARARGS
-    #define CO_VARARGS 0x0004
-    #endif
-    #ifndef CO_VARKEYWORDS
-    #define CO_VARKEYWORDS 0x0008
-    #endif
-    #ifndef CO_ASYNC_GENERATOR
-    #define CO_ASYNC_GENERATOR 0x0200
-    #endif
-    #ifndef CO_GENERATOR
-    #define CO_GENERATOR 0x0020
-    #endif
-    #ifndef CO_COROUTINE
-    #define CO_COROUTINE 0x0080
-    #endif
-#elif PY_VERSION_HEX >= 0x030B0000
-  static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-    PyCodeObject *result;
-    PyObject *empty_bytes = PyBytes_FromStringAndSize("", 0);  // we don't have access to __pyx_empty_bytes here
-    if (!empty_bytes) return NULL;
-    result =
-      #if PY_VERSION_HEX >= 0x030C0000
-        PyUnstable_Code_NewWithPosOnlyArgs
-      #else
-        PyCode_NewWithPosOnlyArgs
-      #endif
-        (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, empty_bytes);
-    Py_DECREF(empty_bytes);
-    return result;
-  }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
-  #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
-  #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is)
-  #define __Pyx_Py_Is(x, y)  Py_Is(x, y)
-#else
-  #define __Pyx_Py_Is(x, y) ((x) == (y))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone)
-  #define __Pyx_Py_IsNone(ob) Py_IsNone(ob)
-#else
-  #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue)
-  #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob)
-#else
-  #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse)
-  #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob)
-#else
-  #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False)
-#endif
-#define __Pyx_NoneAsNull(obj)  (__Pyx_Py_IsNone(obj) ? NULL : (obj))
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef CO_COROUTINE
-  #define CO_COROUTINE 0x80
-#endif
-#ifndef CO_ASYNC_GENERATOR
-  #define CO_ASYNC_GENERATOR 0x200
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef Py_TPFLAGS_SEQUENCE
-  #define Py_TPFLAGS_SEQUENCE 0
-#endif
-#ifndef Py_TPFLAGS_MAPPING
-  #define Py_TPFLAGS_MAPPING 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
-  #define __Pyx_METH_FASTCALL METH_FASTCALL
-  #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
-  #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
-  #define __Pyx_METH_FASTCALL METH_VARARGS
-  #define __Pyx_PyCFunction_FastCall PyCFunction
-  #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
-  #define __pyx_vectorcallfunc vectorcallfunc
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  PY_VECTORCALL_ARGUMENTS_OFFSET
-  #define __Pyx_PyVectorcall_NARGS(n)  PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
-  typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
-                                            size_t nargsf, PyObject *kwnames);
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  ((size_t)1 << (8 * sizeof(size_t) - 1))
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET))
-#else
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  0
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(n))
-#endif
-#if PY_MAJOR_VERSION >= 0x030900B1
-#define __Pyx_PyCFunction_CheckExact(func)  PyCFunction_CheckExact(func)
-#else
-#define __Pyx_PyCFunction_CheckExact(func)  PyCFunction_Check(func)
-#endif
-#define __Pyx_CyOrPyCFunction_Check(func)  PyCFunction_Check(func)
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func)  (((PyCFunctionObject*)(func))->m_ml->ml_meth)
-#elif !CYTHON_COMPILING_IN_LIMITED_API
-#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func)  PyCFunction_GET_FUNCTION(func)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_CyOrPyCFunction_GET_FLAGS(func)  (((PyCFunctionObject*)(func))->m_ml->ml_flags)
-static CYTHON_INLINE PyObject* __Pyx_CyOrPyCFunction_GET_SELF(PyObject *func) {
-    return (__Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_STATIC) ? NULL : ((PyCFunctionObject*)func)->m_self;
-}
-#endif
-static CYTHON_INLINE int __Pyx__IsSameCFunction(PyObject *func, void *cfunc) {
-#if CYTHON_COMPILING_IN_LIMITED_API
-    return PyCFunction_Check(func) && PyCFunction_GetFunction(func) == (PyCFunction) cfunc;
-#else
-    return PyCFunction_Check(func) && PyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc;
-#endif
-}
-#define __Pyx_IsSameCFunction(func, cfunc)   __Pyx__IsSameCFunction(func, cfunc)
-#if __PYX_LIMITED_VERSION_HEX < 0x030900B1
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  ((void)m, PyType_FromSpecWithBases(s, b))
-  typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *);
-#else
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  PyType_FromModuleAndSpec(m, s, b)
-  #define __Pyx_PyCMethod  PyCMethod
-#endif
-#ifndef METH_METHOD
-  #define METH_METHOD 0x200
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyThreadState_Current PyThreadState_Get()
-#elif !CYTHON_FAST_THREAD_STATE
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
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-  #define __Pyx_PyUnicode_KIND(u)         ((int)sizeof(Py_UNICODE))
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-#if PY_VERSION_HEX >= 0x030C00A6
-#define __Pyx_PyErr_Occurred()  (__pyx_tstate->current_exception != NULL)
-#define __Pyx_PyErr_CurrentExceptionType()  (__pyx_tstate->current_exception ? (PyObject*) Py_TYPE(__pyx_tstate->current_exception) : (PyObject*) NULL)
-#else
-#define __Pyx_PyErr_Occurred()  (__pyx_tstate->curexc_type != NULL)
-#define __Pyx_PyErr_CurrentExceptionType()  (__pyx_tstate->curexc_type)
-#endif
-#else
-#define __Pyx_PyThreadState_declare
-#define __Pyx_PyThreadState_assign
-#define __Pyx_PyErr_Occurred()  (PyErr_Occurred() != NULL)
-#define __Pyx_PyErr_CurrentExceptionType()  PyErr_Occurred()
-#endif
-
-/* PyErrFetchRestore.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
-#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6
-#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
-#else
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#endif
-#else
-#define __Pyx_PyErr_Clear() PyErr_Clear()
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
-#endif
-
-/* PyObjectGetAttrStr.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
-#endif
-
-/* PyObjectGetAttrStrNoError.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);
-
-/* GetBuiltinName.proto */
-static PyObject *__Pyx_GetBuiltinName(PyObject *name);
-
-/* TupleAndListFromArray.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n);
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n);
-#endif
-
-/* IncludeStringH.proto */
-#include <string.h>
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* fastcall.proto */
-#if CYTHON_AVOID_BORROWED_REFS
-    #define __Pyx_Arg_VARARGS(args, i) PySequence_GetItem(args, i)
-#elif CYTHON_ASSUME_SAFE_MACROS
-    #define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i)
-#else
-    #define __Pyx_Arg_VARARGS(args, i) PyTuple_GetItem(args, i)
-#endif
-#if CYTHON_AVOID_BORROWED_REFS
-    #define __Pyx_Arg_NewRef_VARARGS(arg) __Pyx_NewRef(arg)
-    #define __Pyx_Arg_XDECREF_VARARGS(arg) Py_XDECREF(arg)
-#else
-    #define __Pyx_Arg_NewRef_VARARGS(arg) arg // no-op
-    #define __Pyx_Arg_XDECREF_VARARGS(arg) // no-op - arg is borrowed
-#endif
-#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds)
-#define __Pyx_KwValues_VARARGS(args, nargs) NULL
-#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s)
-#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw)
-#if CYTHON_METH_FASTCALL
-    #define __Pyx_Arg_FASTCALL(args, i) args[i]
-    #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds)
-    #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs))
-    static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s);
-    #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw)
-    #define __Pyx_Arg_NewRef_FASTCALL(arg) arg // no-op, __Pyx_Arg_FASTCALL is direct and this needs
-    #define __Pyx_Arg_XDECREF_FASTCALL(arg)  // no-op - arg was returned from array
-#else
-    #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS
-    #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS
-    #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS
-    #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS
-    #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS
-    #define __Pyx_Arg_NewRef_FASTCALL(arg) __Pyx_Arg_NewRef_VARARGS(arg)
-    #define __Pyx_Arg_XDECREF_FASTCALL(arg) __Pyx_Arg_XDECREF_VARARGS(arg)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start)
-#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start)
-#else
-#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop)
-#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop)
-#endif
-
-/* RaiseArgTupleInvalid.proto */
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
-    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
-
-/* RaiseDoubleKeywords.proto */
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
-
-/* ParseKeywords.proto */
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,
-    const char* function_name);
-
-/* GetItemInt.proto */
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
-               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-/* AssertionsEnabled.proto */
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define __Pyx_init_assertions_enabled()  (0)
-  #define __pyx_assertions_enabled()  (1)
-#elif CYTHON_COMPILING_IN_LIMITED_API  ||  (CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030C0000)
-  static int __pyx_assertions_enabled_flag;
-  #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag)
-  static int __Pyx_init_assertions_enabled(void) {
-    PyObject *builtins, *debug, *debug_str;
-    int flag;
-    builtins = PyEval_GetBuiltins();
-    if (!builtins) goto bad;
-    debug_str = PyUnicode_FromStringAndSize("__debug__", 9);
-    if (!debug_str) goto bad;
-    debug = PyObject_GetItem(builtins, debug_str);
-    Py_DECREF(debug_str);
-    if (!debug) goto bad;
-    flag = PyObject_IsTrue(debug);
-    Py_DECREF(debug);
-    if (flag == -1) goto bad;
-    __pyx_assertions_enabled_flag = flag;
-    return 0;
-  bad:
-    __pyx_assertions_enabled_flag = 1;
-    return -1;
-  }
-#else
-  #define __Pyx_init_assertions_enabled()  (0)
-  #define __pyx_assertions_enabled()  (!Py_OptimizeFlag)
-#endif
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
-
-/* py_abs.proto */
-#if CYTHON_USE_PYLONG_INTERNALS
-static PyObject *__Pyx_PyLong_AbsNeg(PyObject *num);
-#define __Pyx_PyNumber_Absolute(x)\
-    ((likely(PyLong_CheckExact(x))) ?\
-         (likely(__Pyx_PyLong_IsNonNeg(x)) ? (Py_INCREF(x), (x)) : __Pyx_PyLong_AbsNeg(x)) :\
-         PyNumber_Absolute(x))
-#else
-#define __Pyx_PyNumber_Absolute(x)  PyNumber_Absolute(x)
-#endif
-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* SliceTupleAndList.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(PyObject* src, Py_ssize_t start, Py_ssize_t stop);
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(PyObject* src, Py_ssize_t start, Py_ssize_t stop);
-#else
-#define __Pyx_PyList_GetSlice(seq, start, stop)   PySequence_GetSlice(seq, start, stop)
-#define __Pyx_PyTuple_GetSlice(seq, start, stop)  PySequence_GetSlice(seq, start, stop)
-#endif
-
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_SubtractCObj(op1, op2, intval, inplace, zerodivision_check)\
-    (inplace ? PyNumber_InPlaceSubtract(op1, op2) : PyNumber_Subtract(op1, op2))
-#endif
-
-/* ArgTypeTest.proto */
-#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
-    ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\
-        __Pyx__ArgTypeTest(obj, type, name, exact))
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
-
-/* RaiseUnboundLocalError.proto */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* pep479.proto */
-static void __Pyx_Generator_Replace_StopIteration(int in_async_gen);
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* IterFinish.proto */
-static CYTHON_INLINE int __Pyx_IterFinish(void);
-
-/* UnpackItemEndCheck.proto */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
-
-/* PyObjectCall.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-/* PyDictVersioning.proto */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-#define __PYX_DICT_VERSION_INIT  ((PY_UINT64_T) -1)
-#define __PYX_GET_DICT_VERSION(dict)  (((PyDictObject*)(dict))->ma_version_tag)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\
-    (version_var) = __PYX_GET_DICT_VERSION(dict);\
-    (cache_var) = (value);
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
-        (VAR) = __pyx_dict_cached_value;\
-    } else {\
-        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
-        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
-    }\
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
-#else
-#define __PYX_GET_DICT_VERSION(dict)  (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
-#endif
-
-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  do {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
-        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-} while(0)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  do {\
-    PY_UINT64_T __pyx_dict_version;\
-    PyObject *__pyx_dict_cached_value;\
-    (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-} while(0)
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
-#endif
-
-/* PyFunctionFastCall.proto */
-#if CYTHON_FAST_PYCALL
-#if !CYTHON_VECTORCALL
-#define __Pyx_PyFunction_FastCall(func, args, nargs)\
-    __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);
-#endif
-#define __Pyx_BUILD_ASSERT_EXPR(cond)\
-    (sizeof(char [1 - 2*!(cond)]) - 1)
-#ifndef Py_MEMBER_SIZE
-#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
-#endif
-#if !CYTHON_VECTORCALL
-#if PY_VERSION_HEX >= 0x03080000
-  #include "frameobject.h"
-#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-  #define __Pxy_PyFrame_Initialize_Offsets()
-  #define __Pyx_PyFrame_GetLocalsplus(frame)  ((frame)->f_localsplus)
-#else
-  static size_t __pyx_pyframe_localsplus_offset = 0;
-  #include "frameobject.h"
-  #define __Pxy_PyFrame_Initialize_Offsets()\
-    ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
-     (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus)))
-  #define __Pyx_PyFrame_GetLocalsplus(frame)\
-    (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset))
-#endif
-#endif
-#endif
-
-/* PyObjectCallMethO.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
-#endif
-
-/* PyObjectFastCall.proto */
-#define __Pyx_PyObject_FastCall(func, args, nargs)  __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL)
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs);
-
-/* SliceObject.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
-        PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
-        int has_cstart, int has_cstop, int wraparound);
-
-/* PyObjectCallNoArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-
-/* PyObjectCallOneArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
-
-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* pop.proto */
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L);
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L);
-#define __Pyx_PyObject_Pop(L) (likely(PyList_CheckExact(L)) ?\
-    __Pyx_PyList_Pop(L) : __Pyx__PyObject_Pop(L))
-#else
-#define __Pyx_PyList_Pop(L)  __Pyx__PyObject_Pop(L)
-#define __Pyx_PyObject_Pop(L)  __Pyx__PyObject_Pop(L)
-#endif
-
-/* UnpackUnboundCMethod.proto */
-typedef struct {
-    PyObject *type;
-    PyObject **method_name;
-    PyCFunction func;
-    PyObject *method;
-    int flag;
-} __Pyx_CachedCFunction;
-
-/* CallUnboundCMethod0.proto */
-static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_CallUnboundCMethod0(cfunc, self)\
-    (likely((cfunc)->func) ?\
-        (likely((cfunc)->flag == METH_NOARGS) ?  (*((cfunc)->func))(self, NULL) :\
-         (PY_VERSION_HEX >= 0x030600B1 && likely((cfunc)->flag == METH_FASTCALL) ?\
-            (PY_VERSION_HEX >= 0x030700A0 ?\
-                (*(__Pyx_PyCFunctionFast)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0) :\
-                (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0, NULL)) :\
-          (PY_VERSION_HEX >= 0x030700A0 && (cfunc)->flag == (METH_FASTCALL | METH_KEYWORDS) ?\
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-#define __Pyx_CallUnboundCMethod0(cfunc, self)  __Pyx__CallUnboundCMethod0(cfunc, self)
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-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
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-    Py_DECREF(none);
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-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
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-/* RaiseUnexpectedTypeError.proto */
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-/* PyIntBinop.proto */
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-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
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-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
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-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
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-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
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-/* pyfrozenset_new.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it);
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-/* PySetContains.proto */
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq);
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-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
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-/* ImportFrom.proto */
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-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type);
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-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
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-/* PyType_Ready.proto */
-CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t);
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-/* PyObject_GenericGetAttrNoDict.proto */
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-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
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-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2))
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyErr_ExceptionMatches2(err1, err2)  __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2)
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
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-/* ImportDottedModule.proto */
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple);
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple);
-#endif
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-/* FetchSharedCythonModule.proto */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void);
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-/* FetchCommonType.proto */
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-#else
-static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases);
-#endif
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-/* PyMethodNew.proto */
-#if CYTHON_COMPILING_IN_LIMITED_API
-static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
-    PyObject *typesModule=NULL, *methodType=NULL, *result=NULL;
-    CYTHON_UNUSED_VAR(typ);
-    if (!self)
-        return __Pyx_NewRef(func);
-    typesModule = PyImport_ImportModule("types");
-    if (!typesModule) return NULL;
-    methodType = PyObject_GetAttrString(typesModule, "MethodType");
-    Py_DECREF(typesModule);
-    if (!methodType) return NULL;
-    result = PyObject_CallFunctionObjArgs(methodType, func, self, NULL);
-    Py_DECREF(methodType);
-    return result;
-}
-#elif PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
-    CYTHON_UNUSED_VAR(typ);
-    if (!self)
-        return __Pyx_NewRef(func);
-    return PyMethod_New(func, self);
-}
-#else
-    #define __Pyx_PyMethod_New PyMethod_New
-#endif
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-/* PyVectorcallFastCallDict.proto */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
-#endif
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-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CYFUNCTION_COROUTINE     0x08
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#else
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      ((PyObject*) ((PyCMethodObject *) (f))->mm_class)
-#endif
-#define __Pyx_CyFunction_SetClassObj(f, classobj)\
-    __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj))
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-#if CYTHON_COMPILING_IN_LIMITED_API
-    PyObject_HEAD
-    PyObject *func;
-#elif PY_VERSION_HEX < 0x030900B1
-    PyCFunctionObject func;
-#else
-    PyCMethodObject func;
-#endif
-#if CYTHON_BACKPORT_VECTORCALL
-    __pyx_vectorcallfunc func_vectorcall;
-#endif
-#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    PyObject *func_classobj;
-#endif
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-    PyObject *func_is_coroutine;
-} __pyx_CyFunctionObject;
-#undef __Pyx_CyOrPyCFunction_Check
-#define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_CyOrPyCFunction_Check(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
-#define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
-static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc);
-#undef __Pyx_IsSameCFunction
-#define __Pyx_IsSameCFunction(func, cfunc)   __Pyx__IsSameCyOrCFunction(func, cfunc)
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(PyObject *module);
-#if CYTHON_METH_FASTCALL
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-#if CYTHON_BACKPORT_VECTORCALL
-#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall)
-#else
-#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
-#endif
-#endif
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-/* RealImag.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if defined(__cplusplus) && CYTHON_CCOMPLEX\
-        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_double(a, b) ((a)-(b))
-    #define __Pyx_c_prod_double(a, b) ((a)*(b))
-    #define __Pyx_c_quot_double(a, b) ((a)/(b))
-    #define __Pyx_c_neg_double(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
-    #define __Pyx_c_conj_double(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (::std::abs(z))
-        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_double(z) ((z)==0)
-    #define __Pyx_c_conj_double(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (cabs(z))
-        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    #endif
-#endif
-
-/* FromPy.proto */
-static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject*);
-
-/* GCCDiagnostics.proto */
-#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* ToPy.proto */
-#define __pyx_PyComplex_FromComplex(z)\
-        PyComplex_FromDoubles((double)__Pyx_CREAL(z),\
-                              (double)__Pyx_CIMAG(z))
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* FormatTypeName.proto */
-#if CYTHON_COMPILING_IN_LIMITED_API
-typedef PyObject *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%U"
-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
-#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj)
-#else
-typedef const char *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%.200s"
-#define __Pyx_PyType_GetName(tp) ((tp)->tp_name)
-#define __Pyx_DECREF_TypeName(obj)
-#endif
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* PyObjectCall2Args.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2);
-
-/* PyObjectCallMethod1.proto */
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
-
-/* CoroutineBase.proto */
-struct __pyx_CoroutineObject;
-typedef PyObject *(*__pyx_coroutine_body_t)(struct __pyx_CoroutineObject *, PyThreadState *, PyObject *);
-#if CYTHON_USE_EXC_INFO_STACK
-#define __Pyx_ExcInfoStruct  _PyErr_StackItem
-#else
-typedef struct {
-    PyObject *exc_type;
-    PyObject *exc_value;
-    PyObject *exc_traceback;
-} __Pyx_ExcInfoStruct;
-#endif
-typedef struct __pyx_CoroutineObject {
-    PyObject_HEAD
-    __pyx_coroutine_body_t body;
-    PyObject *closure;
-    __Pyx_ExcInfoStruct gi_exc_state;
-    PyObject *gi_weakreflist;
-    PyObject *classobj;
-    PyObject *yieldfrom;
-    PyObject *gi_name;
-    PyObject *gi_qualname;
-    PyObject *gi_modulename;
-    PyObject *gi_code;
-    PyObject *gi_frame;
-    int resume_label;
-    char is_running;
-} __pyx_CoroutineObject;
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-    PyTypeObject *type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-    PyObject *name, PyObject *qualname, PyObject *module_name);
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name);
-static CYTHON_INLINE void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *self);
-static int __Pyx_Coroutine_clear(PyObject *self);
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value);
-static PyObject *__Pyx_Coroutine_Close(PyObject *self);
-static PyObject *__Pyx_Coroutine_Throw(PyObject *gen, PyObject *args);
-#if CYTHON_USE_EXC_INFO_STACK
-#define __Pyx_Coroutine_SwapException(self)
-#define __Pyx_Coroutine_ResetAndClearException(self)  __Pyx_Coroutine_ExceptionClear(&(self)->gi_exc_state)
-#else
-#define __Pyx_Coroutine_SwapException(self) {\
-    __Pyx_ExceptionSwap(&(self)->gi_exc_state.exc_type, &(self)->gi_exc_state.exc_value, &(self)->gi_exc_state.exc_traceback);\
-    __Pyx_Coroutine_ResetFrameBackpointer(&(self)->gi_exc_state);\
-    }
-#define __Pyx_Coroutine_ResetAndClearException(self) {\
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-#define __pyx_m (PyState_FindModule(&__pyx_moduledef))
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-  Py_CLEAR(clear_module_state->__pyx_codeobj__14);
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-  Py_VISIT(traverse_module_state->__pyx_codeobj__14);
-  return 0;
-}
-#endif
-/* #### Code section: module_state_defines ### */
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
-#endif
-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
-#endif
-#ifdef __Pyx_Generator_USED
-#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType
-#endif
-#ifdef __Pyx_IterableCoroutine_USED
-#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#define __pyx_type_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__genexpr __pyx_mstate_global->__pyx_type_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__genexpr
-#define __pyx_type_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr __pyx_mstate_global->__pyx_type_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr
-#endif
-#define __pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__genexpr __pyx_mstate_global->__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__genexpr
-#define __pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr __pyx_mstate_global->__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr
-#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError
-#define __pyx_n_s_AttributeError __pyx_mstate_global->__pyx_n_s_AttributeError
-#define __pyx_n_s_COMPILED __pyx_mstate_global->__pyx_n_s_COMPILED
-#define __pyx_n_s_ImportError __pyx_mstate_global->__pyx_n_s_ImportError
-#define __pyx_kp_s_Lib_fontTools_qu2cu_qu2cu_py __pyx_mstate_global->__pyx_kp_s_Lib_fontTools_qu2cu_qu2cu_py
-#define __pyx_n_s_List __pyx_mstate_global->__pyx_n_s_List
-#define __pyx_kp_s_List_List_Point __pyx_mstate_global->__pyx_kp_s_List_List_Point
-#define __pyx_kp_s_List_Tuple_Point __pyx_mstate_global->__pyx_kp_s_List_Tuple_Point
-#define __pyx_kp_u_One_random_cubic_turned_into_d_q __pyx_mstate_global->__pyx_kp_u_One_random_cubic_turned_into_d_q
-#define __pyx_kp_u_Original_curve __pyx_mstate_global->__pyx_kp_u_Original_curve
-#define __pyx_n_s_Point __pyx_mstate_global->__pyx_n_s_Point
-#define __pyx_kp_u_Reconstructed_curve_s __pyx_mstate_global->__pyx_kp_u_Reconstructed_curve_s
-#define __pyx_n_s_Solution __pyx_mstate_global->__pyx_n_s_Solution
-#define __pyx_n_u_Solution __pyx_mstate_global->__pyx_n_u_Solution
-#define __pyx_kp_u_Those_quadratics_turned_back_int __pyx_mstate_global->__pyx_kp_u_Those_quadratics_turned_back_int
-#define __pyx_n_s_Tuple __pyx_mstate_global->__pyx_n_s_Tuple
-#define __pyx_n_s_Union __pyx_mstate_global->__pyx_n_s_Union
-#define __pyx_n_s_ZeroDivisionError __pyx_mstate_global->__pyx_n_s_ZeroDivisionError
-#define __pyx_n_s__15 __pyx_mstate_global->__pyx_n_s__15
-#define __pyx_kp_u__3 __pyx_mstate_global->__pyx_kp_u__3
-#define __pyx_n_s__4 __pyx_mstate_global->__pyx_n_s__4
-#define __pyx_n_s_add_implicit_on_curves __pyx_mstate_global->__pyx_n_s_add_implicit_on_curves
-#define __pyx_n_s_all __pyx_mstate_global->__pyx_n_s_all
-#define __pyx_n_s_all_cubic __pyx_mstate_global->__pyx_n_s_all_cubic
-#define __pyx_n_s_args __pyx_mstate_global->__pyx_n_s_args
-#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines
-#define __pyx_n_s_best_sol __pyx_mstate_global->__pyx_n_s_best_sol
-#define __pyx_n_s_bool __pyx_mstate_global->__pyx_n_s_bool
-#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem
-#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback
-#define __pyx_n_s_close __pyx_mstate_global->__pyx_n_s_close
-#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections
-#define __pyx_n_s_cost __pyx_mstate_global->__pyx_n_s_cost
-#define __pyx_n_s_costs __pyx_mstate_global->__pyx_n_s_costs
-#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count
-#define __pyx_kp_u_cu2qu_tolerance_g_qu2cu_toleranc __pyx_mstate_global->__pyx_kp_u_cu2qu_tolerance_g_qu2cu_toleranc
-#define __pyx_n_s_cubic __pyx_mstate_global->__pyx_n_s_cubic
-#define __pyx_n_s_curve __pyx_mstate_global->__pyx_n_s_curve
-#define __pyx_n_s_curve_to_quadratic __pyx_mstate_global->__pyx_n_s_curve_to_quadratic
-#define __pyx_n_s_curves __pyx_mstate_global->__pyx_n_s_curves
-#define __pyx_n_s_cython __pyx_mstate_global->__pyx_n_s_cython
-#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable
-#define __pyx_n_s_elevate_quadratic __pyx_mstate_global->__pyx_n_s_elevate_quadratic
-#define __pyx_n_s_elevated_quadratics __pyx_mstate_global->__pyx_n_s_elevated_quadratics
-#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable
-#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate
-#define __pyx_n_s_err __pyx_mstate_global->__pyx_n_s_err
-#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error
-#define __pyx_n_u_error __pyx_mstate_global->__pyx_n_u_error
-#define __pyx_n_s_float __pyx_mstate_global->__pyx_n_s_float
-#define __pyx_n_s_fontTools_cu2qu __pyx_mstate_global->__pyx_n_s_fontTools_cu2qu
-#define __pyx_n_s_fontTools_cu2qu_benchmark __pyx_mstate_global->__pyx_n_s_fontTools_cu2qu_benchmark
-#define __pyx_n_s_fontTools_misc __pyx_mstate_global->__pyx_n_s_fontTools_misc
-#define __pyx_n_s_fontTools_misc_bezierTools __pyx_mstate_global->__pyx_n_s_fontTools_misc_bezierTools
-#define __pyx_n_s_fontTools_qu2cu_qu2cu __pyx_mstate_global->__pyx_n_s_fontTools_qu2cu_qu2cu
-#define __pyx_n_s_forced __pyx_mstate_global->__pyx_n_s_forced
-#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc
-#define __pyx_n_s_generate_curve __pyx_mstate_global->__pyx_n_s_generate_curve
-#define __pyx_n_s_genexpr __pyx_mstate_global->__pyx_n_s_genexpr
-#define __pyx_n_s_i __pyx_mstate_global->__pyx_n_s_i
-#define __pyx_n_s_i_sol __pyx_mstate_global->__pyx_n_s_i_sol
-#define __pyx_n_s_i_sol_count __pyx_mstate_global->__pyx_n_s_i_sol_count
-#define __pyx_n_s_i_sol_error __pyx_mstate_global->__pyx_n_s_i_sol_error
-#define __pyx_n_s_imag __pyx_mstate_global->__pyx_n_s_imag
-#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import
-#define __pyx_n_s_impossible __pyx_mstate_global->__pyx_n_s_impossible
-#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing
-#define __pyx_n_s_is_complex __pyx_mstate_global->__pyx_n_s_is_complex
-#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine
-#define __pyx_n_s_is_cubic __pyx_mstate_global->__pyx_n_s_is_cubic
-#define __pyx_n_u_is_cubic __pyx_mstate_global->__pyx_n_u_is_cubic
-#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled
-#define __pyx_n_s_j __pyx_mstate_global->__pyx_n_s_j
-#define __pyx_n_s_j_sol_count __pyx_mstate_global->__pyx_n_s_j_sol_count
-#define __pyx_n_s_j_sol_error __pyx_mstate_global->__pyx_n_s_j_sol_error
-#define __pyx_n_s_k __pyx_mstate_global->__pyx_n_s_k
-#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main
-#define __pyx_n_u_main __pyx_mstate_global->__pyx_n_u_main
-#define __pyx_n_s_main_2 __pyx_mstate_global->__pyx_n_s_main_2
-#define __pyx_n_s_math __pyx_mstate_global->__pyx_n_s_math
-#define __pyx_n_s_max_err __pyx_mstate_global->__pyx_n_s_max_err
-#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name
-#define __pyx_n_s_namedtuple __pyx_mstate_global->__pyx_n_s_namedtuple
-#define __pyx_n_s_num_offcurves __pyx_mstate_global->__pyx_n_s_num_offcurves
-#define __pyx_n_s_num_points __pyx_mstate_global->__pyx_n_s_num_points
-#define __pyx_n_u_num_points __pyx_mstate_global->__pyx_n_u_num_points
-#define __pyx_n_s_off1 __pyx_mstate_global->__pyx_n_s_off1
-#define __pyx_n_s_off2 __pyx_mstate_global->__pyx_n_s_off2
-#define __pyx_n_s_on __pyx_mstate_global->__pyx_n_s_on
-#define __pyx_n_s_orig __pyx_mstate_global->__pyx_n_s_orig
-#define __pyx_n_s_p __pyx_mstate_global->__pyx_n_s_p
-#define __pyx_n_s_p0 __pyx_mstate_global->__pyx_n_s_p0
-#define __pyx_n_s_p1 __pyx_mstate_global->__pyx_n_s_p1
-#define __pyx_n_s_p1_2_3 __pyx_mstate_global->__pyx_n_s_p1_2_3
-#define __pyx_n_s_p2 __pyx_mstate_global->__pyx_n_s_p2
-#define __pyx_n_s_p3 __pyx_mstate_global->__pyx_n_s_p3
-#define __pyx_n_s_pop __pyx_mstate_global->__pyx_n_s_pop
-#define __pyx_n_s_print __pyx_mstate_global->__pyx_n_s_print
-#define __pyx_n_s_q __pyx_mstate_global->__pyx_n_s_q
-#define __pyx_n_s_qq __pyx_mstate_global->__pyx_n_s_qq
-#define __pyx_kp_u_quadratic_spline_requires_at_lea __pyx_mstate_global->__pyx_kp_u_quadratic_spline_requires_at_lea
-#define __pyx_n_s_quadratic_to_curves __pyx_mstate_global->__pyx_n_s_quadratic_to_curves
-#define __pyx_n_u_quadratic_to_curves __pyx_mstate_global->__pyx_n_u_quadratic_to_curves
-#define __pyx_n_s_quadratic_to_curves_locals_genex __pyx_mstate_global->__pyx_n_s_quadratic_to_curves_locals_genex
-#define __pyx_n_s_quadratics __pyx_mstate_global->__pyx_n_s_quadratics
-#define __pyx_n_s_quads __pyx_mstate_global->__pyx_n_s_quads
-#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range
-#define __pyx_n_s_real __pyx_mstate_global->__pyx_n_s_real
-#define __pyx_n_s_reconst __pyx_mstate_global->__pyx_n_s_reconst
-#define __pyx_n_s_reconstruct_tolerance __pyx_mstate_global->__pyx_n_s_reconstruct_tolerance
-#define __pyx_n_s_reconstructed __pyx_mstate_global->__pyx_n_s_reconstructed
-#define __pyx_n_s_reconstructed_iter __pyx_mstate_global->__pyx_n_s_reconstructed_iter
-#define __pyx_n_s_return __pyx_mstate_global->__pyx_n_s_return
-#define __pyx_n_s_reversed __pyx_mstate_global->__pyx_n_s_reversed
-#define __pyx_n_s_send __pyx_mstate_global->__pyx_n_s_send
-#define __pyx_n_s_sols __pyx_mstate_global->__pyx_n_s_sols
-#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec
-#define __pyx_n_s_spline_to_curves __pyx_mstate_global->__pyx_n_s_spline_to_curves
-#define __pyx_n_s_spline_to_curves_locals_genexpr __pyx_mstate_global->__pyx_n_s_spline_to_curves_locals_genexpr
-#define __pyx_n_s_splitCubicAtTC __pyx_mstate_global->__pyx_n_s_splitCubicAtTC
-#define __pyx_n_s_splits __pyx_mstate_global->__pyx_n_s_splits
-#define __pyx_n_s_start __pyx_mstate_global->__pyx_n_s_start
-#define __pyx_n_s_start_index __pyx_mstate_global->__pyx_n_s_start_index
-#define __pyx_n_u_start_index __pyx_mstate_global->__pyx_n_u_start_index
-#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test
-#define __pyx_n_s_this_count __pyx_mstate_global->__pyx_n_s_this_count
-#define __pyx_n_s_this_sol_count __pyx_mstate_global->__pyx_n_s_this_sol_count
-#define __pyx_n_s_throw __pyx_mstate_global->__pyx_n_s_throw
-#define __pyx_n_s_tolerance __pyx_mstate_global->__pyx_n_s_tolerance
-#define __pyx_n_s_ts __pyx_mstate_global->__pyx_n_s_ts
-#define __pyx_n_s_typing __pyx_mstate_global->__pyx_n_s_typing
-#define __pyx_n_s_u __pyx_mstate_global->__pyx_n_s_u
-#define __pyx_n_s_v __pyx_mstate_global->__pyx_n_s_v
-#define __pyx_n_s_x __pyx_mstate_global->__pyx_n_s_x
-#define __pyx_n_s_y __pyx_mstate_global->__pyx_n_s_y
-#define __pyx_n_s_zip __pyx_mstate_global->__pyx_n_s_zip
-#define __pyx_int_0 __pyx_mstate_global->__pyx_int_0
-#define __pyx_int_1 __pyx_mstate_global->__pyx_int_1
-#define __pyx_int_3 __pyx_mstate_global->__pyx_int_3
-#define __pyx_slice_ __pyx_mstate_global->__pyx_slice_
-#define __pyx_tuple__2 __pyx_mstate_global->__pyx_tuple__2
-#define __pyx_tuple__5 __pyx_mstate_global->__pyx_tuple__5
-#define __pyx_tuple__7 __pyx_mstate_global->__pyx_tuple__7
-#define __pyx_tuple__9 __pyx_mstate_global->__pyx_tuple__9
-#define __pyx_tuple__11 __pyx_mstate_global->__pyx_tuple__11
-#define __pyx_tuple__13 __pyx_mstate_global->__pyx_tuple__13
-#define __pyx_codeobj__6 __pyx_mstate_global->__pyx_codeobj__6
-#define __pyx_codeobj__8 __pyx_mstate_global->__pyx_codeobj__8
-#define __pyx_codeobj__10 __pyx_mstate_global->__pyx_codeobj__10
-#define __pyx_codeobj__12 __pyx_mstate_global->__pyx_codeobj__12
-#define __pyx_codeobj__14 __pyx_mstate_global->__pyx_codeobj__14
-/* #### Code section: module_code ### */
-
-/* "fontTools/qu2cu/qu2cu.py":43
- * 
- * # Copied from cu2qu
- * @cython.cfunc             # <<<<<<<<<<<<<<
- * @cython.returns(cython.int)
- * @cython.locals(
- */
-
-static int __pyx_f_9fontTools_5qu2cu_5qu2cu_cubic_farthest_fit_inside(__pyx_t_double_complex __pyx_v_p0, __pyx_t_double_complex __pyx_v_p1, __pyx_t_double_complex __pyx_v_p2, __pyx_t_double_complex __pyx_v_p3, double __pyx_v_tolerance) {
-  __pyx_t_double_complex __pyx_v_mid;
-  __pyx_t_double_complex __pyx_v_deriv3;
-  int __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "fontTools/qu2cu/qu2cu.py":72
- *     """
- *     # First check p2 then p1, as p2 has higher error early on.
- *     if abs(p2) <= tolerance and abs(p1) <= tolerance:             # <<<<<<<<<<<<<<
- *         return True
- * 
- */
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-        return __pyx_empty_tuple;
-    }
-    res = PyTuple_New(n);
-    if (unlikely(res == NULL)) return NULL;
-    __Pyx_copy_object_array(src, ((PyTupleObject*)res)->ob_item, n);
-    return res;
-}
-static CYTHON_INLINE PyObject *
-__Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n)
-{
-    PyObject *res;
-    if (n <= 0) {
-        return PyList_New(0);
-    }
-    res = PyList_New(n);
-    if (unlikely(res == NULL)) return NULL;
-    __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n);
-    return res;
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* fastcall */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s)
-{
-    Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames);
-    for (i = 0; i < n; i++)
-    {
-        if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i];
-    }
-    for (i = 0; i < n; i++)
-    {
-        int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ);
-        if (unlikely(eq != 0)) {
-            if (unlikely(eq < 0)) return NULL;  // error
-            return kwvalues[i];
-        }
-    }
-    return NULL;  // not found (no exception set)
-}
-#endif
-
-/* RaiseArgTupleInvalid */
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-/* RaiseDoubleKeywords */
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
-    while (1) {
-        Py_XDECREF(key); key = NULL;
-        Py_XDECREF(value); value = NULL;
-        if (kwds_is_tuple) {
-            Py_ssize_t size;
-#if CYTHON_ASSUME_SAFE_MACROS
-            size = PyTuple_GET_SIZE(kwds);
-#else
-            size = PyTuple_Size(kwds);
-            if (size < 0) goto bad;
-#endif
-            if (pos >= size) break;
-#if CYTHON_AVOID_BORROWED_REFS
-            key = __Pyx_PySequence_ITEM(kwds, pos);
-            if (!key) goto bad;
-#elif CYTHON_ASSUME_SAFE_MACROS
-            key = PyTuple_GET_ITEM(kwds, pos);
-#else
-            key = PyTuple_GetItem(kwds, pos);
-            if (!key) goto bad;
-#endif
-            value = kwvalues[pos];
-            pos++;
-        }
-        else
-        {
-            if (!PyDict_Next(kwds, &pos, &key, &value)) break;
-#if CYTHON_AVOID_BORROWED_REFS
-            Py_INCREF(key);
-#endif
-        }
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-#if CYTHON_AVOID_BORROWED_REFS
-            Py_INCREF(value); // transfer ownership of value to values
-            Py_DECREF(key);
-#endif
-            key = NULL;
-            value = NULL;
-            continue;
-        }
-#if !CYTHON_AVOID_BORROWED_REFS
-        Py_INCREF(key);
-#endif
-        Py_INCREF(value);
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-#if CYTHON_AVOID_BORROWED_REFS
-                    value = NULL;  // ownership transferred to values
-#endif
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key)
-                );
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-#if CYTHON_AVOID_BORROWED_REFS
-                    value = NULL; // ownership transferred to values
-#endif
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    Py_XDECREF(key);
-    Py_XDECREF(value);
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    Py_XDECREF(key);
-    Py_XDECREF(value);
-    return -1;
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (unlikely(!j)) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_subscript) {
-            PyObject *r, *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return NULL;
-            r = mm->mp_subscript(o, key);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    CYTHON_UNUSED_VAR(cause);
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-      #if PY_VERSION_HEX >= 0x030C00A6
-        PyException_SetTraceback(value, tb);
-      #elif CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* py_abs */
-#if CYTHON_USE_PYLONG_INTERNALS
-static PyObject *__Pyx_PyLong_AbsNeg(PyObject *n) {
-#if PY_VERSION_HEX >= 0x030C00A7
-    if (likely(__Pyx_PyLong_IsCompact(n))) {
-        return PyLong_FromSize_t(__Pyx_PyLong_CompactValueUnsigned(n));
-    }
-#else
-    if (likely(Py_SIZE(n) == -1)) {
-        return PyLong_FromUnsignedLong(__Pyx_PyLong_Digits(n)[0]);
-    }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-    {
-        PyObject *copy = _PyLong_Copy((PyLongObject*)n);
-        if (likely(copy)) {
-            #if PY_VERSION_HEX >= 0x030C00A7
-            ((PyLongObject*)copy)->long_value.lv_tag = ((PyLongObject*)copy)->long_value.lv_tag & ~_PyLong_SIGN_MASK;
-            #else
-            __Pyx_SET_SIZE(copy, -Py_SIZE(copy));
-            #endif
-        }
-        return copy;
-    }
-#else
-    return PyNumber_Negative(n);
-#endif
-}
-#endif
-
-/* SliceTupleAndList */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE void __Pyx_crop_slice(Py_ssize_t* _start, Py_ssize_t* _stop, Py_ssize_t* _length) {
-    Py_ssize_t start = *_start, stop = *_stop, length = *_length;
-    if (start < 0) {
-        start += length;
-        if (start < 0)
-            start = 0;
-    }
-    if (stop < 0)
-        stop += length;
-    else if (stop > length)
-        stop = length;
-    *_length = stop - start;
-    *_start = start;
-    *_stop = stop;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    Py_ssize_t length = PyList_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    if (length <= 0) {
-        return PyList_New(0);
-    }
-    return __Pyx_PyList_FromArray(((PyListObject*)src)->ob_item + start, length);
-}
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    Py_ssize_t length = PyTuple_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    return __Pyx_PyTuple_FromArray(((PyTupleObject*)src)->ob_item + start, length);
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_MAYBE_UNUSED_VAR(inplace);
-    CYTHON_UNUSED_VAR(zerodivision_check);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op2))) {
-        const long a = intval;
-        long x;
-        long b = PyInt_AS_LONG(op2);
-        
-            x = (long)((unsigned long)a - (unsigned long)b);
-            if (likely((x^a) >= 0 || (x^~b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op2))) {
-        const long a = intval;
-        long b, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG lla = intval;
-        PY_LONG_LONG llb, llx;
-#endif
-        if (unlikely(__Pyx_PyLong_IsZero(op2))) {
-            return __Pyx_NewRef(op1);
-        }
-        if (likely(__Pyx_PyLong_IsCompact(op2))) {
-            b = __Pyx_PyLong_CompactValue(op2);
-        } else {
-            const digit* digits = __Pyx_PyLong_Digits(op2);
-            const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op2);
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        b = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        b = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        b = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        b = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        b = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        b = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-            }
-        }
-                x = a - b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla - llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op2)) {
-        const long a = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double b = __pyx_PyFloat_AsDouble(op2);
-#else
-        double b = PyFloat_AS_DOUBLE(op2);
-#endif
-            double result;
-            
-            PyFPE_START_PROTECT("subtract", return NULL)
-            result = ((double)a) - (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
-}
-#endif
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    __Pyx_TypeName type_name;
-    __Pyx_TypeName obj_type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    type_name = __Pyx_PyType_GetName(type);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME
-        ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* RaiseUnboundLocalError */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type = NULL, *local_value, *local_tb = NULL;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if PY_VERSION_HEX >= 0x030C00A6
-    local_value = tstate->current_exception;
-    tstate->current_exception = 0;
-    if (likely(local_value)) {
-        local_type = (PyObject*) Py_TYPE(local_value);
-        Py_INCREF(local_type);
-        local_tb = PyException_GetTraceback(local_value);
-    }
-  #else
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-  #endif
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6
-    if (unlikely(tstate->current_exception))
-#elif CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-      #if PY_VERSION_HEX >= 0x030B00a4
-        tmp_value = exc_info->exc_value;
-        exc_info->exc_value = local_value;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-        Py_XDECREF(local_type);
-        Py_XDECREF(local_tb);
-      #else
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-      #endif
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* pep479 */
-static void __Pyx_Generator_Replace_StopIteration(int in_async_gen) {
-    PyObject *exc, *val, *tb, *cur_exc;
-    __Pyx_PyThreadState_declare
-    #ifdef __Pyx_StopAsyncIteration_USED
-    int is_async_stopiteration = 0;
-    #endif
-    CYTHON_MAYBE_UNUSED_VAR(in_async_gen);
-    cur_exc = PyErr_Occurred();
-    if (likely(!__Pyx_PyErr_GivenExceptionMatches(cur_exc, PyExc_StopIteration))) {
-        #ifdef __Pyx_StopAsyncIteration_USED
-        if (in_async_gen && unlikely(__Pyx_PyErr_GivenExceptionMatches(cur_exc, __Pyx_PyExc_StopAsyncIteration))) {
-            is_async_stopiteration = 1;
-        } else
-        #endif
-            return;
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_GetException(&exc, &val, &tb);
-    Py_XDECREF(exc);
-    Py_XDECREF(val);
-    Py_XDECREF(tb);
-    PyErr_SetString(PyExc_RuntimeError,
-        #ifdef __Pyx_StopAsyncIteration_USED
-        is_async_stopiteration ? "async generator raised StopAsyncIteration" :
-        in_async_gen ? "async generator raised StopIteration" :
-        #endif
-        "generator raised StopIteration");
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* IterFinish */
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    PyObject* exc_type = __Pyx_PyErr_CurrentExceptionType();
-    if (unlikely(exc_type)) {
-        if (unlikely(!__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)))
-            return -1;
-        __Pyx_PyErr_Clear();
-        return 0;
-    }
-    return 0;
-}
-
-/* UnpackItemEndCheck */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
-    if (unlikely(retval)) {
-        Py_DECREF(retval);
-        __Pyx_RaiseTooManyValuesError(expected);
-        return -1;
-    }
-    return __Pyx_IterFinish();
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#elif CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(!__pyx_m)) {
-        return NULL;
-    }
-    result = PyObject_GetAttr(__pyx_m, name);
-    if (likely(result)) {
-        return result;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = __Pyx_CyOrPyCFunction_GET_FUNCTION(func);
-    self = __Pyx_CyOrPyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectFastCall */
-#if PY_VERSION_HEX < 0x03090000 || CYTHON_COMPILING_IN_LIMITED_API
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
-    PyObject *argstuple;
-    PyObject *result = 0;
-    size_t i;
-    argstuple = PyTuple_New((Py_ssize_t)nargs);
-    if (unlikely(!argstuple)) return NULL;
-    for (i = 0; i < nargs; i++) {
-        Py_INCREF(args[i]);
-        if (__Pyx_PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]) < 0) goto bad;
-    }
-    result = __Pyx_PyObject_Call(func, argstuple, kwargs);
-  bad:
-    Py_DECREF(argstuple);
-    return result;
-}
-#endif
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
-    Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (nargs == 0 && kwargs == NULL) {
-        if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_NOARGS))
-            return __Pyx_PyObject_CallMethO(func, NULL);
-    }
-    else if (nargs == 1 && kwargs == NULL) {
-        if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_O))
-            return __Pyx_PyObject_CallMethO(func, args[0]);
-    }
-#endif
-    #if PY_VERSION_HEX < 0x030800B1
-    #if CYTHON_FAST_PYCCALL
-    if (PyCFunction_Check(func)) {
-        if (kwargs) {
-            return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
-        } else {
-            return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
-        }
-    }
-    #if PY_VERSION_HEX >= 0x030700A1
-    if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
-        return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
-    }
-    #endif
-    #endif
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
-    }
-    #endif
-    #endif
-    if (kwargs == NULL) {
-        #if CYTHON_VECTORCALL
-        #if Py_VERSION_HEX < 0x03090000
-        vectorcallfunc f = _PyVectorcall_Function(func);
-        #else
-        vectorcallfunc f = PyVectorcall_Function(func);
-        #endif
-        if (f) {
-            return f(func, args, (size_t)nargs, NULL);
-        }
-        #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
-        if (__Pyx_CyFunction_CheckExact(func)) {
-            __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
-            if (f) return f(func, args, (size_t)nargs, NULL);
-        }
-        #endif
-    }
-    if (nargs == 0) {
-        return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
-    }
-    #if PY_VERSION_HEX >= 0x03090000 && !CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_VectorcallDict(func, args, (size_t)nargs, kwargs);
-    #else
-    return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-    #endif
-}
-
-/* SliceObject */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
-        Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
-        int has_cstart, int has_cstop, int wraparound) {
-    __Pyx_TypeName obj_type_name;
-#if CYTHON_USE_TYPE_SLOTS
-    PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
-    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
-    if (likely(ms && ms->sq_slice)) {
-        if (!has_cstart) {
-            if (_py_start && (*_py_start != Py_None)) {
-                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
-                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstart = 0;
-        }
-        if (!has_cstop) {
-            if (_py_stop && (*_py_stop != Py_None)) {
-                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
-                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstop = PY_SSIZE_T_MAX;
-        }
-        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
-            Py_ssize_t l = ms->sq_length(obj);
-            if (likely(l >= 0)) {
-                if (cstop < 0) {
-                    cstop += l;
-                    if (cstop < 0) cstop = 0;
-                }
-                if (cstart < 0) {
-                    cstart += l;
-                    if (cstart < 0) cstart = 0;
-                }
-            } else {
-                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                    goto bad;
-                PyErr_Clear();
-            }
-        }
-        return ms->sq_slice(obj, cstart, cstop);
-    }
-#else
-    CYTHON_UNUSED_VAR(wraparound);
-#endif
-    mp = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(mp && mp->mp_subscript))
-#else
-    CYTHON_UNUSED_VAR(wraparound);
-#endif
-    {
-        PyObject* result;
-        PyObject *py_slice, *py_start, *py_stop;
-        if (_py_slice) {
-            py_slice = *_py_slice;
-        } else {
-            PyObject* owned_start = NULL;
-            PyObject* owned_stop = NULL;
-            if (_py_start) {
-                py_start = *_py_start;
-            } else {
-                if (has_cstart) {
-                    owned_start = py_start = PyInt_FromSsize_t(cstart);
-                    if (unlikely(!py_start)) goto bad;
-                } else
-                    py_start = Py_None;
-            }
-            if (_py_stop) {
-                py_stop = *_py_stop;
-            } else {
-                if (has_cstop) {
-                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
-                    if (unlikely(!py_stop)) {
-                        Py_XDECREF(owned_start);
-                        goto bad;
-                    }
-                } else
-                    py_stop = Py_None;
-            }
-            py_slice = PySlice_New(py_start, py_stop, Py_None);
-            Py_XDECREF(owned_start);
-            Py_XDECREF(owned_stop);
-            if (unlikely(!py_slice)) goto bad;
-        }
-#if CYTHON_USE_TYPE_SLOTS
-        result = mp->mp_subscript(obj, py_slice);
-#else
-        result = PyObject_GetItem(obj, py_slice);
-#endif
-        if (!_py_slice) {
-            Py_DECREF(py_slice);
-        }
-        return result;
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is unsliceable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-bad:
-    return NULL;
-}
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-    PyObject *arg[2] = {NULL, NULL};
-    return __Pyx_PyObject_FastCall(func, arg + 1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *args[2] = {NULL, arg};
-    return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    __Pyx_TypeName type_name;
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
-        if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (likely(descr != NULL)) {
-        *method = descr;
-        return 0;
-    }
-    type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* UnpackUnboundCMethod */
-static PyObject *__Pyx_SelflessCall(PyObject *method, PyObject *args, PyObject *kwargs) {
-    PyObject *selfless_args = PyTuple_GetSlice(args, 1, PyTuple_Size(args));
-    if (unlikely(!selfless_args)) return NULL;
-    PyObject *result = PyObject_Call(method, selfless_args, kwargs);
-    Py_DECREF(selfless_args);
-    return result;
-}
-static PyMethodDef __Pyx_UnboundCMethod_Def = {
-     "CythonUnboundCMethod",
-     __PYX_REINTERPRET_FUNCION(PyCFunction, __Pyx_SelflessCall),
-     METH_VARARGS | METH_KEYWORDS,
-     NULL
-};
-static int __Pyx_TryUnpackUnboundCMethod(__Pyx_CachedCFunction* target) {
-    PyObject *method;
-    method = __Pyx_PyObject_GetAttrStr(target->type, *target->method_name);
-    if (unlikely(!method))
-        return -1;
-    target->method = method;
-#if CYTHON_COMPILING_IN_CPYTHON
-    #if PY_MAJOR_VERSION >= 3
-    if (likely(__Pyx_TypeCheck(method, &PyMethodDescr_Type)))
-    #else
-    if (likely(!__Pyx_CyOrPyCFunction_Check(method)))
-    #endif
-    {
-        PyMethodDescrObject *descr = (PyMethodDescrObject*) method;
-        target->func = descr->d_method->ml_meth;
-        target->flag = descr->d_method->ml_flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_STACKLESS);
-    } else
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-#else
-    if (PyCFunction_Check(method))
-#endif
-    {
-        PyObject *self;
-        int self_found;
-#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_PYPY
-        self = PyObject_GetAttrString(method, "__self__");
-        if (!self) {
-            PyErr_Clear();
-        }
-#else
-        self = PyCFunction_GET_SELF(method);
-#endif
-        self_found = (self && self != Py_None);
-#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_PYPY
-        Py_XDECREF(self);
-#endif
-        if (self_found) {
-            PyObject *unbound_method = PyCFunction_New(&__Pyx_UnboundCMethod_Def, method);
-            if (unlikely(!unbound_method)) return -1;
-            Py_DECREF(method);
-            target->method = unbound_method;
-        }
-    }
-    return 0;
-}
-
-/* CallUnboundCMethod0 */
-static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self) {
-    PyObject *args, *result = NULL;
-    if (unlikely(!cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL;
-#if CYTHON_ASSUME_SAFE_MACROS
-    args = PyTuple_New(1);
-    if (unlikely(!args)) goto bad;
-    Py_INCREF(self);
-    PyTuple_SET_ITEM(args, 0, self);
-#else
-    args = PyTuple_Pack(1, self);
-    if (unlikely(!args)) goto bad;
-#endif
-    result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
-    Py_DECREF(args);
-bad:
-    return result;
-}
-
-/* pop */
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L) {
-    if (__Pyx_IS_TYPE(L, &PySet_Type)) {
-        return PySet_Pop(L);
-    }
-    return __Pyx_PyObject_CallMethod0(L, __pyx_n_s_pop);
-}
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L) {
-    if (likely(PyList_GET_SIZE(L) > (((PyListObject*)L)->allocated >> 1))) {
-        __Pyx_SET_SIZE(L, Py_SIZE(L) - 1);
-        return PyList_GET_ITEM(L, PyList_GET_SIZE(L));
-    }
-    return __Pyx_CallUnboundCMethod0(&__pyx_umethod_PyList_Type_pop, L);
-}
-#endif
-
-/* RaiseUnexpectedTypeError */
-static int
-__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj)
-{
-    __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME,
-                 expected, obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
-    CYTHON_MAYBE_UNUSED_VAR(intval);
-    CYTHON_MAYBE_UNUSED_VAR(inplace);
-    CYTHON_UNUSED_VAR(zerodivision_check);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-        
-            x = (long)((unsigned long)a + (unsigned long)b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        if (unlikely(__Pyx_PyLong_IsZero(op1))) {
-            return __Pyx_NewRef(op2);
-        }
-        if (likely(__Pyx_PyLong_IsCompact(op1))) {
-            a = __Pyx_PyLong_CompactValue(op1);
-        } else {
-            const digit* digits = __Pyx_PyLong_Digits(op1);
-            const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op1);
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-                    #ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-                    #endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        double a = __pyx_PyFloat_AsDouble(op1);
-#else
-        double a = PyFloat_AS_DOUBLE(op1);
-#endif
-            double result;
-            
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    PyObject *exc_value = exc_info->exc_value;
-    if (exc_value == NULL || exc_value == Py_None) {
-        *value = NULL;
-        *type = NULL;
-        *tb = NULL;
-    } else {
-        *value = exc_value;
-        Py_INCREF(*value);
-        *type = (PyObject*) Py_TYPE(exc_value);
-        Py_INCREF(*type);
-        *tb = PyException_GetTraceback(exc_value);
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #endif
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    PyObject *tmp_value = exc_info->exc_value;
-    exc_info->exc_value = value;
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-  #else
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-  #endif
-}
-#endif
-
-/* pyfrozenset_new */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it) {
-    if (it) {
-        PyObject* result;
-#if CYTHON_COMPILING_IN_PYPY
-        PyObject* args;
-        args = PyTuple_Pack(1, it);
-        if (unlikely(!args))
-            return NULL;
-        result = PyObject_Call((PyObject*)&PyFrozenSet_Type, args, NULL);
-        Py_DECREF(args);
-        return result;
-#else
-        if (PyFrozenSet_CheckExact(it)) {
-            Py_INCREF(it);
-            return it;
-        }
-        result = PyFrozenSet_New(it);
-        if (unlikely(!result))
-            return NULL;
-        if ((PY_VERSION_HEX >= 0x031000A1) || likely(PySet_GET_SIZE(result)))
-            return result;
-        Py_DECREF(result);
-#endif
-    }
-#if CYTHON_USE_TYPE_SLOTS
-    return PyFrozenSet_Type.tp_new(&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#else
-    return PyObject_Call((PyObject*)&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#endif
-}
-
-/* PySetContains */
-static int __Pyx_PySet_ContainsUnhashable(PyObject *set, PyObject *key) {
-    int result = -1;
-    if (PySet_Check(key) && PyErr_ExceptionMatches(PyExc_TypeError)) {
-        PyObject *tmpkey;
-        PyErr_Clear();
-        tmpkey = __Pyx_PyFrozenSet_New(key);
-        if (tmpkey != NULL) {
-            result = PySet_Contains(set, tmpkey);
-            Py_DECREF(tmpkey);
-        }
-    }
-    return result;
-}
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq) {
-    int result = PySet_Contains(set, key);
-    if (unlikely(result < 0)) {
-        result = __Pyx_PySet_ContainsUnhashable(set, key);
-    }
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *module = 0;
-    PyObject *empty_dict = 0;
-    PyObject *empty_list = 0;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (unlikely(!py_import))
-        goto bad;
-    if (!from_list) {
-        empty_list = PyList_New(0);
-        if (unlikely(!empty_list))
-            goto bad;
-        from_list = empty_list;
-    }
-    #endif
-    empty_dict = PyDict_New();
-    if (unlikely(!empty_dict))
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if (strchr(__Pyx_MODULE_NAME, '.') != NULL) {
-                module = PyImport_ImportModuleLevelObject(
-                    name, __pyx_d, empty_dict, from_list, 1);
-                if (unlikely(!module)) {
-                    if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (unlikely(!py_level))
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, __pyx_d, empty_dict, from_list, level);
-            #endif
-        }
-    }
-bad:
-    Py_XDECREF(empty_dict);
-    Py_XDECREF(empty_list);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    return module;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        const char* module_name_str = 0;
-        PyObject* module_name = 0;
-        PyObject* module_dot = 0;
-        PyObject* full_name = 0;
-        PyErr_Clear();
-        module_name_str = PyModule_GetName(module);
-        if (unlikely(!module_name_str)) { goto modbad; }
-        module_name = PyUnicode_FromString(module_name_str);
-        if (unlikely(!module_name)) { goto modbad; }
-        module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__3);
-        if (unlikely(!module_dot)) { goto modbad; }
-        full_name = PyUnicode_Concat(module_dot, name);
-        if (unlikely(!full_name)) { goto modbad; }
-        #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-        {
-            PyObject *modules = PyImport_GetModuleDict();
-            if (unlikely(!modules))
-                goto modbad;
-            value = PyObject_GetItem(modules, full_name);
-        }
-        #else
-        value = PyImport_GetModule(full_name);
-        #endif
-      modbad:
-        Py_XDECREF(full_name);
-        Py_XDECREF(module_dot);
-        Py_XDECREF(module_name);
-    }
-    if (unlikely(!value)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    CYTHON_UNUSED_VAR(spec);
-    CYTHON_UNUSED_VAR(type);
-#else
-    const PyType_Slot *slot = spec->slots;
-    while (slot && slot->slot && slot->slot != Py_tp_members)
-        slot++;
-    if (slot && slot->slot == Py_tp_members) {
-        int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
-        const
-#endif
-            PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
-        while (memb && memb->name) {
-            if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
-                if (strcmp(memb->name, "__weaklistoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_weaklistoffset = memb->offset;
-                    changed = 1;
-                }
-                else if (strcmp(memb->name, "__dictoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_dictoffset = memb->offset;
-                    changed = 1;
-                }
-#if CYTHON_METH_FASTCALL
-                else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
-                    type->tp_vectorcall_offset = memb->offset;
-#else
-                    type->tp_print = (printfunc) memb->offset;
-#endif
-                    changed = 1;
-                }
-#endif
-#else
-                if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
-                else if (strcmp(memb->name, "__module__") == 0) {
-                    PyObject *descr;
-                    assert(memb->type == T_OBJECT);
-                    assert(memb->flags == 0 || memb->flags == READONLY);
-                    descr = PyDescr_NewMember(type, memb);
-                    if (unlikely(!descr))
-                        return -1;
-                    if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
-                        Py_DECREF(descr);
-                        return -1;
-                    }
-                    Py_DECREF(descr);
-                    changed = 1;
-                }
-#endif
-            }
-            memb++;
-        }
-        if (changed)
-            PyType_Modified(type);
-    }
-#endif
-    return 0;
-}
-#endif
-
-/* ValidateBasesTuple */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
-    Py_ssize_t i, n;
-#if CYTHON_ASSUME_SAFE_MACROS
-    n = PyTuple_GET_SIZE(bases);
-#else
-    n = PyTuple_Size(bases);
-    if (n < 0) return -1;
-#endif
-    for (i = 1; i < n; i++)
-    {
-#if CYTHON_AVOID_BORROWED_REFS
-        PyObject *b0 = PySequence_GetItem(bases, i);
-        if (!b0) return -1;
-#elif CYTHON_ASSUME_SAFE_MACROS
-        PyObject *b0 = PyTuple_GET_ITEM(bases, i);
-#else
-        PyObject *b0 = PyTuple_GetItem(bases, i);
-        if (!b0) return -1;
-#endif
-        PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
-        if (PyClass_Check(b0))
-        {
-            PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
-                         PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
-#if CYTHON_AVOID_BORROWED_REFS
-            Py_DECREF(b0);
-#endif
-            return -1;
-        }
-#endif
-        b = (PyTypeObject*) b0;
-        if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
-            __Pyx_DECREF_TypeName(b_name);
-#if CYTHON_AVOID_BORROWED_REFS
-            Py_DECREF(b0);
-#endif
-            return -1;
-        }
-#if !CYTHON_USE_TYPE_SLOTS
-        if (dictoffset == 0) {
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%s.200s': "
-                "unable to validate whether bases have a __dict__ "
-                "when CYTHON_USE_TYPE_SLOTS is off "
-                "(likely because you are building in the limited API). "
-                "Therefore, all extension types with multiple bases "
-                "must add 'cdef dict __dict__' in this compilation mode",
-                type_name);
-#if CYTHON_AVOID_BORROWED_REFS
-            Py_DECREF(b0);
-#endif
-            return -1;
-        }
-#else
-        if (dictoffset == 0 && b->tp_dictoffset)
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%.200s' has no __dict__ slot, "
-                "but base type '" __Pyx_FMT_TYPENAME "' has: "
-                "either add 'cdef dict __dict__' to the extension type "
-                "or add '__slots__ = [...]' to the base type",
-                type_name, b_name);
-            __Pyx_DECREF_TypeName(b_name);
-#if CYTHON_AVOID_BORROWED_REFS
-            Py_DECREF(b0);
-#endif
-            return -1;
-        }
-#endif
-#if CYTHON_AVOID_BORROWED_REFS
-        Py_DECREF(b0);
-#endif
-    }
-    return 0;
-}
-#endif
-
-/* PyType_Ready */
-static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
-    (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
-    (void)__Pyx_validate_bases_tuple;
-#endif
-    return PyType_Ready(t);
-#else
-    int r;
-    PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
-    if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
-        return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-    {
-        int gc_was_enabled;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        gc_was_enabled = PyGC_Disable();
-        (void)__Pyx_PyObject_CallMethod0;
-    #else
-        PyObject *ret, *py_status;
-        PyObject *gc = NULL;
-        #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
-        gc = PyImport_GetModule(__pyx_kp_u_gc);
-        #endif
-        if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
-        if (unlikely(!gc)) return -1;
-        py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
-        if (unlikely(!py_status)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-        gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
-        Py_DECREF(py_status);
-        if (gc_was_enabled > 0) {
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
-            if (unlikely(!ret)) {
-                Py_DECREF(gc);
-                return -1;
-            }
-            Py_DECREF(ret);
-        } else if (unlikely(gc_was_enabled == -1)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-    #endif
-        t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#if PY_VERSION_HEX >= 0x030A0000
-        t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE;
-#endif
-#else
-        (void)__Pyx_PyObject_CallMethod0;
-#endif
-    r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-        t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        if (gc_was_enabled)
-            PyGC_Enable();
-    #else
-        if (gc_was_enabled) {
-            PyObject *tp, *v, *tb;
-            PyErr_Fetch(&tp, &v, &tb);
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
-            if (likely(ret || r == -1)) {
-                Py_XDECREF(ret);
-                PyErr_Restore(tp, v, tb);
-            } else {
-                Py_XDECREF(tp);
-                Py_XDECREF(v);
-                Py_XDECREF(tb);
-                r = -1;
-            }
-        }
-        Py_DECREF(gc);
-    #endif
-    }
-#endif
-    return r;
-#endif
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, attr_name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(attr_name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (cls == a || cls == b) return 1;
-    mro = cls->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            PyObject *base = PyTuple_GET_ITEM(mro, i);
-            if (base == (PyObject *)a || base == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    if (exc_type1) {
-        return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
-    } else {
-        return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* ImportDottedModule */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) {
-    PyObject *partial_name = NULL, *slice = NULL, *sep = NULL;
-    if (unlikely(PyErr_Occurred())) {
-        PyErr_Clear();
-    }
-    if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) {
-        partial_name = name;
-    } else {
-        slice = PySequence_GetSlice(parts_tuple, 0, count);
-        if (unlikely(!slice))
-            goto bad;
-        sep = PyUnicode_FromStringAndSize(".", 1);
-        if (unlikely(!sep))
-            goto bad;
-        partial_name = PyUnicode_Join(sep, slice);
-    }
-    PyErr_Format(
-#if PY_MAJOR_VERSION < 3
-        PyExc_ImportError,
-        "No module named '%s'", PyString_AS_STRING(partial_name));
-#else
-#if PY_VERSION_HEX >= 0x030600B1
-        PyExc_ModuleNotFoundError,
-#else
-        PyExc_ImportError,
-#endif
-        "No module named '%U'", partial_name);
-#endif
-bad:
-    Py_XDECREF(sep);
-    Py_XDECREF(slice);
-    Py_XDECREF(partial_name);
-    return NULL;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) {
-    PyObject *imported_module;
-#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-    PyObject *modules = PyImport_GetModuleDict();
-    if (unlikely(!modules))
-        return NULL;
-    imported_module = __Pyx_PyDict_GetItemStr(modules, name);
-    Py_XINCREF(imported_module);
-#else
-    imported_module = PyImport_GetModule(name);
-#endif
-    return imported_module;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) {
-    Py_ssize_t i, nparts;
-    nparts = PyTuple_GET_SIZE(parts_tuple);
-    for (i=1; i < nparts && module; i++) {
-        PyObject *part, *submodule;
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        part = PyTuple_GET_ITEM(parts_tuple, i);
-#else
-        part = PySequence_ITEM(parts_tuple, i);
-#endif
-        submodule = __Pyx_PyObject_GetAttrStrNoError(module, part);
-#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-        Py_DECREF(part);
-#endif
-        Py_DECREF(module);
-        module = submodule;
-    }
-    if (unlikely(!module)) {
-        return __Pyx__ImportDottedModule_Error(name, parts_tuple, i);
-    }
-    return module;
-}
-#endif
-static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if PY_MAJOR_VERSION < 3
-    PyObject *module, *from_list, *star = __pyx_n_s__4;
-    CYTHON_UNUSED_VAR(parts_tuple);
-    from_list = PyList_New(1);
-    if (unlikely(!from_list))
-        return NULL;
-    Py_INCREF(star);
-    PyList_SET_ITEM(from_list, 0, star);
-    module = __Pyx_Import(name, from_list, 0);
-    Py_DECREF(from_list);
-    return module;
-#else
-    PyObject *imported_module;
-    PyObject *module = __Pyx_Import(name, NULL, 0);
-    if (!parts_tuple || unlikely(!module))
-        return module;
-    imported_module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(imported_module)) {
-        Py_DECREF(module);
-        return imported_module;
-    }
-    PyErr_Clear();
-    return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple);
-#endif
-}
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1
-    PyObject *module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(module)) {
-        PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec);
-        if (likely(spec)) {
-            PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing);
-            if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) {
-                Py_DECREF(spec);
-                spec = NULL;
-            }
-            Py_XDECREF(unsafe);
-        }
-        if (likely(!spec)) {
-            PyErr_Clear();
-            return module;
-        }
-        Py_DECREF(spec);
-        Py_DECREF(module);
-    } else if (PyErr_Occurred()) {
-        PyErr_Clear();
-    }
-#endif
-    return __Pyx__ImportDottedModule(name, parts_tuple);
-}
-
-/* FetchSharedCythonModule */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
-}
-
-/* FetchCommonType */
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
-                               const char *name,
-                               Py_ssize_t basicsize,
-                               Py_ssize_t expected_basicsize) {
-    if (!PyType_Check(cached_type)) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s is not a type object", name);
-        return -1;
-    }
-    if (basicsize != expected_basicsize) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s has the wrong size, try recompiling",
-            name);
-        return -1;
-    }
-    return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* abi_module;
-    const char* object_name;
-    PyTypeObject *cached_type = NULL;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    object_name = strrchr(type->tp_name, '.');
-    object_name = object_name ? object_name+1 : type->tp_name;
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        if (__Pyx_VerifyCachedType(
-              (PyObject *)cached_type,
-              object_name,
-              cached_type->tp_basicsize,
-              type->tp_basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    if (PyType_Ready(type) < 0) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
-        goto bad;
-    Py_INCREF(type);
-    cached_type = type;
-done:
-    Py_DECREF(abi_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
-    PyObject *abi_module, *cached_type = NULL;
-    const char* object_name = strrchr(spec->name, '.');
-    object_name = object_name ? object_name+1 : spec->name;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    cached_type = PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        PyObject *py_basicsize;
-        py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
-        if (unlikely(!py_basicsize)) goto bad;
-        basicsize = PyLong_AsSsize_t(py_basicsize);
-        Py_DECREF(py_basicsize);
-        py_basicsize = 0;
-        if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
-        basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
-        if (__Pyx_VerifyCachedType(
-              cached_type,
-              object_name,
-              basicsize,
-              spec->basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    CYTHON_UNUSED_VAR(module);
-    cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
-    if (unlikely(!cached_type)) goto bad;
-    if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
-    Py_DECREF(abi_module);
-    assert(cached_type == NULL || PyType_Check(cached_type));
-    return (PyTypeObject *) cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    PyObject *res = NULL;
-    PyObject *kwnames;
-    PyObject **newargs;
-    PyObject **kwvalues;
-    Py_ssize_t i, pos;
-    size_t j;
-    PyObject *key, *value;
-    unsigned long keys_are_strings;
-    Py_ssize_t nkw = PyDict_GET_SIZE(kw);
-    newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
-    if (unlikely(newargs == NULL)) {
-        PyErr_NoMemory();
-        return NULL;
-    }
-    for (j = 0; j < nargs; j++) newargs[j] = args[j];
-    kwnames = PyTuple_New(nkw);
-    if (unlikely(kwnames == NULL)) {
-        PyMem_Free(newargs);
-        return NULL;
-    }
-    kwvalues = newargs + nargs;
-    pos = i = 0;
-    keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
-    while (PyDict_Next(kw, &pos, &key, &value)) {
-        keys_are_strings &= Py_TYPE(key)->tp_flags;
-        Py_INCREF(key);
-        Py_INCREF(value);
-        PyTuple_SET_ITEM(kwnames, i, key);
-        kwvalues[i] = value;
-        i++;
-    }
-    if (unlikely(!keys_are_strings)) {
-        PyErr_SetString(PyExc_TypeError, "keywords must be strings");
-        goto cleanup;
-    }
-    res = vc(func, newargs, nargs, kwnames);
-cleanup:
-    Py_DECREF(kwnames);
-    for (i = 0; i < nkw; i++)
-        Py_DECREF(kwvalues[i]);
-    PyMem_Free(newargs);
-    return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
-        return vc(func, args, nargs, NULL);
-    }
-    return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) {
-    if (__Pyx_CyFunction_Check(func)) {
-        return PyCFunction_GetFunction(((__pyx_CyFunctionObject*)func)->func) == (PyCFunction) cfunc;
-    } else if (PyCFunction_Check(func)) {
-        return PyCFunction_GetFunction(func) == (PyCFunction) cfunc;
-    }
-    return 0;
-}
-#else
-static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) {
-    return __Pyx_CyOrPyCFunction_Check(func) && __Pyx_CyOrPyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc;
-}
-#endif
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    __Pyx_Py_XDECREF_SET(
-        __Pyx_CyFunction_GetClassObj(f),
-            ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
-    __Pyx_Py_XDECREF_SET(
-        ((PyCMethodObject *) (f))->mm_class,
-        (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
-    CYTHON_UNUSED_VAR(closure);
-    if (unlikely(op->func_doc == NULL)) {
-#if CYTHON_COMPILING_IN_LIMITED_API
-        op->func_doc = PyObject_GetAttrString(op->func, "__doc__");
-        if (unlikely(!op->func_doc)) return NULL;
-#else
-        if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-#endif
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_doc, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_name == NULL)) {
-#if CYTHON_COMPILING_IN_LIMITED_API
-        op->func_name = PyObject_GetAttrString(op->func, "__name__");
-#elif PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_dict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(op);
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = __Pyx_PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = __Pyx_PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_tuple;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_kwdict;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_annotations, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->func_annotations;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
-    int is_coroutine;
-    CYTHON_UNUSED_VAR(context);
-    if (op->func_is_coroutine) {
-        return __Pyx_NewRef(op->func_is_coroutine);
-    }
-    is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
-    if (is_coroutine) {
-        PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
-        fromlist = PyList_New(1);
-        if (unlikely(!fromlist)) return NULL;
-        Py_INCREF(marker);
-#if CYTHON_ASSUME_SAFE_MACROS
-        PyList_SET_ITEM(fromlist, 0, marker);
-#else
-        if (unlikely(PyList_SetItem(fromlist, 0, marker) < 0)) {
-            Py_DECREF(marker);
-            Py_DECREF(fromlist);
-            return NULL;
-        }
-#endif
-        module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
-        Py_DECREF(fromlist);
-        if (unlikely(!module)) goto ignore;
-        op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
-        Py_DECREF(module);
-        if (likely(op->func_is_coroutine)) {
-            return __Pyx_NewRef(op->func_is_coroutine);
-        }
-ignore:
-        PyErr_Clear();
-    }
-#endif
-    op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
-    return __Pyx_NewRef(op->func_is_coroutine);
-}
-#if CYTHON_COMPILING_IN_LIMITED_API
-static PyObject *
-__Pyx_CyFunction_get_module(__pyx_CyFunctionObject *op, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    return PyObject_GetAttrString(op->func, "__module__");
-}
-static int
-__Pyx_CyFunction_set_module(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    return PyObject_SetAttrString(op->func, "__module__", value);
-}
-#endif
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
-#if CYTHON_COMPILING_IN_LIMITED_API
-    {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0},
-#endif
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-#if !CYTHON_COMPILING_IN_LIMITED_API
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#endif
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
-#if !CYTHON_COMPILING_IN_LIMITED_API
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
-    CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-#if !CYTHON_COMPILING_IN_LIMITED_API
-    PyCFunctionObject *cf = (PyCFunctionObject*) op;
-#endif
-    if (unlikely(op == NULL))
-        return NULL;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    op->func = PyCFunction_NewEx(ml, (PyObject*)op, module);
-    if (unlikely(!op->func)) return NULL;
-#endif
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-#if !CYTHON_COMPILING_IN_LIMITED_API
-    cf->m_ml = ml;
-    cf->m_self = (PyObject *) op;
-#endif
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-#if !CYTHON_COMPILING_IN_LIMITED_API
-    Py_XINCREF(module);
-    cf->m_module = module;
-#endif
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    op->func_classobj = NULL;
-#else
-    ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
-    switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
-    case METH_NOARGS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
-        break;
-    case METH_O:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
-        break;
-    case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
-        break;
-    case METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = NULL;
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        Py_DECREF(op);
-        return NULL;
-    }
-#endif
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-#if CYTHON_COMPILING_IN_LIMITED_API
-    Py_CLEAR(m->func);
-#else
-    Py_CLEAR(((PyCFunctionObject*)m)->m_module);
-#endif
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-#if !CYTHON_COMPILING_IN_LIMITED_API
-#if PY_VERSION_HEX < 0x030900B1
-    Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
-    {
-        PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
-        ((PyCMethodObject *) (m))->mm_class = NULL;
-        Py_XDECREF(cls);
-    }
-#endif
-#endif
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    Py_CLEAR(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-#if CYTHON_COMPILING_IN_LIMITED_API
-    Py_VISIT(m->func);
-#else
-    Py_VISIT(((PyCFunctionObject*)m)->m_module);
-#endif
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-#if !CYTHON_COMPILING_IN_LIMITED_API
-    Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
-#endif
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    Py_VISIT(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-#if CYTHON_COMPILING_IN_LIMITED_API
-    PyObject *f = ((__pyx_CyFunctionObject*)func)->func;
-    PyObject *py_name = NULL;
-    PyCFunction meth;
-    int flags;
-    meth = PyCFunction_GetFunction(f);
-    if (unlikely(!meth)) return NULL;
-    flags = PyCFunction_GetFlags(f);
-    if (unlikely(flags < 0)) return NULL;
-#else
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    int flags = f->m_ml->ml_flags;
-#endif
-    Py_ssize_t size;
-    switch (flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-#if CYTHON_ASSUME_SAFE_MACROS
-            size = PyTuple_GET_SIZE(arg);
-#else
-            size = PyTuple_Size(arg);
-            if (unlikely(size < 0)) return NULL;
-#endif
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-#if CYTHON_COMPILING_IN_LIMITED_API
-            py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL);
-            if (!py_name) return NULL;
-            PyErr_Format(PyExc_TypeError,
-                "%.200S() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                py_name, size);
-            Py_DECREF(py_name);
-#else
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-#endif
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-#if CYTHON_ASSUME_SAFE_MACROS
-            size = PyTuple_GET_SIZE(arg);
-#else
-            size = PyTuple_Size(arg);
-            if (unlikely(size < 0)) return NULL;
-#endif
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = __Pyx_PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-#if CYTHON_COMPILING_IN_LIMITED_API
-            py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL);
-            if (!py_name) return NULL;
-            PyErr_Format(PyExc_TypeError,
-                "%.200S() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                py_name, size);
-            Py_DECREF(py_name);
-#else
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-#endif
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        return NULL;
-    }
-#if CYTHON_COMPILING_IN_LIMITED_API
-    py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL);
-    if (!py_name) return NULL;
-    PyErr_Format(PyExc_TypeError, "%.200S() takes no keyword arguments",
-                 py_name);
-    Py_DECREF(py_name);
-#else
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-#endif
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *self, *result;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)func)->func);
-    if (unlikely(!self) && PyErr_Occurred()) return NULL;
-#else
-    self = ((PyCFunctionObject*)func)->m_self;
-#endif
-    result = __Pyx_CyFunction_CallMethod(func, self, arg, kw);
-    return result;
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
-     __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
-    if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
-        return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
-        (void) &__Pyx_PyVectorcall_FastCallDict;
-        return PyVectorcall_Call(func, args, kw);
-#endif
-    }
-#endif
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-#if CYTHON_ASSUME_SAFE_MACROS
-        argc = PyTuple_GET_SIZE(args);
-#else
-        argc = PyTuple_Size(args);
-        if (unlikely(!argc) < 0) return NULL;
-#endif
-        new_args = PyTuple_GetSlice(args, 1, argc);
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-            return NULL;
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-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
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-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
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-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
-    int ret = 0;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        if (unlikely(nargs < 1)) {
-            PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
-                         ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-            return -1;
-        }
-        ret = 1;
-    }
-    if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
-        PyErr_Format(PyExc_TypeError,
-                     "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-        return -1;
-    }
-    return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
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-        break;
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-        return NULL;
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-    if (unlikely(nargs != 0)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 1)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
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-        return NULL;
-    }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
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-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-    PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
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-        return NULL;
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-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
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-    {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
-    {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
-    {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
-    {Py_tp_methods, (void *)__pyx_CyFunction_methods},
-    {Py_tp_members, (void *)__pyx_CyFunction_members},
-    {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
-    {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
-    {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
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-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
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-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
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-    (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
-    0,
-#elif CYTHON_BACKPORT_VECTORCALL
-    (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
-    offsetof(PyCFunctionObject, vectorcall),
-#endif
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_PyMethod_New,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
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-    0,
-    0,
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-#if PY_VERSION_HEX >= 0x030400a1
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-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
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-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
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-    );
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-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError,
-            "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
-        __Pyx_DECREF_TypeName(index_type_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
-    __Pyx_TypeName obj_type_name;
-    if (likely(PyType_Check(obj))) {
-        PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
-        if (meth) {
-            PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
-            Py_DECREF(meth);
-            return result;
-        }
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyMappingMethods *mm = tp->tp_as_mapping;
-    PySequenceMethods *sm = tp->tp_as_sequence;
-    if (likely(mm && mm->mp_subscript)) {
-        return mm->mp_subscript(obj, key);
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-    if (likely(sm && sm->sq_item)) {
-        return __Pyx_PyObject_GetIndex(obj, key);
-    }
-    return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    CYTHON_MAYBE_UNUSED_VAR(tstate);
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
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-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
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-#endif
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-/* CodeObjectCache */
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-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
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-        if (code_line < entries[mid].code_line) {
-            end = mid;
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-             start = mid + 1;
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-            return mid;
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-    if (code_line <= entries[mid].code_line) {
-        return mid;
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-        return mid + 1;
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-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
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-        return NULL;
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-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
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-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
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-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
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-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
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-        return;
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-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
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-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
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-        if (unlikely(!entries)) {
-            return;
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-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
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-    for (i=__pyx_code_cache.count; i>pos; i--) {
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-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-#endif
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
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-#if CYTHON_COMPILING_IN_LIMITED_API
-static PyObject *__Pyx_PyCode_Replace_For_AddTraceback(PyObject *code, PyObject *scratch_dict,
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-    PyObject *replace = NULL;
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-        if (unlikely(PyDict_SetItemString(scratch_dict, "type", (PyObject*)(&PyType_Type)))) return NULL;
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-            "  code.co_argcount, code.co_kwonlyargcount, code.co_nlocals, code.co_stacksize,\n"
-            "  code.co_flags, code.co_code, code.co_consts, code.co_names,\n"
-            "  code.co_varnames, code.co_filename, co_name, co_firstlineno,\n"
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-        Py_DECREF(compiled);
-        if (!result) PyErr_Print();
-        Py_DECREF(result);
-        result = PyDict_GetItemString(scratch_dict, "out");
-        if (result) Py_INCREF(result);
-        return result;
-    }
-    #endif
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyObject *code_object = NULL, *py_py_line = NULL, *py_funcname = NULL, *dict = NULL;
-    PyObject *replace = NULL, *getframe = NULL, *frame = NULL;
-    PyObject *exc_type, *exc_value, *exc_traceback;
-    int success = 0;
-    if (c_line) {
-        (void) __pyx_cfilenm;
-        (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
-    }
-    PyErr_Fetch(&exc_type, &exc_value, &exc_traceback);
-    code_object = Py_CompileString("_getframe()", filename, Py_eval_input);
-    if (unlikely(!code_object)) goto bad;
-    py_py_line = PyLong_FromLong(py_line);
-    if (unlikely(!py_py_line)) goto bad;
-    py_funcname = PyUnicode_FromString(funcname);
-    if (unlikely(!py_funcname)) goto bad;
-    dict = PyDict_New();
-    if (unlikely(!dict)) goto bad;
-    {
-        PyObject *old_code_object = code_object;
-        code_object = __Pyx_PyCode_Replace_For_AddTraceback(code_object, dict, py_py_line, py_funcname);
-        Py_DECREF(old_code_object);
-    }
-    if (unlikely(!code_object)) goto bad;
-    getframe = PySys_GetObject("_getframe");
-    if (unlikely(!getframe)) goto bad;
-    if (unlikely(PyDict_SetItemString(dict, "_getframe", getframe))) goto bad;
-    frame = PyEval_EvalCode(code_object, dict, dict);
-    if (unlikely(!frame) || frame == Py_None) goto bad;
-    success = 1;
-  bad:
-    PyErr_Restore(exc_type, exc_value, exc_traceback);
-    Py_XDECREF(code_object);
-    Py_XDECREF(py_py_line);
-    Py_XDECREF(py_funcname);
-    Py_XDECREF(dict);
-    Py_XDECREF(replace);
-    if (success) {
-        PyTraceBack_Here(
-            (struct _frame*)frame);
-    }
-    Py_XDECREF(frame);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-#endif
-
-/* Declarations */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus)
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabs(b.real) >= fabs(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                double r = b.imag / b.real;
-                double s = (double)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_double_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            double r = b.real / b.imag;
-            double s = (double)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_double_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            double denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_double_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_double(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if ((b.imag == 0) && (a.real >= 0)) {
-                    z.real = pow(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_double(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* FromPy */
-static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject* o) {
-    Py_complex cval;
-#if !CYTHON_COMPILING_IN_PYPY
-    if (PyComplex_CheckExact(o))
-        cval = ((PyComplexObject *)o)->cval;
-    else
-#endif
-        cval = PyComplex_AsCComplex(o);
-    return __pyx_t_double_complex_from_parts(
-               (double)cval.real,
-               (double)cval.imag);
-}
-
-/* CIntFromPyVerify */
-#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* CIntFromPy */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(int) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) {
-                                return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) {
-                                return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) {
-                                return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(int) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(int) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (int) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (int) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (int) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (int) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (int) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((int) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((int) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-#if !CYTHON_COMPILING_IN_LIMITED_API
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-#else
-        PyObject *from_bytes, *result = NULL;
-        PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL;
-        from_bytes = PyObject_GetAttrString((PyObject*)&PyInt_Type, "from_bytes");
-        if (!from_bytes) return NULL;
-        py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(int));
-        if (!py_bytes) goto limited_bad;
-        order_str = PyUnicode_FromString(little ? "little" : "big");
-        if (!order_str) goto limited_bad;
-        arg_tuple = PyTuple_Pack(2, py_bytes, order_str);
-        if (!arg_tuple) goto limited_bad;
-        kwds = PyDict_New();
-        if (!kwds) goto limited_bad;
-        if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(!is_unsigned ? Py_True : Py_False))) goto limited_bad;
-        result = PyObject_Call(from_bytes, arg_tuple, kwds);
-        limited_bad:
-        Py_XDECREF(from_bytes);
-        Py_XDECREF(py_bytes);
-        Py_XDECREF(order_str);
-        Py_XDECREF(arg_tuple);
-        Py_XDECREF(kwds);
-        return result;
-#endif
-    }
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-#if !CYTHON_COMPILING_IN_LIMITED_API
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-#else
-        PyObject *from_bytes, *result = NULL;
-        PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL;
-        from_bytes = PyObject_GetAttrString((PyObject*)&PyInt_Type, "from_bytes");
-        if (!from_bytes) return NULL;
-        py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(long));
-        if (!py_bytes) goto limited_bad;
-        order_str = PyUnicode_FromString(little ? "little" : "big");
-        if (!order_str) goto limited_bad;
-        arg_tuple = PyTuple_Pack(2, py_bytes, order_str);
-        if (!arg_tuple) goto limited_bad;
-        kwds = PyDict_New();
-        if (!kwds) goto limited_bad;
-        if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(!is_unsigned ? Py_True : Py_False))) goto limited_bad;
-        result = PyObject_Call(from_bytes, arg_tuple, kwds);
-        limited_bad:
-        Py_XDECREF(from_bytes);
-        Py_XDECREF(py_bytes);
-        Py_XDECREF(order_str);
-        Py_XDECREF(arg_tuple);
-        Py_XDECREF(kwds);
-        return result;
-#endif
-    }
-}
-
-/* FormatTypeName */
-#if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
-    PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
-                                               __pyx_n_s_name);
-    if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
-        PyErr_Clear();
-        Py_XDECREF(name);
-        name = __Pyx_NewRef(__pyx_n_s__15);
-    }
-    return name;
-}
-#endif
-
-/* CIntFromPy */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(long) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) {
-                                return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) {
-                                return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) {
-                                return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(long) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(long) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (long) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (long) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (long) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (long) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (long) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((long) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((long) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_value = exc_info->exc_value;
-    exc_info->exc_value = *value;
-    if (tmp_value == NULL || tmp_value == Py_None) {
-        Py_XDECREF(tmp_value);
-        tmp_value = NULL;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-    } else {
-        tmp_type = (PyObject*) Py_TYPE(tmp_value);
-        Py_INCREF(tmp_type);
-        #if CYTHON_COMPILING_IN_CPYTHON
-        tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback;
-        Py_XINCREF(tmp_tb);
-        #else
-        tmp_tb = PyException_GetTraceback(tmp_value);
-        #endif
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-  #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-  #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args[3] = {NULL, arg1, arg2};
-    return __Pyx_PyObject_FastCall(function, args+1, 2 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectCallMethod1 */
-static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) {
-    PyObject *result = __Pyx_PyObject_CallOneArg(method, arg);
-    Py_DECREF(method);
-    return result;
-}
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
-    PyObject *method = NULL, *result;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_Call2Args(method, obj, arg);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) return NULL;
-    return __Pyx__PyObject_CallMethod1(method, arg);
-}
-
-/* CoroutineBase */
-#include <frameobject.h>
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
-static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *__pyx_tstate, PyObject **pvalue) {
-    PyObject *et, *ev, *tb;
-    PyObject *value = NULL;
-    CYTHON_UNUSED_VAR(__pyx_tstate);
-    __Pyx_ErrFetch(&et, &ev, &tb);
-    if (!et) {
-        Py_XDECREF(tb);
-        Py_XDECREF(ev);
-        Py_INCREF(Py_None);
-        *pvalue = Py_None;
-        return 0;
-    }
-    if (likely(et == PyExc_StopIteration)) {
-        if (!ev) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#if PY_VERSION_HEX >= 0x030300A0
-        else if (likely(__Pyx_IS_TYPE(ev, (PyTypeObject*)PyExc_StopIteration))) {
-            value = ((PyStopIterationObject *)ev)->value;
-            Py_INCREF(value);
-            Py_DECREF(ev);
-        }
-#endif
-        else if (unlikely(PyTuple_Check(ev))) {
-            if (PyTuple_GET_SIZE(ev) >= 1) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                value = PyTuple_GET_ITEM(ev, 0);
-                Py_INCREF(value);
-#else
-                value = PySequence_ITEM(ev, 0);
-#endif
-            } else {
-                Py_INCREF(Py_None);
-                value = Py_None;
-            }
-            Py_DECREF(ev);
-        }
-        else if (!__Pyx_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) {
-            value = ev;
-        }
-        if (likely(value)) {
-            Py_XDECREF(tb);
-            Py_DECREF(et);
-            *pvalue = value;
-            return 0;
-        }
-    } else if (!__Pyx_PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    PyErr_NormalizeException(&et, &ev, &tb);
-    if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    Py_XDECREF(tb);
-    Py_DECREF(et);
-#if PY_VERSION_HEX >= 0x030300A0
-    value = ((PyStopIterationObject *)ev)->value;
-    Py_INCREF(value);
-    Py_DECREF(ev);
-#else
-    {
-        PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args);
-        Py_DECREF(ev);
-        if (likely(args)) {
-            value = PySequence_GetItem(args, 0);
-            Py_DECREF(args);
-        }
-        if (unlikely(!value)) {
-            __Pyx_ErrRestore(NULL, NULL, NULL);
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-    }
-#endif
-    *pvalue = value;
-    return 0;
-}
-static CYTHON_INLINE
-void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *exc_state) {
-#if PY_VERSION_HEX >= 0x030B00a4
-    Py_CLEAR(exc_state->exc_value);
-#else
-    PyObject *t, *v, *tb;
-    t = exc_state->exc_type;
-    v = exc_state->exc_value;
-    tb = exc_state->exc_traceback;
-    exc_state->exc_type = NULL;
-    exc_state->exc_value = NULL;
-    exc_state->exc_traceback = NULL;
-    Py_XDECREF(t);
-    Py_XDECREF(v);
-    Py_XDECREF(tb);
-#endif
-}
-#define __Pyx_Coroutine_AlreadyRunningError(gen)  (__Pyx__Coroutine_AlreadyRunningError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyRunningError(__pyx_CoroutineObject *gen) {
-    const char *msg;
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check((PyObject*)gen)) {
-        msg = "coroutine already executing";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact((PyObject*)gen)) {
-        msg = "async generator already executing";
-    #endif
-    } else {
-        msg = "generator already executing";
-    }
-    PyErr_SetString(PyExc_ValueError, msg);
-}
-#define __Pyx_Coroutine_NotStartedError(gen)  (__Pyx__Coroutine_NotStartedError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_NotStartedError(PyObject *gen) {
-    const char *msg;
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check(gen)) {
-        msg = "can't send non-None value to a just-started coroutine";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact(gen)) {
-        msg = "can't send non-None value to a just-started async generator";
-    #endif
-    } else {
-        msg = "can't send non-None value to a just-started generator";
-    }
-    PyErr_SetString(PyExc_TypeError, msg);
-}
-#define __Pyx_Coroutine_AlreadyTerminatedError(gen, value, closing)  (__Pyx__Coroutine_AlreadyTerminatedError(gen, value, closing), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyTerminatedError(PyObject *gen, PyObject *value, int closing) {
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    CYTHON_MAYBE_UNUSED_VAR(closing);
-    #ifdef __Pyx_Coroutine_USED
-    if (!closing && __Pyx_Coroutine_Check(gen)) {
-        PyErr_SetString(PyExc_RuntimeError, "cannot reuse already awaited coroutine");
-    } else
-    #endif
-    if (value) {
-        #ifdef __Pyx_AsyncGen_USED
-        if (__Pyx_AsyncGen_CheckExact(gen))
-            PyErr_SetNone(__Pyx_PyExc_StopAsyncIteration);
-        else
-        #endif
-        PyErr_SetNone(PyExc_StopIteration);
-    }
-}
-static
-PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value, int closing) {
-    __Pyx_PyThreadState_declare
-    PyThreadState *tstate;
-    __Pyx_ExcInfoStruct *exc_state;
-    PyObject *retval;
-    assert(!self->is_running);
-    if (unlikely(self->resume_label == 0)) {
-        if (unlikely(value && value != Py_None)) {
-            return __Pyx_Coroutine_NotStartedError((PyObject*)self);
-        }
-    }
-    if (unlikely(self->resume_label == -1)) {
-        return __Pyx_Coroutine_AlreadyTerminatedError((PyObject*)self, value, closing);
-    }
-#if CYTHON_FAST_THREAD_STATE
-    __Pyx_PyThreadState_assign
-    tstate = __pyx_tstate;
-#else
-    tstate = __Pyx_PyThreadState_Current;
-#endif
-    exc_state = &self->gi_exc_state;
-    if (exc_state->exc_value) {
-        #if CYTHON_COMPILING_IN_PYPY
-        #else
-        PyObject *exc_tb;
-        #if PY_VERSION_HEX >= 0x030B00a4 && !CYTHON_COMPILING_IN_CPYTHON
-        exc_tb = PyException_GetTraceback(exc_state->exc_value);
-        #elif PY_VERSION_HEX >= 0x030B00a4
-        exc_tb = ((PyBaseExceptionObject*) exc_state->exc_value)->traceback;
-        #else
-        exc_tb = exc_state->exc_traceback;
-        #endif
-        if (exc_tb) {
-            PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-            PyFrameObject *f = tb->tb_frame;
-            assert(f->f_back == NULL);
-            #if PY_VERSION_HEX >= 0x030B00A1
-            f->f_back = PyThreadState_GetFrame(tstate);
-            #else
-            Py_XINCREF(tstate->frame);
-            f->f_back = tstate->frame;
-            #endif
-            #if PY_VERSION_HEX >= 0x030B00a4 && !CYTHON_COMPILING_IN_CPYTHON
-            Py_DECREF(exc_tb);
-            #endif
-        }
-        #endif
-    }
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state->previous_item = tstate->exc_info;
-    tstate->exc_info = exc_state;
-#else
-    if (exc_state->exc_type) {
-        __Pyx_ExceptionSwap(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    } else {
-        __Pyx_Coroutine_ExceptionClear(exc_state);
-        __Pyx_ExceptionSave(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    }
-#endif
-    self->is_running = 1;
-    retval = self->body(self, tstate, value);
-    self->is_running = 0;
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state = &self->gi_exc_state;
-    tstate->exc_info = exc_state->previous_item;
-    exc_state->previous_item = NULL;
-    __Pyx_Coroutine_ResetFrameBackpointer(exc_state);
-#endif
-    return retval;
-}
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state) {
-#if CYTHON_COMPILING_IN_PYPY
-    CYTHON_UNUSED_VAR(exc_state);
-#else
-    PyObject *exc_tb;
-    #if PY_VERSION_HEX >= 0x030B00a4
-    if (!exc_state->exc_value) return;
-    exc_tb = PyException_GetTraceback(exc_state->exc_value);
-    #else
-    exc_tb = exc_state->exc_traceback;
-    #endif
-    if (likely(exc_tb)) {
-        PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-        PyFrameObject *f = tb->tb_frame;
-        Py_CLEAR(f->f_back);
-        #if PY_VERSION_HEX >= 0x030B00a4
-        Py_DECREF(exc_tb);
-        #endif
-    }
-#endif
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_MethodReturn(PyObject* gen, PyObject *retval) {
-    CYTHON_MAYBE_UNUSED_VAR(gen);
-    if (unlikely(!retval)) {
-        __Pyx_PyThreadState_declare
-        __Pyx_PyThreadState_assign
-        if (!__Pyx_PyErr_Occurred()) {
-            PyObject *exc = PyExc_StopIteration;
-            #ifdef __Pyx_AsyncGen_USED
-            if (__Pyx_AsyncGen_CheckExact(gen))
-                exc = __Pyx_PyExc_StopAsyncIteration;
-            #endif
-            __Pyx_PyErr_SetNone(exc);
-        }
-    }
-    return retval;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-static CYTHON_INLINE
-PyObject *__Pyx_PyGen_Send(PyGenObject *gen, PyObject *arg) {
-#if PY_VERSION_HEX <= 0x030A00A1
-    return _PyGen_Send(gen, arg);
-#else
-    PyObject *result;
-    if (PyIter_Send((PyObject*)gen, arg ? arg : Py_None, &result) == PYGEN_RETURN) {
-        if (PyAsyncGen_CheckExact(gen)) {
-            assert(result == Py_None);
-            PyErr_SetNone(PyExc_StopAsyncIteration);
-        }
-        else if (result == Py_None) {
-            PyErr_SetNone(PyExc_StopIteration);
-        }
-        else {
-            _PyGen_SetStopIterationValue(result);
-        }
-        Py_CLEAR(result);
-    }
-    return result;
-#endif
-}
-#endif
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) {
-    PyObject *ret;
-    PyObject *val = NULL;
-    __Pyx_Coroutine_Undelegate(gen);
-    __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, &val);
-    ret = __Pyx_Coroutine_SendEx(gen, val, 0);
-    Py_XDECREF(val);
-    return ret;
-}
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) {
-    PyObject *retval;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-            ret = __Pyx_async_gen_asend_send(yf, value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03050000 && defined(PyCoro_CheckExact) && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyCoro_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        {
-            if (value == Py_None)
-                ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
-            else
-                ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
-        }
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        retval = __Pyx_Coroutine_FinishDelegation(gen);
-    } else {
-        retval = __Pyx_Coroutine_SendEx(gen, value, 0);
-    }
-    return __Pyx_Coroutine_MethodReturn(self, retval);
-}
-static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) {
-    PyObject *retval = NULL;
-    int err = 0;
-    #ifdef __Pyx_Generator_USED
-    if (__Pyx_Generator_CheckExact(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_Coroutine_USED
-    if (__Pyx_Coroutine_Check(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-        retval = __Pyx_CoroutineAwait_Close((__pyx_CoroutineAwaitObject*)yf, NULL);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-        retval = __Pyx_async_gen_asend_close(yf, NULL);
-    } else
-    if (__pyx_PyAsyncGenAThrow_CheckExact(yf)) {
-        retval = __Pyx_async_gen_athrow_close(yf, NULL);
-    } else
-    #endif
-    {
-        PyObject *meth;
-        gen->is_running = 1;
-        meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_close);
-        if (unlikely(!meth)) {
-            if (unlikely(PyErr_Occurred())) {
-                PyErr_WriteUnraisable(yf);
-            }
-        } else {
-            retval = __Pyx_PyObject_CallNoArg(meth);
-            Py_DECREF(meth);
-            if (unlikely(!retval))
-                err = -1;
-        }
-        gen->is_running = 0;
-    }
-    Py_XDECREF(retval);
-    return err;
-}
-static PyObject *__Pyx_Generator_Next(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Generator_Next(yf);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, NULL);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, Py_None);
-        } else
-        #endif
-            ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        return __Pyx_Coroutine_FinishDelegation(gen);
-    }
-    return __Pyx_Coroutine_SendEx(gen, Py_None, 0);
-}
-static PyObject *__Pyx_Coroutine_Close_Method(PyObject *self, PyObject *arg) {
-    CYTHON_UNUSED_VAR(arg);
-    return __Pyx_Coroutine_Close(self);
-}
-static PyObject *__Pyx_Coroutine_Close(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *retval, *raised_exception;
-    PyObject *yf = gen->yieldfrom;
-    int err = 0;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        Py_INCREF(yf);
-        err = __Pyx_Coroutine_CloseIter(gen, yf);
-        __Pyx_Coroutine_Undelegate(gen);
-        Py_DECREF(yf);
-    }
-    if (err == 0)
-        PyErr_SetNone(PyExc_GeneratorExit);
-    retval = __Pyx_Coroutine_SendEx(gen, NULL, 1);
-    if (unlikely(retval)) {
-        const char *msg;
-        Py_DECREF(retval);
-        if ((0)) {
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_Coroutine_Check(self)) {
-            msg = "coroutine ignored GeneratorExit";
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        } else if (__Pyx_AsyncGen_CheckExact(self)) {
-#if PY_VERSION_HEX < 0x03060000
-            msg = "async generator ignored GeneratorExit - might require Python 3.6+ finalisation (PEP 525)";
-#else
-            msg = "async generator ignored GeneratorExit";
-#endif
-        #endif
-        } else {
-            msg = "generator ignored GeneratorExit";
-        }
-        PyErr_SetString(PyExc_RuntimeError, msg);
-        return NULL;
-    }
-    raised_exception = PyErr_Occurred();
-    if (likely(!raised_exception || __Pyx_PyErr_GivenExceptionMatches2(raised_exception, PyExc_GeneratorExit, PyExc_StopIteration))) {
-        if (raised_exception) PyErr_Clear();
-        Py_INCREF(Py_None);
-        return Py_None;
-    }
-    return NULL;
-}
-static PyObject *__Pyx__Coroutine_Throw(PyObject *self, PyObject *typ, PyObject *val, PyObject *tb,
-                                        PyObject *args, int close_on_genexit) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        Py_INCREF(yf);
-        if (__Pyx_PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit) && close_on_genexit) {
-            int err = __Pyx_Coroutine_CloseIter(gen, yf);
-            Py_DECREF(yf);
-            __Pyx_Coroutine_Undelegate(gen);
-            if (err < 0)
-                return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-            goto throw_here;
-        }
-        gen->is_running = 1;
-        if (0
-        #ifdef __Pyx_Generator_USED
-            || __Pyx_Generator_CheckExact(yf)
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-            || __Pyx_Coroutine_Check(yf)
-        #endif
-            ) {
-            ret = __Pyx__Coroutine_Throw(yf, typ, val, tb, args, close_on_genexit);
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-            ret = __Pyx__Coroutine_Throw(((__pyx_CoroutineAwaitObject*)yf)->coroutine, typ, val, tb, args, close_on_genexit);
-        #endif
-        } else {
-            PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_throw);
-            if (unlikely(!meth)) {
-                Py_DECREF(yf);
-                if (unlikely(PyErr_Occurred())) {
-                    gen->is_running = 0;
-                    return NULL;
-                }
-                __Pyx_Coroutine_Undelegate(gen);
-                gen->is_running = 0;
-                goto throw_here;
-            }
-            if (likely(args)) {
-                ret = __Pyx_PyObject_Call(meth, args, NULL);
-            } else {
-                PyObject *cargs[4] = {NULL, typ, val, tb};
-                ret = __Pyx_PyObject_FastCall(meth, cargs+1, 3 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-            }
-            Py_DECREF(meth);
-        }
-        gen->is_running = 0;
-        Py_DECREF(yf);
-        if (!ret) {
-            ret = __Pyx_Coroutine_FinishDelegation(gen);
-        }
-        return __Pyx_Coroutine_MethodReturn(self, ret);
-    }
-throw_here:
-    __Pyx_Raise(typ, val, tb, NULL);
-    return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-}
-static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) {
-    PyObject *typ;
-    PyObject *val = NULL;
-    PyObject *tb = NULL;
-    if (unlikely(!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb)))
-        return NULL;
-    return __Pyx__Coroutine_Throw(self, typ, val, tb, args, 1);
-}
-static CYTHON_INLINE int __Pyx_Coroutine_traverse_excstate(__Pyx_ExcInfoStruct *exc_state, visitproc visit, void *arg) {
-#if PY_VERSION_HEX >= 0x030B00a4
-    Py_VISIT(exc_state->exc_value);
-#else
-    Py_VISIT(exc_state->exc_type);
-    Py_VISIT(exc_state->exc_value);
-    Py_VISIT(exc_state->exc_traceback);
-#endif
-    return 0;
-}
-static int __Pyx_Coroutine_traverse(__pyx_CoroutineObject *gen, visitproc visit, void *arg) {
-    Py_VISIT(gen->closure);
-    Py_VISIT(gen->classobj);
-    Py_VISIT(gen->yieldfrom);
-    return __Pyx_Coroutine_traverse_excstate(&gen->gi_exc_state, visit, arg);
-}
-static int __Pyx_Coroutine_clear(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    Py_CLEAR(gen->closure);
-    Py_CLEAR(gen->classobj);
-    Py_CLEAR(gen->yieldfrom);
-    __Pyx_Coroutine_ExceptionClear(&gen->gi_exc_state);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)gen)->ag_finalizer);
-    }
-#endif
-    Py_CLEAR(gen->gi_code);
-    Py_CLEAR(gen->gi_frame);
-    Py_CLEAR(gen->gi_name);
-    Py_CLEAR(gen->gi_qualname);
-    Py_CLEAR(gen->gi_modulename);
-    return 0;
-}
-static void __Pyx_Coroutine_dealloc(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject_GC_UnTrack(gen);
-    if (gen->gi_weakreflist != NULL)
-        PyObject_ClearWeakRefs(self);
-    if (gen->resume_label >= 0) {
-        PyObject_GC_Track(self);
-#if PY_VERSION_HEX >= 0x030400a1 && CYTHON_USE_TP_FINALIZE
-        if (unlikely(PyObject_CallFinalizerFromDealloc(self)))
-#else
-        Py_TYPE(gen)->tp_del(self);
-        if (unlikely(Py_REFCNT(self) > 0))
-#endif
-        {
-            return;
-        }
-        PyObject_GC_UnTrack(self);
-    }
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        /* We have to handle this case for asynchronous generators
-           right here, because this code has to be between UNTRACK
-           and GC_Del. */
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)self)->ag_finalizer);
-    }
-#endif
-    __Pyx_Coroutine_clear(self);
-    __Pyx_PyHeapTypeObject_GC_Del(gen);
-}
-static void __Pyx_Coroutine_del(PyObject *self) {
-    PyObject *error_type, *error_value, *error_traceback;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    __Pyx_PyThreadState_declare
-    if (gen->resume_label < 0) {
-        return;
-    }
-#if !CYTHON_USE_TP_FINALIZE
-    assert(self->ob_refcnt == 0);
-    __Pyx_SET_REFCNT(self, 1);
-#endif
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        __pyx_PyAsyncGenObject *agen = (__pyx_PyAsyncGenObject*)self;
-        PyObject *finalizer = agen->ag_finalizer;
-        if (finalizer && !agen->ag_closed) {
-            PyObject *res = __Pyx_PyObject_CallOneArg(finalizer, self);
-            if (unlikely(!res)) {
-                PyErr_WriteUnraisable(self);
-            } else {
-                Py_DECREF(res);
-            }
-            __Pyx_ErrRestore(error_type, error_value, error_traceback);
-            return;
-        }
-    }
-#endif
-    if (unlikely(gen->resume_label == 0 && !error_value)) {
-#ifdef __Pyx_Coroutine_USED
-#ifdef __Pyx_Generator_USED
-    if (!__Pyx_Generator_CheckExact(self))
-#endif
-        {
-        PyObject_GC_UnTrack(self);
-#if PY_MAJOR_VERSION >= 3  || defined(PyErr_WarnFormat)
-        if (unlikely(PyErr_WarnFormat(PyExc_RuntimeWarning, 1, "coroutine '%.50S' was never awaited", gen->gi_qualname) < 0))
-            PyErr_WriteUnraisable(self);
-#else
-        {PyObject *msg;
-        char *cmsg;
-        #if CYTHON_COMPILING_IN_PYPY
-        msg = NULL;
-        cmsg = (char*) "coroutine was never awaited";
-        #else
-        char *cname;
-        PyObject *qualname;
-        qualname = gen->gi_qualname;
-        cname = PyString_AS_STRING(qualname);
-        msg = PyString_FromFormat("coroutine '%.50s' was never awaited", cname);
-        if (unlikely(!msg)) {
-            PyErr_Clear();
-            cmsg = (char*) "coroutine was never awaited";
-        } else {
-            cmsg = PyString_AS_STRING(msg);
-        }
-        #endif
-        if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, cmsg, 1) < 0))
-            PyErr_WriteUnraisable(self);
-        Py_XDECREF(msg);}
-#endif
-        PyObject_GC_Track(self);
-        }
-#endif
-    } else {
-        PyObject *res = __Pyx_Coroutine_Close(self);
-        if (unlikely(!res)) {
-            if (PyErr_Occurred())
-                PyErr_WriteUnraisable(self);
-        } else {
-            Py_DECREF(res);
-        }
-    }
-    __Pyx_ErrRestore(error_type, error_value, error_traceback);
-#if !CYTHON_USE_TP_FINALIZE
-    assert(Py_REFCNT(self) > 0);
-    if (likely(--self->ob_refcnt == 0)) {
-        return;
-    }
-    {
-        Py_ssize_t refcnt = Py_REFCNT(self);
-        _Py_NewReference(self);
-        __Pyx_SET_REFCNT(self, refcnt);
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    assert(PyType_IS_GC(Py_TYPE(self)) &&
-           _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
-    _Py_DEC_REFTOTAL;
-#endif
-#ifdef COUNT_ALLOCS
-    --Py_TYPE(self)->tp_frees;
-    --Py_TYPE(self)->tp_allocs;
-#endif
-#endif
-}
-static PyObject *
-__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *name = self->gi_name;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(self->gi_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *name = self->gi_qualname;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(self->gi_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_frame(__pyx_CoroutineObject *self, void *context)
-{
-    PyObject *frame = self->gi_frame;
-    CYTHON_UNUSED_VAR(context);
-    if (!frame) {
-        if (unlikely(!self->gi_code)) {
-            Py_RETURN_NONE;
-        }
-        frame = (PyObject *) PyFrame_New(
-            PyThreadState_Get(),            /*PyThreadState *tstate,*/
-            (PyCodeObject*) self->gi_code,  /*PyCodeObject *code,*/
-            __pyx_d,                 /*PyObject *globals,*/
-            0                               /*PyObject *locals*/
-        );
-        if (unlikely(!frame))
-            return NULL;
-        self->gi_frame = frame;
-    }
-    Py_INCREF(frame);
-    return frame;
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-            PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type);
-    if (unlikely(!gen))
-        return NULL;
-    return __Pyx__Coroutine_NewInit(gen, body, code, closure, name, qualname, module_name);
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    gen->body = body;
-    gen->closure = closure;
-    Py_XINCREF(closure);
-    gen->is_running = 0;
-    gen->resume_label = 0;
-    gen->classobj = NULL;
-    gen->yieldfrom = NULL;
-    #if PY_VERSION_HEX >= 0x030B00a4
-    gen->gi_exc_state.exc_value = NULL;
-    #else
-    gen->gi_exc_state.exc_type = NULL;
-    gen->gi_exc_state.exc_value = NULL;
-    gen->gi_exc_state.exc_traceback = NULL;
-    #endif
-#if CYTHON_USE_EXC_INFO_STACK
-    gen->gi_exc_state.previous_item = NULL;
-#endif
-    gen->gi_weakreflist = NULL;
-    Py_XINCREF(qualname);
-    gen->gi_qualname = qualname;
-    Py_XINCREF(name);
-    gen->gi_name = name;
-    Py_XINCREF(module_name);
-    gen->gi_modulename = module_name;
-    Py_XINCREF(code);
-    gen->gi_code = code;
-    gen->gi_frame = NULL;
-    PyObject_GC_Track(gen);
-    return gen;
-}
-
-/* PatchModuleWithCoroutine */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    int result;
-    PyObject *globals, *result_obj;
-    globals = PyDict_New();  if (unlikely(!globals)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_coroutine_type",
-    #ifdef __Pyx_Coroutine_USED
-        (PyObject*)__pyx_CoroutineType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_generator_type",
-    #ifdef __Pyx_Generator_USED
-        (PyObject*)__pyx_GeneratorType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore;
-    result_obj = PyRun_String(py_code, Py_file_input, globals, globals);
-    if (unlikely(!result_obj)) goto ignore;
-    Py_DECREF(result_obj);
-    Py_DECREF(globals);
-    return module;
-ignore:
-    Py_XDECREF(globals);
-    PyErr_WriteUnraisable(module);
-    if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) {
-        Py_DECREF(module);
-        module = NULL;
-    }
-#else
-    py_code++;
-#endif
-    return module;
-}
-
-/* PatchGeneratorABC */
-#ifndef CYTHON_REGISTER_ABCS
-#define CYTHON_REGISTER_ABCS 1
-#endif
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-static PyObject* __Pyx_patch_abc_module(PyObject *module);
-static PyObject* __Pyx_patch_abc_module(PyObject *module) {
-    module = __Pyx_Coroutine_patch_module(
-        module, ""
-"if _cython_generator_type is not None:\n"
-"    try: Generator = _module.Generator\n"
-"    except AttributeError: pass\n"
-"    else: Generator.register(_cython_generator_type)\n"
-"if _cython_coroutine_type is not None:\n"
-"    try: Coroutine = _module.Coroutine\n"
-"    except AttributeError: pass\n"
-"    else: Coroutine.register(_cython_coroutine_type)\n"
-    );
-    return module;
-}
-#endif
-static int __Pyx_patch_abc(void) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    static int abc_patched = 0;
-    if (CYTHON_REGISTER_ABCS && !abc_patched) {
-        PyObject *module;
-        module = PyImport_ImportModule((PY_MAJOR_VERSION >= 3) ? "collections.abc" : "collections");
-        if (unlikely(!module)) {
-            PyErr_WriteUnraisable(NULL);
-            if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning,
-                    ((PY_MAJOR_VERSION >= 3) ?
-                        "Cython module failed to register with collections.abc module" :
-                        "Cython module failed to register with collections module"), 1) < 0)) {
-                return -1;
-            }
-        } else {
-            module = __Pyx_patch_abc_module(module);
-            abc_patched = 1;
-            if (unlikely(!module))
-                return -1;
-            Py_DECREF(module);
-        }
-        module = PyImport_ImportModule("backports_abc");
-        if (module) {
-            module = __Pyx_patch_abc_module(module);
-            Py_XDECREF(module);
-        }
-        if (!module) {
-            PyErr_Clear();
-        }
-    }
-#else
-    if ((0)) __Pyx_Coroutine_patch_module(NULL, NULL);
-#endif
-    return 0;
-}
-
-/* Generator */
-static PyMethodDef __pyx_Generator_methods[] = {
-    {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O,
-     (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")},
-    {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS,
-     (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")},
-    {"close", (PyCFunction) __Pyx_Coroutine_Close_Method, METH_NOARGS,
-     (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")},
-    {0, 0, 0, 0}
-};
-static PyMemberDef __pyx_Generator_memberlist[] = {
-    {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL},
-    {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY,
-     (char*) PyDoc_STR("object being iterated by 'yield from', or None")},
-    {(char*) "gi_code", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_code), READONLY, NULL},
-    {(char *) "__module__", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_modulename), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CoroutineObject, gi_weakreflist), READONLY, 0},
-#endif
-    {0, 0, 0, 0, 0}
-};
-static PyGetSetDef __pyx_Generator_getsets[] = {
-    {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name,
-     (char*) PyDoc_STR("name of the generator"), 0},
-    {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname,
-     (char*) PyDoc_STR("qualified name of the generator"), 0},
-    {(char *) "gi_frame", (getter)__Pyx_Coroutine_get_frame, NULL,
-     (char*) PyDoc_STR("Frame of the generator"), 0},
-    {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_GeneratorType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_Coroutine_dealloc},
-    {Py_tp_traverse, (void *)__Pyx_Coroutine_traverse},
-    {Py_tp_iter, (void *)PyObject_SelfIter},
-    {Py_tp_iternext, (void *)__Pyx_Generator_Next},
-    {Py_tp_methods, (void *)__pyx_Generator_methods},
-    {Py_tp_members, (void *)__pyx_Generator_memberlist},
-    {Py_tp_getset, (void *)__pyx_Generator_getsets},
-    {Py_tp_getattro, (void *) __Pyx_PyObject_GenericGetAttrNoDict},
-#if CYTHON_USE_TP_FINALIZE
-    {Py_tp_finalize, (void *)__Pyx_Coroutine_del},
-#endif
-    {0, 0},
-};
-static PyType_Spec __pyx_GeneratorType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "generator",
-    sizeof(__pyx_CoroutineObject),
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
-    __pyx_GeneratorType_slots
-};
-#else
-static PyTypeObject __pyx_GeneratorType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "generator",
-    sizeof(__pyx_CoroutineObject),
-    0,
-    (destructor) __Pyx_Coroutine_dealloc,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
-    0,
-    (traverseproc) __Pyx_Coroutine_traverse,
-    0,
-    0,
-    offsetof(__pyx_CoroutineObject, gi_weakreflist),
-    0,
-    (iternextfunc) __Pyx_Generator_Next,
-    __pyx_Generator_methods,
-    __pyx_Generator_memberlist,
-    __pyx_Generator_getsets,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if CYTHON_USE_TP_FINALIZE
-    0,
-#else
-    __Pyx_Coroutine_del,
-#endif
-    0,
-#if CYTHON_USE_TP_FINALIZE
-    __Pyx_Coroutine_del,
-#elif PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_Generator_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_GeneratorType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_GeneratorType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_GeneratorType_type.tp_getattro = __Pyx_PyObject_GenericGetAttrNoDict;
-    __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter;
-    __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type);
-#endif
-    if (unlikely(!__pyx_GeneratorType)) {
-        return -1;
-    }
-    return 0;
-}
-
-/* CheckBinaryVersion */
-static unsigned long __Pyx_get_runtime_version() {
-#if __PYX_LIMITED_VERSION_HEX >= 0x030B00A4
-    return Py_Version & ~0xFFUL;
-#else
-    const char* rt_version = Py_GetVersion();
-    unsigned long version = 0;
-    unsigned long factor = 0x01000000UL;
-    unsigned int digit = 0;
-    int i = 0;
-    while (factor) {
-        while ('0' <= rt_version[i] && rt_version[i] <= '9') {
-            digit = digit * 10 + (unsigned int) (rt_version[i] - '0');
-            ++i;
-        }
-        version += factor * digit;
-        if (rt_version[i] != '.')
-            break;
-        digit = 0;
-        factor >>= 8;
-        ++i;
-    }
-    return version;
-#endif
-}
-static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer) {
-    const unsigned long MAJOR_MINOR = 0xFFFF0000UL;
-    if ((rt_version & MAJOR_MINOR) == (ct_version & MAJOR_MINOR))
-        return 0;
-    if (likely(allow_newer && (rt_version & MAJOR_MINOR) > (ct_version & MAJOR_MINOR)))
-        return 1;
-    {
-        char message[200];
-        PyOS_snprintf(message, sizeof(message),
-                      "compile time Python version %d.%d "
-                      "of module '%.100s' "
-                      "%s "
-                      "runtime version %d.%d",
-                       (int) (ct_version >> 24), (int) ((ct_version >> 16) & 0xFF),
-                       __Pyx_MODULE_NAME,
-                       (allow_newer) ? "was newer than" : "does not match",
-                       (int) (rt_version >> 24), (int) ((rt_version >> 16) & 0xFF)
-       );
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-}
-
-/* InitStrings */
-#if PY_MAJOR_VERSION >= 3
-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) {
-    if (t.is_unicode | t.is_str) {
-        if (t.intern) {
-            *str = PyUnicode_InternFromString(t.s);
-        } else if (t.encoding) {
-            *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL);
-        } else {
-            *str = PyUnicode_FromStringAndSize(t.s, t.n - 1);
-        }
-    } else {
-        *str = PyBytes_FromStringAndSize(t.s, t.n - 1);
-    }
-    if (!*str)
-        return -1;
-    if (PyObject_Hash(*str) == -1)
-        return -1;
-    return 0;
-}
-#endif
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION >= 3
-        __Pyx_InitString(*t, t->p);
-        #else
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        #endif
-        ++t;
-    }
-    return 0;
-}
-
-#include <string.h>
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) {
-    size_t len = strlen(s);
-    if (unlikely(len > (size_t) PY_SSIZE_T_MAX)) {
-        PyErr_SetString(PyExc_OverflowError, "byte string is too long");
-        return -1;
-    }
-    return (Py_ssize_t) len;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    Py_ssize_t len = __Pyx_ssize_strlen(c_str);
-    if (unlikely(len < 0)) return NULL;
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, len);
-}
-static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char* c_str) {
-    Py_ssize_t len = __Pyx_ssize_strlen(c_str);
-    if (unlikely(len < 0)) return NULL;
-    return PyByteArray_FromStringAndSize(c_str, len);
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-    __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result));
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type " __Pyx_FMT_TYPENAME ").  "
-                "The ability to return an instance of a strict subclass of int is deprecated, "
-                "and may be removed in a future version of Python.",
-                result_type_name)) {
-            __Pyx_DECREF_TypeName(result_type_name);
-            Py_DECREF(result);
-            return NULL;
-        }
-        __Pyx_DECREF_TypeName(result_type_name);
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")",
-                 type_name, type_name, result_type_name);
-    __Pyx_DECREF_TypeName(result_type_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(__Pyx_PyLong_IsCompact(b))) {
-        return __Pyx_PyLong_CompactValue(b);
-    } else {
-      const digit* digits = __Pyx_PyLong_Digits(b);
-      const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b);
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-/* #### Code section: utility_code_pragmas_end ### */
-#ifdef _MSC_VER
-#pragma warning( pop )
-#endif
-
-
-
-/* #### Code section: end ### */
-#endif /* Py_PYTHON_H */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/__init__.py
deleted file mode 100644
index f4cba26bf6ecaf18e96a62db69f70078498451e3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/__init__.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# DON'T EDIT! This file is generated by MetaTools/buildTableList.py.
-def _moduleFinderHint():
-    """Dummy function to let modulefinder know what tables may be
-    dynamically imported. Generated by MetaTools/buildTableList.py.
-
-            >>> _moduleFinderHint()
-    """
-    from . import B_A_S_E_
-    from . import C_B_D_T_
-    from . import C_B_L_C_
-    from . import C_F_F_
-    from . import C_F_F__2
-    from . import C_O_L_R_
-    from . import C_P_A_L_
-    from . import D_S_I_G_
-    from . import D__e_b_g
-    from . import E_B_D_T_
-    from . import E_B_L_C_
-    from . import F_F_T_M_
-    from . import F__e_a_t
-    from . import G_D_E_F_
-    from . import G_M_A_P_
-    from . import G_P_K_G_
-    from . import G_P_O_S_
-    from . import G_S_U_B_
-    from . import G__l_a_t
-    from . import G__l_o_c
-    from . import H_V_A_R_
-    from . import J_S_T_F_
-    from . import L_T_S_H_
-    from . import M_A_T_H_
-    from . import M_E_T_A_
-    from . import M_V_A_R_
-    from . import O_S_2f_2
-    from . import S_I_N_G_
-    from . import S_T_A_T_
-    from . import S_V_G_
-    from . import S__i_l_f
-    from . import S__i_l_l
-    from . import T_S_I_B_
-    from . import T_S_I_C_
-    from . import T_S_I_D_
-    from . import T_S_I_J_
-    from . import T_S_I_P_
-    from . import T_S_I_S_
-    from . import T_S_I_V_
-    from . import T_S_I__0
-    from . import T_S_I__1
-    from . import T_S_I__2
-    from . import T_S_I__3
-    from . import T_S_I__5
-    from . import T_T_F_A_
-    from . import V_D_M_X_
-    from . import V_O_R_G_
-    from . import V_V_A_R_
-    from . import _a_n_k_r
-    from . import _a_v_a_r
-    from . import _b_s_l_n
-    from . import _c_i_d_g
-    from . import _c_m_a_p
-    from . import _c_v_a_r
-    from . import _c_v_t
-    from . import _f_e_a_t
-    from . import _f_p_g_m
-    from . import _f_v_a_r
-    from . import _g_a_s_p
-    from . import _g_c_i_d
-    from . import _g_l_y_f
-    from . import _g_v_a_r
-    from . import _h_d_m_x
-    from . import _h_e_a_d
-    from . import _h_h_e_a
-    from . import _h_m_t_x
-    from . import _k_e_r_n
-    from . import _l_c_a_r
-    from . import _l_o_c_a
-    from . import _l_t_a_g
-    from . import _m_a_x_p
-    from . import _m_e_t_a
-    from . import _m_o_r_t
-    from . import _m_o_r_x
-    from . import _n_a_m_e
-    from . import _o_p_b_d
-    from . import _p_o_s_t
-    from . import _p_r_e_p
-    from . import _p_r_o_p
-    from . import _s_b_i_x
-    from . import _t_r_a_k
-    from . import _v_h_e_a
-    from . import _v_m_t_x
-
-
-if __name__ == "__main__":
-    import doctest, sys
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/themes/upload_theme.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/themes/upload_theme.py
deleted file mode 100644
index ee11e056d488579e818bc4814d6b13892e6b6e0b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/themes/upload_theme.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from __future__ import annotations
-
-import argparse
-
-from gradio.themes import ThemeClass
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Upload a demo to a space")
-    parser.add_argument("theme", type=str, help="Theme json file")
-    parser.add_argument("repo_name", type=str, help="HF repo name to store the theme")
-    parser.add_argument(
-        "--org_name",
-        type=str,
-        help="The name of the org to save the space in. If None (the default), the username corresponding to the logged in user, or hƒ_token is used.",
-    )
-    parser.add_argument("--version", type=str, help="Semver version")
-    parser.add_argument("--hf_token", type=str, help="HF Token")
-    parser.add_argument(
-        "--theme-name",
-        type=str,
-        help="Name of theme.",
-    )
-    parser.add_argument(
-        "--description",
-        type=str,
-        help="Description of theme",
-    )
-    args = parser.parse_args()
-    upload_theme(
-        args.theme,
-        args.repo_name,
-        args.org_name,
-        args.version,
-        args.hf_token,
-        args.theme_name,
-        args.description,
-    )
-
-
-def upload_theme(
-    theme: str,
-    repo_name: str,
-    org_name: str | None = None,
-    version: str | None = None,
-    hf_token: str | None = None,
-    theme_name: str | None = None,
-    description: str | None = None,
-):
-    theme = ThemeClass.load(theme)
-
-    return theme.push_to_hub(
-        repo_name=repo_name,
-        version=version,
-        hf_token=hf_token,
-        theme_name=theme_name,
-        description=description,
-        org_name=org_name,
-    )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio_client/exceptions.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio_client/exceptions.py
deleted file mode 100644
index 5329124e7aa955a95867951d2afc2a93924b121b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio_client/exceptions.py
+++ /dev/null
@@ -1,4 +0,0 @@
-class SerializationSetupError(ValueError):
-    """Raised when a serializers cannot be set up correctly."""
-
-    pass
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk3agg.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk3agg.py
deleted file mode 100644
index a006a4c6f12745da673890f96ec55e82e13d976b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk3agg.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import numpy as np
-
-from .. import cbook, transforms
-from . import backend_agg, backend_gtk3
-from .backend_gtk3 import Gtk, _BackendGTK3
-
-import cairo  # Presence of cairo is already checked by _backend_gtk.
-
-
-class FigureCanvasGTK3Agg(backend_agg.FigureCanvasAgg,
-                          backend_gtk3.FigureCanvasGTK3):
-    def __init__(self, figure):
-        super().__init__(figure=figure)
-        self._bbox_queue = []
-
-    def on_draw_event(self, widget, ctx):
-        scale = self.device_pixel_ratio
-        allocation = self.get_allocation()
-        w = allocation.width * scale
-        h = allocation.height * scale
-
-        if not len(self._bbox_queue):
-            Gtk.render_background(
-                self.get_style_context(), ctx,
-                allocation.x, allocation.y,
-                allocation.width, allocation.height)
-            bbox_queue = [transforms.Bbox([[0, 0], [w, h]])]
-        else:
-            bbox_queue = self._bbox_queue
-
-        for bbox in bbox_queue:
-            x = int(bbox.x0)
-            y = h - int(bbox.y1)
-            width = int(bbox.x1) - int(bbox.x0)
-            height = int(bbox.y1) - int(bbox.y0)
-
-            buf = cbook._unmultiplied_rgba8888_to_premultiplied_argb32(
-                np.asarray(self.copy_from_bbox(bbox)))
-            image = cairo.ImageSurface.create_for_data(
-                buf.ravel().data, cairo.FORMAT_ARGB32, width, height)
-            image.set_device_scale(scale, scale)
-            ctx.set_source_surface(image, x / scale, y / scale)
-            ctx.paint()
-
-        if len(self._bbox_queue):
-            self._bbox_queue = []
-
-        return False
-
-    def blit(self, bbox=None):
-        # If bbox is None, blit the entire canvas to gtk. Otherwise
-        # blit only the area defined by the bbox.
-        if bbox is None:
-            bbox = self.figure.bbox
-
-        scale = self.device_pixel_ratio
-        allocation = self.get_allocation()
-        x = int(bbox.x0 / scale)
-        y = allocation.height - int(bbox.y1 / scale)
-        width = (int(bbox.x1) - int(bbox.x0)) // scale
-        height = (int(bbox.y1) - int(bbox.y0)) // scale
-
-        self._bbox_queue.append(bbox)
-        self.queue_draw_area(x, y, width, height)
-
-
-@_BackendGTK3.export
-class _BackendGTK3Cairo(_BackendGTK3):
-    FigureCanvas = FigureCanvasGTK3Agg
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_pdf.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_pdf.py
deleted file mode 100644
index aa4883135d92fdeb2105510f5e072701c7dcd97f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_pdf.py
+++ /dev/null
@@ -1,2827 +0,0 @@
-"""
-A PDF Matplotlib backend.
-
-Author: Jouni K Seppänen <jks@iki.fi> and others.
-"""
-
-import codecs
-from datetime import timezone
-from datetime import datetime
-from enum import Enum
-from functools import total_ordering
-from io import BytesIO
-import itertools
-import logging
-import math
-import os
-import string
-import struct
-import sys
-import time
-import types
-import warnings
-import zlib
-
-import numpy as np
-from PIL import Image
-
-import matplotlib as mpl
-from matplotlib import _api, _text_helpers, _type1font, cbook, dviread
-from matplotlib._pylab_helpers import Gcf
-from matplotlib.backend_bases import (
-    _Backend, FigureCanvasBase, FigureManagerBase, GraphicsContextBase,
-    RendererBase)
-from matplotlib.backends.backend_mixed import MixedModeRenderer
-from matplotlib.figure import Figure
-from matplotlib.font_manager import get_font, fontManager as _fontManager
-from matplotlib._afm import AFM
-from matplotlib.ft2font import (FIXED_WIDTH, ITALIC, LOAD_NO_SCALE,
-                                LOAD_NO_HINTING, KERNING_UNFITTED, FT2Font)
-from matplotlib.transforms import Affine2D, BboxBase
-from matplotlib.path import Path
-from matplotlib.dates import UTC
-from matplotlib import _path
-from . import _backend_pdf_ps
-
-_log = logging.getLogger(__name__)
-
-# Overview
-#
-# The low-level knowledge about pdf syntax lies mainly in the pdfRepr
-# function and the classes Reference, Name, Operator, and Stream.  The
-# PdfFile class knows about the overall structure of pdf documents.
-# It provides a "write" method for writing arbitrary strings in the
-# file, and an "output" method that passes objects through the pdfRepr
-# function before writing them in the file.  The output method is
-# called by the RendererPdf class, which contains the various draw_foo
-# methods.  RendererPdf contains a GraphicsContextPdf instance, and
-# each draw_foo calls self.check_gc before outputting commands.  This
-# method checks whether the pdf graphics state needs to be modified
-# and outputs the necessary commands.  GraphicsContextPdf represents
-# the graphics state, and its "delta" method returns the commands that
-# modify the state.
-
-# Add "pdf.use14corefonts: True" in your configuration file to use only
-# the 14 PDF core fonts. These fonts do not need to be embedded; every
-# PDF viewing application is required to have them. This results in very
-# light PDF files you can use directly in LaTeX or ConTeXt documents
-# generated with pdfTeX, without any conversion.
-
-# These fonts are: Helvetica, Helvetica-Bold, Helvetica-Oblique,
-# Helvetica-BoldOblique, Courier, Courier-Bold, Courier-Oblique,
-# Courier-BoldOblique, Times-Roman, Times-Bold, Times-Italic,
-# Times-BoldItalic, Symbol, ZapfDingbats.
-#
-# Some tricky points:
-#
-# 1. The clip path can only be widened by popping from the state
-# stack.  Thus the state must be pushed onto the stack before narrowing
-# the clip path.  This is taken care of by GraphicsContextPdf.
-#
-# 2. Sometimes it is necessary to refer to something (e.g., font,
-# image, or extended graphics state, which contains the alpha value)
-# in the page stream by a name that needs to be defined outside the
-# stream.  PdfFile provides the methods fontName, imageObject, and
-# alphaState for this purpose.  The implementations of these methods
-# should perhaps be generalized.
-
-# TODOs:
-#
-# * encoding of fonts, including mathtext fonts and Unicode support
-# * TTF support has lots of small TODOs, e.g., how do you know if a font
-#   is serif/sans-serif, or symbolic/non-symbolic?
-# * draw_quad_mesh
-
-
-def _fill(strings, linelen=75):
-    """
-    Make one string from sequence of strings, with whitespace in between.
-
-    The whitespace is chosen to form lines of at most *linelen* characters,
-    if possible.
-    """
-    currpos = 0
-    lasti = 0
-    result = []
-    for i, s in enumerate(strings):
-        length = len(s)
-        if currpos + length < linelen:
-            currpos += length + 1
-        else:
-            result.append(b' '.join(strings[lasti:i]))
-            lasti = i
-            currpos = length
-    result.append(b' '.join(strings[lasti:]))
-    return b'\n'.join(result)
-
-
-def _create_pdf_info_dict(backend, metadata):
-    """
-    Create a PDF infoDict based on user-supplied metadata.
-
-    A default ``Creator``, ``Producer``, and ``CreationDate`` are added, though
-    the user metadata may override it. The date may be the current time, or a
-    time set by the ``SOURCE_DATE_EPOCH`` environment variable.
-
-    Metadata is verified to have the correct keys and their expected types. Any
-    unknown keys/types will raise a warning.
-
-    Parameters
-    ----------
-    backend : str
-        The name of the backend to use in the Producer value.
-
-    metadata : dict[str, Union[str, datetime, Name]]
-        A dictionary of metadata supplied by the user with information
-        following the PDF specification, also defined in
-        `~.backend_pdf.PdfPages` below.
-
-        If any value is *None*, then the key will be removed. This can be used
-        to remove any pre-defined values.
-
-    Returns
-    -------
-    dict[str, Union[str, datetime, Name]]
-        A validated dictionary of metadata.
-    """
-
-    # get source date from SOURCE_DATE_EPOCH, if set
-    # See https://reproducible-builds.org/specs/source-date-epoch/
-    source_date_epoch = os.getenv("SOURCE_DATE_EPOCH")
-    if source_date_epoch:
-        source_date = datetime.fromtimestamp(int(source_date_epoch), timezone.utc)
-        source_date = source_date.replace(tzinfo=UTC)
-    else:
-        source_date = datetime.today()
-
-    info = {
-        'Creator': f'Matplotlib v{mpl.__version__}, https://matplotlib.org',
-        'Producer': f'Matplotlib {backend} backend v{mpl.__version__}',
-        'CreationDate': source_date,
-        **metadata
-    }
-    info = {k: v for (k, v) in info.items() if v is not None}
-
-    def is_string_like(x):
-        return isinstance(x, str)
-    is_string_like.text_for_warning = "an instance of str"
-
-    def is_date(x):
-        return isinstance(x, datetime)
-    is_date.text_for_warning = "an instance of datetime.datetime"
-
-    def check_trapped(x):
-        if isinstance(x, Name):
-            return x.name in (b'True', b'False', b'Unknown')
-        else:
-            return x in ('True', 'False', 'Unknown')
-    check_trapped.text_for_warning = 'one of {"True", "False", "Unknown"}'
-
-    keywords = {
-        'Title': is_string_like,
-        'Author': is_string_like,
-        'Subject': is_string_like,
-        'Keywords': is_string_like,
-        'Creator': is_string_like,
-        'Producer': is_string_like,
-        'CreationDate': is_date,
-        'ModDate': is_date,
-        'Trapped': check_trapped,
-    }
-    for k in info:
-        if k not in keywords:
-            _api.warn_external(f'Unknown infodict keyword: {k!r}. '
-                               f'Must be one of {set(keywords)!r}.')
-        elif not keywords[k](info[k]):
-            _api.warn_external(f'Bad value for infodict keyword {k}. '
-                               f'Got {info[k]!r} which is not '
-                               f'{keywords[k].text_for_warning}.')
-    if 'Trapped' in info:
-        info['Trapped'] = Name(info['Trapped'])
-
-    return info
-
-
-def _datetime_to_pdf(d):
-    """
-    Convert a datetime to a PDF string representing it.
-
-    Used for PDF and PGF.
-    """
-    r = d.strftime('D:%Y%m%d%H%M%S')
-    z = d.utcoffset()
-    if z is not None:
-        z = z.seconds
-    else:
-        if time.daylight:
-            z = time.altzone
-        else:
-            z = time.timezone
-    if z == 0:
-        r += 'Z'
-    elif z < 0:
-        r += "+%02d'%02d'" % ((-z) // 3600, (-z) % 3600)
-    else:
-        r += "-%02d'%02d'" % (z // 3600, z % 3600)
-    return r
-
-
-def _calculate_quad_point_coordinates(x, y, width, height, angle=0):
-    """
-    Calculate the coordinates of rectangle when rotated by angle around x, y
-    """
-
-    angle = math.radians(-angle)
-    sin_angle = math.sin(angle)
-    cos_angle = math.cos(angle)
-    a = x + height * sin_angle
-    b = y + height * cos_angle
-    c = x + width * cos_angle + height * sin_angle
-    d = y - width * sin_angle + height * cos_angle
-    e = x + width * cos_angle
-    f = y - width * sin_angle
-    return ((x, y), (e, f), (c, d), (a, b))
-
-
-def _get_coordinates_of_block(x, y, width, height, angle=0):
-    """
-    Get the coordinates of rotated rectangle and rectangle that covers the
-    rotated rectangle.
-    """
-
-    vertices = _calculate_quad_point_coordinates(x, y, width,
-                                                 height, angle)
-
-    # Find min and max values for rectangle
-    # adjust so that QuadPoints is inside Rect
-    # PDF docs says that QuadPoints should be ignored if any point lies
-    # outside Rect, but for Acrobat it is enough that QuadPoints is on the
-    # border of Rect.
-
-    pad = 0.00001 if angle % 90 else 0
-    min_x = min(v[0] for v in vertices) - pad
-    min_y = min(v[1] for v in vertices) - pad
-    max_x = max(v[0] for v in vertices) + pad
-    max_y = max(v[1] for v in vertices) + pad
-    return (tuple(itertools.chain.from_iterable(vertices)),
-            (min_x, min_y, max_x, max_y))
-
-
-def _get_link_annotation(gc, x, y, width, height, angle=0):
-    """
-    Create a link annotation object for embedding URLs.
-    """
-    quadpoints, rect = _get_coordinates_of_block(x, y, width, height, angle)
-    link_annotation = {
-        'Type': Name('Annot'),
-        'Subtype': Name('Link'),
-        'Rect': rect,
-        'Border': [0, 0, 0],
-        'A': {
-            'S': Name('URI'),
-            'URI': gc.get_url(),
-        },
-    }
-    if angle % 90:
-        # Add QuadPoints
-        link_annotation['QuadPoints'] = quadpoints
-    return link_annotation
-
-
-# PDF strings are supposed to be able to include any eight-bit data, except
-# that unbalanced parens and backslashes must be escaped by a backslash.
-# However, sf bug #2708559 shows that the carriage return character may get
-# read as a newline; these characters correspond to \gamma and \Omega in TeX's
-# math font encoding. Escaping them fixes the bug.
-_str_escapes = str.maketrans({
-    '\\': '\\\\', '(': '\\(', ')': '\\)', '\n': '\\n', '\r': '\\r'})
-
-
-def pdfRepr(obj):
-    """Map Python objects to PDF syntax."""
-
-    # Some objects defined later have their own pdfRepr method.
-    if hasattr(obj, 'pdfRepr'):
-        return obj.pdfRepr()
-
-    # Floats. PDF does not have exponential notation (1.0e-10) so we
-    # need to use %f with some precision.  Perhaps the precision
-    # should adapt to the magnitude of the number?
-    elif isinstance(obj, (float, np.floating)):
-        if not np.isfinite(obj):
-            raise ValueError("Can only output finite numbers in PDF")
-        r = b"%.10f" % obj
-        return r.rstrip(b'0').rstrip(b'.')
-
-    # Booleans. Needs to be tested before integers since
-    # isinstance(True, int) is true.
-    elif isinstance(obj, bool):
-        return [b'false', b'true'][obj]
-
-    # Integers are written as such.
-    elif isinstance(obj, (int, np.integer)):
-        return b"%d" % obj
-
-    # Non-ASCII Unicode strings are encoded in UTF-16BE with byte-order mark.
-    elif isinstance(obj, str):
-        return pdfRepr(obj.encode('ascii') if obj.isascii()
-                       else codecs.BOM_UTF16_BE + obj.encode('UTF-16BE'))
-
-    # Strings are written in parentheses, with backslashes and parens
-    # escaped. Actually balanced parens are allowed, but it is
-    # simpler to escape them all. TODO: cut long strings into lines;
-    # I believe there is some maximum line length in PDF.
-    # Despite the extra decode/encode, translate is faster than regex.
-    elif isinstance(obj, bytes):
-        return (
-            b'(' +
-            obj.decode('latin-1').translate(_str_escapes).encode('latin-1')
-            + b')')
-
-    # Dictionaries. The keys must be PDF names, so if we find strings
-    # there, we make Name objects from them. The values may be
-    # anything, so the caller must ensure that PDF names are
-    # represented as Name objects.
-    elif isinstance(obj, dict):
-        return _fill([
-            b"<<",
-            *[Name(k).pdfRepr() + b" " + pdfRepr(v) for k, v in obj.items()],
-            b">>",
-        ])
-
-    # Lists.
-    elif isinstance(obj, (list, tuple)):
-        return _fill([b"[", *[pdfRepr(val) for val in obj], b"]"])
-
-    # The null keyword.
-    elif obj is None:
-        return b'null'
-
-    # A date.
-    elif isinstance(obj, datetime):
-        return pdfRepr(_datetime_to_pdf(obj))
-
-    # A bounding box
-    elif isinstance(obj, BboxBase):
-        return _fill([pdfRepr(val) for val in obj.bounds])
-
-    else:
-        raise TypeError(f"Don't know a PDF representation for {type(obj)} "
-                        "objects")
-
-
-def _font_supports_glyph(fonttype, glyph):
-    """
-    Returns True if the font is able to provide codepoint *glyph* in a PDF.
-
-    For a Type 3 font, this method returns True only for single-byte
-    characters. For Type 42 fonts this method return True if the character is
-    from the Basic Multilingual Plane.
-    """
-    if fonttype == 3:
-        return glyph <= 255
-    if fonttype == 42:
-        return glyph <= 65535
-    raise NotImplementedError()
-
-
-class Reference:
-    """
-    PDF reference object.
-
-    Use PdfFile.reserveObject() to create References.
-    """
-
-    def __init__(self, id):
-        self.id = id
-
-    def __repr__(self):
-        return "<Reference %d>" % self.id
-
-    def pdfRepr(self):
-        return b"%d 0 R" % self.id
-
-    def write(self, contents, file):
-        write = file.write
-        write(b"%d 0 obj\n" % self.id)
-        write(pdfRepr(contents))
-        write(b"\nendobj\n")
-
-
-@total_ordering
-class Name:
-    """PDF name object."""
-    __slots__ = ('name',)
-    _hexify = {c: '#%02x' % c
-               for c in {*range(256)} - {*range(ord('!'), ord('~') + 1)}}
-
-    def __init__(self, name):
-        if isinstance(name, Name):
-            self.name = name.name
-        else:
-            if isinstance(name, bytes):
-                name = name.decode('ascii')
-            self.name = name.translate(self._hexify).encode('ascii')
-
-    def __repr__(self):
-        return "<Name %s>" % self.name
-
-    def __str__(self):
-        return '/' + self.name.decode('ascii')
-
-    def __eq__(self, other):
-        return isinstance(other, Name) and self.name == other.name
-
-    def __lt__(self, other):
-        return isinstance(other, Name) and self.name < other.name
-
-    def __hash__(self):
-        return hash(self.name)
-
-    def pdfRepr(self):
-        return b'/' + self.name
-
-
-class Verbatim:
-    """Store verbatim PDF command content for later inclusion in the stream."""
-    def __init__(self, x):
-        self._x = x
-
-    def pdfRepr(self):
-        return self._x
-
-
-class Op(Enum):
-    """PDF operators (not an exhaustive list)."""
-
-    close_fill_stroke = b'b'
-    fill_stroke = b'B'
-    fill = b'f'
-    closepath = b'h'
-    close_stroke = b's'
-    stroke = b'S'
-    endpath = b'n'
-    begin_text = b'BT'
-    end_text = b'ET'
-    curveto = b'c'
-    rectangle = b're'
-    lineto = b'l'
-    moveto = b'm'
-    concat_matrix = b'cm'
-    use_xobject = b'Do'
-    setgray_stroke = b'G'
-    setgray_nonstroke = b'g'
-    setrgb_stroke = b'RG'
-    setrgb_nonstroke = b'rg'
-    setcolorspace_stroke = b'CS'
-    setcolorspace_nonstroke = b'cs'
-    setcolor_stroke = b'SCN'
-    setcolor_nonstroke = b'scn'
-    setdash = b'd'
-    setlinejoin = b'j'
-    setlinecap = b'J'
-    setgstate = b'gs'
-    gsave = b'q'
-    grestore = b'Q'
-    textpos = b'Td'
-    selectfont = b'Tf'
-    textmatrix = b'Tm'
-    show = b'Tj'
-    showkern = b'TJ'
-    setlinewidth = b'w'
-    clip = b'W'
-    shading = b'sh'
-
-    def pdfRepr(self):
-        return self.value
-
-    @classmethod
-    def paint_path(cls, fill, stroke):
-        """
-        Return the PDF operator to paint a path.
-
-        Parameters
-        ----------
-        fill : bool
-            Fill the path with the fill color.
-        stroke : bool
-            Stroke the outline of the path with the line color.
-        """
-        if stroke:
-            if fill:
-                return cls.fill_stroke
-            else:
-                return cls.stroke
-        else:
-            if fill:
-                return cls.fill
-            else:
-                return cls.endpath
-
-
-class Stream:
-    """
-    PDF stream object.
-
-    This has no pdfRepr method. Instead, call begin(), then output the
-    contents of the stream by calling write(), and finally call end().
-    """
-    __slots__ = ('id', 'len', 'pdfFile', 'file', 'compressobj', 'extra', 'pos')
-
-    def __init__(self, id, len, file, extra=None, png=None):
-        """
-        Parameters
-        ----------
-        id : int
-            Object id of the stream.
-        len : Reference or None
-            An unused Reference object for the length of the stream;
-            None means to use a memory buffer so the length can be inlined.
-        file : PdfFile
-            The underlying object to write the stream to.
-        extra : dict from Name to anything, or None
-            Extra key-value pairs to include in the stream header.
-        png : dict or None
-            If the data is already png encoded, the decode parameters.
-        """
-        self.id = id            # object id
-        self.len = len          # id of length object
-        self.pdfFile = file
-        self.file = file.fh      # file to which the stream is written
-        self.compressobj = None  # compression object
-        if extra is None:
-            self.extra = dict()
-        else:
-            self.extra = extra.copy()
-        if png is not None:
-            self.extra.update({'Filter':      Name('FlateDecode'),
-                               'DecodeParms': png})
-
-        self.pdfFile.recordXref(self.id)
-        if mpl.rcParams['pdf.compression'] and not png:
-            self.compressobj = zlib.compressobj(
-                mpl.rcParams['pdf.compression'])
-        if self.len is None:
-            self.file = BytesIO()
-        else:
-            self._writeHeader()
-            self.pos = self.file.tell()
-
-    def _writeHeader(self):
-        write = self.file.write
-        write(b"%d 0 obj\n" % self.id)
-        dict = self.extra
-        dict['Length'] = self.len
-        if mpl.rcParams['pdf.compression']:
-            dict['Filter'] = Name('FlateDecode')
-
-        write(pdfRepr(dict))
-        write(b"\nstream\n")
-
-    def end(self):
-        """Finalize stream."""
-
-        self._flush()
-        if self.len is None:
-            contents = self.file.getvalue()
-            self.len = len(contents)
-            self.file = self.pdfFile.fh
-            self._writeHeader()
-            self.file.write(contents)
-            self.file.write(b"\nendstream\nendobj\n")
-        else:
-            length = self.file.tell() - self.pos
-            self.file.write(b"\nendstream\nendobj\n")
-            self.pdfFile.writeObject(self.len, length)
-
-    def write(self, data):
-        """Write some data on the stream."""
-
-        if self.compressobj is None:
-            self.file.write(data)
-        else:
-            compressed = self.compressobj.compress(data)
-            self.file.write(compressed)
-
-    def _flush(self):
-        """Flush the compression object."""
-
-        if self.compressobj is not None:
-            compressed = self.compressobj.flush()
-            self.file.write(compressed)
-            self.compressobj = None
-
-
-def _get_pdf_charprocs(font_path, glyph_ids):
-    font = get_font(font_path, hinting_factor=1)
-    conv = 1000 / font.units_per_EM  # Conversion to PS units (1/1000's).
-    procs = {}
-    for glyph_id in glyph_ids:
-        g = font.load_glyph(glyph_id, LOAD_NO_SCALE)
-        # NOTE: We should be using round(), but instead use
-        # "(x+.5).astype(int)" to keep backcompat with the old ttconv code
-        # (this is different for negative x's).
-        d1 = (np.array([g.horiAdvance, 0, *g.bbox]) * conv + .5).astype(int)
-        v, c = font.get_path()
-        v = (v * 64).astype(int)  # Back to TrueType's internal units (1/64's).
-        # Backcompat with old ttconv code: control points between two quads are
-        # omitted if they are exactly at the midpoint between the control of
-        # the quad before and the quad after, but ttconv used to interpolate
-        # *after* conversion to PS units, causing floating point errors.  Here
-        # we reproduce ttconv's logic, detecting these "implicit" points and
-        # re-interpolating them.  Note that occasionally (e.g. with DejaVu Sans
-        # glyph "0") a point detected as "implicit" is actually explicit, and
-        # will thus be shifted by 1.
-        quads, = np.nonzero(c == 3)
-        quads_on = quads[1::2]
-        quads_mid_on = np.array(
-            sorted({*quads_on} & {*(quads - 1)} & {*(quads + 1)}), int)
-        implicit = quads_mid_on[
-            (v[quads_mid_on]  # As above, use astype(int), not // division
-             == ((v[quads_mid_on - 1] + v[quads_mid_on + 1]) / 2).astype(int))
-            .all(axis=1)]
-        if (font.postscript_name, glyph_id) in [
-                ("DejaVuSerif-Italic", 77),  # j
-                ("DejaVuSerif-Italic", 135),  # \AA
-        ]:
-            v[:, 0] -= 1  # Hard-coded backcompat (FreeType shifts glyph by 1).
-        v = (v * conv + .5).astype(int)  # As above re: truncation vs rounding.
-        v[implicit] = ((  # Fix implicit points; again, truncate.
-            (v[implicit - 1] + v[implicit + 1]) / 2).astype(int))
-        procs[font.get_glyph_name(glyph_id)] = (
-            " ".join(map(str, d1)).encode("ascii") + b" d1\n"
-            + _path.convert_to_string(
-                Path(v, c), None, None, False, None, -1,
-                # no code for quad Beziers triggers auto-conversion to cubics.
-                [b"m", b"l", b"", b"c", b"h"], True)
-            + b"f")
-    return procs
-
-
-class PdfFile:
-    """PDF file object."""
-
-    def __init__(self, filename, metadata=None):
-        """
-        Parameters
-        ----------
-        filename : str or path-like or file-like
-            Output target; if a string, a file will be opened for writing.
-
-        metadata : dict from strings to strings and dates
-            Information dictionary object (see PDF reference section 10.2.1
-            'Document Information Dictionary'), e.g.:
-            ``{'Creator': 'My software', 'Author': 'Me', 'Title': 'Awesome'}``.
-
-            The standard keys are 'Title', 'Author', 'Subject', 'Keywords',
-            'Creator', 'Producer', 'CreationDate', 'ModDate', and
-            'Trapped'. Values have been predefined for 'Creator', 'Producer'
-            and 'CreationDate'. They can be removed by setting them to `None`.
-        """
-        super().__init__()
-
-        self._object_seq = itertools.count(1)  # consumed by reserveObject
-        self.xrefTable = [[0, 65535, 'the zero object']]
-        self.passed_in_file_object = False
-        self.original_file_like = None
-        self.tell_base = 0
-        fh, opened = cbook.to_filehandle(filename, "wb", return_opened=True)
-        if not opened:
-            try:
-                self.tell_base = filename.tell()
-            except OSError:
-                fh = BytesIO()
-                self.original_file_like = filename
-            else:
-                fh = filename
-                self.passed_in_file_object = True
-
-        self.fh = fh
-        self.currentstream = None  # stream object to write to, if any
-        fh.write(b"%PDF-1.4\n")    # 1.4 is the first version to have alpha
-        # Output some eight-bit chars as a comment so various utilities
-        # recognize the file as binary by looking at the first few
-        # lines (see note in section 3.4.1 of the PDF reference).
-        fh.write(b"%\254\334 \253\272\n")
-
-        self.rootObject = self.reserveObject('root')
-        self.pagesObject = self.reserveObject('pages')
-        self.pageList = []
-        self.fontObject = self.reserveObject('fonts')
-        self._extGStateObject = self.reserveObject('extended graphics states')
-        self.hatchObject = self.reserveObject('tiling patterns')
-        self.gouraudObject = self.reserveObject('Gouraud triangles')
-        self.XObjectObject = self.reserveObject('external objects')
-        self.resourceObject = self.reserveObject('resources')
-
-        root = {'Type': Name('Catalog'),
-                'Pages': self.pagesObject}
-        self.writeObject(self.rootObject, root)
-
-        self.infoDict = _create_pdf_info_dict('pdf', metadata or {})
-
-        self.fontNames = {}     # maps filenames to internal font names
-        self._internal_font_seq = (Name(f'F{i}') for i in itertools.count(1))
-        self.dviFontInfo = {}   # maps dvi font names to embedding information
-        # differently encoded Type-1 fonts may share the same descriptor
-        self.type1Descriptors = {}
-        self._character_tracker = _backend_pdf_ps.CharacterTracker()
-
-        self.alphaStates = {}   # maps alpha values to graphics state objects
-        self._alpha_state_seq = (Name(f'A{i}') for i in itertools.count(1))
-        self._soft_mask_states = {}
-        self._soft_mask_seq = (Name(f'SM{i}') for i in itertools.count(1))
-        self._soft_mask_groups = []
-        self.hatchPatterns = {}
-        self._hatch_pattern_seq = (Name(f'H{i}') for i in itertools.count(1))
-        self.gouraudTriangles = []
-
-        self._images = {}
-        self._image_seq = (Name(f'I{i}') for i in itertools.count(1))
-
-        self.markers = {}
-        self.multi_byte_charprocs = {}
-
-        self.paths = []
-
-        # A list of annotations for each page. Each entry is a tuple of the
-        # overall Annots object reference that's inserted into the page object,
-        # followed by a list of the actual annotations.
-        self._annotations = []
-        # For annotations added before a page is created; mostly for the
-        # purpose of newTextnote.
-        self.pageAnnotations = []
-
-        # The PDF spec recommends to include every procset
-        procsets = [Name(x) for x in "PDF Text ImageB ImageC ImageI".split()]
-
-        # Write resource dictionary.
-        # Possibly TODO: more general ExtGState (graphics state dictionaries)
-        #                ColorSpace Pattern Shading Properties
-        resources = {'Font': self.fontObject,
-                     'XObject': self.XObjectObject,
-                     'ExtGState': self._extGStateObject,
-                     'Pattern': self.hatchObject,
-                     'Shading': self.gouraudObject,
-                     'ProcSet': procsets}
-        self.writeObject(self.resourceObject, resources)
-
-    def newPage(self, width, height):
-        self.endStream()
-
-        self.width, self.height = width, height
-        contentObject = self.reserveObject('page contents')
-        annotsObject = self.reserveObject('annotations')
-        thePage = {'Type': Name('Page'),
-                   'Parent': self.pagesObject,
-                   'Resources': self.resourceObject,
-                   'MediaBox': [0, 0, 72 * width, 72 * height],
-                   'Contents': contentObject,
-                   'Annots': annotsObject,
-                   }
-        pageObject = self.reserveObject('page')
-        self.writeObject(pageObject, thePage)
-        self.pageList.append(pageObject)
-        self._annotations.append((annotsObject, self.pageAnnotations))
-
-        self.beginStream(contentObject.id,
-                         self.reserveObject('length of content stream'))
-        # Initialize the pdf graphics state to match the default Matplotlib
-        # graphics context (colorspace and joinstyle).
-        self.output(Name('DeviceRGB'), Op.setcolorspace_stroke)
-        self.output(Name('DeviceRGB'), Op.setcolorspace_nonstroke)
-        self.output(GraphicsContextPdf.joinstyles['round'], Op.setlinejoin)
-
-        # Clear the list of annotations for the next page
-        self.pageAnnotations = []
-
-    def newTextnote(self, text, positionRect=[-100, -100, 0, 0]):
-        # Create a new annotation of type text
-        theNote = {'Type': Name('Annot'),
-                   'Subtype': Name('Text'),
-                   'Contents': text,
-                   'Rect': positionRect,
-                   }
-        self.pageAnnotations.append(theNote)
-
-    def _get_subsetted_psname(self, ps_name, charmap):
-        def toStr(n, base):
-            if n < base:
-                return string.ascii_uppercase[n]
-            else:
-                return (
-                    toStr(n // base, base) + string.ascii_uppercase[n % base]
-                )
-
-        # encode to string using base 26
-        hashed = hash(frozenset(charmap.keys())) % ((sys.maxsize + 1) * 2)
-        prefix = toStr(hashed, 26)
-
-        # get first 6 characters from prefix
-        return prefix[:6] + "+" + ps_name
-
-    def finalize(self):
-        """Write out the various deferred objects and the pdf end matter."""
-
-        self.endStream()
-        self._write_annotations()
-        self.writeFonts()
-        self.writeExtGSTates()
-        self._write_soft_mask_groups()
-        self.writeHatches()
-        self.writeGouraudTriangles()
-        xobjects = {
-            name: ob for image, name, ob in self._images.values()}
-        for tup in self.markers.values():
-            xobjects[tup[0]] = tup[1]
-        for name, value in self.multi_byte_charprocs.items():
-            xobjects[name] = value
-        for name, path, trans, ob, join, cap, padding, filled, stroked \
-                in self.paths:
-            xobjects[name] = ob
-        self.writeObject(self.XObjectObject, xobjects)
-        self.writeImages()
-        self.writeMarkers()
-        self.writePathCollectionTemplates()
-        self.writeObject(self.pagesObject,
-                         {'Type': Name('Pages'),
-                          'Kids': self.pageList,
-                          'Count': len(self.pageList)})
-        self.writeInfoDict()
-
-        # Finalize the file
-        self.writeXref()
-        self.writeTrailer()
-
-    def close(self):
-        """Flush all buffers and free all resources."""
-
-        self.endStream()
-        if self.passed_in_file_object:
-            self.fh.flush()
-        else:
-            if self.original_file_like is not None:
-                self.original_file_like.write(self.fh.getvalue())
-            self.fh.close()
-
-    def write(self, data):
-        if self.currentstream is None:
-            self.fh.write(data)
-        else:
-            self.currentstream.write(data)
-
-    def output(self, *data):
-        self.write(_fill([pdfRepr(x) for x in data]))
-        self.write(b'\n')
-
-    def beginStream(self, id, len, extra=None, png=None):
-        assert self.currentstream is None
-        self.currentstream = Stream(id, len, self, extra, png)
-
-    def endStream(self):
-        if self.currentstream is not None:
-            self.currentstream.end()
-            self.currentstream = None
-
-    def outputStream(self, ref, data, *, extra=None):
-        self.beginStream(ref.id, None, extra)
-        self.currentstream.write(data)
-        self.endStream()
-
-    def _write_annotations(self):
-        for annotsObject, annotations in self._annotations:
-            self.writeObject(annotsObject, annotations)
-
-    def fontName(self, fontprop):
-        """
-        Select a font based on fontprop and return a name suitable for
-        Op.selectfont. If fontprop is a string, it will be interpreted
-        as the filename of the font.
-        """
-
-        if isinstance(fontprop, str):
-            filenames = [fontprop]
-        elif mpl.rcParams['pdf.use14corefonts']:
-            filenames = _fontManager._find_fonts_by_props(
-                fontprop, fontext='afm', directory=RendererPdf._afm_font_dir
-            )
-        else:
-            filenames = _fontManager._find_fonts_by_props(fontprop)
-        first_Fx = None
-        for fname in filenames:
-            Fx = self.fontNames.get(fname)
-            if not first_Fx:
-                first_Fx = Fx
-            if Fx is None:
-                Fx = next(self._internal_font_seq)
-                self.fontNames[fname] = Fx
-                _log.debug('Assigning font %s = %r', Fx, fname)
-                if not first_Fx:
-                    first_Fx = Fx
-
-        # find_fontsprop's first value always adheres to
-        # findfont's value, so technically no behaviour change
-        return first_Fx
-
-    def dviFontName(self, dvifont):
-        """
-        Given a dvi font object, return a name suitable for Op.selectfont.
-        This registers the font information in ``self.dviFontInfo`` if not yet
-        registered.
-        """
-
-        dvi_info = self.dviFontInfo.get(dvifont.texname)
-        if dvi_info is not None:
-            return dvi_info.pdfname
-
-        tex_font_map = dviread.PsfontsMap(dviread.find_tex_file('pdftex.map'))
-        psfont = tex_font_map[dvifont.texname]
-        if psfont.filename is None:
-            raise ValueError(
-                "No usable font file found for {} (TeX: {}); "
-                "the font may lack a Type-1 version"
-                .format(psfont.psname, dvifont.texname))
-
-        pdfname = next(self._internal_font_seq)
-        _log.debug('Assigning font %s = %s (dvi)', pdfname, dvifont.texname)
-        self.dviFontInfo[dvifont.texname] = types.SimpleNamespace(
-            dvifont=dvifont,
-            pdfname=pdfname,
-            fontfile=psfont.filename,
-            basefont=psfont.psname,
-            encodingfile=psfont.encoding,
-            effects=psfont.effects)
-        return pdfname
-
-    def writeFonts(self):
-        fonts = {}
-        for dviname, info in sorted(self.dviFontInfo.items()):
-            Fx = info.pdfname
-            _log.debug('Embedding Type-1 font %s from dvi.', dviname)
-            fonts[Fx] = self._embedTeXFont(info)
-        for filename in sorted(self.fontNames):
-            Fx = self.fontNames[filename]
-            _log.debug('Embedding font %s.', filename)
-            if filename.endswith('.afm'):
-                # from pdf.use14corefonts
-                _log.debug('Writing AFM font.')
-                fonts[Fx] = self._write_afm_font(filename)
-            else:
-                # a normal TrueType font
-                _log.debug('Writing TrueType font.')
-                chars = self._character_tracker.used.get(filename)
-                if chars:
-                    fonts[Fx] = self.embedTTF(filename, chars)
-        self.writeObject(self.fontObject, fonts)
-
-    def _write_afm_font(self, filename):
-        with open(filename, 'rb') as fh:
-            font = AFM(fh)
-        fontname = font.get_fontname()
-        fontdict = {'Type': Name('Font'),
-                    'Subtype': Name('Type1'),
-                    'BaseFont': Name(fontname),
-                    'Encoding': Name('WinAnsiEncoding')}
-        fontdictObject = self.reserveObject('font dictionary')
-        self.writeObject(fontdictObject, fontdict)
-        return fontdictObject
-
-    def _embedTeXFont(self, fontinfo):
-        _log.debug('Embedding TeX font %s - fontinfo=%s',
-                   fontinfo.dvifont.texname, fontinfo.__dict__)
-
-        # Widths
-        widthsObject = self.reserveObject('font widths')
-        self.writeObject(widthsObject, fontinfo.dvifont.widths)
-
-        # Font dictionary
-        fontdictObject = self.reserveObject('font dictionary')
-        fontdict = {
-            'Type':      Name('Font'),
-            'Subtype':   Name('Type1'),
-            'FirstChar': 0,
-            'LastChar':  len(fontinfo.dvifont.widths) - 1,
-            'Widths':    widthsObject,
-            }
-
-        # Encoding (if needed)
-        if fontinfo.encodingfile is not None:
-            fontdict['Encoding'] = {
-                'Type': Name('Encoding'),
-                'Differences': [
-                    0, *map(Name, dviread._parse_enc(fontinfo.encodingfile))],
-            }
-
-        # If no file is specified, stop short
-        if fontinfo.fontfile is None:
-            _log.warning(
-                "Because of TeX configuration (pdftex.map, see updmap option "
-                "pdftexDownloadBase14) the font %s is not embedded. This is "
-                "deprecated as of PDF 1.5 and it may cause the consumer "
-                "application to show something that was not intended.",
-                fontinfo.basefont)
-            fontdict['BaseFont'] = Name(fontinfo.basefont)
-            self.writeObject(fontdictObject, fontdict)
-            return fontdictObject
-
-        # We have a font file to embed - read it in and apply any effects
-        t1font = _type1font.Type1Font(fontinfo.fontfile)
-        if fontinfo.effects:
-            t1font = t1font.transform(fontinfo.effects)
-        fontdict['BaseFont'] = Name(t1font.prop['FontName'])
-
-        # Font descriptors may be shared between differently encoded
-        # Type-1 fonts, so only create a new descriptor if there is no
-        # existing descriptor for this font.
-        effects = (fontinfo.effects.get('slant', 0.0),
-                   fontinfo.effects.get('extend', 1.0))
-        fontdesc = self.type1Descriptors.get((fontinfo.fontfile, effects))
-        if fontdesc is None:
-            fontdesc = self.createType1Descriptor(t1font, fontinfo.fontfile)
-            self.type1Descriptors[(fontinfo.fontfile, effects)] = fontdesc
-        fontdict['FontDescriptor'] = fontdesc
-
-        self.writeObject(fontdictObject, fontdict)
-        return fontdictObject
-
-    def createType1Descriptor(self, t1font, fontfile):
-        # Create and write the font descriptor and the font file
-        # of a Type-1 font
-        fontdescObject = self.reserveObject('font descriptor')
-        fontfileObject = self.reserveObject('font file')
-
-        italic_angle = t1font.prop['ItalicAngle']
-        fixed_pitch = t1font.prop['isFixedPitch']
-
-        flags = 0
-        # fixed width
-        if fixed_pitch:
-            flags |= 1 << 0
-        # TODO: serif
-        if 0:
-            flags |= 1 << 1
-        # TODO: symbolic (most TeX fonts are)
-        if 1:
-            flags |= 1 << 2
-        # non-symbolic
-        else:
-            flags |= 1 << 5
-        # italic
-        if italic_angle:
-            flags |= 1 << 6
-        # TODO: all caps
-        if 0:
-            flags |= 1 << 16
-        # TODO: small caps
-        if 0:
-            flags |= 1 << 17
-        # TODO: force bold
-        if 0:
-            flags |= 1 << 18
-
-        ft2font = get_font(fontfile)
-
-        descriptor = {
-            'Type':        Name('FontDescriptor'),
-            'FontName':    Name(t1font.prop['FontName']),
-            'Flags':       flags,
-            'FontBBox':    ft2font.bbox,
-            'ItalicAngle': italic_angle,
-            'Ascent':      ft2font.ascender,
-            'Descent':     ft2font.descender,
-            'CapHeight':   1000,  # TODO: find this out
-            'XHeight':     500,  # TODO: this one too
-            'FontFile':    fontfileObject,
-            'FontFamily':  t1font.prop['FamilyName'],
-            'StemV':       50,  # TODO
-            # (see also revision 3874; but not all TeX distros have AFM files!)
-            # 'FontWeight': a number where 400 = Regular, 700 = Bold
-            }
-
-        self.writeObject(fontdescObject, descriptor)
-
-        self.outputStream(fontfileObject, b"".join(t1font.parts[:2]),
-                          extra={'Length1': len(t1font.parts[0]),
-                                 'Length2': len(t1font.parts[1]),
-                                 'Length3': 0})
-
-        return fontdescObject
-
-    def _get_xobject_glyph_name(self, filename, glyph_name):
-        Fx = self.fontName(filename)
-        return "-".join([
-            Fx.name.decode(),
-            os.path.splitext(os.path.basename(filename))[0],
-            glyph_name])
-
-    _identityToUnicodeCMap = b"""/CIDInit /ProcSet findresource begin
-12 dict begin
-begincmap
-/CIDSystemInfo
-<< /Registry (Adobe)
-   /Ordering (UCS)
-   /Supplement 0
->> def
-/CMapName /Adobe-Identity-UCS def
-/CMapType 2 def
-1 begincodespacerange
-<0000> <ffff>
-endcodespacerange
-%d beginbfrange
-%s
-endbfrange
-endcmap
-CMapName currentdict /CMap defineresource pop
-end
-end"""
-
-    def embedTTF(self, filename, characters):
-        """Embed the TTF font from the named file into the document."""
-
-        font = get_font(filename)
-        fonttype = mpl.rcParams['pdf.fonttype']
-
-        def cvt(length, upe=font.units_per_EM, nearest=True):
-            """Convert font coordinates to PDF glyph coordinates."""
-            value = length / upe * 1000
-            if nearest:
-                return round(value)
-            # Best(?) to round away from zero for bounding boxes and the like.
-            if value < 0:
-                return math.floor(value)
-            else:
-                return math.ceil(value)
-
-        def embedTTFType3(font, characters, descriptor):
-            """The Type 3-specific part of embedding a Truetype font"""
-            widthsObject = self.reserveObject('font widths')
-            fontdescObject = self.reserveObject('font descriptor')
-            fontdictObject = self.reserveObject('font dictionary')
-            charprocsObject = self.reserveObject('character procs')
-            differencesArray = []
-            firstchar, lastchar = 0, 255
-            bbox = [cvt(x, nearest=False) for x in font.bbox]
-
-            fontdict = {
-                'Type': Name('Font'),
-                'BaseFont': ps_name,
-                'FirstChar': firstchar,
-                'LastChar': lastchar,
-                'FontDescriptor': fontdescObject,
-                'Subtype': Name('Type3'),
-                'Name': descriptor['FontName'],
-                'FontBBox': bbox,
-                'FontMatrix': [.001, 0, 0, .001, 0, 0],
-                'CharProcs': charprocsObject,
-                'Encoding': {
-                    'Type': Name('Encoding'),
-                    'Differences': differencesArray},
-                'Widths': widthsObject
-                }
-
-            from encodings import cp1252
-
-            # Make the "Widths" array
-            def get_char_width(charcode):
-                s = ord(cp1252.decoding_table[charcode])
-                width = font.load_char(
-                    s, flags=LOAD_NO_SCALE | LOAD_NO_HINTING).horiAdvance
-                return cvt(width)
-            with warnings.catch_warnings():
-                # Ignore 'Required glyph missing from current font' warning
-                # from ft2font: here we're just building the widths table, but
-                # the missing glyphs may not even be used in the actual string.
-                warnings.filterwarnings("ignore")
-                widths = [get_char_width(charcode)
-                          for charcode in range(firstchar, lastchar+1)]
-            descriptor['MaxWidth'] = max(widths)
-
-            # Make the "Differences" array, sort the ccodes < 255 from
-            # the multi-byte ccodes, and build the whole set of glyph ids
-            # that we need from this font.
-            glyph_ids = []
-            differences = []
-            multi_byte_chars = set()
-            for c in characters:
-                ccode = c
-                gind = font.get_char_index(ccode)
-                glyph_ids.append(gind)
-                glyph_name = font.get_glyph_name(gind)
-                if ccode <= 255:
-                    differences.append((ccode, glyph_name))
-                else:
-                    multi_byte_chars.add(glyph_name)
-            differences.sort()
-
-            last_c = -2
-            for c, name in differences:
-                if c != last_c + 1:
-                    differencesArray.append(c)
-                differencesArray.append(Name(name))
-                last_c = c
-
-            # Make the charprocs array.
-            rawcharprocs = _get_pdf_charprocs(filename, glyph_ids)
-            charprocs = {}
-            for charname in sorted(rawcharprocs):
-                stream = rawcharprocs[charname]
-                charprocDict = {}
-                # The 2-byte characters are used as XObjects, so they
-                # need extra info in their dictionary
-                if charname in multi_byte_chars:
-                    charprocDict = {'Type': Name('XObject'),
-                                    'Subtype': Name('Form'),
-                                    'BBox': bbox}
-                    # Each glyph includes bounding box information,
-                    # but xpdf and ghostscript can't handle it in a
-                    # Form XObject (they segfault!!!), so we remove it
-                    # from the stream here.  It's not needed anyway,
-                    # since the Form XObject includes it in its BBox
-                    # value.
-                    stream = stream[stream.find(b"d1") + 2:]
-                charprocObject = self.reserveObject('charProc')
-                self.outputStream(charprocObject, stream, extra=charprocDict)
-
-                # Send the glyphs with ccode > 255 to the XObject dictionary,
-                # and the others to the font itself
-                if charname in multi_byte_chars:
-                    name = self._get_xobject_glyph_name(filename, charname)
-                    self.multi_byte_charprocs[name] = charprocObject
-                else:
-                    charprocs[charname] = charprocObject
-
-            # Write everything out
-            self.writeObject(fontdictObject, fontdict)
-            self.writeObject(fontdescObject, descriptor)
-            self.writeObject(widthsObject, widths)
-            self.writeObject(charprocsObject, charprocs)
-
-            return fontdictObject
-
-        def embedTTFType42(font, characters, descriptor):
-            """The Type 42-specific part of embedding a Truetype font"""
-            fontdescObject = self.reserveObject('font descriptor')
-            cidFontDictObject = self.reserveObject('CID font dictionary')
-            type0FontDictObject = self.reserveObject('Type 0 font dictionary')
-            cidToGidMapObject = self.reserveObject('CIDToGIDMap stream')
-            fontfileObject = self.reserveObject('font file stream')
-            wObject = self.reserveObject('Type 0 widths')
-            toUnicodeMapObject = self.reserveObject('ToUnicode map')
-
-            subset_str = "".join(chr(c) for c in characters)
-            _log.debug("SUBSET %s characters: %s", filename, subset_str)
-            fontdata = _backend_pdf_ps.get_glyphs_subset(filename, subset_str)
-            _log.debug(
-                "SUBSET %s %d -> %d", filename,
-                os.stat(filename).st_size, fontdata.getbuffer().nbytes
-            )
-
-            # We need this ref for XObjects
-            full_font = font
-
-            # reload the font object from the subset
-            # (all the necessary data could probably be obtained directly
-            # using fontLib.ttLib)
-            font = FT2Font(fontdata)
-
-            cidFontDict = {
-                'Type': Name('Font'),
-                'Subtype': Name('CIDFontType2'),
-                'BaseFont': ps_name,
-                'CIDSystemInfo': {
-                    'Registry': 'Adobe',
-                    'Ordering': 'Identity',
-                    'Supplement': 0},
-                'FontDescriptor': fontdescObject,
-                'W': wObject,
-                'CIDToGIDMap': cidToGidMapObject
-                }
-
-            type0FontDict = {
-                'Type': Name('Font'),
-                'Subtype': Name('Type0'),
-                'BaseFont': ps_name,
-                'Encoding': Name('Identity-H'),
-                'DescendantFonts': [cidFontDictObject],
-                'ToUnicode': toUnicodeMapObject
-                }
-
-            # Make fontfile stream
-            descriptor['FontFile2'] = fontfileObject
-            self.outputStream(
-                fontfileObject, fontdata.getvalue(),
-                extra={'Length1': fontdata.getbuffer().nbytes})
-
-            # Make the 'W' (Widths) array, CidToGidMap and ToUnicode CMap
-            # at the same time
-            cid_to_gid_map = ['\0'] * 65536
-            widths = []
-            max_ccode = 0
-            for c in characters:
-                ccode = c
-                gind = font.get_char_index(ccode)
-                glyph = font.load_char(ccode,
-                                       flags=LOAD_NO_SCALE | LOAD_NO_HINTING)
-                widths.append((ccode, cvt(glyph.horiAdvance)))
-                if ccode < 65536:
-                    cid_to_gid_map[ccode] = chr(gind)
-                max_ccode = max(ccode, max_ccode)
-            widths.sort()
-            cid_to_gid_map = cid_to_gid_map[:max_ccode + 1]
-
-            last_ccode = -2
-            w = []
-            max_width = 0
-            unicode_groups = []
-            for ccode, width in widths:
-                if ccode != last_ccode + 1:
-                    w.append(ccode)
-                    w.append([width])
-                    unicode_groups.append([ccode, ccode])
-                else:
-                    w[-1].append(width)
-                    unicode_groups[-1][1] = ccode
-                max_width = max(max_width, width)
-                last_ccode = ccode
-
-            unicode_bfrange = []
-            for start, end in unicode_groups:
-                # Ensure the CID map contains only chars from BMP
-                if start > 65535:
-                    continue
-                end = min(65535, end)
-
-                unicode_bfrange.append(
-                    b"<%04x> <%04x> [%s]" %
-                    (start, end,
-                     b" ".join(b"<%04x>" % x for x in range(start, end+1))))
-            unicode_cmap = (self._identityToUnicodeCMap %
-                            (len(unicode_groups), b"\n".join(unicode_bfrange)))
-
-            # Add XObjects for unsupported chars
-            glyph_ids = []
-            for ccode in characters:
-                if not _font_supports_glyph(fonttype, ccode):
-                    gind = full_font.get_char_index(ccode)
-                    glyph_ids.append(gind)
-
-            bbox = [cvt(x, nearest=False) for x in full_font.bbox]
-            rawcharprocs = _get_pdf_charprocs(filename, glyph_ids)
-            for charname in sorted(rawcharprocs):
-                stream = rawcharprocs[charname]
-                charprocDict = {'Type': Name('XObject'),
-                                'Subtype': Name('Form'),
-                                'BBox': bbox}
-                # Each glyph includes bounding box information,
-                # but xpdf and ghostscript can't handle it in a
-                # Form XObject (they segfault!!!), so we remove it
-                # from the stream here.  It's not needed anyway,
-                # since the Form XObject includes it in its BBox
-                # value.
-                stream = stream[stream.find(b"d1") + 2:]
-                charprocObject = self.reserveObject('charProc')
-                self.outputStream(charprocObject, stream, extra=charprocDict)
-
-                name = self._get_xobject_glyph_name(filename, charname)
-                self.multi_byte_charprocs[name] = charprocObject
-
-            # CIDToGIDMap stream
-            cid_to_gid_map = "".join(cid_to_gid_map).encode("utf-16be")
-            self.outputStream(cidToGidMapObject, cid_to_gid_map)
-
-            # ToUnicode CMap
-            self.outputStream(toUnicodeMapObject, unicode_cmap)
-
-            descriptor['MaxWidth'] = max_width
-
-            # Write everything out
-            self.writeObject(cidFontDictObject, cidFontDict)
-            self.writeObject(type0FontDictObject, type0FontDict)
-            self.writeObject(fontdescObject, descriptor)
-            self.writeObject(wObject, w)
-
-            return type0FontDictObject
-
-        # Beginning of main embedTTF function...
-
-        ps_name = self._get_subsetted_psname(
-            font.postscript_name,
-            font.get_charmap()
-        )
-        ps_name = ps_name.encode('ascii', 'replace')
-        ps_name = Name(ps_name)
-        pclt = font.get_sfnt_table('pclt') or {'capHeight': 0, 'xHeight': 0}
-        post = font.get_sfnt_table('post') or {'italicAngle': (0, 0)}
-        ff = font.face_flags
-        sf = font.style_flags
-
-        flags = 0
-        symbolic = False  # ps_name.name in ('Cmsy10', 'Cmmi10', 'Cmex10')
-        if ff & FIXED_WIDTH:
-            flags |= 1 << 0
-        if 0:  # TODO: serif
-            flags |= 1 << 1
-        if symbolic:
-            flags |= 1 << 2
-        else:
-            flags |= 1 << 5
-        if sf & ITALIC:
-            flags |= 1 << 6
-        if 0:  # TODO: all caps
-            flags |= 1 << 16
-        if 0:  # TODO: small caps
-            flags |= 1 << 17
-        if 0:  # TODO: force bold
-            flags |= 1 << 18
-
-        descriptor = {
-            'Type': Name('FontDescriptor'),
-            'FontName': ps_name,
-            'Flags': flags,
-            'FontBBox': [cvt(x, nearest=False) for x in font.bbox],
-            'Ascent': cvt(font.ascender, nearest=False),
-            'Descent': cvt(font.descender, nearest=False),
-            'CapHeight': cvt(pclt['capHeight'], nearest=False),
-            'XHeight': cvt(pclt['xHeight']),
-            'ItalicAngle': post['italicAngle'][1],  # ???
-            'StemV': 0  # ???
-            }
-
-        if fonttype == 3:
-            return embedTTFType3(font, characters, descriptor)
-        elif fonttype == 42:
-            return embedTTFType42(font, characters, descriptor)
-
-    def alphaState(self, alpha):
-        """Return name of an ExtGState that sets alpha to the given value."""
-
-        state = self.alphaStates.get(alpha, None)
-        if state is not None:
-            return state[0]
-
-        name = next(self._alpha_state_seq)
-        self.alphaStates[alpha] = \
-            (name, {'Type': Name('ExtGState'),
-                    'CA': alpha[0], 'ca': alpha[1]})
-        return name
-
-    def _soft_mask_state(self, smask):
-        """
-        Return an ExtGState that sets the soft mask to the given shading.
-
-        Parameters
-        ----------
-        smask : Reference
-            Reference to a shading in DeviceGray color space, whose luminosity
-            is to be used as the alpha channel.
-
-        Returns
-        -------
-        Name
-        """
-
-        state = self._soft_mask_states.get(smask, None)
-        if state is not None:
-            return state[0]
-
-        name = next(self._soft_mask_seq)
-        groupOb = self.reserveObject('transparency group for soft mask')
-        self._soft_mask_states[smask] = (
-            name,
-            {
-                'Type': Name('ExtGState'),
-                'AIS': False,
-                'SMask': {
-                    'Type': Name('Mask'),
-                    'S': Name('Luminosity'),
-                    'BC': [1],
-                    'G': groupOb
-                }
-            }
-        )
-        self._soft_mask_groups.append((
-            groupOb,
-            {
-                'Type': Name('XObject'),
-                'Subtype': Name('Form'),
-                'FormType': 1,
-                'Group': {
-                    'S': Name('Transparency'),
-                    'CS': Name('DeviceGray')
-                },
-                'Matrix': [1, 0, 0, 1, 0, 0],
-                'Resources': {'Shading': {'S': smask}},
-                'BBox': [0, 0, 1, 1]
-            },
-            [Name('S'), Op.shading]
-        ))
-        return name
-
-    def writeExtGSTates(self):
-        self.writeObject(
-            self._extGStateObject,
-            dict([
-                *self.alphaStates.values(),
-                *self._soft_mask_states.values()
-            ])
-        )
-
-    def _write_soft_mask_groups(self):
-        for ob, attributes, content in self._soft_mask_groups:
-            self.beginStream(ob.id, None, attributes)
-            self.output(*content)
-            self.endStream()
-
-    def hatchPattern(self, hatch_style):
-        # The colors may come in as numpy arrays, which aren't hashable
-        if hatch_style is not None:
-            edge, face, hatch = hatch_style
-            if edge is not None:
-                edge = tuple(edge)
-            if face is not None:
-                face = tuple(face)
-            hatch_style = (edge, face, hatch)
-
-        pattern = self.hatchPatterns.get(hatch_style, None)
-        if pattern is not None:
-            return pattern
-
-        name = next(self._hatch_pattern_seq)
-        self.hatchPatterns[hatch_style] = name
-        return name
-
-    def writeHatches(self):
-        hatchDict = dict()
-        sidelen = 72.0
-        for hatch_style, name in self.hatchPatterns.items():
-            ob = self.reserveObject('hatch pattern')
-            hatchDict[name] = ob
-            res = {'Procsets':
-                   [Name(x) for x in "PDF Text ImageB ImageC ImageI".split()]}
-            self.beginStream(
-                ob.id, None,
-                {'Type': Name('Pattern'),
-                 'PatternType': 1, 'PaintType': 1, 'TilingType': 1,
-                 'BBox': [0, 0, sidelen, sidelen],
-                 'XStep': sidelen, 'YStep': sidelen,
-                 'Resources': res,
-                 # Change origin to match Agg at top-left.
-                 'Matrix': [1, 0, 0, 1, 0, self.height * 72]})
-
-            stroke_rgb, fill_rgb, hatch = hatch_style
-            self.output(stroke_rgb[0], stroke_rgb[1], stroke_rgb[2],
-                        Op.setrgb_stroke)
-            if fill_rgb is not None:
-                self.output(fill_rgb[0], fill_rgb[1], fill_rgb[2],
-                            Op.setrgb_nonstroke,
-                            0, 0, sidelen, sidelen, Op.rectangle,
-                            Op.fill)
-
-            self.output(mpl.rcParams['hatch.linewidth'], Op.setlinewidth)
-
-            self.output(*self.pathOperations(
-                Path.hatch(hatch),
-                Affine2D().scale(sidelen),
-                simplify=False))
-            self.output(Op.fill_stroke)
-
-            self.endStream()
-        self.writeObject(self.hatchObject, hatchDict)
-
-    def addGouraudTriangles(self, points, colors):
-        """
-        Add a Gouraud triangle shading.
-
-        Parameters
-        ----------
-        points : np.ndarray
-            Triangle vertices, shape (n, 3, 2)
-            where n = number of triangles, 3 = vertices, 2 = x, y.
-        colors : np.ndarray
-            Vertex colors, shape (n, 3, 1) or (n, 3, 4)
-            as with points, but last dimension is either (gray,)
-            or (r, g, b, alpha).
-
-        Returns
-        -------
-        Name, Reference
-        """
-        name = Name('GT%d' % len(self.gouraudTriangles))
-        ob = self.reserveObject(f'Gouraud triangle {name}')
-        self.gouraudTriangles.append((name, ob, points, colors))
-        return name, ob
-
-    def writeGouraudTriangles(self):
-        gouraudDict = dict()
-        for name, ob, points, colors in self.gouraudTriangles:
-            gouraudDict[name] = ob
-            shape = points.shape
-            flat_points = points.reshape((shape[0] * shape[1], 2))
-            colordim = colors.shape[2]
-            assert colordim in (1, 4)
-            flat_colors = colors.reshape((shape[0] * shape[1], colordim))
-            if colordim == 4:
-                # strip the alpha channel
-                colordim = 3
-            points_min = np.min(flat_points, axis=0) - (1 << 8)
-            points_max = np.max(flat_points, axis=0) + (1 << 8)
-            factor = 0xffffffff / (points_max - points_min)
-
-            self.beginStream(
-                ob.id, None,
-                {'ShadingType': 4,
-                 'BitsPerCoordinate': 32,
-                 'BitsPerComponent': 8,
-                 'BitsPerFlag': 8,
-                 'ColorSpace': Name(
-                     'DeviceRGB' if colordim == 3 else 'DeviceGray'
-                 ),
-                 'AntiAlias': False,
-                 'Decode': ([points_min[0], points_max[0],
-                             points_min[1], points_max[1]]
-                            + [0, 1] * colordim),
-                 })
-
-            streamarr = np.empty(
-                (shape[0] * shape[1],),
-                dtype=[('flags', 'u1'),
-                       ('points', '>u4', (2,)),
-                       ('colors', 'u1', (colordim,))])
-            streamarr['flags'] = 0
-            streamarr['points'] = (flat_points - points_min) * factor
-            streamarr['colors'] = flat_colors[:, :colordim] * 255.0
-
-            self.write(streamarr.tobytes())
-            self.endStream()
-        self.writeObject(self.gouraudObject, gouraudDict)
-
-    def imageObject(self, image):
-        """Return name of an image XObject representing the given image."""
-
-        entry = self._images.get(id(image), None)
-        if entry is not None:
-            return entry[1]
-
-        name = next(self._image_seq)
-        ob = self.reserveObject(f'image {name}')
-        self._images[id(image)] = (image, name, ob)
-        return name
-
-    def _unpack(self, im):
-        """
-        Unpack image array *im* into ``(data, alpha)``, which have shape
-        ``(height, width, 3)`` (RGB) or ``(height, width, 1)`` (grayscale or
-        alpha), except that alpha is None if the image is fully opaque.
-        """
-        im = im[::-1]
-        if im.ndim == 2:
-            return im, None
-        else:
-            rgb = im[:, :, :3]
-            rgb = np.array(rgb, order='C')
-            # PDF needs a separate alpha image
-            if im.shape[2] == 4:
-                alpha = im[:, :, 3][..., None]
-                if np.all(alpha == 255):
-                    alpha = None
-                else:
-                    alpha = np.array(alpha, order='C')
-            else:
-                alpha = None
-            return rgb, alpha
-
-    def _writePng(self, img):
-        """
-        Write the image *img* into the pdf file using png
-        predictors with Flate compression.
-        """
-        buffer = BytesIO()
-        img.save(buffer, format="png")
-        buffer.seek(8)
-        png_data = b''
-        bit_depth = palette = None
-        while True:
-            length, type = struct.unpack(b'!L4s', buffer.read(8))
-            if type in [b'IHDR', b'PLTE', b'IDAT']:
-                data = buffer.read(length)
-                if len(data) != length:
-                    raise RuntimeError("truncated data")
-                if type == b'IHDR':
-                    bit_depth = int(data[8])
-                elif type == b'PLTE':
-                    palette = data
-                elif type == b'IDAT':
-                    png_data += data
-            elif type == b'IEND':
-                break
-            else:
-                buffer.seek(length, 1)
-            buffer.seek(4, 1)   # skip CRC
-        return png_data, bit_depth, palette
-
-    def _writeImg(self, data, id, smask=None):
-        """
-        Write the image *data*, of shape ``(height, width, 1)`` (grayscale) or
-        ``(height, width, 3)`` (RGB), as pdf object *id* and with the soft mask
-        (alpha channel) *smask*, which should be either None or a ``(height,
-        width, 1)`` array.
-        """
-        height, width, color_channels = data.shape
-        obj = {'Type': Name('XObject'),
-               'Subtype': Name('Image'),
-               'Width': width,
-               'Height': height,
-               'ColorSpace': Name({1: 'DeviceGray', 3: 'DeviceRGB'}[color_channels]),
-               'BitsPerComponent': 8}
-        if smask:
-            obj['SMask'] = smask
-        if mpl.rcParams['pdf.compression']:
-            if data.shape[-1] == 1:
-                data = data.squeeze(axis=-1)
-            png = {'Predictor': 10, 'Colors': color_channels, 'Columns': width}
-            img = Image.fromarray(data)
-            img_colors = img.getcolors(maxcolors=256)
-            if color_channels == 3 and img_colors is not None:
-                # Convert to indexed color if there are 256 colors or fewer. This can
-                # significantly reduce the file size.
-                num_colors = len(img_colors)
-                palette = np.array([comp for _, color in img_colors for comp in color],
-                                   dtype=np.uint8)
-                palette24 = ((palette[0::3].astype(np.uint32) << 16) |
-                             (palette[1::3].astype(np.uint32) << 8) |
-                             palette[2::3])
-                rgb24 = ((data[:, :, 0].astype(np.uint32) << 16) |
-                         (data[:, :, 1].astype(np.uint32) << 8) |
-                         data[:, :, 2])
-                indices = np.argsort(palette24).astype(np.uint8)
-                rgb8 = indices[np.searchsorted(palette24, rgb24, sorter=indices)]
-                img = Image.fromarray(rgb8, mode='P')
-                img.putpalette(palette)
-                png_data, bit_depth, palette = self._writePng(img)
-                if bit_depth is None or palette is None:
-                    raise RuntimeError("invalid PNG header")
-                palette = palette[:num_colors * 3]  # Trim padding; remove for Pillow>=9
-                obj['ColorSpace'] = [Name('Indexed'), Name('DeviceRGB'),
-                                     num_colors - 1, palette]
-                obj['BitsPerComponent'] = bit_depth
-                png['Colors'] = 1
-                png['BitsPerComponent'] = bit_depth
-            else:
-                png_data, _, _ = self._writePng(img)
-        else:
-            png = None
-        self.beginStream(
-            id,
-            self.reserveObject('length of image stream'),
-            obj,
-            png=png
-            )
-        if png:
-            self.currentstream.write(png_data)
-        else:
-            self.currentstream.write(data.tobytes())
-        self.endStream()
-
-    def writeImages(self):
-        for img, name, ob in self._images.values():
-            data, adata = self._unpack(img)
-            if adata is not None:
-                smaskObject = self.reserveObject("smask")
-                self._writeImg(adata, smaskObject.id)
-            else:
-                smaskObject = None
-            self._writeImg(data, ob.id, smaskObject)
-
-    def markerObject(self, path, trans, fill, stroke, lw, joinstyle,
-                     capstyle):
-        """Return name of a marker XObject representing the given path."""
-        # self.markers used by markerObject, writeMarkers, close:
-        # mapping from (path operations, fill?, stroke?) to
-        #   [name, object reference, bounding box, linewidth]
-        # This enables different draw_markers calls to share the XObject
-        # if the gc is sufficiently similar: colors etc can vary, but
-        # the choices of whether to fill and whether to stroke cannot.
-        # We need a bounding box enclosing all of the XObject path,
-        # but since line width may vary, we store the maximum of all
-        # occurring line widths in self.markers.
-        # close() is somewhat tightly coupled in that it expects the
-        # first two components of each value in self.markers to be the
-        # name and object reference.
-        pathops = self.pathOperations(path, trans, simplify=False)
-        key = (tuple(pathops), bool(fill), bool(stroke), joinstyle, capstyle)
-        result = self.markers.get(key)
-        if result is None:
-            name = Name('M%d' % len(self.markers))
-            ob = self.reserveObject('marker %d' % len(self.markers))
-            bbox = path.get_extents(trans)
-            self.markers[key] = [name, ob, bbox, lw]
-        else:
-            if result[-1] < lw:
-                result[-1] = lw
-            name = result[0]
-        return name
-
-    def writeMarkers(self):
-        for ((pathops, fill, stroke, joinstyle, capstyle),
-             (name, ob, bbox, lw)) in self.markers.items():
-            # bbox wraps the exact limits of the control points, so half a line
-            # will appear outside it. If the join style is miter and the line
-            # is not parallel to the edge, then the line will extend even
-            # further. From the PDF specification, Section 8.4.3.5, the miter
-            # limit is miterLength / lineWidth and from Table 52, the default
-            # is 10. With half the miter length outside, that works out to the
-            # following padding:
-            bbox = bbox.padded(lw * 5)
-            self.beginStream(
-                ob.id, None,
-                {'Type': Name('XObject'), 'Subtype': Name('Form'),
-                 'BBox': list(bbox.extents)})
-            self.output(GraphicsContextPdf.joinstyles[joinstyle],
-                        Op.setlinejoin)
-            self.output(GraphicsContextPdf.capstyles[capstyle], Op.setlinecap)
-            self.output(*pathops)
-            self.output(Op.paint_path(fill, stroke))
-            self.endStream()
-
-    def pathCollectionObject(self, gc, path, trans, padding, filled, stroked):
-        name = Name('P%d' % len(self.paths))
-        ob = self.reserveObject('path %d' % len(self.paths))
-        self.paths.append(
-            (name, path, trans, ob, gc.get_joinstyle(), gc.get_capstyle(),
-             padding, filled, stroked))
-        return name
-
-    def writePathCollectionTemplates(self):
-        for (name, path, trans, ob, joinstyle, capstyle, padding, filled,
-             stroked) in self.paths:
-            pathops = self.pathOperations(path, trans, simplify=False)
-            bbox = path.get_extents(trans)
-            if not np.all(np.isfinite(bbox.extents)):
-                extents = [0, 0, 0, 0]
-            else:
-                bbox = bbox.padded(padding)
-                extents = list(bbox.extents)
-            self.beginStream(
-                ob.id, None,
-                {'Type': Name('XObject'), 'Subtype': Name('Form'),
-                 'BBox': extents})
-            self.output(GraphicsContextPdf.joinstyles[joinstyle],
-                        Op.setlinejoin)
-            self.output(GraphicsContextPdf.capstyles[capstyle], Op.setlinecap)
-            self.output(*pathops)
-            self.output(Op.paint_path(filled, stroked))
-            self.endStream()
-
-    @staticmethod
-    def pathOperations(path, transform, clip=None, simplify=None, sketch=None):
-        return [Verbatim(_path.convert_to_string(
-            path, transform, clip, simplify, sketch,
-            6,
-            [Op.moveto.value, Op.lineto.value, b'', Op.curveto.value,
-             Op.closepath.value],
-            True))]
-
-    def writePath(self, path, transform, clip=False, sketch=None):
-        if clip:
-            clip = (0.0, 0.0, self.width * 72, self.height * 72)
-            simplify = path.should_simplify
-        else:
-            clip = None
-            simplify = False
-        cmds = self.pathOperations(path, transform, clip, simplify=simplify,
-                                   sketch=sketch)
-        self.output(*cmds)
-
-    def reserveObject(self, name=''):
-        """
-        Reserve an ID for an indirect object.
-
-        The name is used for debugging in case we forget to print out
-        the object with writeObject.
-        """
-        id = next(self._object_seq)
-        self.xrefTable.append([None, 0, name])
-        return Reference(id)
-
-    def recordXref(self, id):
-        self.xrefTable[id][0] = self.fh.tell() - self.tell_base
-
-    def writeObject(self, object, contents):
-        self.recordXref(object.id)
-        object.write(contents, self)
-
-    def writeXref(self):
-        """Write out the xref table."""
-        self.startxref = self.fh.tell() - self.tell_base
-        self.write(b"xref\n0 %d\n" % len(self.xrefTable))
-        for i, (offset, generation, name) in enumerate(self.xrefTable):
-            if offset is None:
-                raise AssertionError(
-                    'No offset for object %d (%s)' % (i, name))
-            else:
-                key = b"f" if name == 'the zero object' else b"n"
-                text = b"%010d %05d %b \n" % (offset, generation, key)
-                self.write(text)
-
-    def writeInfoDict(self):
-        """Write out the info dictionary, checking it for good form"""
-
-        self.infoObject = self.reserveObject('info')
-        self.writeObject(self.infoObject, self.infoDict)
-
-    def writeTrailer(self):
-        """Write out the PDF trailer."""
-
-        self.write(b"trailer\n")
-        self.write(pdfRepr(
-            {'Size': len(self.xrefTable),
-             'Root': self.rootObject,
-             'Info': self.infoObject}))
-        # Could add 'ID'
-        self.write(b"\nstartxref\n%d\n%%%%EOF\n" % self.startxref)
-
-
-class RendererPdf(_backend_pdf_ps.RendererPDFPSBase):
-
-    _afm_font_dir = cbook._get_data_path("fonts/pdfcorefonts")
-    _use_afm_rc_name = "pdf.use14corefonts"
-
-    def __init__(self, file, image_dpi, height, width):
-        super().__init__(width, height)
-        self.file = file
-        self.gc = self.new_gc()
-        self.image_dpi = image_dpi
-
-    def finalize(self):
-        self.file.output(*self.gc.finalize())
-
-    def check_gc(self, gc, fillcolor=None):
-        orig_fill = getattr(gc, '_fillcolor', (0., 0., 0.))
-        gc._fillcolor = fillcolor
-
-        orig_alphas = getattr(gc, '_effective_alphas', (1.0, 1.0))
-
-        if gc.get_rgb() is None:
-            # It should not matter what color here since linewidth should be
-            # 0 unless affected by global settings in rcParams, hence setting
-            # zero alpha just in case.
-            gc.set_foreground((0, 0, 0, 0), isRGBA=True)
-
-        if gc._forced_alpha:
-            gc._effective_alphas = (gc._alpha, gc._alpha)
-        elif fillcolor is None or len(fillcolor) < 4:
-            gc._effective_alphas = (gc._rgb[3], 1.0)
-        else:
-            gc._effective_alphas = (gc._rgb[3], fillcolor[3])
-
-        delta = self.gc.delta(gc)
-        if delta:
-            self.file.output(*delta)
-
-        # Restore gc to avoid unwanted side effects
-        gc._fillcolor = orig_fill
-        gc._effective_alphas = orig_alphas
-
-    def get_image_magnification(self):
-        return self.image_dpi/72.0
-
-    def draw_image(self, gc, x, y, im, transform=None):
-        # docstring inherited
-
-        h, w = im.shape[:2]
-        if w == 0 or h == 0:
-            return
-
-        if transform is None:
-            # If there's no transform, alpha has already been applied
-            gc.set_alpha(1.0)
-
-        self.check_gc(gc)
-
-        w = 72.0 * w / self.image_dpi
-        h = 72.0 * h / self.image_dpi
-
-        imob = self.file.imageObject(im)
-
-        if transform is None:
-            self.file.output(Op.gsave,
-                             w, 0, 0, h, x, y, Op.concat_matrix,
-                             imob, Op.use_xobject, Op.grestore)
-        else:
-            tr1, tr2, tr3, tr4, tr5, tr6 = transform.frozen().to_values()
-
-            self.file.output(Op.gsave,
-                             1, 0, 0, 1, x, y, Op.concat_matrix,
-                             tr1, tr2, tr3, tr4, tr5, tr6, Op.concat_matrix,
-                             imob, Op.use_xobject, Op.grestore)
-
-    def draw_path(self, gc, path, transform, rgbFace=None):
-        # docstring inherited
-        self.check_gc(gc, rgbFace)
-        self.file.writePath(
-            path, transform,
-            rgbFace is None and gc.get_hatch_path() is None,
-            gc.get_sketch_params())
-        self.file.output(self.gc.paint())
-
-    def draw_path_collection(self, gc, master_transform, paths, all_transforms,
-                             offsets, offset_trans, facecolors, edgecolors,
-                             linewidths, linestyles, antialiaseds, urls,
-                             offset_position):
-        # We can only reuse the objects if the presence of fill and
-        # stroke (and the amount of alpha for each) is the same for
-        # all of them
-        can_do_optimization = True
-        facecolors = np.asarray(facecolors)
-        edgecolors = np.asarray(edgecolors)
-
-        if not len(facecolors):
-            filled = False
-            can_do_optimization = not gc.get_hatch()
-        else:
-            if np.all(facecolors[:, 3] == facecolors[0, 3]):
-                filled = facecolors[0, 3] != 0.0
-            else:
-                can_do_optimization = False
-
-        if not len(edgecolors):
-            stroked = False
-        else:
-            if np.all(np.asarray(linewidths) == 0.0):
-                stroked = False
-            elif np.all(edgecolors[:, 3] == edgecolors[0, 3]):
-                stroked = edgecolors[0, 3] != 0.0
-            else:
-                can_do_optimization = False
-
-        # Is the optimization worth it? Rough calculation:
-        # cost of emitting a path in-line is len_path * uses_per_path
-        # cost of XObject is len_path + 5 for the definition,
-        #    uses_per_path for the uses
-        len_path = len(paths[0].vertices) if len(paths) > 0 else 0
-        uses_per_path = self._iter_collection_uses_per_path(
-            paths, all_transforms, offsets, facecolors, edgecolors)
-        should_do_optimization = \
-            len_path + uses_per_path + 5 < len_path * uses_per_path
-
-        if (not can_do_optimization) or (not should_do_optimization):
-            return RendererBase.draw_path_collection(
-                self, gc, master_transform, paths, all_transforms,
-                offsets, offset_trans, facecolors, edgecolors,
-                linewidths, linestyles, antialiaseds, urls,
-                offset_position)
-
-        padding = np.max(linewidths)
-        path_codes = []
-        for i, (path, transform) in enumerate(self._iter_collection_raw_paths(
-                master_transform, paths, all_transforms)):
-            name = self.file.pathCollectionObject(
-                gc, path, transform, padding, filled, stroked)
-            path_codes.append(name)
-
-        output = self.file.output
-        output(*self.gc.push())
-        lastx, lasty = 0, 0
-        for xo, yo, path_id, gc0, rgbFace in self._iter_collection(
-                gc, path_codes, offsets, offset_trans,
-                facecolors, edgecolors, linewidths, linestyles,
-                antialiaseds, urls, offset_position):
-
-            self.check_gc(gc0, rgbFace)
-            dx, dy = xo - lastx, yo - lasty
-            output(1, 0, 0, 1, dx, dy, Op.concat_matrix, path_id,
-                   Op.use_xobject)
-            lastx, lasty = xo, yo
-        output(*self.gc.pop())
-
-    def draw_markers(self, gc, marker_path, marker_trans, path, trans,
-                     rgbFace=None):
-        # docstring inherited
-
-        # Same logic as in draw_path_collection
-        len_marker_path = len(marker_path)
-        uses = len(path)
-        if len_marker_path * uses < len_marker_path + uses + 5:
-            RendererBase.draw_markers(self, gc, marker_path, marker_trans,
-                                      path, trans, rgbFace)
-            return
-
-        self.check_gc(gc, rgbFace)
-        fill = gc.fill(rgbFace)
-        stroke = gc.stroke()
-
-        output = self.file.output
-        marker = self.file.markerObject(
-            marker_path, marker_trans, fill, stroke, self.gc._linewidth,
-            gc.get_joinstyle(), gc.get_capstyle())
-
-        output(Op.gsave)
-        lastx, lasty = 0, 0
-        for vertices, code in path.iter_segments(
-                trans,
-                clip=(0, 0, self.file.width*72, self.file.height*72),
-                simplify=False):
-            if len(vertices):
-                x, y = vertices[-2:]
-                if not (0 <= x <= self.file.width * 72
-                        and 0 <= y <= self.file.height * 72):
-                    continue
-                dx, dy = x - lastx, y - lasty
-                output(1, 0, 0, 1, dx, dy, Op.concat_matrix,
-                       marker, Op.use_xobject)
-                lastx, lasty = x, y
-        output(Op.grestore)
-
-    def draw_gouraud_triangle(self, gc, points, colors, trans):
-        self.draw_gouraud_triangles(gc, points.reshape((1, 3, 2)),
-                                    colors.reshape((1, 3, 4)), trans)
-
-    def draw_gouraud_triangles(self, gc, points, colors, trans):
-        assert len(points) == len(colors)
-        if len(points) == 0:
-            return
-        assert points.ndim == 3
-        assert points.shape[1] == 3
-        assert points.shape[2] == 2
-        assert colors.ndim == 3
-        assert colors.shape[1] == 3
-        assert colors.shape[2] in (1, 4)
-
-        shape = points.shape
-        points = points.reshape((shape[0] * shape[1], 2))
-        tpoints = trans.transform(points)
-        tpoints = tpoints.reshape(shape)
-        name, _ = self.file.addGouraudTriangles(tpoints, colors)
-        output = self.file.output
-
-        if colors.shape[2] == 1:
-            # grayscale
-            gc.set_alpha(1.0)
-            self.check_gc(gc)
-            output(name, Op.shading)
-            return
-
-        alpha = colors[0, 0, 3]
-        if np.allclose(alpha, colors[:, :, 3]):
-            # single alpha value
-            gc.set_alpha(alpha)
-            self.check_gc(gc)
-            output(name, Op.shading)
-        else:
-            # varying alpha: use a soft mask
-            alpha = colors[:, :, 3][:, :, None]
-            _, smask_ob = self.file.addGouraudTriangles(tpoints, alpha)
-            gstate = self.file._soft_mask_state(smask_ob)
-            output(Op.gsave, gstate, Op.setgstate,
-                   name, Op.shading,
-                   Op.grestore)
-
-    def _setup_textpos(self, x, y, angle, oldx=0, oldy=0, oldangle=0):
-        if angle == oldangle == 0:
-            self.file.output(x - oldx, y - oldy, Op.textpos)
-        else:
-            angle = math.radians(angle)
-            self.file.output(math.cos(angle), math.sin(angle),
-                             -math.sin(angle), math.cos(angle),
-                             x, y, Op.textmatrix)
-            self.file.output(0, 0, Op.textpos)
-
-    def draw_mathtext(self, gc, x, y, s, prop, angle):
-        # TODO: fix positioning and encoding
-        width, height, descent, glyphs, rects = \
-            self._text2path.mathtext_parser.parse(s, 72, prop)
-
-        if gc.get_url() is not None:
-            self.file._annotations[-1][1].append(_get_link_annotation(
-                gc, x, y, width, height, angle))
-
-        fonttype = mpl.rcParams['pdf.fonttype']
-
-        # Set up a global transformation matrix for the whole math expression
-        a = math.radians(angle)
-        self.file.output(Op.gsave)
-        self.file.output(math.cos(a), math.sin(a),
-                         -math.sin(a), math.cos(a),
-                         x, y, Op.concat_matrix)
-
-        self.check_gc(gc, gc._rgb)
-        prev_font = None, None
-        oldx, oldy = 0, 0
-        unsupported_chars = []
-
-        self.file.output(Op.begin_text)
-        for font, fontsize, num, ox, oy in glyphs:
-            self.file._character_tracker.track_glyph(font, num)
-            fontname = font.fname
-            if not _font_supports_glyph(fonttype, num):
-                # Unsupported chars (i.e. multibyte in Type 3 or beyond BMP in
-                # Type 42) must be emitted separately (below).
-                unsupported_chars.append((font, fontsize, ox, oy, num))
-            else:
-                self._setup_textpos(ox, oy, 0, oldx, oldy)
-                oldx, oldy = ox, oy
-                if (fontname, fontsize) != prev_font:
-                    self.file.output(self.file.fontName(fontname), fontsize,
-                                     Op.selectfont)
-                    prev_font = fontname, fontsize
-                self.file.output(self.encode_string(chr(num), fonttype),
-                                 Op.show)
-        self.file.output(Op.end_text)
-
-        for font, fontsize, ox, oy, num in unsupported_chars:
-            self._draw_xobject_glyph(
-                font, fontsize, font.get_char_index(num), ox, oy)
-
-        # Draw any horizontal lines in the math layout
-        for ox, oy, width, height in rects:
-            self.file.output(Op.gsave, ox, oy, width, height,
-                             Op.rectangle, Op.fill, Op.grestore)
-
-        # Pop off the global transformation
-        self.file.output(Op.grestore)
-
-    def draw_tex(self, gc, x, y, s, prop, angle, *, mtext=None):
-        # docstring inherited
-        texmanager = self.get_texmanager()
-        fontsize = prop.get_size_in_points()
-        dvifile = texmanager.make_dvi(s, fontsize)
-        with dviread.Dvi(dvifile, 72) as dvi:
-            page, = dvi
-
-        if gc.get_url() is not None:
-            self.file._annotations[-1][1].append(_get_link_annotation(
-                gc, x, y, page.width, page.height, angle))
-
-        # Gather font information and do some setup for combining
-        # characters into strings. The variable seq will contain a
-        # sequence of font and text entries. A font entry is a list
-        # ['font', name, size] where name is a Name object for the
-        # font. A text entry is ['text', x, y, glyphs, x+w] where x
-        # and y are the starting coordinates, w is the width, and
-        # glyphs is a list; in this phase it will always contain just
-        # one single-character string, but later it may have longer
-        # strings interspersed with kern amounts.
-        oldfont, seq = None, []
-        for x1, y1, dvifont, glyph, width in page.text:
-            if dvifont != oldfont:
-                pdfname = self.file.dviFontName(dvifont)
-                seq += [['font', pdfname, dvifont.size]]
-                oldfont = dvifont
-            seq += [['text', x1, y1, [bytes([glyph])], x1+width]]
-
-        # Find consecutive text strings with constant y coordinate and
-        # combine into a sequence of strings and kerns, or just one
-        # string (if any kerns would be less than 0.1 points).
-        i, curx, fontsize = 0, 0, None
-        while i < len(seq)-1:
-            elt, nxt = seq[i:i+2]
-            if elt[0] == 'font':
-                fontsize = elt[2]
-            elif elt[0] == nxt[0] == 'text' and elt[2] == nxt[2]:
-                offset = elt[4] - nxt[1]
-                if abs(offset) < 0.1:
-                    elt[3][-1] += nxt[3][0]
-                    elt[4] += nxt[4]-nxt[1]
-                else:
-                    elt[3] += [offset*1000.0/fontsize, nxt[3][0]]
-                    elt[4] = nxt[4]
-                del seq[i+1]
-                continue
-            i += 1
-
-        # Create a transform to map the dvi contents to the canvas.
-        mytrans = Affine2D().rotate_deg(angle).translate(x, y)
-
-        # Output the text.
-        self.check_gc(gc, gc._rgb)
-        self.file.output(Op.begin_text)
-        curx, cury, oldx, oldy = 0, 0, 0, 0
-        for elt in seq:
-            if elt[0] == 'font':
-                self.file.output(elt[1], elt[2], Op.selectfont)
-            elif elt[0] == 'text':
-                curx, cury = mytrans.transform((elt[1], elt[2]))
-                self._setup_textpos(curx, cury, angle, oldx, oldy)
-                oldx, oldy = curx, cury
-                if len(elt[3]) == 1:
-                    self.file.output(elt[3][0], Op.show)
-                else:
-                    self.file.output(elt[3], Op.showkern)
-            else:
-                assert False
-        self.file.output(Op.end_text)
-
-        # Then output the boxes (e.g., variable-length lines of square
-        # roots).
-        boxgc = self.new_gc()
-        boxgc.copy_properties(gc)
-        boxgc.set_linewidth(0)
-        pathops = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO,
-                   Path.CLOSEPOLY]
-        for x1, y1, h, w in page.boxes:
-            path = Path([[x1, y1], [x1+w, y1], [x1+w, y1+h], [x1, y1+h],
-                         [0, 0]], pathops)
-            self.draw_path(boxgc, path, mytrans, gc._rgb)
-
-    def encode_string(self, s, fonttype):
-        if fonttype in (1, 3):
-            return s.encode('cp1252', 'replace')
-        return s.encode('utf-16be', 'replace')
-
-    def draw_text(self, gc, x, y, s, prop, angle, ismath=False, mtext=None):
-        # docstring inherited
-
-        # TODO: combine consecutive texts into one BT/ET delimited section
-
-        self.check_gc(gc, gc._rgb)
-        if ismath:
-            return self.draw_mathtext(gc, x, y, s, prop, angle)
-
-        fontsize = prop.get_size_in_points()
-
-        if mpl.rcParams['pdf.use14corefonts']:
-            font = self._get_font_afm(prop)
-            fonttype = 1
-        else:
-            font = self._get_font_ttf(prop)
-            self.file._character_tracker.track(font, s)
-            fonttype = mpl.rcParams['pdf.fonttype']
-
-        if gc.get_url() is not None:
-            font.set_text(s)
-            width, height = font.get_width_height()
-            self.file._annotations[-1][1].append(_get_link_annotation(
-                gc, x, y, width / 64, height / 64, angle))
-
-        # If fonttype is neither 3 nor 42, emit the whole string at once
-        # without manual kerning.
-        if fonttype not in [3, 42]:
-            self.file.output(Op.begin_text,
-                             self.file.fontName(prop), fontsize, Op.selectfont)
-            self._setup_textpos(x, y, angle)
-            self.file.output(self.encode_string(s, fonttype),
-                             Op.show, Op.end_text)
-
-        # A sequence of characters is broken into multiple chunks. The chunking
-        # serves two purposes:
-        #   - For Type 3 fonts, there is no way to access multibyte characters,
-        #     as they cannot have a CIDMap.  Therefore, in this case we break
-        #     the string into chunks, where each chunk contains either a string
-        #     of consecutive 1-byte characters or a single multibyte character.
-        #   - A sequence of 1-byte characters is split into chunks to allow for
-        #     kerning adjustments between consecutive chunks.
-        #
-        # Each chunk is emitted with a separate command: 1-byte characters use
-        # the regular text show command (TJ) with appropriate kerning between
-        # chunks, whereas multibyte characters use the XObject command (Do).
-        else:
-            # List of (ft_object, start_x, [prev_kern, char, char, ...]),
-            # w/o zero kerns.
-            singlebyte_chunks = []
-            # List of (ft_object, start_x, glyph_index).
-            multibyte_glyphs = []
-            prev_was_multibyte = True
-            prev_font = font
-            for item in _text_helpers.layout(
-                    s, font, kern_mode=KERNING_UNFITTED):
-                if _font_supports_glyph(fonttype, ord(item.char)):
-                    if prev_was_multibyte or item.ft_object != prev_font:
-                        singlebyte_chunks.append((item.ft_object, item.x, []))
-                        prev_font = item.ft_object
-                    if item.prev_kern:
-                        singlebyte_chunks[-1][2].append(item.prev_kern)
-                    singlebyte_chunks[-1][2].append(item.char)
-                    prev_was_multibyte = False
-                else:
-                    multibyte_glyphs.append(
-                        (item.ft_object, item.x, item.glyph_idx)
-                    )
-                    prev_was_multibyte = True
-            # Do the rotation and global translation as a single matrix
-            # concatenation up front
-            self.file.output(Op.gsave)
-            a = math.radians(angle)
-            self.file.output(math.cos(a), math.sin(a),
-                             -math.sin(a), math.cos(a),
-                             x, y, Op.concat_matrix)
-            # Emit all the 1-byte characters in a BT/ET group.
-
-            self.file.output(Op.begin_text)
-            prev_start_x = 0
-            for ft_object, start_x, kerns_or_chars in singlebyte_chunks:
-                ft_name = self.file.fontName(ft_object.fname)
-                self.file.output(ft_name, fontsize, Op.selectfont)
-                self._setup_textpos(start_x, 0, 0, prev_start_x, 0, 0)
-                self.file.output(
-                    # See pdf spec "Text space details" for the 1000/fontsize
-                    # (aka. 1000/T_fs) factor.
-                    [-1000 * next(group) / fontsize if tp == float  # a kern
-                     else self.encode_string("".join(group), fonttype)
-                     for tp, group in itertools.groupby(kerns_or_chars, type)],
-                    Op.showkern)
-                prev_start_x = start_x
-            self.file.output(Op.end_text)
-            # Then emit all the multibyte characters, one at a time.
-            for ft_object, start_x, glyph_idx in multibyte_glyphs:
-                self._draw_xobject_glyph(
-                    ft_object, fontsize, glyph_idx, start_x, 0
-                )
-            self.file.output(Op.grestore)
-
-    def _draw_xobject_glyph(self, font, fontsize, glyph_idx, x, y):
-        """Draw a multibyte character from a Type 3 font as an XObject."""
-        glyph_name = font.get_glyph_name(glyph_idx)
-        name = self.file._get_xobject_glyph_name(font.fname, glyph_name)
-        self.file.output(
-            Op.gsave,
-            0.001 * fontsize, 0, 0, 0.001 * fontsize, x, y, Op.concat_matrix,
-            Name(name), Op.use_xobject,
-            Op.grestore,
-        )
-
-    def new_gc(self):
-        # docstring inherited
-        return GraphicsContextPdf(self.file)
-
-
-class GraphicsContextPdf(GraphicsContextBase):
-
-    def __init__(self, file):
-        super().__init__()
-        self._fillcolor = (0.0, 0.0, 0.0)
-        self._effective_alphas = (1.0, 1.0)
-        self.file = file
-        self.parent = None
-
-    def __repr__(self):
-        d = dict(self.__dict__)
-        del d['file']
-        del d['parent']
-        return repr(d)
-
-    def stroke(self):
-        """
-        Predicate: does the path need to be stroked (its outline drawn)?
-        This tests for the various conditions that disable stroking
-        the path, in which case it would presumably be filled.
-        """
-        # _linewidth > 0: in pdf a line of width 0 is drawn at minimum
-        #   possible device width, but e.g., agg doesn't draw at all
-        return (self._linewidth > 0 and self._alpha > 0 and
-                (len(self._rgb) <= 3 or self._rgb[3] != 0.0))
-
-    def fill(self, *args):
-        """
-        Predicate: does the path need to be filled?
-
-        An optional argument can be used to specify an alternative
-        _fillcolor, as needed by RendererPdf.draw_markers.
-        """
-        if len(args):
-            _fillcolor = args[0]
-        else:
-            _fillcolor = self._fillcolor
-        return (self._hatch or
-                (_fillcolor is not None and
-                 (len(_fillcolor) <= 3 or _fillcolor[3] != 0.0)))
-
-    def paint(self):
-        """
-        Return the appropriate pdf operator to cause the path to be
-        stroked, filled, or both.
-        """
-        return Op.paint_path(self.fill(), self.stroke())
-
-    capstyles = {'butt': 0, 'round': 1, 'projecting': 2}
-    joinstyles = {'miter': 0, 'round': 1, 'bevel': 2}
-
-    def capstyle_cmd(self, style):
-        return [self.capstyles[style], Op.setlinecap]
-
-    def joinstyle_cmd(self, style):
-        return [self.joinstyles[style], Op.setlinejoin]
-
-    def linewidth_cmd(self, width):
-        return [width, Op.setlinewidth]
-
-    def dash_cmd(self, dashes):
-        offset, dash = dashes
-        if dash is None:
-            dash = []
-            offset = 0
-        return [list(dash), offset, Op.setdash]
-
-    def alpha_cmd(self, alpha, forced, effective_alphas):
-        name = self.file.alphaState(effective_alphas)
-        return [name, Op.setgstate]
-
-    def hatch_cmd(self, hatch, hatch_color):
-        if not hatch:
-            if self._fillcolor is not None:
-                return self.fillcolor_cmd(self._fillcolor)
-            else:
-                return [Name('DeviceRGB'), Op.setcolorspace_nonstroke]
-        else:
-            hatch_style = (hatch_color, self._fillcolor, hatch)
-            name = self.file.hatchPattern(hatch_style)
-            return [Name('Pattern'), Op.setcolorspace_nonstroke,
-                    name, Op.setcolor_nonstroke]
-
-    def rgb_cmd(self, rgb):
-        if mpl.rcParams['pdf.inheritcolor']:
-            return []
-        if rgb[0] == rgb[1] == rgb[2]:
-            return [rgb[0], Op.setgray_stroke]
-        else:
-            return [*rgb[:3], Op.setrgb_stroke]
-
-    def fillcolor_cmd(self, rgb):
-        if rgb is None or mpl.rcParams['pdf.inheritcolor']:
-            return []
-        elif rgb[0] == rgb[1] == rgb[2]:
-            return [rgb[0], Op.setgray_nonstroke]
-        else:
-            return [*rgb[:3], Op.setrgb_nonstroke]
-
-    def push(self):
-        parent = GraphicsContextPdf(self.file)
-        parent.copy_properties(self)
-        parent.parent = self.parent
-        self.parent = parent
-        return [Op.gsave]
-
-    def pop(self):
-        assert self.parent is not None
-        self.copy_properties(self.parent)
-        self.parent = self.parent.parent
-        return [Op.grestore]
-
-    def clip_cmd(self, cliprect, clippath):
-        """Set clip rectangle. Calls `.pop()` and `.push()`."""
-        cmds = []
-        # Pop graphics state until we hit the right one or the stack is empty
-        while ((self._cliprect, self._clippath) != (cliprect, clippath)
-                and self.parent is not None):
-            cmds.extend(self.pop())
-        # Unless we hit the right one, set the clip polygon
-        if ((self._cliprect, self._clippath) != (cliprect, clippath) or
-                self.parent is None):
-            cmds.extend(self.push())
-            if self._cliprect != cliprect:
-                cmds.extend([cliprect, Op.rectangle, Op.clip, Op.endpath])
-            if self._clippath != clippath:
-                path, affine = clippath.get_transformed_path_and_affine()
-                cmds.extend(
-                    PdfFile.pathOperations(path, affine, simplify=False) +
-                    [Op.clip, Op.endpath])
-        return cmds
-
-    commands = (
-        # must come first since may pop
-        (('_cliprect', '_clippath'), clip_cmd),
-        (('_alpha', '_forced_alpha', '_effective_alphas'), alpha_cmd),
-        (('_capstyle',), capstyle_cmd),
-        (('_fillcolor',), fillcolor_cmd),
-        (('_joinstyle',), joinstyle_cmd),
-        (('_linewidth',), linewidth_cmd),
-        (('_dashes',), dash_cmd),
-        (('_rgb',), rgb_cmd),
-        # must come after fillcolor and rgb
-        (('_hatch', '_hatch_color'), hatch_cmd),
-        )
-
-    def delta(self, other):
-        """
-        Copy properties of other into self and return PDF commands
-        needed to transform *self* into *other*.
-        """
-        cmds = []
-        fill_performed = False
-        for params, cmd in self.commands:
-            different = False
-            for p in params:
-                ours = getattr(self, p)
-                theirs = getattr(other, p)
-                try:
-                    if ours is None or theirs is None:
-                        different = ours is not theirs
-                    else:
-                        different = bool(ours != theirs)
-                except ValueError:
-                    ours = np.asarray(ours)
-                    theirs = np.asarray(theirs)
-                    different = (ours.shape != theirs.shape or
-                                 np.any(ours != theirs))
-                if different:
-                    break
-
-            # Need to update hatching if we also updated fillcolor
-            if params == ('_hatch', '_hatch_color') and fill_performed:
-                different = True
-
-            if different:
-                if params == ('_fillcolor',):
-                    fill_performed = True
-                theirs = [getattr(other, p) for p in params]
-                cmds.extend(cmd(self, *theirs))
-                for p in params:
-                    setattr(self, p, getattr(other, p))
-        return cmds
-
-    def copy_properties(self, other):
-        """
-        Copy properties of other into self.
-        """
-        super().copy_properties(other)
-        fillcolor = getattr(other, '_fillcolor', self._fillcolor)
-        effective_alphas = getattr(other, '_effective_alphas',
-                                   self._effective_alphas)
-        self._fillcolor = fillcolor
-        self._effective_alphas = effective_alphas
-
-    def finalize(self):
-        """
-        Make sure every pushed graphics state is popped.
-        """
-        cmds = []
-        while self.parent is not None:
-            cmds.extend(self.pop())
-        return cmds
-
-
-class PdfPages:
-    """
-    A multi-page PDF file.
-
-    Examples
-    --------
-    >>> import matplotlib.pyplot as plt
-    >>> # Initialize:
-    >>> with PdfPages('foo.pdf') as pdf:
-    ...     # As many times as you like, create a figure fig and save it:
-    ...     fig = plt.figure()
-    ...     pdf.savefig(fig)
-    ...     # When no figure is specified the current figure is saved
-    ...     pdf.savefig()
-
-    Notes
-    -----
-    In reality `PdfPages` is a thin wrapper around `PdfFile`, in order to avoid
-    confusion when using `~.pyplot.savefig` and forgetting the format argument.
-    """
-
-    _UNSET = object()
-
-    def __init__(self, filename, keep_empty=_UNSET, metadata=None):
-        """
-        Create a new PdfPages object.
-
-        Parameters
-        ----------
-        filename : str or path-like or file-like
-            Plots using `PdfPages.savefig` will be written to a file at this location.
-            The file is opened when a figure is saved for the first time (overwriting
-            any older file with the same name).
-
-        keep_empty : bool, optional
-            If set to False, then empty pdf files will be deleted automatically
-            when closed.
-
-        metadata : dict, optional
-            Information dictionary object (see PDF reference section 10.2.1
-            'Document Information Dictionary'), e.g.:
-            ``{'Creator': 'My software', 'Author': 'Me', 'Title': 'Awesome'}``.
-
-            The standard keys are 'Title', 'Author', 'Subject', 'Keywords',
-            'Creator', 'Producer', 'CreationDate', 'ModDate', and
-            'Trapped'. Values have been predefined for 'Creator', 'Producer'
-            and 'CreationDate'. They can be removed by setting them to `None`.
-        """
-        self._filename = filename
-        self._metadata = metadata
-        self._file = None
-        if keep_empty and keep_empty is not self._UNSET:
-            _api.warn_deprecated("3.8", message=(
-                "Keeping empty pdf files is deprecated since %(since)s and support "
-                "will be removed %(removal)s."))
-        self._keep_empty = keep_empty
-
-    keep_empty = _api.deprecate_privatize_attribute("3.8")
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
-
-    def _ensure_file(self):
-        if self._file is None:
-            self._file = PdfFile(self._filename, metadata=self._metadata)  # init.
-        return self._file
-
-    def close(self):
-        """
-        Finalize this object, making the underlying file a complete
-        PDF file.
-        """
-        if self._file is not None:
-            self._file.finalize()
-            self._file.close()
-            self._file = None
-        elif self._keep_empty:  # True *or* UNSET.
-            _api.warn_deprecated("3.8", message=(
-                "Keeping empty pdf files is deprecated since %(since)s and support "
-                "will be removed %(removal)s."))
-            PdfFile(self._filename, metadata=self._metadata)  # touch the file.
-
-    def infodict(self):
-        """
-        Return a modifiable information dictionary object
-        (see PDF reference section 10.2.1 'Document Information
-        Dictionary').
-        """
-        return self._ensure_file().infoDict
-
-    def savefig(self, figure=None, **kwargs):
-        """
-        Save a `.Figure` to this file as a new page.
-
-        Any other keyword arguments are passed to `~.Figure.savefig`.
-
-        Parameters
-        ----------
-        figure : `.Figure` or int, default: the active figure
-            The figure, or index of the figure, that is saved to the file.
-        """
-        if not isinstance(figure, Figure):
-            if figure is None:
-                manager = Gcf.get_active()
-            else:
-                manager = Gcf.get_fig_manager(figure)
-            if manager is None:
-                raise ValueError(f"No figure {figure}")
-            figure = manager.canvas.figure
-        # Force use of pdf backend, as PdfPages is tightly coupled with it.
-        with cbook._setattr_cm(figure, canvas=FigureCanvasPdf(figure)):
-            figure.savefig(self, format="pdf", **kwargs)
-
-    def get_pagecount(self):
-        """Return the current number of pages in the multipage pdf file."""
-        return len(self._ensure_file().pageList)
-
-    def attach_note(self, text, positionRect=[-100, -100, 0, 0]):
-        """
-        Add a new text note to the page to be saved next. The optional
-        positionRect specifies the position of the new note on the
-        page. It is outside the page per default to make sure it is
-        invisible on printouts.
-        """
-        self._ensure_file().newTextnote(text, positionRect)
-
-
-class FigureCanvasPdf(FigureCanvasBase):
-    # docstring inherited
-
-    fixed_dpi = 72
-    filetypes = {'pdf': 'Portable Document Format'}
-
-    def get_default_filetype(self):
-        return 'pdf'
-
-    def print_pdf(self, filename, *,
-                  bbox_inches_restore=None, metadata=None):
-
-        dpi = self.figure.dpi
-        self.figure.dpi = 72  # there are 72 pdf points to an inch
-        width, height = self.figure.get_size_inches()
-        if isinstance(filename, PdfPages):
-            file = filename._ensure_file()
-        else:
-            file = PdfFile(filename, metadata=metadata)
-        try:
-            file.newPage(width, height)
-            renderer = MixedModeRenderer(
-                self.figure, width, height, dpi,
-                RendererPdf(file, dpi, height, width),
-                bbox_inches_restore=bbox_inches_restore)
-            self.figure.draw(renderer)
-            renderer.finalize()
-            if not isinstance(filename, PdfPages):
-                file.finalize()
-        finally:
-            if isinstance(filename, PdfPages):  # finish off this page
-                file.endStream()
-            else:            # we opened the file above; now finish it off
-                file.close()
-
-    def draw(self):
-        self.figure.draw_without_rendering()
-        return super().draw()
-
-
-FigureManagerPdf = FigureManagerBase
-
-
-@_Backend.export
-class _BackendPdf(_Backend):
-    FigureCanvas = FigureCanvasPdf
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mdurl/_url.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mdurl/_url.py
deleted file mode 100644
index f866e7a179c8854e37c9bba6294f48681e5d99d7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mdurl/_url.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from __future__ import annotations
-
-from typing import NamedTuple
-
-
-class URL(NamedTuple):
-    protocol: str | None
-    slashes: bool
-    auth: str | None
-    port: str | None
-    hostname: str | None
-    hash: str | None  # noqa: A003
-    search: str | None
-    pathname: str | None
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/_machar.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/_machar.py
deleted file mode 100644
index 59d71014ff20053fecd69bf3448b152030842491..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/_machar.py
+++ /dev/null
@@ -1,356 +0,0 @@
-"""
-Machine arithmetic - determine the parameters of the
-floating-point arithmetic system
-
-Author: Pearu Peterson, September 2003
-
-"""
-__all__ = ['MachAr']
-
-from .fromnumeric import any
-from ._ufunc_config import errstate
-from .._utils import set_module
-
-# Need to speed this up...especially for longfloat
-
-# Deprecated 2021-10-20, NumPy 1.22
-class MachAr:
-    """
-    Diagnosing machine parameters.
-
-    Attributes
-    ----------
-    ibeta : int
-        Radix in which numbers are represented.
-    it : int
-        Number of base-`ibeta` digits in the floating point mantissa M.
-    machep : int
-        Exponent of the smallest (most negative) power of `ibeta` that,
-        added to 1.0, gives something different from 1.0
-    eps : float
-        Floating-point number ``beta**machep`` (floating point precision)
-    negep : int
-        Exponent of the smallest power of `ibeta` that, subtracted
-        from 1.0, gives something different from 1.0.
-    epsneg : float
-        Floating-point number ``beta**negep``.
-    iexp : int
-        Number of bits in the exponent (including its sign and bias).
-    minexp : int
-        Smallest (most negative) power of `ibeta` consistent with there
-        being no leading zeros in the mantissa.
-    xmin : float
-        Floating-point number ``beta**minexp`` (the smallest [in
-        magnitude] positive floating point number with full precision).
-    maxexp : int
-        Smallest (positive) power of `ibeta` that causes overflow.
-    xmax : float
-        ``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
-        usable floating value).
-    irnd : int
-        In ``range(6)``, information on what kind of rounding is done
-        in addition, and on how underflow is handled.
-    ngrd : int
-        Number of 'guard digits' used when truncating the product
-        of two mantissas to fit the representation.
-    epsilon : float
-        Same as `eps`.
-    tiny : float
-        An alias for `smallest_normal`, kept for backwards compatibility.
-    huge : float
-        Same as `xmax`.
-    precision : float
-        ``- int(-log10(eps))``
-    resolution : float
-        ``- 10**(-precision)``
-    smallest_normal : float
-        The smallest positive floating point number with 1 as leading bit in
-        the mantissa following IEEE-754. Same as `xmin`.
-    smallest_subnormal : float
-        The smallest positive floating point number with 0 as leading bit in
-        the mantissa following IEEE-754.
-
-    Parameters
-    ----------
-    float_conv : function, optional
-        Function that converts an integer or integer array to a float
-        or float array. Default is `float`.
-    int_conv : function, optional
-        Function that converts a float or float array to an integer or
-        integer array. Default is `int`.
-    float_to_float : function, optional
-        Function that converts a float array to float. Default is `float`.
-        Note that this does not seem to do anything useful in the current
-        implementation.
-    float_to_str : function, optional
-        Function that converts a single float to a string. Default is
-        ``lambda v:'%24.16e' %v``.
-    title : str, optional
-        Title that is printed in the string representation of `MachAr`.
-
-    See Also
-    --------
-    finfo : Machine limits for floating point types.
-    iinfo : Machine limits for integer types.
-
-    References
-    ----------
-    .. [1] Press, Teukolsky, Vetterling and Flannery,
-           "Numerical Recipes in C++," 2nd ed,
-           Cambridge University Press, 2002, p. 31.
-
-    """
-
-    def __init__(self, float_conv=float,int_conv=int,
-                 float_to_float=float,
-                 float_to_str=lambda v:'%24.16e' % v,
-                 title='Python floating point number'):
-        """
-
-        float_conv - convert integer to float (array)
-        int_conv   - convert float (array) to integer
-        float_to_float - convert float array to float
-        float_to_str - convert array float to str
-        title        - description of used floating point numbers
-
-        """
-        # We ignore all errors here because we are purposely triggering
-        # underflow to detect the properties of the runninng arch.
-        with errstate(under='ignore'):
-            self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
-
-    def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
-        max_iterN = 10000
-        msg = "Did not converge after %d tries with %s"
-        one = float_conv(1)
-        two = one + one
-        zero = one - one
-
-        # Do we really need to do this?  Aren't they 2 and 2.0?
-        # Determine ibeta and beta
-        a = one
-        for _ in range(max_iterN):
-            a = a + a
-            temp = a + one
-            temp1 = temp - a
-            if any(temp1 - one != zero):
-                break
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        b = one
-        for _ in range(max_iterN):
-            b = b + b
-            temp = a + b
-            itemp = int_conv(temp-a)
-            if any(itemp != 0):
-                break
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        ibeta = itemp
-        beta = float_conv(ibeta)
-
-        # Determine it and irnd
-        it = -1
-        b = one
-        for _ in range(max_iterN):
-            it = it + 1
-            b = b * beta
-            temp = b + one
-            temp1 = temp - b
-            if any(temp1 - one != zero):
-                break
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-
-        betah = beta / two
-        a = one
-        for _ in range(max_iterN):
-            a = a + a
-            temp = a + one
-            temp1 = temp - a
-            if any(temp1 - one != zero):
-                break
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        temp = a + betah
-        irnd = 0
-        if any(temp-a != zero):
-            irnd = 1
-        tempa = a + beta
-        temp = tempa + betah
-        if irnd == 0 and any(temp-tempa != zero):
-            irnd = 2
-
-        # Determine negep and epsneg
-        negep = it + 3
-        betain = one / beta
-        a = one
-        for i in range(negep):
-            a = a * betain
-        b = a
-        for _ in range(max_iterN):
-            temp = one - a
-            if any(temp-one != zero):
-                break
-            a = a * beta
-            negep = negep - 1
-            # Prevent infinite loop on PPC with gcc 4.0:
-            if negep < 0:
-                raise RuntimeError("could not determine machine tolerance "
-                                   "for 'negep', locals() -> %s" % (locals()))
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        negep = -negep
-        epsneg = a
-
-        # Determine machep and eps
-        machep = - it - 3
-        a = b
-
-        for _ in range(max_iterN):
-            temp = one + a
-            if any(temp-one != zero):
-                break
-            a = a * beta
-            machep = machep + 1
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        eps = a
-
-        # Determine ngrd
-        ngrd = 0
-        temp = one + eps
-        if irnd == 0 and any(temp*one - one != zero):
-            ngrd = 1
-
-        # Determine iexp
-        i = 0
-        k = 1
-        z = betain
-        t = one + eps
-        nxres = 0
-        for _ in range(max_iterN):
-            y = z
-            z = y*y
-            a = z*one  # Check here for underflow
-            temp = z*t
-            if any(a+a == zero) or any(abs(z) >= y):
-                break
-            temp1 = temp * betain
-            if any(temp1*beta == z):
-                break
-            i = i + 1
-            k = k + k
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        if ibeta != 10:
-            iexp = i + 1
-            mx = k + k
-        else:
-            iexp = 2
-            iz = ibeta
-            while k >= iz:
-                iz = iz * ibeta
-                iexp = iexp + 1
-            mx = iz + iz - 1
-
-        # Determine minexp and xmin
-        for _ in range(max_iterN):
-            xmin = y
-            y = y * betain
-            a = y * one
-            temp = y * t
-            if any((a + a) != zero) and any(abs(y) < xmin):
-                k = k + 1
-                temp1 = temp * betain
-                if any(temp1*beta == y) and any(temp != y):
-                    nxres = 3
-                    xmin = y
-                    break
-            else:
-                break
-        else:
-            raise RuntimeError(msg % (_, one.dtype))
-        minexp = -k
-
-        # Determine maxexp, xmax
-        if mx <= k + k - 3 and ibeta != 10:
-            mx = mx + mx
-            iexp = iexp + 1
-        maxexp = mx + minexp
-        irnd = irnd + nxres
-        if irnd >= 2:
-            maxexp = maxexp - 2
-        i = maxexp + minexp
-        if ibeta == 2 and not i:
-            maxexp = maxexp - 1
-        if i > 20:
-            maxexp = maxexp - 1
-        if any(a != y):
-            maxexp = maxexp - 2
-        xmax = one - epsneg
-        if any(xmax*one != xmax):
-            xmax = one - beta*epsneg
-        xmax = xmax / (xmin*beta*beta*beta)
-        i = maxexp + minexp + 3
-        for j in range(i):
-            if ibeta == 2:
-                xmax = xmax + xmax
-            else:
-                xmax = xmax * beta
-
-        smallest_subnormal = abs(xmin / beta ** (it))
-
-        self.ibeta = ibeta
-        self.it = it
-        self.negep = negep
-        self.epsneg = float_to_float(epsneg)
-        self._str_epsneg = float_to_str(epsneg)
-        self.machep = machep
-        self.eps = float_to_float(eps)
-        self._str_eps = float_to_str(eps)
-        self.ngrd = ngrd
-        self.iexp = iexp
-        self.minexp = minexp
-        self.xmin = float_to_float(xmin)
-        self._str_xmin = float_to_str(xmin)
-        self.maxexp = maxexp
-        self.xmax = float_to_float(xmax)
-        self._str_xmax = float_to_str(xmax)
-        self.irnd = irnd
-
-        self.title = title
-        # Commonly used parameters
-        self.epsilon = self.eps
-        self.tiny = self.xmin
-        self.huge = self.xmax
-        self.smallest_normal = self.xmin
-        self._str_smallest_normal = float_to_str(self.xmin)
-        self.smallest_subnormal = float_to_float(smallest_subnormal)
-        self._str_smallest_subnormal = float_to_str(smallest_subnormal)
-
-        import math
-        self.precision = int(-math.log10(float_to_float(self.eps)))
-        ten = two + two + two + two + two
-        resolution = ten ** (-self.precision)
-        self.resolution = float_to_float(resolution)
-        self._str_resolution = float_to_str(resolution)
-
-    def __str__(self):
-        fmt = (
-           'Machine parameters for %(title)s\n'
-           '---------------------------------------------------------------------\n'
-           'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
-           'machep=%(machep)s     eps=%(_str_eps)s (beta**machep == epsilon)\n'
-           'negep =%(negep)s  epsneg=%(_str_epsneg)s (beta**epsneg)\n'
-           'minexp=%(minexp)s   xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
-           'maxexp=%(maxexp)s    xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
-           'smallest_normal=%(smallest_normal)s    '
-           'smallest_subnormal=%(smallest_subnormal)s\n'
-           '---------------------------------------------------------------------\n'
-           )
-        return fmt % self.__dict__
-
-
-if __name__ == '__main__':
-    print(MachAr())
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_records.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_records.py
deleted file mode 100644
index a76ae2d999780fa7aa245923f24e7fe17cdf038e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_records.py
+++ /dev/null
@@ -1,520 +0,0 @@
-import collections.abc
-import textwrap
-from io import BytesIO
-from os import path
-from pathlib import Path
-import pytest
-
-import numpy as np
-from numpy.testing import (
-    assert_, assert_equal, assert_array_equal, assert_array_almost_equal,
-    assert_raises, temppath,
-    )
-from numpy.compat import pickle
-
-
-class TestFromrecords:
-    def test_fromrecords(self):
-        r = np.rec.fromrecords([[456, 'dbe', 1.2], [2, 'de', 1.3]],
-                            names='col1,col2,col3')
-        assert_equal(r[0].item(), (456, 'dbe', 1.2))
-        assert_equal(r['col1'].dtype.kind, 'i')
-        assert_equal(r['col2'].dtype.kind, 'U')
-        assert_equal(r['col2'].dtype.itemsize, 12)
-        assert_equal(r['col3'].dtype.kind, 'f')
-
-    def test_fromrecords_0len(self):
-        """ Verify fromrecords works with a 0-length input """
-        dtype = [('a', float), ('b', float)]
-        r = np.rec.fromrecords([], dtype=dtype)
-        assert_equal(r.shape, (0,))
-
-    def test_fromrecords_2d(self):
-        data = [
-            [(1, 2), (3, 4), (5, 6)],
-            [(6, 5), (4, 3), (2, 1)]
-        ]
-        expected_a = [[1, 3, 5], [6, 4, 2]]
-        expected_b = [[2, 4, 6], [5, 3, 1]]
-
-        # try with dtype
-        r1 = np.rec.fromrecords(data, dtype=[('a', int), ('b', int)])
-        assert_equal(r1['a'], expected_a)
-        assert_equal(r1['b'], expected_b)
-
-        # try with names
-        r2 = np.rec.fromrecords(data, names=['a', 'b'])
-        assert_equal(r2['a'], expected_a)
-        assert_equal(r2['b'], expected_b)
-
-        assert_equal(r1, r2)
-
-    def test_method_array(self):
-        r = np.rec.array(b'abcdefg' * 100, formats='i2,a3,i4', shape=3, byteorder='big')
-        assert_equal(r[1].item(), (25444, b'efg', 1633837924))
-
-    def test_method_array2(self):
-        r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
-                     (6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
-        assert_equal(r[1].item(), (2, 22.0, b'b'))
-
-    def test_recarray_slices(self):
-        r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
-                     (6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
-        assert_equal(r[1::2][1].item(), (4, 44.0, b'd'))
-
-    def test_recarray_fromarrays(self):
-        x1 = np.array([1, 2, 3, 4])
-        x2 = np.array(['a', 'dd', 'xyz', '12'])
-        x3 = np.array([1.1, 2, 3, 4])
-        r = np.rec.fromarrays([x1, x2, x3], names='a,b,c')
-        assert_equal(r[1].item(), (2, 'dd', 2.0))
-        x1[1] = 34
-        assert_equal(r.a, np.array([1, 2, 3, 4]))
-
-    def test_recarray_fromfile(self):
-        data_dir = path.join(path.dirname(__file__), 'data')
-        filename = path.join(data_dir, 'recarray_from_file.fits')
-        fd = open(filename, 'rb')
-        fd.seek(2880 * 2)
-        r1 = np.rec.fromfile(fd, formats='f8,i4,a5', shape=3, byteorder='big')
-        fd.seek(2880 * 2)
-        r2 = np.rec.array(fd, formats='f8,i4,a5', shape=3, byteorder='big')
-        fd.seek(2880 * 2)
-        bytes_array = BytesIO()
-        bytes_array.write(fd.read())
-        bytes_array.seek(0)
-        r3 = np.rec.fromfile(bytes_array, formats='f8,i4,a5', shape=3, byteorder='big')
-        fd.close()
-        assert_equal(r1, r2)
-        assert_equal(r2, r3)
-
-    def test_recarray_from_obj(self):
-        count = 10
-        a = np.zeros(count, dtype='O')
-        b = np.zeros(count, dtype='f8')
-        c = np.zeros(count, dtype='f8')
-        for i in range(len(a)):
-            a[i] = list(range(1, 10))
-
-        mine = np.rec.fromarrays([a, b, c], names='date,data1,data2')
-        for i in range(len(a)):
-            assert_((mine.date[i] == list(range(1, 10))))
-            assert_((mine.data1[i] == 0.0))
-            assert_((mine.data2[i] == 0.0))
-
-    def test_recarray_repr(self):
-        a = np.array([(1, 0.1), (2, 0.2)],
-                     dtype=[('foo', '<i4'), ('bar', '<f8')])
-        a = np.rec.array(a)
-        assert_equal(
-            repr(a),
-            textwrap.dedent("""\
-            rec.array([(1, 0.1), (2, 0.2)],
-                      dtype=[('foo', '<i4'), ('bar', '<f8')])""")
-        )
-
-        # make sure non-structured dtypes also show up as rec.array
-        a = np.array(np.ones(4, dtype='f8'))
-        assert_(repr(np.rec.array(a)).startswith('rec.array'))
-
-        # check that the 'np.record' part of the dtype isn't shown
-        a = np.rec.array(np.ones(3, dtype='i4,i4'))
-        assert_equal(repr(a).find('numpy.record'), -1)
-        a = np.rec.array(np.ones(3, dtype='i4'))
-        assert_(repr(a).find('dtype=int32') != -1)
-
-    def test_0d_recarray_repr(self):
-        arr_0d = np.rec.array((1, 2.0, '2003'), dtype='<i4,<f8,<M8[Y]')
-        assert_equal(repr(arr_0d), textwrap.dedent("""\
-            rec.array((1, 2., '2003'),
-                      dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '<M8[Y]')])"""))
-
-        record = arr_0d[()]
-        assert_equal(repr(record), "(1, 2., '2003')")
-        # 1.13 converted to python scalars before the repr
-        try:
-            np.set_printoptions(legacy='1.13')
-            assert_equal(repr(record), '(1, 2.0, datetime.date(2003, 1, 1))')
-        finally:
-            np.set_printoptions(legacy=False)
-
-    def test_recarray_from_repr(self):
-        a = np.array([(1,'ABC'), (2, "DEF")],
-                     dtype=[('foo', int), ('bar', 'S4')])
-        recordarr = np.rec.array(a)
-        recarr = a.view(np.recarray)
-        recordview = a.view(np.dtype((np.record, a.dtype)))
-
-        recordarr_r = eval("numpy." + repr(recordarr), {'numpy': np})
-        recarr_r = eval("numpy." + repr(recarr), {'numpy': np})
-        recordview_r = eval("numpy." + repr(recordview), {'numpy': np})
-
-        assert_equal(type(recordarr_r), np.recarray)
-        assert_equal(recordarr_r.dtype.type, np.record)
-        assert_equal(recordarr, recordarr_r)
-
-        assert_equal(type(recarr_r), np.recarray)
-        assert_equal(recarr_r.dtype.type, np.record)
-        assert_equal(recarr, recarr_r)
-
-        assert_equal(type(recordview_r), np.ndarray)
-        assert_equal(recordview.dtype.type, np.record)
-        assert_equal(recordview, recordview_r)
-
-    def test_recarray_views(self):
-        a = np.array([(1,'ABC'), (2, "DEF")],
-                     dtype=[('foo', int), ('bar', 'S4')])
-        b = np.array([1,2,3,4,5], dtype=np.int64)
-
-        #check that np.rec.array gives right dtypes
-        assert_equal(np.rec.array(a).dtype.type, np.record)
-        assert_equal(type(np.rec.array(a)), np.recarray)
-        assert_equal(np.rec.array(b).dtype.type, np.int64)
-        assert_equal(type(np.rec.array(b)), np.recarray)
-
-        #check that viewing as recarray does the same
-        assert_equal(a.view(np.recarray).dtype.type, np.record)
-        assert_equal(type(a.view(np.recarray)), np.recarray)
-        assert_equal(b.view(np.recarray).dtype.type, np.int64)
-        assert_equal(type(b.view(np.recarray)), np.recarray)
-
-        #check that view to non-structured dtype preserves type=np.recarray
-        r = np.rec.array(np.ones(4, dtype="f4,i4"))
-        rv = r.view('f8').view('f4,i4')
-        assert_equal(type(rv), np.recarray)
-        assert_equal(rv.dtype.type, np.record)
-
-        #check that getitem also preserves np.recarray and np.record
-        r = np.rec.array(np.ones(4, dtype=[('a', 'i4'), ('b', 'i4'),
-                                           ('c', 'i4,i4')]))
-        assert_equal(r['c'].dtype.type, np.record)
-        assert_equal(type(r['c']), np.recarray)
-
-        #and that it preserves subclasses (gh-6949)
-        class C(np.recarray):
-            pass
-
-        c = r.view(C)
-        assert_equal(type(c['c']), C)
-
-        # check that accessing nested structures keep record type, but
-        # not for subarrays, non-void structures, non-structured voids
-        test_dtype = [('a', 'f4,f4'), ('b', 'V8'), ('c', ('f4',2)),
-                      ('d', ('i8', 'i4,i4'))]
-        r = np.rec.array([((1,1), b'11111111', [1,1], 1),
-                          ((1,1), b'11111111', [1,1], 1)], dtype=test_dtype)
-        assert_equal(r.a.dtype.type, np.record)
-        assert_equal(r.b.dtype.type, np.void)
-        assert_equal(r.c.dtype.type, np.float32)
-        assert_equal(r.d.dtype.type, np.int64)
-        # check the same, but for views
-        r = np.rec.array(np.ones(4, dtype='i4,i4'))
-        assert_equal(r.view('f4,f4').dtype.type, np.record)
-        assert_equal(r.view(('i4',2)).dtype.type, np.int32)
-        assert_equal(r.view('V8').dtype.type, np.void)
-        assert_equal(r.view(('i8', 'i4,i4')).dtype.type, np.int64)
-
-        #check that we can undo the view
-        arrs = [np.ones(4, dtype='f4,i4'), np.ones(4, dtype='f8')]
-        for arr in arrs:
-            rec = np.rec.array(arr)
-            # recommended way to view as an ndarray:
-            arr2 = rec.view(rec.dtype.fields or rec.dtype, np.ndarray)
-            assert_equal(arr2.dtype.type, arr.dtype.type)
-            assert_equal(type(arr2), type(arr))
-
-    def test_recarray_from_names(self):
-        ra = np.rec.array([
-            (1, 'abc', 3.7000002861022949, 0),
-            (2, 'xy', 6.6999998092651367, 1),
-            (0, ' ', 0.40000000596046448, 0)],
-                       names='c1, c2, c3, c4')
-        pa = np.rec.fromrecords([
-            (1, 'abc', 3.7000002861022949, 0),
-            (2, 'xy', 6.6999998092651367, 1),
-            (0, ' ', 0.40000000596046448, 0)],
-                       names='c1, c2, c3, c4')
-        assert_(ra.dtype == pa.dtype)
-        assert_(ra.shape == pa.shape)
-        for k in range(len(ra)):
-            assert_(ra[k].item() == pa[k].item())
-
-    def test_recarray_conflict_fields(self):
-        ra = np.rec.array([(1, 'abc', 2.3), (2, 'xyz', 4.2),
-                        (3, 'wrs', 1.3)],
-                       names='field, shape, mean')
-        ra.mean = [1.1, 2.2, 3.3]
-        assert_array_almost_equal(ra['mean'], [1.1, 2.2, 3.3])
-        assert_(type(ra.mean) is type(ra.var))
-        ra.shape = (1, 3)
-        assert_(ra.shape == (1, 3))
-        ra.shape = ['A', 'B', 'C']
-        assert_array_equal(ra['shape'], [['A', 'B', 'C']])
-        ra.field = 5
-        assert_array_equal(ra['field'], [[5, 5, 5]])
-        assert_(isinstance(ra.field, collections.abc.Callable))
-
-    def test_fromrecords_with_explicit_dtype(self):
-        a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')],
-                                dtype=[('a', int), ('b', object)])
-        assert_equal(a.a, [1, 2])
-        assert_equal(a[0].a, 1)
-        assert_equal(a.b, ['a', 'bbb'])
-        assert_equal(a[-1].b, 'bbb')
-        #
-        ndtype = np.dtype([('a', int), ('b', object)])
-        a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')], dtype=ndtype)
-        assert_equal(a.a, [1, 2])
-        assert_equal(a[0].a, 1)
-        assert_equal(a.b, ['a', 'bbb'])
-        assert_equal(a[-1].b, 'bbb')
-
-    def test_recarray_stringtypes(self):
-        # Issue #3993
-        a = np.array([('abc ', 1), ('abc', 2)],
-                     dtype=[('foo', 'S4'), ('bar', int)])
-        a = a.view(np.recarray)
-        assert_equal(a.foo[0] == a.foo[1], False)
-
-    def test_recarray_returntypes(self):
-        qux_fields = {'C': (np.dtype('S5'), 0), 'D': (np.dtype('S5'), 6)}
-        a = np.rec.array([('abc ', (1,1), 1, ('abcde', 'fgehi')),
-                          ('abc', (2,3), 1, ('abcde', 'jklmn'))],
-                         dtype=[('foo', 'S4'),
-                                ('bar', [('A', int), ('B', int)]),
-                                ('baz', int), ('qux', qux_fields)])
-        assert_equal(type(a.foo), np.ndarray)
-        assert_equal(type(a['foo']), np.ndarray)
-        assert_equal(type(a.bar), np.recarray)
-        assert_equal(type(a['bar']), np.recarray)
-        assert_equal(a.bar.dtype.type, np.record)
-        assert_equal(type(a['qux']), np.recarray)
-        assert_equal(a.qux.dtype.type, np.record)
-        assert_equal(dict(a.qux.dtype.fields), qux_fields)
-        assert_equal(type(a.baz), np.ndarray)
-        assert_equal(type(a['baz']), np.ndarray)
-        assert_equal(type(a[0].bar), np.record)
-        assert_equal(type(a[0]['bar']), np.record)
-        assert_equal(a[0].bar.A, 1)
-        assert_equal(a[0].bar['A'], 1)
-        assert_equal(a[0]['bar'].A, 1)
-        assert_equal(a[0]['bar']['A'], 1)
-        assert_equal(a[0].qux.D, b'fgehi')
-        assert_equal(a[0].qux['D'], b'fgehi')
-        assert_equal(a[0]['qux'].D, b'fgehi')
-        assert_equal(a[0]['qux']['D'], b'fgehi')
-
-    def test_zero_width_strings(self):
-        # Test for #6430, based on the test case from #1901
-
-        cols = [['test'] * 3, [''] * 3]
-        rec = np.rec.fromarrays(cols)
-        assert_equal(rec['f0'], ['test', 'test', 'test'])
-        assert_equal(rec['f1'], ['', '', ''])
-
-        dt = np.dtype([('f0', '|S4'), ('f1', '|S')])
-        rec = np.rec.fromarrays(cols, dtype=dt)
-        assert_equal(rec.itemsize, 4)
-        assert_equal(rec['f0'], [b'test', b'test', b'test'])
-        assert_equal(rec['f1'], [b'', b'', b''])
-
-
-class TestPathUsage:
-    # Test that pathlib.Path can be used
-    def test_tofile_fromfile(self):
-        with temppath(suffix='.bin') as path:
-            path = Path(path)
-            np.random.seed(123)
-            a = np.random.rand(10).astype('f8,i4,a5')
-            a[5] = (0.5,10,'abcde')
-            with path.open("wb") as fd:
-                a.tofile(fd)
-            x = np.core.records.fromfile(path,
-                                         formats='f8,i4,a5',
-                                         shape=10)
-            assert_array_equal(x, a)
-
-
-class TestRecord:
-    def setup_method(self):
-        self.data = np.rec.fromrecords([(1, 2, 3), (4, 5, 6)],
-                            dtype=[("col1", "<i4"),
-                                   ("col2", "<i4"),
-                                   ("col3", "<i4")])
-
-    def test_assignment1(self):
-        a = self.data
-        assert_equal(a.col1[0], 1)
-        a[0].col1 = 0
-        assert_equal(a.col1[0], 0)
-
-    def test_assignment2(self):
-        a = self.data
-        assert_equal(a.col1[0], 1)
-        a.col1[0] = 0
-        assert_equal(a.col1[0], 0)
-
-    def test_invalid_assignment(self):
-        a = self.data
-
-        def assign_invalid_column(x):
-            x[0].col5 = 1
-
-        assert_raises(AttributeError, assign_invalid_column, a)
-
-    def test_nonwriteable_setfield(self):
-        # gh-8171
-        r = np.rec.array([(0,), (1,)], dtype=[('f', 'i4')])
-        r.flags.writeable = False
-        with assert_raises(ValueError):
-            r.f = [2, 3]
-        with assert_raises(ValueError):
-            r.setfield([2,3], *r.dtype.fields['f'])
-
-    def test_out_of_order_fields(self):
-        # names in the same order, padding added to descr
-        x = self.data[['col1', 'col2']]
-        assert_equal(x.dtype.names, ('col1', 'col2'))
-        assert_equal(x.dtype.descr,
-                     [('col1', '<i4'), ('col2', '<i4'), ('', '|V4')])
-
-        # names change order to match indexing, as of 1.14 - descr can't
-        # represent that
-        y = self.data[['col2', 'col1']]
-        assert_equal(y.dtype.names, ('col2', 'col1'))
-        assert_raises(ValueError, lambda: y.dtype.descr)
-
-    def test_pickle_1(self):
-        # Issue #1529
-        a = np.array([(1, [])], dtype=[('a', np.int32), ('b', np.int32, 0)])
-        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
-            assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))
-            assert_equal(a[0], pickle.loads(pickle.dumps(a[0],
-                                                         protocol=proto)))
-
-    def test_pickle_2(self):
-        a = self.data
-        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
-            assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))
-            assert_equal(a[0], pickle.loads(pickle.dumps(a[0],
-                                                         protocol=proto)))
-
-    def test_pickle_3(self):
-        # Issue #7140
-        a = self.data
-        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
-            pa = pickle.loads(pickle.dumps(a[0], protocol=proto))
-            assert_(pa.flags.c_contiguous)
-            assert_(pa.flags.f_contiguous)
-            assert_(pa.flags.writeable)
-            assert_(pa.flags.aligned)
-
-    def test_pickle_void(self):
-        # issue gh-13593
-        dt = np.dtype([('obj', 'O'), ('int', 'i')])
-        a = np.empty(1, dtype=dt)
-        data = (bytearray(b'eman'),)
-        a['obj'] = data
-        a['int'] = 42
-        ctor, args = a[0].__reduce__()
-        # check the constructor is what we expect before interpreting the arguments
-        assert ctor is np.core.multiarray.scalar
-        dtype, obj = args
-        # make sure we did not pickle the address
-        assert not isinstance(obj, bytes)
-
-        assert_raises(RuntimeError, ctor, dtype, 13)
-
-        # Test roundtrip:
-        dump = pickle.dumps(a[0])
-        unpickled = pickle.loads(dump)
-        assert a[0] == unpickled
-
-        # Also check the similar (impossible) "object scalar" path:
-        with pytest.warns(DeprecationWarning):
-            assert ctor(np.dtype("O"), data) is data
-
-    def test_objview_record(self):
-        # https://github.com/numpy/numpy/issues/2599
-        dt = np.dtype([('foo', 'i8'), ('bar', 'O')])
-        r = np.zeros((1,3), dtype=dt).view(np.recarray)
-        r.foo = np.array([1, 2, 3])  # TypeError?
-
-        # https://github.com/numpy/numpy/issues/3256
-        ra = np.recarray((2,), dtype=[('x', object), ('y', float), ('z', int)])
-        ra[['x','y']]  # TypeError?
-
-    def test_record_scalar_setitem(self):
-        # https://github.com/numpy/numpy/issues/3561
-        rec = np.recarray(1, dtype=[('x', float, 5)])
-        rec[0].x = 1
-        assert_equal(rec[0].x, np.ones(5))
-
-    def test_missing_field(self):
-        # https://github.com/numpy/numpy/issues/4806
-        arr = np.zeros((3,), dtype=[('x', int), ('y', int)])
-        assert_raises(KeyError, lambda: arr[['nofield']])
-
-    def test_fromarrays_nested_structured_arrays(self):
-        arrays = [
-            np.arange(10),
-            np.ones(10, dtype=[('a', '<u2'), ('b', '<f4')]),
-        ]
-        arr = np.rec.fromarrays(arrays)  # ValueError?
-
-    @pytest.mark.parametrize('nfields', [0, 1, 2])
-    def test_assign_dtype_attribute(self, nfields):
-        dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])
-        data = np.zeros(3, dt).view(np.recarray)
-
-        # the original and resulting dtypes differ on whether they are records
-        assert data.dtype.type == np.record
-        assert dt.type != np.record
-
-        # ensure that the dtype remains a record even when assigned
-        data.dtype = dt
-        assert data.dtype.type == np.record
-
-    @pytest.mark.parametrize('nfields', [0, 1, 2])
-    def test_nested_fields_are_records(self, nfields):
-        """ Test that nested structured types are treated as records too """
-        dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])
-        dt_outer = np.dtype([('inner', dt)])
-
-        data = np.zeros(3, dt_outer).view(np.recarray)
-        assert isinstance(data, np.recarray)
-        assert isinstance(data['inner'], np.recarray)
-
-        data0 = data[0]
-        assert isinstance(data0, np.record)
-        assert isinstance(data0['inner'], np.record)
-
-    def test_nested_dtype_padding(self):
-        """ test that trailing padding is preserved """
-        # construct a dtype with padding at the end
-        dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)])
-        dt_padded_end = dt[['a', 'b']]
-        assert dt_padded_end.itemsize == dt.itemsize
-
-        dt_outer = np.dtype([('inner', dt_padded_end)])
-
-        data = np.zeros(3, dt_outer).view(np.recarray)
-        assert_equal(data['inner'].dtype, dt_padded_end)
-
-        data0 = data[0]
-        assert_equal(data0['inner'].dtype, dt_padded_end)
-
-
-def test_find_duplicate():
-    l1 = [1, 2, 3, 4, 5, 6]
-    assert_(np.rec.find_duplicate(l1) == [])
-
-    l2 = [1, 2, 1, 4, 5, 6]
-    assert_(np.rec.find_duplicate(l2) == [1])
-
-    l3 = [1, 2, 1, 4, 1, 6, 2, 3]
-    assert_(np.rec.find_duplicate(l3) == [1, 2])
-
-    l3 = [2, 2, 1, 4, 1, 6, 2, 3]
-    assert_(np.rec.find_duplicate(l3) == [2, 1])
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_sse42.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_sse42.c
deleted file mode 100644
index f60e18f3c4f13d58bc9e8ac84752612b5ad11830..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_sse42.c
+++ /dev/null
@@ -1,20 +0,0 @@
-#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
-    /*
-     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
-     * whether or not the build options for those features are specified.
-     * Therefore, we must test #definitions of CPU features when option native/host
-     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
-     * the test will be broken and leads to enable all possible features.
-     */
-    #ifndef __SSE4_2__
-        #error "HOST/ARCH doesn't support SSE42"
-    #endif
-#endif
-
-#include <smmintrin.h>
-
-int main(void)
-{
-    __m128 a = _mm_hadd_ps(_mm_setzero_ps(), _mm_setzero_ps());
-    return (int)_mm_cvtss_f32(a);
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/__init__.py
deleted file mode 100644
index 75c61da03795af0d4f60cd4d4a8b8e0dd45e3d5e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/__init__.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from pandas.plotting._matplotlib.boxplot import (
-    BoxPlot,
-    boxplot,
-    boxplot_frame,
-    boxplot_frame_groupby,
-)
-from pandas.plotting._matplotlib.converter import (
-    deregister,
-    register,
-)
-from pandas.plotting._matplotlib.core import (
-    AreaPlot,
-    BarhPlot,
-    BarPlot,
-    HexBinPlot,
-    LinePlot,
-    PiePlot,
-    ScatterPlot,
-)
-from pandas.plotting._matplotlib.hist import (
-    HistPlot,
-    KdePlot,
-    hist_frame,
-    hist_series,
-)
-from pandas.plotting._matplotlib.misc import (
-    andrews_curves,
-    autocorrelation_plot,
-    bootstrap_plot,
-    lag_plot,
-    parallel_coordinates,
-    radviz,
-    scatter_matrix,
-)
-from pandas.plotting._matplotlib.tools import table
-
-if TYPE_CHECKING:
-    from pandas.plotting._matplotlib.core import MPLPlot
-
-PLOT_CLASSES: dict[str, type[MPLPlot]] = {
-    "line": LinePlot,
-    "bar": BarPlot,
-    "barh": BarhPlot,
-    "box": BoxPlot,
-    "hist": HistPlot,
-    "kde": KdePlot,
-    "area": AreaPlot,
-    "pie": PiePlot,
-    "scatter": ScatterPlot,
-    "hexbin": HexBinPlot,
-}
-
-
-def plot(data, kind, **kwargs):
-    # Importing pyplot at the top of the file (before the converters are
-    # registered) causes problems in matplotlib 2 (converters seem to not
-    # work)
-    import matplotlib.pyplot as plt
-
-    if kwargs.pop("reuse_plot", False):
-        ax = kwargs.get("ax")
-        if ax is None and len(plt.get_fignums()) > 0:
-            with plt.rc_context():
-                ax = plt.gca()
-            kwargs["ax"] = getattr(ax, "left_ax", ax)
-    plot_obj = PLOT_CLASSES[kind](data, **kwargs)
-    plot_obj.generate()
-    plot_obj.draw()
-    return plot_obj.result
-
-
-__all__ = [
-    "plot",
-    "hist_series",
-    "hist_frame",
-    "boxplot",
-    "boxplot_frame",
-    "boxplot_frame_groupby",
-    "table",
-    "andrews_curves",
-    "autocorrelation_plot",
-    "bootstrap_plot",
-    "lag_plot",
-    "parallel_coordinates",
-    "radviz",
-    "scatter_matrix",
-    "register",
-    "deregister",
-]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_groupby.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_groupby.py
deleted file mode 100644
index ab00e18fc481280a08f83f34e074bbc742848ca3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_groupby.py
+++ /dev/null
@@ -1,1276 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    Index,
-    MultiIndex,
-    Series,
-    Timestamp,
-    date_range,
-    to_datetime,
-)
-import pandas._testing as tm
-from pandas.api.indexers import BaseIndexer
-from pandas.core.groupby.groupby import get_groupby
-
-
-@pytest.fixture
-def times_frame():
-    """Frame for testing times argument in EWM groupby."""
-    return DataFrame(
-        {
-            "A": ["a", "b", "c", "a", "b", "c", "a", "b", "c", "a"],
-            "B": [0, 0, 0, 1, 1, 1, 2, 2, 2, 3],
-            "C": to_datetime(
-                [
-                    "2020-01-01",
-                    "2020-01-01",
-                    "2020-01-01",
-                    "2020-01-02",
-                    "2020-01-10",
-                    "2020-01-22",
-                    "2020-01-03",
-                    "2020-01-23",
-                    "2020-01-23",
-                    "2020-01-04",
-                ]
-            ),
-        }
-    )
-
-
-@pytest.fixture
-def roll_frame():
-    return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
-
-
-class TestRolling:
-    def test_groupby_unsupported_argument(self, roll_frame):
-        msg = r"groupby\(\) got an unexpected keyword argument 'foo'"
-        with pytest.raises(TypeError, match=msg):
-            roll_frame.groupby("A", foo=1)
-
-    def test_getitem(self, roll_frame):
-        g = roll_frame.groupby("A")
-        g_mutated = get_groupby(roll_frame, by="A")
-
-        expected = g_mutated.B.apply(lambda x: x.rolling(2).mean())
-
-        result = g.rolling(2).mean().B
-        tm.assert_series_equal(result, expected)
-
-        result = g.rolling(2).B.mean()
-        tm.assert_series_equal(result, expected)
-
-        result = g.B.rolling(2).mean()
-        tm.assert_series_equal(result, expected)
-
-        result = roll_frame.B.groupby(roll_frame.A).rolling(2).mean()
-        tm.assert_series_equal(result, expected)
-
-    def test_getitem_multiple(self, roll_frame):
-        # GH 13174
-        g = roll_frame.groupby("A")
-        r = g.rolling(2, min_periods=0)
-        g_mutated = get_groupby(roll_frame, by="A")
-        expected = g_mutated.B.apply(lambda x: x.rolling(2, min_periods=0).count())
-
-        result = r.B.count()
-        tm.assert_series_equal(result, expected)
-
-        result = r.B.count()
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "f",
-        [
-            "sum",
-            "mean",
-            "min",
-            "max",
-            "count",
-            "kurt",
-            "skew",
-        ],
-    )
-    def test_rolling(self, f, roll_frame):
-        g = roll_frame.groupby("A", group_keys=False)
-        r = g.rolling(window=4)
-
-        result = getattr(r, f)()
-        expected = g.apply(lambda x: getattr(x.rolling(4), f)())
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("f", ["std", "var"])
-    def test_rolling_ddof(self, f, roll_frame):
-        g = roll_frame.groupby("A", group_keys=False)
-        r = g.rolling(window=4)
-
-        result = getattr(r, f)(ddof=1)
-        expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
-    )
-    def test_rolling_quantile(self, interpolation, roll_frame):
-        g = roll_frame.groupby("A", group_keys=False)
-        r = g.rolling(window=4)
-
-        result = r.quantile(0.4, interpolation=interpolation)
-        expected = g.apply(
-            lambda x: x.rolling(4).quantile(0.4, interpolation=interpolation)
-        )
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("f, expected_val", [["corr", 1], ["cov", 0.5]])
-    def test_rolling_corr_cov_other_same_size_as_groups(self, f, expected_val):
-        # GH 42915
-        df = DataFrame(
-            {"value": range(10), "idx1": [1] * 5 + [2] * 5, "idx2": [1, 2, 3, 4, 5] * 2}
-        ).set_index(["idx1", "idx2"])
-        other = DataFrame({"value": range(5), "idx2": [1, 2, 3, 4, 5]}).set_index(
-            "idx2"
-        )
-        result = getattr(df.groupby(level=0).rolling(2), f)(other)
-        expected_data = ([np.nan] + [expected_val] * 4) * 2
-        expected = DataFrame(
-            expected_data,
-            columns=["value"],
-            index=MultiIndex.from_arrays(
-                [
-                    [1] * 5 + [2] * 5,
-                    [1] * 5 + [2] * 5,
-                    list(range(1, 6)) * 2,
-                ],
-                names=["idx1", "idx1", "idx2"],
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("f", ["corr", "cov"])
-    def test_rolling_corr_cov_other_diff_size_as_groups(self, f, roll_frame):
-        g = roll_frame.groupby("A")
-        r = g.rolling(window=4)
-
-        result = getattr(r, f)(roll_frame)
-
-        def func(x):
-            return getattr(x.rolling(4), f)(roll_frame)
-
-        expected = g.apply(func)
-        # GH 39591: The grouped column should be all np.nan
-        # (groupby.apply inserts 0s for cov)
-        expected["A"] = np.nan
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("f", ["corr", "cov"])
-    def test_rolling_corr_cov_pairwise(self, f, roll_frame):
-        g = roll_frame.groupby("A")
-        r = g.rolling(window=4)
-
-        result = getattr(r.B, f)(pairwise=True)
-
-        def func(x):
-            return getattr(x.B.rolling(4), f)(pairwise=True)
-
-        expected = g.apply(func)
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "func, expected_values",
-        [("cov", [[1.0, 1.0], [1.0, 4.0]]), ("corr", [[1.0, 0.5], [0.5, 1.0]])],
-    )
-    def test_rolling_corr_cov_unordered(self, func, expected_values):
-        # GH 43386
-        df = DataFrame(
-            {
-                "a": ["g1", "g2", "g1", "g1"],
-                "b": [0, 0, 1, 2],
-                "c": [2, 0, 6, 4],
-            }
-        )
-        rol = df.groupby("a").rolling(3)
-        result = getattr(rol, func)()
-        expected = DataFrame(
-            {
-                "b": 4 * [np.nan] + expected_values[0] + 2 * [np.nan],
-                "c": 4 * [np.nan] + expected_values[1] + 2 * [np.nan],
-            },
-            index=MultiIndex.from_tuples(
-                [
-                    ("g1", 0, "b"),
-                    ("g1", 0, "c"),
-                    ("g1", 2, "b"),
-                    ("g1", 2, "c"),
-                    ("g1", 3, "b"),
-                    ("g1", 3, "c"),
-                    ("g2", 1, "b"),
-                    ("g2", 1, "c"),
-                ],
-                names=["a", None, None],
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_rolling_apply(self, raw, roll_frame):
-        g = roll_frame.groupby("A", group_keys=False)
-        r = g.rolling(window=4)
-
-        # reduction
-        result = r.apply(lambda x: x.sum(), raw=raw)
-        expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum(), raw=raw))
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    def test_rolling_apply_mutability(self):
-        # GH 14013
-        df = DataFrame({"A": ["foo"] * 3 + ["bar"] * 3, "B": [1] * 6})
-        g = df.groupby("A")
-
-        mi = MultiIndex.from_tuples(
-            [("bar", 3), ("bar", 4), ("bar", 5), ("foo", 0), ("foo", 1), ("foo", 2)]
-        )
-
-        mi.names = ["A", None]
-        # Grouped column should not be a part of the output
-        expected = DataFrame([np.nan, 2.0, 2.0] * 2, columns=["B"], index=mi)
-
-        result = g.rolling(window=2).sum()
-        tm.assert_frame_equal(result, expected)
-
-        # Call an arbitrary function on the groupby
-        g.sum()
-
-        # Make sure nothing has been mutated
-        result = g.rolling(window=2).sum()
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("expected_value,raw_value", [[1.0, True], [0.0, False]])
-    def test_groupby_rolling(self, expected_value, raw_value):
-        # GH 31754
-
-        def isnumpyarray(x):
-            return int(isinstance(x, np.ndarray))
-
-        df = DataFrame({"id": [1, 1, 1], "value": [1, 2, 3]})
-        result = df.groupby("id").value.rolling(1).apply(isnumpyarray, raw=raw_value)
-        expected = Series(
-            [expected_value] * 3,
-            index=MultiIndex.from_tuples(((1, 0), (1, 1), (1, 2)), names=["id", None]),
-            name="value",
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_groupby_rolling_center_center(self):
-        # GH 35552
-        series = Series(range(1, 6))
-        result = series.groupby(series).rolling(center=True, window=3).mean()
-        expected = Series(
-            [np.nan] * 5,
-            index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3), (5, 4))),
-        )
-        tm.assert_series_equal(result, expected)
-
-        series = Series(range(1, 5))
-        result = series.groupby(series).rolling(center=True, window=3).mean()
-        expected = Series(
-            [np.nan] * 4,
-            index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3))),
-        )
-        tm.assert_series_equal(result, expected)
-
-        df = DataFrame({"a": ["a"] * 5 + ["b"] * 6, "b": range(11)})
-        result = df.groupby("a").rolling(center=True, window=3).mean()
-        expected = DataFrame(
-            [np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, 9, np.nan],
-            index=MultiIndex.from_tuples(
-                (
-                    ("a", 0),
-                    ("a", 1),
-                    ("a", 2),
-                    ("a", 3),
-                    ("a", 4),
-                    ("b", 5),
-                    ("b", 6),
-                    ("b", 7),
-                    ("b", 8),
-                    ("b", 9),
-                    ("b", 10),
-                ),
-                names=["a", None],
-            ),
-            columns=["b"],
-        )
-        tm.assert_frame_equal(result, expected)
-
-        df = DataFrame({"a": ["a"] * 5 + ["b"] * 5, "b": range(10)})
-        result = df.groupby("a").rolling(center=True, window=3).mean()
-        expected = DataFrame(
-            [np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, np.nan],
-            index=MultiIndex.from_tuples(
-                (
-                    ("a", 0),
-                    ("a", 1),
-                    ("a", 2),
-                    ("a", 3),
-                    ("a", 4),
-                    ("b", 5),
-                    ("b", 6),
-                    ("b", 7),
-                    ("b", 8),
-                    ("b", 9),
-                ),
-                names=["a", None],
-            ),
-            columns=["b"],
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_rolling_center_on(self):
-        # GH 37141
-        df = DataFrame(
-            data={
-                "Date": date_range("2020-01-01", "2020-01-10"),
-                "gb": ["group_1"] * 6 + ["group_2"] * 4,
-                "value": range(10),
-            }
-        )
-        result = (
-            df.groupby("gb")
-            .rolling(6, on="Date", center=True, min_periods=1)
-            .value.mean()
-        )
-        expected = Series(
-            [1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 7.0, 7.5, 7.5, 7.5],
-            name="value",
-            index=MultiIndex.from_tuples(
-                (
-                    ("group_1", Timestamp("2020-01-01")),
-                    ("group_1", Timestamp("2020-01-02")),
-                    ("group_1", Timestamp("2020-01-03")),
-                    ("group_1", Timestamp("2020-01-04")),
-                    ("group_1", Timestamp("2020-01-05")),
-                    ("group_1", Timestamp("2020-01-06")),
-                    ("group_2", Timestamp("2020-01-07")),
-                    ("group_2", Timestamp("2020-01-08")),
-                    ("group_2", Timestamp("2020-01-09")),
-                    ("group_2", Timestamp("2020-01-10")),
-                ),
-                names=["gb", "Date"],
-            ),
-        )
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("min_periods", [5, 4, 3])
-    def test_groupby_rolling_center_min_periods(self, min_periods):
-        # GH 36040
-        df = DataFrame({"group": ["A"] * 10 + ["B"] * 10, "data": range(20)})
-
-        window_size = 5
-        result = (
-            df.groupby("group")
-            .rolling(window_size, center=True, min_periods=min_periods)
-            .mean()
-        )
-        result = result.reset_index()[["group", "data"]]
-
-        grp_A_mean = [1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 7.5, 8.0]
-        grp_B_mean = [x + 10.0 for x in grp_A_mean]
-
-        num_nans = max(0, min_periods - 3)  # For window_size of 5
-        nans = [np.nan] * num_nans
-        grp_A_expected = nans + grp_A_mean[num_nans : 10 - num_nans] + nans
-        grp_B_expected = nans + grp_B_mean[num_nans : 10 - num_nans] + nans
-
-        expected = DataFrame(
-            {"group": ["A"] * 10 + ["B"] * 10, "data": grp_A_expected + grp_B_expected}
-        )
-
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_subselect_rolling(self):
-        # GH 35486
-        df = DataFrame(
-            {"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0], "c": [10, 20, 30, 20]}
-        )
-        result = df.groupby("a")[["b"]].rolling(2).max()
-        expected = DataFrame(
-            [np.nan, np.nan, 2.0, np.nan],
-            columns=["b"],
-            index=MultiIndex.from_tuples(
-                ((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None]
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-        result = df.groupby("a")["b"].rolling(2).max()
-        expected = Series(
-            [np.nan, np.nan, 2.0, np.nan],
-            index=MultiIndex.from_tuples(
-                ((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None]
-            ),
-            name="b",
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_groupby_rolling_custom_indexer(self):
-        # GH 35557
-        class SimpleIndexer(BaseIndexer):
-            def get_window_bounds(
-                self,
-                num_values=0,
-                min_periods=None,
-                center=None,
-                closed=None,
-                step=None,
-            ):
-                min_periods = self.window_size if min_periods is None else 0
-                end = np.arange(num_values, dtype=np.int64) + 1
-                start = end.copy() - self.window_size
-                start[start < 0] = min_periods
-                return start, end
-
-        df = DataFrame(
-            {"a": [1.0, 2.0, 3.0, 4.0, 5.0] * 3}, index=[0] * 5 + [1] * 5 + [2] * 5
-        )
-        result = (
-            df.groupby(df.index)
-            .rolling(SimpleIndexer(window_size=3), min_periods=1)
-            .sum()
-        )
-        expected = df.groupby(df.index).rolling(window=3, min_periods=1).sum()
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_rolling_subset_with_closed(self):
-        # GH 35549
-        df = DataFrame(
-            {
-                "column1": range(6),
-                "column2": range(6),
-                "group": 3 * ["A", "B"],
-                "date": [Timestamp("2019-01-01")] * 6,
-            }
-        )
-        result = (
-            df.groupby("group").rolling("1D", on="date", closed="left")["column1"].sum()
-        )
-        expected = Series(
-            [np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
-            index=MultiIndex.from_tuples(
-                [("A", Timestamp("2019-01-01"))] * 3
-                + [("B", Timestamp("2019-01-01"))] * 3,
-                names=["group", "date"],
-            ),
-            name="column1",
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_groupby_subset_rolling_subset_with_closed(self):
-        # GH 35549
-        df = DataFrame(
-            {
-                "column1": range(6),
-                "column2": range(6),
-                "group": 3 * ["A", "B"],
-                "date": [Timestamp("2019-01-01")] * 6,
-            }
-        )
-
-        result = (
-            df.groupby("group")[["column1", "date"]]
-            .rolling("1D", on="date", closed="left")["column1"]
-            .sum()
-        )
-        expected = Series(
-            [np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
-            index=MultiIndex.from_tuples(
-                [("A", Timestamp("2019-01-01"))] * 3
-                + [("B", Timestamp("2019-01-01"))] * 3,
-                names=["group", "date"],
-            ),
-            name="column1",
-        )
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("func", ["max", "min"])
-    def test_groupby_rolling_index_changed(self, func):
-        # GH: #36018 nlevels of MultiIndex changed
-        ds = Series(
-            [1, 2, 2],
-            index=MultiIndex.from_tuples(
-                [("a", "x"), ("a", "y"), ("c", "z")], names=["1", "2"]
-            ),
-            name="a",
-        )
-
-        result = getattr(ds.groupby(ds).rolling(2), func)()
-        expected = Series(
-            [np.nan, np.nan, 2.0],
-            index=MultiIndex.from_tuples(
-                [(1, "a", "x"), (2, "a", "y"), (2, "c", "z")], names=["a", "1", "2"]
-            ),
-            name="a",
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_groupby_rolling_empty_frame(self):
-        # GH 36197
-        expected = DataFrame({"s1": []})
-        result = expected.groupby("s1").rolling(window=1).sum()
-        # GH 32262
-        expected = expected.drop(columns="s1")
-        # GH-38057 from_tuples gives empty object dtype, we now get float/int levels
-        # expected.index = MultiIndex.from_tuples([], names=["s1", None])
-        expected.index = MultiIndex.from_product(
-            [Index([], dtype="float64"), Index([], dtype="int64")], names=["s1", None]
-        )
-        tm.assert_frame_equal(result, expected)
-
-        expected = DataFrame({"s1": [], "s2": []})
-        result = expected.groupby(["s1", "s2"]).rolling(window=1).sum()
-        # GH 32262
-        expected = expected.drop(columns=["s1", "s2"])
-        expected.index = MultiIndex.from_product(
-            [
-                Index([], dtype="float64"),
-                Index([], dtype="float64"),
-                Index([], dtype="int64"),
-            ],
-            names=["s1", "s2", None],
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_rolling_string_index(self):
-        # GH: 36727
-        df = DataFrame(
-            [
-                ["A", "group_1", Timestamp(2019, 1, 1, 9)],
-                ["B", "group_1", Timestamp(2019, 1, 2, 9)],
-                ["Z", "group_2", Timestamp(2019, 1, 3, 9)],
-                ["H", "group_1", Timestamp(2019, 1, 6, 9)],
-                ["E", "group_2", Timestamp(2019, 1, 20, 9)],
-            ],
-            columns=["index", "group", "eventTime"],
-        ).set_index("index")
-
-        groups = df.groupby("group")
-        df["count_to_date"] = groups.cumcount()
-        rolling_groups = groups.rolling("10d", on="eventTime")
-        result = rolling_groups.apply(lambda df: df.shape[0])
-        expected = DataFrame(
-            [
-                ["A", "group_1", Timestamp(2019, 1, 1, 9), 1.0],
-                ["B", "group_1", Timestamp(2019, 1, 2, 9), 2.0],
-                ["H", "group_1", Timestamp(2019, 1, 6, 9), 3.0],
-                ["Z", "group_2", Timestamp(2019, 1, 3, 9), 1.0],
-                ["E", "group_2", Timestamp(2019, 1, 20, 9), 1.0],
-            ],
-            columns=["index", "group", "eventTime", "count_to_date"],
-        ).set_index(["group", "index"])
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_rolling_no_sort(self):
-        # GH 36889
-        result = (
-            DataFrame({"foo": [2, 1], "bar": [2, 1]})
-            .groupby("foo", sort=False)
-            .rolling(1)
-            .min()
-        )
-        expected = DataFrame(
-            np.array([[2.0, 2.0], [1.0, 1.0]]),
-            columns=["foo", "bar"],
-            index=MultiIndex.from_tuples([(2, 0), (1, 1)], names=["foo", None]),
-        )
-        # GH 32262
-        expected = expected.drop(columns="foo")
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_rolling_count_closed_on(self):
-        # GH 35869
-        df = DataFrame(
-            {
-                "column1": range(6),
-                "column2": range(6),
-                "group": 3 * ["A", "B"],
-                "date": date_range(end="20190101", periods=6),
-            }
-        )
-        result = (
-            df.groupby("group")
-            .rolling("3d", on="date", closed="left")["column1"]
-            .count()
-        )
-        expected = Series(
-            [np.nan, 1.0, 1.0, np.nan, 1.0, 1.0],
-            name="column1",
-            index=MultiIndex.from_tuples(
-                [
-                    ("A", Timestamp("2018-12-27")),
-                    ("A", Timestamp("2018-12-29")),
-                    ("A", Timestamp("2018-12-31")),
-                    ("B", Timestamp("2018-12-28")),
-                    ("B", Timestamp("2018-12-30")),
-                    ("B", Timestamp("2019-01-01")),
-                ],
-                names=["group", "date"],
-            ),
-        )
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        ("func", "kwargs"),
-        [("rolling", {"window": 2, "min_periods": 1}), ("expanding", {})],
-    )
-    def test_groupby_rolling_sem(self, func, kwargs):
-        # GH: 26476
-        df = DataFrame(
-            [["a", 1], ["a", 2], ["b", 1], ["b", 2], ["b", 3]], columns=["a", "b"]
-        )
-        result = getattr(df.groupby("a"), func)(**kwargs).sem()
-        expected = DataFrame(
-            {"a": [np.nan] * 5, "b": [np.nan, 0.70711, np.nan, 0.70711, 0.70711]},
-            index=MultiIndex.from_tuples(
-                [("a", 0), ("a", 1), ("b", 2), ("b", 3), ("b", 4)], names=["a", None]
-            ),
-        )
-        # GH 32262
-        expected = expected.drop(columns="a")
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        ("rollings", "key"), [({"on": "a"}, "a"), ({"on": None}, "index")]
-    )
-    def test_groupby_rolling_nans_in_index(self, rollings, key):
-        # GH: 34617
-        df = DataFrame(
-            {
-                "a": to_datetime(["2020-06-01 12:00", "2020-06-01 14:00", np.nan]),
-                "b": [1, 2, 3],
-                "c": [1, 1, 1],
-            }
-        )
-        if key == "index":
-            df = df.set_index("a")
-        with pytest.raises(ValueError, match=f"{key} values must not have NaT"):
-            df.groupby("c").rolling("60min", **rollings)
-
-    @pytest.mark.parametrize("group_keys", [True, False])
-    def test_groupby_rolling_group_keys(self, group_keys):
-        # GH 37641
-        # GH 38523: GH 37641 actually was not a bug.
-        # group_keys only applies to groupby.apply directly
-        arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
-        index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
-
-        s = Series([1, 2, 3], index=index)
-        result = s.groupby(["idx1", "idx2"], group_keys=group_keys).rolling(1).mean()
-        expected = Series(
-            [1.0, 2.0, 3.0],
-            index=MultiIndex.from_tuples(
-                [
-                    ("val1", "val1", "val1", "val1"),
-                    ("val1", "val1", "val1", "val1"),
-                    ("val2", "val2", "val2", "val2"),
-                ],
-                names=["idx1", "idx2", "idx1", "idx2"],
-            ),
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_groupby_rolling_index_level_and_column_label(self):
-        # The groupby keys should not appear as a resulting column
-        arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
-        index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
-
-        df = DataFrame({"A": [1, 1, 2], "B": range(3)}, index=index)
-        result = df.groupby(["idx1", "A"]).rolling(1).mean()
-        expected = DataFrame(
-            {"B": [0.0, 1.0, 2.0]},
-            index=MultiIndex.from_tuples(
-                [
-                    ("val1", 1, "val1", "val1"),
-                    ("val1", 1, "val1", "val1"),
-                    ("val2", 2, "val2", "val2"),
-                ],
-                names=["idx1", "A", "idx1", "idx2"],
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_groupby_rolling_resulting_multiindex(self):
-        # a few different cases checking the created MultiIndex of the result
-        # https://github.com/pandas-dev/pandas/pull/38057
-
-        # grouping by 1 columns -> 2-level MI as result
-        df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4})
-        result = df.groupby("b").rolling(3).mean()
-        expected_index = MultiIndex.from_tuples(
-            [(1, 0), (1, 2), (1, 4), (1, 6), (2, 1), (2, 3), (2, 5), (2, 7)],
-            names=["b", None],
-        )
-        tm.assert_index_equal(result.index, expected_index)
-
-    def test_groupby_rolling_resulting_multiindex2(self):
-        # grouping by 2 columns -> 3-level MI as result
-        df = DataFrame({"a": np.arange(12.0), "b": [1, 2] * 6, "c": [1, 2, 3, 4] * 3})
-        result = df.groupby(["b", "c"]).rolling(2).sum()
-        expected_index = MultiIndex.from_tuples(
-            [
-                (1, 1, 0),
-                (1, 1, 4),
-                (1, 1, 8),
-                (1, 3, 2),
-                (1, 3, 6),
-                (1, 3, 10),
-                (2, 2, 1),
-                (2, 2, 5),
-                (2, 2, 9),
-                (2, 4, 3),
-                (2, 4, 7),
-                (2, 4, 11),
-            ],
-            names=["b", "c", None],
-        )
-        tm.assert_index_equal(result.index, expected_index)
-
-    def test_groupby_rolling_resulting_multiindex3(self):
-        # grouping with 1 level on dataframe with 2-level MI -> 3-level MI as result
-        df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4, "c": [1, 2, 3, 4] * 2})
-        df = df.set_index("c", append=True)
-        result = df.groupby("b").rolling(3).mean()
-        expected_index = MultiIndex.from_tuples(
-            [
-                (1, 0, 1),
-                (1, 2, 3),
-                (1, 4, 1),
-                (1, 6, 3),
-                (2, 1, 2),
-                (2, 3, 4),
-                (2, 5, 2),
-                (2, 7, 4),
-            ],
-            names=["b", None, "c"],
-        )
-        tm.assert_index_equal(result.index, expected_index, exact="equiv")
-
-    def test_groupby_rolling_object_doesnt_affect_groupby_apply(self, roll_frame):
-        # GH 39732
-        g = roll_frame.groupby("A", group_keys=False)
-        expected = g.apply(lambda x: x.rolling(4).sum()).index
-        _ = g.rolling(window=4)
-        result = g.apply(lambda x: x.rolling(4).sum()).index
-        tm.assert_index_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        ("window", "min_periods", "closed", "expected"),
-        [
-            (2, 0, "left", [None, 0.0, 1.0, 1.0, None, 0.0, 1.0, 1.0]),
-            (2, 2, "left", [None, None, 1.0, 1.0, None, None, 1.0, 1.0]),
-            (4, 4, "left", [None, None, None, None, None, None, None, None]),
-            (4, 4, "right", [None, None, None, 5.0, None, None, None, 5.0]),
-        ],
-    )
-    def test_groupby_rolling_var(self, window, min_periods, closed, expected):
-        df = DataFrame([1, 2, 3, 4, 5, 6, 7, 8])
-        result = (
-            df.groupby([1, 2, 1, 2, 1, 2, 1, 2])
-            .rolling(window=window, min_periods=min_periods, closed=closed)
-            .var(0)
-        )
-        expected_result = DataFrame(
-            np.array(expected, dtype="float64"),
-            index=MultiIndex(
-                levels=[np.array([1, 2]), [0, 1, 2, 3, 4, 5, 6, 7]],
-                codes=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 2, 4, 6, 1, 3, 5, 7]],
-            ),
-        )
-        tm.assert_frame_equal(result, expected_result)
-
-    @pytest.mark.parametrize(
-        "columns", [MultiIndex.from_tuples([("A", ""), ("B", "C")]), ["A", "B"]]
-    )
-    def test_by_column_not_in_values(self, columns):
-        # GH 32262
-        df = DataFrame([[1, 0]] * 20 + [[2, 0]] * 12 + [[3, 0]] * 8, columns=columns)
-        g = df.groupby("A")
-        original_obj = g.obj.copy(deep=True)
-        r = g.rolling(4)
-        result = r.sum()
-        assert "A" not in result.columns
-        tm.assert_frame_equal(g.obj, original_obj)
-
-    def test_groupby_level(self):
-        # GH 38523, 38787
-        arrays = [
-            ["Falcon", "Falcon", "Parrot", "Parrot"],
-            ["Captive", "Wild", "Captive", "Wild"],
-        ]
-        index = MultiIndex.from_arrays(arrays, names=("Animal", "Type"))
-        df = DataFrame({"Max Speed": [390.0, 350.0, 30.0, 20.0]}, index=index)
-        result = df.groupby(level=0)["Max Speed"].rolling(2).sum()
-        expected = Series(
-            [np.nan, 740.0, np.nan, 50.0],
-            index=MultiIndex.from_tuples(
-                [
-                    ("Falcon", "Falcon", "Captive"),
-                    ("Falcon", "Falcon", "Wild"),
-                    ("Parrot", "Parrot", "Captive"),
-                    ("Parrot", "Parrot", "Wild"),
-                ],
-                names=["Animal", "Animal", "Type"],
-            ),
-            name="Max Speed",
-        )
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "by, expected_data",
-        [
-            [["id"], {"num": [100.0, 150.0, 150.0, 200.0]}],
-            [
-                ["id", "index"],
-                {
-                    "date": [
-                        Timestamp("2018-01-01"),
-                        Timestamp("2018-01-02"),
-                        Timestamp("2018-01-01"),
-                        Timestamp("2018-01-02"),
-                    ],
-                    "num": [100.0, 200.0, 150.0, 250.0],
-                },
-            ],
-        ],
-    )
-    def test_as_index_false(self, by, expected_data):
-        # GH 39433
-        data = [
-            ["A", "2018-01-01", 100.0],
-            ["A", "2018-01-02", 200.0],
-            ["B", "2018-01-01", 150.0],
-            ["B", "2018-01-02", 250.0],
-        ]
-        df = DataFrame(data, columns=["id", "date", "num"])
-        df["date"] = to_datetime(df["date"])
-        df = df.set_index(["date"])
-
-        gp_by = [getattr(df, attr) for attr in by]
-        result = (
-            df.groupby(gp_by, as_index=False).rolling(window=2, min_periods=1).mean()
-        )
-
-        expected = {"id": ["A", "A", "B", "B"]}
-        expected.update(expected_data)
-        expected = DataFrame(
-            expected,
-            index=df.index,
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_nan_and_zero_endpoints(self, any_int_numpy_dtype):
-        # https://github.com/twosigma/pandas/issues/53
-        typ = np.dtype(any_int_numpy_dtype).type
-        size = 1000
-        idx = np.repeat(typ(0), size)
-        idx[-1] = 1
-
-        val = 5e25
-        arr = np.repeat(val, size)
-        arr[0] = np.nan
-        arr[-1] = 0
-
-        df = DataFrame(
-            {
-                "index": idx,
-                "adl2": arr,
-            }
-        ).set_index("index")
-        result = df.groupby("index")["adl2"].rolling(window=10, min_periods=1).mean()
-        expected = Series(
-            arr,
-            name="adl2",
-            index=MultiIndex.from_arrays(
-                [
-                    Index([0] * 999 + [1], dtype=typ, name="index"),
-                    Index([0] * 999 + [1], dtype=typ, name="index"),
-                ],
-            ),
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_groupby_rolling_non_monotonic(self):
-        # GH 43909
-
-        shuffled = [3, 0, 1, 2]
-        sec = 1_000
-        df = DataFrame(
-            [{"t": Timestamp(2 * x * sec), "x": x + 1, "c": 42} for x in shuffled]
-        )
-        with pytest.raises(ValueError, match=r".* must be monotonic"):
-            df.groupby("c").rolling(on="t", window="3s")
-
-    def test_groupby_monotonic(self):
-        # GH 15130
-        # we don't need to validate monotonicity when grouping
-
-        # GH 43909 we should raise an error here to match
-        # behaviour of non-groupby rolling.
-
-        data = [
-            ["David", "1/1/2015", 100],
-            ["David", "1/5/2015", 500],
-            ["David", "5/30/2015", 50],
-            ["David", "7/25/2015", 50],
-            ["Ryan", "1/4/2014", 100],
-            ["Ryan", "1/19/2015", 500],
-            ["Ryan", "3/31/2016", 50],
-            ["Joe", "7/1/2015", 100],
-            ["Joe", "9/9/2015", 500],
-            ["Joe", "10/15/2015", 50],
-        ]
-
-        df = DataFrame(data=data, columns=["name", "date", "amount"])
-        df["date"] = to_datetime(df["date"])
-        df = df.sort_values("date")
-
-        expected = (
-            df.set_index("date")
-            .groupby("name")
-            .apply(lambda x: x.rolling("180D")["amount"].sum())
-        )
-        result = df.groupby("name").rolling("180D", on="date")["amount"].sum()
-        tm.assert_series_equal(result, expected)
-
-    def test_datelike_on_monotonic_within_each_group(self):
-        # GH 13966 (similar to #15130, closed by #15175)
-
-        # superseded by 43909
-        # GH 46061: OK if the on is monotonic relative to each each group
-
-        dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s")
-        df = DataFrame(
-            {
-                "A": [1] * 20 + [2] * 12 + [3] * 8,
-                "B": np.concatenate((dates, dates)),
-                "C": np.arange(40),
-            }
-        )
-
-        expected = (
-            df.set_index("B").groupby("A").apply(lambda x: x.rolling("4s")["C"].mean())
-        )
-        result = df.groupby("A").rolling("4s", on="B").C.mean()
-        tm.assert_series_equal(result, expected)
-
-    def test_datelike_on_not_monotonic_within_each_group(self):
-        # GH 46061
-        df = DataFrame(
-            {
-                "A": [1] * 3 + [2] * 3,
-                "B": [Timestamp(year, 1, 1) for year in [2020, 2021, 2019]] * 2,
-                "C": range(6),
-            }
-        )
-        with pytest.raises(ValueError, match="Each group within B must be monotonic."):
-            df.groupby("A").rolling("365D", on="B")
-
-
-class TestExpanding:
-    @pytest.fixture
-    def frame(self):
-        return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
-
-    @pytest.mark.parametrize(
-        "f", ["sum", "mean", "min", "max", "count", "kurt", "skew"]
-    )
-    def test_expanding(self, f, frame):
-        g = frame.groupby("A", group_keys=False)
-        r = g.expanding()
-
-        result = getattr(r, f)()
-        expected = g.apply(lambda x: getattr(x.expanding(), f)())
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("f", ["std", "var"])
-    def test_expanding_ddof(self, f, frame):
-        g = frame.groupby("A", group_keys=False)
-        r = g.expanding()
-
-        result = getattr(r, f)(ddof=0)
-        expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
-    )
-    def test_expanding_quantile(self, interpolation, frame):
-        g = frame.groupby("A", group_keys=False)
-        r = g.expanding()
-
-        result = r.quantile(0.4, interpolation=interpolation)
-        expected = g.apply(
-            lambda x: x.expanding().quantile(0.4, interpolation=interpolation)
-        )
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("f", ["corr", "cov"])
-    def test_expanding_corr_cov(self, f, frame):
-        g = frame.groupby("A")
-        r = g.expanding()
-
-        result = getattr(r, f)(frame)
-
-        def func_0(x):
-            return getattr(x.expanding(), f)(frame)
-
-        expected = g.apply(func_0)
-        # GH 39591: groupby.apply returns 1 instead of nan for windows
-        # with all nan values
-        null_idx = list(range(20, 61)) + list(range(72, 113))
-        expected.iloc[null_idx, 1] = np.nan
-        # GH 39591: The grouped column should be all np.nan
-        # (groupby.apply inserts 0s for cov)
-        expected["A"] = np.nan
-        tm.assert_frame_equal(result, expected)
-
-        result = getattr(r.B, f)(pairwise=True)
-
-        def func_1(x):
-            return getattr(x.B.expanding(), f)(pairwise=True)
-
-        expected = g.apply(func_1)
-        tm.assert_series_equal(result, expected)
-
-    def test_expanding_apply(self, raw, frame):
-        g = frame.groupby("A", group_keys=False)
-        r = g.expanding()
-
-        # reduction
-        result = r.apply(lambda x: x.sum(), raw=raw)
-        expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw))
-        # groupby.apply doesn't drop the grouped-by column
-        expected = expected.drop("A", axis=1)
-        # GH 39732
-        expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
-        expected.index = expected_index
-        tm.assert_frame_equal(result, expected)
-
-
-class TestEWM:
-    @pytest.mark.parametrize(
-        "method, expected_data",
-        [
-            ["mean", [0.0, 0.6666666666666666, 1.4285714285714286, 2.2666666666666666]],
-            ["std", [np.nan, 0.707107, 0.963624, 1.177164]],
-            ["var", [np.nan, 0.5, 0.9285714285714286, 1.3857142857142857]],
-        ],
-    )
-    def test_methods(self, method, expected_data):
-        # GH 16037
-        df = DataFrame({"A": ["a"] * 4, "B": range(4)})
-        result = getattr(df.groupby("A").ewm(com=1.0), method)()
-        expected = DataFrame(
-            {"B": expected_data},
-            index=MultiIndex.from_tuples(
-                [
-                    ("a", 0),
-                    ("a", 1),
-                    ("a", 2),
-                    ("a", 3),
-                ],
-                names=["A", None],
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "method, expected_data",
-        [["corr", [np.nan, 1.0, 1.0, 1]], ["cov", [np.nan, 0.5, 0.928571, 1.385714]]],
-    )
-    def test_pairwise_methods(self, method, expected_data):
-        # GH 16037
-        df = DataFrame({"A": ["a"] * 4, "B": range(4)})
-        result = getattr(df.groupby("A").ewm(com=1.0), method)()
-        expected = DataFrame(
-            {"B": expected_data},
-            index=MultiIndex.from_tuples(
-                [
-                    ("a", 0, "B"),
-                    ("a", 1, "B"),
-                    ("a", 2, "B"),
-                    ("a", 3, "B"),
-                ],
-                names=["A", None, None],
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.groupby("A").apply(lambda x: getattr(x.ewm(com=1.0), method)())
-        tm.assert_frame_equal(result, expected)
-
-    def test_times(self, times_frame):
-        # GH 40951
-        halflife = "23 days"
-        # GH#42738
-        times = times_frame.pop("C")
-        result = times_frame.groupby("A").ewm(halflife=halflife, times=times).mean()
-        expected = DataFrame(
-            {
-                "B": [
-                    0.0,
-                    0.507534,
-                    1.020088,
-                    1.537661,
-                    0.0,
-                    0.567395,
-                    1.221209,
-                    0.0,
-                    0.653141,
-                    1.195003,
-                ]
-            },
-            index=MultiIndex.from_tuples(
-                [
-                    ("a", 0),
-                    ("a", 3),
-                    ("a", 6),
-                    ("a", 9),
-                    ("b", 1),
-                    ("b", 4),
-                    ("b", 7),
-                    ("c", 2),
-                    ("c", 5),
-                    ("c", 8),
-                ],
-                names=["A", None],
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_times_array(self, times_frame):
-        # GH 40951
-        halflife = "23 days"
-        times = times_frame.pop("C")
-        gb = times_frame.groupby("A")
-        result = gb.ewm(halflife=halflife, times=times).mean()
-        expected = gb.ewm(halflife=halflife, times=times.values).mean()
-        tm.assert_frame_equal(result, expected)
-
-    def test_dont_mutate_obj_after_slicing(self):
-        # GH 43355
-        df = DataFrame(
-            {
-                "id": ["a", "a", "b", "b", "b"],
-                "timestamp": date_range("2021-9-1", periods=5, freq="H"),
-                "y": range(5),
-            }
-        )
-        grp = df.groupby("id").rolling("1H", on="timestamp")
-        result = grp.count()
-        expected_df = DataFrame(
-            {
-                "timestamp": date_range("2021-9-1", periods=5, freq="H"),
-                "y": [1.0] * 5,
-            },
-            index=MultiIndex.from_arrays(
-                [["a", "a", "b", "b", "b"], list(range(5))], names=["id", None]
-            ),
-        )
-        tm.assert_frame_equal(result, expected_df)
-
-        result = grp["y"].count()
-        expected_series = Series(
-            [1.0] * 5,
-            index=MultiIndex.from_arrays(
-                [
-                    ["a", "a", "b", "b", "b"],
-                    date_range("2021-9-1", periods=5, freq="H"),
-                ],
-                names=["id", "timestamp"],
-            ),
-            name="y",
-        )
-        tm.assert_series_equal(result, expected_series)
-        # This is the key test
-        result = grp.count()
-        tm.assert_frame_equal(result, expected_df)
-
-
-def test_rolling_corr_with_single_integer_in_index():
-    # GH 44078
-    df = DataFrame({"a": [(1,), (1,), (1,)], "b": [4, 5, 6]})
-    gb = df.groupby(["a"])
-    result = gb.rolling(2).corr(other=df)
-    index = MultiIndex.from_tuples([((1,), 0), ((1,), 1), ((1,), 2)], names=["a", None])
-    expected = DataFrame(
-        {"a": [np.nan, np.nan, np.nan], "b": [np.nan, 1.0, 1.0]}, index=index
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_corr_with_tuples_in_index():
-    # GH 44078
-    df = DataFrame(
-        {
-            "a": [
-                (
-                    1,
-                    2,
-                ),
-                (
-                    1,
-                    2,
-                ),
-                (
-                    1,
-                    2,
-                ),
-            ],
-            "b": [4, 5, 6],
-        }
-    )
-    gb = df.groupby(["a"])
-    result = gb.rolling(2).corr(other=df)
-    index = MultiIndex.from_tuples(
-        [((1, 2), 0), ((1, 2), 1), ((1, 2), 2)], names=["a", None]
-    )
-    expected = DataFrame(
-        {"a": [np.nan, np.nan, np.nan], "b": [np.nan, 1.0, 1.0]}, index=index
-    )
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/resolution/resolvelib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/resolution/resolvelib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/__main__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/__main__.py
deleted file mode 100644
index 8692d37e00b32771a40a85d206642f550e1f9eeb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/__main__.py
+++ /dev/null
@@ -1,280 +0,0 @@
-import colorsys
-import io
-from time import process_time
-
-from pip._vendor.rich import box
-from pip._vendor.rich.color import Color
-from pip._vendor.rich.console import Console, ConsoleOptions, Group, RenderableType, RenderResult
-from pip._vendor.rich.markdown import Markdown
-from pip._vendor.rich.measure import Measurement
-from pip._vendor.rich.pretty import Pretty
-from pip._vendor.rich.segment import Segment
-from pip._vendor.rich.style import Style
-from pip._vendor.rich.syntax import Syntax
-from pip._vendor.rich.table import Table
-from pip._vendor.rich.text import Text
-
-
-class ColorBox:
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        for y in range(0, 5):
-            for x in range(options.max_width):
-                h = x / options.max_width
-                l = 0.1 + ((y / 5) * 0.7)
-                r1, g1, b1 = colorsys.hls_to_rgb(h, l, 1.0)
-                r2, g2, b2 = colorsys.hls_to_rgb(h, l + 0.7 / 10, 1.0)
-                bgcolor = Color.from_rgb(r1 * 255, g1 * 255, b1 * 255)
-                color = Color.from_rgb(r2 * 255, g2 * 255, b2 * 255)
-                yield Segment("▄", Style(color=color, bgcolor=bgcolor))
-            yield Segment.line()
-
-    def __rich_measure__(
-        self, console: "Console", options: ConsoleOptions
-    ) -> Measurement:
-        return Measurement(1, options.max_width)
-
-
-def make_test_card() -> Table:
-    """Get a renderable that demonstrates a number of features."""
-    table = Table.grid(padding=1, pad_edge=True)
-    table.title = "Rich features"
-    table.add_column("Feature", no_wrap=True, justify="center", style="bold red")
-    table.add_column("Demonstration")
-
-    color_table = Table(
-        box=None,
-        expand=False,
-        show_header=False,
-        show_edge=False,
-        pad_edge=False,
-    )
-    color_table.add_row(
-        # "[bold yellow]256[/] colors or [bold green]16.7 million[/] colors [blue](if supported by your terminal)[/].",
-        (
-            "✓ [bold green]4-bit color[/]\n"
-            "✓ [bold blue]8-bit color[/]\n"
-            "✓ [bold magenta]Truecolor (16.7 million)[/]\n"
-            "✓ [bold yellow]Dumb terminals[/]\n"
-            "✓ [bold cyan]Automatic color conversion"
-        ),
-        ColorBox(),
-    )
-
-    table.add_row("Colors", color_table)
-
-    table.add_row(
-        "Styles",
-        "All ansi styles: [bold]bold[/], [dim]dim[/], [italic]italic[/italic], [underline]underline[/], [strike]strikethrough[/], [reverse]reverse[/], and even [blink]blink[/].",
-    )
-
-    lorem = "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque in metus sed sapien ultricies pretium a at justo. Maecenas luctus velit et auctor maximus."
-    lorem_table = Table.grid(padding=1, collapse_padding=True)
-    lorem_table.pad_edge = False
-    lorem_table.add_row(
-        Text(lorem, justify="left", style="green"),
-        Text(lorem, justify="center", style="yellow"),
-        Text(lorem, justify="right", style="blue"),
-        Text(lorem, justify="full", style="red"),
-    )
-    table.add_row(
-        "Text",
-        Group(
-            Text.from_markup(
-                """Word wrap text. Justify [green]left[/], [yellow]center[/], [blue]right[/] or [red]full[/].\n"""
-            ),
-            lorem_table,
-        ),
-    )
-
-    def comparison(renderable1: RenderableType, renderable2: RenderableType) -> Table:
-        table = Table(show_header=False, pad_edge=False, box=None, expand=True)
-        table.add_column("1", ratio=1)
-        table.add_column("2", ratio=1)
-        table.add_row(renderable1, renderable2)
-        return table
-
-    table.add_row(
-        "Asian\nlanguage\nsupport",
-        ":flag_for_china:  该库支持中文，日文和韩文文本！\n:flag_for_japan:  ライブラリは中国語、日本語、韓国語のテキストをサポートしています\n:flag_for_south_korea:  이 라이브러리는 중국어, 일본어 및 한국어 텍스트를 지원합니다",
-    )
-
-    markup_example = (
-        "[bold magenta]Rich[/] supports a simple [i]bbcode[/i]-like [b]markup[/b] for [yellow]color[/], [underline]style[/], and emoji! "
-        ":+1: :apple: :ant: :bear: :baguette_bread: :bus: "
-    )
-    table.add_row("Markup", markup_example)
-
-    example_table = Table(
-        show_edge=False,
-        show_header=True,
-        expand=False,
-        row_styles=["none", "dim"],
-        box=box.SIMPLE,
-    )
-    example_table.add_column("[green]Date", style="green", no_wrap=True)
-    example_table.add_column("[blue]Title", style="blue")
-    example_table.add_column(
-        "[cyan]Production Budget",
-        style="cyan",
-        justify="right",
-        no_wrap=True,
-    )
-    example_table.add_column(
-        "[magenta]Box Office",
-        style="magenta",
-        justify="right",
-        no_wrap=True,
-    )
-    example_table.add_row(
-        "Dec 20, 2019",
-        "Star Wars: The Rise of Skywalker",
-        "$275,000,000",
-        "$375,126,118",
-    )
-    example_table.add_row(
-        "May 25, 2018",
-        "[b]Solo[/]: A Star Wars Story",
-        "$275,000,000",
-        "$393,151,347",
-    )
-    example_table.add_row(
-        "Dec 15, 2017",
-        "Star Wars Ep. VIII: The Last Jedi",
-        "$262,000,000",
-        "[bold]$1,332,539,889[/bold]",
-    )
-    example_table.add_row(
-        "May 19, 1999",
-        "Star Wars Ep. [b]I[/b]: [i]The phantom Menace",
-        "$115,000,000",
-        "$1,027,044,677",
-    )
-
-    table.add_row("Tables", example_table)
-
-    code = '''\
-def iter_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'''
-
-    pretty_data = {
-        "foo": [
-            3.1427,
-            (
-                "Paul Atreides",
-                "Vladimir Harkonnen",
-                "Thufir Hawat",
-            ),
-        ],
-        "atomic": (False, True, None),
-    }
-    table.add_row(
-        "Syntax\nhighlighting\n&\npretty\nprinting",
-        comparison(
-            Syntax(code, "python3", line_numbers=True, indent_guides=True),
-            Pretty(pretty_data, indent_guides=True),
-        ),
-    )
-
-    markdown_example = """\
-# Markdown
-
-Supports much of the *markdown* __syntax__!
-
-- Headers
-- Basic formatting: **bold**, *italic*, `code`
-- Block quotes
-- Lists, and more...
-    """
-    table.add_row(
-        "Markdown", comparison("[cyan]" + markdown_example, Markdown(markdown_example))
-    )
-
-    table.add_row(
-        "+more!",
-        """Progress bars, columns, styled logging handler, tracebacks, etc...""",
-    )
-    return table
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    console = Console(
-        file=io.StringIO(),
-        force_terminal=True,
-    )
-    test_card = make_test_card()
-
-    # Print once to warm cache
-    start = process_time()
-    console.print(test_card)
-    pre_cache_taken = round((process_time() - start) * 1000.0, 1)
-
-    console.file = io.StringIO()
-
-    start = process_time()
-    console.print(test_card)
-    taken = round((process_time() - start) * 1000.0, 1)
-
-    text = console.file.getvalue()
-    # https://bugs.python.org/issue37871
-    for line in text.splitlines(True):
-        print(line, end="")
-
-    print(f"rendered in {pre_cache_taken}ms (cold cache)")
-    print(f"rendered in {taken}ms (warm cache)")
-
-    from pip._vendor.rich.panel import Panel
-
-    console = Console()
-
-    sponsor_message = Table.grid(padding=1)
-    sponsor_message.add_column(style="green", justify="right")
-    sponsor_message.add_column(no_wrap=True)
-
-    sponsor_message.add_row(
-        "Buy devs a :coffee:",
-        "[u blue link=https://ko-fi.com/textualize]https://ko-fi.com/textualize",
-    )
-    sponsor_message.add_row(
-        "Twitter",
-        "[u blue link=https://twitter.com/willmcgugan]https://twitter.com/willmcgugan",
-    )
-    sponsor_message.add_row(
-        "Blog", "[u blue link=https://www.willmcgugan.com]https://www.willmcgugan.com"
-    )
-
-    intro_message = Text.from_markup(
-        """\
-We hope you enjoy using Rich!
-
-Rich is maintained with :heart: by [link=https://www.textualize.io]Textualize.io[/]
-
-- Will McGugan"""
-    )
-
-    message = Table.grid(padding=2)
-    message.add_column()
-    message.add_column(no_wrap=True)
-    message.add_row(intro_message, sponsor_message)
-
-    console.print(
-        Panel.fit(
-            message,
-            box=box.ROUNDED,
-            padding=(1, 2),
-            title="[b red]Thanks for trying out Rich!",
-            border_style="bright_blue",
-        ),
-        justify="center",
-    )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/tenacity/nap.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/tenacity/nap.py
deleted file mode 100644
index 72aa5bfd4b60d8e6ef6ed0cf2ae4f763d12195cc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/tenacity/nap.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright 2016 Étienne Bersac
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import time
-import typing
-
-if typing.TYPE_CHECKING:
-    import threading
-
-
-def sleep(seconds: float) -> None:
-    """
-    Sleep strategy that delays execution for a given number of seconds.
-
-    This is the default strategy, and may be mocked out for unit testing.
-    """
-    time.sleep(seconds)
-
-
-class sleep_using_event:
-    """Sleep strategy that waits on an event to be set."""
-
-    def __init__(self, event: "threading.Event") -> None:
-        self.event = event
-
-    def __call__(self, timeout: typing.Optional[float]) -> None:
-        # NOTE(harlowja): this may *not* actually wait for timeout
-        # seconds if the event is set (ie this may eject out early).
-        self.event.wait(timeout=timeout)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/scanner.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/scanner.py
deleted file mode 100644
index 32a2f303296d1bffce917413b2e7150a5194e32f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/scanner.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""
-    pygments.scanner
-    ~~~~~~~~~~~~~~~~
-
-    This library implements a regex based scanner. Some languages
-    like Pascal are easy to parse but have some keywords that
-    depend on the context. Because of this it's impossible to lex
-    that just by using a regular expression lexer like the
-    `RegexLexer`.
-
-    Have a look at the `DelphiLexer` to get an idea of how to use
-    this scanner.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-import re
-
-
-class EndOfText(RuntimeError):
-    """
-    Raise if end of text is reached and the user
-    tried to call a match function.
-    """
-
-
-class Scanner:
-    """
-    Simple scanner
-
-    All method patterns are regular expression strings (not
-    compiled expressions!)
-    """
-
-    def __init__(self, text, flags=0):
-        """
-        :param text:    The text which should be scanned
-        :param flags:   default regular expression flags
-        """
-        self.data = text
-        self.data_length = len(text)
-        self.start_pos = 0
-        self.pos = 0
-        self.flags = flags
-        self.last = None
-        self.match = None
-        self._re_cache = {}
-
-    def eos(self):
-        """`True` if the scanner reached the end of text."""
-        return self.pos >= self.data_length
-    eos = property(eos, eos.__doc__)
-
-    def check(self, pattern):
-        """
-        Apply `pattern` on the current position and return
-        the match object. (Doesn't touch pos). Use this for
-        lookahead.
-        """
-        if self.eos:
-            raise EndOfText()
-        if pattern not in self._re_cache:
-            self._re_cache[pattern] = re.compile(pattern, self.flags)
-        return self._re_cache[pattern].match(self.data, self.pos)
-
-    def test(self, pattern):
-        """Apply a pattern on the current position and check
-        if it patches. Doesn't touch pos.
-        """
-        return self.check(pattern) is not None
-
-    def scan(self, pattern):
-        """
-        Scan the text for the given pattern and update pos/match
-        and related fields. The return value is a boolean that
-        indicates if the pattern matched. The matched value is
-        stored on the instance as ``match``, the last value is
-        stored as ``last``. ``start_pos`` is the position of the
-        pointer before the pattern was matched, ``pos`` is the
-        end position.
-        """
-        if self.eos:
-            raise EndOfText()
-        if pattern not in self._re_cache:
-            self._re_cache[pattern] = re.compile(pattern, self.flags)
-        self.last = self.match
-        m = self._re_cache[pattern].match(self.data, self.pos)
-        if m is None:
-            return False
-        self.start_pos = m.start()
-        self.pos = m.end()
-        self.match = m.group()
-        return True
-
-    def get_char(self):
-        """Scan exactly one char."""
-        self.scan('.')
-
-    def __repr__(self):
-        return '<%s %d/%d>' % (
-            self.__class__.__name__,
-            self.pos,
-            self.data_length
-        )
diff --git a/spaces/pycoming/bingo/src/components/header.tsx b/spaces/pycoming/bingo/src/components/header.tsx
deleted file mode 100644
index dc298b722154d1ac6d7a7e148204605562d6cc58..0000000000000000000000000000000000000000
--- a/spaces/pycoming/bingo/src/components/header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as React from 'react'
-import { UserMenu } from './user-menu'
-
-export async function Header() {
-  return (
-    <header className="sticky top-0 z-50 flex items-center justify-between w-full h-16 px-4 border-b shrink-0 bg-gradient-to-b from-background/10 via-background/50 to-background/80 backdrop-blur-xl">
-      <div className="flex items-center justify-end space-x-2 w-full">
-        <UserMenu />
-      </div>
-    </header>
-  )
-}
diff --git a/spaces/pytorch/AlexNet/README.md b/spaces/pytorch/AlexNet/README.md
deleted file mode 100644
index 84d0b3896c8d5ad1a9a4014b1d1a58d2a6c1cce2..0000000000000000000000000000000000000000
--- a/spaces/pytorch/AlexNet/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AlexNet
-emoji: 💩
-colorFrom: green
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git "a/spaces/qingxu98/gpt-academic/crazy_functions/\344\272\244\344\272\222\345\212\237\350\203\275\345\207\275\346\225\260\346\250\241\346\235\277.py" "b/spaces/qingxu98/gpt-academic/crazy_functions/\344\272\244\344\272\222\345\212\237\350\203\275\345\207\275\346\225\260\346\250\241\346\235\277.py"
deleted file mode 100644
index d57fc2b0f0fb604be1dc19f789815eb7833bef7f..0000000000000000000000000000000000000000
--- "a/spaces/qingxu98/gpt-academic/crazy_functions/\344\272\244\344\272\222\345\212\237\350\203\275\345\207\275\346\225\260\346\250\241\346\235\277.py"
+++ /dev/null
@@ -1,63 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-
-@CatchException
-def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
-    plugin_kwargs   插件模型的参数, 如温度和top_p等, 一般原样传递下去就行
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态
-
-    if state is None:
-        chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数'      # 赋予插件锁定 锁定插件回调路径，当下一次用户提交时，会直接转到该函数
-        chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword'                              # 赋予插件状态
-
-        chatbot.append(("第一次调用：", "请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中，请直接提交即可。"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if state == 'wait_user_keyword':
-        chatbot._cookies['lock_plugin'] = None          # 解除插件锁定，避免遗忘导致死锁
-        chatbot._cookies['plugin_state_0001'] = None    # 解除插件状态，避免遗忘导致死锁
-
-        # 解除插件锁定
-        chatbot.append((f"获取关键词：{txt}", ""))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        page_return = get_image_page_by_keyword(txt)
-        inputs=inputs_show_user=f"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \n\n {page_return}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=inputs, inputs_show_user=inputs_show_user,
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-            sys_prompt="When you want to show an image, use markdown format. e.g. ![image_description](image_url). If there are no image url provided, answer 'no image url provided'"
-        )
-        chatbot[-1] = [chatbot[-1][0], gpt_say]
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-
-
-# ---------------------------------------------------------------------------------
-
-def get_image_page_by_keyword(keyword):
-    import requests
-    from bs4 import BeautifulSoup
-    response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
-    res = "image urls: \n"
-    for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
-        try:
-            res += image_element["data-src"]
-            res += "\n"
-        except:
-            pass
-    return res
diff --git a/spaces/qinzhu/diy-girlfriend/text/cleaners.py b/spaces/qinzhu/diy-girlfriend/text/cleaners.py
deleted file mode 100644
index eedbeaee8ad73dd4aaf6c12e3f900fc34a1ee630..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/diy-girlfriend/text/cleaners.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import re
-import pyopenjtalk
-
-pyopenjtalk._lazy_init()
-
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from text.mandarin import chinese_to_romaji
-    from text.japanese import japanese_to_romaji_with_accent
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    if text[-1] != '।':
-        text += ' ।'
-    return text
-
-
-def cjks_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_lazy_ipa
-    from text.sanskrit import devanagari_to_ipa
-    from text.english import english_to_lazy_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn') + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz') + ' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    from text.mandarin import chinese_to_ipa
-    from text.japanese import japanese_to_ipa2
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    from text.thai import num_to_thai, latin_to_thai
-    text = num_to_thai(text)
-    text = latin_to_thai(text)
-    return text
-
-
-def shanghainese_cleaners(text):
-    from text.shanghainese import shanghainese_to_ipa
-    text = shanghainese_to_ipa(text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def chinese_dialect_cleaners(text):
-    from text.mandarin import chinese_to_ipa2
-    from text.japanese import japanese_to_ipa3
-    from text.shanghainese import shanghainese_to_ipa
-    from text.cantonese import cantonese_to_ipa
-    from text.english import english_to_lazy_ipa2
-    from text.ngu_dialect import ngu_dialect_to_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ') + ' ', text)
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-                                                                                                             '˧˧˦').replace(
-        '6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e') + ' ', text)
-    text = re.sub(r'\[GD\](.*?)\[GD\]',
-                  lambda x: cantonese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-        1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Basic Instinct 3 2010 Hindi Dubbed Movie Watch Online.md b/spaces/quidiaMuxgu/Expedit-SAM/Basic Instinct 3 2010 Hindi Dubbed Movie Watch Online.md
deleted file mode 100644
index c74843d471373d5168c6c4b036ae1cb75feb8041..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Basic Instinct 3 2010 Hindi Dubbed Movie Watch Online.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Basic Instinct 3 2010 Hindi Dubbed Movie Watch Online</h2><br /><p><b><b>Download Zip</b> &#10003;&#10003;&#10003; <a href="https://geags.com/2uCrJL">https://geags.com/2uCrJL</a></b></p><br /><br />
-
-Nonton & Download Film Basic Instinct (1992) BluRay 480p, 720p, & 1080p HD ... Movie Genre: Hindi Dubbed English Comedy Adventure 720p Dual. ... MKV Subtitle Download Person of Interest S03 Complete Season 480p HDTV x264 3. ... of mkv shrek Index of mkv shrek Shop online at x-cite for the best deals in Kuwait. 4d29de3e1b<br />
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Bilgi Sosyolojisi Ve Hermeneutik Pdf 14 !!TOP!!.md b/spaces/quidiaMuxgu/Expedit-SAM/Bilgi Sosyolojisi Ve Hermeneutik Pdf 14 !!TOP!!.md
deleted file mode 100644
index cd815337e9528f6b5a42e9f3eb4f35542ba28eaf..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Bilgi Sosyolojisi Ve Hermeneutik Pdf 14 !!TOP!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>bilgi sosyolojisi ve hermeneutik pdf 14</h2><br /><p><b><b>Download</b> &#10038; <a href="https://geags.com/2uCqdJ">https://geags.com/2uCqdJ</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Ediabas Inpa Software Download Deutsch __LINK__.md b/spaces/quidiaMuxgu/Expedit-SAM/Ediabas Inpa Software Download Deutsch __LINK__.md
deleted file mode 100644
index 88dfdffbcc238a20c3cc4d4ed559ed83627d6319..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Ediabas Inpa Software Download Deutsch __LINK__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ediabas Inpa Software Download Deutsch</h2><br /><p><b><b>Download Zip</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://geags.com/2uCrvG">https://geags.com/2uCrvG</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/r3gm/RVC_HF/extract_locale.py b/spaces/r3gm/RVC_HF/extract_locale.py
deleted file mode 100644
index a4ff5ea3ddd7c612c640544099ab98a861b8fe35..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/extract_locale.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import json
-import re
-
-# Define regular expression patterns
-pattern = r"""i18n\([\s\n\t]*(["'][^"']+["'])[\s\n\t]*\)"""
-
-# Initialize the dictionary to store key-value pairs
-data = {}
-
-
-def process(fn: str):
-    global data
-    with open(fn, "r", encoding="utf-8") as f:
-        contents = f.read()
-        matches = re.findall(pattern, contents)
-        for key in matches:
-            key = eval(key)
-            print("extract:", key)
-            data[key] = key
-
-
-print("processing infer-web.py")
-process("infer-web.py")
-
-print("processing gui_v0.py")
-process("gui_v0.py")
-
-print("processing gui_v1.py")
-process("gui_v1.py")
-
-# Save as a JSON file
-with open("./i18n/en_US.json", "w", encoding="utf-8") as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)
-    f.write("\n")
diff --git a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/__init__.py b/spaces/rachana219/MODT2/trackers/strongsort/deep/models/__init__.py
deleted file mode 100644
index 3c60ba6f59ca7fa5ff9f3c6a4dcef1357c353dde..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/__init__.py
+++ /dev/null
@@ -1,122 +0,0 @@
-from __future__ import absolute_import
-import torch
-
-from .pcb import *
-from .mlfn import *
-from .hacnn import *
-from .osnet import *
-from .senet import *
-from .mudeep import *
-from .nasnet import *
-from .resnet import *
-from .densenet import *
-from .xception import *
-from .osnet_ain import *
-from .resnetmid import *
-from .shufflenet import *
-from .squeezenet import *
-from .inceptionv4 import *
-from .mobilenetv2 import *
-from .resnet_ibn_a import *
-from .resnet_ibn_b import *
-from .shufflenetv2 import *
-from .inceptionresnetv2 import *
-
-__model_factory = {
-    # image classification models
-    'resnet18': resnet18,
-    'resnet34': resnet34,
-    'resnet50': resnet50,
-    'resnet101': resnet101,
-    'resnet152': resnet152,
-    'resnext50_32x4d': resnext50_32x4d,
-    'resnext101_32x8d': resnext101_32x8d,
-    'resnet50_fc512': resnet50_fc512,
-    'se_resnet50': se_resnet50,
-    'se_resnet50_fc512': se_resnet50_fc512,
-    'se_resnet101': se_resnet101,
-    'se_resnext50_32x4d': se_resnext50_32x4d,
-    'se_resnext101_32x4d': se_resnext101_32x4d,
-    'densenet121': densenet121,
-    'densenet169': densenet169,
-    'densenet201': densenet201,
-    'densenet161': densenet161,
-    'densenet121_fc512': densenet121_fc512,
-    'inceptionresnetv2': inceptionresnetv2,
-    'inceptionv4': inceptionv4,
-    'xception': xception,
-    'resnet50_ibn_a': resnet50_ibn_a,
-    'resnet50_ibn_b': resnet50_ibn_b,
-    # lightweight models
-    'nasnsetmobile': nasnetamobile,
-    'mobilenetv2_x1_0': mobilenetv2_x1_0,
-    'mobilenetv2_x1_4': mobilenetv2_x1_4,
-    'shufflenet': shufflenet,
-    'squeezenet1_0': squeezenet1_0,
-    'squeezenet1_0_fc512': squeezenet1_0_fc512,
-    'squeezenet1_1': squeezenet1_1,
-    'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
-    'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
-    'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
-    'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
-    # reid-specific models
-    'mudeep': MuDeep,
-    'resnet50mid': resnet50mid,
-    'hacnn': HACNN,
-    'pcb_p6': pcb_p6,
-    'pcb_p4': pcb_p4,
-    'mlfn': mlfn,
-    'osnet_x1_0': osnet_x1_0,
-    'osnet_x0_75': osnet_x0_75,
-    'osnet_x0_5': osnet_x0_5,
-    'osnet_x0_25': osnet_x0_25,
-    'osnet_ibn_x1_0': osnet_ibn_x1_0,
-    'osnet_ain_x1_0': osnet_ain_x1_0,
-    'osnet_ain_x0_75': osnet_ain_x0_75,
-    'osnet_ain_x0_5': osnet_ain_x0_5,
-    'osnet_ain_x0_25': osnet_ain_x0_25
-}
-
-
-def show_avai_models():
-    """Displays available models.
-
-    Examples::
-        >>> from torchreid import models
-        >>> models.show_avai_models()
-    """
-    print(list(__model_factory.keys()))
-
-
-def build_model(
-    name, num_classes, loss='softmax', pretrained=True, use_gpu=True
-):
-    """A function wrapper for building a model.
-
-    Args:
-        name (str): model name.
-        num_classes (int): number of training identities.
-        loss (str, optional): loss function to optimize the model. Currently
-            supports "softmax" and "triplet". Default is "softmax".
-        pretrained (bool, optional): whether to load ImageNet-pretrained weights.
-            Default is True.
-        use_gpu (bool, optional): whether to use gpu. Default is True.
-
-    Returns:
-        nn.Module
-
-    Examples::
-        >>> from torchreid import models
-        >>> model = models.build_model('resnet50', 751, loss='softmax')
-    """
-    avai_models = list(__model_factory.keys())
-    if name not in avai_models:
-        raise KeyError(
-            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
-        )
-    return __model_factory[name](
-        num_classes=num_classes,
-        loss=loss,
-        pretrained=pretrained,
-        use_gpu=use_gpu
-    )
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/mtcnn/mtcnn_pytorch/src/align_trans.py b/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/mtcnn/mtcnn_pytorch/src/align_trans.py
deleted file mode 100644
index ab5f1df002bc19556ae8a75cabf56310084785a9..0000000000000000000000000000000000000000
--- a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/mtcnn/mtcnn_pytorch/src/align_trans.py
+++ /dev/null
@@ -1,304 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Apr 24 15:43:29 2017
-@author: zhaoy
-"""
-import numpy as np
-import cv2
-
-# from scipy.linalg import lstsq
-# from scipy.ndimage import geometric_transform  # , map_coordinates
-
-from models.mtcnn.mtcnn_pytorch.src.matlab_cp2tform import get_similarity_transform_for_cv2
-
-# reference facial points, a list of coordinates (x,y)
-REFERENCE_FACIAL_POINTS = [
-    [30.29459953, 51.69630051],
-    [65.53179932, 51.50139999],
-    [48.02519989, 71.73660278],
-    [33.54930115, 92.3655014],
-    [62.72990036, 92.20410156]
-]
-
-DEFAULT_CROP_SIZE = (96, 112)
-
-
-class FaceWarpException(Exception):
-    def __str__(self):
-        return 'In File {}:{}'.format(
-            __file__, super.__str__(self))
-
-
-def get_reference_facial_points(output_size=None,
-                                inner_padding_factor=0.0,
-                                outer_padding=(0, 0),
-                                default_square=False):
-    """
-    Function:
-    ----------
-        get reference 5 key points according to crop settings:
-        0. Set default crop_size:
-            if default_square: 
-                crop_size = (112, 112)
-            else: 
-                crop_size = (96, 112)
-        1. Pad the crop_size by inner_padding_factor in each side;
-        2. Resize crop_size into (output_size - outer_padding*2),
-            pad into output_size with outer_padding;
-        3. Output reference_5point;
-    Parameters:
-    ----------
-        @output_size: (w, h) or None
-            size of aligned face image
-        @inner_padding_factor: (w_factor, h_factor)
-            padding factor for inner (w, h)
-        @outer_padding: (w_pad, h_pad)
-            each row is a pair of coordinates (x, y)
-        @default_square: True or False
-            if True:
-                default crop_size = (112, 112)
-            else:
-                default crop_size = (96, 112);
-        !!! make sure, if output_size is not None:
-                (output_size - outer_padding) 
-                = some_scale * (default crop_size * (1.0 + inner_padding_factor))
-    Returns:
-    ----------
-        @reference_5point: 5x2 np.array
-            each row is a pair of transformed coordinates (x, y)
-    """
-    # print('\n===> get_reference_facial_points():')
-
-    # print('---> Params:')
-    # print('            output_size: ', output_size)
-    # print('            inner_padding_factor: ', inner_padding_factor)
-    # print('            outer_padding:', outer_padding)
-    # print('            default_square: ', default_square)
-
-    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
-    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
-
-    # 0) make the inner region a square
-    if default_square:
-        size_diff = max(tmp_crop_size) - tmp_crop_size
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += size_diff
-
-    # print('---> default:')
-    # print('              crop_size = ', tmp_crop_size)
-    # print('              reference_5pts = ', tmp_5pts)
-
-    if (output_size and
-            output_size[0] == tmp_crop_size[0] and
-            output_size[1] == tmp_crop_size[1]):
-        # print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size))
-        return tmp_5pts
-
-    if (inner_padding_factor == 0 and
-            outer_padding == (0, 0)):
-        if output_size is None:
-            # print('No paddings to do: return default reference points')
-            return tmp_5pts
-        else:
-            raise FaceWarpException(
-                'No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
-
-    # check output size
-    if not (0 <= inner_padding_factor <= 1.0):
-        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
-
-    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0)
-            and output_size is None):
-        output_size = tmp_crop_size * \
-                      (1 + inner_padding_factor * 2).astype(np.int32)
-        output_size += np.array(outer_padding)
-        # print('              deduced from paddings, output_size = ', output_size)
-
-    if not (outer_padding[0] < output_size[0]
-            and outer_padding[1] < output_size[1]):
-        raise FaceWarpException('Not (outer_padding[0] < output_size[0]'
-                                'and outer_padding[1] < output_size[1])')
-
-    # 1) pad the inner region according inner_padding_factor
-    # print('---> STEP1: pad the inner region according inner_padding_factor')
-    if inner_padding_factor > 0:
-        size_diff = tmp_crop_size * inner_padding_factor * 2
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += np.round(size_diff).astype(np.int32)
-
-    # print('              crop_size = ', tmp_crop_size)
-    # print('              reference_5pts = ', tmp_5pts)
-
-    # 2) resize the padded inner region
-    # print('---> STEP2: resize the padded inner region')
-    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
-    # print('              crop_size = ', tmp_crop_size)
-    # print('              size_bf_outer_pad = ', size_bf_outer_pad)
-
-    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
-        raise FaceWarpException('Must have (output_size - outer_padding)'
-                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
-
-    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
-    # print('              resize scale_factor = ', scale_factor)
-    tmp_5pts = tmp_5pts * scale_factor
-    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
-    #    tmp_5pts = tmp_5pts + size_diff / 2
-    tmp_crop_size = size_bf_outer_pad
-    # print('              crop_size = ', tmp_crop_size)
-    # print('              reference_5pts = ', tmp_5pts)
-
-    # 3) add outer_padding to make output_size
-    reference_5point = tmp_5pts + np.array(outer_padding)
-    tmp_crop_size = output_size
-    # print('---> STEP3: add outer_padding to make output_size')
-    # print('              crop_size = ', tmp_crop_size)
-    # print('              reference_5pts = ', tmp_5pts)
-
-    # print('===> end get_reference_facial_points\n')
-
-    return reference_5point
-
-
-def get_affine_transform_matrix(src_pts, dst_pts):
-    """
-    Function:
-    ----------
-        get affine transform matrix 'tfm' from src_pts to dst_pts
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points matrix, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points matrix, each row is a pair of coordinates (x, y)
-    Returns:
-    ----------
-        @tfm: 2x3 np.array
-            transform matrix from src_pts to dst_pts
-    """
-
-    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
-    n_pts = src_pts.shape[0]
-    ones = np.ones((n_pts, 1), src_pts.dtype)
-    src_pts_ = np.hstack([src_pts, ones])
-    dst_pts_ = np.hstack([dst_pts, ones])
-
-    #    #print(('src_pts_:\n' + str(src_pts_))
-    #    #print(('dst_pts_:\n' + str(dst_pts_))
-
-    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
-
-    #    #print(('np.linalg.lstsq return A: \n' + str(A))
-    #    #print(('np.linalg.lstsq return res: \n' + str(res))
-    #    #print(('np.linalg.lstsq return rank: \n' + str(rank))
-    #    #print(('np.linalg.lstsq return s: \n' + str(s))
-
-    if rank == 3:
-        tfm = np.float32([
-            [A[0, 0], A[1, 0], A[2, 0]],
-            [A[0, 1], A[1, 1], A[2, 1]]
-        ])
-    elif rank == 2:
-        tfm = np.float32([
-            [A[0, 0], A[1, 0], 0],
-            [A[0, 1], A[1, 1], 0]
-        ])
-
-    return tfm
-
-
-def warp_and_crop_face(src_img,
-                       facial_pts,
-                       reference_pts=None,
-                       crop_size=(96, 112),
-                       align_type='smilarity'):
-    """
-    Function:
-    ----------
-        apply affine transform 'trans' to uv
-    Parameters:
-    ----------
-        @src_img: 3x3 np.array
-            input image
-        @facial_pts: could be
-            1)a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        @reference_pts: could be
-            1) a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        or
-            3) None
-            if None, use default reference facial points
-        @crop_size: (w, h)
-            output face image size
-        @align_type: transform type, could be one of
-            1) 'similarity': use similarity transform
-            2) 'cv2_affine': use the first 3 points to do affine transform,
-                    by calling cv2.getAffineTransform()
-            3) 'affine': use all points to do affine transform
-    Returns:
-    ----------
-        @face_img: output face image with size (w, h) = @crop_size
-    """
-
-    if reference_pts is None:
-        if crop_size[0] == 96 and crop_size[1] == 112:
-            reference_pts = REFERENCE_FACIAL_POINTS
-        else:
-            default_square = False
-            inner_padding_factor = 0
-            outer_padding = (0, 0)
-            output_size = crop_size
-
-            reference_pts = get_reference_facial_points(output_size,
-                                                        inner_padding_factor,
-                                                        outer_padding,
-                                                        default_square)
-
-    ref_pts = np.float32(reference_pts)
-    ref_pts_shp = ref_pts.shape
-    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
-        raise FaceWarpException(
-            'reference_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if ref_pts_shp[0] == 2:
-        ref_pts = ref_pts.T
-
-    src_pts = np.float32(facial_pts)
-    src_pts_shp = src_pts.shape
-    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
-        raise FaceWarpException(
-            'facial_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if src_pts_shp[0] == 2:
-        src_pts = src_pts.T
-
-    #    #print('--->src_pts:\n', src_pts
-    #    #print('--->ref_pts\n', ref_pts
-
-    if src_pts.shape != ref_pts.shape:
-        raise FaceWarpException(
-            'facial_pts and reference_pts must have the same shape')
-
-    if align_type is 'cv2_affine':
-        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
-    #        #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm))
-    elif align_type is 'affine':
-        tfm = get_affine_transform_matrix(src_pts, ref_pts)
-    #        #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm))
-    else:
-        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
-    #        #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm))
-
-    #    #print('--->Transform matrix: '
-    #    #print(('type(tfm):' + str(type(tfm)))
-    #    #print(('tfm.dtype:' + str(tfm.dtype))
-    #    #print( tfm
-
-    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
-
-    return face_img, tfm
diff --git a/spaces/rainy3/chatgpt_academic/crazy_functions/test_project/latex/attention/background.tex b/spaces/rainy3/chatgpt_academic/crazy_functions/test_project/latex/attention/background.tex
deleted file mode 100644
index 785069dc0f9143bad24e640056dd1072d5c6e5b5..0000000000000000000000000000000000000000
--- a/spaces/rainy3/chatgpt_academic/crazy_functions/test_project/latex/attention/background.tex
+++ /dev/null
@@ -1,58 +0,0 @@
-The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
-
-Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
-
-End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
-
-To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
-In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
-
-
-%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
-
-%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
-
-%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
-
-%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-
-
-%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
-
-%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
-
-%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
-
-%\begin{table}[h!]
-%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
-%\label{tab:op_complexities}
-%\begin{center}
-%\vspace{-5pt}
-%\scalebox{0.75}{
-
-%\begin{tabular}{l|c|c|c}
-%\hline \hline
-%Layer Type & Receptive & Complexity & Sequential  \\
-%           & Field     &            & Operations  \\
-%\hline
-%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
-%\hline
-%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
-%\hline
-%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
-%\hline \hline
-%\end{tabular}
-%}
-%\end{center}
-%\end{table}
\ No newline at end of file
diff --git a/spaces/rajeshradhakrishnan/english-malayalam/Dockerfile b/spaces/rajeshradhakrishnan/english-malayalam/Dockerfile
deleted file mode 100644
index b2a302495f094efdb61d992e2665054298186ee0..0000000000000000000000000000000000000000
--- a/spaces/rajeshradhakrishnan/english-malayalam/Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
-# Use the official Python 3.9 image
-FROM python:3.9
-
-# Set the working directory to /code
-WORKDIR /code
-
-# Copy the current directory contents into the container at /code
-COPY ./requirements.txt /code/requirements.txt
-
-# Install requirements.txt 
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-# Switch to the "user" user
-USER user
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-RUN --mount=type=secret,id=API_KEY,mode=0444,required=true \
- echo "const API_KEY = '$(cat /run/secrets/API_KEY)';" > $HOME/app/static/config.js
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/README.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/README.md
deleted file mode 100644
index cfe77ca32003b0dbea5a1dad91aaf8157b019cb1..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatGPT With Voice Cloning For All
-emoji: 🌖
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.25.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: Kevin676/ChatGPT-with-Voice-Cloning-for-All
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aff Repair Station Crack Free 41 REPACK.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aff Repair Station Crack Free 41 REPACK.md
deleted file mode 100644
index edd905f6c5b8b6c780b3d1a7b40c4bd9e8214712..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aff Repair Station Crack Free 41 REPACK.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Aff Repair Station Crack Free 41</h2><br /><p><b><b>Download File</b> &#10037;&#10037;&#10037; <a href="https://urlgoal.com/2uCJJR">https://urlgoal.com/2uCJJR</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Avatar Extended Collectors Edition 2009.720p.BrRip.x264.YIFY.mp4 !!LINK!!.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Avatar Extended Collectors Edition 2009.720p.BrRip.x264.YIFY.mp4 !!LINK!!.md
deleted file mode 100644
index 2abec7033421ab5c91cae4dce5d4752de309cf60..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Avatar Extended Collectors Edition 2009.720p.BrRip.x264.YIFY.mp4 !!LINK!!.md	
+++ /dev/null
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-<h2>Avatar Extended Collectors Edition 2009.720p.BrRip.x264.YIFY.mp4</h2><br /><p><b><b>DOWNLOAD</b> > <a href="https://urlgoal.com/2uCK5s">https://urlgoal.com/2uCK5s</a></b></p><br /><br />
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diff --git a/spaces/rinsora/White-box-Cartoonization/wbc/cartoonize.py b/spaces/rinsora/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/rinsora/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
-    h, w, c = np.shape(image)
-    if min(h, w) > 720:
-        if h > w:
-            h, w = int(720 * h / w), 720
-        else:
-            h, w = 720, int(720 * w / h)
-    image = cv2.resize(image, (w, h),
-                       interpolation=cv2.INTER_AREA)
-    h, w = (h // 8) * 8, (w // 8) * 8
-    image = image[:h, :w, :]
-    return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
-    print(model_path)
-    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-    network_out = network.unet_generator(input_photo)
-    final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
-    all_vars = tf.trainable_variables()
-    gene_vars = [var for var in all_vars if 'generator' in var.name]
-    saver = tf.train.Saver(var_list=gene_vars)
-
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    sess = tf.Session(config=config)
-
-    sess.run(tf.global_variables_initializer())
-    saver.restore(sess, tf.train.latest_checkpoint(model_path))
-    name_list = os.listdir(load_folder)
-    for name in tqdm(name_list):
-        try:
-            load_path = os.path.join(load_folder, name)
-            save_path = os.path.join(save_folder, name)
-            image = cv2.imread(load_path)
-            image = resize_crop(image)
-            batch_image = image.astype(np.float32) / 127.5 - 1
-            batch_image = np.expand_dims(batch_image, axis=0)
-            output = sess.run(final_out, feed_dict={input_photo: batch_image})
-            output = (np.squeeze(output) + 1) * 127.5
-            output = np.clip(output, 0, 255).astype(np.uint8)
-            cv2.imwrite(save_path, output)
-        except:
-            print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
-    def __init__(self, model_path):
-        print(model_path)
-        self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-        network_out = network.unet_generator(self.input_photo)
-        self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
-        all_vars = tf.trainable_variables()
-        gene_vars = [var for var in all_vars if 'generator' in var.name]
-        saver = tf.train.Saver(var_list=gene_vars)
-
-        config = tf.ConfigProto()
-        config.gpu_options.allow_growth = True
-        self.sess = tf.Session(config=config)
-
-        self.sess.run(tf.global_variables_initializer())
-        saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
-    def run(self, load_folder, save_folder):
-        name_list = os.listdir(load_folder)
-        for name in tqdm(name_list):
-            try:
-                load_path = os.path.join(load_folder, name)
-                save_path = os.path.join(save_folder, name)
-                image = cv2.imread(load_path)
-                image = resize_crop(image)
-                batch_image = image.astype(np.float32) / 127.5 - 1
-                batch_image = np.expand_dims(batch_image, axis=0)
-                output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
-                output = (np.squeeze(output) + 1) * 127.5
-                output = np.clip(output, 0, 255).astype(np.uint8)
-                cv2.imwrite(save_path, output)
-            except:
-                print('cartoonize {} failed'.format(load_path))
-
-    def run_sigle(self, load_path, save_path):
-        try:
-            image = cv2.imread(load_path)
-            image = resize_crop(image)
-            batch_image = image.astype(np.float32) / 127.5 - 1
-            batch_image = np.expand_dims(batch_image, axis=0)
-            output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
-            output = (np.squeeze(output) + 1) * 127.5
-            output = np.clip(output, 0, 255).astype(np.uint8)
-            cv2.imwrite(save_path, output)
-        except:
-            print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
-    model_path = 'saved_models'
-    load_folder = 'test_images'
-    save_folder = 'cartoonized_images'
-    if not os.path.exists(save_folder):
-        os.mkdir(save_folder)
-    cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/rishirajacharya/picspeaks-hindi/app.py b/spaces/rishirajacharya/picspeaks-hindi/app.py
deleted file mode 100644
index c258bca8925cccf43f3c053faaee9c3175241690..0000000000000000000000000000000000000000
--- a/spaces/rishirajacharya/picspeaks-hindi/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import torch
-import re
-import os
-import requests
-import gradio as gr
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
-
-
-device = 'cpu'
-encoder_checkpoint = os.environ['encoder_checkpoint']
-decoder_checkpoint = os.environ['decoder_checkpoint']
-model_checkpoint = os.environ['model_checkpoint']
-feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
-tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
-model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
-
-
-def predict(image,max_length=64, num_beams=4):
-  image = image.convert('RGB')
-  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-  caption_ids = model.generate(image, max_length = max_length)[0]
-  caption_text = clean_text(tokenizer.decode(caption_ids))
-  tts_url = os.environ['secret_url'] + str(caption_text)
-  caption_audio = requests.get(tts_url)
-  with open('output.mp3', 'wb') as f:
-    f.write(caption_audio.content)
-  return 'output.mp3'
-
-
-input = gr.inputs.Image(label="𝕐𝕠𝕦𝕣 𝕡𝕚𝕔 𝕘𝕠𝕖𝕤 𝕙𝕖𝕣𝕖 📸", type = 'pil', optional=False)
-output = gr.outputs.Audio(type="auto",label="𝕐𝕠𝕦𝕣 𝕡𝕚𝕔 𝕤𝕡𝕖𝕒𝕜𝕤 𝕙𝕖𝕣𝕖 🔊")
-
-
-article = "𝕋𝕙𝕚𝕤 𝕕𝕖𝕞𝕠 𝕒𝕡𝕡 𝕘𝕖𝕟𝕖𝕣𝕒𝕥𝕖𝕤 𝕙𝕚𝕟𝕕𝕚 𝕤𝕡𝕖𝕖𝕔𝕙 𝕗𝕣𝕠𝕞 𝕚𝕞𝕒𝕘𝕖𝕤 𝕒𝕟𝕕 𝕚𝕤 𝕓𝕦𝕚𝕝𝕥 𝕓𝕪 ℝ𝕚𝕤𝕙𝕚𝕣𝕒𝕛 𝔸𝕔𝕙𝕒𝕣𝕪𝕒 𝕨𝕚𝕥𝕙 𝕍𝕀𝕋 𝔼𝕟𝕔𝕠𝕕𝕖𝕣 𝕒𝕟𝕕 𝔾ℙ𝕋𝟚 𝔻𝕖𝕔𝕠𝕕𝕖𝕣"
-title = "ℙ𝕚𝕔𝕊𝕡𝕖𝕒𝕜𝕤 - 𝕀𝕞𝕒𝕘𝕖 𝕥𝕠 ℍ𝕚𝕟𝕕𝕚 𝕊𝕡𝕖𝕖𝕔𝕙 𝕊𝕪𝕤𝕥𝕖𝕞"
-examples = [f"./example_{i}.jpg" for i in range(1,4)]
-
-
-interface = gr.Interface(
-        fn=predict,
-        inputs = input,
-        theme="huggingface",
-        outputs=output,
-        examples = examples,
-        title=title,
-        description=article,
-    )
-interface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Teks Simtudduror Pdf Writer [CRACKED].md b/spaces/rorallitri/biomedical-language-models/logs/Download Teks Simtudduror Pdf Writer [CRACKED].md
deleted file mode 100644
index 89fd2f35875049ba64624dd1e2f382017181e3e2..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Teks Simtudduror Pdf Writer [CRACKED].md	
+++ /dev/null
@@ -1,89 +0,0 @@
-<br />
-<h1>Download Teks Simtudduror Pdf Writer: A Comprehensive Review</h1>
-
-<p>Teks simtudduror is a popular book of maulid, or praise and celebration of the birth of Prophet Muhammad (peace be upon him), written by Habib Ali bin Muhammad al-Habsyi, a renowned scholar and poet from Yemen. This book contains beautiful verses and stories about the life, character, and miracles of the Prophet, as well as prayers and blessings for him and his family.</p>
-<h2>Download Teks Simtudduror Pdf Writer</h2><br /><p><b><b>Download</b> &#10022; <a href="https://tinurll.com/2uzoMa">https://tinurll.com/2uzoMa</a></b></p><br /><br />
-
-<p>Many Muslims around the world read and recite teks simtudduror as a way of expressing their love and gratitude to the Prophet, who is the best of creation and the mercy to all worlds. Reading and reciting teks simtudduror also brings many benefits and rewards, such as increasing one's faith, knowledge, and good deeds, following the example of the Prophet.</p>
-
-<p>If you are interested in reading and reciting teks simtudduror, you may want to download teks simtudduror pdf writer. This is a digital version of the book that you can access on your device or computer. Downloading teks simtudduror pdf writer will allow you to read and recite teks simtudduror anytime and anywhere, without having to carry a physical copy of the book.</p>
-
-<h2>How to Download Teks Simtudduror Pdf Writer</h2>
-
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-
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-<p></p>
-
-<ol>
-<li>Go to https://islamiques.net/download-maulid-simtudduror/</li>
-<li>Choose the version of teks simtudduror pdf writer that you want to download. You can select from Kitab Maulid Simthud Dhurror (Arabic), Kitab Maulid Simtudduror Syarifah Nafidatul Jannah (Indonesian), or both.</li>
-<li>Click on the download link or button that corresponds to your choice.</li>
-<li>Save the file to your device or computer.</li>
-<li>Open the file with a pdf reader or writer program.</li>
-</ol>
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-
-<h2>How to Use Teks Simtudduror Pdf Writer</h2>
-
-<p>After downloading teks simtudduror pdf writer, you can use it for various purposes. You can read it for yourself or with your family and friends. You can also recite it in a gathering or event to commemorate the Prophet's birth. You can also listen to it or watch it online or offline.</p>
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-
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-
-<h2>Conclusion</h2>
-
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-<h2>How to Improve Your Reading and Recitation of Teks Simtudduror Pdf Writer</h2>
-
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-</ul>
-
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-
-<h2>Conclusion</h2>
-
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-<h2>Where to Download Teks Simtudduror Pdf Writer for Free</h2>
-
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-<h2>Conclusion</h2>
-
-<p>Teks simtudduror pdf writer is a valuable and beneficial book for Muslims who want to celebrate and learn more about the Prophet. It contains 12 chapters that praise and describe the Prophet's lineage, birth, character, attributes, miracles, migration, battles, prophethood, leadership, love, mercy, intercession, protection, family, companions, and community. It also contains prayers and invocations that can be recited by the readers.</p>
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diff --git a/spaces/runa91/barc_gradio/src/smal_pytorch/renderer/differentiable_renderer.py b/spaces/runa91/barc_gradio/src/smal_pytorch/renderer/differentiable_renderer.py
deleted file mode 100644
index c3d76f1f34a16ee6b559e18d95ecaca4fa267b31..0000000000000000000000000000000000000000
--- a/spaces/runa91/barc_gradio/src/smal_pytorch/renderer/differentiable_renderer.py
+++ /dev/null
@@ -1,280 +0,0 @@
-
-# part of the code from 
-#   https://github.com/benjiebob/SMALify/blob/master/smal_fitter/p3d_renderer.py
-
-import torch
-import torch.nn.functional as F
-from scipy.io  import loadmat
-import numpy as np
-# import config
-
-import pytorch3d
-from pytorch3d.structures import Meshes
-from pytorch3d.renderer import (
-    PerspectiveCameras, look_at_view_transform, look_at_rotation, 
-    RasterizationSettings, MeshRenderer, MeshRasterizer, BlendParams,
-    PointLights, HardPhongShader, SoftSilhouetteShader, Materials, Textures, 
-    DirectionalLights
-)
-from pytorch3d.renderer import TexturesVertex, SoftPhongShader
-from pytorch3d.io import load_objs_as_meshes
-
-MESH_COLOR_0 = [0, 172, 223]
-MESH_COLOR_1 = [172, 223, 0]
-
-
-'''
-Explanation of the shift between projection results from opendr and pytorch3d:
-    (0, 0, ?) will be projected to 127.5 (pytorch3d) instead of 128 (opendr)
-    imagine you have an image of size 4:
-    middle of the first pixel is 0
-    middle of the last pixel is 3
-    => middle of the imgae would be 1.5 and not 2!
-    so in order to go from pytorch3d predictions to opendr we would calculate: p_odr = p_p3d * (128/127.5)
-To reproject points (p3d) by hand according to this pytorch3d renderer we would do the following steps:
-    1.) build camera matrix
-        K = np.array([[flength, 0, c_x],
-                    [0, flength, c_y],
-                    [0, 0, 1]], np.float)
-    2.) we don't need to add extrinsics, as the mesh comes with translation (which is 
-        added within smal_pytorch). all 3d points are already in the camera coordinate system.
-        -> projection reduces to p2d_proj = K*p3d
-    3.) convert to pytorch3d conventions (0 in the middle of the first pixel)
-        p2d_proj_pytorch3d = p2d_proj / image_size * (image_size-1.)
-renderer.py - project_points_p3d: shows an example of what is described above, but 
-    same focal length for the whole batch
-
-'''
-
-class SilhRenderer(torch.nn.Module):
-    def __init__(self, image_size, adapt_R_wldo=False):
-        super(SilhRenderer, self).__init__()
-        # see: https://pytorch3d.org/files/fit_textured_mesh.py, line 315
-        # adapt_R=True is True for all my experiments
-        # image_size: one number, integer
-        # -----
-        # set mesh color
-        self.register_buffer('mesh_color_0', torch.FloatTensor(MESH_COLOR_0))
-        self.register_buffer('mesh_color_1', torch.FloatTensor(MESH_COLOR_1))
-        # prepare extrinsics, which in our case don't change
-        R = torch.Tensor(np.eye(3)).float()[None, :, :]
-        T = torch.Tensor(np.zeros((1, 3))).float()
-        if adapt_R_wldo:
-            R[0, 0, 0] = -1
-        else:       # used for all my own experiments
-            R[0, 0, 0] = -1
-            R[0, 1, 1] = -1            
-        self.register_buffer('R', R)
-        self.register_buffer('T', T)
-        # prepare that part of the intrinsics which does not change either
-        # principal_point_prep = torch.Tensor([self.image_size / 2., self.image_size / 2.]).float()[None, :].float().to(device) 
-        # image_size_prep = torch.Tensor([self.image_size, self.image_size]).float()[None, :].float().to(device) 
-        self.img_size_scalar = image_size
-        self.register_buffer('image_size', torch.Tensor([image_size, image_size]).float()[None, :].float())
-        self.register_buffer('principal_point', torch.Tensor([image_size / 2., image_size / 2.]).float()[None, :].float())
-        # Rasterization settings for differentiable rendering, where the blur_radius
-        # initialization is based on Liu et al, 'Soft Rasterizer: A Differentiable 
-        # Renderer for Image-based 3D Reasoning', ICCV 2019
-        self.blend_params = BlendParams(sigma=1e-4, gamma=1e-4)
-        self.raster_settings_soft = RasterizationSettings(
-            image_size=image_size,      # 128
-            blur_radius=np.log(1. / 1e-4 - 1.)*self.blend_params.sigma,  
-            faces_per_pixel=100)     #50, 
-        # Renderer for Image-based 3D Reasoning', body part segmentation
-        self.blend_params_parts = BlendParams(sigma=2*1e-4, gamma=1e-4)
-        self.raster_settings_soft_parts = RasterizationSettings(
-            image_size=image_size,      # 128
-            blur_radius=np.log(1. / 1e-4 - 1.)*self.blend_params_parts.sigma,  
-            faces_per_pixel=60)     #50, 
-        # settings for visualization renderer
-        self.raster_settings_vis = RasterizationSettings(
-                image_size=image_size, 
-                blur_radius=0.0, 
-                faces_per_pixel=1)
-
-    def _get_cam(self, focal_lengths):
-        device = focal_lengths.device
-        bs = focal_lengths.shape[0]
-        if pytorch3d.__version__ == '0.2.5':
-            cameras = PerspectiveCameras(device=device,
-                focal_length=focal_lengths.repeat((1, 2)), 
-                principal_point=self.principal_point.repeat((bs, 1)), 
-                R=self.R.repeat((bs, 1, 1)), T=self.T.repeat((bs, 1)), 
-                image_size=self.image_size.repeat((bs, 1)))
-        elif pytorch3d.__version__ == '0.6.1':
-            cameras = PerspectiveCameras(device=device, in_ndc=False,
-            focal_length=focal_lengths.repeat((1, 2)), 
-            principal_point=self.principal_point.repeat((bs, 1)), 
-            R=self.R.repeat((bs, 1, 1)), T=self.T.repeat((bs, 1)), 
-            image_size=self.image_size.repeat((bs, 1)))
-        else: 
-            print('this part depends on the version of pytorch3d, code was developed with 0.2.5')
-            raise ValueError
-        return cameras
-
-    def _get_visualization_from_mesh(self, mesh, cameras, lights=None):
-        # color renderer for visualization
-        with torch.no_grad():
-            device = mesh.device
-            # renderer for visualization
-            if lights is None:
-                lights = PointLights(device=device, location=[[0.0, 0.0, 3.0]])
-            vis_renderer = MeshRenderer(
-                rasterizer=MeshRasterizer(
-                    cameras=cameras, 
-                    raster_settings=self.raster_settings_vis),
-                shader=HardPhongShader(
-                    device=device, 
-                    cameras=cameras,
-                    lights=lights))
-            # render image:
-            visualization = vis_renderer(mesh).permute(0, 3, 1, 2)[:, :3, :, :]
-        return visualization
-
-
-    def calculate_vertex_visibility(self, vertices, faces, focal_lengths, soft=False):
-        tex = torch.ones_like(vertices) * self.mesh_color_0    # (1, V, 3)
-        textures = Textures(verts_rgb=tex)
-        mesh = Meshes(verts=vertices, faces=faces, textures=textures)
-        cameras = self._get_cam(focal_lengths)
-        # NEW: use the rasterizer to check vertex visibility
-        #   see: https://github.com/facebookresearch/pytorch3d/issues/126
-        # Get a rasterizer
-        if soft:
-            rasterizer = MeshRasterizer(cameras=cameras, 
-                                        raster_settings=self.raster_settings_soft)
-        else:
-            rasterizer = MeshRasterizer(cameras=cameras, 
-                            raster_settings=self.raster_settings_vis)
-        # Get the output from rasterization
-        fragments = rasterizer(mesh)
-        # pix_to_face is of shape (N, H, W, 1)
-        pix_to_face = fragments.pix_to_face  
-        # (F, 3) where F is the total number of faces across all the meshes in the batch
-        packed_faces = mesh.faces_packed() 
-        # (V, 3) where V is the total number of verts across all the meshes in the batch
-        packed_verts = mesh.verts_packed() 
-        vertex_visibility_map = torch.zeros(packed_verts.shape[0])   # (V,)
-        # Indices of unique visible faces
-        visible_faces = pix_to_face.unique()    # [0]   # (num_visible_faces )
-        # Get Indices of unique visible verts using the vertex indices in the faces
-        visible_verts_idx = packed_faces[visible_faces]    # (num_visible_faces,  3)
-        unique_visible_verts_idx = torch.unique(visible_verts_idx)   # (num_visible_verts, )
-        # Update visibility indicator to 1 for all visible vertices 
-        vertex_visibility_map[unique_visible_verts_idx] = 1.0
-        # since all meshes have the same amount of vertices, we can reshape the result 
-        bs = vertices.shape[0]
-        vertex_visibility_map_resh = vertex_visibility_map.reshape((bs, -1))
-        return pix_to_face, vertex_visibility_map_resh
-
-
-    def get_torch_meshes(self, vertices, faces, color=0):
-        # create pytorch mesh
-        if color == 0:
-            mesh_color = self.mesh_color_0
-        else:
-            mesh_color = self.mesh_color_1
-        tex = torch.ones_like(vertices) * mesh_color    # (1, V, 3)
-        textures = Textures(verts_rgb=tex)
-        mesh = Meshes(verts=vertices, faces=faces, textures=textures)  
-        return mesh
-
-
-    def get_visualization_nograd(self, vertices, faces, focal_lengths, color=0):
-        # vertices: torch.Size([bs, 3889, 3])
-        # faces: torch.Size([bs, 7774, 3]), int
-        # focal_lengths: torch.Size([bs, 1])
-        device = vertices.device
-        # create cameras
-        cameras = self._get_cam(focal_lengths)
-        # create pytorch mesh
-        if color == 0:
-            mesh_color = self.mesh_color_0      # blue
-        elif color == 1:
-            mesh_color = self.mesh_color_1      
-        elif color ==  2:   
-            MESH_COLOR_2 = [240, 250, 240]      # white
-            mesh_color =  torch.FloatTensor(MESH_COLOR_2).to(device)
-        elif color ==  3:   
-            # MESH_COLOR_3 = [223, 0, 172]         # pink
-            # MESH_COLOR_3 = [245, 245, 220]         # beige
-            MESH_COLOR_3 = [166, 173, 164] 
-            mesh_color =  torch.FloatTensor(MESH_COLOR_3).to(device)        
-        else:
-            MESH_COLOR_2 = [240, 250, 240]
-            mesh_color =  torch.FloatTensor(MESH_COLOR_2).to(device)
-        tex = torch.ones_like(vertices) * mesh_color    # (1, V, 3)
-        textures = Textures(verts_rgb=tex)
-        mesh = Meshes(verts=vertices, faces=faces, textures=textures)  
-        # render mesh (no gradients)
-        # lights = PointLights(device=device, location=[[0.0, 0.0, 3.0]])
-        # lights = PointLights(device=device, location=[[2.0, 2.0, -2.0]])
-        lights = DirectionalLights(device=device, direction=[[0.0, -5.0, -10.0]])
-        visualization = self._get_visualization_from_mesh(mesh, cameras, lights=lights)
-        return visualization
-              
-    def project_points(self, points, focal_lengths=None, cameras=None):
-        # points: torch.Size([bs, n_points, 3])
-        # either focal_lengths or cameras is needed:
-        #   focal_lenghts: torch.Size([bs, 1])
-        #   cameras: pytorch camera, for example PerspectiveCameras()
-        bs = points.shape[0]
-        device = points.device
-        screen_size = self.image_size.repeat((bs, 1))
-        if cameras is None:
-            cameras = self._get_cam(focal_lengths)
-        if pytorch3d.__version__ == '0.2.5':
-            proj_points_orig = cameras.transform_points_screen(points, screen_size)[:, :, [1, 0]]       # used in the original virtuel environment (for cvpr BARC submission) 
-        elif pytorch3d.__version__ == '0.6.1':
-            proj_points_orig = cameras.transform_points_screen(points)[:, :, [1, 0]]        
-        else: 
-            print('this part depends on the version of pytorch3d, code was developed with 0.2.5')
-            raise ValueError
-        # flip, otherwise the 1st and 2nd row are exchanged compared to the ground truth
-        proj_points = torch.flip(proj_points_orig, [2])   
-        # --- project points 'manually'
-        # j_proj = project_points_p3d(image_size, focal_length, points, device)
-        return proj_points      
-
-    def forward(self, vertices, points, faces, focal_lengths, color=None):
-        # vertices: torch.Size([bs, 3889, 3])
-        # points: torch.Size([bs, n_points, 3]) (or None)
-        # faces: torch.Size([bs, 7774, 3]), int
-        # focal_lengths: torch.Size([bs, 1])
-        # color: if None we don't render a visualization, else it should
-        #   either be 0 or 1
-        # ---> important: results are around 0.5 pixels off compared to chumpy!
-        #   have a look at renderer.py for an explanation
-        # create cameras
-        cameras = self._get_cam(focal_lengths)
-        # create pytorch mesh
-        if color is None or color == 0:
-            mesh_color = self.mesh_color_0
-        else:
-            mesh_color = self.mesh_color_1           
-        tex = torch.ones_like(vertices) * mesh_color    # (1, V, 3)
-        textures = Textures(verts_rgb=tex)
-        mesh = Meshes(verts=vertices, faces=faces, textures=textures)
-        # silhouette renderer 
-        renderer_silh = MeshRenderer(
-            rasterizer=MeshRasterizer(
-                cameras=cameras, 
-                raster_settings=self.raster_settings_soft),
-            shader=SoftSilhouetteShader(blend_params=self.blend_params))
-        # project silhouette
-        silh_images = renderer_silh(mesh)[..., -1].unsqueeze(1)
-        # project points
-        if points is None:
-            proj_points = None
-        else:
-            proj_points = self.project_points(points=points, cameras=cameras)
-        if color is not None:
-            # color renderer for visualization (no gradients)
-            visualization = self._get_visualization_from_mesh(mesh, cameras)    
-            return silh_images, proj_points, visualization
-        else:
-            return silh_images, proj_points
-
-
-
-  
diff --git a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/datasets/transforms.py b/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/datasets/transforms.py
deleted file mode 100644
index 91cf9269e4b31008a3ddca34a19b038a9b399991..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/datasets/transforms.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Transforms and data augmentation for both image + bbox.
-"""
-import os
-import random
-
-import PIL
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
-
-from groundingdino.util.box_ops import box_xyxy_to_cxcywh
-from groundingdino.util.misc import interpolate
-
-
-def crop(image, target, region):
-    cropped_image = F.crop(image, *region)
-
-    target = target.copy()
-    i, j, h, w = region
-
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor([h, w])
-
-    fields = ["labels", "area", "iscrowd", "positive_map"]
-
-    if "boxes" in target:
-        boxes = target["boxes"]
-        max_size = torch.as_tensor([w, h], dtype=torch.float32)
-        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
-        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
-        cropped_boxes = cropped_boxes.clamp(min=0)
-        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
-        target["boxes"] = cropped_boxes.reshape(-1, 4)
-        target["area"] = area
-        fields.append("boxes")
-
-    if "masks" in target:
-        # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i : i + h, j : j + w]
-        fields.append("masks")
-
-    # remove elements for which the boxes or masks that have zero area
-    if "boxes" in target or "masks" in target:
-        # favor boxes selection when defining which elements to keep
-        # this is compatible with previous implementation
-        if "boxes" in target:
-            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
-            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
-        else:
-            keep = target["masks"].flatten(1).any(1)
-
-        for field in fields:
-            if field in target:
-                target[field] = target[field][keep]
-
-    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
-        # for debug and visualization only.
-        if "strings_positive" in target:
-            target["strings_positive"] = [
-                _i for _i, _j in zip(target["strings_positive"], keep) if _j
-            ]
-
-    return cropped_image, target
-
-
-def hflip(image, target):
-    flipped_image = F.hflip(image)
-
-    w, h = image.size
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
-            [w, 0, w, 0]
-        )
-        target["boxes"] = boxes
-
-    if "masks" in target:
-        target["masks"] = target["masks"].flip(-1)
-
-    return flipped_image, target
-
-
-def resize(image, target, size, max_size=None):
-    # size can be min_size (scalar) or (w, h) tuple
-
-    def get_size_with_aspect_ratio(image_size, size, max_size=None):
-        w, h = image_size
-        if max_size is not None:
-            min_original_size = float(min((w, h)))
-            max_original_size = float(max((w, h)))
-            if max_original_size / min_original_size * size > max_size:
-                size = int(round(max_size * min_original_size / max_original_size))
-
-        if (w <= h and w == size) or (h <= w and h == size):
-            return (h, w)
-
-        if w < h:
-            ow = size
-            oh = int(size * h / w)
-        else:
-            oh = size
-            ow = int(size * w / h)
-
-        return (oh, ow)
-
-    def get_size(image_size, size, max_size=None):
-        if isinstance(size, (list, tuple)):
-            return size[::-1]
-        else:
-            return get_size_with_aspect_ratio(image_size, size, max_size)
-
-    size = get_size(image.size, size, max_size)
-    rescaled_image = F.resize(image, size)
-
-    if target is None:
-        return rescaled_image, None
-
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
-    ratio_width, ratio_height = ratios
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        scaled_boxes = boxes * torch.as_tensor(
-            [ratio_width, ratio_height, ratio_width, ratio_height]
-        )
-        target["boxes"] = scaled_boxes
-
-    if "area" in target:
-        area = target["area"]
-        scaled_area = area * (ratio_width * ratio_height)
-        target["area"] = scaled_area
-
-    h, w = size
-    target["size"] = torch.tensor([h, w])
-
-    if "masks" in target:
-        target["masks"] = (
-            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
-        )
-
-    return rescaled_image, target
-
-
-def pad(image, target, padding):
-    # assumes that we only pad on the bottom right corners
-    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
-    if target is None:
-        return padded_image, None
-    target = target.copy()
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor(padded_image.size[::-1])
-    if "masks" in target:
-        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
-    return padded_image, target
-
-
-class ResizeDebug(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        return resize(img, target, self.size)
-
-
-class RandomCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        region = T.RandomCrop.get_params(img, self.size)
-        return crop(img, target, region)
-
-
-class RandomSizeCrop(object):
-    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
-        # respect_boxes:    True to keep all boxes
-        #                   False to tolerence box filter
-        self.min_size = min_size
-        self.max_size = max_size
-        self.respect_boxes = respect_boxes
-
-    def __call__(self, img: PIL.Image.Image, target: dict):
-        init_boxes = len(target["boxes"])
-        max_patience = 10
-        for i in range(max_patience):
-            w = random.randint(self.min_size, min(img.width, self.max_size))
-            h = random.randint(self.min_size, min(img.height, self.max_size))
-            region = T.RandomCrop.get_params(img, [h, w])
-            result_img, result_target = crop(img, target, region)
-            if (
-                not self.respect_boxes
-                or len(result_target["boxes"]) == init_boxes
-                or i == max_patience - 1
-            ):
-                return result_img, result_target
-        return result_img, result_target
-
-
-class CenterCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        image_width, image_height = img.size
-        crop_height, crop_width = self.size
-        crop_top = int(round((image_height - crop_height) / 2.0))
-        crop_left = int(round((image_width - crop_width) / 2.0))
-        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
-
-
-class RandomHorizontalFlip(object):
-    def __init__(self, p=0.5):
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return hflip(img, target)
-        return img, target
-
-
-class RandomResize(object):
-    def __init__(self, sizes, max_size=None):
-        assert isinstance(sizes, (list, tuple))
-        self.sizes = sizes
-        self.max_size = max_size
-
-    def __call__(self, img, target=None):
-        size = random.choice(self.sizes)
-        return resize(img, target, size, self.max_size)
-
-
-class RandomPad(object):
-    def __init__(self, max_pad):
-        self.max_pad = max_pad
-
-    def __call__(self, img, target):
-        pad_x = random.randint(0, self.max_pad)
-        pad_y = random.randint(0, self.max_pad)
-        return pad(img, target, (pad_x, pad_y))
-
-
-class RandomSelect(object):
-    """
-    Randomly selects between transforms1 and transforms2,
-    with probability p for transforms1 and (1 - p) for transforms2
-    """
-
-    def __init__(self, transforms1, transforms2, p=0.5):
-        self.transforms1 = transforms1
-        self.transforms2 = transforms2
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return self.transforms1(img, target)
-        return self.transforms2(img, target)
-
-
-class ToTensor(object):
-    def __call__(self, img, target):
-        return F.to_tensor(img), target
-
-
-class RandomErasing(object):
-    def __init__(self, *args, **kwargs):
-        self.eraser = T.RandomErasing(*args, **kwargs)
-
-    def __call__(self, img, target):
-        return self.eraser(img), target
-
-
-class Normalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, image, target=None):
-        image = F.normalize(image, mean=self.mean, std=self.std)
-        if target is None:
-            return image, None
-        target = target.copy()
-        h, w = image.shape[-2:]
-        if "boxes" in target:
-            boxes = target["boxes"]
-            boxes = box_xyxy_to_cxcywh(boxes)
-            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
-            target["boxes"] = boxes
-        return image, target
-
-
-class Compose(object):
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, image, target):
-        for t in self.transforms:
-            image, target = t(image, target)
-        return image, target
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + "("
-        for t in self.transforms:
-            format_string += "\n"
-            format_string += "    {0}".format(t)
-        format_string += "\n)"
-        return format_string
diff --git a/spaces/sanjaykamath/BLIP2/app.py b/spaces/sanjaykamath/BLIP2/app.py
deleted file mode 100644
index 1581dbfa83b8fefb14ab3acefcdd75b47b465574..0000000000000000000000000000000000000000
--- a/spaces/sanjaykamath/BLIP2/app.py
+++ /dev/null
@@ -1,282 +0,0 @@
-from io import BytesIO
-
-import string
-import gradio as gr
-import requests
-from utils import Endpoint, get_token
-
-
-def encode_image(image):
-    buffered = BytesIO()
-    image.save(buffered, format="JPEG")
-    buffered.seek(0)
-
-    return buffered
-
-
-def query_chat_api(
-    image, prompt, decoding_method, temperature, len_penalty, repetition_penalty
-):
-
-    url = endpoint.url
-    url = url + "/api/generate"
-
-    headers = {
-        "User-Agent": "BLIP-2 HuggingFace Space",
-        "Auth-Token": get_token(),
-    }
-
-    data = {
-        "prompt": prompt,
-        "use_nucleus_sampling": decoding_method == "Nucleus sampling",
-        "temperature": temperature,
-        "length_penalty": len_penalty,
-        "repetition_penalty": repetition_penalty,
-    }
-
-    image = encode_image(image)
-    files = {"image": image}
-
-    response = requests.post(url, data=data, files=files, headers=headers)
-
-    if response.status_code == 200:
-        return response.json()
-    else:
-        return "Error: " + response.text
-
-
-def query_caption_api(
-    image, decoding_method, temperature, len_penalty, repetition_penalty
-):
-
-    url = endpoint.url
-    url = url + "/api/caption"
-
-    headers = {
-        "User-Agent": "BLIP-2 HuggingFace Space",
-        "Auth-Token": get_token(),
-    }
-
-    data = {
-        "use_nucleus_sampling": decoding_method == "Nucleus sampling",
-        "temperature": temperature,
-        "length_penalty": len_penalty,
-        "repetition_penalty": repetition_penalty,
-    }
-
-    image = encode_image(image)
-    files = {"image": image}
-
-    response = requests.post(url, data=data, files=files, headers=headers)
-
-    if response.status_code == 200:
-        return response.json()
-    else:
-        return "Error: " + response.text
-
-
-def postprocess_output(output):
-    # if last character is not a punctuation, add a full stop
-    if not output[0][-1] in string.punctuation:
-        output[0] += "."
-
-    return output
-
-
-def inference_chat(
-    image,
-    text_input,
-    decoding_method,
-    temperature,
-    length_penalty,
-    repetition_penalty,
-    history=[],
-):
-    text_input = text_input
-    history.append(text_input)
-
-    prompt = " ".join(history)
-
-    output = query_chat_api(
-        image, prompt, decoding_method, temperature, length_penalty, repetition_penalty
-    )
-    output = postprocess_output(output)
-    history += output
-
-    chat = [
-        (history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)
-    ]  # convert to tuples of list
-
-    return {chatbot: chat, state: history}
-
-
-def inference_caption(
-    image,
-    decoding_method,
-    temperature,
-    length_penalty,
-    repetition_penalty,
-):
-    output = query_caption_api(
-        image, decoding_method, temperature, length_penalty, repetition_penalty
-    )
-
-    return output[0]
-
-
-title = """<h1 align="center">BLIP-2</h1>"""
-description = """Gradio demo for BLIP-2, image-to-text generation from Salesforce Research. To use it, simply upload your image, or click one of the examples to load them.
-<br> <strong>Disclaimer</strong>: This is a research prototype and is not intended for production use. No data including but not restricted to text and images is collected."""
-article = """<strong>Paper</strong>: <a href='https://arxiv.org/abs/2301.12597' target='_blank'>BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models</a>
-<br> <strong>Code</strong>: BLIP2 is now integrated into GitHub repo: <a href='https://github.com/salesforce/LAVIS' target='_blank'>LAVIS: a One-stop Library for Language and Vision</a>
-<br> <strong>🤗 `transformers` integration</strong>: You can now use `transformers` to use our BLIP-2 models! Check out the <a href='https://huggingface.co/docs/transformers/main/en/model_doc/blip-2' target='_blank'> official docs </a>
-<p> <strong>Project Page</strong>: <a href='https://github.com/salesforce/LAVIS/tree/main/projects/blip2' target='_blank'> BLIP2 on LAVIS</a>
-<br> <strong>Description</strong>: Captioning results from <strong>BLIP2_OPT_6.7B</strong>. Chat results from <strong>BLIP2_FlanT5xxl</strong>.
-"""
-
-endpoint = Endpoint()
-
-examples = [
-    ["house.png", "How could someone get out of the house?"],
-    ["flower.jpg", "Question: What is this flower and where is it's origin? Answer:"],
-    ["pizza.jpg", "What are steps to cook it?"],
-    ["sunset.jpg", "Here is a romantic message going along the photo:"],
-    ["forbidden_city.webp", "In what dynasties was this place built?"],
-]
-
-with gr.Blocks(
-    css="""
-    .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
-    #component-21 > div.wrap.svelte-w6rprc {height: 600px;}
-    """
-) as iface:
-    state = gr.State([])
-
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown(article)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="pil")
-
-            # with gr.Row():
-            sampling = gr.Radio(
-                choices=["Beam search", "Nucleus sampling"],
-                value="Beam search",
-                label="Text Decoding Method",
-                interactive=True,
-            )
-
-            temperature = gr.Slider(
-                minimum=0.5,
-                maximum=1.0,
-                value=1.0,
-                step=0.1,
-                interactive=True,
-                label="Temperature (used with nucleus sampling)",
-            )
-
-            len_penalty = gr.Slider(
-                minimum=-1.0,
-                maximum=2.0,
-                value=1.0,
-                step=0.2,
-                interactive=True,
-                label="Length Penalty (set to larger for longer sequence, used with beam search)",
-            )
-
-            rep_penalty = gr.Slider(
-                minimum=1.0,
-                maximum=5.0,
-                value=1.5,
-                step=0.5,
-                interactive=True,
-                label="Repeat Penalty (larger value prevents repetition)",
-            )
-
-        with gr.Column(scale=1.8):
-
-            with gr.Column():
-                caption_output = gr.Textbox(lines=1, label="Caption Output")
-                caption_button = gr.Button(
-                    value="Caption it!", interactive=True, variant="primary"
-                )
-                caption_button.click(
-                    inference_caption,
-                    [
-                        image_input,
-                        sampling,
-                        temperature,
-                        len_penalty,
-                        rep_penalty,
-                    ],
-                    [caption_output],
-                )
-
-            gr.Markdown("""Trying prompting your input for chat; e.g. example prompt for QA, \"Question: {} Answer:\" Use proper punctuation (e.g., question mark).""")
-            with gr.Row():
-                with gr.Column(
-                    scale=1.5, 
-                ):
-                    chatbot = gr.Chatbot(
-                        label="Chat Output (from FlanT5)",
-                    )
-
-                # with gr.Row():
-                with gr.Column(scale=1):
-                    chat_input = gr.Textbox(lines=1, label="Chat Input")
-                    chat_input.submit(
-                        inference_chat,
-                        [
-                            image_input,
-                            chat_input,
-                            sampling,
-                            temperature,
-                            len_penalty,
-                            rep_penalty,
-                            state,
-                        ],
-                        [chatbot, state],
-                    )
-
-                    with gr.Row():
-                        clear_button = gr.Button(value="Clear", interactive=True)
-                        clear_button.click(
-                            lambda: ("", [], []),
-                            [],
-                            [chat_input, chatbot, state],
-                            queue=False,
-                        )
-
-                        submit_button = gr.Button(
-                            value="Submit", interactive=True, variant="primary"
-                        )
-                        submit_button.click(
-                            inference_chat,
-                            [
-                                image_input,
-                                chat_input,
-                                sampling,
-                                temperature,
-                                len_penalty,
-                                rep_penalty,
-                                state,
-                            ],
-                            [chatbot, state],
-                        )
-
-            image_input.change(
-                lambda: ("", "", []),
-                [],
-                [chatbot, caption_output, state],
-                queue=False,
-            )
-
-    examples = gr.Examples(
-        examples=examples,
-        inputs=[image_input, chat_input],
-    )
-
-iface.queue(concurrency_count=1, api_open=False, max_size=10)
-iface.launch(enable_queue=True)
diff --git a/spaces/sanzgiri/cartoonify/Notes.md b/spaces/sanzgiri/cartoonify/Notes.md
deleted file mode 100644
index 4fc2921a0aea64e2dd0ead60985266dd54112c63..0000000000000000000000000000000000000000
--- a/spaces/sanzgiri/cartoonify/Notes.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Steps to get binary files working 
-
-```buildoutcfg
-# clone repo from huggingface
-git clone https://huggingface.co/spaces/sanzgiri/cartoonify
-cd cartoonify
-
-# create a directory called saved_model and put binaries in there
-# this is the directory referenced in code
-mkdir saved_model
-cp <model-files> saved_model
-
-# track model-files using git-lfs
-# git lfs install
-git lfs track saved_model/*
-git add <other files> saved_model
-
-# git lfs ls-files 
-# this should now list the files
-
-git commit -m "adding files" .
-git push
-```
\ No newline at end of file
diff --git a/spaces/sarinam/speaker-anonymization-gan/README.md b/spaces/sarinam/speaker-anonymization-gan/README.md
deleted file mode 100644
index d15d92c7f5efc9b225adfaf2b578149ac20becce..0000000000000000000000000000000000000000
--- a/spaces/sarinam/speaker-anonymization-gan/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Speaker Anonymization
-emoji: 🐠
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/scedlatioru/img-to-music/example/Camtasia Studio 2019.0.65004 Cracked Serial Is Here Crack __LINK__.md b/spaces/scedlatioru/img-to-music/example/Camtasia Studio 2019.0.65004 Cracked Serial Is Here Crack __LINK__.md
deleted file mode 100644
index b9bb72a2ca39d6a32d73b510898b8e2c9e57e293..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Camtasia Studio 2019.0.65004 Cracked Serial Is Here Crack __LINK__.md	
+++ /dev/null
@@ -1,65 +0,0 @@
-<br />
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\ No newline at end of file
diff --git a/spaces/sczhou/CodeFormer/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/spaces/sczhou/CodeFormer/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda.cpp
deleted file mode 100644
index 5d9424908ed2dbd4ac3cdb98d13e09287a4d2f2d..0000000000000000000000000000000000000000
--- a/spaces/sczhou/CodeFormer/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda.cpp
+++ /dev/null
@@ -1,685 +0,0 @@
-// modify from
-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
-
-#include <torch/extension.h>
-#include <ATen/DeviceGuard.h>
-
-#include <cmath>
-#include <vector>
-
-void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset,
-                       const int channels, const int height, const int width,
-                       const int ksize_h, const int ksize_w, const int pad_h,
-                       const int pad_w, const int stride_h, const int stride_w,
-                       const int dilation_h, const int dilation_w,
-                       const int parallel_imgs, const int deformable_group,
-                       at::Tensor data_col);
-
-void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset,
-                       const int channels, const int height, const int width,
-                       const int ksize_h, const int ksize_w, const int pad_h,
-                       const int pad_w, const int stride_h, const int stride_w,
-                       const int dilation_h, const int dilation_w,
-                       const int parallel_imgs, const int deformable_group,
-                       at::Tensor grad_im);
-
-void deformable_col2im_coord(
-    const at::Tensor data_col, const at::Tensor data_im,
-    const at::Tensor data_offset, const int channels, const int height,
-    const int width, const int ksize_h, const int ksize_w, const int pad_h,
-    const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int parallel_imgs,
-    const int deformable_group, at::Tensor grad_offset);
-
-void modulated_deformable_im2col_cuda(
-    const at::Tensor data_im, const at::Tensor data_offset,
-    const at::Tensor data_mask, const int batch_size, const int channels,
-    const int height_im, const int width_im, const int height_col,
-    const int width_col, const int kernel_h, const int kenerl_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int deformable_group,
-    at::Tensor data_col);
-
-void modulated_deformable_col2im_cuda(
-    const at::Tensor data_col, const at::Tensor data_offset,
-    const at::Tensor data_mask, const int batch_size, const int channels,
-    const int height_im, const int width_im, const int height_col,
-    const int width_col, const int kernel_h, const int kenerl_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int deformable_group,
-    at::Tensor grad_im);
-
-void modulated_deformable_col2im_coord_cuda(
-    const at::Tensor data_col, const at::Tensor data_im,
-    const at::Tensor data_offset, const at::Tensor data_mask,
-    const int batch_size, const int channels, const int height_im,
-    const int width_im, const int height_col, const int width_col,
-    const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
-    const int stride_h, const int stride_w, const int dilation_h,
-    const int dilation_w, const int deformable_group, at::Tensor grad_offset,
-    at::Tensor grad_mask);
-
-void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput,
-                 at::Tensor weight, int kH, int kW, int dH, int dW, int padH,
-                 int padW, int dilationH, int dilationW, int group,
-                 int deformable_group) {
-  TORCH_CHECK(weight.ndimension() == 4,
-           "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, "
-           "but got: %s",
-           weight.ndimension());
-
-  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-
-  TORCH_CHECK(kW > 0 && kH > 0,
-           "kernel size should be greater than zero, but got kH: %d kW: %d", kH,
-           kW);
-
-  TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW),
-           "kernel size should be consistent with weight, ",
-           "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH,
-           kW, weight.size(2), weight.size(3));
-
-  TORCH_CHECK(dW > 0 && dH > 0,
-           "stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
-
-  TORCH_CHECK(
-      dilationW > 0 && dilationH > 0,
-      "dilation should be greater than 0, but got dilationH: %d dilationW: %d",
-      dilationH, dilationW);
-
-  int ndim = input.ndimension();
-  int dimf = 0;
-  int dimh = 1;
-  int dimw = 2;
-
-  if (ndim == 4) {
-    dimf++;
-    dimh++;
-    dimw++;
-  }
-
-  TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
-           ndim);
-
-  long nInputPlane = weight.size(1) * group;
-  long inputHeight = input.size(dimh);
-  long inputWidth = input.size(dimw);
-  long nOutputPlane = weight.size(0);
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-
-  TORCH_CHECK(nInputPlane % deformable_group == 0,
-           "input channels must divide deformable group size");
-
-  if (outputWidth < 1 || outputHeight < 1)
-    AT_ERROR(
-        "Given input size: (%ld x %ld x %ld). "
-        "Calculated output size: (%ld x %ld x %ld). Output size is too small",
-        nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
-        outputWidth);
-
-  TORCH_CHECK(input.size(1) == nInputPlane,
-           "invalid number of input planes, expected: %d, but got: %d",
-           nInputPlane, input.size(1));
-
-  TORCH_CHECK((inputHeight >= kH && inputWidth >= kW),
-           "input image is smaller than kernel");
-
-  TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
-           "invalid spatial size of offset, expected height: %d width: %d, but "
-           "got height: %d width: %d",
-           outputHeight, outputWidth, offset.size(2), offset.size(3));
-
-  TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
-           "invalid number of channels of offset");
-
-  if (gradOutput != NULL) {
-    TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane,
-             "invalid number of gradOutput planes, expected: %d, but got: %d",
-             nOutputPlane, gradOutput->size(dimf));
-
-    TORCH_CHECK((gradOutput->size(dimh) == outputHeight &&
-              gradOutput->size(dimw) == outputWidth),
-             "invalid size of gradOutput, expected height: %d width: %d , but "
-             "got height: %d width: %d",
-             outputHeight, outputWidth, gradOutput->size(dimh),
-             gradOutput->size(dimw));
-  }
-}
-
-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
-                             at::Tensor offset, at::Tensor output,
-                             at::Tensor columns, at::Tensor ones, int kW,
-                             int kH, int dW, int dH, int padW, int padH,
-                             int dilationW, int dilationH, int group,
-                             int deformable_group, int im2col_step) {
-  // todo: resize columns to include im2col: done
-  // todo: add im2col_step as input
-  // todo: add new output buffer and transpose it to output (or directly
-  // transpose output) todo: possibly change data indexing because of
-  // parallel_imgs
-
-  shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW,
-              dilationH, dilationW, group, deformable_group);
-  at::DeviceGuard guard(input.device());
-
-  input = input.contiguous();
-  offset = offset.contiguous();
-  weight = weight.contiguous();
-
-  int batch = 1;
-  if (input.ndimension() == 3) {
-    // Force batch
-    batch = 0;
-    input.unsqueeze_(0);
-    offset.unsqueeze_(0);
-  }
-
-  // todo: assert batchsize dividable by im2col_step
-
-  long batchSize = input.size(0);
-  long nInputPlane = input.size(1);
-  long inputHeight = input.size(2);
-  long inputWidth = input.size(3);
-
-  long nOutputPlane = weight.size(0);
-
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
-  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
-
-  output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane,
-                        outputHeight, outputWidth});
-  columns = at::zeros(
-      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
-      input.options());
-
-  if (ones.ndimension() != 2 ||
-      ones.size(0) * ones.size(1) < outputHeight * outputWidth) {
-    ones = at::ones({outputHeight, outputWidth}, input.options());
-  }
-
-  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                      inputHeight, inputWidth});
-  offset =
-      offset.view({batchSize / im2col_step, im2col_step,
-                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  at::Tensor output_buffer =
-      at::zeros({batchSize / im2col_step, nOutputPlane,
-                 im2col_step * outputHeight, outputWidth},
-                output.options());
-
-  output_buffer = output_buffer.view(
-      {output_buffer.size(0), group, output_buffer.size(1) / group,
-       output_buffer.size(2), output_buffer.size(3)});
-
-  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
-    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
-                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
-                      dilationW, im2col_step, deformable_group, columns);
-
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-
-    for (int g = 0; g < group; g++) {
-      output_buffer[elt][g] = output_buffer[elt][g]
-                                  .flatten(1)
-                                  .addmm_(weight[g].flatten(1), columns[g])
-                                  .view_as(output_buffer[elt][g]);
-    }
-  }
-
-  output_buffer = output_buffer.view(
-      {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2),
-       output_buffer.size(3), output_buffer.size(4)});
-
-  output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane,
-                                      im2col_step, outputHeight, outputWidth});
-  output_buffer.transpose_(1, 2);
-  output.copy_(output_buffer);
-  output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
-  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  offset = offset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  if (batch == 0) {
-    output = output.view({nOutputPlane, outputHeight, outputWidth});
-    input = input.view({nInputPlane, inputHeight, inputWidth});
-    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
-  }
-
-  return 1;
-}
-
-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
-                                    at::Tensor gradOutput, at::Tensor gradInput,
-                                    at::Tensor gradOffset, at::Tensor weight,
-                                    at::Tensor columns, int kW, int kH, int dW,
-                                    int dH, int padW, int padH, int dilationW,
-                                    int dilationH, int group,
-                                    int deformable_group, int im2col_step) {
-  shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW,
-              dilationH, dilationW, group, deformable_group);
-  at::DeviceGuard guard(input.device());
-
-  input = input.contiguous();
-  offset = offset.contiguous();
-  gradOutput = gradOutput.contiguous();
-  weight = weight.contiguous();
-
-  int batch = 1;
-
-  if (input.ndimension() == 3) {
-    // Force batch
-    batch = 0;
-    input = input.view({1, input.size(0), input.size(1), input.size(2)});
-    offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)});
-    gradOutput = gradOutput.view(
-        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
-  }
-
-  long batchSize = input.size(0);
-  long nInputPlane = input.size(1);
-  long inputHeight = input.size(2);
-  long inputWidth = input.size(3);
-
-  long nOutputPlane = weight.size(0);
-
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
-  TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
-  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  columns = at::zeros(
-      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
-      input.options());
-
-  // change order of grad output
-  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
-                                nOutputPlane, outputHeight, outputWidth});
-  gradOutput.transpose_(1, 2);
-
-  gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                              inputHeight, inputWidth});
-  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                      inputHeight, inputWidth});
-  gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step,
-                                deformable_group * 2 * kH * kW, outputHeight,
-                                outputWidth});
-  offset =
-      offset.view({batchSize / im2col_step, im2col_step,
-                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
-    // divide into groups
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-    gradOutput = gradOutput.view(
-        {gradOutput.size(0), group, gradOutput.size(1) / group,
-         gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)});
-
-    for (int g = 0; g < group; g++) {
-      columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
-                                     gradOutput[elt][g].flatten(1), 0.0f, 1.0f);
-    }
-
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    gradOutput = gradOutput.view(
-        {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2),
-         gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)});
-
-    deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane,
-                            inputHeight, inputWidth, kH, kW, padH, padW, dH, dW,
-                            dilationH, dilationW, im2col_step, deformable_group,
-                            gradOffset[elt]);
-
-    deformable_col2im(columns, offset[elt], nInputPlane, inputHeight,
-                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
-                      dilationW, im2col_step, deformable_group, gradInput[elt]);
-  }
-
-  gradOutput.transpose_(1, 2);
-  gradOutput =
-      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
-  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  gradOffset = gradOffset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-  offset = offset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  if (batch == 0) {
-    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
-    input = input.view({nInputPlane, inputHeight, inputWidth});
-    gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth});
-    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
-    gradOffset =
-        gradOffset.view({offset.size(1), offset.size(2), offset.size(3)});
-  }
-
-  return 1;
-}
-
-int deform_conv_backward_parameters_cuda(
-    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
-    at::Tensor gradWeight,  // at::Tensor gradBias,
-    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
-    int padW, int padH, int dilationW, int dilationH, int group,
-    int deformable_group, float scale, int im2col_step) {
-  // todo: transpose and reshape outGrad
-  // todo: reshape columns
-  // todo: add im2col_step as input
-
-  shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH,
-              padW, dilationH, dilationW, group, deformable_group);
-  at::DeviceGuard guard(input.device());
-
-  input = input.contiguous();
-  offset = offset.contiguous();
-  gradOutput = gradOutput.contiguous();
-
-  int batch = 1;
-
-  if (input.ndimension() == 3) {
-    // Force batch
-    batch = 0;
-    input = input.view(
-        at::IntList({1, input.size(0), input.size(1), input.size(2)}));
-    gradOutput = gradOutput.view(
-        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
-  }
-
-  long batchSize = input.size(0);
-  long nInputPlane = input.size(1);
-  long inputHeight = input.size(2);
-  long inputWidth = input.size(3);
-
-  long nOutputPlane = gradWeight.size(0);
-
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
-  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
-
-  columns = at::zeros(
-      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
-      input.options());
-
-  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
-                                nOutputPlane, outputHeight, outputWidth});
-  gradOutput.transpose_(1, 2);
-
-  at::Tensor gradOutputBuffer = at::zeros_like(gradOutput);
-  gradOutputBuffer =
-      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step,
-                             outputHeight, outputWidth});
-  gradOutputBuffer.copy_(gradOutput);
-  gradOutputBuffer =
-      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane,
-                             im2col_step * outputHeight, outputWidth});
-
-  gradOutput.transpose_(1, 2);
-  gradOutput =
-      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
-  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                      inputHeight, inputWidth});
-  offset =
-      offset.view({batchSize / im2col_step, im2col_step,
-                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
-    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
-                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
-                      dilationW, im2col_step, deformable_group, columns);
-
-    // divide into group
-    gradOutputBuffer = gradOutputBuffer.view(
-        {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group,
-         gradOutputBuffer.size(2), gradOutputBuffer.size(3)});
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    gradWeight =
-        gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1),
-                         gradWeight.size(2), gradWeight.size(3)});
-
-    for (int g = 0; g < group; g++) {
-      gradWeight[g] = gradWeight[g]
-                          .flatten(1)
-                          .addmm_(gradOutputBuffer[elt][g].flatten(1),
-                                  columns[g].transpose(1, 0), 1.0, scale)
-                          .view_as(gradWeight[g]);
-    }
-    gradOutputBuffer = gradOutputBuffer.view(
-        {gradOutputBuffer.size(0),
-         gradOutputBuffer.size(1) * gradOutputBuffer.size(2),
-         gradOutputBuffer.size(3), gradOutputBuffer.size(4)});
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1),
-                                  gradWeight.size(2), gradWeight.size(3),
-                                  gradWeight.size(4)});
-  }
-
-  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  offset = offset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  if (batch == 0) {
-    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
-    input = input.view({nInputPlane, inputHeight, inputWidth});
-  }
-
-  return 1;
-}
-
-void modulated_deform_conv_cuda_forward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
-    int kernel_h, 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 group, const int deformable_group,
-    const bool with_bias) {
-  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
-  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-  at::DeviceGuard guard(input.device());
-
-  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);
-
-  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
-    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
-             kernel_h_, kernel_w, kernel_h_, kernel_w_);
-  if (channels != channels_kernel * group)
-    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
-             channels, channels_kernel * group);
-
-  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;
-
-  if (ones.ndimension() != 2 ||
-      ones.size(0) * ones.size(1) < height_out * width_out) {
-    // Resize plane and fill with ones...
-    ones = at::ones({height_out, width_out}, input.options());
-  }
-
-  // resize output
-  output = output.view({batch, channels_out, height_out, width_out}).zero_();
-  // resize temporary columns
-  columns =
-      at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out},
-                input.options());
-
-  output = output.view({output.size(0), group, output.size(1) / group,
-                        output.size(2), output.size(3)});
-
-  for (int b = 0; b < batch; b++) {
-    modulated_deformable_im2col_cuda(
-        input[b], offset[b], mask[b], 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);
-
-    // divide into group
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-
-    for (int g = 0; g < group; g++) {
-      output[b][g] = output[b][g]
-                         .flatten(1)
-                         .addmm_(weight[g].flatten(1), columns[g])
-                         .view_as(output[b][g]);
-    }
-
-    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
-                          weight.size(3), weight.size(4)});
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-  }
-
-  output = output.view({output.size(0), output.size(1) * output.size(2),
-                        output.size(3), output.size(4)});
-
-  if (with_bias) {
-    output += bias.view({1, bias.size(0), 1, 1});
-  }
-}
-
-void modulated_deform_conv_cuda_backward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor columns,
-    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
-    at::Tensor grad_offset, at::Tensor grad_mask, 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 group, int deformable_group,
-    const bool with_bias) {
-  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
-  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-  at::DeviceGuard guard(input.device());
-
-  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_kernel = weight.size(1);
-  const int kernel_h_ = weight.size(2);
-  const int kernel_w_ = weight.size(3);
-  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
-    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
-             kernel_h_, kernel_w, kernel_h_, kernel_w_);
-  if (channels != channels_kernel * group)
-    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
-             channels, channels_kernel * group);
-
-  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;
-
-  if (ones.ndimension() != 2 ||
-      ones.size(0) * ones.size(1) < height_out * width_out) {
-    // Resize plane and fill with ones...
-    ones = at::ones({height_out, width_out}, input.options());
-  }
-
-  grad_input = grad_input.view({batch, channels, height, width});
-  columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out},
-                      input.options());
-
-  grad_output =
-      grad_output.view({grad_output.size(0), group, grad_output.size(1) / group,
-                        grad_output.size(2), grad_output.size(3)});
-
-  for (int b = 0; b < batch; b++) {
-    // divide int group
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-
-    for (int g = 0; g < group; g++) {
-      columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
-                        grad_output[b][g].flatten(1), 0.0f, 1.0f);
-    }
-
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
-                          weight.size(3), weight.size(4)});
-
-    // gradient w.r.t. input coordinate data
-    modulated_deformable_col2im_coord_cuda(
-        columns, input[b], offset[b], mask[b], 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[b],
-        grad_mask[b]);
-    // gradient w.r.t. input data
-    modulated_deformable_col2im_cuda(
-        columns, offset[b], mask[b], 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[b]);
-
-    // gradient w.r.t. weight, dWeight should accumulate across the batch and
-    // group
-    modulated_deformable_im2col_cuda(
-        input[b], offset[b], mask[b], 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);
-
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    grad_weight = grad_weight.view({group, grad_weight.size(0) / group,
-                                    grad_weight.size(1), grad_weight.size(2),
-                                    grad_weight.size(3)});
-    if (with_bias)
-      grad_bias = grad_bias.view({group, grad_bias.size(0) / group});
-
-    for (int g = 0; g < group; g++) {
-      grad_weight[g] =
-          grad_weight[g]
-              .flatten(1)
-              .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1))
-              .view_as(grad_weight[g]);
-      if (with_bias) {
-        grad_bias[g] =
-            grad_bias[g]
-                .view({-1, 1})
-                .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1}))
-                .view(-1);
-      }
-    }
-
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
-                                    grad_weight.size(2), grad_weight.size(3),
-                                    grad_weight.size(4)});
-    if (with_bias)
-      grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
-  }
-  grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1),
-                                  grad_output.size(2), grad_output.size(3),
-                                  grad_output.size(4)});
-}
diff --git a/spaces/sczhou/ProPainter/README.md b/spaces/sczhou/ProPainter/README.md
deleted file mode 100644
index 7f0e254106d4c0c5d7b1ef2befa5faea7cd13079..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ProPainter
-emoji: 👨‍🎨
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.50.2
-app_file: web-demos/hugging_face/app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sdhsdhk/bingo111/src/lib/isomorphic/node.ts b/spaces/sdhsdhk/bingo111/src/lib/isomorphic/node.ts
deleted file mode 100644
index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/lib/isomorphic/node.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import Debug from 'debug'
-
-const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici')
-const { HttpsProxyAgent } = require('https-proxy-agent')
-const ws = require('ws')
-
-const debug = Debug('bingo')
-
-const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY;
-let WebSocket = ws.WebSocket
-
-if (httpProxy) {
-  setGlobalDispatcher(new ProxyAgent(httpProxy))
-  const agent = new HttpsProxyAgent(httpProxy)
-  // @ts-ignore
-  WebSocket = class extends ws.WebSocket {
-    constructor(address: string | URL, options: typeof ws.WebSocket) {
-      super(address, {
-        ...options,
-        agent,
-      })
-    }
-  }
-}
-
-export default { fetch, WebSocket, debug }
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/conformer/encoder.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/conformer/encoder.py
deleted file mode 100644
index 980d15a18b8b5c87e14c7edc808a6b19ba999dee..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/conformer/encoder.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
-#                Northwestern Polytechnical University (Pengcheng Guo)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Encoder definition."""
-
-import logging
-import torch
-
-from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
-from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
-from espnet.nets.pytorch_backend.nets_utils import get_activation
-from espnet.nets.pytorch_backend.transducer.vgg2l import VGG2L
-from espnet.nets.pytorch_backend.transformer.attention import (
-    MultiHeadedAttention,  # noqa: H301
-    RelPositionMultiHeadedAttention,  # noqa: H301
-    LegacyRelPositionMultiHeadedAttention,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.embedding import (
-    PositionalEncoding,  # noqa: H301
-    ScaledPositionalEncoding,  # noqa: H301
-    RelPositionalEncoding,  # noqa: H301
-    LegacyRelPositionalEncoding,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
-from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear
-from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d
-from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
-    PositionwiseFeedForward,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.repeat import repeat
-from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling
-
-
-class Encoder(torch.nn.Module):
-    """Conformer encoder module.
-
-    Args:
-        idim (int): Input dimension.
-        attention_dim (int): Dimention of attention.
-        attention_heads (int): The number of heads of multi head attention.
-        linear_units (int): The number of units of position-wise feed forward.
-        num_blocks (int): The number of decoder blocks.
-        dropout_rate (float): Dropout rate.
-        positional_dropout_rate (float): Dropout rate after adding positional encoding.
-        attention_dropout_rate (float): Dropout rate in attention.
-        input_layer (Union[str, torch.nn.Module]): Input layer type.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
-            if True, additional linear will be applied.
-            i.e. x -> x + linear(concat(x, att(x)))
-            if False, no additional linear will be applied. i.e. x -> x + att(x)
-        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
-        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
-        macaron_style (bool): Whether to use macaron style for positionwise layer.
-        pos_enc_layer_type (str): Encoder positional encoding layer type.
-        selfattention_layer_type (str): Encoder attention layer type.
-        activation_type (str): Encoder activation function type.
-        use_cnn_module (bool): Whether to use convolution module.
-        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
-        cnn_module_kernel (int): Kernerl size of convolution module.
-        padding_idx (int): Padding idx for input_layer=embed.
-
-    """
-
-    def __init__(
-        self,
-        idim,
-        attention_dim=256,
-        attention_heads=4,
-        linear_units=2048,
-        num_blocks=6,
-        dropout_rate=0.1,
-        positional_dropout_rate=0.1,
-        attention_dropout_rate=0.0,
-        input_layer="conv2d",
-        normalize_before=True,
-        concat_after=False,
-        positionwise_layer_type="linear",
-        positionwise_conv_kernel_size=1,
-        macaron_style=False,
-        pos_enc_layer_type="abs_pos",
-        selfattention_layer_type="selfattn",
-        activation_type="swish",
-        use_cnn_module=False,
-        zero_triu=False,
-        cnn_module_kernel=31,
-        padding_idx=-1,
-    ):
-        """Construct an Encoder object."""
-        super(Encoder, self).__init__()
-
-        activation = get_activation(activation_type)
-        if pos_enc_layer_type == "abs_pos":
-            pos_enc_class = PositionalEncoding
-        elif pos_enc_layer_type == "scaled_abs_pos":
-            pos_enc_class = ScaledPositionalEncoding
-        elif pos_enc_layer_type == "rel_pos":
-            assert selfattention_layer_type == "rel_selfattn"
-            pos_enc_class = RelPositionalEncoding
-        elif pos_enc_layer_type == "legacy_rel_pos":
-            pos_enc_class = LegacyRelPositionalEncoding
-            assert selfattention_layer_type == "legacy_rel_selfattn"
-        else:
-            raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
-
-        self.conv_subsampling_factor = 1
-        if input_layer == "linear":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Linear(idim, attention_dim),
-                torch.nn.LayerNorm(attention_dim),
-                torch.nn.Dropout(dropout_rate),
-                pos_enc_class(attention_dim, positional_dropout_rate),
-            )
-        elif input_layer == "conv2d":
-            self.embed = Conv2dSubsampling(
-                idim,
-                attention_dim,
-                dropout_rate,
-                pos_enc_class(attention_dim, positional_dropout_rate),
-            )
-            self.conv_subsampling_factor = 4
-        elif input_layer == "vgg2l":
-            self.embed = VGG2L(idim, attention_dim)
-            self.conv_subsampling_factor = 4
-        elif input_layer == "embed":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
-                pos_enc_class(attention_dim, positional_dropout_rate),
-            )
-        elif isinstance(input_layer, torch.nn.Module):
-            self.embed = torch.nn.Sequential(
-                input_layer,
-                pos_enc_class(attention_dim, positional_dropout_rate),
-            )
-        elif input_layer is None:
-            self.embed = torch.nn.Sequential(
-                pos_enc_class(attention_dim, positional_dropout_rate)
-            )
-        else:
-            raise ValueError("unknown input_layer: " + input_layer)
-        self.normalize_before = normalize_before
-
-        # self-attention module definition
-        if selfattention_layer_type == "selfattn":
-            logging.info("encoder self-attention layer type = self-attention")
-            encoder_selfattn_layer = MultiHeadedAttention
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                attention_dim,
-                attention_dropout_rate,
-            )
-        elif selfattention_layer_type == "legacy_rel_selfattn":
-            assert pos_enc_layer_type == "legacy_rel_pos"
-            encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                attention_dim,
-                attention_dropout_rate,
-            )
-        elif selfattention_layer_type == "rel_selfattn":
-            logging.info("encoder self-attention layer type = relative self-attention")
-            assert pos_enc_layer_type == "rel_pos"
-            encoder_selfattn_layer = RelPositionMultiHeadedAttention
-            encoder_selfattn_layer_args = (
-                attention_heads,
-                attention_dim,
-                attention_dropout_rate,
-                zero_triu,
-            )
-        else:
-            raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type)
-
-        # feed-forward module definition
-        if positionwise_layer_type == "linear":
-            positionwise_layer = PositionwiseFeedForward
-            positionwise_layer_args = (
-                attention_dim,
-                linear_units,
-                dropout_rate,
-                activation,
-            )
-        elif positionwise_layer_type == "conv1d":
-            positionwise_layer = MultiLayeredConv1d
-            positionwise_layer_args = (
-                attention_dim,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        elif positionwise_layer_type == "conv1d-linear":
-            positionwise_layer = Conv1dLinear
-            positionwise_layer_args = (
-                attention_dim,
-                linear_units,
-                positionwise_conv_kernel_size,
-                dropout_rate,
-            )
-        else:
-            raise NotImplementedError("Support only linear or conv1d.")
-
-        # convolution module definition
-        convolution_layer = ConvolutionModule
-        convolution_layer_args = (attention_dim, cnn_module_kernel, activation)
-
-        self.encoders = repeat(
-            num_blocks,
-            lambda lnum: EncoderLayer(
-                attention_dim,
-                encoder_selfattn_layer(*encoder_selfattn_layer_args),
-                positionwise_layer(*positionwise_layer_args),
-                positionwise_layer(*positionwise_layer_args) if macaron_style else None,
-                convolution_layer(*convolution_layer_args) if use_cnn_module else None,
-                dropout_rate,
-                normalize_before,
-                concat_after,
-            ),
-        )
-        if self.normalize_before:
-            self.after_norm = LayerNorm(attention_dim)
-
-    def forward(self, xs, masks):
-        """Encode input sequence.
-
-        Args:
-            xs (torch.Tensor): Input tensor (#batch, time, idim).
-            masks (torch.Tensor): Mask tensor (#batch, time).
-
-        Returns:
-            torch.Tensor: Output tensor (#batch, time, attention_dim).
-            torch.Tensor: Mask tensor (#batch, time).
-
-        """
-        if isinstance(self.embed, (Conv2dSubsampling, VGG2L)):
-            xs, masks = self.embed(xs, masks)
-        else:
-            xs = self.embed(xs)
-
-        xs, masks = self.encoders(xs, masks)
-        if isinstance(xs, tuple):
-            xs = xs[0]
-
-        if self.normalize_before:
-            xs = self.after_norm(xs)
-        return xs, masks
diff --git a/spaces/segments-tobias/conex/espnet2/bin/asr_inference.py b/spaces/segments-tobias/conex/espnet2/bin/asr_inference.py
deleted file mode 100644
index 7b7e45732bac6d32126011b9924f997851389e11..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/bin/asr_inference.py
+++ /dev/null
@@ -1,463 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import logging
-from pathlib import Path
-import sys
-from typing import Optional
-from typing import Sequence
-from typing import Tuple
-from typing import Union
-
-import numpy as np
-import torch
-from typeguard import check_argument_types
-from typeguard import check_return_type
-from typing import List
-
-from espnet.nets.batch_beam_search import BatchBeamSearch
-from espnet.nets.batch_beam_search_online_sim import BatchBeamSearchOnlineSim
-from espnet.nets.beam_search import BeamSearch
-from espnet.nets.beam_search import Hypothesis
-from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
-from espnet.nets.scorer_interface import BatchScorerInterface
-from espnet.nets.scorers.ctc import CTCPrefixScorer
-from espnet.nets.scorers.length_bonus import LengthBonus
-from espnet.utils.cli_utils import get_commandline_args
-from espnet2.fileio.datadir_writer import DatadirWriter
-from espnet2.tasks.asr import ASRTask
-from espnet2.tasks.lm import LMTask
-from espnet2.text.build_tokenizer import build_tokenizer
-from espnet2.text.token_id_converter import TokenIDConverter
-from espnet2.torch_utils.device_funcs import to_device
-from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
-from espnet2.utils import config_argparse
-from espnet2.utils.types import str2bool
-from espnet2.utils.types import str2triple_str
-from espnet2.utils.types import str_or_none
-
-
-class Speech2Text:
-    """Speech2Text class
-
-    Examples:
-        >>> import soundfile
-        >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
-        >>> audio, rate = soundfile.read("speech.wav")
-        >>> speech2text(audio)
-        [(text, token, token_int, hypothesis object), ...]
-
-    """
-
-    def __init__(
-        self,
-        asr_train_config: Union[Path, str],
-        asr_model_file: Union[Path, str] = None,
-        lm_train_config: Union[Path, str] = None,
-        lm_file: Union[Path, str] = None,
-        token_type: str = None,
-        bpemodel: str = None,
-        device: str = "cpu",
-        maxlenratio: float = 0.0,
-        minlenratio: float = 0.0,
-        batch_size: int = 1,
-        dtype: str = "float32",
-        beam_size: int = 20,
-        ctc_weight: float = 0.5,
-        lm_weight: float = 1.0,
-        penalty: float = 0.0,
-        nbest: int = 1,
-        streaming: bool = False,
-    ):
-        assert check_argument_types()
-
-        # 1. Build ASR model
-        scorers = {}
-        asr_model, asr_train_args = ASRTask.build_model_from_file(
-            asr_train_config, asr_model_file, device
-        )
-        asr_model.to(dtype=getattr(torch, dtype)).eval()
-
-        decoder = asr_model.decoder
-        ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
-        token_list = asr_model.token_list
-        scorers.update(
-            decoder=decoder,
-            ctc=ctc,
-            length_bonus=LengthBonus(len(token_list)),
-        )
-
-        # 2. Build Language model
-        if lm_train_config is not None:
-            lm, lm_train_args = LMTask.build_model_from_file(
-                lm_train_config, lm_file, device
-            )
-            scorers["lm"] = lm.lm
-
-        # 3. Build BeamSearch object
-        weights = dict(
-            decoder=1.0 - ctc_weight,
-            ctc=ctc_weight,
-            lm=lm_weight,
-            length_bonus=penalty,
-        )
-        beam_search = BeamSearch(
-            beam_size=beam_size,
-            weights=weights,
-            scorers=scorers,
-            sos=asr_model.sos,
-            eos=asr_model.eos,
-            vocab_size=len(token_list),
-            token_list=token_list,
-            pre_beam_score_key=None if ctc_weight == 1.0 else "full",
-        )
-        # TODO(karita): make all scorers batchfied
-        if batch_size == 1:
-            non_batch = [
-                k
-                for k, v in beam_search.full_scorers.items()
-                if not isinstance(v, BatchScorerInterface)
-            ]
-            if len(non_batch) == 0:
-                if streaming:
-                    beam_search.__class__ = BatchBeamSearchOnlineSim
-                    beam_search.set_streaming_config(asr_train_config)
-                    logging.info("BatchBeamSearchOnlineSim implementation is selected.")
-                else:
-                    beam_search.__class__ = BatchBeamSearch
-                    logging.info("BatchBeamSearch implementation is selected.")
-            else:
-                logging.warning(
-                    f"As non-batch scorers {non_batch} are found, "
-                    f"fall back to non-batch implementation."
-                )
-        beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
-        for scorer in scorers.values():
-            if isinstance(scorer, torch.nn.Module):
-                scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
-        logging.info(f"Beam_search: {beam_search}")
-        logging.info(f"Decoding device={device}, dtype={dtype}")
-
-        # 4. [Optional] Build Text converter: e.g. bpe-sym -> Text
-        if token_type is None:
-            token_type = asr_train_args.token_type
-        if bpemodel is None:
-            bpemodel = asr_train_args.bpemodel
-
-        if token_type is None:
-            tokenizer = None
-        elif token_type == "bpe":
-            if bpemodel is not None:
-                tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
-            else:
-                tokenizer = None
-        else:
-            tokenizer = build_tokenizer(token_type=token_type)
-        converter = TokenIDConverter(token_list=token_list)
-        logging.info(f"Text tokenizer: {tokenizer}")
-
-        self.asr_model = asr_model
-        self.asr_train_args = asr_train_args
-        self.converter = converter
-        self.tokenizer = tokenizer
-        self.beam_search = beam_search
-        self.maxlenratio = maxlenratio
-        self.minlenratio = minlenratio
-        self.device = device
-        self.dtype = dtype
-        self.nbest = nbest
-
-    @torch.no_grad()
-    def __call__(
-        self, speech: Union[torch.Tensor, np.ndarray]
-    ) -> List[Tuple[Optional[str], List[str], List[int], Hypothesis]]:
-        """Inference
-
-        Args:
-            data: Input speech data
-        Returns:
-            text, token, token_int, hyp
-
-        """
-        assert check_argument_types()
-
-        # Input as audio signal
-        if isinstance(speech, np.ndarray):
-            speech = torch.tensor(speech)
-
-        # data: (Nsamples,) -> (1, Nsamples)
-        speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
-        # lenghts: (1,)
-        lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
-        batch = {"speech": speech, "speech_lengths": lengths}
-
-        # a. To device
-        batch = to_device(batch, device=self.device)
-
-        # b. Forward Encoder
-        enc, _ = self.asr_model.encode(**batch)
-        assert len(enc) == 1, len(enc)
-
-        # c. Passed the encoder result and the beam search
-        nbest_hyps = self.beam_search(
-            x=enc[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
-        )
-        nbest_hyps = nbest_hyps[: self.nbest]
-
-        results = []
-        for hyp in nbest_hyps:
-            assert isinstance(hyp, Hypothesis), type(hyp)
-
-            # remove sos/eos and get results
-            token_int = hyp.yseq[1:-1].tolist()
-
-            # remove blank symbol id, which is assumed to be 0
-            token_int = list(filter(lambda x: x != 0, token_int))
-
-            # Change integer-ids to tokens
-            token = self.converter.ids2tokens(token_int)
-
-            if self.tokenizer is not None:
-                text = self.tokenizer.tokens2text(token)
-            else:
-                text = None
-            results.append((text, token, token_int, hyp))
-
-        assert check_return_type(results)
-        return results
-
-
-def inference(
-    output_dir: str,
-    maxlenratio: float,
-    minlenratio: float,
-    batch_size: int,
-    dtype: str,
-    beam_size: int,
-    ngpu: int,
-    seed: int,
-    ctc_weight: float,
-    lm_weight: float,
-    penalty: float,
-    nbest: int,
-    num_workers: int,
-    log_level: Union[int, str],
-    data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
-    key_file: Optional[str],
-    asr_train_config: str,
-    asr_model_file: str,
-    lm_train_config: Optional[str],
-    lm_file: Optional[str],
-    word_lm_train_config: Optional[str],
-    word_lm_file: Optional[str],
-    token_type: Optional[str],
-    bpemodel: Optional[str],
-    allow_variable_data_keys: bool,
-    streaming: bool,
-):
-    assert check_argument_types()
-    if batch_size > 1:
-        raise NotImplementedError("batch decoding is not implemented")
-    if word_lm_train_config is not None:
-        raise NotImplementedError("Word LM is not implemented")
-    if ngpu > 1:
-        raise NotImplementedError("only single GPU decoding is supported")
-
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
-    )
-
-    if ngpu >= 1:
-        device = "cuda"
-    else:
-        device = "cpu"
-
-    # 1. Set random-seed
-    set_all_random_seed(seed)
-
-    # 2. Build speech2text
-    speech2text = Speech2Text(
-        asr_train_config=asr_train_config,
-        asr_model_file=asr_model_file,
-        lm_train_config=lm_train_config,
-        lm_file=lm_file,
-        token_type=token_type,
-        bpemodel=bpemodel,
-        device=device,
-        maxlenratio=maxlenratio,
-        minlenratio=minlenratio,
-        dtype=dtype,
-        beam_size=beam_size,
-        ctc_weight=ctc_weight,
-        lm_weight=lm_weight,
-        penalty=penalty,
-        nbest=nbest,
-        streaming=streaming,
-    )
-
-    # 3. Build data-iterator
-    loader = ASRTask.build_streaming_iterator(
-        data_path_and_name_and_type,
-        dtype=dtype,
-        batch_size=batch_size,
-        key_file=key_file,
-        num_workers=num_workers,
-        preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-        collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-        allow_variable_data_keys=allow_variable_data_keys,
-        inference=True,
-    )
-
-    # 7 .Start for-loop
-    # FIXME(kamo): The output format should be discussed about
-    with DatadirWriter(output_dir) as writer:
-        for keys, batch in loader:
-            assert isinstance(batch, dict), type(batch)
-            assert all(isinstance(s, str) for s in keys), keys
-            _bs = len(next(iter(batch.values())))
-            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
-            batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
-
-            # N-best list of (text, token, token_int, hyp_object)
-            try:
-                results = speech2text(**batch)
-            except TooShortUttError as e:
-                logging.warning(f"Utterance {keys} {e}")
-                hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
-                results = [[" ", ["<space>"], [2], hyp]] * nbest
-
-            # Only supporting batch_size==1
-            key = keys[0]
-            for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
-                # Create a directory: outdir/{n}best_recog
-                ibest_writer = writer[f"{n}best_recog"]
-
-                # Write the result to each file
-                ibest_writer["token"][key] = " ".join(token)
-                ibest_writer["token_int"][key] = " ".join(map(str, token_int))
-                ibest_writer["score"][key] = str(hyp.score)
-
-                if text is not None:
-                    ibest_writer["text"][key] = text
-
-
-def get_parser():
-    parser = config_argparse.ArgumentParser(
-        description="ASR Decoding",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-    )
-
-    # Note(kamo): Use '_' instead of '-' as separator.
-    # '-' is confusing if written in yaml.
-    parser.add_argument(
-        "--log_level",
-        type=lambda x: x.upper(),
-        default="INFO",
-        choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
-        help="The verbose level of logging",
-    )
-
-    parser.add_argument("--output_dir", type=str, required=True)
-    parser.add_argument(
-        "--ngpu",
-        type=int,
-        default=0,
-        help="The number of gpus. 0 indicates CPU mode",
-    )
-    parser.add_argument("--seed", type=int, default=0, help="Random seed")
-    parser.add_argument(
-        "--dtype",
-        default="float32",
-        choices=["float16", "float32", "float64"],
-        help="Data type",
-    )
-    parser.add_argument(
-        "--num_workers",
-        type=int,
-        default=1,
-        help="The number of workers used for DataLoader",
-    )
-
-    group = parser.add_argument_group("Input data related")
-    group.add_argument(
-        "--data_path_and_name_and_type",
-        type=str2triple_str,
-        required=True,
-        action="append",
-    )
-    group.add_argument("--key_file", type=str_or_none)
-    group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
-
-    group = parser.add_argument_group("The model configuration related")
-    group.add_argument("--asr_train_config", type=str, required=True)
-    group.add_argument("--asr_model_file", type=str, required=True)
-    group.add_argument("--lm_train_config", type=str)
-    group.add_argument("--lm_file", type=str)
-    group.add_argument("--word_lm_train_config", type=str)
-    group.add_argument("--word_lm_file", type=str)
-
-    group = parser.add_argument_group("Beam-search related")
-    group.add_argument(
-        "--batch_size",
-        type=int,
-        default=1,
-        help="The batch size for inference",
-    )
-    group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
-    group.add_argument("--beam_size", type=int, default=20, help="Beam size")
-    group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
-    group.add_argument(
-        "--maxlenratio",
-        type=float,
-        default=0.0,
-        help="Input length ratio to obtain max output length. "
-        "If maxlenratio=0.0 (default), it uses a end-detect "
-        "function "
-        "to automatically find maximum hypothesis lengths",
-    )
-    group.add_argument(
-        "--minlenratio",
-        type=float,
-        default=0.0,
-        help="Input length ratio to obtain min output length",
-    )
-    group.add_argument(
-        "--ctc_weight",
-        type=float,
-        default=0.5,
-        help="CTC weight in joint decoding",
-    )
-    group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
-    group.add_argument("--streaming", type=str2bool, default=False)
-
-    group = parser.add_argument_group("Text converter related")
-    group.add_argument(
-        "--token_type",
-        type=str_or_none,
-        default=None,
-        choices=["char", "bpe", None],
-        help="The token type for ASR model. "
-        "If not given, refers from the training args",
-    )
-    group.add_argument(
-        "--bpemodel",
-        type=str_or_none,
-        default=None,
-        help="The model path of sentencepiece. "
-        "If not given, refers from the training args",
-    )
-
-    return parser
-
-
-def main(cmd=None):
-    print(get_commandline_args(), file=sys.stderr)
-    parser = get_parser()
-    args = parser.parse_args(cmd)
-    kwargs = vars(args)
-    kwargs.pop("config", None)
-    inference(**kwargs)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h b/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h
deleted file mode 100644
index 51bb27e9ee828f967e8aa854c2d55574040c6d7e..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h
+++ /dev/null
@@ -1,38 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-/*!
-* Copyright (c) Facebook, Inc. and its affiliates.
-* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-*/
-
-#pragma once
-#include <torch/extension.h>
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-
diff --git a/spaces/shriarul5273/Yolov7/utils/google_utils.py b/spaces/shriarul5273/Yolov7/utils/google_utils.py
deleted file mode 100644
index f363408e63981702e63dcda189cbc2099d0a9499..0000000000000000000000000000000000000000
--- a/spaces/shriarul5273/Yolov7/utils/google_utils.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Google utils: https://cloud.google.com/storage/docs/reference/libraries
-
-import os
-import platform
-import subprocess
-import time
-from pathlib import Path
-
-import requests
-import torch
-
-
-def gsutil_getsize(url=''):
-    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
-    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
-    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
-
-
-def attempt_download(file, repo='WongKinYiu/yolov7'):
-    # Attempt file download if does not exist
-    file = Path(str(file).strip().replace("'", '').lower())
-
-    if not file.exists():
-        try:
-            response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
-            assets = [x['name'] for x in response['assets']]  # release assets
-            tag = response['tag_name']  # i.e. 'v1.0'
-        except:  # fallback plan
-            assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt', 
-                      'yolov7-e6e.pt', 'yolov7-w6.pt']
-            tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]
-
-        name = file.name
-        if name in assets:
-            msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
-            redundant = False  # second download option
-            try:  # GitHub
-                url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
-                print(f'Downloading {url} to {file}...')
-                torch.hub.download_url_to_file(url, file)
-                assert file.exists() and file.stat().st_size > 1E6  # check
-            except Exception as e:  # GCP
-                print(f'Download error: {e}')
-                assert redundant, 'No secondary mirror'
-                url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
-                print(f'Downloading {url} to {file}...')
-                os.system(f'curl -L {url} -o {file}')  # torch.hub.download_url_to_file(url, weights)
-            finally:
-                if not file.exists() or file.stat().st_size < 1E6:  # check
-                    file.unlink(missing_ok=True)  # remove partial downloads
-                    print(f'ERROR: Download failure: {msg}')
-                print('')
-                return
-
-
-def gdrive_download(id='', file='tmp.zip'):
-    # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download()
-    t = time.time()
-    file = Path(file)
-    cookie = Path('cookie')  # gdrive cookie
-    print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
-    file.unlink(missing_ok=True)  # remove existing file
-    cookie.unlink(missing_ok=True)  # remove existing cookie
-
-    # Attempt file download
-    out = "NUL" if platform.system() == "Windows" else "/dev/null"
-    os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
-    if os.path.exists('cookie'):  # large file
-        s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
-    else:  # small file
-        s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
-    r = os.system(s)  # execute, capture return
-    cookie.unlink(missing_ok=True)  # remove existing cookie
-
-    # Error check
-    if r != 0:
-        file.unlink(missing_ok=True)  # remove partial
-        print('Download error ')  # raise Exception('Download error')
-        return r
-
-    # Unzip if archive
-    if file.suffix == '.zip':
-        print('unzipping... ', end='')
-        os.system(f'unzip -q {file}')  # unzip
-        file.unlink()  # remove zip to free space
-
-    print(f'Done ({time.time() - t:.1f}s)')
-    return r
-
-
-def get_token(cookie="./cookie"):
-    with open(cookie) as f:
-        for line in f:
-            if "download" in line:
-                return line.split()[-1]
-    return ""
-
-# def upload_blob(bucket_name, source_file_name, destination_blob_name):
-#     # Uploads a file to a bucket
-#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
-#
-#     storage_client = storage.Client()
-#     bucket = storage_client.get_bucket(bucket_name)
-#     blob = bucket.blob(destination_blob_name)
-#
-#     blob.upload_from_filename(source_file_name)
-#
-#     print('File {} uploaded to {}.'.format(
-#         source_file_name,
-#         destination_blob_name))
-#
-#
-# def download_blob(bucket_name, source_blob_name, destination_file_name):
-#     # Uploads a blob from a bucket
-#     storage_client = storage.Client()
-#     bucket = storage_client.get_bucket(bucket_name)
-#     blob = bucket.blob(source_blob_name)
-#
-#     blob.download_to_filename(destination_file_name)
-#
-#     print('Blob {} downloaded to {}.'.format(
-#         source_blob_name,
-#         destination_file_name))
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/metrics/__init__.py b/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/metrics/__init__.py
deleted file mode 100644
index db8124b132f91216c0ded226f20ea3a046734728..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/metrics/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-# empty
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/op/upfirdn2d.py b/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/op/upfirdn2d.py
deleted file mode 100644
index 9ca1f5c72098debfb0ffa1ba1b81eb92eb64d428..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/op/upfirdn2d.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import os
-import platform
-
-import torch
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-use_fallback = False
-
-# Try loading precompiled, otherwise use native fallback
-try:
-    import upfirdn2d_op
-except ModuleNotFoundError as e:
-    print('StyleGAN2: Optimized CUDA op UpFirDn2d not available, using native PyTorch fallback.')
-    use_fallback = True
-
-class UpFirDn2dBackward(Function):
-    @staticmethod
-    def forward(
-        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
-    ):
-
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_op.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_op.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(
-            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
-        )
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_op.upfirdn2d(
-            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-        )
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    if use_fallback or input.device.type == "cpu":
-        out = upfirdn2d_native(
-            input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
-        )
-    else:
-        out = UpFirDn2d.apply(
-            input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
-        )
-
-    return out
-
-
-def upfirdn2d_native(
-    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/simonschoe/Call2Vec/app.py b/spaces/simonschoe/Call2Vec/app.py
deleted file mode 100644
index daf104e14bb3fe772c2e760bf62e00769a79205e..0000000000000000000000000000000000000000
--- a/spaces/simonschoe/Call2Vec/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import numpy as np
-import pandas as pd
-from datetime import datetime
-import os
-import re
-
-from huggingface_hub import hf_hub_url, cached_download
-from gensim.models.fasttext import load_facebook_model
-
-ACCESS_KEY = os.environ.get('ACCESS_KEY')
-
-
-# Setup model
-url = hf_hub_url(repo_id="simonschoe/call2vec", filename="model.bin")
-cached_download(url)
-model = load_facebook_model(cached_download(url))
-
-def semantic_search(_input, n):
-    """ Perform semantic search """
-
-    _input = re.split('[,;\n]', _input)
-    _input = [s.strip().lower().replace(' ', '_') for s in _input if s]
-
-    if _input[0] != ACCESS_KEY:
-        with open('log.txt', 'a') as f:
-            f.write(str(datetime.now()) + '+++' + '___'.join(_input) + '\n')
-
-    if len(_input) > 1:
-        avg_input = np.stack([model.wv[w] for w in _input], axis=0).mean(axis=0)
-        nearest_neighbours = model.wv.most_similar(positive=avg_input, topn=n)
-        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours]
-    else:
-        nearest_neighbours = model.wv.most_similar(positive=_input[0], topn=n)
-        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours]
-    
-    if _input[0] == ACCESS_KEY:
-        with open('log.txt', 'r') as f:
-            prompts = f.readlines()
-            prompts = [p.strip().split('+++') for p in prompts]
-        result = pd.DataFrame(prompts,
-                              columns=['Time', 'Prompt'])
-    else:
-        result = pd.DataFrame([(a[0],a[1],b) for a,b in zip(nearest_neighbours, frequencies)],
-                              columns=['Token', 'Cosine Similarity', 'Corpus Frequency'])
-    
-    result.to_csv('result.csv')
-    return result, 'result.csv', '\n'.join(_input)
-
-app = gr.Blocks()
-
-with app:
-    gr.Markdown("# Call2Vec")
-    gr.Markdown("## Semantic Search in Quarterly Earnings Conference Calls")
-    with gr.Row():
-        with gr.Column():
-            text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query")
-            with gr.Row():
-                n = gr.Slider(value=50, minimum=5, maximum=500, step=5, label="Number of Neighbours")
-                compute_bt = gr.Button("Search")
-            df_out = gr.Dataframe(interactive=False)
-            f_out = gr.File(interactive=False, label="Download")
-        with gr.Column():
-            gr.Markdown(
-                """
-                #### Project Description
-                Call2Vec is a [fastText](https://fasttext.cc/) word embedding model trained via [Gensim](https://radimrehurek.com/gensim/). It maps each token in the vocabulary into a dense, 300-dimensional vector space, designed for performing semantic search.
-                The model is trained on a large sample of quarterly earnings conference calls, held by U.S. firms during the 2006-2022 period. In particular, the training data is restriced to the (rather sponentous) executives' remarks of the Q&A section of the call. The data has been preprocessed prior to model training via stop word removal, lemmatization, named entity masking, and coocurrence modeling.
-                """
-            )
-            gr.Markdown(
-                """
-                #### App usage
-                The model is intented to be used for **semantic search**: It encodes the search query (entered in the textbox on the right) in a dense vector space and finds semantic neighbours, i.e., token which frequently occur within similar contexts in the underlying training data.
-                The model allows for two use cases:
-                1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence").
-                2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token.
-                """
-            )
-            gr.Examples(
-                examples = [
-                    ["transformation", 20],
-                    ["climate_change", 50],
-                    ["risk, political_risk, uncertainty", 250],
-                ],
-                inputs = [text_in, n],
-                outputs = [df_out, f_out, text_in],
-                fn = semantic_search,
-                cache_examples=True
-            )
-    gr.Markdown(
-        """
-        <p style="text-align: center;">
-            Call2Vec by X and Y
-            <br>
-            <img 'id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" />
-        </p>
-        """
-    )
-    compute_bt.click(semantic_search, inputs=[text_in, n], outputs=[df_out, f_out, text_in])
-
-app.launch()
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Best Love Songs of 2022 in MP3 Quality.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Best Love Songs of 2022 in MP3 Quality.md
deleted file mode 100644
index 8c3c3db37b4f802f0aa29c2d22d2003067e13440..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Best Love Songs of 2022 in MP3 Quality.md	
+++ /dev/null
@@ -1,248 +0,0 @@
-
-<h1>Love Songs MP3 Download 2022: How to Find and Enjoy the Best Romantic Songs of the Year</h1>
- <p>Are you looking for some new love songs to spice up your playlist? Do you want to express your feelings to your partner with some sweet tunes? Or do you just want to relax and enjoy some soothing melodies that make you feel good?</p>
- <p>If you answered yes to any of these questions, then you are in the right place. In this article, we will tell you everything you need to know about love songs mp3 download 2022. We will explain what are love songs and why people listen to them, what are the benefits of listening to love songs, and how to find the best love songs of 2022 by genre. We will also give you some examples of the most popular and romantic songs of the year and where to download them for free or at a low cost.</p>
-<h2>love songs mp3 download 2022</h2><br /><p><b><b>Download Zip</b> &#128504; <a href="https://ssurll.com/2uNRet">https://ssurll.com/2uNRet</a></b></p><br /><br />
- <p>So, without further ado, let's get started!</p>
- <h2>Introduction</h2>
- <h3>What are love songs and why do people listen to them?</h3>
- <p>Love songs are songs that express or evoke emotions related to love, such as affection, attraction, admiration, devotion, passion, or heartbreak. They can be sung by solo artists or duets, and they can have different styles, tempos, and lyrics. Some love songs are upbeat and cheerful, while others are slow and sad. Some love songs are realistic and honest, while others are idealistic and poetic.</p>
- <p>People listen to love songs for various reasons. Some of the most common ones are:</p>
- <ul>
-<li>To celebrate their love for someone or their relationship status.</li>
-<li>To cope with their feelings of loneliness or rejection.</li>
-<li>To express their emotions or communicate their message to someone.</li>
-<li>To reminisce about their past experiences or memories.</li>
-<li>To relax and enjoy some pleasant music.</li>
-</ul>
- <h3>What are the benefits of listening to love songs?</h3>
- <p>Listening to love songs can have many positive effects on your mood, health, and well-being. Some of the benefits are:</p>
- <ul>
-<li>They can boost your happiness and reduce your stress levels.</li>
-<li>They can enhance your creativity and imagination.</li>
-<li>They can improve your memory and cognitive skills.</li>
-<li>They can increase your empathy and compassion.</li>
-<li>They can strengthen your bond with your partner or friends.</li>
-</ul>
- <h3>How to find the best love songs of 2022?</h3>
- <p>Finding the best love songs of 2022 can be a challenging task, especially if you have a specific taste or preference. However, there are some tips and tricks that can help you discover new and amazing songs that suit your mood and style. Here are some of them:</p>
- <ul>
-<li>Browse online platforms that offer music streaming or downloading services, such as Spotify, YouTube, Apple Music, Amazon Music, Wynk Music, etc. You can search by genre, artist, album, playlist, or keyword. You can also check out the charts, recommendations, reviews, ratings, or comments from other users.</li>
-<li>Follow social media accounts or blogs that share music news, updates, reviews, or suggestions. You can also join online communities or forums that discuss music topics or genres. You can ask for recommendations, opinions, feedback, or advice from other music lovers or experts.</li>
-<li <li>Listen to radio stations or podcasts that play or feature love songs or music genres that you like. You can also tune in to live shows or events that showcase new or upcoming artists or songs. You can also call or text the hosts or guests and request your favorite songs or ask for suggestions.</li>
-<li>Ask your friends, family, or partner for their opinions or recommendations. You can also share your playlists or songs with them and get their feedback. You can also make a collaborative playlist with them and add songs that you both enjoy.</li>
-</ul>
- <h2>Best Love Songs 2022 by Genre</h2>
- <p>Now that you know how to find the best love songs of 2022, let's take a look at some of the most popular and romantic songs of the year by genre. We have selected four genres that are known for producing amazing love songs: pop, rock, R&B, and country. We have also included some examples of the best love songs 2022 in each genre and where to download them.</p>
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- <h3>Pop Love Songs 2022</h3>
- <p>Pop love songs are catchy, upbeat, and fun. They are perfect for dancing, singing along, or just having a good time. They often have simple and catchy lyrics, catchy melodies, and electronic beats. Some of the most popular pop love songs 2022 are:</p>
- <h4>Examples of pop love songs 2022 and where to download them</h4>
- <table>
-<tr>
-<th>Song</th>
-<th>Artist</th>
-<th>Description</th>
-<th>Download Link</th>
-</tr>
-<tr>
-<td>Leave The Door Open</td>
-<td>Bruno Mars, Anderson .Paak, Silk Sonic</td>
-<td>A smooth and soulful song that invites a lover to come over and spend some quality time together.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Drivers License</td>
-<td>Olivia Rodrigo</td>
-<td>A heartbreaking song that expresses the pain of losing a lover to someone else.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Save Your Tears (Remix)</td>
-<td>The Weeknd, Ariana Grande</td>
-<td>A synth-pop song that tells the story of a complicated relationship between two lovers who hurt each other.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Levitating</td>
-<td>Dua Lipa, DaBaby</td>
-<td>A disco-inspired song that celebrates the feeling of being in love and flying high.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Butter</td>
-<td>BTS</td>
-<td>A funky and groovy song that showcases the charm and charisma of the Korean boy band.</td>
-<td><a href=""></a></td>
-</tr>
-</table>
- <h3>Rock Love Songs 2022</h3>
- <p>Rock love songs are powerful, passionate, and intense. They are perfect for expressing your emotions, releasing your energy, or rocking out. They often have electric guitars, drums, bass, and vocals. Some of the most popular rock love songs 2022 are:</p>
- <h4>Examples of rock love songs 2022 and where to download them</h4>
- <table>
-<tr>
-<th>Song</th>
-<th>Artist</th>
-<th>Description</th>
-<th>Download Link</th>
-</tr> <h3>R&B Love Songs 2022</h3>
- <p>R&B love songs are smooth, soulful, and sensual. They are perfect for setting the mood, expressing your feelings, or making love. They often have rich vocals, harmonies, grooves, and beats. Some of the most popular R&B love songs 2022 are:</p>
- <h4>Examples of R&B love songs 2022 and where to download them</h4>
- <table>
-<tr>
-<th>Song</th>
-<th>Artist</th>
-<th>Description</th>
-<th>Download Link</th>
-</tr>
-<tr>
-<td>The Wolf You Feed</td>
-<td>Nita Strauss feat. Alissa White-Gluz</td>
-<td>A powerful and passionate song that explores the duality of human nature and the choice between love and hate.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Lift Me Up</td>
-<td>Rihanna</td>
-<td>A beautiful and uplifting song that celebrates the spiritual bond between two lovers who support each other through hard times.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Free Mind</td>
-<td>Tems</td>
-<td>A mellow and soothing song that encourages self-love and freedom from toxic relationships.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Shirt</td>
-<td>SZA</td>
-<td>A catchy and playful song that expresses the desire to wear a lover's shirt as a reminder of their intimacy.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Heartbreak Anniversary</td>
-<td>Givēon</td>
-<td>A sad and soulful song that reflects on the pain of missing a lover on the anniversary of their breakup.</td>
-<td><a href=""></a></td>
-</tr>
-</table>
- <h3>Country Love Songs 2022</h3>
- <p>Country love songs are heartfelt, honest, and authentic. They are perfect for telling a story, sharing a memory, or celebrating a connection. They often have acoustic guitars, fiddles, banjos, and vocals. Some of the most popular country love songs 2022 are:</p>
- <h4>Examples of country love songs 2022 and where to download them</h4>
- <table>
-<tr>
-<th>Song</th>
-<th>Artist</th>
-<th>Description</th>
-<th>Download Link</th>
-</tr> <tr>
-<td>Forever After All</td>
-<td>Luke Combs</td>
-<td>A romantic and sincere song that expresses the commitment and devotion of a couple who are meant to be together.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Starting Over</td>
-<td>Chris Stapleton</td>
-<td>A hopeful and optimistic song that celebrates the opportunity to start a new chapter in life and love with someone special.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>One Of Them Girls</td>
-<td>Lee Brice</td>
-<td>A fun and flirty song that praises the qualities of a girl who is independent, adventurous, and irresistible.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Settling Down</td>
-<td>Miranda Lambert</td>
-<td>A sweet and honest song that explores the dilemma of choosing between a wild and free lifestyle or a stable and cozy relationship.</td>
-<td><a href=""></a></td>
-</tr>
-<tr>
-<td>Hole In The Bottle</td>
-<td>Kelsea Ballerini</td>
-<td>A humorous and catchy song that describes the coping mechanism of drinking wine to forget about a breakup.</td>
-<td><a href=""></a></td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, love songs are songs that express or evoke emotions related to love, such as affection, attraction, admiration, devotion, passion, or heartbreak. People listen to love songs for various reasons, such as celebrating their love, coping with their feelings, expressing their emotions, reminiscing about their memories, or relaxing and enjoying some music. Listening to love songs can have many benefits, such as boosting happiness, enhancing creativity, improving memory, increasing empathy, and strengthening bonds.</p>
- <p>To find the best love songs of 2022, you can browse online platforms that offer music streaming or downloading services, follow social media accounts or blogs that share music news or suggestions, listen to radio stations or podcasts that play or feature love songs, ask your friends or family for their opinions or recommendations, or share your playlists or songs with them. You can also search by genre, artist, album, playlist, or keyword. Some of the most popular and romantic songs of 2022 by genre are pop love songs 2022, rock love songs 2022, R&B love songs 2022, and country love songs 2022. We have given you some examples of the best love songs 2022 in each genre and where to download them.</p>
- <h3>Call to action and recommendations</h3>
- <p>We hope you enjoyed this article and found it helpful and informative. If you did, please share it with your friends, family, or partner who might also be interested in love songs mp3 download 2022. You can also leave us a comment below and tell us what are your favorite love songs of 2022 or any other suggestions or feedback you have. We would love to hear from you!</p>
- <p>Now that you have learned how to find and enjoy the best love songs of 2022, why not give it a try? Download some of the songs we recommended or find your own favorites and create your own playlist. You can also play them on your speakers or headphones and listen to them alone or with someone special. You can also sing along, dance along, or just relax and feel the emotions. Whatever you do, we hope you have a wonderful time listening to the best love songs of 2022!</p>
- <h3>Frequently Asked Questions (FAQs)</h3>
- <p>Here are some of the most common questions that people ask about love songs mp3 download 2022:</p>
- <ol>
-<li>What is the best website to download love songs mp3 for free?</li>
-<p>There is no definitive answer to this question, as different websites may have different features, qualities, and availability of songs. However, some of the most popular and reliable websites that offer free mp3 downloads are MP3Juices.cc, FreeMP3Cloud.com, MP3Skull.to, MP3Quack.lol, and BeeMP3s.net. You can also use online converters or downloaders that allow you to convert YouTube videos or other online sources into mp3 files.</p>
- <li>What is the best app to download love songs mp3 for Android or iOS?</li>
-<p>Again, there is no definitive answer to this question, as different apps may have different features, qualities, and availability of songs. However, some of the most popular and reliable apps that offer mp3 downloads for Android or iOS are Music Downloader & Free MP3 Song Download, Free Music Downloader & Mp3 Music Download Songs, MP3 Music Downloader - Download Free Music, and Free Music - Music Player, MP3 Player, Song Download. You can also use online platforms that offer music streaming or downloading services, such as Spotify, YouTube, Apple Music, Amazon Music, Wynk Music, etc.</p>
- <li>What is the best way to organize my love songs mp3 playlist?</li>
-<p>There is no definitive answer to this question, as different people may have different preferences and tastes. However, some of the most common and effective ways to organize your love songs mp3 playlist are:</p>
- <ul>
-<li>By genre: You can group your songs by the music genre they belong to, such as pop, rock, R&B, country, etc. This way, you can easily find the songs that match your mood and style.</li>
-<li>By mood: You can group your songs by the mood they create or reflect, such as happy, sad, romantic, angry, etc. This way, you can easily find the songs that suit your emotions and feelings.</li>
-<li>By theme: You can group your songs by the theme they explore or convey, such as love, breakup, friendship, etc. This way, you can easily find the songs that relate to your situation or experience.</li>
-<li>By artist: You can group your songs by the artist who sings or performs them. This way, you can easily find the songs that showcase your favorite singers or bands.</li>
-<li>By date: You can group your songs by the date they were released or added to your playlist. This way, you can easily find the latest or oldest songs in your collection.</li>
-</ul>
- <li>How can I make my love songs mp3 sound better?</li>
-<p>There are some tips and tricks that can help you improve the quality and clarity of your love songs mp3. Some of them are:</p>
- <ul>
-<li>Use a good quality device and headphones or speakers to play your songs. You can also adjust the volume, bass, treble, and equalizer settings to suit your preferences.</li>
-<li>Download your songs from reliable and reputable sources that offer high-quality mp3 files. You can also check the bitrate and format of your files before downloading them.</li>
-<li>Use a good quality software or app to edit or enhance your songs. You can also use online tools or converters that allow you to change the bitrate, format, speed, pitch, or volume of your files.</li>
-<li>Delete or replace any corrupted or damaged files that cause noise or distortion in your songs. You can also scan your device for viruses or malware that may affect your files.</li>
-</ul>
- <li>How can I share my love songs mp3 with others?</li>
-<p>There are many ways to share your love songs mp3 with others who might appreciate them. Some of them are:</p>
- <ul>
-<li>Send them via email, message, or social media platforms. You can also attach a note or a message to express your thoughts or feelings about the songs.</li>
-<li>Create a playlist or a mixtape and share it online or offline. You can also add a title or a cover art to personalize it.</li>
-<li>Burn them onto a CD or a USB drive and give it as a gift or a souvenir. You can also add a label or a sticker to decorate it.</li>
-<li>Play them on your device or speakers and invite others to listen with you. You can also sing along, dance along, or just enjoy the music together.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/pretrain_randeng_bart/pretrain_bart_base.sh b/spaces/skf15963/summary/fengshen/examples/pretrain_randeng_bart/pretrain_bart_base.sh
deleted file mode 100644
index 2ac4d8d40a2135c7439c150d7b208f94ba002a0d..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/pretrain_randeng_bart/pretrain_bart_base.sh
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=pretrain_bart # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks-per-node=8 # number of tasks to run per node
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:8 # number of gpus per node
-#SBATCH -o %x-%j.log # output and error log file names (%x for job id)
-#SBATCH -x dgx050
-
-# pwd=Fengshenbang-LM/fengshen/examples/pretrain_erlangshen
-ROOT_DIR=../../workspace
-export TORCH_EXTENSIONS_DIR=${ROOT_DIR}/torch_extendsions
-
-MODEL_NAME=randeng-bart-base
-MODEL_ROOT_DIR=$ROOT_DIR/${MODEL_NAME}
-if [ ! -d ${MODEL_ROOT_DIR} ];then
-  mkdir ${MODEL_ROOT_DIR}
-fi
-
-NNODES=1
-GPUS_PER_NODE=1
-
-MICRO_BATCH_SIZE=32
-
-# 如果你不用Deepspeed的话 下面的一段话都可以删掉 Begin
-CONFIG_JSON="$MODEL_ROOT_DIR/${MODEL_NAME}.ds_config.json"
-ZERO_STAGE=1
-# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
-cat <<EOT > $CONFIG_JSON
-{
-    "zero_optimization": {
-        "stage": ${ZERO_STAGE}
-    },
-    "fp16": {
-        "enabled": true
-    },
-    "gradient_clipping": 1,
-    "train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE
-}
-EOT
-export PL_DEEPSPEED_CONFIG_PATH=$CONFIG_JSON
-### End
-
-DATA_ARGS="\
-        --dataloader_workers 2 \
-        --train_batchsize $MICRO_BATCH_SIZE  \
-        --val_batchsize $MICRO_BATCH_SIZE \
-        --test_batchsize $MICRO_BATCH_SIZE  \
-        "
-# 如果你有一批数据，可以参照IDEA-CCNL/PretrainCorpusDemo的格式处理，通过参数传入
-# --train_file train.json
-# --val_file val.json
-# --test_file test.json
-
-MODEL_ARGS="\
-        --model_path $MODEL_ROOT_DIR/pretrain \
-        --learning_rate 1e-4 \
-        --weight_decay 1e-1 \
-        --warmup_ratio 0.01 \
-        "
-
-MODEL_CHECKPOINT_ARGS="\
-        --save_last \
-        --save_ckpt_path ${MODEL_ROOT_DIR}/ckpt \
-        --load_ckpt_path ${MODEL_ROOT_DIR}/ckpt/last.ckpt \
-        "
-
-TRAINER_ARGS="\
-        --max_epoch 10 \
-        --gpus $GPUS_PER_NODE \
-        --num_nodes $NNODES \
-        --strategy deepspeed_stage_${ZERO_STAGE} \
-        --log_every_n_steps 1 \
-        --precision 16 \
-        --default_root_dir ${MODEL_ROOT_DIR} \
-        --replace_sampler_ddp False \
-        "
-
-export options=" \
-        $DATA_ARGS \
-        $MODEL_ARGS \
-        $MODEL_CHECKPOINT_ARGS \
-        $TRAINER_ARGS \
-        "
-
-python3 pretrain_bart.py $options
-#srun -N $NNODES --gres=gpu:$GPUS_PER_NODE --ntasks-per-node=$GPUS_PER_NODE --cpus-per-task=20 python3 pretrain_bart.py $options
diff --git a/spaces/skf15963/summary/fengshen/models/megatron_t5/tokenization_megatron_t5.py b/spaces/skf15963/summary/fengshen/models/megatron_t5/tokenization_megatron_t5.py
deleted file mode 100644
index d96b7e1743ae8c7ecb4aa3871907a9dc070cf74b..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/megatron_t5/tokenization_megatron_t5.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" T5Tokenizer """
-
-from transformers import BertTokenizer
-
-
-class T5Tokenizer():
-    def __init__(self, extra_id_num=118):
-        self.extra_id_num = extra_id_num
-
-    @classmethod
-    def from_pretrained(self, vocab_path):
-        self.extra_id_num = 118
-        self.T5_special_tokens = ['[BOS]', '[EOS]']
-        for i in range(self.extra_id_num):
-            self.T5_special_tokens.append(f'<extra_id_{str(i)}>')
-        tokenizer = BertTokenizer.from_pretrained(vocab_path, additional_special_tokens=self.T5_special_tokens)
-
-        return tokenizer
diff --git a/spaces/smangrul/peft-lora-sd-dreambooth/trainer.py b/spaces/smangrul/peft-lora-sd-dreambooth/trainer.py
deleted file mode 100644
index b2b8e412dbbc2a7745926606924415584488a194..0000000000000000000000000000000000000000
--- a/spaces/smangrul/peft-lora-sd-dreambooth/trainer.py
+++ /dev/null
@@ -1,156 +0,0 @@
-from __future__ import annotations
-
-import os
-import pathlib
-import shlex
-import shutil
-import subprocess
-
-import gradio as gr
-import PIL.Image
-import torch
-
-
-def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
-    w, h = image.size
-    if w == h:
-        return image
-    elif w > h:
-        new_image = PIL.Image.new(image.mode, (w, w), (0, 0, 0))
-        new_image.paste(image, (0, (w - h) // 2))
-        return new_image
-    else:
-        new_image = PIL.Image.new(image.mode, (h, h), (0, 0, 0))
-        new_image.paste(image, ((h - w) // 2, 0))
-        return new_image
-
-
-class Trainer:
-    def __init__(self):
-        self.is_running = False
-        self.is_running_message = "Another training is in progress."
-
-        self.output_dir = pathlib.Path("results")
-        self.instance_data_dir = self.output_dir / "training_data"
-
-    def check_if_running(self) -> dict:
-        if self.is_running:
-            return gr.update(value=self.is_running_message)
-        else:
-            return gr.update(value="No training is running.")
-
-    def cleanup_dirs(self) -> None:
-        shutil.rmtree(self.output_dir, ignore_errors=True)
-
-    def prepare_dataset(self, concept_images: list, resolution: int) -> None:
-        self.instance_data_dir.mkdir(parents=True)
-        for i, temp_path in enumerate(concept_images):
-            image = PIL.Image.open(temp_path.name)
-            image = pad_image(image)
-            image = image.resize((resolution, resolution))
-            image = image.convert("RGB")
-            out_path = self.instance_data_dir / f"{i:03d}.jpg"
-            image.save(out_path, format="JPEG", quality=100)
-
-    def run(
-        self,
-        base_model: str,
-        resolution_s: str,
-        n_steps: int,
-        concept_images: list | None,
-        concept_prompt: str,
-        learning_rate: float,
-        gradient_accumulation: int,
-        fp16: bool,
-        use_8bit_adam: bool,
-        gradient_checkpointing: bool,
-        train_text_encoder: bool,
-        with_prior_preservation: bool,
-        prior_loss_weight: float,
-        class_prompt: str,
-        num_class_images: int,
-        lora_r: int,
-        lora_alpha: int,
-        lora_bias: str,
-        lora_dropout: float,
-        lora_text_encoder_r: int,
-        lora_text_encoder_alpha: int,
-        lora_text_encoder_bias: str,
-        lora_text_encoder_dropout: float,
-    ) -> tuple[dict, list[pathlib.Path]]:
-        if not torch.cuda.is_available():
-            raise gr.Error("CUDA is not available.")
-
-        if self.is_running:
-            return gr.update(value=self.is_running_message), []
-
-        if concept_images is None:
-            raise gr.Error("You need to upload images.")
-        if not concept_prompt:
-            raise gr.Error("The concept prompt is missing.")
-
-        resolution = int(resolution_s)
-
-        self.cleanup_dirs()
-        self.prepare_dataset(concept_images, resolution)
-
-        command = f"""
-        accelerate launch train_dreambooth.py \
-            --pretrained_model_name_or_path={base_model}  \
-            --instance_data_dir={self.instance_data_dir} \
-            --output_dir={self.output_dir} \
-            --train_text_encoder \
-            --instance_prompt="{concept_prompt}" \
-            --resolution={resolution} \
-            --gradient_accumulation_steps={gradient_accumulation} \
-            --learning_rate={learning_rate} \
-            --max_train_steps={n_steps} \
-            --train_batch_size=1 \
-            --lr_scheduler=constant \
-            --lr_warmup_steps=0 \
-            --num_class_images={num_class_images} \
-        """
-        if train_text_encoder:
-            command += f" --train_text_encoder"
-        if with_prior_preservation:
-            command += f""" --with_prior_preservation \
-                --prior_loss_weight={prior_loss_weight} \
-                --class_prompt="{class_prompt}" \
-                --class_data_dir={self.output_dir / 'class_data'}
-                """
-
-        command += f""" --use_lora \
-            --lora_r={lora_r} \
-            --lora_alpha={lora_alpha} \
-            --lora_bias={lora_bias} \
-            --lora_dropout={lora_dropout}
-            """
-
-        if train_text_encoder:
-            command += f""" --lora_text_encoder_r={lora_text_encoder_r} \
-                --lora_text_encoder_alpha={lora_text_encoder_alpha} \
-                --lora_text_encoder_bias={lora_text_encoder_bias} \
-                --lora_text_encoder_dropout={lora_text_encoder_dropout}
-                """
-        if fp16:
-            command += " --mixed_precision fp16"
-        if use_8bit_adam:
-            command += " --use_8bit_adam"
-        if gradient_checkpointing:
-            command += " --gradient_checkpointing"
-
-        with open(self.output_dir / "train.sh", "w") as f:
-            command_s = " ".join(command.split())
-            f.write(command_s)
-
-        self.is_running = True
-        res = subprocess.run(shlex.split(command))
-        self.is_running = False
-
-        if res.returncode == 0:
-            result_message = "Training Completed!"
-        else:
-            result_message = "Training Failed!"
-        weight_paths = sorted(self.output_dir.glob("*.pt"))
-        config_paths = sorted(self.output_dir.glob("*.json"))
-        return gr.update(value=result_message), weight_paths + config_paths
diff --git a/spaces/soggys/tavern/greeting.md b/spaces/soggys/tavern/greeting.md
deleted file mode 100644
index b60b424f056da4311ecea5fbdcf4df13aeae9f32..0000000000000000000000000000000000000000
--- a/spaces/soggys/tavern/greeting.md
+++ /dev/null
@@ -1,2 +0,0 @@
-alckconnect@proton.me :3</br>
-https://rentry.org/alckconnect for stats
\ No newline at end of file
diff --git a/spaces/spookyspaghetti/Speech-Analyser/README.md b/spaces/spookyspaghetti/Speech-Analyser/README.md
deleted file mode 100644
index b55875e28b29536eaaf1dac8cb92d272d9eadc9a..0000000000000000000000000000000000000000
--- a/spaces/spookyspaghetti/Speech-Analyser/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Speech Analyser
-emoji: 🏃
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Download Windows Last Xp V22 Sp3 Torent FREE.md b/spaces/stomexserde/gpt4-ui/Examples/Download Windows Last Xp V22 Sp3 Torent FREE.md
deleted file mode 100644
index 16050fb2452986ab883c4c9a6f1240375c1a9839..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Download Windows Last Xp V22 Sp3 Torent FREE.md	
+++ /dev/null
@@ -1,68 +0,0 @@
-<br />
-<table>
-<tr>
-<td>
- <h1>Download Windows Last XP V22 SP3 Torrent: A Guide for Windows XP Lovers</h1>
- <p>Are you a fan of Windows XP, the classic operating system that was released by Microsoft in 2001? Do you miss its simple and user-friendly interface, its fast and stable performance, and its compatibility with many applications and games? If you answered yes to these questions, then you might be interested in downloading Windows Last XP V22 SP3 torrent.</p>
- <p>Windows Last XP is a modified version of Windows XP that includes many features and enhancements that make it more suitable for modern users. It is based on Windows XP Professional with Service Pack 3 (SP3), which is the last official update for Windows XP from Microsoft. However, Windows Last XP also adds many customizations and tweaks that improve its functionality and appearance.</p>
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- <p>In this article, we will explain what Windows Last XP is, what are its features and benefits, how to download it as a torrent file, how to install it on your PC, and how to optimize it for better performance and security. By the end of this article, you will have a clear idea of whether Windows Last XP is right for you and how to get it.</p>
- <h2>What is Windows Last XP?</h2>
- <p>Windows Last XP is a fan-made project that aims to provide a better version of Windows XP for users who still love this operating system. It was created by a group of enthusiasts who wanted to improve Windows XP with new features, updates, drivers, themes, wallpapers, icons, sounds, and more. It is not an official product from Microsoft, nor is it affiliated with them in any way.</p>
- <p>Windows Last XP was first released in 2008 as Windows LastXP v20. Since then, it has been updated several times with new versions that add more improvements and fixes. The latest version is Windows LastXP v22 SP3, which was released in 2010. This version is based on the original Windows XP Professional with Service Pack 3 (SP3), which was released by Microsoft in 2008.</p>
- <p>Windows LastXP v22 SP3 is also known as Windows LastXP v22 USB Edition or RON Core Edition. This is because it can be installed from a DVD or a USB drive, unlike the original Windows XP which required a CD-ROM drive. It also has a core edition that allows users to customize their installation by choosing which components they want to install or remove.</p>
- <h2>Features of Windows LastXP v22 SP3</h2>
- <p>Windows LastXP v22 SP3 has many features that make it different from the original Windows XP. Some of these features are:</p>
- <ul>
-<li>It includes all the updates and patches that were released for Windows XP until April 2019.</li>
-<li>It supports SATA, AHCI, RAID, SCSI, IDE, USB, Firewire, and other types of hard disk drives.</li>
-<li>It has built-in drivers for many devices such as graphics cards, sound cards , network cards, webcams, printers, scanners, and more.</li>
-<li>It has a new and improved user interface that includes many themes, wallpapers, icons, sounds, cursors, and fonts.</li>
-<li>It has many useful applications and tools that are pre-installed or can be installed optionally. Some of these are: Internet Explorer 8, Firefox, Chrome, Opera, Safari, WinRAR, 7-Zip, VLC Media Player, K-Lite Codec Pack, Adobe Reader, Flash Player, Java Runtime Environment, Microsoft Office 2007, Nero Burning ROM, Daemon Tools Lite, CCleaner, Defraggler, Recuva, Speccy, Malwarebytes Anti-Malware, Avast Free Antivirus, Spybot Search & Destroy, ZoneAlarm Free Firewall, and more.</li>
-<li>It has many tweaks and optimizations that improve its speed, stability, security, and performance. Some of these are: disabling unnecessary services and programs, enabling faster boot and shutdown times, increasing the internet speed and connection quality, enhancing the system security and privacy settings, fixing common errors and bugs, and more.</li>
-</ul>
- <h2>Benefits of Windows LastXP v22 SP3</h2>
- <p>Windows LastXP v22 SP3 has many benefits that make it a better choice than the original Windows XP for some users. Some of these benefits are:</p>
- <ul>
-<li>It is compatible with most of the hardware and software that were designed for Windows XP.</li>
-<li>It is more secure and updated than Windows XP, which is no longer supported by Microsoft since 2014.</li>
-<li>It is more customizable and flexible than Windows XP, as it allows users to choose which components they want to install or remove.</li>
-<li>It is more attractive and modern than Windows XP, as it has a new and improved user interface that includes many themes, wallpapers, icons, sounds, cursors, and fonts.</li>
-<li>It is more functional and convenient than Windows XP, as it has many useful applications and tools that are pre-installed or can be installed optionally.</li>
-<li>It is faster and smoother than Windows XP, as it has many tweaks and optimizations that improve its speed, stability , security, and performance.</li>
-</ul>
- <p>Of course, Windows LastXP v22 SP3 also has some drawbacks that make it a worse choice than the original Windows XP for some users. Some of these drawbacks are:</p>
-<p></p>
- <ul>
-<li>It is not an official product from Microsoft, nor is it affiliated with them in any way. Therefore, it may not be legal or ethical to use it without their permission.</li>
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-</ul>
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- <ul>
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-<li>A DVD or USB drive that has at least 4 GB of free space.</li>
-<li>A torrent client that can download torrent files. Some examples are: uTorrent, BitTorrent, qBittorrent, Vuze, Deluge, and Transmission.</li>
-<li>A reliable internet connection that can download large files quickly and securely.</li>
-</ul>
- <h3>Steps to download Windows LastXP v22 SP3 torrent</h3>
- <p>After you have checked that you have the requirements for downloading and installing Windows LastXP v22 SP3, you can proceed with the following steps:</p>
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- <p>Depending on your internet connection and the availability of the file, downloading the Windows LastXP v22 SP3 torrent file may take from a few minutes to several hours. You should wait until the download is complete before proceeding to the next step.</p>
- <h4>Step 4: Mount or burn the ISO file to a DVD or USB drive</h4>
- <p>The fourth step is to mount or burn the ISO file to a DVD or USB drive. An ISO file is an image file that contains all the data of a CD or DVD in a single file. The Windows LastXP v22 SP3 torrent file that you have downloaded is an ISO file that contains all the data of the Windows LastXP v22 SP3 installation disc. To install Windows LastXP v22 SP3 on your PC, you will need to either mount the ISO file to a virtual drive or burn the ISO file to a physical DVD or USB drive. To mount the ISO file to a virtual drive, you will need to use a software program that can create and manage virtual drives. A virtual drive is a simulated drive that can read ISO files as if they were real discs. Some examples of software programs that can create and manage virtual drives are: Daemon Tools Lite, PowerISO, Virtual CloneDrive, and WinCDEmu. To mount the ISO file to a virtual drive using Daemon Tools Lite, you will need to follow these steps: - Download and install Daemon Tools Lite from its official website: https://www.daemon-tools.cc/products/dtLite - Launch Daemon Tools Lite and click on the Add Image button on the main window. - Browse and select the Windows LastXP v22 SP3 ISO file that you have downloaded and click on Open. - The ISO file will be added to the Images list on the main window. Right-click on it and select Mount. - A new virtual drive will be created and assigned a letter. You can see it on the My Computer or This PC window. - Double-click on the virtual drive to open it and access the contents of the Windows LastXP v22 SP3 installation disc. To burn the ISO file to a physical DVD or USB drive, you will need to use a software program that can write ISO files to discs or drives. Some examples of software programs that can write ISO files to discs or drives are: Nero Burning ROM, ImgBurn, Rufus, and Etcher. To burn the ISO file to a physical DVD using Nero Burning ROM, you will need to follow these steps: - Download and install Nero Burning ROM from its official website: https://www.nero.com/enu/products/nero-burning-rom/ - Launch Nero Burning ROM and select DVD-ROM (Boot) from the New Compilation window. - Click on the Burn tab and check the Finalize Disc option. - Click on the Label tab and enter a name for your disc. - Click on the New button to open the File Browser window. - Browse and drag the Windows LastXP v22 SP3 ISO file that you have downloaded to the left pane of the File Browser window. - Insert a blank DVD into your DVD writer drive and select it from the Destination drop-down menu on the main window. - Click on the Burn button to start burning the ISO file to the DVD. To burn the ISO file to a USB drive using Rufus, you will need to follow these steps: - Download and install Rufus from its official website: https://rufus.ie/ - Launch Rufus and insert a USB drive into your computer. Rufus will automatically detect it and select it as the Device. - Click on the Select button and browse and select the Windows LastXP v22 SP3 ISO file that you have downloaded. - Rufus will automatically set the Boot selection, Partition scheme, File system, Cluster size, and Volume label options according to the ISO file. You can change them if you want, but it is recommended to leave them as they are. - Click on the Start button to start burning the ISO file to the USB drive.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MusicGen-Continuation/tests/modules/test_transformer.py b/spaces/sub314xxl/MusicGen-Continuation/tests/modules/test_transformer.py
deleted file mode 100644
index 8c9953d9e8f139db7b8ce3063e3d5a78d2f5d088..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen-Continuation/tests/modules/test_transformer.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.transformer import StreamingMultiheadAttention, StreamingTransformer
-
-
-def test_transformer_causal_streaming():
-    torch.manual_seed(1234)
-
-    for context, custom in product([None, 10], [False, True]):
-        # Test that causality and receptive fields are properly handled.
-        # looking at the gradients
-        tr = StreamingTransformer(
-            16, 4, 1 if context else 2,
-            causal=True, past_context=context, custom=custom,
-            dropout=0.)
-        steps = 20
-        for k in [0, 10, 15, 19]:
-            x = torch.randn(4, steps, 16, requires_grad=True)
-            y = tr(x)
-            y[:, k].abs().sum().backward()
-            if k + 1 < steps:
-                assert torch.allclose(x.grad[:, k + 1:], torch.tensor(0.)), x.grad[:, k + 1:].norm()
-            assert not torch.allclose(x.grad[:, :k + 1], torch.tensor(0.)), x.grad[:, :k + 1].norm()
-            if context is not None and k > context:
-                limit = k - context - 1
-                assert torch.allclose(x.grad[:, :limit],
-                                      torch.tensor(0.)), x.grad[:, :limit].norm()
-
-        # Now check that streaming gives the same result at batch eval.
-        x = torch.randn(4, steps, 16)
-        y = tr(x)
-        ys = []
-        with tr.streaming():
-            for k in range(steps):
-                chunk = x[:, k:k + 1, :]
-                ys.append(tr(chunk))
-        y_stream = torch.cat(ys, dim=1)
-        delta = torch.norm(y_stream - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_transformer_vs_pytorch():
-    torch.manual_seed(1234)
-    # Check that in the non causal setting, we get the same result as
-    # PyTorch Transformer encoder.
-    for custom in [False, True]:
-        tr = StreamingTransformer(
-            16, 4, 2,
-            causal=False, custom=custom, dropout=0., positional_scale=0.)
-        layer = torch.nn.TransformerEncoderLayer(16, 4, dropout=0., batch_first=True)
-        tr_ref = torch.nn.TransformerEncoder(layer, 2)
-        tr.load_state_dict(tr_ref.state_dict())
-
-        x = torch.randn(4, 20, 16)
-        y = tr(x)
-        y2 = tr_ref(x)
-        delta = torch.norm(y2 - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_streaming_api():
-    tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0.)
-    tr.eval()
-    steps = 12
-    x = torch.randn(1, steps, 16)
-
-    with torch.no_grad():
-        with tr.streaming():
-            _ = tr(x[:, :1])
-            state = {k: v.clone() for k, v in tr.get_streaming_state().items()}
-            y = tr(x[:, 1:2])
-            tr.set_streaming_state(state)
-            y2 = tr(x[:, 1:2])
-            assert torch.allclose(y, y2), (y - y2).norm()
-            assert tr.flush() is None
-
-
-def test_memory_efficient():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., layer_scale=0.1)
-    tr_mem_efficient = StreamingTransformer(
-        16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1)
-    tr_mem_efficient.load_state_dict(tr.state_dict())
-    tr.eval()
-    steps = 12
-    x = torch.randn(3, steps, 16)
-
-    with torch.no_grad():
-        y = tr(x)
-        y2 = tr_mem_efficient(x)
-        assert torch.allclose(y, y2), (y - y2).norm()
-
-
-def test_attention_as_float32():
-    torch.manual_seed(1234)
-    cases = [
-        {'custom': True},
-        {'custom': False},
-    ]
-    for case in cases:
-        tr = StreamingTransformer(16, 4, 2, dropout=0., dtype=torch.bfloat16, **case)
-        tr_float32 = StreamingTransformer(
-            16, 4, 2, dropout=0., attention_as_float32=True, dtype=torch.bfloat16, **case)
-        if not case['custom']:
-            # we are not using autocast here because it doesn't really
-            # work as expected on CPU, so we have to manually cast the weights of the MHA.
-            for layer in tr_float32.layers:
-                layer.self_attn.mha.to(torch.float32)
-        tr_float32.load_state_dict(tr.state_dict())
-        steps = 12
-        x = torch.randn(3, steps, 16, dtype=torch.bfloat16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_float32(x)
-            assert not torch.allclose(y, y2), (y - y2).norm()
-
-
-@torch.no_grad()
-def test_streaming_memory_efficient():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0., custom=True)
-    tr_mem_efficient = StreamingTransformer(
-        16, 4, 2, dropout=0., memory_efficient=True, causal=True)
-    tr.load_state_dict(tr_mem_efficient.state_dict())
-    tr.eval()
-    tr_mem_efficient.eval()
-    steps = 12
-    x = torch.randn(3, steps, 16)
-
-    ref = tr(x)
-
-    with tr_mem_efficient.streaming():
-        outs = []
-        # frame_sizes = [2] + [1] * (steps - 2)
-        frame_sizes = [1] * steps
-
-        for frame_size in frame_sizes:
-            frame = x[:, :frame_size]
-            x = x[:, frame_size:]
-            outs.append(tr_mem_efficient(frame))
-
-    out = torch.cat(outs, dim=1)
-    delta = torch.norm(out - ref) / torch.norm(out)
-    assert delta < 1e-6, delta
-
-
-def test_cross_attention():
-    torch.manual_seed(1234)
-    for norm_first in [True, False]:
-        m = StreamingTransformer(
-            16, 4, 2, cross_attention=False, norm_first=norm_first, dropout=0., custom=True)
-        m_cross = StreamingTransformer(
-            16, 4, 2, cross_attention=True, norm_first=norm_first, dropout=0., custom=True)
-        m_cross.load_state_dict(m.state_dict(), strict=False)
-        x = torch.randn(2, 5, 16)
-        cross_x = torch.randn(2, 3, 16)
-        y_ref = m(x)
-        y_cross_zero = m_cross(x, cross_attention_src=0 * cross_x)
-        # With norm_first, the two should be exactly yhe same,
-        # but with norm_first=False, we get 2 normalization in a row
-        # and the epsilon value leads to a tiny change.
-        atol = 0. if norm_first else 1e-6
-        print((y_ref - y_cross_zero).norm() / y_ref.norm())
-        assert torch.allclose(y_ref, y_cross_zero, atol=atol)
-
-        # We now expect a difference even with a generous atol of 1e-2.
-        y_cross = m_cross(x, cross_attention_src=cross_x)
-        assert not torch.allclose(y_cross, y_cross_zero, atol=1e-2)
-
-        with pytest.raises(AssertionError):
-            _ = m_cross(x)
-            _ = m(x, cross_attention_src=cross_x)
-
-
-def test_cross_attention_compat():
-    torch.manual_seed(1234)
-    num_heads = 2
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        StreamingMultiheadAttention(dim, num_heads, causal=True, cross_attention=True)
-
-    cross_attn = StreamingMultiheadAttention(
-        dim, num_heads, dropout=0, cross_attention=True, custom=True)
-    ref_attn = torch.nn.MultiheadAttention(dim, num_heads, dropout=0, batch_first=True)
-
-    # We can load the regular attention state dict
-    # so we have compat when loading old checkpoints.
-    cross_attn.load_state_dict(ref_attn.state_dict())
-
-    queries = torch.randn(3, 7, dim)
-    keys = torch.randn(3, 9, dim)
-    values = torch.randn(3, 9, dim)
-
-    y = cross_attn(queries, keys, values)[0]
-    y_ref = ref_attn(queries, keys, values)[0]
-    assert torch.allclose(y, y_ref, atol=1e-7)
-
-    # Now let's check that streaming is working properly.
-    with cross_attn.streaming():
-        ys = []
-        for step in range(queries.shape[1]):
-            ys.append(cross_attn(queries[:, step: step + 1], keys, values)[0])
-    y_streaming = torch.cat(ys, dim=1)
-    assert torch.allclose(y_streaming, y, atol=1e-7)
-
-
-def test_repeat_kv():
-    torch.manual_seed(1234)
-    num_heads = 8
-    kv_repeat = 4
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat, cross_attention=True)
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat)
-    mha = StreamingMultiheadAttention(
-        dim, num_heads, causal=True, kv_repeat=kv_repeat, custom=True)
-    x = torch.randn(4, 18, dim)
-    y = mha(x, x, x)[0]
-    assert x.shape == y.shape
-
-
-def test_qk_layer_norm():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, bias_attn=False)
-    steps = 12
-    x = torch.randn(3, steps, 16)
-    y = tr(x)
-
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, cross_attention=True)
-    z = torch.randn(3, 21, 16)
-    y = tr(x, cross_attention_src=z)
-    assert y.shape == x.shape
diff --git a/spaces/sujithvamshi/vehicle-color-recognition/utils.py b/spaces/sujithvamshi/vehicle-color-recognition/utils.py
deleted file mode 100644
index 1746c0deb286d12ca100a594e811b52b51c74356..0000000000000000000000000000000000000000
--- a/spaces/sujithvamshi/vehicle-color-recognition/utils.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import tensorflow as tf
-import numpy as np
-# Load TensorFlowLite model
-interpreter = tf.lite.Interpreter(model_path="model.tflite")
-interpreter.allocate_tensors()
-input_details = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
-# Define the input interface      
-
-def predict(img):
-    input_data = img.astype(np.float32)
-    input_data = input_data / 255.0
-    input_data = np.expand_dims(input_data, axis=0)
-
-    interpreter.set_tensor(input_details[0]['index'], input_data)
-    interpreter.invoke()
-    output_data = interpreter.get_tensor(output_details[0]['index'])
-    colours = ['black', 'blue', 'brown', 'green', 'grey', 'red', 'silver', 'white', 'yellow']
-    return colours[output_data.argmax()]
\ No newline at end of file
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/deepbooru_model.py b/spaces/supertori/files/stable-diffusion-webui/modules/deepbooru_model.py
deleted file mode 100644
index 7a53884624e96284c35214ce02b8a2891d92c3e8..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/deepbooru_model.py
+++ /dev/null
@@ -1,678 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from modules import devices
-
-# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
-
-
-class DeepDanbooruModel(nn.Module):
-    def __init__(self):
-        super(DeepDanbooruModel, self).__init__()
-
-        self.tags = []
-
-        self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
-        self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
-        self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
-        self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
-        self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
-        self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
-        self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
-        self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
-        self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
-        self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
-        self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
-        self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
-        self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
-        self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
-        self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
-        self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
-        self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
-        self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
-        self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
-        self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
-        self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
-        self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
-        self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
-        self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
-        self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
-        self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
-        self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
-        self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
-        self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
-        self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
-        self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
-        self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
-        self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
-        self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
-        self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
-        self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
-        self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
-        self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
-        self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
-        self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
-        self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
-        self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
-        self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
-        self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
-        self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
-        self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
-        self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
-
-    def forward(self, *inputs):
-        t_358, = inputs
-        t_359 = t_358.permute(*[0, 3, 1, 2])
-        t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
-        t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
-        t_361 = F.relu(t_360)
-        t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
-        t_362 = self.n_MaxPool_0(t_361)
-        t_363 = self.n_Conv_1(t_362)
-        t_364 = self.n_Conv_2(t_362)
-        t_365 = F.relu(t_364)
-        t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
-        t_366 = self.n_Conv_3(t_365_padded)
-        t_367 = F.relu(t_366)
-        t_368 = self.n_Conv_4(t_367)
-        t_369 = torch.add(t_368, t_363)
-        t_370 = F.relu(t_369)
-        t_371 = self.n_Conv_5(t_370)
-        t_372 = F.relu(t_371)
-        t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
-        t_373 = self.n_Conv_6(t_372_padded)
-        t_374 = F.relu(t_373)
-        t_375 = self.n_Conv_7(t_374)
-        t_376 = torch.add(t_375, t_370)
-        t_377 = F.relu(t_376)
-        t_378 = self.n_Conv_8(t_377)
-        t_379 = F.relu(t_378)
-        t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
-        t_380 = self.n_Conv_9(t_379_padded)
-        t_381 = F.relu(t_380)
-        t_382 = self.n_Conv_10(t_381)
-        t_383 = torch.add(t_382, t_377)
-        t_384 = F.relu(t_383)
-        t_385 = self.n_Conv_11(t_384)
-        t_386 = self.n_Conv_12(t_384)
-        t_387 = F.relu(t_386)
-        t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
-        t_388 = self.n_Conv_13(t_387_padded)
-        t_389 = F.relu(t_388)
-        t_390 = self.n_Conv_14(t_389)
-        t_391 = torch.add(t_390, t_385)
-        t_392 = F.relu(t_391)
-        t_393 = self.n_Conv_15(t_392)
-        t_394 = F.relu(t_393)
-        t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
-        t_395 = self.n_Conv_16(t_394_padded)
-        t_396 = F.relu(t_395)
-        t_397 = self.n_Conv_17(t_396)
-        t_398 = torch.add(t_397, t_392)
-        t_399 = F.relu(t_398)
-        t_400 = self.n_Conv_18(t_399)
-        t_401 = F.relu(t_400)
-        t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
-        t_402 = self.n_Conv_19(t_401_padded)
-        t_403 = F.relu(t_402)
-        t_404 = self.n_Conv_20(t_403)
-        t_405 = torch.add(t_404, t_399)
-        t_406 = F.relu(t_405)
-        t_407 = self.n_Conv_21(t_406)
-        t_408 = F.relu(t_407)
-        t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
-        t_409 = self.n_Conv_22(t_408_padded)
-        t_410 = F.relu(t_409)
-        t_411 = self.n_Conv_23(t_410)
-        t_412 = torch.add(t_411, t_406)
-        t_413 = F.relu(t_412)
-        t_414 = self.n_Conv_24(t_413)
-        t_415 = F.relu(t_414)
-        t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
-        t_416 = self.n_Conv_25(t_415_padded)
-        t_417 = F.relu(t_416)
-        t_418 = self.n_Conv_26(t_417)
-        t_419 = torch.add(t_418, t_413)
-        t_420 = F.relu(t_419)
-        t_421 = self.n_Conv_27(t_420)
-        t_422 = F.relu(t_421)
-        t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
-        t_423 = self.n_Conv_28(t_422_padded)
-        t_424 = F.relu(t_423)
-        t_425 = self.n_Conv_29(t_424)
-        t_426 = torch.add(t_425, t_420)
-        t_427 = F.relu(t_426)
-        t_428 = self.n_Conv_30(t_427)
-        t_429 = F.relu(t_428)
-        t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
-        t_430 = self.n_Conv_31(t_429_padded)
-        t_431 = F.relu(t_430)
-        t_432 = self.n_Conv_32(t_431)
-        t_433 = torch.add(t_432, t_427)
-        t_434 = F.relu(t_433)
-        t_435 = self.n_Conv_33(t_434)
-        t_436 = F.relu(t_435)
-        t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
-        t_437 = self.n_Conv_34(t_436_padded)
-        t_438 = F.relu(t_437)
-        t_439 = self.n_Conv_35(t_438)
-        t_440 = torch.add(t_439, t_434)
-        t_441 = F.relu(t_440)
-        t_442 = self.n_Conv_36(t_441)
-        t_443 = self.n_Conv_37(t_441)
-        t_444 = F.relu(t_443)
-        t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
-        t_445 = self.n_Conv_38(t_444_padded)
-        t_446 = F.relu(t_445)
-        t_447 = self.n_Conv_39(t_446)
-        t_448 = torch.add(t_447, t_442)
-        t_449 = F.relu(t_448)
-        t_450 = self.n_Conv_40(t_449)
-        t_451 = F.relu(t_450)
-        t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
-        t_452 = self.n_Conv_41(t_451_padded)
-        t_453 = F.relu(t_452)
-        t_454 = self.n_Conv_42(t_453)
-        t_455 = torch.add(t_454, t_449)
-        t_456 = F.relu(t_455)
-        t_457 = self.n_Conv_43(t_456)
-        t_458 = F.relu(t_457)
-        t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
-        t_459 = self.n_Conv_44(t_458_padded)
-        t_460 = F.relu(t_459)
-        t_461 = self.n_Conv_45(t_460)
-        t_462 = torch.add(t_461, t_456)
-        t_463 = F.relu(t_462)
-        t_464 = self.n_Conv_46(t_463)
-        t_465 = F.relu(t_464)
-        t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
-        t_466 = self.n_Conv_47(t_465_padded)
-        t_467 = F.relu(t_466)
-        t_468 = self.n_Conv_48(t_467)
-        t_469 = torch.add(t_468, t_463)
-        t_470 = F.relu(t_469)
-        t_471 = self.n_Conv_49(t_470)
-        t_472 = F.relu(t_471)
-        t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
-        t_473 = self.n_Conv_50(t_472_padded)
-        t_474 = F.relu(t_473)
-        t_475 = self.n_Conv_51(t_474)
-        t_476 = torch.add(t_475, t_470)
-        t_477 = F.relu(t_476)
-        t_478 = self.n_Conv_52(t_477)
-        t_479 = F.relu(t_478)
-        t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
-        t_480 = self.n_Conv_53(t_479_padded)
-        t_481 = F.relu(t_480)
-        t_482 = self.n_Conv_54(t_481)
-        t_483 = torch.add(t_482, t_477)
-        t_484 = F.relu(t_483)
-        t_485 = self.n_Conv_55(t_484)
-        t_486 = F.relu(t_485)
-        t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
-        t_487 = self.n_Conv_56(t_486_padded)
-        t_488 = F.relu(t_487)
-        t_489 = self.n_Conv_57(t_488)
-        t_490 = torch.add(t_489, t_484)
-        t_491 = F.relu(t_490)
-        t_492 = self.n_Conv_58(t_491)
-        t_493 = F.relu(t_492)
-        t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
-        t_494 = self.n_Conv_59(t_493_padded)
-        t_495 = F.relu(t_494)
-        t_496 = self.n_Conv_60(t_495)
-        t_497 = torch.add(t_496, t_491)
-        t_498 = F.relu(t_497)
-        t_499 = self.n_Conv_61(t_498)
-        t_500 = F.relu(t_499)
-        t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
-        t_501 = self.n_Conv_62(t_500_padded)
-        t_502 = F.relu(t_501)
-        t_503 = self.n_Conv_63(t_502)
-        t_504 = torch.add(t_503, t_498)
-        t_505 = F.relu(t_504)
-        t_506 = self.n_Conv_64(t_505)
-        t_507 = F.relu(t_506)
-        t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
-        t_508 = self.n_Conv_65(t_507_padded)
-        t_509 = F.relu(t_508)
-        t_510 = self.n_Conv_66(t_509)
-        t_511 = torch.add(t_510, t_505)
-        t_512 = F.relu(t_511)
-        t_513 = self.n_Conv_67(t_512)
-        t_514 = F.relu(t_513)
-        t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
-        t_515 = self.n_Conv_68(t_514_padded)
-        t_516 = F.relu(t_515)
-        t_517 = self.n_Conv_69(t_516)
-        t_518 = torch.add(t_517, t_512)
-        t_519 = F.relu(t_518)
-        t_520 = self.n_Conv_70(t_519)
-        t_521 = F.relu(t_520)
-        t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
-        t_522 = self.n_Conv_71(t_521_padded)
-        t_523 = F.relu(t_522)
-        t_524 = self.n_Conv_72(t_523)
-        t_525 = torch.add(t_524, t_519)
-        t_526 = F.relu(t_525)
-        t_527 = self.n_Conv_73(t_526)
-        t_528 = F.relu(t_527)
-        t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
-        t_529 = self.n_Conv_74(t_528_padded)
-        t_530 = F.relu(t_529)
-        t_531 = self.n_Conv_75(t_530)
-        t_532 = torch.add(t_531, t_526)
-        t_533 = F.relu(t_532)
-        t_534 = self.n_Conv_76(t_533)
-        t_535 = F.relu(t_534)
-        t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
-        t_536 = self.n_Conv_77(t_535_padded)
-        t_537 = F.relu(t_536)
-        t_538 = self.n_Conv_78(t_537)
-        t_539 = torch.add(t_538, t_533)
-        t_540 = F.relu(t_539)
-        t_541 = self.n_Conv_79(t_540)
-        t_542 = F.relu(t_541)
-        t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
-        t_543 = self.n_Conv_80(t_542_padded)
-        t_544 = F.relu(t_543)
-        t_545 = self.n_Conv_81(t_544)
-        t_546 = torch.add(t_545, t_540)
-        t_547 = F.relu(t_546)
-        t_548 = self.n_Conv_82(t_547)
-        t_549 = F.relu(t_548)
-        t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
-        t_550 = self.n_Conv_83(t_549_padded)
-        t_551 = F.relu(t_550)
-        t_552 = self.n_Conv_84(t_551)
-        t_553 = torch.add(t_552, t_547)
-        t_554 = F.relu(t_553)
-        t_555 = self.n_Conv_85(t_554)
-        t_556 = F.relu(t_555)
-        t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
-        t_557 = self.n_Conv_86(t_556_padded)
-        t_558 = F.relu(t_557)
-        t_559 = self.n_Conv_87(t_558)
-        t_560 = torch.add(t_559, t_554)
-        t_561 = F.relu(t_560)
-        t_562 = self.n_Conv_88(t_561)
-        t_563 = F.relu(t_562)
-        t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
-        t_564 = self.n_Conv_89(t_563_padded)
-        t_565 = F.relu(t_564)
-        t_566 = self.n_Conv_90(t_565)
-        t_567 = torch.add(t_566, t_561)
-        t_568 = F.relu(t_567)
-        t_569 = self.n_Conv_91(t_568)
-        t_570 = F.relu(t_569)
-        t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
-        t_571 = self.n_Conv_92(t_570_padded)
-        t_572 = F.relu(t_571)
-        t_573 = self.n_Conv_93(t_572)
-        t_574 = torch.add(t_573, t_568)
-        t_575 = F.relu(t_574)
-        t_576 = self.n_Conv_94(t_575)
-        t_577 = F.relu(t_576)
-        t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
-        t_578 = self.n_Conv_95(t_577_padded)
-        t_579 = F.relu(t_578)
-        t_580 = self.n_Conv_96(t_579)
-        t_581 = torch.add(t_580, t_575)
-        t_582 = F.relu(t_581)
-        t_583 = self.n_Conv_97(t_582)
-        t_584 = F.relu(t_583)
-        t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
-        t_585 = self.n_Conv_98(t_584_padded)
-        t_586 = F.relu(t_585)
-        t_587 = self.n_Conv_99(t_586)
-        t_588 = self.n_Conv_100(t_582)
-        t_589 = torch.add(t_587, t_588)
-        t_590 = F.relu(t_589)
-        t_591 = self.n_Conv_101(t_590)
-        t_592 = F.relu(t_591)
-        t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
-        t_593 = self.n_Conv_102(t_592_padded)
-        t_594 = F.relu(t_593)
-        t_595 = self.n_Conv_103(t_594)
-        t_596 = torch.add(t_595, t_590)
-        t_597 = F.relu(t_596)
-        t_598 = self.n_Conv_104(t_597)
-        t_599 = F.relu(t_598)
-        t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
-        t_600 = self.n_Conv_105(t_599_padded)
-        t_601 = F.relu(t_600)
-        t_602 = self.n_Conv_106(t_601)
-        t_603 = torch.add(t_602, t_597)
-        t_604 = F.relu(t_603)
-        t_605 = self.n_Conv_107(t_604)
-        t_606 = F.relu(t_605)
-        t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
-        t_607 = self.n_Conv_108(t_606_padded)
-        t_608 = F.relu(t_607)
-        t_609 = self.n_Conv_109(t_608)
-        t_610 = torch.add(t_609, t_604)
-        t_611 = F.relu(t_610)
-        t_612 = self.n_Conv_110(t_611)
-        t_613 = F.relu(t_612)
-        t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
-        t_614 = self.n_Conv_111(t_613_padded)
-        t_615 = F.relu(t_614)
-        t_616 = self.n_Conv_112(t_615)
-        t_617 = torch.add(t_616, t_611)
-        t_618 = F.relu(t_617)
-        t_619 = self.n_Conv_113(t_618)
-        t_620 = F.relu(t_619)
-        t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
-        t_621 = self.n_Conv_114(t_620_padded)
-        t_622 = F.relu(t_621)
-        t_623 = self.n_Conv_115(t_622)
-        t_624 = torch.add(t_623, t_618)
-        t_625 = F.relu(t_624)
-        t_626 = self.n_Conv_116(t_625)
-        t_627 = F.relu(t_626)
-        t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
-        t_628 = self.n_Conv_117(t_627_padded)
-        t_629 = F.relu(t_628)
-        t_630 = self.n_Conv_118(t_629)
-        t_631 = torch.add(t_630, t_625)
-        t_632 = F.relu(t_631)
-        t_633 = self.n_Conv_119(t_632)
-        t_634 = F.relu(t_633)
-        t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
-        t_635 = self.n_Conv_120(t_634_padded)
-        t_636 = F.relu(t_635)
-        t_637 = self.n_Conv_121(t_636)
-        t_638 = torch.add(t_637, t_632)
-        t_639 = F.relu(t_638)
-        t_640 = self.n_Conv_122(t_639)
-        t_641 = F.relu(t_640)
-        t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
-        t_642 = self.n_Conv_123(t_641_padded)
-        t_643 = F.relu(t_642)
-        t_644 = self.n_Conv_124(t_643)
-        t_645 = torch.add(t_644, t_639)
-        t_646 = F.relu(t_645)
-        t_647 = self.n_Conv_125(t_646)
-        t_648 = F.relu(t_647)
-        t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
-        t_649 = self.n_Conv_126(t_648_padded)
-        t_650 = F.relu(t_649)
-        t_651 = self.n_Conv_127(t_650)
-        t_652 = torch.add(t_651, t_646)
-        t_653 = F.relu(t_652)
-        t_654 = self.n_Conv_128(t_653)
-        t_655 = F.relu(t_654)
-        t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
-        t_656 = self.n_Conv_129(t_655_padded)
-        t_657 = F.relu(t_656)
-        t_658 = self.n_Conv_130(t_657)
-        t_659 = torch.add(t_658, t_653)
-        t_660 = F.relu(t_659)
-        t_661 = self.n_Conv_131(t_660)
-        t_662 = F.relu(t_661)
-        t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
-        t_663 = self.n_Conv_132(t_662_padded)
-        t_664 = F.relu(t_663)
-        t_665 = self.n_Conv_133(t_664)
-        t_666 = torch.add(t_665, t_660)
-        t_667 = F.relu(t_666)
-        t_668 = self.n_Conv_134(t_667)
-        t_669 = F.relu(t_668)
-        t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
-        t_670 = self.n_Conv_135(t_669_padded)
-        t_671 = F.relu(t_670)
-        t_672 = self.n_Conv_136(t_671)
-        t_673 = torch.add(t_672, t_667)
-        t_674 = F.relu(t_673)
-        t_675 = self.n_Conv_137(t_674)
-        t_676 = F.relu(t_675)
-        t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
-        t_677 = self.n_Conv_138(t_676_padded)
-        t_678 = F.relu(t_677)
-        t_679 = self.n_Conv_139(t_678)
-        t_680 = torch.add(t_679, t_674)
-        t_681 = F.relu(t_680)
-        t_682 = self.n_Conv_140(t_681)
-        t_683 = F.relu(t_682)
-        t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
-        t_684 = self.n_Conv_141(t_683_padded)
-        t_685 = F.relu(t_684)
-        t_686 = self.n_Conv_142(t_685)
-        t_687 = torch.add(t_686, t_681)
-        t_688 = F.relu(t_687)
-        t_689 = self.n_Conv_143(t_688)
-        t_690 = F.relu(t_689)
-        t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
-        t_691 = self.n_Conv_144(t_690_padded)
-        t_692 = F.relu(t_691)
-        t_693 = self.n_Conv_145(t_692)
-        t_694 = torch.add(t_693, t_688)
-        t_695 = F.relu(t_694)
-        t_696 = self.n_Conv_146(t_695)
-        t_697 = F.relu(t_696)
-        t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
-        t_698 = self.n_Conv_147(t_697_padded)
-        t_699 = F.relu(t_698)
-        t_700 = self.n_Conv_148(t_699)
-        t_701 = torch.add(t_700, t_695)
-        t_702 = F.relu(t_701)
-        t_703 = self.n_Conv_149(t_702)
-        t_704 = F.relu(t_703)
-        t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
-        t_705 = self.n_Conv_150(t_704_padded)
-        t_706 = F.relu(t_705)
-        t_707 = self.n_Conv_151(t_706)
-        t_708 = torch.add(t_707, t_702)
-        t_709 = F.relu(t_708)
-        t_710 = self.n_Conv_152(t_709)
-        t_711 = F.relu(t_710)
-        t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
-        t_712 = self.n_Conv_153(t_711_padded)
-        t_713 = F.relu(t_712)
-        t_714 = self.n_Conv_154(t_713)
-        t_715 = torch.add(t_714, t_709)
-        t_716 = F.relu(t_715)
-        t_717 = self.n_Conv_155(t_716)
-        t_718 = F.relu(t_717)
-        t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
-        t_719 = self.n_Conv_156(t_718_padded)
-        t_720 = F.relu(t_719)
-        t_721 = self.n_Conv_157(t_720)
-        t_722 = torch.add(t_721, t_716)
-        t_723 = F.relu(t_722)
-        t_724 = self.n_Conv_158(t_723)
-        t_725 = self.n_Conv_159(t_723)
-        t_726 = F.relu(t_725)
-        t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
-        t_727 = self.n_Conv_160(t_726_padded)
-        t_728 = F.relu(t_727)
-        t_729 = self.n_Conv_161(t_728)
-        t_730 = torch.add(t_729, t_724)
-        t_731 = F.relu(t_730)
-        t_732 = self.n_Conv_162(t_731)
-        t_733 = F.relu(t_732)
-        t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
-        t_734 = self.n_Conv_163(t_733_padded)
-        t_735 = F.relu(t_734)
-        t_736 = self.n_Conv_164(t_735)
-        t_737 = torch.add(t_736, t_731)
-        t_738 = F.relu(t_737)
-        t_739 = self.n_Conv_165(t_738)
-        t_740 = F.relu(t_739)
-        t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
-        t_741 = self.n_Conv_166(t_740_padded)
-        t_742 = F.relu(t_741)
-        t_743 = self.n_Conv_167(t_742)
-        t_744 = torch.add(t_743, t_738)
-        t_745 = F.relu(t_744)
-        t_746 = self.n_Conv_168(t_745)
-        t_747 = self.n_Conv_169(t_745)
-        t_748 = F.relu(t_747)
-        t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
-        t_749 = self.n_Conv_170(t_748_padded)
-        t_750 = F.relu(t_749)
-        t_751 = self.n_Conv_171(t_750)
-        t_752 = torch.add(t_751, t_746)
-        t_753 = F.relu(t_752)
-        t_754 = self.n_Conv_172(t_753)
-        t_755 = F.relu(t_754)
-        t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
-        t_756 = self.n_Conv_173(t_755_padded)
-        t_757 = F.relu(t_756)
-        t_758 = self.n_Conv_174(t_757)
-        t_759 = torch.add(t_758, t_753)
-        t_760 = F.relu(t_759)
-        t_761 = self.n_Conv_175(t_760)
-        t_762 = F.relu(t_761)
-        t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
-        t_763 = self.n_Conv_176(t_762_padded)
-        t_764 = F.relu(t_763)
-        t_765 = self.n_Conv_177(t_764)
-        t_766 = torch.add(t_765, t_760)
-        t_767 = F.relu(t_766)
-        t_768 = self.n_Conv_178(t_767)
-        t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
-        t_770 = torch.squeeze(t_769, 3)
-        t_770 = torch.squeeze(t_770, 2)
-        t_771 = torch.sigmoid(t_770)
-        return t_771
-
-    def load_state_dict(self, state_dict, **kwargs):
-        self.tags = state_dict.get('tags', [])
-
-        super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
-
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Airbag Dump Collection.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Airbag Dump Collection.md
deleted file mode 100644
index 4977f338da7e3139b00e4a52764d1e503be29c2b..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Airbag Dump Collection.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>airbag dump collection</h2><br /><p><b><b>Download Zip</b> &rArr;&rArr;&rArr; <a href="https://cinurl.com/2uEYuH">https://cinurl.com/2uEYuH</a></b></p><br /><br />
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Miki Falls Spring Pdf Download 2021.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Miki Falls Spring Pdf Download 2021.md
deleted file mode 100644
index bb06a545d0ffaa8b84ffc32326e6511f054ac4f6..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Miki Falls Spring Pdf Download 2021.md	
+++ /dev/null
@@ -1,46 +0,0 @@
-<h2>Miki Falls Spring Pdf Download</h2><br /><p><b><b>Download File</b> &#128504;&#128504;&#128504; <a href="https://cinurl.com/2uEYDo">https://cinurl.com/2uEYDo</a></b></p><br /><br />
-<br />
- . . completely in control. Her life is a complete lie. Nothing that she says, thinks or does is real. It is all a charade to give Miki the freedom she needs to focus on what really matters. . . . it's time to finally become who Miki is . . . finally get her life in order. So, when her parents are transferred, there's no stopping Miki. The only problem is, she finds herself stuck in her new home...Miki ends up in a world she never knew existed. What happens to Miki and her new friends as their school year progresses is nothing short of a miracle.
-
-Cast
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-Miki Yoshida is portrayed by actor and model Ayano Takeda.
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-Shinichi is portrayed by actor Yuichi Nakamura.
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-Hiroshi is portrayed by actor Yudai Chiba.
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-Chizuko is portrayed by actor and model Maho Satō.
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-Teru is portrayed by actor and model Ryuhei Maruyama.
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-Release
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-Box office
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-Miki Nakayama and the Mind of Misora (Kōgeki no MISORA) grossed  on its opening weekend in Japan. As of November 18, 2011, the film has grossed  domestically.
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-References
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-Category:2011 films
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-Category:Japanese films
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-Category:Japanese drama films
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-Category:2010s drama films
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-Category:Japanese-language filmsI am an advocate of restraint and of patience and of the thoughtful use of capital to create prosperity. Yet I am also an advocate of revolution, a revolution which is a revolution of the heart. And the revolution of the heart has more than one face.
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-My mask today is a wicked rictus, my face a face of terror and of rage. I was once cruel. I was once white. I was once a slave. I was once a human being. I was once a slave. I was once a slave. I was once a human being.
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Advanced C Programming By Example John Perry 51.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Advanced C Programming By Example John Perry 51.md
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--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Advanced C Programming By Example John Perry 51.md	
+++ /dev/null
@@ -1,108 +0,0 @@
-
-<h1>Advanced C Programming by Example: A Book Review</h1>
-<p>If you are an intermediate-level C programmer who wants to take your skills to the next level, you might be interested in reading <strong>Advanced C Programming by Example</strong> by John W. Perry. This book is a practical, example-driven, code-centered guide that covers various topics such as ANSI C libraries, dynamic data structures, string parsing, numeric conversion, memory management, bit-level manipulation, interactions with operating systems, and more.</p>
-<p>In this article, we will review the main features of this book, its strengths and weaknesses, and how it can help you improve your C programming skills.</p>
-<h2>Advanced C Programming By Example John Perry 51</h2><br /><p><b><b>Download File</b> &#127775; <a href="https://urluss.com/2uCFpa">https://urluss.com/2uCFpa</a></b></p><br /><br />
-<h2>What is Advanced C Programming by Example?</h2>
-<p><strong>Advanced C Programming by Example</strong> is a book that was published in 1998 by PWS Publishing. It has 320 pages and contains 12 chapters that cover different aspects of advanced C programming. The book assumes that the reader has a basic knowledge of C and is familiar with the syntax and semantics of the language. The book does not teach C from scratch, but rather builds on the reader's existing background in C to complete their knowledge of ANSI C libraries and the conceptual and syntactic structures needed to master advanced techniques.</p>
-<p>The book is divided into three parts: Part I introduces the ANSI C libraries and how to use them effectively; Part II covers dynamic data structures such as linked lists, stacks, queues, trees, and graphs; Part III explores string parsing and numeric conversion, memory management, bit-level manipulation, interactions with operating systems, and other advanced topics. Each chapter contains a brief introduction, a series of examples that illustrate the concepts and techniques discussed, exercises that test the reader's understanding and challenge them to apply what they learned, and a summary that reviews the main points of the chapter.</p>
-<h2>What are the strengths of Advanced C Programming by Example?</h2>
-<p>One of the main strengths of <strong>Advanced C Programming by Example</strong> is its "blue collar" approach to the art of programming. The book does not present abstract ideas or pseudocode, but rather shows how to master the "down in the trenches" C details to implement them successfully. The book presents actual C code that is clear, concise, and well-commented. The code examples are not trivial or toy programs, but rather realistic and useful applications that demonstrate how to solve real-world problems with C.</p>
-<p>Another strength of <strong>Advanced C Programming by Example</strong> is its comprehensive coverage of various topics that are often overlooked or treated superficially in other books. The book goes beyond the basics of C and delves into topics such as dynamic data structures, string parsing and numeric conversion, memory management, bit-level manipulation, interactions with operating systems, and more. The book also covers some of the features of ANSI C that are not widely used or understood by many programmers, such as function pointers, variable-length arrays, unions, bit fields, and type qualifiers.</p>
-<h2>What are the weaknesses of Advanced C Programming by Example?</h2>
-<p>One of the possible weaknesses of <strong>Advanced C Programming by Example</strong> is its age. The book was published in 1998 and does not reflect some of the changes and developments that have occurred in the C language and its environment since then. For example, the book does not cover some of the features that were introduced in later versions of ANSI C (such as C99 or C11), such as designated initializers, compound literals, flexible array members, static assertions, _Bool type, _Generic keyword, etc. The book also does not address some of the issues and challenges that modern C programmers face today, such as portability across different platforms and compilers, security and reliability concerns, concurrency and parallelism issues, etc.</p>
-<p>Another possible weakness of <strong>Advanced C Programming by Example</strong> is its level of difficulty. The book is intended for intermediate-level C programmers who want to take their skills to the next level. However, some of the topics and examples presented in the book may be too advanced or complex for some readers who are not familiar with them or who do not have enough background or experience in C programming. The book does not provide much explanation or guidance for some of the concepts and techniques discussed; it assumes that the reader can follow along and understand them by looking at the code examples. Therefore, some readers may find some parts of the book too challenging or frustrating to follow.</p>
-<h2>How can Advanced C Programming by Example help you improve your C programming skills?</h2>
-<p>If you are an intermediate-level C programmer who wants to take your skills to the next level,<strong>Advanced C Programming by Example</strong> can help you improve your C programming skills by providing you with a practical,
-example-driven,
-code-centered guide that covers various topics such as ANSI C libraries,
-dynamic data structures,
-string parsing,
-numeric conversion,
-memory management,
-bit-level manipulation,
-interactions with operating systems,
-and more.
-The book can help you learn how to master the "down in
-the trenches" C details to implement abstract ideas successfully.
-The book can also help you expand your knowledge
-of ANSI C libraries
-and
-the conceptual
-and syntactic structures needed
-to master advanced techniques.
-The book can also expose you
-to some
-of
-the features
-of ANSI
-C
-that are not widely used or understood
-by many programmers,
-such as function pointers,
-variable-length arrays,
-unions,
-bit fields,
-and type qualifiers.</p>
-<p></p>
-<p>If you want to read <strong>Advanced C Programming by Example</strong>, you can find it on Amazon or Google Books. You can also find more information about it on ACM Digital Library or Semantic Scholar.</p>
-<h2>Who is John W. Perry and why did he write Advanced C Programming by Example?</h2>
-<p>John W. Perry is the author of <strong>Advanced C Programming by Example</strong>. He is a computer scientist and a professor of computer science at the University of Texas at San Antonio. He has a Ph.D. in computer science from the University of Texas at Austin and has over 20 years of experience in teaching and research in various areas of computer science, such as programming languages, compilers, operating systems, distributed systems, and artificial intelligence.</p>
-<p>He wrote <strong>Advanced C Programming by Example</strong> because he noticed that there was a lack of books that addressed the needs of intermediate-level C programmers who wanted to improve their skills and learn more about the advanced features and techniques of C programming. He wanted to provide a book that was practical, example-driven, and code-centered, rather than theoretical, abstract, or pseudocode-based. He also wanted to present a "blue collar" approach to the art of programming, which emphasizes how to master the "down in the trenches" C details to implement abstract ideas successfully.</p>
-<h2>What are the benefits of reading Advanced C Programming by Example?</h2>
-<p>Reading <strong>Advanced C Programming by Example</strong> can provide you with several benefits, such as:</p>
-<ul>
-<li>You can learn how to use ANSI C libraries effectively and efficiently.</li>
-<li>You can master dynamic data structures such as linked lists, stacks, queues, trees, and graphs.</li>
-<li>You can explore string parsing and numeric conversion techniques.</li>
-<li>You can understand memory management and allocation strategies.</li>
-<li>You can manipulate bits and bytes with ease and confidence.</li>
-<li>You can interact with operating systems and perform system calls.</li>
-<li>You can apply advanced techniques to solve real-world problems with C.</li>
-</ul>
-<p>By reading <strong>Advanced C Programming by Example</strong>, you can enhance your C programming skills and become a more proficient and professional C programmer.</p>
-<h2>How to get the most out of Advanced C Programming by Example?</h2>
-<p>To get the most out of <strong>Advanced C Programming by Example</strong>, you should:</p>
-<ul>
-<li>Have a basic knowledge of C and be familiar with the syntax and semantics of the language.</li>
-<li>Read each chapter carefully and pay attention to the introduction, examples, exercises, and summary.</li>
-<li>Try to understand the concepts and techniques presented in each chapter and how they relate to each other.</li>
-<li>Run the code examples on your own computer and experiment with them by modifying or extending them.</li>
-<li>Solve the exercises at the end of each chapter and check your answers with the solutions provided in the appendix.</li>
-<li>Use the book as a reference when you encounter a problem or a question related to advanced C programming.</li>
-</ul>
-<p><strong>Advanced C Programming by Example</strong> is a book that can help you take your skills to the next level. If you are interested in learning more about advanced C programming, you should give this book a try. You will not regret it.</p>
-<h2>What are some of the topics covered in Advanced C Programming by Example?</h2>
-<p><strong>Advanced C Programming by Example</strong> covers a wide range of topics that are relevant and useful for intermediate-level C programmers who want to improve their skills and learn more about the advanced features and techniques of C programming. Some of the topics covered in the book are:</p>
-<ul>
-<li>ANSI C libraries: The book introduces the reader to the standard libraries that are part of ANSI C, such as stdio.h, stdlib.h, string.h, math.h, time.h, etc. The book explains how to use these libraries effectively and efficiently, and how to avoid common pitfalls and errors. The book also covers some of the lesser-known or rarely-used libraries, such as ctype.h, setjmp.h, signal.h, etc.</li>
-<li>Dynamic data structures: The book covers various types of dynamic data structures that are commonly used in C programming, such as linked lists, stacks, queues, trees, and graphs. The book shows how to implement these data structures using pointers, structures, and dynamic memory allocation. The book also explains how to manipulate these data structures using functions, recursion, and algorithms.</li>
-<li>String parsing and numeric conversion: The book explores various techniques for parsing strings and converting numbers in C programming. The book shows how to use functions such as strtok, sscanf, sprintf, atoi, atof, etc. The book also shows how to write custom functions for parsing and converting complex or non-standard formats.</li>
-<li>Memory management: The book discusses various aspects of memory management in C programming, such as memory allocation, deallocation, fragmentation, leakage, etc. The book shows how to use functions such as malloc, calloc, realloc, free, etc. The book also shows how to write custom memory management functions and how to use memory debugging tools.</li>
-<li>Bit-level manipulation: The book demonstrates various techniques for manipulating bits and bytes in C programming, such as bitwise operators, bit fields, bit masks, bit vectors, etc. The book shows how to use these techniques for various purposes, such as encryption, compression, error detection, etc.</li>
-<li>Interactions with operating systems: The book explains how to interact with operating systems and perform system calls in C programming. The book shows how to use functions such as fork, exec, waitpid,
-pipe,
-dup,
-open,
-close,
-read,
-write,
-etc.
-The book also shows how to handle signals,
-processes,
-files,
-and directories.</li>
-</ul>
-<p>These are some of the topics covered in <strong>Advanced C Programming by Example</strong>. The book also covers other topics such as variable-length arrays,
-function pointers,
-unions,
-type qualifiers,
-and more.</p>
-<h2>Who should read Advanced C Programming by Example?</h2>
-<p><strong>Advanced C Programming by Example</strong> is a book that is suitable for intermediate-level C programmers who want to take their skills to the next level. The book is not intended for beginners who are just learning C or for experts who already know everything about C. The book is designed for programmers who have a basic knowledge of C and are familiar with the syntax and semantics of the language. The book is also suitable for programmers who want to refresh or update their knowledge of ANSI C libraries and the conceptual and syntactic structures needed to master advanced techniques.</p>
-<p>If you are interested in learning more about advanced C programming,<strong>Advanced C Programming by Example</strong> is a book that can help you achieve your goals. You can find it on Amazon or Google Books. You can also find more information about it on ACM Digital Library or Semantic Scholar.</p>
-<h2>Conclusion</h2>
-<p><strong>Advanced C Programming by Example</strong> is a book that can help you improve your C programming skills by providing you with a practical, example-driven, code-centered guide that covers various topics such as ANSI C libraries, dynamic data structures, string parsing and numeric conversion, memory management, bit-level manipulation, interactions with operating systems, and more. The book can help you learn how to master the "down in the trenches" C details to implement abstract ideas successfully. The book can also help you expand your knowledge of ANSI C libraries and the conceptual and syntactic structures needed to master advanced techniques. The book can also expose you to some of the features of ANSI C that are not widely used or understood by many programmers, such as function pointers, variable-length arrays, unions, bit fields, and type qualifiers.</p>
-<p>If you are an intermediate-level C programmer who wants to take your skills to the next level,<strong>Advanced C Programming by Example</strong> is a book that you should read. You will not regret it.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suryabbrj/ContentModX/app.py b/spaces/suryabbrj/ContentModX/app.py
deleted file mode 100644
index 6ab5277f9f935411ae38fdbc8bb6fc8f18f12865..0000000000000000000000000000000000000000
--- a/spaces/suryabbrj/ContentModX/app.py
+++ /dev/null
@@ -1,291 +0,0 @@
-import gradio as gr
-import PIL.Image
-import skimage.io as io
-from tqdm import tqdm, trange
-from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup
-from typing import Tuple, List, Union, Optional
-import sys
-import torch.nn.functional as nnf
-import torch
-import numpy as np
-from torch import nn
-import clip
-from huggingface_hub import hf_hub_download
-import os
-
-conceptual_weight = hf_hub_download(
-    repo_id="akhaliq/CLIP-prefix-captioning-conceptual-weights", filename="conceptual_weights.pt")
-coco_weight = hf_hub_download(
-    repo_id="akhaliq/CLIP-prefix-captioning-COCO-weights", filename="coco_weights.pt")
-
-N = type(None)
-V = np.array
-ARRAY = np.ndarray
-ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]]
-VS = Union[Tuple[V, ...], List[V]]
-VN = Union[V, N]
-VNS = Union[VS, N]
-T = torch.Tensor
-TS = Union[Tuple[T, ...], List[T]]
-TN = Optional[T]
-TNS = Union[Tuple[TN, ...], List[TN]]
-TSN = Optional[TS]
-TA = Union[T, ARRAY]
-
-
-D = torch.device
-CPU = torch.device('cpu')
-
-
-def get_device(device_id: int) -> D:
-    if not torch.cuda.is_available():
-        return CPU
-    device_id = min(torch.cuda.device_count() - 1, device_id)
-    return torch.device(f'cuda:{device_id}')
-
-
-CUDA = get_device
-
-
-class MLP(nn.Module):
-
-    def forward(self, x: T) -> T:
-        return self.model(x)
-
-    def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
-        super(MLP, self).__init__()
-        layers = []
-        for i in range(len(sizes) - 1):
-            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
-            if i < len(sizes) - 2:
-                layers.append(act())
-        self.model = nn.Sequential(*layers)
-
-
-class ClipCaptionModel(nn.Module):
-
-    # @functools.lru_cache #FIXME
-    def get_dummy_token(self, batch_size: int, device: D) -> T:
-        return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)
-
-    def forward(self, tokens: T, prefix: T, mask: Optional[T] = None, labels: Optional[T] = None):
-        embedding_text = self.gpt.transformer.wte(tokens)
-        prefix_projections = self.clip_project(
-            prefix).view(-1, self.prefix_length, self.gpt_embedding_size)
-        # print(embedding_text.size()) #torch.Size([5, 67, 768])
-        # print(prefix_projections.size()) #torch.Size([5, 1, 768])
-        embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)
-        if labels is not None:
-            dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)
-            labels = torch.cat((dummy_token, tokens), dim=1)
-        out = self.gpt(inputs_embeds=embedding_cat,
-                       labels=labels, attention_mask=mask)
-        return out
-
-    def __init__(self, prefix_length: int, prefix_size: int = 512):
-        super(ClipCaptionModel, self).__init__()
-        self.prefix_length = prefix_length
-        self.gpt = GPT2LMHeadModel.from_pretrained('gpt2')
-        self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
-        if prefix_length > 10:  # not enough memory
-            self.clip_project = nn.Linear(
-                prefix_size, self.gpt_embedding_size * prefix_length)
-        else:
-            self.clip_project = MLP((prefix_size, (self.gpt_embedding_size *
-                                    prefix_length) // 2, self.gpt_embedding_size * prefix_length))
-
-
-class ClipCaptionPrefix(ClipCaptionModel):
-
-    def parameters(self, recurse: bool = True):
-        return self.clip_project.parameters()
-
-    def train(self, mode: bool = True):
-        super(ClipCaptionPrefix, self).train(mode)
-        self.gpt.eval()
-        return self
-
-
-# @title Caption prediction
-
-def generate_beam(model, tokenizer, beam_size: int = 5, prompt=None, embed=None,
-                  entry_length=67, temperature=1., stop_token: str = '.'):
-
-    model.eval()
-    stop_token_index = tokenizer.encode(stop_token)[0]
-    tokens = None
-    scores = None
-    device = next(model.parameters()).device
-    seq_lengths = torch.ones(beam_size, device=device)
-    is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)
-    with torch.no_grad():
-        if embed is not None:
-            generated = embed
-        else:
-            if tokens is None:
-                tokens = torch.tensor(tokenizer.encode(prompt))
-                tokens = tokens.unsqueeze(0).to(device)
-                generated = model.gpt.transformer.wte(tokens)
-        for i in range(entry_length):
-            outputs = model.gpt(inputs_embeds=generated)
-            logits = outputs.logits
-            logits = logits[:, -1, :] / \
-                (temperature if temperature > 0 else 1.0)
-            logits = logits.softmax(-1).log()
-            if scores is None:
-                scores, next_tokens = logits.topk(beam_size, -1)
-                generated = generated.expand(beam_size, *generated.shape[1:])
-                next_tokens, scores = next_tokens.permute(
-                    1, 0), scores.squeeze(0)
-                if tokens is None:
-                    tokens = next_tokens
-                else:
-                    tokens = tokens.expand(beam_size, *tokens.shape[1:])
-                    tokens = torch.cat((tokens, next_tokens), dim=1)
-            else:
-                logits[is_stopped] = -float(np.inf)
-                logits[is_stopped, 0] = 0
-                scores_sum = scores[:, None] + logits
-                seq_lengths[~is_stopped] += 1
-                scores_sum_average = scores_sum / seq_lengths[:, None]
-                scores_sum_average, next_tokens = scores_sum_average.view(
-                    -1).topk(beam_size, -1)
-                next_tokens_source = next_tokens // scores_sum.shape[1]
-                seq_lengths = seq_lengths[next_tokens_source]
-                next_tokens = next_tokens % scores_sum.shape[1]
-                next_tokens = next_tokens.unsqueeze(1)
-                tokens = tokens[next_tokens_source]
-                tokens = torch.cat((tokens, next_tokens), dim=1)
-                generated = generated[next_tokens_source]
-                scores = scores_sum_average * seq_lengths
-                is_stopped = is_stopped[next_tokens_source]
-            next_token_embed = model.gpt.transformer.wte(
-                next_tokens.squeeze()).view(generated.shape[0], 1, -1)
-            generated = torch.cat((generated, next_token_embed), dim=1)
-            is_stopped = is_stopped + \
-                next_tokens.eq(stop_token_index).squeeze()
-            if is_stopped.all():
-                break
-    scores = scores / seq_lengths
-    output_list = tokens.cpu().numpy()
-    output_texts = [tokenizer.decode(output[:int(length)])
-                    for output, length in zip(output_list, seq_lengths)]
-    order = scores.argsort(descending=True)
-    output_texts = [output_texts[i] for i in order]
-    return output_texts
-
-
-def generate2(
-        model,
-        tokenizer,
-        tokens=None,
-        prompt=None,
-        embed=None,
-        entry_count=1,
-        entry_length=67,  # maximum number of words
-        top_p=0.8,
-        temperature=1.,
-        stop_token: str = '.',
-):
-    model.eval()
-    generated_num = 0
-    generated_list = []
-    stop_token_index = tokenizer.encode(stop_token)[0]
-    filter_value = -float("Inf")
-    device = next(model.parameters()).device
-
-    with torch.no_grad():
-
-        for entry_idx in trange(entry_count):
-            if embed is not None:
-                generated = embed
-            else:
-                if tokens is None:
-                    tokens = torch.tensor(tokenizer.encode(prompt))
-                    tokens = tokens.unsqueeze(0).to(device)
-
-                generated = model.gpt.transformer.wte(tokens)
-
-            for i in range(entry_length):
-
-                outputs = model.gpt(inputs_embeds=generated)
-                logits = outputs.logits
-                logits = logits[:, -1, :] / \
-                    (temperature if temperature > 0 else 1.0)
-                sorted_logits, sorted_indices = torch.sort(
-                    logits, descending=True)
-                cumulative_probs = torch.cumsum(
-                    nnf.softmax(sorted_logits, dim=-1), dim=-1)
-                sorted_indices_to_remove = cumulative_probs > top_p
-                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[
-                    ..., :-1
-                ].clone()
-                sorted_indices_to_remove[..., 0] = 0
-
-                indices_to_remove = sorted_indices[sorted_indices_to_remove]
-                logits[:, indices_to_remove] = filter_value
-                next_token = torch.argmax(logits, -1).unsqueeze(0)
-                next_token_embed = model.gpt.transformer.wte(next_token)
-                if tokens is None:
-                    tokens = next_token
-                else:
-                    tokens = torch.cat((tokens, next_token), dim=1)
-                generated = torch.cat((generated, next_token_embed), dim=1)
-                if stop_token_index == next_token.item():
-                    break
-
-            output_list = list(tokens.squeeze().cpu().numpy())
-            output_text = tokenizer.decode(output_list)
-            generated_list.append(output_text)
-
-    return generated_list[0]
-
-
-is_gpu = False
-device = CUDA(0) if is_gpu else "cpu"
-clip_model, preprocess = clip.load("ViT-B/32", device=device, jit=False)
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-
-
-def inference(img, model_name):
-    prefix_length = 10
-
-    model = ClipCaptionModel(prefix_length)
-
-    if model_name == "COCO":
-        model_path = coco_weight
-    else:
-        model_path = conceptual_weight
-    model.load_state_dict(torch.load(model_path, map_location=CPU))
-    model = model.eval()
-    device = CUDA(0) if is_gpu else "cpu"
-    model = model.to(device)
-
-    use_beam_search = False
-    image = io.imread(img.name)
-    pil_image = PIL.Image.fromarray(image)
-    image = preprocess(pil_image).unsqueeze(0).to(device)
-    with torch.no_grad():
-        prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)
-        prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)
-    if use_beam_search:
-        generated_text_prefix = generate_beam(
-            model, tokenizer, embed=prefix_embed)[0]
-    else:
-        generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed)
-    return generated_text_prefix
-
-
-title = "ProjectX"
-description = "Front-End Application used for ContentModX engine built using Python. To use it, simply upload your image, or click one of the examples to load them."
-article = "<p style='text-align: center'><a href='https://github.com/suryabbrj/python_ml' target='_blank'>Github Repo</a></p>"
-
-gr.Interface(
-    inference,
-    [gr.inputs.Image(type="filepath", label="Input"), gr.inputs.Radio(choices=[
-        "Yes", "No"], type="value", default="COCO", label="would you like to constribute this result to the model training dataset (do this only if the image used is not a personal image, of you or anyone else you know.)")],
-    gr.outputs.Textbox(label="Output"),
-    title=title,
-    description=description,
-    article=article,
-).launch(debug=True, share=True)
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/roipoint_pool3d.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/roipoint_pool3d.py
deleted file mode 100644
index 0a21412c0728431c04b84245bc2e3109eea9aefc..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/roipoint_pool3d.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from torch import nn as nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['roipoint_pool3d_forward'])
-
-
-class RoIPointPool3d(nn.Module):
-    """Encode the geometry-specific features of each 3D proposal.
-
-    Please refer to `Paper of PartA2 <https://arxiv.org/pdf/1907.03670.pdf>`_
-    for more details.
-
-    Args:
-        num_sampled_points (int, optional): Number of samples in each roi.
-            Default: 512.
-    """
-
-    def __init__(self, num_sampled_points=512):
-        super().__init__()
-        self.num_sampled_points = num_sampled_points
-
-    def forward(self, points, point_features, boxes3d):
-        """
-        Args:
-            points (torch.Tensor): Input points whose shape is (B, N, C).
-            point_features (torch.Tensor): Features of input points whose shape
-                is (B, N, C).
-            boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7).
-
-        Returns:
-            pooled_features (torch.Tensor): The output pooled features whose
-                shape is (B, M, 512, 3 + C).
-            pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M).
-        """
-        return RoIPointPool3dFunction.apply(points, point_features, boxes3d,
-                                            self.num_sampled_points)
-
-
-class RoIPointPool3dFunction(Function):
-
-    @staticmethod
-    def forward(ctx, points, point_features, boxes3d, num_sampled_points=512):
-        """
-        Args:
-            points (torch.Tensor): Input points whose shape is (B, N, C).
-            point_features (torch.Tensor): Features of input points whose shape
-                is (B, N, C).
-            boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7).
-            num_sampled_points (int, optional): The num of sampled points.
-                Default: 512.
-
-        Returns:
-            pooled_features (torch.Tensor): The output pooled features whose
-                shape is (B, M, 512, 3 + C).
-            pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M).
-        """
-        assert len(points.shape) == 3 and points.shape[2] == 3
-        batch_size, boxes_num, feature_len = points.shape[0], boxes3d.shape[
-            1], point_features.shape[2]
-        pooled_boxes3d = boxes3d.view(batch_size, -1, 7)
-        pooled_features = point_features.new_zeros(
-            (batch_size, boxes_num, num_sampled_points, 3 + feature_len))
-        pooled_empty_flag = point_features.new_zeros(
-            (batch_size, boxes_num)).int()
-
-        ext_module.roipoint_pool3d_forward(points.contiguous(),
-                                           pooled_boxes3d.contiguous(),
-                                           point_features.contiguous(),
-                                           pooled_features, pooled_empty_flag)
-
-        return pooled_features, pooled_empty_flag
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        raise NotImplementedError
diff --git a/spaces/szukevin/VISOR-GPT/train/inference/run_chid_infer.py b/spaces/szukevin/VISOR-GPT/train/inference/run_chid_infer.py
deleted file mode 100644
index 028a823f66c30878496b3a6f687ce0db4198d823..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/inference/run_chid_infer.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""
-  This script provides an example to wrap TencentPretrain for ChID (a multiple choice dataset) inference.
-"""
-import sys
-import os
-import torch
-import json
-import argparse
-import collections
-import numpy as np
-
-tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(tencentpretrain_dir)
-
-from tencentpretrain.utils.constants import *
-from tencentpretrain.utils.tokenizers import *
-from tencentpretrain.utils.config import load_hyperparam
-from tencentpretrain.model_loader import load_model
-from tencentpretrain.opts import infer_opts
-from finetune.run_classifier import batch_loader
-from finetune.run_c3 import MultipleChoice
-from finetune.run_chid import read_dataset
-
-
-def postprocess_chid_predictions(results):
-    index2tag = {index: tag for index, (tag, logits) in enumerate(results)}
-    logits_matrix = [logits for _, logits in results]
-
-    logits_matrix = np.transpose(np.array(logits_matrix))
-    logits_matrix_list = []
-    for i, row in enumerate(logits_matrix):
-        for j, value in enumerate(row):
-            logits_matrix_list.append((i, j, value))
-    else:
-        choices = set(range(i + 1))
-        blanks = set(range(j + 1))
-    logits_matrix_list = sorted(logits_matrix_list, key=lambda x: x[2], reverse=True)
-    results = []
-    for i, j, v in logits_matrix_list:
-        if (j in blanks) and (i in choices):
-            results.append((i, j))
-            blanks.remove(j)
-            choices.remove(i)
-    results = sorted(results, key=lambda x: x[1], reverse=False)
-    results = [[index2tag[j], i] for i, j in results]
-    return results
-
-
-def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    infer_opts(parser)
-
-    parser.add_argument("--vocab_path", default=None, type=str,
-                        help="Path of the vocabulary file.")
-    parser.add_argument("--spm_model_path", default=None, type=str,
-                        help="Path of the sentence piece model.")
-    parser.add_argument("--max_choices_num", default=10, type=int,
-                        help="The maximum number of cadicate answer, shorter than this will be padded.")
-
-    args = parser.parse_args()
-
-    # Load the hyperparameters from the config file.
-    args = load_hyperparam(args)
-
-    # Build tokenizer.
-    args.tokenizer = CharTokenizer(args)
-
-    # Build classification model and load parameters.
-    model = MultipleChoice(args)
-    model = load_model(model, args.load_model_path)
-
-    # For simplicity, we use DataParallel wrapper to use multiple GPUs.
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(device)
-    if torch.cuda.device_count() > 1:
-        print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
-        model = torch.nn.DataParallel(model)
-
-    dataset = read_dataset(args, args.test_path, None)
-
-    model.eval()
-    batch_size = args.batch_size
-    results_final = []
-    dataset_by_group = {}
-    print("The number of prediction instances: ", len(dataset))
-
-    for example in dataset:
-        if example[-1] not in dataset_by_group:
-            dataset_by_group[example[-1]] = [example]
-        else:
-            dataset_by_group[example[-1]].append(example)
-
-    for group_index, examples in dataset_by_group.items():
-        src = torch.LongTensor([example[0] for example in examples])
-        tgt = torch.LongTensor([example[1] for example in examples])
-        seg = torch.LongTensor([example[2] for example in examples])
-        index = 0
-        results = []
-        for i, (src_batch, _, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
-
-            src_batch = src_batch.to(device)
-            seg_batch = seg_batch.to(device)
-
-            with torch.no_grad():
-                _, logits = model(src_batch, None, seg_batch)
-                pred = torch.argmax(logits, dim=1)
-                pred = pred.cpu().numpy().tolist()
-                for j in range(len(pred)):
-                    results.append((examples[index][-2], logits[index].cpu().numpy()))
-                    index += 1
-        results_final.extend(postprocess_chid_predictions(results))
-
-    with open(args.prediction_path, "w") as f:
-        json.dump({tag: pred for tag, pred in results_final}, f, indent=2)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/taquynhnga/CNNs-interpretation-visualization/pages/4_ImageNet1k.py b/spaces/taquynhnga/CNNs-interpretation-visualization/pages/4_ImageNet1k.py
deleted file mode 100644
index db28ec7939885aa4678b163ddb8cdeb9cee14f47..0000000000000000000000000000000000000000
--- a/spaces/taquynhnga/CNNs-interpretation-visualization/pages/4_ImageNet1k.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-from backend.utils import load_dataset, use_container_width_percentage
-
-st.set_page_config(layout='wide')
-
-st.title('ImageNet-1k')
-st.markdown('This page shows the summary of 50,000 images in the validation set of [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)')
-
-# SCREEN_WIDTH, SCREEN_HEIGHT = 2560, 1664
-
-with st.spinner("Loading dataset..."):
-    dataset_dict = {}
-    for data_index in range(5):
-        dataset_dict[data_index] = load_dataset(data_index)
-        
-imagenet_df = pd.read_csv('./data/ImageNet_metadata.csv')
-
-class_labels = imagenet_df.ClassLabel.unique().tolist()
-class_labels.sort()
-selected_classes = st.multiselect('Class filter: ', options=['All'] + class_labels)
-if not ('All' in selected_classes or len(selected_classes) == 0):
-    imagenet_df = imagenet_df[imagenet_df['ClassLabel'].isin(selected_classes)]
-# st.write(class_labels)
-
-col1, col2 = st.columns([2, 1])
-with col1:
-    st.dataframe(imagenet_df)
-    use_container_width_percentage(100)
-
-with col2:
-    st.text_area('Type anything here to copy later :)')
-    image = None
-    with st.form("display image"):
-        img_index = st.text_input('Image ID to display')
-    
-        submitted = st.form_submit_button('Display this image')
-        error_container = st.empty()
-
-        if submitted:
-            try:
-                img_index = int(img_index)
-                if img_index > 50000-1 or img_index < 0:
-                    error_container.error('The Image ID must be in range from 0 to 49999', icon="🚫")
-                else:
-                    image = dataset_dict[img_index//10_000][img_index%10_000]['image']
-                    class_label = dataset_dict[img_index//10_000][img_index%10_000]['label']
-                    class_id = dataset_dict[img_index//10_000][img_index%10_000]['id']
-            except ValueError:
-                error_container.error('Please enter an integer number for Image ID', icon = "🚫")
-            
-    if image != None:
-        st.image(image)
-        st.write('**Class label:** ', class_label)
-        st.write('\n**Class id:** ', str(class_id))
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Automodeller Pro Full Crack VERIFIEDed.md b/spaces/terfces0erbo/CollegeProjectV2/Automodeller Pro Full Crack VERIFIEDed.md
deleted file mode 100644
index 18b15d4b72320702f25f69df9a9df038aba2a959..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Automodeller Pro Full Crack VERIFIEDed.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-<br />
-<h1>How to Download and Install Automodeller Pro Full Cracked for 3ds Max</h1>
-<p>Automodeller Pro is a powerful plugin for 3ds Max that allows you to create realistic and complex models by applying or painting a group of meshes as "geometry-texture" onto your existing scene geometry[^1^]. It is compatible with 3ds Max versions from 2010 to 2018 and supports multi-threading for faster performance[^2^].</p>
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-<ol>
-<li>Go to the official website of Automodeller Pro at <a href="http://www.automodeller.com/">www.automodeller.com</a> and click on the "Download" button.</li>
-<li>Choose the version of Automodeller Pro that matches your 3ds Max version and click on the "Download Now" button.</li>
-<li>You will be redirected to a file hosting site where you can download the plugin as a zip file.</li>
-<li>Extract the zip file to a folder on your computer.</li>
-<li>Copy the "AutoModellerPro.dlo" file from the extracted folder to your 3ds Max plugins folder (usually located at C:\Program Files\Autodesk\3ds Max [version]\plugins).</li>
-<li>Run 3ds Max and go to Customize > Plugin Manager. You should see Automodeller Pro listed under "Loaded Plug-ins".</li>
-<li>Click on the "License" button and enter the serial number that you received by email when you purchased Automodeller Pro.</li>
-<li>Click on "Activate" and wait for the confirmation message.</li>
-<li>Congratulations! You have successfully installed Automodeller Pro Full Cracked for 3ds Max.</li>
-</ol>
-<p>To learn how to use Automodeller Pro, you can watch the tutorial videos on the official website or read the user manual that comes with the plugin. You can also visit the forum and the gallery to see what other users have created with Automodeller Pro.</p>
-<p>Automodeller Pro is a game-changing tool that will take your 3d modelling skills to the next level. Download it today and unleash your creativity!</p>
-
-<h2>Why You Should Use Automodeller Pro for 3ds Max</h2>
-<p>Automodeller Pro is not just a plugin, it is a game-changing modelling tool that will revolutionize your workflow and unleash your creativity. Here are some of the benefits of using Automodeller Pro for 3ds Max:</p>
-<p></p>
-<ul>
-<li>It saves you time and effort. You don't have to model every detail by hand, you can simply paint them on with a few clicks. You can also reuse your existing geometry textures for different projects and scenarios.</li>
-<li>It gives you more control and flexibility. You can adjust the position, scale, rotation, alignment and randomization of your geometry textures using UV mapping or the intuitive UI. You can also combine different geometry textures and switch between them easily.</li>
-<li>It enhances your realism and quality. You can create complex and detailed models that look amazing from any angle and distance. You can also use instances and proxies to optimize your scene performance and memory usage.</li>
-<li>It inspires your imagination and innovation. You can experiment with different geometry textures and create unique and original models that stand out from the crowd. You can also use Automodeller Pro for any genre or style, from sci-fi to fantasy to modern architecture.</li>
-</ul>
-<p>Automodeller Pro is compatible with 3ds Max versions from 2010 to 2022 and supports multi-threading for faster performance[^1^] [^3^]. It is also easy to use and stable, with a user-friendly interface and a comprehensive user manual[^2^]. Whether you are a beginner or a professional, Automodeller Pro will take your 3d modelling skills to the next level.</p> d5da3c52bf<br />
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/HD Online Player (The Fast And The Furious 5 In Hindi ) !LINK!.md b/spaces/terfces0erbo/CollegeProjectV2/HD Online Player (The Fast And The Furious 5 In Hindi ) !LINK!.md
deleted file mode 100644
index af1efe4b38894a9e4b7f8954b711a554cc270dc5..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/HD Online Player (The Fast And The Furious 5 In Hindi ) !LINK!.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-<h1>HD Online Player (The Fast And The Furious 5 In Hindi ): How to Watch the Action-Packed Movie Online</h1>
-
-<p>If you are a fan of the Fast and Furious franchise, you might be wondering how to watch the fifth installment of the series, Fast Five, online in HD quality with Hindi dubbing. Fast Five is one of the most popular and successful movies in the franchise, featuring an all-star cast of Vin Diesel, Paul Walker, Dwayne Johnson, Jordana Brewster, Tyrese Gibson, Ludacris, and more. The movie follows Dominic Toretto and his crew of street racers as they plan a massive heist to buy their freedom while in the sights of a powerful Brazilian drug lord and a dangerous federal agent.</p>
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diff --git a/spaces/theaster/RVC-New-Arknights/config.py b/spaces/theaster/RVC-New-Arknights/config.py
deleted file mode 100644
index 26e5c63e0ea863a89453424ab8fc3190151b79a7..0000000000000000000000000000000000000000
--- a/spaces/theaster/RVC-New-Arknights/config.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import argparse
-import torch
-from multiprocessing import cpu_count
-
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        (
-            self.python_cmd,
-            self.listen_port,
-            self.colab,
-            self.noparallel,
-            self.noautoopen,
-            self.api
-        ) = self.arg_parse()
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--port", type=int, default=7865, help="Listen port")
-        parser.add_argument(
-            "--pycmd", type=str, default="python", help="Python command"
-        )
-        parser.add_argument("--colab", action="store_true", help="Launch in colab")
-        parser.add_argument(
-            "--noparallel", action="store_true", help="Disable parallel processing"
-        )
-        parser.add_argument(
-            "--noautoopen",
-            action="store_true",
-            help="Do not open in browser automatically",
-        )
-        parser.add_argument('--api', action="store_true", default=False)
-        cmd_opts = parser.parse_args()
-
-        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
-        return (
-            cmd_opts.pycmd,
-            cmd_opts.port,
-            cmd_opts.colab,
-            cmd_opts.noparallel,
-            cmd_opts.noautoopen,
-            cmd_opts.api,
-        )
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("16系/10系显卡和P40强制单精度")
-                self.is_half = False
-                for config_file in ["32k.json", "40k.json", "48k.json"]:
-                    with open(f"configs/{config_file}", "r") as f:
-                        strr = f.read().replace("true", "false")
-                    with open(f"configs/{config_file}", "w") as f:
-                        f.write(strr)
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("没有发现支持的N卡, 使用MPS进行推理")
-            self.device = "mps"
-            self.is_half = False
-        else:
-            print("没有发现支持的N卡, 使用CPU进行推理")
-            self.device = "cpu"
-            self.is_half = False
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/3dmgame dll mediafire download The most popular and reliable link.md b/spaces/tialenAdioni/chat-gpt-api/logs/3dmgame dll mediafire download The most popular and reliable link.md
deleted file mode 100644
index dbf5828407ff139ff570b80a7862a4fc4c67a11d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/3dmgame dll mediafire download The most popular and reliable link.md	
+++ /dev/null
@@ -1,134 +0,0 @@
-<br />
-<h1>3dmgame dll mediafire download: What is it and how to fix it?</h1>
- <p>If you are a gamer or a software user, you may have encountered an error message that says "The program can't start because 3dmgame.dll is missing from your computer. Try reinstalling the program to fix this problem." This can be frustrating and prevent you from enjoying your favorite games or programs. In this article, we will explain what 3dmgame dll is, why it is missing or corrupted, how to download it from mediafire, and how to fix 3dmgame dll errors.</p>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Handycafe V 1.1.16 Win7 Download NEWs.rar Hit.md b/spaces/tialenAdioni/chat-gpt-api/logs/Handycafe V 1.1.16 Win7 Download NEWs.rar Hit.md
deleted file mode 100644
index 36c0095b9fc6198edb8bdedcfab6dd34419e0eb1..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Handycafe V 1.1.16 Win7 Download NEWs.rar Hit.md	
+++ /dev/null
@@ -1,33 +0,0 @@
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-<h1>How to Download and Install Handycafe V 1.1.16 on Windows 7</h1>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Backgammon APK Play Online with Top Board Game Players.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Backgammon APK Play Online with Top Board Game Players.md
deleted file mode 100644
index eaea0da035e31a051d105bf51ceb57c01335cf7d..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Backgammon APK Play Online with Top Board Game Players.md	
+++ /dev/null
@@ -1,127 +0,0 @@
-<br />
-<h1>Download Backgammon APK: How to Play the Classic Board Game on Your Android Device</h1>
- <p>Backgammon is one of the oldest and most popular board games in the world. It is a game of strategy, skill, and luck that can be played by two players or online with millions of others. If you love backgammon and want to play it on your Android device, you might be wondering how to download backgammon apk. In this article, we will explain what backgammon is, what an apk is, how to download and install backgammon apk, and how to play backgammon on your Android device.</p>
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- <p>Backgammon is a game that involves moving checkers around a board with 24 triangular points, called pips. The objective of the game is to move all your checkers into your home board and then bear them off before your opponent does. The first player to bear off all their checkers wins the game.</p>
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- <p>Backgammon has a long and rich history that dates back thousands of years. The earliest evidence of backgammon comes from ancient Mesopotamia, where a board game called the Royal Game of Ur was played around 2600 BC. The game spread to other civilizations, such as Egypt, Persia, Rome, China, India, and Greece. The modern version of backgammon emerged in Europe in the 17th century and became popular among the aristocracy and the common people alike. Today, backgammon is played all over the world, both offline and online.</p>
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-<li>Each player has 15 checkers of their own color (black or white).</li>
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-<li>The checkers move in opposite directions, from the 24-point to the 1-point for black, and from the 1-point to the 24-point for white.</li>
-<li>Each player rolls two dice at the beginning of their turn and moves their checkers according to the numbers shown on the dice.</li>
-<li>A checker can only move to an open point, meaning a point that is not occupied by two or more opposing checkers.</li>
-<li>If a point has only one opposing checker, it can be hit and sent to the bar. The hit checker must re-enter from the opponent's home board before any other move can be made.</li>
-<li>A player can double the stakes of the game by offering a cube with a higher number on it. The opponent can either accept or decline the cube. If they accept, they take control of the cube and can re-double later. If they decline, they lose the game immediately.</li>
-<li>A player can win a single game, worth one point; a gammon, worth two points if they bear off all their checkers before their opponent bears off any; or a backgammon, worth three points if they bear off all their checkers while their opponent still has checkers on the bar or in their home board.</li>
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- <h2>What is an APK?</h2>
- <p>An APK (Android Package Kit) is a file format that contains all the components needed to install an app on an Android device. APKs are usually downloaded from official app stores, such as Google Play, or from third-party sources, such as websites or forums. APKs are useful for installing apps that are not available in your region, updating apps before they are officially released, or accessing apps that are removed from the app store.</p>
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-<li>You may miss out on important updates, security patches, or bug fixes that are only available through the official app store.</li>
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- <h2>How to Download Backgammon APK</h2>
- <p>If you want to download backgammon apk and play the classic board game on your Android device, you need to follow these steps:</p>
- <h3>Step 1: Enable Unknown Sources</h3>
- <p>Before you can install any APK file on your device, you need to enable the option to allow installation from unknown sources. This option is usually disabled by default for security reasons. To enable it, you need to go to your device settings, then security or privacy, then find and toggle on the option to allow installation from unknown sources. Depending on your device model and operating system, the exact steps may vary slightly.</p>
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- <h2>How to Play Backgammon on Your Android Device</h2>
- <p>Playing backgammon on your Android device is similar to playing it on a physical board. You can play against another player online or offline, or against an artificial intelligence (AI) opponent with different difficulty levels. You can also choose from different game modes, such as classic backgammon, hyper backgammon, nackgammon, acey-deucey, and more. Here are some basic steps to play backgammon on your Android device:</p>
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- <p>When you launch the app, you will see a menu with different options to choose from. You can select a game mode that suits your preference and skill level. For example, you can choose classic backgammon if you want to play the standard rules of the game; hyper backgammon if you want to play with only three checkers per player; nackgammon if you want to play with a different starting position; acey-deucey if you want to play with a special rule involving rolling 1-2; and so on. You can also customize the game settings, such as the board design, the checker color, the sound effects, and more.</p>
- <h3>Set Up the Board</h3>
- <p>After you choose a game mode, you will see the board with 24 pips and 15 checkers for each player. The checkers are arranged in a specific pattern according to the game mode. For example, in classic backgammon, each player has five checkers on their 6-point, three checkers on their 8-point, five checkers on their 13-point, and two checkers on their 24-point. The points are numbered from 1 to 24 for white, and from 24 to 1 for black.</p>
- <h3>Roll the Dice and Move Your Checkers</h3>
- <p>Each player rolls two dice at the beginning of their turn and moves their checkers according to the numbers shown on the dice. For example, if you roll a 4 and a 2, you can move one checker four points and another checker two points, or you can move one checker six points. You can also move the same checker twice, as long as it lands on open points. If you roll a double, such as 5 and 5, you can move four checkers five points each, or any combination of moves that adds up to 20 points.</p>
- <p>You can hit your opponent's checkers if they are on a point with only one checker. The hit checker is sent to the bar and must re-enter from your home board before any other move can be made. You can also block your opponent's checkers by occupying six consecutive points in your home board, creating a wall that prevents them from re-entering.</p>
- <p>You can bear off your checkers once all of them are in your home board. To bear off a checker, you need to roll a number that corresponds to the point where the checker is located. For example, if you have a checker on your 5-point, you need to roll a 5 to bear it off. If you roll a higher number than the highest point where you have a checker, you can bear off a checker from that point. For example, if you have checkers on your 6-point and 4-point, and you roll a 6 and a 3, you can bear off a checker from your 6-point with the 6, and a checker from your 4-point with the 3.</p>
- <h2>Conclusion</h2>
- <p>Backgammon is a fun and challenging game that can be enjoyed by anyone who loves strategy, skill, and luck. By downloading backgammon apk, you can play this classic board game on your Android device anytime and anywhere. You can choose from different game modes, customize your game settings, and play against other players or AI opponents. However, you need to be careful when downloading APK files from unknown sources, as they may contain harmful or malicious content. You also need to follow the rules of backgammon and use your best moves to win the game.</p>
- <h4>FAQs</h4>
- <p>Here are some frequently asked questions about backgammon apk:</p>
-<ul>
-<li>Q: Is backgammon apk free to download and play?<br>
-A: Yes, backgammon apk is free to download and play. However, some apps may offer in-app purchases or ads that require real money.</li>
-<li>Q: What are the best sources to download backgammon apk?<br>
-A: Some of the best sources to download backgammon apk are APKPure, APKMirror, Uptodown, and Aptoide. These sources are reputable and reliable, and offer various versions of backgammon apk for different devices.</li>
-<li>Q: How can I play backgammon online with other players?<br>
-A: Some backgammon apps allow you to play online with other players from around the world. You can either join an existing game or create your own game and invite your friends. You can also chat with other players and view their profiles and ratings.</li>
-<li>Q: How can I improve my backgammon skills?<br>
-A: The best way to improve your backgammon skills is to practice regularly and learn from your mistakes. You can also read books, watch videos, or take courses on backgammon strategies and tactics. You can also use tools such as analysis software or online tutors to help you improve your game.</li>
-<li>Q: What are some tips and tricks for playing backgammon?<br>
-A: Some tips and tricks for playing backgammon are:</li>
-<ul>
-<li>Use the doubling cube wisely. You can use it to increase the stakes of the game when you have an advantage or when you want to bluff your opponent.</li>
-<li>Avoid leaving blots (single checkers) on the board. They are vulnerable to being hit by your opponent and sent to the bar.</li>
-<li>Make points (two or more checkers on a point) as much as possible. They provide security for your checkers and block your opponent's movement.</li>
-<li>Try to escape your back checkers (the ones furthest from your home board) as soon as possible. They can be trapped by your opponent's prime (six consecutive points) and prevent you from bearing off.</li>
-<li>Balance your distribution of checkers on the board. You want to have enough checkers to move flexibly and avoid wastage (rolling numbers that you cannot use).</li>
-</ul>
-</ul>
- <p>I hope you enjoyed this article and learned something new about backgammon apk. If you have any questions or feedback, please leave a comment below. Thank you for reading and happy gaming!</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pep517/meta.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pep517/meta.py
deleted file mode 100644
index d525de5c6c8791def8dbb2f460027e200c934874..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pep517/meta.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""Build metadata for a project using PEP 517 hooks.
-"""
-import argparse
-import logging
-import os
-import shutil
-import functools
-
-try:
-    import importlib.metadata as imp_meta
-except ImportError:
-    import importlib_metadata as imp_meta
-
-try:
-    from zipfile import Path
-except ImportError:
-    from zipp import Path
-
-from .envbuild import BuildEnvironment
-from .wrappers import Pep517HookCaller, quiet_subprocess_runner
-from .dirtools import tempdir, mkdir_p, dir_to_zipfile
-from .build import validate_system, load_system, compat_system
-
-log = logging.getLogger(__name__)
-
-
-def _prep_meta(hooks, env, dest):
-    reqs = hooks.get_requires_for_build_wheel({})
-    log.info('Got build requires: %s', reqs)
-
-    env.pip_install(reqs)
-    log.info('Installed dynamic build dependencies')
-
-    with tempdir() as td:
-        log.info('Trying to build metadata in %s', td)
-        filename = hooks.prepare_metadata_for_build_wheel(td, {})
-        source = os.path.join(td, filename)
-        shutil.move(source, os.path.join(dest, os.path.basename(filename)))
-
-
-def build(source_dir='.', dest=None, system=None):
-    system = system or load_system(source_dir)
-    dest = os.path.join(source_dir, dest or 'dist')
-    mkdir_p(dest)
-    validate_system(system)
-    hooks = Pep517HookCaller(
-        source_dir, system['build-backend'], system.get('backend-path')
-    )
-
-    with hooks.subprocess_runner(quiet_subprocess_runner):
-        with BuildEnvironment() as env:
-            env.pip_install(system['requires'])
-            _prep_meta(hooks, env, dest)
-
-
-def build_as_zip(builder=build):
-    with tempdir() as out_dir:
-        builder(dest=out_dir)
-        return dir_to_zipfile(out_dir)
-
-
-def load(root):
-    """
-    Given a source directory (root) of a package,
-    return an importlib.metadata.Distribution object
-    with metadata build from that package.
-    """
-    root = os.path.expanduser(root)
-    system = compat_system(root)
-    builder = functools.partial(build, source_dir=root, system=system)
-    path = Path(build_as_zip(builder))
-    return imp_meta.PathDistribution(path)
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    'source_dir',
-    help="A directory containing pyproject.toml",
-)
-parser.add_argument(
-    '--out-dir', '-o',
-    help="Destination in which to save the builds relative to source dir",
-)
-
-
-def main():
-    args = parser.parse_args()
-    build(args.source_dir, args.out_dir)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/tmtsmrsl/twitter-sentiment/helper_functions.py b/spaces/tmtsmrsl/twitter-sentiment/helper_functions.py
deleted file mode 100644
index 09f6ab30c9e65ba3b597d00d47427efca160fad6..0000000000000000000000000000000000000000
--- a/spaces/tmtsmrsl/twitter-sentiment/helper_functions.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import numpy as np
-import pandas as pd
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import pickle
-import snscrape.modules.twitter as sntwitter
-import datetime as dt
-import nltk
-
-nltk.download(
-    ["punkt", "wordnet", "omw-1.4", "averaged_perceptron_tagger", "universal_tagset"]
-)
-from nltk.stem import WordNetLemmatizer
-from nltk.tag import pos_tag
-from nltk.tokenize import word_tokenize
-import re
-from sklearn.feature_extraction.text import CountVectorizer
-import plotly.express as px
-import plotly.io as pio
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-from wordcloud import WordCloud
-from PIL import Image
-
-
-# Create a custom plotly theme and set it as default
-pio.templates["custom"] = pio.templates["plotly_white"]
-pio.templates["custom"].layout.margin = {"b": 25, "l": 25, "r": 25, "t": 50}
-pio.templates["custom"].layout.width = 600
-pio.templates["custom"].layout.height = 450
-pio.templates["custom"].layout.autosize = False
-pio.templates["custom"].layout.font.update(
-    {"family": "Arial", "size": 12, "color": "#707070"}
-)
-pio.templates["custom"].layout.title.update(
-    {
-        "xref": "container",
-        "yref": "container",
-        "x": 0.5,
-        "yanchor": "top",
-        "font_size": 16,
-        "y": 0.95,
-        "font_color": "#353535",
-    }
-)
-pio.templates["custom"].layout.xaxis.update(
-    {"showline": True, "linecolor": "lightgray", "title_font_size": 14}
-)
-pio.templates["custom"].layout.yaxis.update(
-    {"showline": True, "linecolor": "lightgray", "title_font_size": 14}
-)
-pio.templates["custom"].layout.colorway = [
-    "#1F77B4",
-    "#FF7F0E",
-    "#54A24B",
-    "#D62728",
-    "#C355FA",
-    "#8C564B",
-    "#E377C2",
-    "#7F7F7F",
-    "#FFE323",
-    "#17BECF",
-]
-pio.templates.default = "custom"
-
-
-def get_latest_tweet_df(search_term, num_tweets):
-    tweet_data = []
-    for i, tweet in enumerate(
-        sntwitter.TwitterSearchScraper("{} lang:en".format(search_term)).get_items()
-    ):
-        if i >= num_tweets or i >= 5000:
-            break
-        tweet_data.append(
-            [tweet.user.username, tweet.date, tweet.likeCount, tweet.content]
-        )
-
-    tweet_df = pd.DataFrame(
-        tweet_data, columns=["Username", "Date", "Like Count", "Tweet"]
-    )
-    return tweet_df
-
-
-def text_preprocessing(text):
-    stopwords = set()
-    with open("static/en_stopwords.txt", "r") as file:
-        for word in file:
-            stopwords.add(word.rstrip("\n"))
-    lemmatizer = WordNetLemmatizer()
-    try:
-        url_pattern = r"((http://)[^ ]*|(https://)[^ ]*|(www\.)[^ ]*)"
-        user_pattern = r"@[^\s]+"
-        entity_pattern = r"&.*;"
-        neg_contraction = r"n't\W"
-        non_alpha = "[^a-z]"
-        cleaned_text = text.lower()
-        cleaned_text = re.sub(neg_contraction, " not ", cleaned_text)
-        cleaned_text = re.sub(url_pattern, " ", cleaned_text)
-        cleaned_text = re.sub(user_pattern, " ", cleaned_text)
-        cleaned_text = re.sub(entity_pattern, " ", cleaned_text)
-        cleaned_text = re.sub(non_alpha, " ", cleaned_text)
-        tokens = word_tokenize(cleaned_text)
-        # provide POS tag for lemmatization to yield better result
-        word_tag_tuples = pos_tag(tokens, tagset="universal")
-        tag_dict = {"NOUN": "n", "VERB": "v", "ADJ": "a", "ADV": "r"}
-        final_tokens = []
-        for word, tag in word_tag_tuples:
-            if len(word) > 1 and word not in stopwords:
-                if tag in tag_dict:
-                    final_tokens.append(lemmatizer.lemmatize(word, tag_dict[tag]))
-                else:
-                    final_tokens.append(lemmatizer.lemmatize(word))
-        return " ".join(final_tokens)
-    except:
-        return np.nan
-
-
-def predict_sentiment(tweet_df):
-    model = load_model("static/lstm_model.h5")
-    with open("static/tokenizer.pickle", "rb") as handle:
-        custom_tokenizer = pickle.load(handle)
-    temp_df = tweet_df.copy()
-    temp_df["Cleaned Tweet"] = temp_df["Tweet"].apply(text_preprocessing)
-    temp_df = temp_df[
-        (temp_df["Cleaned Tweet"].notna()) & (temp_df["Cleaned Tweet"] != "")
-    ]
-    sequences = pad_sequences(
-        custom_tokenizer.texts_to_sequences(temp_df["Cleaned Tweet"]), maxlen=54
-    )
-    score = model.predict(sequences)
-    temp_df["Score"] = score
-    temp_df["Sentiment"] = temp_df["Score"].apply(
-        lambda x: "Positive" if x >= 0.50 else "Negative"
-    )
-    return temp_df
-
-
-def plot_sentiment(tweet_df):
-    sentiment_count = tweet_df["Sentiment"].value_counts()
-    fig = px.pie(
-        values=sentiment_count.values,
-        names=sentiment_count.index,
-        hole=0.3,
-        title="<b>Sentiment Distribution</b>",
-        color=sentiment_count.index,
-        color_discrete_map={"Positive": "#1F77B4", "Negative": "#FF7F0E"},
-    )
-    fig.update_traces(
-        textposition="inside",
-        texttemplate="%{label}<br>%{value} (%{percent})",
-        hovertemplate="<b>%{label}</b><br>Percentage=%{percent}<br>Count=%{value}",
-    )
-    fig.update_layout(showlegend=False)
-    return fig
-
-
-def plot_wordcloud(tweet_df, colormap="Greens"):
-    stopwords = set()
-    with open("static/en_stopwords_viz.txt", "r") as file:
-        for word in file:
-            stopwords.add(word.rstrip("\n"))
-    cmap = mpl.cm.get_cmap(colormap)(np.linspace(0, 1, 20))
-    cmap = mpl.colors.ListedColormap(cmap[10:15])
-    mask = np.array(Image.open("static/twitter_mask.png"))
-    font = "static/quartzo.ttf"
-    text = " ".join(tweet_df["Cleaned Tweet"])
-    wc = WordCloud(
-        background_color="white",
-        font_path=font,
-        stopwords=stopwords,
-        max_words=90,
-        colormap=cmap,
-        mask=mask,
-        random_state=42,
-        collocations=False,
-        min_word_length=2,
-        max_font_size=200,
-    )
-    wc.generate(text)
-    fig = plt.figure(figsize=(8, 8))
-    ax = fig.add_subplot(1, 1, 1)
-    plt.imshow(wc, interpolation="bilinear")
-    plt.axis("off")
-    plt.title("Wordcloud", fontdict={"fontsize": 16}, fontweight="heavy", pad=20, y=1.0)
-    return fig
-
-
-def get_top_n_gram(tweet_df, ngram_range, n=10):
-    stopwords = set()
-    with open("static/en_stopwords_viz.txt", "r") as file:
-        for word in file:
-            stopwords.add(word.rstrip("\n"))
-    corpus = tweet_df["Cleaned Tweet"]
-    vectorizer = CountVectorizer(
-        analyzer="word", ngram_range=ngram_range, stop_words=stopwords
-    )
-    X = vectorizer.fit_transform(corpus.astype(str).values)
-    words = vectorizer.get_feature_names_out()
-    words_count = np.ravel(X.sum(axis=0))
-    df = pd.DataFrame(zip(words, words_count))
-    df.columns = ["words", "counts"]
-    df = df.sort_values(by="counts", ascending=False).head(n)
-    df["words"] = df["words"].str.title()
-    return df
-
-
-def plot_n_gram(n_gram_df, title, color="#54A24B"):
-    fig = px.bar(
-        x=n_gram_df.counts,
-        y=n_gram_df.words,
-        title="<b>{}</b>".format(title),
-        text_auto=True,
-    )
-    fig.update_layout(plot_bgcolor="white")
-    fig.update_xaxes(title=None)
-    fig.update_yaxes(autorange="reversed", title=None)
-    fig.update_traces(hovertemplate="<b>%{y}</b><br>Count=%{x}", marker_color=color)
-    return fig
diff --git a/spaces/tomofi/Hive-OCR/app.py b/spaces/tomofi/Hive-OCR/app.py
deleted file mode 100644
index ce1ea1f6c0c3eec17988b3ebbc6507b99f1b0f19..0000000000000000000000000000000000000000
--- a/spaces/tomofi/Hive-OCR/app.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import requests
-import gradio as gr
-import pandas as pd
-from PIL import Image, ImageDraw
-
-def infer(im):
-  im.save('converted.png')
-  url = 'https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition'
-  files = {
-      'image': ('converted.png', open('converted.png', 'rb'), 'image/png'),
-      'model_type': (None, 'detection'),
-      'media_type': (None, 'photo'),
-  }
-  headers = {
-      'referer': 'https://thehive.ai/'
-  }
-  r = requests.post(url, headers=headers, files=files)
-  res = r.json()
-  print(r, res)
-  img = im.convert('RGB')
-
-  words = []
-  draw = ImageDraw.Draw(img,'RGBA')
-  for output in res['response']['output']:
-    for poly in output['bounding_poly']:
-      words += [c['class'] for c in poly['classes']]
-      draw.rectangle((poly['dimensions']['left']-2,poly['dimensions']['top']-2,poly['dimensions']['right']+2,poly['dimensions']['bottom']+2), outline=(0,255,0,255), fill=(0,255,0,50),width=2)
-
-  img.save('result.png')
-  return 'result.png', '\n'.join([o['block_text'] for o in res['response']['output']]), pd.DataFrame(words)
-
-iface = gr.Interface(
-    fn=infer,
-    title="Hive OCR",
-    description="Demo for Hive OCR.Transcribe and analyze media depicting typed, written, or graphic text",
-    inputs=[gr.inputs.Image(label='image', type='pil')],
-    outputs=['image', 'text', gr.outputs.Dataframe(headers=['word'])],
-    examples=['testocr.png', 'receipt.webp', '20131216170659.jpg'],
-    article="<a href=\"https://thehive.ai/hive-ocr-solutions\">Hive OCR</a>",
-).launch()
\ No newline at end of file
diff --git a/spaces/tomofi/MMOCR/tools/test.py b/spaces/tomofi/MMOCR/tools/test.py
deleted file mode 100644
index d7f50120163c197a5f9d6a0e1e1ab5fc1cb791b3..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/tools/test.py
+++ /dev/null
@@ -1,235 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) OpenMMLab. All rights reserved.
-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
-
-from mmocr.apis.test import single_gpu_test
-from mmocr.apis.utils import (disable_text_recog_aug_test,
-                              replace_image_to_tensor)
-from mmocr.datasets import build_dataloader, build_dataset
-from mmocr.models import build_detector
-from mmocr.utils import revert_sync_batchnorm, setup_multi_processes
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='MMOCR 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(
-        '--gpu-id',
-        type=int,
-        default=0,
-        help='id of gpu to use '
-        '(only applicable to non-distributed testing)')
-    parser.add_argument(
-        '--format-only',
-        action='store_true',
-        help='Format the output results without performing evaluation. It is'
-        'useful when you want to format the results to a specific format and '
-        'submit them to the test server.')
-    parser.add_argument(
-        '--eval',
-        type=str,
-        nargs='+',
-        help='The evaluation metrics, which depends on the dataset, e.g.,'
-        '"bbox", "seg", "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 the output 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='The 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 the config file. If the value '
-        'to be overwritten is a list, it should be of the form of either '
-        'key="[a,b]" or key=a,b. The argument 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='Options for 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)
-    setup_multi_processes(cfg)
-
-    # set cudnn_benchmark
-    if cfg.get('cudnn_benchmark', False):
-        torch.backends.cudnn.benchmark = True
-    if cfg.model.get('pretrained'):
-        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 = (cfg.data.get('test_dataloader', {})).get(
-        'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
-    if samples_per_gpu > 1:
-        cfg = disable_text_recog_aug_test(cfg)
-        cfg = replace_image_to_tensor(cfg)
-
-    # init distributed env first, since logger depends on the dist info.
-    if args.launcher == 'none':
-        cfg.gpu_ids = [args.gpu_id]
-        distributed = False
-    else:
-        distributed = True
-        init_dist(args.launcher, **cfg.dist_params)
-
-    # build the dataloader
-    dataset = build_dataset(cfg.data.test, dict(test_mode=True))
-    # step 1: give default values and override (if exist) from cfg.data
-    loader_cfg = {
-        **dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
-        **({} if torch.__version__ != 'parrots' else dict(
-               prefetch_num=2,
-               pin_memory=False,
-           )),
-        **dict((k, cfg.data[k]) for k in [
-                   'workers_per_gpu',
-                   'seed',
-                   'prefetch_num',
-                   'pin_memory',
-                   'persistent_workers',
-               ] if k in cfg.data)
-    }
-    test_loader_cfg = {
-        **loader_cfg,
-        **dict(shuffle=False, drop_last=False),
-        **cfg.data.get('test_dataloader', {}),
-        **dict(samples_per_gpu=samples_per_gpu)
-    }
-
-    data_loader = build_dataloader(dataset, **test_loader_cfg)
-
-    # build the model and load checkpoint
-    cfg.model.train_cfg = None
-    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
-    model = revert_sync_batchnorm(model)
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        wrap_fp16_model(model)
-    load_checkpoint(model, args.checkpoint, map_location='cpu')
-    if args.fuse_conv_bn:
-        model = fuse_conv_bn(model)
-
-    if not distributed:
-        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
-        is_kie = cfg.model.type in ['SDMGR']
-        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
-                                  is_kie, 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()
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
deleted file mode 100644
index b371ed757bf7dd95ef9ecfc2e609ca5ab03795d6..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,38 +0,0 @@
-_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
-
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(
-        norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
-
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py
deleted file mode 100644
index 18daadd6a9d3024f30157aea1f1cef3e13326b5a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './ga_retinanet_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/preview.py b/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/preview.py
deleted file mode 100644
index f86acaacc7f83c814d73b29186e019e97034a45e..0000000000000000000000000000000000000000
--- a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/preview.py
+++ /dev/null
@@ -1,121 +0,0 @@
-from time import sleep
-from typing import Any, Dict, Tuple, List, Optional
-import cv2
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.capturer import get_video_frame, get_video_frame_total
-from DeepFakeAI.face_analyser import get_one_face
-from DeepFakeAI.face_reference import get_face_reference, set_face_reference
-from DeepFakeAI.predictor import predict_frame
-from DeepFakeAI.processors.frame.core import load_frame_processor_module
-from DeepFakeAI.typing import Frame
-from DeepFakeAI.uis import core as ui
-from DeepFakeAI.uis.typing import ComponentName, Update
-from DeepFakeAI.utilities import is_video, is_image
-
-PREVIEW_IMAGE : Optional[gradio.Image] = None
-PREVIEW_FRAME_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global PREVIEW_IMAGE
-	global PREVIEW_FRAME_SLIDER
-
-	with gradio.Box():
-		preview_image_args: Dict[str, Any] = {
-			'label': wording.get('preview_image_label')
-		}
-		preview_frame_slider_args: Dict[str, Any] = {
-			'label': wording.get('preview_frame_slider_label'),
-			'step': 1,
-			'visible': False
-		}
-		if is_image(DeepFakeAI.globals.target_path):
-			target_frame = cv2.imread(DeepFakeAI.globals.target_path)
-			preview_frame = extract_preview_frame(target_frame)
-			preview_image_args['value'] = ui.normalize_frame(preview_frame)
-		if is_video(DeepFakeAI.globals.target_path):
-			temp_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-			preview_frame = extract_preview_frame(temp_frame)
-			preview_image_args['value'] = ui.normalize_frame(preview_frame)
-			preview_image_args['visible'] = True
-			preview_frame_slider_args['value'] = DeepFakeAI.globals.reference_frame_number
-			preview_frame_slider_args['maximum'] = get_video_frame_total(DeepFakeAI.globals.target_path)
-			preview_frame_slider_args['visible'] = True
-		PREVIEW_IMAGE = gradio.Image(**preview_image_args)
-		PREVIEW_FRAME_SLIDER = gradio.Slider(**preview_frame_slider_args)
-		ui.register_component('preview_frame_slider', PREVIEW_FRAME_SLIDER)
-
-
-def listen() -> None:
-	PREVIEW_FRAME_SLIDER.change(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
-	update_component_names : List[ComponentName] =\
-	[
-		'source_file',
-		'target_file',
-		'face_recognition_dropdown',
-		'reference_face_distance_slider',
-		'frame_processors_checkbox_group'
-	]
-	for component_name in update_component_names:
-		component = ui.get_component(component_name)
-		if component:
-			component.change(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
-	select_component_names : List[ComponentName] =\
-	[
-		'reference_face_position_gallery',
-		'face_analyser_direction_dropdown',
-		'face_analyser_age_dropdown',
-		'face_analyser_gender_dropdown'
-	]
-	for component_name in select_component_names:
-		component = ui.get_component(component_name)
-		if component:
-			component.select(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
-
-
-def update(frame_number : int = 0) -> Tuple[Update, Update]:
-	sleep(0.1)
-	if is_image(DeepFakeAI.globals.target_path):
-		target_frame = cv2.imread(DeepFakeAI.globals.target_path)
-		preview_frame = extract_preview_frame(target_frame)
-		return gradio.update(value = ui.normalize_frame(preview_frame)), gradio.update(value = None, maximum = None, visible = False)
-	if is_video(DeepFakeAI.globals.target_path):
-		DeepFakeAI.globals.reference_frame_number = frame_number
-		video_frame_total = get_video_frame_total(DeepFakeAI.globals.target_path)
-		temp_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-		preview_frame = extract_preview_frame(temp_frame)
-		return gradio.update(value = ui.normalize_frame(preview_frame)), gradio.update(maximum = video_frame_total, visible = True)
-	return gradio.update(value = None), gradio.update(value = None, maximum = None, visible = False)
-
-
-def extract_preview_frame(temp_frame : Frame) -> Frame:
-	if predict_frame(temp_frame):
-		return cv2.GaussianBlur(temp_frame, (99, 99), 0)
-	source_face = get_one_face(cv2.imread(DeepFakeAI.globals.source_path)) if DeepFakeAI.globals.source_path else None
-	temp_frame = reduce_preview_frame(temp_frame)
-	if 'reference' in DeepFakeAI.globals.face_recognition and not get_face_reference():
-		reference_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-		reference_face = get_one_face(reference_frame, DeepFakeAI.globals.reference_face_position)
-		set_face_reference(reference_face)
-	reference_face = get_face_reference() if 'reference' in DeepFakeAI.globals.face_recognition else None
-	for frame_processor in DeepFakeAI.globals.frame_processors:
-		frame_processor_module = load_frame_processor_module(frame_processor)
-		if frame_processor_module.pre_process():
-			temp_frame = frame_processor_module.process_frame(
-				source_face,
-				reference_face,
-				temp_frame
-			)
-	return temp_frame
-
-
-def reduce_preview_frame(temp_frame : Frame, max_height : int = 480) -> Frame:
-	height, width = temp_frame.shape[:2]
-	if height > max_height:
-		scale = max_height / height
-		max_width = int(width * scale)
-		temp_frame = cv2.resize(temp_frame, (max_width, max_height))
-	return temp_frame
diff --git a/spaces/trl-internal-testing/rlhf_dialog_experiment/app.py b/spaces/trl-internal-testing/rlhf_dialog_experiment/app.py
deleted file mode 100644
index 1531cef23bc42d338e19d2a0bd5c549e4fa90a72..0000000000000000000000000000000000000000
--- a/spaces/trl-internal-testing/rlhf_dialog_experiment/app.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import datetime
-import hashlib
-import json
-import os
-import random
-from io import BytesIO
-import gradio as gr
-from datasets import load_dataset
-from huggingface_hub import upload_file
-
-
-
-dataset = load_dataset("edbeeching/rlhf_dialog_experiment_cosmo_dialog_generation", 
-use_auth_token=os.environ['EB_TOKEN'])["validation"]
-
-def sample_to_markdown(sample, index):
-    conversation = sample["conversations"][index]
-    trucation_length = sample["trucation_length"]
-    output = ""
-    aligns = ["left", "right"]
-    strongs1 = ["", "<strong>"]
-    strongs2 = ["", "</strong>"]
-    for i,conv in enumerate(conversation):
-        if i == trucation_length:
-            output += '<p style="text-align:center"> --- START OF DIALOG GENERATION --- </p><br>'
-        align = aligns[i%2]
-        strong1 = strongs1[i%2]
-        strong2 = strongs2[i%2]
-        
-        output += f'<div style="text-align: {align}"> {strong1}{conv}{strong2} \n </div> <br>'
-
-    return output
-
-
-
-sample = None
-def get_sample():
-    # I set the seed here as the randomness was a bit off otherwise
-    print(abs(hash(datetime.datetime.now().strftime("%Y%m%d_%H%M%s"))) % (10 ** 8))
-    random.seed(abs(hash(datetime.datetime.now().strftime("%Y%m%d_%H%M%s"))) % (10 ** 8))
-    dataset_size = len(dataset)
-    sample_index = random.randint(0, dataset_size-1)
-    sample = dataset[sample_index]
-    return sample
-
-def check_and_submit_preferences(sample, preferred_text, text_quality):
-    if preferred_text is None:
-        print("not submitted due to unselected preferred text")
-        return
-
-    if text_quality is None:
-        print("not submitted due to unselected text_quality")
-        return
-
-    data = {
-        "sample": sample,
-        "preferred_text": preferred_text,
-        "text_quality": text_quality,
-        "date_time": datetime.datetime.now().strftime("%m/%d/%Y %H:%M:%S")
-        # add other info like user etc?
-    }
-
-    task_hash = hashlib.md5(sample["situation"].encode())
-    time_now = datetime.datetime.now().strftime("%Y%m%d_%H%M%s")
-    task_directory = f"{time_now}_{task_hash.hexdigest()}"
-    upload_file(
-                path_or_fileobj=BytesIO(bytes(json.dumps(data), 'utf-8')),
-                path_in_repo=task_directory,
-                repo_id='edbeeching/rlhf_dialog_experiment_dataset',
-                repo_type='dataset',
-                token=os.environ['EB_TOKEN']
-            )
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-    This Space is an experiment to model human preferences on dialog generated with the [Cosmo-XL](https://huggingface.co/allenai/cosmo-xl) model, prompted with parts of conversations from the [SODA](https://huggingface.co/datasets/allenai/soda) dataset.
-    The following conversation was created with the following prompt:
-
-    """
-    )
-
-    sample = get_sample()
-
-    with gr.Column() as details_col:
-        summary = gr.Markdown(f"## {sample['situation']}", label='Description')
-        with gr.Row():
-            with gr.Column():
-                with gr.Box():
-                    dialog1 = gr.Markdown(sample_to_markdown(sample, 0), label='Dialog 1')
-
-            with gr.Column():
-                with gr.Box():
-                    dialog2 = gr.Markdown(sample_to_markdown(sample, 1), label='Dialog 2')
-
-        with gr.Column():
-            dialog_choice = gr.Radio(["Left dialog", "Right dialog"], label="Preferred text", interactive=True)
-            quality_of_dialog = gr.Radio(["Terrible", "Poor", "Ok", "Good", "Excellent"], label="Quality of preferred text", interactive=True)
-            next_button = gr.Button("Submit")
-
-
-    def on_next(preferred_text, text_quality):
-        # check and submit the current response
-        global sample
-        check_and_submit_preferences(sample, preferred_text, text_quality)
-
-        sample = get_sample()
-
-        return (
-            gr.Markdown.update(f"## {sample['situation']}"), 
-            gr.Markdown.update(sample_to_markdown(sample, 0)), 
-            gr.Markdown.update(sample_to_markdown(sample, 1)), 
-            gr.Radio.update(value=None),
-            gr.Radio.update(value=None)
-        )
-
-    next_button.click(on_next, inputs=[dialog_choice, quality_of_dialog], outputs=[summary, dialog1, dialog2, dialog_choice, quality_of_dialog])
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/uSerNameDDHL/bingo/src/components/turn-counter.tsx b/spaces/uSerNameDDHL/bingo/src/components/turn-counter.tsx
deleted file mode 100644
index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/src/components/turn-counter.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
-  throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
-  if (!throttling) {
-    return null
-  }
-
-  return (
-    <div className="turn-counter">
-      <div className="text">
-        <span>{throttling.numUserMessagesInConversation}</span>
-        <span> 共 </span>
-        <span>{throttling.maxNumUserMessagesInConversation}</span>
-      </div>
-      <div className="indicator"></div>
-    </div>
-  )
-}
diff --git a/spaces/ucalyptus/PTI/torch_utils/ops/grid_sample_gradfix.py b/spaces/ucalyptus/PTI/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index ca6b3413ea72a734703c34382c023b84523601fd..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import warnings
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    if not enabled:
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
-    return False
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------
diff --git a/spaces/ulysses115/diffsvc_test/modules/parallel_wavegan/__init__.py b/spaces/ulysses115/diffsvc_test/modules/parallel_wavegan/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/image_sharpening.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/image_sharpening.py
deleted file mode 100644
index 6d12b5d5bffa496c245a09d823cbaa9989c52435..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/image_sharpening.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import cv2
-import numpy as np
-
-def unsharp_mask(img, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0, mask=None):
-    if amount == 0:
-        return img
-    # Return a sharpened version of the image, using an unsharp mask.
-    # If mask is not None, only areas under mask are handled
-    blurred = cv2.GaussianBlur(img, kernel_size, sigma)
-    sharpened = float(amount + 1) * img - float(amount) * blurred
-    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
-    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
-    sharpened = sharpened.round().astype(np.uint8)
-    if threshold > 0:
-        low_contrast_mask = np.absolute(img - blurred) < threshold
-        np.copyto(sharpened, img, where=low_contrast_mask)
-    if mask is not None:
-        mask = np.array(mask)
-        masked_sharpened = cv2.bitwise_and(sharpened, sharpened, mask=mask)
-        masked_img = cv2.bitwise_and(img, img, mask=255-mask)
-        sharpened = cv2.add(masked_img, masked_sharpened)
-    return sharpened
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/sd_models_config.py b/spaces/user238921933/stable-diffusion-webui/modules/sd_models_config.py
deleted file mode 100644
index 222793d451b3659f7954c208260af71840b475a2..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/sd_models_config.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import re
-import os
-
-import torch
-
-from modules import shared, paths, sd_disable_initialization
-
-sd_configs_path = shared.sd_configs_path
-sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
-
-
-config_default = shared.sd_default_config
-config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
-config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
-config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
-config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
-config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
-config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
-config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
-
-
-def is_using_v_parameterization_for_sd2(state_dict):
-    """
-    Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
-    """
-
-    import ldm.modules.diffusionmodules.openaimodel
-    from modules import devices
-
-    device = devices.cpu
-
-    with sd_disable_initialization.DisableInitialization():
-        unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
-            use_checkpoint=True,
-            use_fp16=False,
-            image_size=32,
-            in_channels=4,
-            out_channels=4,
-            model_channels=320,
-            attention_resolutions=[4, 2, 1],
-            num_res_blocks=2,
-            channel_mult=[1, 2, 4, 4],
-            num_head_channels=64,
-            use_spatial_transformer=True,
-            use_linear_in_transformer=True,
-            transformer_depth=1,
-            context_dim=1024,
-            legacy=False
-        )
-        unet.eval()
-
-    with torch.no_grad():
-        unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
-        unet.load_state_dict(unet_sd, strict=True)
-        unet.to(device=device, dtype=torch.float)
-
-        test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
-        x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
-
-        out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
-
-    return out < -1
-
-
-def guess_model_config_from_state_dict(sd, filename):
-    sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
-    diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
-
-    if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
-        return config_depth_model
-
-    if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
-        if diffusion_model_input.shape[1] == 9:
-            return config_sd2_inpainting
-        elif is_using_v_parameterization_for_sd2(sd):
-            return config_sd2v
-        else:
-            return config_sd2
-
-    if diffusion_model_input is not None:
-        if diffusion_model_input.shape[1] == 9:
-            return config_inpainting
-        if diffusion_model_input.shape[1] == 8:
-            return config_instruct_pix2pix
-
-    if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
-        return config_alt_diffusion
-
-    return config_default
-
-
-def find_checkpoint_config(state_dict, info):
-    if info is None:
-        return guess_model_config_from_state_dict(state_dict, "")
-
-    config = find_checkpoint_config_near_filename(info)
-    if config is not None:
-        return config
-
-    return guess_model_config_from_state_dict(state_dict, info.filename)
-
-
-def find_checkpoint_config_near_filename(info):
-    if info is None:
-        return None
-
-    config = os.path.splitext(info.filename)[0] + ".yaml"
-    if os.path.exists(config):
-        return config
-
-    return None
-
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/nn/modules/conv.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/nn/modules/conv.md
deleted file mode 100644
index 6a5df52c3949d96678dab3d252a6228470c84a59..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/nn/modules/conv.md
+++ /dev/null
@@ -1,74 +0,0 @@
----
-description: Explore convolutional neural network modules & techniques such as LightConv, DWConv, ConvTranspose, GhostConv, CBAM & autopad with Ultralytics Docs.
-keywords: Ultralytics, Convolutional Neural Network, Conv2, DWConv, ConvTranspose, GhostConv, ChannelAttention, CBAM, autopad
----
-
-## Conv
----
-### ::: ultralytics.nn.modules.conv.Conv
-<br><br>
-
-## Conv2
----
-### ::: ultralytics.nn.modules.conv.Conv2
-<br><br>
-
-## LightConv
----
-### ::: ultralytics.nn.modules.conv.LightConv
-<br><br>
-
-## DWConv
----
-### ::: ultralytics.nn.modules.conv.DWConv
-<br><br>
-
-## DWConvTranspose2d
----
-### ::: ultralytics.nn.modules.conv.DWConvTranspose2d
-<br><br>
-
-## ConvTranspose
----
-### ::: ultralytics.nn.modules.conv.ConvTranspose
-<br><br>
-
-## Focus
----
-### ::: ultralytics.nn.modules.conv.Focus
-<br><br>
-
-## GhostConv
----
-### ::: ultralytics.nn.modules.conv.GhostConv
-<br><br>
-
-## RepConv
----
-### ::: ultralytics.nn.modules.conv.RepConv
-<br><br>
-
-## ChannelAttention
----
-### ::: ultralytics.nn.modules.conv.ChannelAttention
-<br><br>
-
-## SpatialAttention
----
-### ::: ultralytics.nn.modules.conv.SpatialAttention
-<br><br>
-
-## CBAM
----
-### ::: ultralytics.nn.modules.conv.CBAM
-<br><br>
-
-## Concat
----
-### ::: ultralytics.nn.modules.conv.Concat
-<br><br>
-
-## autopad
----
-### ::: ultralytics.nn.modules.conv.autopad
-<br><br>
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/callbacks/neptune.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/callbacks/neptune.py
deleted file mode 100644
index be6434124283b352db8ca040ee85ba892f5264b1..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/callbacks/neptune.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-
-from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
-from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
-
-try:
-    import neptune
-    from neptune.types import File
-
-    assert not TESTS_RUNNING  # do not log pytest
-    assert hasattr(neptune, '__version__')
-except (ImportError, AssertionError):
-    neptune = None
-
-run = None  # NeptuneAI experiment logger instance
-
-
-def _log_scalars(scalars, step=0):
-    """Log scalars to the NeptuneAI experiment logger."""
-    if run:
-        for k, v in scalars.items():
-            run[k].append(value=v, step=step)
-
-
-def _log_images(imgs_dict, group=''):
-    """Log scalars to the NeptuneAI experiment logger."""
-    if run:
-        for k, v in imgs_dict.items():
-            run[f'{group}/{k}'].upload(File(v))
-
-
-def _log_plot(title, plot_path):
-    """Log plots to the NeptuneAI experiment logger."""
-    """
-        Log image as plot in the plot section of NeptuneAI
-
-        arguments:
-        title (str) Title of the plot
-        plot_path (PosixPath or str) Path to the saved image file
-        """
-    img = mpimg.imread(plot_path)
-    fig = plt.figure()
-    ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[])  # no ticks
-    ax.imshow(img)
-    run[f'Plots/{title}'].upload(fig)
-
-
-def on_pretrain_routine_start(trainer):
-    """Callback function called before the training routine starts."""
-    try:
-        global run
-        run = neptune.init_run(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, tags=['YOLOv8'])
-        run['Configuration/Hyperparameters'] = {k: '' if v is None else v for k, v in vars(trainer.args).items()}
-    except Exception as e:
-        LOGGER.warning(f'WARNING ⚠️ NeptuneAI installed but not initialized correctly, not logging this run. {e}')
-
-
-def on_train_epoch_end(trainer):
-    """Callback function called at end of each training epoch."""
-    _log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1)
-    _log_scalars(trainer.lr, trainer.epoch + 1)
-    if trainer.epoch == 1:
-        _log_images({f.stem: str(f) for f in trainer.save_dir.glob('train_batch*.jpg')}, 'Mosaic')
-
-
-def on_fit_epoch_end(trainer):
-    """Callback function called at end of each fit (train+val) epoch."""
-    if run and trainer.epoch == 0:
-        run['Configuration/Model'] = model_info_for_loggers(trainer)
-    _log_scalars(trainer.metrics, trainer.epoch + 1)
-
-
-def on_val_end(validator):
-    """Callback function called at end of each validation."""
-    if run:
-        # Log val_labels and val_pred
-        _log_images({f.stem: str(f) for f in validator.save_dir.glob('val*.jpg')}, 'Validation')
-
-
-def on_train_end(trainer):
-    """Callback function called at end of training."""
-    if run:
-        # Log final results, CM matrix + PR plots
-        files = [
-            'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
-            *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
-        files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()]  # filter
-        for f in files:
-            _log_plot(title=f.stem, plot_path=f)
-        # Log the final model
-        run[f'weights/{trainer.args.name or trainer.args.task}/{str(trainer.best.name)}'].upload(File(str(
-            trainer.best)))
-
-
-callbacks = {
-    'on_pretrain_routine_start': on_pretrain_routine_start,
-    'on_train_epoch_end': on_train_epoch_end,
-    'on_fit_epoch_end': on_fit_epoch_end,
-    'on_val_end': on_val_end,
-    'on_train_end': on_train_end} if neptune else {}
diff --git a/spaces/vinthony/SadTalker/src/facerender/modules/util.py b/spaces/vinthony/SadTalker/src/facerender/modules/util.py
deleted file mode 100644
index b916deefbb8b957ad6ab3cd7403c28513e5ae18e..0000000000000000000000000000000000000000
--- a/spaces/vinthony/SadTalker/src/facerender/modules/util.py
+++ /dev/null
@@ -1,564 +0,0 @@
-from torch import nn
-
-import torch.nn.functional as F
-import torch
-
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
-
-import torch.nn.utils.spectral_norm as spectral_norm
-
-
-def kp2gaussian(kp, spatial_size, kp_variance):
-    """
-    Transform a keypoint into gaussian like representation
-    """
-    mean = kp['value']
-
-    coordinate_grid = make_coordinate_grid(spatial_size, mean.type())
-    number_of_leading_dimensions = len(mean.shape) - 1
-    shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
-    coordinate_grid = coordinate_grid.view(*shape)
-    repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1)
-    coordinate_grid = coordinate_grid.repeat(*repeats)
-
-    # Preprocess kp shape
-    shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3)
-    mean = mean.view(*shape)
-
-    mean_sub = (coordinate_grid - mean)
-
-    out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
-
-    return out
-
-def make_coordinate_grid_2d(spatial_size, type):
-    """
-    Create a meshgrid [-1,1] x [-1,1] of given spatial_size.
-    """
-    h, w = spatial_size
-    x = torch.arange(w).type(type)
-    y = torch.arange(h).type(type)
-
-    x = (2 * (x / (w - 1)) - 1)
-    y = (2 * (y / (h - 1)) - 1)
-
-    yy = y.view(-1, 1).repeat(1, w)
-    xx = x.view(1, -1).repeat(h, 1)
-
-    meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
-
-    return meshed
-
-
-def make_coordinate_grid(spatial_size, type):
-    d, h, w = spatial_size
-    x = torch.arange(w).type(type)
-    y = torch.arange(h).type(type)
-    z = torch.arange(d).type(type)
-
-    x = (2 * (x / (w - 1)) - 1)
-    y = (2 * (y / (h - 1)) - 1)
-    z = (2 * (z / (d - 1)) - 1)
-   
-    yy = y.view(1, -1, 1).repeat(d, 1, w)
-    xx = x.view(1, 1, -1).repeat(d, h, 1)
-    zz = z.view(-1, 1, 1).repeat(1, h, w)
-
-    meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3)
-
-    return meshed
-
-
-class ResBottleneck(nn.Module):
-    def __init__(self, in_features, stride):
-        super(ResBottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features//4, kernel_size=1)
-        self.conv2 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features//4, kernel_size=3, padding=1, stride=stride)
-        self.conv3 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features, kernel_size=1)
-        self.norm1 = BatchNorm2d(in_features//4, affine=True)
-        self.norm2 = BatchNorm2d(in_features//4, affine=True)
-        self.norm3 = BatchNorm2d(in_features, affine=True)
-
-        self.stride = stride
-        if self.stride != 1:
-            self.skip = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=1, stride=stride)
-            self.norm4 = BatchNorm2d(in_features, affine=True)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.norm1(out)
-        out = F.relu(out)
-        out = self.conv2(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-        out = self.conv3(out)
-        out = self.norm3(out)
-        if self.stride != 1:
-            x = self.skip(x)
-            x = self.norm4(x)
-        out += x
-        out = F.relu(out)
-        return out
-
-
-class ResBlock2d(nn.Module):
-    """
-    Res block, preserve spatial resolution.
-    """
-
-    def __init__(self, in_features, kernel_size, padding):
-        super(ResBlock2d, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.norm1 = BatchNorm2d(in_features, affine=True)
-        self.norm2 = BatchNorm2d(in_features, affine=True)
-
-    def forward(self, x):
-        out = self.norm1(x)
-        out = F.relu(out)
-        out = self.conv1(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-        out = self.conv2(out)
-        out += x
-        return out
-
-
-class ResBlock3d(nn.Module):
-    """
-    Res block, preserve spatial resolution.
-    """
-
-    def __init__(self, in_features, kernel_size, padding):
-        super(ResBlock3d, self).__init__()
-        self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.norm1 = BatchNorm3d(in_features, affine=True)
-        self.norm2 = BatchNorm3d(in_features, affine=True)
-
-    def forward(self, x):
-        out = self.norm1(x)
-        out = F.relu(out)
-        out = self.conv1(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-        out = self.conv2(out)
-        out += x
-        return out
-
-
-class UpBlock2d(nn.Module):
-    """
-    Upsampling block for use in decoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(UpBlock2d, self).__init__()
-
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm2d(out_features, affine=True)
-
-    def forward(self, x):
-        out = F.interpolate(x, scale_factor=2)
-        out = self.conv(out)
-        out = self.norm(out)
-        out = F.relu(out)
-        return out
-
-class UpBlock3d(nn.Module):
-    """
-    Upsampling block for use in decoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(UpBlock3d, self).__init__()
-
-        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm3d(out_features, affine=True)
-
-    def forward(self, x):
-        # out = F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear')
-        out = F.interpolate(x, scale_factor=(1, 2, 2))
-        out = self.conv(out)
-        out = self.norm(out)
-        out = F.relu(out)
-        return out
-
-
-class DownBlock2d(nn.Module):
-    """
-    Downsampling block for use in encoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(DownBlock2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm2d(out_features, affine=True)
-        self.pool = nn.AvgPool2d(kernel_size=(2, 2))
-
-    def forward(self, x):
-        out = self.conv(x)
-        out = self.norm(out)
-        out = F.relu(out)
-        out = self.pool(out)
-        return out
-
-
-class DownBlock3d(nn.Module):
-    """
-    Downsampling block for use in encoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(DownBlock3d, self).__init__()
-        '''
-        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups, stride=(1, 2, 2))
-        '''
-        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm3d(out_features, affine=True)
-        self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2))
-
-    def forward(self, x):
-        out = self.conv(x)
-        out = self.norm(out)
-        out = F.relu(out)
-        out = self.pool(out)
-        return out
-
-
-class SameBlock2d(nn.Module):
-    """
-    Simple block, preserve spatial resolution.
-    """
-
-    def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False):
-        super(SameBlock2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,
-                              kernel_size=kernel_size, padding=padding, groups=groups)
-        self.norm = BatchNorm2d(out_features, affine=True)
-        if lrelu:
-            self.ac = nn.LeakyReLU()
-        else:
-            self.ac = nn.ReLU()
-
-    def forward(self, x):
-        out = self.conv(x)
-        out = self.norm(out)
-        out = self.ac(out)
-        return out
-
-
-class Encoder(nn.Module):
-    """
-    Hourglass Encoder
-    """
-
-    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
-        super(Encoder, self).__init__()
-
-        down_blocks = []
-        for i in range(num_blocks):
-            down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
-                                           min(max_features, block_expansion * (2 ** (i + 1))),
-                                           kernel_size=3, padding=1))
-        self.down_blocks = nn.ModuleList(down_blocks)
-
-    def forward(self, x):
-        outs = [x]
-        for down_block in self.down_blocks:
-            outs.append(down_block(outs[-1]))
-        return outs
-
-
-class Decoder(nn.Module):
-    """
-    Hourglass Decoder
-    """
-
-    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
-        super(Decoder, self).__init__()
-
-        up_blocks = []
-
-        for i in range(num_blocks)[::-1]:
-            in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
-            out_filters = min(max_features, block_expansion * (2 ** i))
-            up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
-
-        self.up_blocks = nn.ModuleList(up_blocks)
-        # self.out_filters = block_expansion
-        self.out_filters = block_expansion + in_features
-
-        self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1)
-        self.norm = BatchNorm3d(self.out_filters, affine=True)
-
-    def forward(self, x):
-        out = x.pop()
-        # for up_block in self.up_blocks[:-1]:
-        for up_block in self.up_blocks:
-            out = up_block(out)
-            skip = x.pop()
-            out = torch.cat([out, skip], dim=1)
-        # out = self.up_blocks[-1](out)
-        out = self.conv(out)
-        out = self.norm(out)
-        out = F.relu(out)
-        return out
-
-
-class Hourglass(nn.Module):
-    """
-    Hourglass architecture.
-    """
-
-    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
-        super(Hourglass, self).__init__()
-        self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
-        self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
-        self.out_filters = self.decoder.out_filters
-
-    def forward(self, x):
-        return self.decoder(self.encoder(x))
-
-
-class KPHourglass(nn.Module):
-    """
-    Hourglass architecture.
-    """ 
-
-    def __init__(self, block_expansion, in_features, reshape_features, reshape_depth, num_blocks=3, max_features=256):
-        super(KPHourglass, self).__init__()
-        
-        self.down_blocks = nn.Sequential()
-        for i in range(num_blocks):
-            self.down_blocks.add_module('down'+ str(i), DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
-                                                                   min(max_features, block_expansion * (2 ** (i + 1))),
-                                                                   kernel_size=3, padding=1))
-
-        in_filters = min(max_features, block_expansion * (2 ** num_blocks))
-        self.conv = nn.Conv2d(in_channels=in_filters, out_channels=reshape_features, kernel_size=1)
-
-        self.up_blocks = nn.Sequential()
-        for i in range(num_blocks):
-            in_filters = min(max_features, block_expansion * (2 ** (num_blocks - i)))
-            out_filters = min(max_features, block_expansion * (2 ** (num_blocks - i - 1)))
-            self.up_blocks.add_module('up'+ str(i), UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
-
-        self.reshape_depth = reshape_depth
-        self.out_filters = out_filters
-
-    def forward(self, x):
-        out = self.down_blocks(x)
-        out = self.conv(out)
-        bs, c, h, w = out.shape
-        out = out.view(bs, c//self.reshape_depth, self.reshape_depth, h, w)
-        out = self.up_blocks(out)
-
-        return out
-        
-
-
-class AntiAliasInterpolation2d(nn.Module):
-    """
-    Band-limited downsampling, for better preservation of the input signal.
-    """
-    def __init__(self, channels, scale):
-        super(AntiAliasInterpolation2d, self).__init__()
-        sigma = (1 / scale - 1) / 2
-        kernel_size = 2 * round(sigma * 4) + 1
-        self.ka = kernel_size // 2
-        self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
-
-        kernel_size = [kernel_size, kernel_size]
-        sigma = [sigma, sigma]
-        # The gaussian kernel is the product of the
-        # gaussian function of each dimension.
-        kernel = 1
-        meshgrids = torch.meshgrid(
-            [
-                torch.arange(size, dtype=torch.float32)
-                for size in kernel_size
-                ]
-        )
-        for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
-            mean = (size - 1) / 2
-            kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))
-
-        # Make sure sum of values in gaussian kernel equals 1.
-        kernel = kernel / torch.sum(kernel)
-        # Reshape to depthwise convolutional weight
-        kernel = kernel.view(1, 1, *kernel.size())
-        kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
-
-        self.register_buffer('weight', kernel)
-        self.groups = channels
-        self.scale = scale
-        inv_scale = 1 / scale
-        self.int_inv_scale = int(inv_scale)
-
-    def forward(self, input):
-        if self.scale == 1.0:
-            return input
-
-        out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
-        out = F.conv2d(out, weight=self.weight, groups=self.groups)
-        out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale]
-
-        return out
-
-
-class SPADE(nn.Module):
-    def __init__(self, norm_nc, label_nc):
-        super().__init__()
-
-        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
-        nhidden = 128
-
-        self.mlp_shared = nn.Sequential(
-            nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1),
-            nn.ReLU())
-        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
-        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
-
-    def forward(self, x, segmap):
-        normalized = self.param_free_norm(x)
-        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
-        actv = self.mlp_shared(segmap)
-        gamma = self.mlp_gamma(actv)
-        beta = self.mlp_beta(actv)
-        out = normalized * (1 + gamma) + beta
-        return out
-    
-
-class SPADEResnetBlock(nn.Module):
-    def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1):
-        super().__init__()
-        # Attributes
-        self.learned_shortcut = (fin != fout)
-        fmiddle = min(fin, fout)
-        self.use_se = use_se
-        # create conv layers
-        self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation)
-        self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation)
-        if self.learned_shortcut:
-            self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
-        # apply spectral norm if specified
-        if 'spectral' in norm_G:
-            self.conv_0 = spectral_norm(self.conv_0)
-            self.conv_1 = spectral_norm(self.conv_1)
-            if self.learned_shortcut:
-                self.conv_s = spectral_norm(self.conv_s)
-        # define normalization layers
-        self.norm_0 = SPADE(fin, label_nc)
-        self.norm_1 = SPADE(fmiddle, label_nc)
-        if self.learned_shortcut:
-            self.norm_s = SPADE(fin, label_nc)
-
-    def forward(self, x, seg1):
-        x_s = self.shortcut(x, seg1)
-        dx = self.conv_0(self.actvn(self.norm_0(x, seg1)))
-        dx = self.conv_1(self.actvn(self.norm_1(dx, seg1)))
-        out = x_s + dx
-        return out
-
-    def shortcut(self, x, seg1):
-        if self.learned_shortcut:
-            x_s = self.conv_s(self.norm_s(x, seg1))
-        else:
-            x_s = x
-        return x_s
-
-    def actvn(self, x):
-        return F.leaky_relu(x, 2e-1)
-
-class audio2image(nn.Module):
-    def __init__(self, generator, kp_extractor, he_estimator_video, he_estimator_audio, train_params):
-        super().__init__()
-        # Attributes
-        self.generator = generator
-        self.kp_extractor = kp_extractor
-        self.he_estimator_video = he_estimator_video
-        self.he_estimator_audio = he_estimator_audio
-        self.train_params = train_params
-
-    def headpose_pred_to_degree(self, pred):
-        device = pred.device
-        idx_tensor = [idx for idx in range(66)]
-        idx_tensor = torch.FloatTensor(idx_tensor).to(device)
-        pred = F.softmax(pred)
-        degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
-
-        return degree
-    
-    def get_rotation_matrix(self, yaw, pitch, roll):
-        yaw = yaw / 180 * 3.14
-        pitch = pitch / 180 * 3.14
-        roll = roll / 180 * 3.14
-
-        roll = roll.unsqueeze(1)
-        pitch = pitch.unsqueeze(1)
-        yaw = yaw.unsqueeze(1)
-
-        roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), 
-                          torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll),
-                          torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1)
-        roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
-
-        pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), 
-                           torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch),
-                           -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1)
-        pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
-
-        yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw),  
-                         torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw),
-                         torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1)
-        yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
-
-        rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat)
-
-        return rot_mat
-
-    def keypoint_transformation(self, kp_canonical, he):
-        kp = kp_canonical['value']    # (bs, k, 3)
-        yaw, pitch, roll = he['yaw'], he['pitch'], he['roll']
-        t, exp = he['t'], he['exp']
-    
-        yaw = self.headpose_pred_to_degree(yaw)
-        pitch = self.headpose_pred_to_degree(pitch)
-        roll = self.headpose_pred_to_degree(roll)
-
-        rot_mat = self.get_rotation_matrix(yaw, pitch, roll)    # (bs, 3, 3)
-    
-        # keypoint rotation
-        kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
-
-    
-
-        # keypoint translation
-        t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
-        kp_t = kp_rotated + t
-
-        # add expression deviation 
-        exp = exp.view(exp.shape[0], -1, 3)
-        kp_transformed = kp_t + exp
-
-        return {'value': kp_transformed}
-
-    def forward(self, source_image, target_audio):
-        pose_source = self.he_estimator_video(source_image)
-        pose_generated = self.he_estimator_audio(target_audio)
-        kp_canonical = self.kp_extractor(source_image)
-        kp_source = self.keypoint_transformation(kp_canonical, pose_source)
-        kp_transformed_generated = self.keypoint_transformation(kp_canonical, pose_generated)
-        generated = self.generator(source_image, kp_source=kp_source, kp_driving=kp_transformed_generated)
-        return generated
\ No newline at end of file
diff --git a/spaces/vonbarnekowa/stable-diffusion/ldm/models/diffusion/dpm_solver/__init__.py b/spaces/vonbarnekowa/stable-diffusion/ldm/models/diffusion/dpm_solver/__init__.py
deleted file mode 100644
index 7427f38c07530afbab79154ea8aaf88c4bf70a08..0000000000000000000000000000000000000000
--- a/spaces/vonbarnekowa/stable-diffusion/ldm/models/diffusion/dpm_solver/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/spaces/vrajeshbhatt/Automated-Ticket-Management-System/static/js/popper.js b/spaces/vrajeshbhatt/Automated-Ticket-Management-System/static/js/popper.js
deleted file mode 100644
index f1da1f940c5ef2eabde299ffb3385f387c969a11..0000000000000000000000000000000000000000
--- a/spaces/vrajeshbhatt/Automated-Ticket-Management-System/static/js/popper.js
+++ /dev/null
@@ -1,5 +0,0 @@
-/*
- Copyright (C) Federico Zivolo 2019
- Distributed under the MIT License (license terms are at http://opensource.org/licenses/MIT).
- */(function(e,t){'object'==typeof exports&&'undefined'!=typeof module?module.exports=t():'function'==typeof define&&define.amd?define(t):e.Popper=t()})(this,function(){'use strict';function e(e){return e&&'[object Function]'==={}.toString.call(e)}function t(e,t){if(1!==e.nodeType)return[];var o=e.ownerDocument.defaultView,n=o.getComputedStyle(e,null);return t?n[t]:n}function o(e){return'HTML'===e.nodeName?e:e.parentNode||e.host}function n(e){if(!e)return document.body;switch(e.nodeName){case'HTML':case'BODY':return e.ownerDocument.body;case'#document':return e.body;}var i=t(e),r=i.overflow,p=i.overflowX,s=i.overflowY;return /(auto|scroll|overlay)/.test(r+s+p)?e:n(o(e))}function r(e){return 11===e?pe:10===e?se:pe||se}function p(e){if(!e)return document.documentElement;for(var o=r(10)?document.body:null,n=e.offsetParent||null;n===o&&e.nextElementSibling;)n=(e=e.nextElementSibling).offsetParent;var i=n&&n.nodeName;return 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diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/iter_based_runner.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/iter_based_runner.py
deleted file mode 100644
index 1df4de8c0285669dec9b014dfd1f3dd1600f0831..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/iter_based_runner.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-from torch.optim import Optimizer
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .hooks import IterTimerHook
-from .utils import get_host_info
-
-
-class IterLoader:
-
-    def __init__(self, dataloader):
-        self._dataloader = dataloader
-        self.iter_loader = iter(self._dataloader)
-        self._epoch = 0
-
-    @property
-    def epoch(self):
-        return self._epoch
-
-    def __next__(self):
-        try:
-            data = next(self.iter_loader)
-        except StopIteration:
-            self._epoch += 1
-            if hasattr(self._dataloader.sampler, 'set_epoch'):
-                self._dataloader.sampler.set_epoch(self._epoch)
-            time.sleep(2)  # Prevent possible deadlock during epoch transition
-            self.iter_loader = iter(self._dataloader)
-            data = next(self.iter_loader)
-
-        return data
-
-    def __len__(self):
-        return len(self._dataloader)
-
-
-@RUNNERS.register_module()
-class IterBasedRunner(BaseRunner):
-    """Iteration-based Runner.
-
-    This runner train models iteration by iteration.
-    """
-
-    def train(self, data_loader, **kwargs):
-        self.model.train()
-        self.mode = 'train'
-        self.data_loader = data_loader
-        self._epoch = data_loader.epoch
-        data_batch = next(data_loader)
-        self.call_hook('before_train_iter')
-        outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('model.train_step() must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-        self.call_hook('after_train_iter')
-        self._inner_iter += 1
-        self._iter += 1
-
-    @torch.no_grad()
-    def val(self, data_loader, **kwargs):
-        self.model.eval()
-        self.mode = 'val'
-        self.data_loader = data_loader
-        data_batch = next(data_loader)
-        self.call_hook('before_val_iter')
-        outputs = self.model.val_step(data_batch, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('model.val_step() must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-        self.call_hook('after_val_iter')
-        self._inner_iter += 1
-
-    def run(self, data_loaders, workflow, max_iters=None, **kwargs):
-        """Start running.
-
-        Args:
-            data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
-                and validation.
-            workflow (list[tuple]): A list of (phase, iters) to specify the
-                running order and iterations. E.g, [('train', 10000),
-                ('val', 1000)] means running 10000 iterations for training and
-                1000 iterations for validation, iteratively.
-        """
-        assert isinstance(data_loaders, list)
-        assert mmcv.is_list_of(workflow, tuple)
-        assert len(data_loaders) == len(workflow)
-        if max_iters is not None:
-            warnings.warn(
-                'setting max_iters in run is deprecated, '
-                'please set max_iters in runner_config', DeprecationWarning)
-            self._max_iters = max_iters
-        assert self._max_iters is not None, (
-            'max_iters must be specified during instantiation')
-
-        work_dir = self.work_dir if self.work_dir is not None else 'NONE'
-        self.logger.info('Start running, host: %s, work_dir: %s',
-                         get_host_info(), work_dir)
-        self.logger.info('Hooks will be executed in the following order:\n%s',
-                         self.get_hook_info())
-        self.logger.info('workflow: %s, max: %d iters', workflow,
-                         self._max_iters)
-        self.call_hook('before_run')
-
-        iter_loaders = [IterLoader(x) for x in data_loaders]
-
-        self.call_hook('before_epoch')
-
-        while self.iter < self._max_iters:
-            for i, flow in enumerate(workflow):
-                self._inner_iter = 0
-                mode, iters = flow
-                if not isinstance(mode, str) or not hasattr(self, mode):
-                    raise ValueError(
-                        'runner has no method named "{}" to run a workflow'.
-                        format(mode))
-                iter_runner = getattr(self, mode)
-                for _ in range(iters):
-                    if mode == 'train' and self.iter >= self._max_iters:
-                        break
-                    iter_runner(iter_loaders[i], **kwargs)
-
-        time.sleep(1)  # wait for some hooks like loggers to finish
-        self.call_hook('after_epoch')
-        self.call_hook('after_run')
-
-    def resume(self,
-               checkpoint,
-               resume_optimizer=True,
-               map_location='default'):
-        """Resume model from checkpoint.
-
-        Args:
-            checkpoint (str): Checkpoint to resume from.
-            resume_optimizer (bool, optional): Whether resume the optimizer(s)
-                if the checkpoint file includes optimizer(s). Default to True.
-            map_location (str, optional): Same as :func:`torch.load`.
-                Default to 'default'.
-        """
-        if map_location == 'default':
-            device_id = torch.cuda.current_device()
-            checkpoint = self.load_checkpoint(
-                checkpoint,
-                map_location=lambda storage, loc: storage.cuda(device_id))
-        else:
-            checkpoint = self.load_checkpoint(
-                checkpoint, map_location=map_location)
-
-        self._epoch = checkpoint['meta']['epoch']
-        self._iter = checkpoint['meta']['iter']
-        self._inner_iter = checkpoint['meta']['iter']
-        if 'optimizer' in checkpoint and resume_optimizer:
-            if isinstance(self.optimizer, Optimizer):
-                self.optimizer.load_state_dict(checkpoint['optimizer'])
-            elif isinstance(self.optimizer, dict):
-                for k in self.optimizer.keys():
-                    self.optimizer[k].load_state_dict(
-                        checkpoint['optimizer'][k])
-            else:
-                raise TypeError(
-                    'Optimizer should be dict or torch.optim.Optimizer '
-                    f'but got {type(self.optimizer)}')
-
-        self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}')
-
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='iter_{}.pth',
-                        meta=None,
-                        save_optimizer=True,
-                        create_symlink=True):
-        """Save checkpoint to file.
-
-        Args:
-            out_dir (str): Directory to save checkpoint files.
-            filename_tmpl (str, optional): Checkpoint file template.
-                Defaults to 'iter_{}.pth'.
-            meta (dict, optional): Metadata to be saved in checkpoint.
-                Defaults to None.
-            save_optimizer (bool, optional): Whether save optimizer.
-                Defaults to True.
-            create_symlink (bool, optional): Whether create symlink to the
-                latest checkpoint file. Defaults to True.
-        """
-        if meta is None:
-            meta = {}
-        elif not isinstance(meta, dict):
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-            # Note: meta.update(self.meta) should be done before
-            # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
-            # there will be problems with resumed checkpoints.
-            # More details in https://github.com/open-mmlab/mmcv/pull/1108
-        meta.update(epoch=self.epoch + 1, iter=self.iter)
-
-        filename = filename_tmpl.format(self.iter + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-
-    def register_training_hooks(self,
-                                lr_config,
-                                optimizer_config=None,
-                                checkpoint_config=None,
-                                log_config=None,
-                                momentum_config=None,
-                                custom_hooks_config=None):
-        """Register default hooks for iter-based training.
-
-        Checkpoint hook, optimizer stepper hook and logger hooks will be set to
-        `by_epoch=False` by default.
-
-        Default hooks include:
-
-        +----------------------+-------------------------+
-        | Hooks                | Priority                |
-        +======================+=========================+
-        | LrUpdaterHook        | VERY_HIGH (10)          |
-        +----------------------+-------------------------+
-        | MomentumUpdaterHook  | HIGH (30)               |
-        +----------------------+-------------------------+
-        | OptimizerStepperHook | ABOVE_NORMAL (40)       |
-        +----------------------+-------------------------+
-        | CheckpointSaverHook  | NORMAL (50)             |
-        +----------------------+-------------------------+
-        | IterTimerHook        | LOW (70)                |
-        +----------------------+-------------------------+
-        | LoggerHook(s)        | VERY_LOW (90)           |
-        +----------------------+-------------------------+
-        | CustomHook(s)        | defaults to NORMAL (50) |
-        +----------------------+-------------------------+
-
-        If custom hooks have same priority with default hooks, custom hooks
-        will be triggered after default hooks.
-        """
-        if checkpoint_config is not None:
-            checkpoint_config.setdefault('by_epoch', False)
-        if lr_config is not None:
-            lr_config.setdefault('by_epoch', False)
-        if log_config is not None:
-            for info in log_config['hooks']:
-                info.setdefault('by_epoch', False)
-        super(IterBasedRunner, self).register_training_hooks(
-            lr_config=lr_config,
-            momentum_config=momentum_config,
-            optimizer_config=optimizer_config,
-            checkpoint_config=checkpoint_config,
-            log_config=log_config,
-            timer_config=IterTimerHook(),
-            custom_hooks_config=custom_hooks_config)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/dm_head.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/dm_head.py
deleted file mode 100644
index 19c963923126b53ce22f60813540a35badf24b3d..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/dm_head.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
-
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class DCM(nn.Module):
-    """Dynamic Convolutional Module used in DMNet.
-
-    Args:
-        filter_size (int): The filter size of generated convolution kernel
-            used in Dynamic Convolutional Module.
-        fusion (bool): Add one conv to fuse DCM output feature.
-        in_channels (int): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        conv_cfg (dict | None): Config of conv layers.
-        norm_cfg (dict | None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-    """
-
-    def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
-                 norm_cfg, act_cfg):
-        super(DCM, self).__init__()
-        self.filter_size = filter_size
-        self.fusion = fusion
-        self.in_channels = in_channels
-        self.channels = channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1,
-                                         0)
-
-        self.input_redu_conv = ConvModule(
-            self.in_channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        if self.norm_cfg is not None:
-            self.norm = build_norm_layer(self.norm_cfg, self.channels)[1]
-        else:
-            self.norm = None
-        self.activate = build_activation_layer(self.act_cfg)
-
-        if self.fusion:
-            self.fusion_conv = ConvModule(
-                self.channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-
-    def forward(self, x):
-        """Forward function."""
-        generated_filter = self.filter_gen_conv(
-            F.adaptive_avg_pool2d(x, self.filter_size))
-        x = self.input_redu_conv(x)
-        b, c, h, w = x.shape
-        # [1, b * c, h, w], c = self.channels
-        x = x.view(1, b * c, h, w)
-        # [b * c, 1, filter_size, filter_size]
-        generated_filter = generated_filter.view(b * c, 1, self.filter_size,
-                                                 self.filter_size)
-        pad = (self.filter_size - 1) // 2
-        if (self.filter_size - 1) % 2 == 0:
-            p2d = (pad, pad, pad, pad)
-        else:
-            p2d = (pad + 1, pad, pad + 1, pad)
-        x = F.pad(input=x, pad=p2d, mode='constant', value=0)
-        # [1, b * c, h, w]
-        output = F.conv2d(input=x, weight=generated_filter, groups=b * c)
-        # [b, c, h, w]
-        output = output.view(b, c, h, w)
-        if self.norm is not None:
-            output = self.norm(output)
-        output = self.activate(output)
-
-        if self.fusion:
-            output = self.fusion_conv(output)
-
-        return output
-
-
-@HEADS.register_module()
-class DMHead(BaseDecodeHead):
-    """Dynamic Multi-scale Filters for Semantic Segmentation.
-
-    This head is the implementation of
-    `DMNet <https://openaccess.thecvf.com/content_ICCV_2019/papers/\
-        He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_\
-            ICCV_2019_paper.pdf>`_.
-
-    Args:
-        filter_sizes (tuple[int]): The size of generated convolutional filters
-            used in Dynamic Convolutional Module. Default: (1, 3, 5, 7).
-        fusion (bool): Add one conv to fuse DCM output feature.
-    """
-
-    def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
-        super(DMHead, self).__init__(**kwargs)
-        assert isinstance(filter_sizes, (list, tuple))
-        self.filter_sizes = filter_sizes
-        self.fusion = fusion
-        dcm_modules = []
-        for filter_size in self.filter_sizes:
-            dcm_modules.append(
-                DCM(filter_size,
-                    self.fusion,
-                    self.in_channels,
-                    self.channels,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg))
-        self.dcm_modules = nn.ModuleList(dcm_modules)
-        self.bottleneck = ConvModule(
-            self.in_channels + len(filter_sizes) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        dcm_outs = [x]
-        for dcm_module in self.dcm_modules:
-            dcm_outs.append(dcm_module(x))
-        dcm_outs = torch.cat(dcm_outs, dim=1)
-        output = self.bottleneck(dcm_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/weiren119/AudiogramDigitization/app.py b/spaces/weiren119/AudiogramDigitization/app.py
deleted file mode 100644
index 9d8a0c814de02e7e686f91b571f36d173fa23e6b..0000000000000000000000000000000000000000
--- a/spaces/weiren119/AudiogramDigitization/app.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""
-Original Algorithm:
-- https://github.com/GreenCUBIC/AudiogramDigitization
-
-Source:
-- huggingface app
-    - https://huggingface.co/spaces/aravinds1811/neural-style-transfer/blob/main/app.py
-    - https://huggingface.co/spaces/keras-io/ocr-for-captcha/blob/main/app.py
-    - https://huggingface.co/spaces/hugginglearners/image-style-transfer/blob/main/app.py
-    - https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial
-- huggingface push
-    - https://huggingface.co/welcome
-"""
-import os 
-import sys 
-from pathlib import Path 
-
-from PIL import Image
-from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np 
-import gradio as gr
-
-sys.path.append(os.path.join(os.path.dirname(__file__), "src"))
-from digitizer.digitization import generate_partial_annotation, extract_thresholds
-
-EXAMPLES_PATH = Path('./examples')
-
-max_length = 5
-img_width = 200
-img_height = 50
-
-  
-def load_image(path, zoom=1):
-    return OffsetImage(plt.imread(path), zoom=zoom)
-
-def plot_audiogram(digital_result):
-    thresholds = pd.DataFrame(digital_result)
-
-    # Figure
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-
-    # x axis
-    axis = [250, 500, 1000, 2000, 4000, 8000, 16000]
-    ax.set_xscale('log')
-    ax.xaxis.tick_top()
-    ax.xaxis.set_major_formatter(plt.FuncFormatter('{:.0f}'.format))
-    ax.set_xlabel('Frequency (Hz)')
-    ax.xaxis.set_label_position('top') 
-    ax.set_xlim(125,16000)
-    plt.xticks(axis)
-
-    # y axis
-    ax.set_ylim(-20, 120)
-    ax.invert_yaxis()
-    ax.set_ylabel('Threshold (dB HL)')
-
-    plt.grid()
-
-    for conduction in ("air", "bone"):
-        for masking in (True, False):
-            for ear in ("left", "right"):
-                symbol_name = f"{ear}_{conduction}_{'unmasked' if not masking else 'masked'}"
-                selection = thresholds[(thresholds.conduction == conduction) & (thresholds.ear == ear) & (thresholds.masking == masking)]
-                selection = selection.sort_values("frequency")
-
-                # Plot the symbols
-                for i, threshold in selection.iterrows():
-                    ab = AnnotationBbox(load_image(f"src/digitizer/assets/symbols/{symbol_name}.png", zoom=0.1), (threshold.frequency, threshold.threshold), frameon=False)
-                    ax.add_artist(ab)
-
-                # Add joining line for air conduction thresholds
-                if conduction == "air":
-                    plt.plot(selection.frequency, selection.threshold, color="red" if ear == "right" else "blue", linewidth=0.5)
-
-    return plt.gcf()
-
-# Function for Audiogram Digit Recognition
-def audiogram_digit_recognition(img_path):
-    digital_result = extract_thresholds(img_path, gpu=False)
-    return [plot_audiogram(digital_result), digital_result]
-
-
-output = [gr.Plot(), gr.JSON()]
-examples = [
-            f'{EXAMPLES_PATH}/audiogram_example01.png',
-            f'{EXAMPLES_PATH}/audiogram_example02.png'
-            ]
-
-iface = gr.Interface(
-    fn=audiogram_digit_recognition,
-    inputs = gr.inputs.Image(type='filepath'),
-    outputs = output , #"image",
-    title=" AudiogramDigitization",
-	description = "facilitate the digitization of audiology reports based on pytorch",
-    article = "Algorithm Authors: <a href=\"francoischarih@sce.carleton.ca\">Francois Charih \
-                    and <a href=\"jrgreen@sce.carleton.ca\"> James R. Green </a>. \
-                    Based on the AudiogramDigitization  <a href=\"https://github.com/GreenCUBIC/AudiogramDigitization\">github repo</a>",
-    examples = examples,
-    allow_flagging='never', 
-    cache_examples=False,
-)
-
-
-iface.launch(
-    enable_queue=True, debug=False, inbrowser=False
-)
diff --git a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/tools/test_web_browser_engine_playwright.py b/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/tools/test_web_browser_engine_playwright.py
deleted file mode 100644
index add2b2f63488c95fdb4de473160a7c63f864b42a..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/tools/test_web_browser_engine_playwright.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""
-@Modified By: mashenquan, 2023/8/20. Remove global configuration `CONFIG`, enable configuration support for business isolation.
-"""
-
-import pytest
-
-from metagpt.config import Config
-from metagpt.tools import web_browser_engine_playwright
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize(
-    "browser_type, use_proxy, kwagrs, url, urls",
-    [
-        ("chromium", {"proxy": True}, {}, "https://fuzhi.ai", ("https://fuzhi.ai",)),
-        ("firefox", {}, {"ignore_https_errors": True}, "https://fuzhi.ai", ("https://fuzhi.ai",)),
-        ("webkit", {}, {"ignore_https_errors": True}, "https://fuzhi.ai", ("https://fuzhi.ai",)),
-    ],
-    ids=["chromium-normal", "firefox-normal", "webkit-normal"],
-)
-async def test_scrape_web_page(browser_type, use_proxy, kwagrs, url, urls, proxy, capfd):
-    conf = Config()
-    global_proxy = conf.global_proxy
-    try:
-        if use_proxy:
-            conf.global_proxy = proxy
-        browser = web_browser_engine_playwright.PlaywrightWrapper(options=conf.runtime_options,
-                                                                  browser_type=browser_type, **kwagrs)
-        result = await browser.run(url)
-        result = result.inner_text
-        assert isinstance(result, str)
-        assert "DeepWisdom" in result
-
-        if urls:
-            results = await browser.run(url, *urls)
-            assert isinstance(results, list)
-            assert len(results) == len(urls) + 1
-            assert all(("DeepWisdom" in i) for i in results)
-        if use_proxy:
-            assert "Proxy:" in capfd.readouterr().out
-    finally:
-        conf.global_proxy = global_proxy
diff --git a/spaces/williamberman/stable-diffusion-xl-inpainting/header.html b/spaces/williamberman/stable-diffusion-xl-inpainting/header.html
deleted file mode 100644
index 3d54d05ccc8fdea2ed662dd0dbc9ccbfd07524be..0000000000000000000000000000000000000000
--- a/spaces/williamberman/stable-diffusion-xl-inpainting/header.html
+++ /dev/null
@@ -1,17 +0,0 @@
-<div style="text-align: center; max-width: 650px; margin: 0 auto;">
-  <div style="
-        display: inline-flex;
-        gap: 0.8rem;
-        font-size: 1.75rem;
-        justify-content: center;
-        margin-bottom: 10px;
-      ">
-    <h1 style="font-weight: 900; align-items: center; margin-bottom: 7px; margin-top: 20px;">
-      Stable Diffusion XL Inpainting 🎨
-    </h1>
-  </div>
-  <div>
-    <p style="align-items: center; margin-bottom: 7px;">
-      Demo for the <a href="https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1" target="_blank">Stable Diffusion XL Inpainting model</a>, add a mask and text prompt for what you want to replace
-  </div>
-</div>
\ No newline at end of file
diff --git a/spaces/wilson1/bingo/src/lib/isomorphic/index.ts b/spaces/wilson1/bingo/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/wilson1/bingo/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
-  Object.assign(exportsModel, require('./browser').default)
-} else {
-  Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/wonderit-safeai/tts-announcer/design.css b/spaces/wonderit-safeai/tts-announcer/design.css
deleted file mode 100644
index b70e8f4c0a221f38958789cb3614af54281b992b..0000000000000000000000000000000000000000
--- a/spaces/wonderit-safeai/tts-announcer/design.css
+++ /dev/null
@@ -1,15 +0,0 @@
-div.css-1kyxreq.etr89bj2{
-    display: flex;
-    flex-flow: row wrap;
-    padding-top: 0.5rem;
-    row-gap: 1rem;
-    justify-content: center;
-}
-
-
-div.css-1avcm0n.e8zbici2{
-    padding-top: 0rem;
-}
-
-div.block-container
-{padding-top:1.5rem;}
diff --git a/spaces/wy213/213a/src/app/layout.tsx b/spaces/wy213/213a/src/app/layout.tsx
deleted file mode 100644
index 8b5122759987177b8dc4e4356d1d06cea25c15ea..0000000000000000000000000000000000000000
--- a/spaces/wy213/213a/src/app/layout.tsx
+++ /dev/null
@@ -1,47 +0,0 @@
-import { Metadata } from 'next'
-import { Toaster } from 'react-hot-toast'
-import { TailwindIndicator } from '@/components/tailwind-indicator'
-import { Providers } from '@/components/providers'
-import { Header } from '@/components/header'
-
-import '@/app/globals.scss'
-
-
-export const metadata: Metadata = {
-  title: {
-    default: 'Bing AI Chatbot',
-    template: `%s - Bing AI Chatbot`
-  },
-  description: 'Bing AI Chatbot Web App.',
-  themeColor: [
-    { media: '(prefers-color-scheme: light)', color: 'white' },
-    { media: '(prefers-color-scheme: dark)', color: 'dark' }
-  ],
-  icons: {
-    icon: '/favicon.ico',
-    shortcut: '../assets/images/logo.svg',
-    apple: '../assets/images/logo.svg'
-  }
-}
-
-interface RootLayoutProps {
-  children: React.ReactNode
-}
-
-export default function RootLayout({ children }: RootLayoutProps) {
-  return (
-    <html lang="zh-CN" suppressHydrationWarning>
-      <body>
-        <Toaster />
-        <Providers attribute="class" defaultTheme="system" enableSystem>
-          <div className="flex flex-col min-h-screen">
-            {/* @ts-ignore */}
-            <Header />
-            <main className="flex flex-col flex-1">{children}</main>
-          </div>
-          <TailwindIndicator />
-        </Providers>
-      </body>
-    </html>
-  )
-}
diff --git a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/text/english.py b/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('æ', 'e'),
-    ('ɑ', 'a'),
-    ('ɔ', 'o'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ɛ', 'e'),
-    ('ɪ', 'i'),
-    ('ʊ', 'u'),
-    ('ʒ', 'ʥ'),
-    ('ʤ', 'ʥ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ʒ', 'ʑ'),
-    ('ʤ', 'dʑ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ʤ', 'dʒ'),
-    ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def collapse_whitespace(text):
-    return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
-    return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
-    else:
-        return 'zero dollars'
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return 'two thousand'
-        elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
-        elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
-        else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
-    else:
-        return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
-
-
-def mark_dark_l(text):
-    return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
-    text = unidecode(text).lower()
-    text = expand_abbreviations(text)
-    text = normalize_numbers(text)
-    phonemes = ipa.convert(text)
-    phonemes = collapse_whitespace(phonemes)
-    return phonemes
-
-
-def english_to_lazy_ipa(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def english_to_ipa2(text):
-    text = english_to_ipa(text)
-    text = mark_dark_l(text)
-    for regex, replacement in _ipa_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/projects/attribute_recognition/datasets/__init__.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/projects/attribute_recognition/datasets/__init__.py
deleted file mode 100644
index 664598c71865da78ce937605de3bbf29ef304152..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/projects/attribute_recognition/datasets/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from __future__ import division, print_function, absolute_import
-
-from .pa100k import PA100K
-
-__datasets = {'pa100k': PA100K}
-
-
-def init_dataset(name, **kwargs):
-    avai_datasets = list(__datasets.keys())
-    if name not in avai_datasets:
-        raise ValueError(
-            'Invalid dataset name. Received "{}", '
-            'but expected to be one of {}'.format(name, avai_datasets)
-        )
-    return __datasets[name](**kwargs)
diff --git a/spaces/xiangdy/chatGPT/modules/models/__init__.py b/spaces/xiangdy/chatGPT/modules/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/xiangdy/chatGPT/modules/models/models.py b/spaces/xiangdy/chatGPT/modules/models/models.py
deleted file mode 100644
index 4105dd3dbcdf7a1dba564c527639787697d2e2eb..0000000000000000000000000000000000000000
--- a/spaces/xiangdy/chatGPT/modules/models/models.py
+++ /dev/null
@@ -1,651 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import platform
-import base64
-from io import BytesIO
-from PIL import Image
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
-
-from ..presets import *
-from ..llama_func import *
-from ..utils import *
-from .. import shared
-from ..config import retrieve_proxy, usage_limit
-from modules import config
-from .base_model import BaseLLMModel, ModelType
-
-
-class OpenAIClient(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-        user_name=""
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-            user=user_name
-        )
-        self.api_key = api_key
-        self.need_api_key = True
-        self._refresh_header()
-
-    def get_answer_stream_iter(self):
-        response = self._get_response(stream=True)
-        if response is not None:
-            iter = self._decode_chat_response(response)
-            partial_text = ""
-            for i in iter:
-                partial_text += i
-                yield partial_text
-        else:
-            yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG
-
-    def get_answer_at_once(self):
-        response = self._get_response()
-        response = json.loads(response.text)
-        content = response["choices"][0]["message"]["content"]
-        total_token_count = response["usage"]["total_tokens"]
-        return content, total_token_count
-
-    def count_token(self, user_input):
-        input_token_count = count_token(construct_user(user_input))
-        if self.system_prompt is not None and len(self.all_token_counts) == 0:
-            system_prompt_token_count = count_token(
-                construct_system(self.system_prompt)
-            )
-            return input_token_count + system_prompt_token_count
-        return input_token_count
-
-    def billing_info(self):
-        try:
-            curr_time = datetime.datetime.now()
-            last_day_of_month = get_last_day_of_month(
-                curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = self._get_billing_data(usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:" + str(e))
-                return i18n("**获取API使用情况失败**")
-            # rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            rounded_usage = round(usage_data["total_usage"] / 100, 5)
-            usage_percent = round(usage_data["total_usage"] / usage_limit, 2)
-            # return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-            return """\
-                <b>""" + i18n("本月使用金额") + f"""</b>
-                <div class="progress-bar">
-                    <div class="progress" style="width: {usage_percent}%;">
-                        <span class="progress-text">{usage_percent}%</span>
-                    </div>
-                </div>
-                <div style="display: flex; justify-content: space-between;"><span>${rounded_usage}</span><span>${usage_limit}</span></div>
-                """
-        except requests.exceptions.ConnectTimeout:
-            status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            )
-            return status_text
-        except requests.exceptions.ReadTimeout:
-            status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            return status_text
-        except Exception as e:
-            import traceback
-            traceback.print_exc()
-            logging.error(i18n("获取API使用情况失败:") + str(e))
-            return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
-    def set_token_upper_limit(self, new_upper_limit):
-        pass
-
-    @shared.state.switching_api_key  # 在不开启多账号模式的时候，这个装饰器不会起作用
-    def _get_response(self, stream=False):
-        openai_api_key = self.api_key
-        system_prompt = self.system_prompt
-        history = self.history
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-
-        if system_prompt is not None:
-            history = [construct_system(system_prompt), *history]
-
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n_choices,
-            "stream": stream,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-        }
-
-        if self.max_generation_token is not None:
-            payload["max_tokens"] = self.max_generation_token
-        if self.stop_sequence is not None:
-            payload["stop"] = self.stop_sequence
-        if self.logit_bias is not None:
-            payload["logit_bias"] = self.logit_bias
-        if self.user_identifier:
-            payload["user"] = self.user_identifier
-
-        if stream:
-            timeout = TIMEOUT_STREAMING
-        else:
-            timeout = TIMEOUT_ALL
-
-        # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
-        if shared.state.completion_url != COMPLETION_URL:
-            logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            try:
-                response = requests.post(
-                    shared.state.completion_url,
-                    headers=headers,
-                    json=payload,
-                    stream=stream,
-                    timeout=timeout,
-                )
-            except:
-                return None
-        return response
-
-    def _refresh_header(self):
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-    def _get_billing_data(self, billing_url):
-        with retrieve_proxy():
-            response = requests.get(
-                billing_url,
-                headers=self.headers,
-                timeout=TIMEOUT_ALL,
-            )
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            raise Exception(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
-
-    def _decode_chat_response(self, response):
-        error_msg = ""
-        for chunk in response.iter_lines():
-            if chunk:
-                chunk = chunk.decode()
-                chunk_length = len(chunk)
-                try:
-                    chunk = json.loads(chunk[6:])
-                except json.JSONDecodeError:
-                    print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
-                    error_msg += chunk
-                    continue
-                if chunk_length > 6 and "delta" in chunk["choices"][0]:
-                    if chunk["choices"][0]["finish_reason"] == "stop":
-                        break
-                    try:
-                        yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        # logging.error(f"Error: {e}")
-                        continue
-        if error_msg:
-            raise Exception(error_msg)
-
-    def set_key(self, new_access_key):
-        ret = super().set_key(new_access_key)
-        self._refresh_header()
-        return ret
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        from transformers import AutoTokenizer, AutoModel
-        import torch
-        global CHATGLM_TOKENIZER, CHATGLM_MODEL
-        if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
-            system_name = platform.system()
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"THUDM/{model_name}"
-            CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            quantified = False
-            if "int4" in model_name:
-                quantified = True
-            model = AutoModel.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            if torch.cuda.is_available():
-                # run on CUDA
-                logging.info("CUDA is available, using CUDA")
-                model = model.half().cuda()
-            # mps加速还存在一些问题，暂时不使用
-            elif system_name == "Darwin" and model_path is not None and not quantified:
-                logging.info("Running on macOS, using MPS")
-                # running on macOS and model already downloaded
-                model = model.half().to("mps")
-            else:
-                logging.info("GPU is not available, using CPU")
-                model = model.float()
-            model = model.eval()
-            CHATGLM_MODEL = model
-
-    def _get_glm_style_input(self):
-        history = [x["content"] for x in self.history]
-        query = history.pop()
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        assert (
-            len(history) % 2 == 0
-        ), f"History should be even length. current history is: {history}"
-        history = [[history[i], history[i + 1]]
-                   for i in range(0, len(history), 2)]
-        return history, query
-
-    def get_answer_at_once(self):
-        history, query = self._get_glm_style_input()
-        response, _ = CHATGLM_MODEL.chat(
-            CHATGLM_TOKENIZER, query, history=history)
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        history, query = self._get_glm_style_input()
-        for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
-        ):
-            yield response
-
-
-class LLaMA_Client(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        lora_path=None,
-        user_name=""
-    ) -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        from lmflow.datasets.dataset import Dataset
-        from lmflow.pipeline.auto_pipeline import AutoPipeline
-        from lmflow.models.auto_model import AutoModel
-        from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
-        self.max_generation_token = 1000
-        self.end_string = "\n\n"
-        # We don't need input data
-        data_args = DatasetArguments(dataset_path=None)
-        self.dataset = Dataset(data_args)
-        self.system_prompt = ""
-
-        global LLAMA_MODEL, LLAMA_INFERENCER
-        if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"decapoda-research/{model_name}"
-                # raise Exception(f"models目录下没有这个模型: {model_name}")
-            if lora_path is not None:
-                lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
-            pipeline_args = InferencerArguments(
-                local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
-            with open(pipeline_args.deepspeed, "r") as f:
-                ds_config = json.load(f)
-            LLAMA_MODEL = AutoModel.get_model(
-                model_args,
-                tune_strategy="none",
-                ds_config=ds_config,
-            )
-            LLAMA_INFERENCER = AutoPipeline.get_pipeline(
-                pipeline_name="inferencer",
-                model_args=model_args,
-                data_args=data_args,
-                pipeline_args=pipeline_args,
-            )
-
-    def _get_llama_style_input(self):
-        history = []
-        instruction = ""
-        if self.system_prompt:
-            instruction = (f"Instruction: {self.system_prompt}\n")
-        for x in self.history:
-            if x["role"] == "user":
-                history.append(f"{instruction}Input: {x['content']}")
-            else:
-                history.append(f"Output: {x['content']}")
-        context = "\n\n".join(history)
-        context += "\n\nOutput: "
-        return context
-
-    def get_answer_at_once(self):
-        context = self._get_llama_style_input()
-
-        input_dataset = self.dataset.from_dict(
-            {"type": "text_only", "instances": [{"text": context}]}
-        )
-
-        output_dataset = LLAMA_INFERENCER.inference(
-            model=LLAMA_MODEL,
-            dataset=input_dataset,
-            max_new_tokens=self.max_generation_token,
-            temperature=self.temperature,
-        )
-
-        response = output_dataset.to_dict()["instances"][0]["text"]
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        context = self._get_llama_style_input()
-        partial_text = ""
-        step = 1
-        for _ in range(0, self.max_generation_token, step):
-            input_dataset = self.dataset.from_dict(
-                {"type": "text_only", "instances": [
-                    {"text": context + partial_text}]}
-            )
-            output_dataset = LLAMA_INFERENCER.inference(
-                model=LLAMA_MODEL,
-                dataset=input_dataset,
-                max_new_tokens=step,
-                temperature=self.temperature,
-            )
-            response = output_dataset.to_dict()["instances"][0]["text"]
-            if response == "" or response == self.end_string:
-                break
-            partial_text += response
-            yield partial_text
-
-
-class XMChat(BaseLLMModel):
-    def __init__(self, api_key, user_name=""):
-        super().__init__(model_name="xmchat", user=user_name)
-        self.api_key = api_key
-        self.session_id = None
-        self.reset()
-        self.image_bytes = None
-        self.image_path = None
-        self.xm_history = []
-        self.url = "https://xmbot.net/web"
-        self.last_conv_id = None
-
-    def reset(self):
-        self.session_id = str(uuid.uuid4())
-        self.last_conv_id = None
-        return [], "已重置"
-
-    def image_to_base64(self, image_path):
-        # 打开并加载图片
-        img = Image.open(image_path)
-
-        # 获取图片的宽度和高度
-        width, height = img.size
-
-        # 计算压缩比例，以确保最长边小于4096像素
-        max_dimension = 2048
-        scale_ratio = min(max_dimension / width, max_dimension / height)
-
-        if scale_ratio < 1:
-            # 按压缩比例调整图片大小
-            new_width = int(width * scale_ratio)
-            new_height = int(height * scale_ratio)
-            img = img.resize((new_width, new_height), Image.ANTIALIAS)
-
-        # 将图片转换为jpg格式的二进制数据
-        buffer = BytesIO()
-        if img.mode == "RGBA":
-            img = img.convert("RGB")
-        img.save(buffer, format='JPEG')
-        binary_image = buffer.getvalue()
-
-        # 对二进制数据进行Base64编码
-        base64_image = base64.b64encode(binary_image).decode('utf-8')
-
-        return base64_image
-
-    def try_read_image(self, filepath):
-        def is_image_file(filepath):
-            # 判断文件是否为图片
-            valid_image_extensions = [
-                ".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
-            file_extension = os.path.splitext(filepath)[1].lower()
-            return file_extension in valid_image_extensions
-
-        if is_image_file(filepath):
-            logging.info(f"读取图片文件: {filepath}")
-            self.image_bytes = self.image_to_base64(filepath)
-            self.image_path = filepath
-        else:
-            self.image_bytes = None
-            self.image_path = None
-
-    def like(self):
-        if self.last_conv_id is None:
-            return "点赞失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "good"
-        }
-        requests.post(self.url, json=data)
-        return "👍点赞成功，感谢反馈～"
-
-    def dislike(self):
-        if self.last_conv_id is None:
-            return "点踩失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "bad"
-        }
-        requests.post(self.url, json=data)
-        return "👎点踩成功，感谢反馈～"
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = real_inputs
-        display_append = ""
-        limited_context = False
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        if files:
-            for file in files:
-                if file.name:
-                    logging.info(f"尝试读取图像: {file.name}")
-                    self.try_read_image(file.name)
-            if self.image_path is not None:
-                chatbot = chatbot + [((self.image_path,), None)]
-            if self.image_bytes is not None:
-                logging.info("使用图片作为输入")
-                # XMChat的一轮对话中实际上只能处理一张图片
-                self.reset()
-                conv_id = str(uuid.uuid4())
-                data = {
-                    "user_id": self.api_key,
-                    "session_id": self.session_id,
-                    "uuid": conv_id,
-                    "data_type": "imgbase64",
-                    "data": self.image_bytes
-                }
-                response = requests.post(self.url, json=data)
-                response = json.loads(response.text)
-                logging.info(f"图片回复: {response['data']}")
-        return None, chatbot, None
-
-    def get_answer_at_once(self):
-        question = self.history[-1]["content"]
-        conv_id = str(uuid.uuid4())
-        self.last_conv_id = conv_id
-        data = {
-            "user_id": self.api_key,
-            "session_id": self.session_id,
-            "uuid": conv_id,
-            "data_type": "text",
-            "data": question
-        }
-        response = requests.post(self.url, json=data)
-        try:
-            response = json.loads(response.text)
-            return response["data"], len(response["data"])
-        except Exception as e:
-            return response.text, len(response.text)
-
-
-def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-    user_name=""
-) -> BaseLLMModel:
-    msg = i18n("模型设置为了：") + f" {model_name}"
-    model_type = ModelType.get_type(model_name)
-    lora_selector_visibility = False
-    lora_choices = []
-    dont_change_lora_selector = False
-    if model_type != ModelType.OpenAI:
-        config.local_embedding = True
-    # del current_model.model
-    model = None
-    try:
-        if model_type == ModelType.OpenAI:
-            logging.info(f"正在加载OpenAI模型: {model_name}")
-            model = OpenAIClient(
-                model_name=model_name,
-                api_key=access_key,
-                system_prompt=system_prompt,
-                temperature=temperature,
-                top_p=top_p,
-                user_name=user_name,
-            )
-        elif model_type == ModelType.ChatGLM:
-            logging.info(f"正在加载ChatGLM模型: {model_name}")
-            model = ChatGLM_Client(model_name, user_name=user_name)
-        elif model_type == ModelType.LLaMA and lora_model_path == "":
-            msg = f"现在请为 {model_name} 选择LoRA模型"
-            logging.info(msg)
-            lora_selector_visibility = True
-            if os.path.isdir("lora"):
-                lora_choices = get_file_names(
-                    "lora", plain=True, filetypes=[""])
-            lora_choices = ["No LoRA"] + lora_choices
-        elif model_type == ModelType.LLaMA and lora_model_path != "":
-            logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
-            dont_change_lora_selector = True
-            if lora_model_path == "No LoRA":
-                lora_model_path = None
-                msg += " + No LoRA"
-            else:
-                msg += f" + {lora_model_path}"
-            model = LLaMA_Client(
-                model_name, lora_model_path, user_name=user_name)
-        elif model_type == ModelType.XMChat:
-            if os.environ.get("XMCHAT_API_KEY") != "":
-                access_key = os.environ.get("XMCHAT_API_KEY")
-            model = XMChat(api_key=access_key, user_name=user_name)
-        elif model_type == ModelType.StableLM:
-            from .StableLM import StableLM_Client
-            model = StableLM_Client(model_name, user_name=user_name)
-        elif model_type == ModelType.MOSS:
-            from .MOSS import MOSS_Client
-            model = MOSS_Client(model_name, user_name=user_name)
-        elif model_type == ModelType.YuanAI:
-            from .inspurai import Yuan_Client
-            model = Yuan_Client(model_name, api_key=access_key, user_name=user_name, system_prompt=system_prompt)
-        elif model_type == ModelType.Unknown:
-            raise ValueError(f"未知模型: {model_name}")
-        logging.info(msg)
-        chatbot = gr.Chatbot.update(label=model_name)
-    except Exception as e:
-        logging.error(e)
-        msg = f"{STANDARD_ERROR_MSG}: {e}"
-    if dont_change_lora_selector:
-        return model, msg, chatbot
-    else:
-        return model, msg, chatbot, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
-    with open("config.json", "r") as f:
-        openai_api_key = cjson.load(f)["openai_api_key"]
-    # set logging level to debug
-    logging.basicConfig(level=logging.DEBUG)
-    # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
-    client = get_model(model_name="chatglm-6b-int4")
-    chatbot = []
-    stream = False
-    # 测试账单功能
-    logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
-    logging.info(client.billing_info())
-    # 测试问答
-    logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
-    question = "巴黎是中国的首都吗？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试问答后history : {client.history}")
-    # 测试记忆力
-    logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
-    question = "我刚刚问了你什么问题？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试记忆力后history : {client.history}")
-    # 测试重试功能
-    logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
-    for i in client.retry(chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"重试后history : {client.history}")
-    # # 测试总结功能
-    # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
-    # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
-    # print(chatbot, msg)
-    # print(f"总结后history: {client.history}")
diff --git a/spaces/xu1998hz/sescore_german_mt/sescore_german_mt.py b/spaces/xu1998hz/sescore_german_mt/sescore_german_mt.py
deleted file mode 100644
index fcb340a62047b0b97be21697536e7365015d8eb2..0000000000000000000000000000000000000000
--- a/spaces/xu1998hz/sescore_german_mt/sescore_german_mt.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""SEScore: a text generation evaluation metric """
-
-import evaluate
-import datasets
-
-import comet
-from typing import Dict
-import torch
-from comet.encoders.base import Encoder
-from comet.encoders.bert import BERTEncoder
-from transformers import AutoModel, AutoTokenizer
-
-class robertaEncoder(BERTEncoder):
-    def __init__(self, pretrained_model: str) -> None:
-        super(Encoder, self).__init__()
-        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
-        self.model = AutoModel.from_pretrained(
-            pretrained_model, add_pooling_layer=False
-        )
-        self.model.encoder.output_hidden_states = True
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model: str) -> Encoder:
-        return robertaEncoder(pretrained_model)
-
-    def forward(
-        self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs
-    ) -> Dict[str, torch.Tensor]:
-        last_hidden_states, _, all_layers = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-            return_dict=False,
-        )
-        return {
-            "sentemb": last_hidden_states[:, 0, :],
-            "wordemb": last_hidden_states,
-            "all_layers": all_layers,
-            "attention_mask": attention_mask,
-        }
-
-
-# TODO: Add BibTeX citation
-_CITATION = """\
-@inproceedings{xu-etal-2022-not,
-  title={Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis},
-  author={Xu, Wenda and Tuan, Yi-lin and Lu, Yujie and Saxon, Michael and Li, Lei and Wang, William Yang},
-  booktitle ={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
-  month={dec},
-  year={2022},
-  url={https://arxiv.org/abs/2210.05035}
-}
-"""
-
-_DESCRIPTION = """\
-SEScore is an evaluation metric that trys to compute an overall score to measure text generation quality.
-"""
-
-_KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references
-Args:
-    predictions: list of candidate outputs
-    references: list of references 
-Returns:
-    {"mean_score": mean_score, "scores": scores}
-    
-Examples:
-    >>> import evaluate
-    >>> sescore = evaluate.load("xu1998hz/sescore")
-    >>> score = sescore.compute(
-            references=['sescore is a simple but effective next-generation text evaluation metric'],
-            predictions=['sescore is simple effective text evaluation metric for next generation']
-        )
-"""
-
-# TODO: Define external resources urls if needed
-BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class SEScore(evaluate.Metric):
-    """SEScore"""
-
-    def _info(self):
-        # TODO: Specifies the evaluate.EvaluationModuleInfo object
-        return evaluate.MetricInfo(
-            # This is the description that will appear on the modules page.
-            module_type="metric",
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            # This defines the format of each prediction and reference
-            features=datasets.Features({
-                'predictions': datasets.Value("string", id="sequence"),
-                'references': datasets.Value("string", id="sequence"),
-            }),
-            # Homepage of the module for documentation
-            homepage="http://module.homepage",
-            # Additional links to the codebase or references
-            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
-        )
-
-    def _download_and_prepare(self, dl_manager):
-        """download SEScore checkpoints to compute the scores"""
-        # Download SEScore checkpoint
-        from comet import load_from_checkpoint
-        import os
-        from huggingface_hub import snapshot_download
-        # initialize roberta into str2encoder
-        comet.encoders.str2encoder['RoBERTa'] = robertaEncoder
-        destination = snapshot_download(repo_id="xu1998hz/sescore_german_mt", revision="main")
-        self.scorer = load_from_checkpoint(f'{destination}/checkpoint/sescore_german.ckpt')
-
-    def _compute(self, predictions, references, gpus=None, progress_bar=False):
-        if gpus is None:
-            gpus = 1 if torch.cuda.is_available() else 0
-
-        data = {"src": references, "mt": predictions}
-        data = [dict(zip(data, t)) for t in zip(*data.values())]
-        scores, mean_score = self.scorer.predict(data, gpus=gpus, progress_bar=progress_bar)
-        return {"mean_score": mean_score, "scores": scores}
diff --git a/spaces/xxie92/antibody_visulization/diffab/modules/common/so3.py b/spaces/xxie92/antibody_visulization/diffab/modules/common/so3.py
deleted file mode 100644
index 794b65c7bff9289e0cc5b6cf2c1a37f9db2cb6f5..0000000000000000000000000000000000000000
--- a/spaces/xxie92/antibody_visulization/diffab/modules/common/so3.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .geometry import quaternion_to_rotation_matrix
-
-
-def log_rotation(R):
-    trace = R[..., range(3), range(3)].sum(-1)
-    if torch.is_grad_enabled():
-        # The derivative of acos at -1.0 is -inf, so to stablize the gradient, we use -0.9999
-        min_cos = -0.999
-    else:
-        min_cos = -1.0
-    cos_theta = ( (trace-1) / 2 ).clamp_min(min=min_cos)
-    sin_theta = torch.sqrt(1 - cos_theta**2)
-    theta = torch.acos(cos_theta)
-    coef = ((theta+1e-8)/(2*sin_theta+2e-8))[..., None, None]
-    logR = coef * (R - R.transpose(-1, -2))
-    return logR
-
-
-def skewsym_to_so3vec(S):
-    x = S[..., 1, 2]
-    y = S[..., 2, 0]
-    z = S[..., 0, 1]
-    w = torch.stack([x,y,z], dim=-1)
-    return w
-
-
-def so3vec_to_skewsym(w):
-    x, y, z = torch.unbind(w, dim=-1)
-    o = torch.zeros_like(x)
-    S = torch.stack([
-        o, z, -y,
-        -z, o, x,
-        y, -x, o,
-    ], dim=-1).reshape(w.shape[:-1] + (3, 3))
-    return S
-
-
-def exp_skewsym(S):
-    x = torch.linalg.norm(skewsym_to_so3vec(S), dim=-1)
-    I = torch.eye(3).to(S).view([1 for _ in range(S.dim()-2)] + [3, 3])
-    
-    sinx, cosx = torch.sin(x), torch.cos(x)
-    b = (sinx + 1e-8) / (x + 1e-8)
-    c = (1-cosx + 1e-8) / (x**2 + 2e-8)  # lim_{x->0} (1-cosx)/(x^2) = 0.5
-
-    S2 = S @ S
-    return I + b[..., None, None]*S + c[..., None, None]*S2
-
-
-def so3vec_to_rotation(w):
-    return exp_skewsym(so3vec_to_skewsym(w))
-
-
-def rotation_to_so3vec(R):
-    logR = log_rotation(R)
-    w = skewsym_to_so3vec(logR)
-    return w
-
-
-def random_uniform_so3(size, device='cpu'):
-    q = F.normalize(torch.randn(list(size)+[4,], device=device), dim=-1)    # (..., 4)
-    return rotation_to_so3vec(quaternion_to_rotation_matrix(q))
-
-
-class ApproxAngularDistribution(nn.Module):
-
-    def __init__(self, stddevs, std_threshold=0.1, num_bins=8192, num_iters=1024):
-        super().__init__()
-        self.std_threshold = std_threshold
-        self.num_bins = num_bins
-        self.num_iters = num_iters
-        self.register_buffer('stddevs', torch.FloatTensor(stddevs))
-        self.register_buffer('approx_flag', self.stddevs <= std_threshold)
-        self._precompute_histograms()
-
-    @staticmethod
-    def _pdf(x, e, L):
-        """
-        Args:
-            x:  (N, )
-            e:  Float
-            L:  Integer
-        """
-        x = x[:, None]  # (N, *)
-        c = ((1 - torch.cos(x)) / math.pi)  # (N, *)
-        l = torch.arange(0, L)[None, :]  # (*, L)
-        a = (2*l+1) * torch.exp(-l*(l+1)*(e**2))  # (*, L)
-        b = (torch.sin( (l+0.5)* x ) + 1e-6) / (torch.sin( x / 2 ) + 1e-6) # (N, L)
-        
-        f = (c * a * b).sum(dim=1)
-        return f
-
-    def _precompute_histograms(self):
-        X, Y = [], []
-        for std in self.stddevs:
-            std = std.item()
-            x = torch.linspace(0, math.pi, self.num_bins)   # (n_bins,)
-            y = self._pdf(x, std, self.num_iters)    # (n_bins,)
-            y = torch.nan_to_num(y).clamp_min(0)
-            X.append(x)
-            Y.append(y)
-        self.register_buffer('X', torch.stack(X, dim=0))  # (n_stddevs, n_bins)
-        self.register_buffer('Y', torch.stack(Y, dim=0))  # (n_stddevs, n_bins)
-
-    def sample(self, std_idx):
-        """
-        Args:
-            std_idx:  Indices of standard deviation.
-        Returns:
-            samples:  Angular samples [0, PI), same size as std.
-        """
-        size = std_idx.size()
-        std_idx = std_idx.flatten() # (N,)
-        
-        # Samples from histogram
-        prob = self.Y[std_idx]  # (N, n_bins)
-        bin_idx = torch.multinomial(prob[:, :-1], num_samples=1).squeeze(-1)    # (N,)
-        bin_start = self.X[std_idx, bin_idx]    # (N,)
-        bin_width = self.X[std_idx, bin_idx+1] - self.X[std_idx, bin_idx]
-        samples_hist = bin_start + torch.rand_like(bin_start) * bin_width    # (N,)
-
-        # Samples from Gaussian approximation
-        mean_gaussian = self.stddevs[std_idx]*2
-        std_gaussian = self.stddevs[std_idx]
-        samples_gaussian = mean_gaussian + torch.randn_like(mean_gaussian) * std_gaussian
-        samples_gaussian = samples_gaussian.abs() % math.pi
-
-        # Choose from histogram or Gaussian
-        gaussian_flag = self.approx_flag[std_idx]
-        samples = torch.where(gaussian_flag, samples_gaussian, samples_hist)
-
-        return samples.reshape(size)
-
-
-def random_normal_so3(std_idx, angular_distrib, device='cpu'):
-    size = std_idx.size()
-    u = F.normalize(torch.randn(list(size)+[3,], device=device), dim=-1)
-    theta = angular_distrib.sample(std_idx)
-    w = u * theta[..., None]
-    return w
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bert/modeling_flax_bert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bert/modeling_flax_bert.py
deleted file mode 100644
index 99dfa2a0e2f9ab53c15e4442d99e089ba3598316..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bert/modeling_flax_bert.py
+++ /dev/null
@@ -1,1712 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, Optional, Tuple
-
-import flax
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
-from flax.linen import combine_masks, make_causal_mask
-from flax.linen import partitioning as nn_partitioning
-from flax.linen.attention import dot_product_attention_weights
-from flax.traverse_util import flatten_dict, unflatten_dict
-from jax import lax
-
-from ...modeling_flax_outputs import (
-    FlaxBaseModelOutputWithPastAndCrossAttentions,
-    FlaxBaseModelOutputWithPooling,
-    FlaxBaseModelOutputWithPoolingAndCrossAttentions,
-    FlaxCausalLMOutputWithCrossAttentions,
-    FlaxMaskedLMOutput,
-    FlaxMultipleChoiceModelOutput,
-    FlaxNextSentencePredictorOutput,
-    FlaxQuestionAnsweringModelOutput,
-    FlaxSequenceClassifierOutput,
-    FlaxTokenClassifierOutput,
-)
-from ...modeling_flax_utils import (
-    ACT2FN,
-    FlaxPreTrainedModel,
-    append_call_sample_docstring,
-    append_replace_return_docstrings,
-    overwrite_call_docstring,
-)
-from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
-from .configuration_bert import BertConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "bert-base-uncased"
-_CONFIG_FOR_DOC = "BertConfig"
-
-remat = nn_partitioning.remat
-
-
-@flax.struct.dataclass
-class FlaxBertForPreTrainingOutput(ModelOutput):
-    """
-    Output type of [`BertForPreTraining`].
-
-    Args:
-        prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        seq_relationship_logits (`jnp.ndarray` of shape `(batch_size, 2)`):
-            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
-            before SoftMax).
-        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
-            `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    prediction_logits: jnp.ndarray = None
-    seq_relationship_logits: jnp.ndarray = None
-    hidden_states: Optional[Tuple[jnp.ndarray]] = None
-    attentions: Optional[Tuple[jnp.ndarray]] = None
-
-
-BERT_START_DOCSTRING = r"""
-
-    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
-
-    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
-    general usage and behavior.
-
-    Finally, this model supports inherent JAX features such as:
-
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        config ([`BertConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
-        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
-            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
-            `jax.numpy.bfloat16` (on TPUs).
-
-            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
-            specified all the computation will be performed with the given `dtype`.
-
-            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
-            parameters.**
-
-            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
-            [`~FlaxPreTrainedModel.to_bf16`].
-        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
-            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
-            `jax.numpy.bfloat16` (on TPUs).
-
-            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
-            specified all the computation will be performed with the given `dtype`.
-
-            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
-            parameters.**
-
-            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
-            [`~FlaxPreTrainedModel.to_bf16`].
-
-"""
-
-BERT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`numpy.ndarray` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-        head_mask (`numpy.ndarray` of shape `({0})`, `optional):
-            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-
-"""
-
-
-class FlaxBertEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.word_embeddings = nn.Embed(
-            self.config.vocab_size,
-            self.config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
-            dtype=self.dtype,
-        )
-        self.position_embeddings = nn.Embed(
-            self.config.max_position_embeddings,
-            self.config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
-            dtype=self.dtype,
-        )
-        self.token_type_embeddings = nn.Embed(
-            self.config.type_vocab_size,
-            self.config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
-            dtype=self.dtype,
-        )
-        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
-        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
-
-    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
-        # Embed
-        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
-        position_embeds = self.position_embeddings(position_ids.astype("i4"))
-        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))
-
-        # Sum all embeddings
-        hidden_states = inputs_embeds + token_type_embeddings + position_embeds
-
-        # Layer Norm
-        hidden_states = self.LayerNorm(hidden_states)
-        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
-        return hidden_states
-
-
-class FlaxBertSelfAttention(nn.Module):
-    config: BertConfig
-    causal: bool = False
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
-        if self.config.hidden_size % self.config.num_attention_heads != 0:
-            raise ValueError(
-                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
-                "                   : {self.config.num_attention_heads}"
-            )
-
-        self.query = nn.Dense(
-            self.config.hidden_size,
-            dtype=self.dtype,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-        )
-        self.key = nn.Dense(
-            self.config.hidden_size,
-            dtype=self.dtype,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-        )
-        self.value = nn.Dense(
-            self.config.hidden_size,
-            dtype=self.dtype,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-        )
-
-        if self.causal:
-            self.causal_mask = make_causal_mask(
-                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
-            )
-
-    def _split_heads(self, hidden_states):
-        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))
-
-    def _merge_heads(self, hidden_states):
-        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))
-
-    @nn.compact
-    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
-    def _concatenate_to_cache(self, key, value, query, attention_mask):
-        """
-        This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
-        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
-        """
-        # detect if we're initializing by absence of existing cache data.
-        is_initialized = self.has_variable("cache", "cached_key")
-        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
-        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
-        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
-
-        if is_initialized:
-            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
-            # update key, value caches with our new 1d spatial slices
-            cur_index = cache_index.value
-            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
-            key = lax.dynamic_update_slice(cached_key.value, key, indices)
-            value = lax.dynamic_update_slice(cached_value.value, value, indices)
-            cached_key.value = key
-            cached_value.value = value
-            num_updated_cache_vectors = query.shape[1]
-            cache_index.value = cache_index.value + num_updated_cache_vectors
-            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
-            pad_mask = jnp.broadcast_to(
-                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
-                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
-            )
-            attention_mask = combine_masks(pad_mask, attention_mask)
-        return key, value, attention_mask
-
-    def __call__(
-        self,
-        hidden_states,
-        attention_mask,
-        layer_head_mask,
-        key_value_states: Optional[jnp.array] = None,
-        init_cache: bool = False,
-        deterministic=True,
-        output_attentions: bool = False,
-    ):
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-        batch_size = hidden_states.shape[0]
-
-        # get query proj
-        query_states = self.query(hidden_states)
-        # get key, value proj
-        if is_cross_attention:
-            # cross_attentions
-            key_states = self.key(key_value_states)
-            value_states = self.value(key_value_states)
-        else:
-            # self_attention
-            key_states = self.key(hidden_states)
-            value_states = self.value(hidden_states)
-
-        query_states = self._split_heads(query_states)
-        key_states = self._split_heads(key_states)
-        value_states = self._split_heads(value_states)
-
-        # handle cache prepare causal attention mask
-        if self.causal:
-            query_length, key_length = query_states.shape[1], key_states.shape[1]
-            if self.has_variable("cache", "cached_key"):
-                mask_shift = self.variables["cache"]["cache_index"]
-                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
-                causal_mask = lax.dynamic_slice(
-                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
-                )
-            else:
-                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
-            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
-
-        # combine masks if needed
-        if attention_mask is not None and self.causal:
-            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
-            attention_mask = combine_masks(attention_mask, causal_mask)
-        elif self.causal:
-            attention_mask = causal_mask
-        elif attention_mask is not None:
-            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
-
-        # During fast autoregressive decoding, we feed one position at a time,
-        # and cache the keys and values step by step.
-        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
-            key_states, value_states, attention_mask = self._concatenate_to_cache(
-                key_states, value_states, query_states, attention_mask
-            )
-
-        # Convert the boolean attention mask to an attention bias.
-        if attention_mask is not None:
-            # attention mask in the form of attention bias
-            attention_bias = lax.select(
-                attention_mask > 0,
-                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
-                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
-            )
-        else:
-            attention_bias = None
-
-        dropout_rng = None
-        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
-            dropout_rng = self.make_rng("dropout")
-
-        attn_weights = dot_product_attention_weights(
-            query_states,
-            key_states,
-            bias=attention_bias,
-            dropout_rng=dropout_rng,
-            dropout_rate=self.config.attention_probs_dropout_prob,
-            broadcast_dropout=True,
-            deterministic=deterministic,
-            dtype=self.dtype,
-            precision=None,
-        )
-
-        # Mask heads if we want to
-        if layer_head_mask is not None:
-            attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)
-
-        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
-        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
-
-        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
-        return outputs
-
-
-class FlaxBertSelfOutput(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.dense = nn.Dense(
-            self.config.hidden_size,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-            dtype=self.dtype,
-        )
-        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
-        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
-
-    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class FlaxBertAttention(nn.Module):
-    config: BertConfig
-    causal: bool = False
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.self = FlaxBertSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
-        self.output = FlaxBertSelfOutput(self.config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        hidden_states,
-        attention_mask,
-        layer_head_mask,
-        key_value_states=None,
-        init_cache=False,
-        deterministic=True,
-        output_attentions: bool = False,
-    ):
-        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
-        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
-        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
-        attn_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            layer_head_mask=layer_head_mask,
-            key_value_states=key_value_states,
-            init_cache=init_cache,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-        )
-        attn_output = attn_outputs[0]
-        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_outputs[1],)
-
-        return outputs
-
-
-class FlaxBertIntermediate(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.dense = nn.Dense(
-            self.config.intermediate_size,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-            dtype=self.dtype,
-        )
-        self.activation = ACT2FN[self.config.hidden_act]
-
-    def __call__(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.activation(hidden_states)
-        return hidden_states
-
-
-class FlaxBertOutput(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.dense = nn.Dense(
-            self.config.hidden_size,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-            dtype=self.dtype,
-        )
-        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
-        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
-
-    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
-        hidden_states = self.LayerNorm(hidden_states + attention_output)
-        return hidden_states
-
-
-class FlaxBertLayer(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.attention = FlaxBertAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
-        self.intermediate = FlaxBertIntermediate(self.config, dtype=self.dtype)
-        self.output = FlaxBertOutput(self.config, dtype=self.dtype)
-        if self.config.add_cross_attention:
-            self.crossattention = FlaxBertAttention(self.config, causal=False, dtype=self.dtype)
-
-    def __call__(
-        self,
-        hidden_states,
-        attention_mask,
-        layer_head_mask,
-        encoder_hidden_states: Optional[jnp.ndarray] = None,
-        encoder_attention_mask: Optional[jnp.ndarray] = None,
-        init_cache: bool = False,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-    ):
-        # Self Attention
-        attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            layer_head_mask=layer_head_mask,
-            init_cache=init_cache,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-        )
-        attention_output = attention_outputs[0]
-
-        # Cross-Attention Block
-        if encoder_hidden_states is not None:
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask=encoder_attention_mask,
-                layer_head_mask=layer_head_mask,
-                key_value_states=encoder_hidden_states,
-                deterministic=deterministic,
-                output_attentions=output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-
-        hidden_states = self.intermediate(attention_output)
-        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attention_outputs[1],)
-            if encoder_hidden_states is not None:
-                outputs += (cross_attention_outputs[1],)
-        return outputs
-
-
-class FlaxBertLayerCollection(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        if self.gradient_checkpointing:
-            FlaxBertCheckpointLayer = remat(FlaxBertLayer, static_argnums=(5, 6, 7))
-            self.layers = [
-                FlaxBertCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
-                for i in range(self.config.num_hidden_layers)
-            ]
-        else:
-            self.layers = [
-                FlaxBertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
-            ]
-
-    def __call__(
-        self,
-        hidden_states,
-        attention_mask,
-        head_mask,
-        encoder_hidden_states: Optional[jnp.ndarray] = None,
-        encoder_attention_mask: Optional[jnp.ndarray] = None,
-        init_cache: bool = False,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        all_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
-
-        # Check if head_mask has a correct number of layers specified if desired
-        if head_mask is not None:
-            if head_mask.shape[0] != (len(self.layers)):
-                raise ValueError(
-                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for                  "
-                    f"       {head_mask.shape[0]}."
-                )
-
-        for i, layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = layer(
-                hidden_states,
-                attention_mask,
-                head_mask[i] if head_mask is not None else None,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                init_cache,
-                deterministic,
-                output_attentions,
-            )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions += (layer_outputs[1],)
-
-                if encoder_hidden_states is not None:
-                    all_cross_attentions += (layer_outputs[2],)
-
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)
-
-        if not return_dict:
-            return tuple(v for v in outputs if v is not None)
-
-        return FlaxBaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-            attentions=all_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class FlaxBertEncoder(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.layer = FlaxBertLayerCollection(
-            self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-
-    def __call__(
-        self,
-        hidden_states,
-        attention_mask,
-        head_mask,
-        encoder_hidden_states: Optional[jnp.ndarray] = None,
-        encoder_attention_mask: Optional[jnp.ndarray] = None,
-        init_cache: bool = False,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        return self.layer(
-            hidden_states,
-            attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            init_cache=init_cache,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-
-class FlaxBertPooler(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        self.dense = nn.Dense(
-            self.config.hidden_size,
-            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
-            dtype=self.dtype,
-        )
-
-    def __call__(self, hidden_states):
-        cls_hidden_state = hidden_states[:, 0]
-        cls_hidden_state = self.dense(cls_hidden_state)
-        return nn.tanh(cls_hidden_state)
-
-
-class FlaxBertPredictionHeadTransform(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
-        self.activation = ACT2FN[self.config.hidden_act]
-        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
-
-    def __call__(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.activation(hidden_states)
-        return self.LayerNorm(hidden_states)
-
-
-class FlaxBertLMPredictionHead(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
-
-    def setup(self):
-        self.transform = FlaxBertPredictionHeadTransform(self.config, dtype=self.dtype)
-        self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
-        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))
-
-    def __call__(self, hidden_states, shared_embedding=None):
-        hidden_states = self.transform(hidden_states)
-
-        if shared_embedding is not None:
-            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
-        else:
-            hidden_states = self.decoder(hidden_states)
-
-        bias = jnp.asarray(self.bias, self.dtype)
-        hidden_states += bias
-        return hidden_states
-
-
-class FlaxBertOnlyMLMHead(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
-
-    def __call__(self, hidden_states, shared_embedding=None):
-        hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding)
-        return hidden_states
-
-
-class FlaxBertOnlyNSPHead(nn.Module):
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.seq_relationship = nn.Dense(2, dtype=self.dtype)
-
-    def __call__(self, pooled_output):
-        return self.seq_relationship(pooled_output)
-
-
-class FlaxBertPreTrainingHeads(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
-        self.seq_relationship = nn.Dense(2, dtype=self.dtype)
-
-    def __call__(self, hidden_states, pooled_output, shared_embedding=None):
-        prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-
-class FlaxBertPreTrainedModel(FlaxPreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    module_class: nn.Module = None
-
-    def __init__(
-        self,
-        config: BertConfig,
-        input_shape: Tuple = (1, 1),
-        seed: int = 0,
-        dtype: jnp.dtype = jnp.float32,
-        _do_init: bool = True,
-        gradient_checkpointing: bool = False,
-        **kwargs,
-    ):
-        module = self.module_class(
-            config=config,
-            dtype=dtype,
-            gradient_checkpointing=gradient_checkpointing,
-            **kwargs,
-        )
-        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
-
-    def enable_gradient_checkpointing(self):
-        self._module = self.module_class(
-            config=self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=True,
-        )
-
-    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
-        # init input tensors
-        input_ids = jnp.zeros(input_shape, dtype="i4")
-        token_type_ids = jnp.zeros_like(input_ids)
-        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
-        attention_mask = jnp.ones_like(input_ids)
-        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        if self.config.add_cross_attention:
-            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
-            encoder_attention_mask = attention_mask
-            module_init_outputs = self.module.init(
-                rngs,
-                input_ids,
-                attention_mask,
-                token_type_ids,
-                position_ids,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                return_dict=False,
-            )
-        else:
-            module_init_outputs = self.module.init(
-                rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False
-            )
-
-        random_params = module_init_outputs["params"]
-
-        if params is not None:
-            random_params = flatten_dict(unfreeze(random_params))
-            params = flatten_dict(unfreeze(params))
-            for missing_key in self._missing_keys:
-                params[missing_key] = random_params[missing_key]
-            self._missing_keys = set()
-            return freeze(unflatten_dict(params))
-        else:
-            return random_params
-
-    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
-    def init_cache(self, batch_size, max_length):
-        r"""
-        Args:
-            batch_size (`int`):
-                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
-            max_length (`int`):
-                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
-                cache.
-        """
-        # init input variables to retrieve cache
-        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
-        attention_mask = jnp.ones_like(input_ids, dtype="i4")
-        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
-
-        init_variables = self.module.init(
-            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
-        )
-        return unfreeze(init_variables["cache"])
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    def __call__(
-        self,
-        input_ids,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        params: dict = None,
-        dropout_rng: jax.random.PRNGKey = None,
-        train: bool = False,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        past_key_values: dict = None,
-    ):
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
-        # init input tensors if not passed
-        if token_type_ids is None:
-            token_type_ids = jnp.zeros_like(input_ids)
-
-        if position_ids is None:
-            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
-
-        if attention_mask is None:
-            attention_mask = jnp.ones_like(input_ids)
-
-        if head_mask is None:
-            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
-
-        # Handle any PRNG if needed
-        rngs = {}
-        if dropout_rng is not None:
-            rngs["dropout"] = dropout_rng
-
-        inputs = {"params": params or self.params}
-
-        if self.config.add_cross_attention:
-            # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
-            # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
-            # changed by FlaxBertAttention module
-            if past_key_values:
-                inputs["cache"] = past_key_values
-                mutable = ["cache"]
-            else:
-                mutable = False
-
-            outputs = self.module.apply(
-                inputs,
-                jnp.array(input_ids, dtype="i4"),
-                jnp.array(attention_mask, dtype="i4"),
-                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
-                position_ids=jnp.array(position_ids, dtype="i4"),
-                head_mask=jnp.array(head_mask, dtype="i4"),
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_attention_mask=encoder_attention_mask,
-                deterministic=not train,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                rngs=rngs,
-                mutable=mutable,
-            )
-
-            # add updated cache to model output
-            if past_key_values is not None and return_dict:
-                outputs, past_key_values = outputs
-                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
-                return outputs
-            elif past_key_values is not None and not return_dict:
-                outputs, past_key_values = outputs
-                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
-
-        else:
-            outputs = self.module.apply(
-                inputs,
-                jnp.array(input_ids, dtype="i4"),
-                jnp.array(attention_mask, dtype="i4"),
-                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
-                position_ids=jnp.array(position_ids, dtype="i4"),
-                head_mask=jnp.array(head_mask, dtype="i4"),
-                deterministic=not train,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                rngs=rngs,
-            )
-
-        return outputs
-
-
-class FlaxBertModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-    add_pooling_layer: bool = True
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.embeddings = FlaxBertEmbeddings(self.config, dtype=self.dtype)
-        self.encoder = FlaxBertEncoder(
-            self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.pooler = FlaxBertPooler(self.config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids: Optional[jnp.ndarray] = None,
-        position_ids: Optional[jnp.ndarray] = None,
-        head_mask: Optional[jnp.ndarray] = None,
-        encoder_hidden_states: Optional[jnp.ndarray] = None,
-        encoder_attention_mask: Optional[jnp.ndarray] = None,
-        init_cache: bool = False,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # make sure `token_type_ids` is correctly initialized when not passed
-        if token_type_ids is None:
-            token_type_ids = jnp.zeros_like(input_ids)
-
-        # make sure `position_ids` is correctly initialized when not passed
-        if position_ids is None:
-            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
-
-        hidden_states = self.embeddings(
-            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
-        )
-        outputs = self.encoder(
-            hidden_states,
-            attention_mask,
-            head_mask=head_mask,
-            deterministic=deterministic,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            init_cache=init_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = outputs[0]
-        pooled = self.pooler(hidden_states) if self.add_pooling_layer else None
-
-        if not return_dict:
-            # if pooled is None, don't return it
-            if pooled is None:
-                return (hidden_states,) + outputs[1:]
-            return (hidden_states, pooled) + outputs[1:]
-
-        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            pooler_output=pooled,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
-    BERT_START_DOCSTRING,
-)
-class FlaxBertModel(FlaxBertPreTrainedModel):
-    module_class = FlaxBertModule
-
-
-append_call_sample_docstring(FlaxBertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)
-
-
-class FlaxBertForPreTrainingModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.cls = FlaxBertPreTrainingHeads(config=self.config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if self.config.tie_word_embeddings:
-            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
-        else:
-            shared_embedding = None
-
-        hidden_states = outputs[0]
-        pooled_output = outputs[1]
-
-        prediction_scores, seq_relationship_score = self.cls(
-            hidden_states, pooled_output, shared_embedding=shared_embedding
-        )
-
-        if not return_dict:
-            return (prediction_scores, seq_relationship_score) + outputs[2:]
-
-        return FlaxBertForPreTrainingOutput(
-            prediction_logits=prediction_scores,
-            seq_relationship_logits=seq_relationship_score,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
-    sentence prediction (classification)` head.
-    """,
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForPreTraining(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForPreTrainingModule
-
-
-FLAX_BERT_FOR_PRETRAINING_DOCSTRING = """
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, FlaxBertForPreTraining
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    >>> model = FlaxBertForPreTraining.from_pretrained("bert-base-uncased")
-
-    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
-    >>> outputs = model(**inputs)
-
-    >>> prediction_logits = outputs.prediction_logits
-    >>> seq_relationship_logits = outputs.seq_relationship_logits
-    ```
-"""
-
-overwrite_call_docstring(
-    FlaxBertForPreTraining,
-    BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BERT_FOR_PRETRAINING_DOCSTRING,
-)
-append_replace_return_docstrings(
-    FlaxBertForPreTraining, output_type=FlaxBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
-)
-
-
-class FlaxBertForMaskedLMModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            add_pooling_layer=False,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.cls = FlaxBertOnlyMLMHead(config=self.config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        if self.config.tie_word_embeddings:
-            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
-        else:
-            shared_embedding = None
-
-        # Compute the prediction scores
-        logits = self.cls(hidden_states, shared_embedding=shared_embedding)
-
-        if not return_dict:
-            return (logits,) + outputs[1:]
-
-        return FlaxMaskedLMOutput(
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings("""Bert Model with a `language modeling` head on top.""", BERT_START_DOCSTRING)
-class FlaxBertForMaskedLM(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForMaskedLMModule
-
-
-append_call_sample_docstring(FlaxBertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)
-
-
-class FlaxBertForNextSentencePredictionModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.cls = FlaxBertOnlyNSPHead(dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-        seq_relationship_scores = self.cls(pooled_output)
-
-        if not return_dict:
-            return (seq_relationship_scores,) + outputs[2:]
-
-        return FlaxNextSentencePredictorOutput(
-            logits=seq_relationship_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """Bert Model with a `next sentence prediction (classification)` head on top.""",
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForNextSentencePrediction(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForNextSentencePredictionModule
-
-
-FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING = """
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, FlaxBertForNextSentencePrediction
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    >>> model = FlaxBertForNextSentencePrediction.from_pretrained("bert-base-uncased")
-
-    >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-    >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-    >>> encoding = tokenizer(prompt, next_sentence, return_tensors="jax")
-
-    >>> outputs = model(**encoding)
-    >>> logits = outputs.logits
-    >>> assert logits[0, 0] < logits[0, 1]  # next sentence was random
-    ```
-"""
-
-
-overwrite_call_docstring(
-    FlaxBertForNextSentencePrediction,
-    BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING,
-)
-append_replace_return_docstrings(
-    FlaxBertForNextSentencePrediction, output_type=FlaxNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC
-)
-
-
-class FlaxBertForSequenceClassificationModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        classifier_dropout = (
-            self.config.classifier_dropout
-            if self.config.classifier_dropout is not None
-            else self.config.hidden_dropout_prob
-        )
-        self.dropout = nn.Dropout(rate=classifier_dropout)
-        self.classifier = nn.Dense(
-            self.config.num_labels,
-            dtype=self.dtype,
-        )
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
-        logits = self.classifier(pooled_output)
-
-        if not return_dict:
-            return (logits,) + outputs[2:]
-
-        return FlaxSequenceClassifierOutput(
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForSequenceClassification(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForSequenceClassificationModule
-
-
-append_call_sample_docstring(
-    FlaxBertForSequenceClassification,
-    _CHECKPOINT_FOR_DOC,
-    FlaxSequenceClassifierOutput,
-    _CONFIG_FOR_DOC,
-)
-
-
-class FlaxBertForMultipleChoiceModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
-        self.classifier = nn.Dense(1, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        num_choices = input_ids.shape[1]
-        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
-        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
-        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
-        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None
-
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
-        logits = self.classifier(pooled_output)
-
-        reshaped_logits = logits.reshape(-1, num_choices)
-
-        if not return_dict:
-            return (reshaped_logits,) + outputs[2:]
-
-        return FlaxMultipleChoiceModelOutput(
-            logits=reshaped_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForMultipleChoice(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForMultipleChoiceModule
-
-
-overwrite_call_docstring(
-    FlaxBertForMultipleChoice, BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
-)
-append_call_sample_docstring(
-    FlaxBertForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC
-)
-
-
-class FlaxBertForTokenClassificationModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            dtype=self.dtype,
-            add_pooling_layer=False,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        classifier_dropout = (
-            self.config.classifier_dropout
-            if self.config.classifier_dropout is not None
-            else self.config.hidden_dropout_prob
-        )
-        self.dropout = nn.Dropout(rate=classifier_dropout)
-        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
-        logits = self.classifier(hidden_states)
-
-        if not return_dict:
-            return (logits,) + outputs[1:]
-
-        return FlaxTokenClassifierOutput(
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForTokenClassification(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForTokenClassificationModule
-
-
-append_call_sample_docstring(
-    FlaxBertForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC
-)
-
-
-class FlaxBertForQuestionAnsweringModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            dtype=self.dtype,
-            add_pooling_layer=False,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        head_mask,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-
-        logits = self.qa_outputs(hidden_states)
-        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
-        start_logits = start_logits.squeeze(-1)
-        end_logits = end_logits.squeeze(-1)
-
-        if not return_dict:
-            return (start_logits, end_logits) + outputs[1:]
-
-        return FlaxQuestionAnsweringModelOutput(
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForQuestionAnswering(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForQuestionAnsweringModule
-
-
-append_call_sample_docstring(
-    FlaxBertForQuestionAnswering,
-    _CHECKPOINT_FOR_DOC,
-    FlaxQuestionAnsweringModelOutput,
-    _CONFIG_FOR_DOC,
-)
-
-
-class FlaxBertForCausalLMModule(nn.Module):
-    config: BertConfig
-    dtype: jnp.dtype = jnp.float32
-    gradient_checkpointing: bool = False
-
-    def setup(self):
-        self.bert = FlaxBertModule(
-            config=self.config,
-            add_pooling_layer=False,
-            dtype=self.dtype,
-            gradient_checkpointing=self.gradient_checkpointing,
-        )
-        self.cls = FlaxBertOnlyMLMHead(config=self.config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        position_ids,
-        token_type_ids: Optional[jnp.ndarray] = None,
-        head_mask: Optional[jnp.ndarray] = None,
-        encoder_hidden_states: Optional[jnp.ndarray] = None,
-        encoder_attention_mask: Optional[jnp.ndarray] = None,
-        init_cache: bool = False,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.bert(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            init_cache=init_cache,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        if self.config.tie_word_embeddings:
-            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
-        else:
-            shared_embedding = None
-
-        # Compute the prediction scores
-        logits = self.cls(hidden_states, shared_embedding=shared_embedding)
-
-        if not return_dict:
-            return (logits,) + outputs[1:]
-
-        return FlaxCausalLMOutputWithCrossAttentions(
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
-    autoregressive tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class FlaxBertForCausalLM(FlaxBertPreTrainedModel):
-    module_class = FlaxBertForCausalLMModule
-
-    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
-        # initializing the cache
-        batch_size, seq_length = input_ids.shape
-
-        past_key_values = self.init_cache(batch_size, max_length)
-        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
-        # But since the decoder uses a causal mask, those positions are masked anyway.
-        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
-        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
-        if attention_mask is not None:
-            position_ids = attention_mask.cumsum(axis=-1) - 1
-            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
-        else:
-            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
-
-        return {
-            "past_key_values": past_key_values,
-            "attention_mask": extended_attention_mask,
-            "position_ids": position_ids,
-        }
-
-    def update_inputs_for_generation(self, model_outputs, model_kwargs):
-        model_kwargs["past_key_values"] = model_outputs.past_key_values
-        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
-        return model_kwargs
-
-
-append_call_sample_docstring(
-    FlaxBertForCausalLM,
-    _CHECKPOINT_FOR_DOC,
-    FlaxCausalLMOutputWithCrossAttentions,
-    _CONFIG_FOR_DOC,
-)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/markuplm/configuration_markuplm.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/markuplm/configuration_markuplm.py
deleted file mode 100644
index 1455150598acc1a6ac1ad4fdf38e1aaa508711a4..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/markuplm/configuration_markuplm.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# coding=utf-8
-# Copyright 2021, The Microsoft Research Asia MarkupLM Team authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" MarkupLM model configuration"""
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
-    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
-}
-
-
-class MarkupLMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
-    MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the MarkupLM
-    [microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
-
-    Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
-    documentation from [`BertConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 30522):
-            Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
-            *inputs_ids* passed to the forward method of [`MarkupLMModel`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"silu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (`int`, *optional*, defaults to 512):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (`int`, *optional*, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
-            The maximum value that the tree id unit embedding might ever use. Typically set this to something large
-            just in case (e.g., 1024).
-        max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
-            The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
-            just in case (e.g., 256).
-        max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
-            The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
-            large just in case (e.g., 1024).
-        tag_pad_id (`int`, *optional*, defaults to 216):
-            The id of the padding token in the xpath tags.
-        subs_pad_id (`int`, *optional*, defaults to 1001):
-            The id of the padding token in the xpath subscripts.
-        xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
-            The hidden size of each tree id unit. One complete tree index will have
-            (50*xpath_tag_unit_hidden_size)-dim.
-        max_depth (`int`, *optional*, defaults to 50):
-            The maximum depth in xpath.
-
-    Examples:
-
-    ```python
-    >>> from transformers import MarkupLMModel, MarkupLMConfig
-
-    >>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
-    >>> configuration = MarkupLMConfig()
-
-    >>> # Initializing a model from the microsoft/markuplm-base style configuration
-    >>> model = MarkupLMModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "markuplm"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        bos_token_id=0,
-        eos_token_id=2,
-        max_xpath_tag_unit_embeddings=256,
-        max_xpath_subs_unit_embeddings=1024,
-        tag_pad_id=216,
-        subs_pad_id=1001,
-        xpath_unit_hidden_size=32,
-        max_depth=50,
-        position_embedding_type="absolute",
-        use_cache=True,
-        classifier_dropout=None,
-        **kwargs,
-    ):
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            **kwargs,
-        )
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.position_embedding_type = position_embedding_type
-        self.use_cache = use_cache
-        self.classifier_dropout = classifier_dropout
-        # additional properties
-        self.max_depth = max_depth
-        self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
-        self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
-        self.tag_pad_id = tag_pad_id
-        self.subs_pad_id = subs_pad_id
-        self.xpath_unit_hidden_size = xpath_unit_hidden_size
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py
deleted file mode 100644
index 443bf8fd7e4efde677392d220a32bf18c0905222..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert MobileNetV2 checkpoints from the tensorflow/models library."""
-
-
-import argparse
-import json
-import re
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import (
-    MobileNetV2Config,
-    MobileNetV2ForImageClassification,
-    MobileNetV2ForSemanticSegmentation,
-    MobileNetV2ImageProcessor,
-    load_tf_weights_in_mobilenet_v2,
-)
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_mobilenet_v2_config(model_name):
-    config = MobileNetV2Config(layer_norm_eps=0.001)
-
-    if "quant" in model_name:
-        raise ValueError("Quantized models are not supported.")
-
-    matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
-    if matches:
-        config.depth_multiplier = float(matches[1])
-        config.image_size = int(matches[2])
-
-    if model_name.startswith("deeplabv3_"):
-        config.output_stride = 8
-        config.num_labels = 21
-        filename = "pascal-voc-id2label.json"
-    else:
-        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
-        # of the usual 1000. The first class (index 0) is "background".
-        config.num_labels = 1001
-        filename = "imagenet-1k-id2label.json"
-
-    repo_id = "huggingface/label-files"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-
-    if config.num_labels == 1001:
-        id2label = {int(k) + 1: v for k, v in id2label.items()}
-        id2label[0] = "background"
-    else:
-        id2label = {int(k): v for k, v in id2label.items()}
-
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-
-    return config
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
-    """
-    Copy/paste/tweak model's weights to our MobileNetV2 structure.
-    """
-    config = get_mobilenet_v2_config(model_name)
-
-    # Load 🤗 model
-    if model_name.startswith("deeplabv3_"):
-        model = MobileNetV2ForSemanticSegmentation(config).eval()
-    else:
-        model = MobileNetV2ForImageClassification(config).eval()
-
-    # Load weights from TensorFlow checkpoint
-    load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path)
-
-    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
-    image_processor = MobileNetV2ImageProcessor(
-        crop_size={"width": config.image_size, "height": config.image_size},
-        size={"shortest_edge": config.image_size + 32},
-    )
-    encoding = image_processor(images=prepare_img(), return_tensors="pt")
-    outputs = model(**encoding)
-    logits = outputs.logits
-
-    if model_name.startswith("deeplabv3_"):
-        assert logits.shape == (1, 21, 65, 65)
-
-        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
-            expected_logits = torch.tensor(
-                [
-                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
-                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
-                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
-                ]
-            )
-
-        else:
-            raise ValueError(f"Unknown model name: {model_name}")
-
-        assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
-    else:
-        assert logits.shape == (1, 1001)
-
-        if model_name == "mobilenet_v2_1.4_224":
-            expected_logits = torch.tensor([0.0181, -1.0015, 0.4688])
-        elif model_name == "mobilenet_v2_1.0_224":
-            expected_logits = torch.tensor([0.2445, -1.1993, 0.1905])
-        elif model_name == "mobilenet_v2_0.75_160":
-            expected_logits = torch.tensor([0.2482, 0.4136, 0.6669])
-        elif model_name == "mobilenet_v2_0.35_96":
-            expected_logits = torch.tensor([0.1451, -0.4624, 0.7192])
-        else:
-            expected_logits = None
-
-        if expected_logits is not None:
-            assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
-    model.save_pretrained(pytorch_dump_folder_path)
-    print(f"Saving image processor to {pytorch_dump_folder_path}")
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        print("Pushing to the hub...")
-        repo_id = "google/" + model_name
-        image_processor.push_to_hub(repo_id)
-        model.push_to_hub(repo_id)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--model_name",
-        default="mobilenet_v2_1.0_224",
-        type=str,
-        help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
-    )
-    parser.add_argument(
-        "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-
-    args = parser.parse_args()
-    convert_movilevit_checkpoint(
-        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
-    )
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/__init__.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/__init__.py
deleted file mode 100644
index 3d015c530b3e33de8ea60943a0a98b135f013dd7..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .batch_norm import FrozenBatchNorm2d, get_norm, NaiveSyncBatchNorm, CycleBatchNormList
-from .deform_conv import DeformConv, ModulatedDeformConv
-from .mask_ops import paste_masks_in_image
-from .nms import batched_nms, batched_nms_rotated, nms, nms_rotated
-from .roi_align import ROIAlign, roi_align
-from .roi_align_rotated import ROIAlignRotated, roi_align_rotated
-from .shape_spec import ShapeSpec
-from .wrappers import (
-    BatchNorm2d,
-    Conv2d,
-    ConvTranspose2d,
-    cat,
-    interpolate,
-    Linear,
-    nonzero_tuple,
-    cross_entropy,
-    shapes_to_tensor,
-)
-from .blocks import CNNBlockBase, DepthwiseSeparableConv2d
-from .aspp import ASPP
-from .losses import ciou_loss, diou_loss
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docker/Dockerfile b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docker/Dockerfile
deleted file mode 100644
index 4eec16dd0beac8b80c5446c9dd6cf15feaf87303..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docker/Dockerfile
+++ /dev/null
@@ -1,47 +0,0 @@
-FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04
-# use an older system (18.04) to avoid opencv incompatibility (issue#3524)
-
-ENV DEBIAN_FRONTEND noninteractive
-RUN apt-get update && apt-get install -y \
-	python3-opencv ca-certificates python3-dev git wget sudo ninja-build
-RUN ln -sv /usr/bin/python3 /usr/bin/python
-
-# create a non-root user
-ARG USER_ID=1000
-RUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g sudo
-RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
-USER appuser
-WORKDIR /home/appuser
-
-ENV PATH="/home/appuser/.local/bin:${PATH}"
-RUN wget https://bootstrap.pypa.io/get-pip.py && \
-	python3 get-pip.py --user && \
-	rm get-pip.py
-
-# install dependencies
-# See https://pytorch.org/ for other options if you use a different version of CUDA
-RUN pip install --user tensorboard cmake   # cmake from apt-get is too old
-RUN pip install --user torch==1.10 torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
-
-RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'
-# install detectron2
-RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo
-# set FORCE_CUDA because during `docker build` cuda is not accessible
-ENV FORCE_CUDA="1"
-# This will by default build detectron2 for all common cuda architectures and take a lot more time,
-# because inside `docker build`, there is no way to tell which architecture will be used.
-ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
-ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"
-
-RUN pip install --user -e detectron2_repo
-
-# Set a fixed model cache directory.
-ENV FVCORE_CACHE="/tmp"
-WORKDIR /home/appuser/detectron2_repo
-
-# run detectron2 under user "appuser":
-# wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg
-# python3 demo/demo.py  \
-	#--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-	#--input input.jpg --output outputs/ \
-	#--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
diff --git a/spaces/ysharma/LLaVA_v1/docs/LLaVA_Bench.md b/spaces/ysharma/LLaVA_v1/docs/LLaVA_Bench.md
deleted file mode 100644
index 5921964c43f599b2d820de5092a1c3b4c39de60f..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LLaVA_v1/docs/LLaVA_Bench.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# LLaVA-Bench [[Download](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild)]
-
-**-Introduction-**  Large commercial multimodal chatbots have been released in this week, including 
-- [Multimodal Bing-Chat by Microsoft](https://blogs.bing.com/search/july-2023/Bing-Chat-Enterprise-announced,-multimodal-Visual-Search-rolling-out-to-Bing-Chat) (July 18, 2023) 
-- [Multimodal Bard by Google](https://bard.google.com/). 
-
-These chatbots are presumably supported by proprietary large multimodal models (LMM). Compared with the open-source LMM such as LLaVA, proprietary LMM represent the scaling success upperbound of the current SoTA techniques. They share the goal of developing multimodal chatbots that follow human intents to complete various daily-life visual tasks in the wild. While it remains less unexplored how to evaluate multimodal chat ability, it provides useful feedback to study open-source LMMs against the commercial multimodal chatbots. In addition to the *LLaVA-Bench (COCO)* dataset we used to develop the early versions of LLaVA, we are releasing  [*LLaVA-Bench (In-the-Wild)*](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to the community for the public use.
-
-## LLaVA-Bench (In-the-Wild *[Ongoing work]*)
-
-To evaluate the model's capability in more challenging tasks and generalizability to novel domains, we collect a diverse set of 24 images with 60 questions in total, including indoor and outdoor scenes, memes, paintings, sketches, etc, and associate each image with a highly-detailed and manually-curated description and a proper selection of questions. Such design also assesses the model's robustness to different prompts. In this release, we also categorize questions into three categories: conversation (simple QA), detailed description, and complex reasoning. We continue to expand and improve the diversity of the LLaVA-Bench (In-the-Wild).  We manually query Bing-Chat and Bard to get the responses. 
-
-### Results
-
-The score is measured by comparing against a reference answer generated by text-only GPT-4. It is generated by feeding the question, along with the ground truth image annotations as the context. A text-only GPT-4 evaluator rates both answers. We query GPT-4 by putting the reference answer first, and then the answer generated by the candidate model. We upload images at their original resolution to Bard and Bing-Chat to obtain the results.
-
-| Approach       | Conversation | Detail | Reasoning | Overall |
-|----------------|--------------|--------|-----------|---------|
-| Bard-0718      | 83.7         | 69.7   | 78.7      | 77.8    |
-| Bing-Chat-0629 | 59.6         | 52.2   | 90.1      | 71.5    |
-| LLaVA-13B-v1-336px-0719 (beam=1) | 64.3         | 55.9   | 81.7      | 70.1    |
-| LLaVA-13B-v1-336px-0719 (beam=5) | 68.4         | 59.9   | 84.3      | 73.5    |
-
-Note that Bard sometimes refuses to answer questions about images containing humans, and Bing-Chat blurs the human faces in the images. We also provide the benchmark score for the subset without humans.
-
-| Approach       | Conversation | Detail | Reasoning | Overall |
-|----------------|--------------|--------|-----------|---------|
-| Bard-0718      | 94.9         | 74.3   | 84.3      | 84.6    |
-| Bing-Chat-0629 | 55.8         | 53.6   | 93.5      | 72.6    |
-| LLaVA-13B-v1-336px-0719 (beam=1) | 62.2         | 56.4   | 82.2      | 70.0    |
-| LLaVA-13B-v1-336px-0719 (beam=5) | 65.6         | 61.7   | 85.0      | 73.6    |
diff --git a/spaces/ysharma/falcon-180b-demo/app.py b/spaces/ysharma/falcon-180b-demo/app.py
deleted file mode 100644
index 8284b6c0607b312cfe90c367aa3ff91cdcc06d3f..0000000000000000000000000000000000000000
--- a/spaces/ysharma/falcon-180b-demo/app.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import json
-import os
-import shutil
-import requests
-
-import gradio as gr
-from huggingface_hub import Repository, InferenceClient
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-180B-chat"
-BOT_NAME = "Falcon"
-
-STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"]
-
-EXAMPLES = [
-    ["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
-    ["What's the Everett interpretation of quantum mechanics?"],
-    ["Give me a list of the top 10 dive sites you would recommend around the world."],
-    ["Can you tell me more about deep-water soloing?"],
-    ["Can you write a short tweet about the release of our latest AI model, Falcon LLM?"]
-    ]
-
-client = InferenceClient(
-    API_URL,
-    headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-
-def format_prompt(message, history, system_prompt):
-  prompt = ""
-  if system_prompt:
-    prompt += f"System: {system_prompt}\n"
-  for user_prompt, bot_response in history:
-    prompt += f"User: {user_prompt}\n"
-    prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
-  prompt += f"""User: {message}
-Falcon:"""
-  return prompt
-
-seed = 42
-
-def generate(
-    prompt, history, system_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-    global seed
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        stop_sequences=STOP_SEQUENCES,
-        do_sample=True,
-        seed=seed,
-    )
-    seed = seed + 1
-    formatted_prompt = format_prompt(prompt, history, system_prompt)
-
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-
-        for stop_str in STOP_SEQUENCES:
-            if output.endswith(stop_str):
-                output = output[:-len(stop_str)]
-                output = output.rstrip()
-                yield output
-        yield output
-    return output
-
-
-additional_inputs=[
-    gr.Textbox("", label="Optional system prompt"),
-    gr.Slider(
-        label="Temperature",
-        value=0.9,
-        minimum=0.0,
-        maximum=1.0,
-        step=0.05,
-        interactive=True,
-        info="Higher values produce more diverse outputs",
-    ),
-    gr.Slider(
-        label="Max new tokens",
-        value=256,
-        minimum=0,
-        maximum=8192,
-        step=64,
-        interactive=True,
-        info="The maximum numbers of new tokens",
-    ),
-    gr.Slider(
-        label="Top-p (nucleus sampling)",
-        value=0.90,
-        minimum=0.0,
-        maximum=1,
-        step=0.05,
-        interactive=True,
-        info="Higher values sample more low-probability tokens",
-    ),
-    gr.Slider(
-        label="Repetition penalty",
-        value=1.2,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-        info="Penalize repeated tokens",
-    )
-]
-
-
-def vote(data: gr.LikeData):
-    if data.liked:
-        print("You upvoted this response: " + data.value)
-    else:
-        print("You downvoted this response: " + data.value)
-        
-
-chatbot = gr.Chatbot(avatar_images=('user.png', 'bot.png'),bubble_full_width = False)
-
-chat_interface = gr.ChatInterface(
-    generate, 
-    chatbot = chatbot,
-    examples=EXAMPLES,
-    additional_inputs=additional_inputs,
-    ) 
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=0.4):
-            gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
-        with gr.Column():
-            gr.Markdown(
-                """# Falcon-180B Demo 
-
-                ## Gradio ChatInterface allows for dynamic Chat Bubble Sizing, User and Bot Avatars, and Like/Dislike buttons.   
-                
-                **Chat with [Falcon-180B-Chat](https://huggingface.co/tiiuae/falcon-180b-chat), brainstorm ideas, discuss your holiday plans, and more!**
-                
-                ✨ This demo is powered by [Falcon-180B](https://huggingface.co/tiiuae/falcon-180B) and finetuned on a mixture of [Ultrachat](https://huggingface.co/datasets/stingning/ultrachat), [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) and [Airoboros](https://huggingface.co/datasets/jondurbin/airoboros-2.1). [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b) is a state-of-the-art large language model built by the [Technology Innovation Institute](https://www.tii.ae) in Abu Dhabi. It is trained on 3.5 trillion tokens (including [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)) and available under the [Falcon-180B TII License](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt). It currently holds the 🥇 1st place on the [🤗 Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for a pretrained model. 
-                
-                🧪 This is only a **first experimental preview**: we intend to provide increasingly capable versions of Falcon in the future, based on improved datasets and RLHF/RLAIF.
-                
-                👀 **Learn more about Falcon LLM:** [falconllm.tii.ae](https://falconllm.tii.ae/)
-                
-                ➡️️ **Intended Use**: this demo is intended to showcase an early finetuning of [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b), to illustrate the impact (and limitations) of finetuning on a dataset of conversations and instructions. We encourage the community to further build upon the base model, and to create even better instruct/chat versions!
-                
-                ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
-                """
-            )
-            
-    chatbot.like(vote, None, None)
-    chat_interface.render()
-    
-demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)
diff --git a/spaces/zetavg/LLaMA-LoRA-Tuner-UI-Demo/download_base_model.py b/spaces/zetavg/LLaMA-LoRA-Tuner-UI-Demo/download_base_model.py
deleted file mode 100644
index fa9576e1d3c20acc9b7df72a78d3e8569bf7cb98..0000000000000000000000000000000000000000
--- a/spaces/zetavg/LLaMA-LoRA-Tuner-UI-Demo/download_base_model.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import fire
-
-from huggingface_hub import snapshot_download
-
-
-def main(
-    base_model_names: str = "",
-):
-    '''
-    Download and cache base models form Hugging Face.
-
-    :param base_model_names: Names of the base model you want to download, seperated by ",". For example: 'decapoda-research/llama-7b-hf,nomic-ai/gpt4all-j'.
-    '''
-
-    assert (
-        base_model_names
-    ), "Please specify --base_model_names, e.g. --base_model_names='decapoda-research/llama-7b-hf,nomic-ai/gpt4all-j'"
-
-    base_model_names_list = base_model_names.split(',')
-    base_model_names_list = [name.strip() for name in base_model_names_list]
-
-    print(f"Base models: {', '.join(base_model_names_list)}.")
-
-    for name in base_model_names_list:
-        print(f"Preparing {name}...")
-        snapshot_download(name)
-
-    print("")
-    print("Done.")
-
-
-if __name__ == "__main__":
-    fire.Fire(main)
diff --git a/spaces/zeynepgulhan/whisper-medium-cv-tr-demo/README.md b/spaces/zeynepgulhan/whisper-medium-cv-tr-demo/README.md
deleted file mode 100644
index 7843cbfd85cf6bb73dfbef2f8863bb01aed27aa2..0000000000000000000000000000000000000000
--- a/spaces/zeynepgulhan/whisper-medium-cv-tr-demo/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Whisper Demo
-emoji: 🤫
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-tags:
-- whisper-event
-duplicated_from: whisper-event/whisper-demo
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zhangyd/bingo/src/components/chat-image.tsx b/spaces/zhangyd/bingo/src/components/chat-image.tsx
deleted file mode 100644
index 05ecc9771eada27a0f2d160bb01cba170d37bb09..0000000000000000000000000000000000000000
--- a/spaces/zhangyd/bingo/src/components/chat-image.tsx
+++ /dev/null
@@ -1,170 +0,0 @@
-import {
-  useEffect,
-  useState,
-  useCallback,
-  ChangeEvent,
-  ClipboardEvent,
-  MouseEventHandler,
-  FormEvent,
-  useRef
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-
-interface ChatImageProps extends Pick<ReturnType<typeof useBing>, 'uploadImage'> {}
-
-const preventDefault: MouseEventHandler<HTMLDivElement> = (event) => {
-  event.nativeEvent.stopImmediatePropagation()
-}
-
-const toBase64 = (file: File): Promise<string> => new Promise((resolve, reject) => {
-  const reader = new FileReader()
-  reader.readAsDataURL(file)
-  reader.onload = () => resolve(reader.result as string)
-  reader.onerror = reject
-})
-
-export function ChatImage({ children, uploadImage }: React.PropsWithChildren<ChatImageProps>) {
-  const videoRef = useRef<HTMLVideoElement>(null)
-  const canvasRef = useRef<HTMLCanvasElement>(null)
-  const mediaStream = useRef<MediaStream>()
-  const [panel, setPanel] = useState('none')
-
-  const upload = useCallback((url: string) => {
-    if (url) {
-      uploadImage(url)
-    }
-    setPanel('none')
-  }, [panel])
-
-  const onUpload = useCallback(async (event: ChangeEvent<HTMLInputElement>) => {
-    const file = event.target.files?.[0]
-    if (file) {
-      const fileDataUrl = await toBase64(file)
-      if (fileDataUrl) {
-        upload(fileDataUrl)
-      }
-    }
-  }, [])
-
-  const onPaste = useCallback((event: ClipboardEvent<HTMLInputElement>) => {
-    const pasteUrl = event.clipboardData.getData('text') ?? ''
-    upload(pasteUrl)
-  }, [])
-
-  const onEnter = useCallback((event: FormEvent<HTMLFormElement>) => {
-    event.preventDefault()
-    event.stopPropagation()
-    // @ts-ignore
-    const inputUrl = event.target.elements.image.value
-    if (inputUrl) {
-      upload(inputUrl)
-    }
-  }, [])
-
-  const openVideo: MouseEventHandler<HTMLButtonElement> = async (event) => {
-    event.stopPropagation()
-    setPanel('camera-mode')
-  }
-
-  const onCapture = () => {
-    if (canvasRef.current && videoRef.current) {
-      const canvas = canvasRef.current
-      canvas.width = videoRef.current!.videoWidth
-      canvas.height = videoRef.current!.videoHeight
-      canvas.getContext('2d')?.drawImage(videoRef.current, 0, 0, canvas.width, canvas.height)
-      const cameraUrl = canvas.toDataURL('image/jpeg')
-      upload(cameraUrl)
-    }
-  }
-
-  useEffect(() => {
-    const handleBlur = () => {
-      if (panel !== 'none') {
-        setPanel('none')
-      }
-    }
-    document.addEventListener('click', handleBlur)
-    return () => {
-      document.removeEventListener('click', handleBlur)
-    }
-  }, [panel])
-
-  useEffect(() => {
-    if (panel === 'camera-mode') {
-      navigator.mediaDevices.getUserMedia({ video: true, audio: false })
-      .then(videoStream => {
-        mediaStream.current = videoStream
-        if (videoRef.current) {
-          videoRef.current.srcObject = videoStream
-        }
-      })
-    } else {
-      if (mediaStream.current) {
-        mediaStream.current.getTracks().forEach(function(track) {
-          track.stop()
-        })
-        mediaStream.current = undefined
-      }
-    }
-  }, [panel])
-
-  return (
-    <div className="visual-search-container">
-      <div onClick={() => panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}</div>
-      <div className={cn('visual-search', panel)} onClick={preventDefault}>
-        <div className="normal-content">
-          <div className="header">
-            <h4>添加图像</h4>
-          </div>
-          <div className="paste">
-            <Image alt="paste" src={PasteIcon} width={24} />
-            <form onSubmitCapture={onEnter}>
-              <input
-                className="paste-input"
-                id="sb_imgpst"
-                type="text"
-                name="image"
-                placeholder="粘贴图像 URL"
-                aria-label="粘贴图像 URL"
-                onPaste={onPaste}
-                onClickCapture={(e) => e.stopPropagation()}
-              />
-            </form>
-          </div>
-          <div className="buttons">
-            <button type="button" aria-label="从此设备上传">
-              <input
-                id="vs_fileinput"
-                className="fileinput"
-                type="file"
-                accept="image/gif, image/jpeg, image/png, image/webp"
-                onChange={onUpload}
-              />
-              <Image alt="uplaod" src={UploadIcon} width={20} />
-              从此设备上传
-            </button>
-            <button type="button" aria-label="拍照" onClick={openVideo}>
-              <Image alt="camera" src={CameraIcon} width={20} />
-              拍照
-            </button>
-          </div>
-        </div>
-        {panel === 'camera-mode' && <div className="cam-content">
-          <div className="webvideo-container">
-            <video className="webvideo" autoPlay muted playsInline ref={videoRef} />
-            <canvas className="webcanvas" ref={canvasRef} />
-          </div>
-          <div className="cambtn" role="button" aria-label="拍照" onClick={onCapture}>
-            <div className="cam-btn-circle-large"></div>
-            <div className="cam-btn-circle-small"></div>
-          </div>
-        </div>}
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/zhuge09/CompVis-stable-diffusion-v4/README.md b/spaces/zhuge09/CompVis-stable-diffusion-v4/README.md
deleted file mode 100644
index e1b20ba5defff3f0822c7b152457bbb17ec21576..0000000000000000000000000000000000000000
--- a/spaces/zhuge09/CompVis-stable-diffusion-v4/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CompVis Stable Diffusion V4
-emoji: 🏆
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zideliu/styledrop/timm/models/rexnet.py b/spaces/zideliu/styledrop/timm/models/rexnet.py
deleted file mode 100644
index 6444b3c8c415f31ef67d7864c7906be9c3b3d154..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/timm/models/rexnet.py
+++ /dev/null
@@ -1,262 +0,0 @@
-""" ReXNet
-
-A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
-https://arxiv.org/abs/2007.00992
-
-Adapted from original impl at https://github.com/clovaai/rexnet
-Copyright (c) 2020-present NAVER Corp. MIT license
-
-Changes for timm, feature extraction, and rounded channel variant hacked together by Ross Wightman
-Copyright 2020 Ross Wightman
-"""
-
-import torch.nn as nn
-from math import ceil
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import ClassifierHead, create_act_layer, ConvBnAct, DropPath
-from .registry import register_model
-from .efficientnet_builder import efficientnet_init_weights
-
-
-def _cfg(url=''):
-    return {
-        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'crop_pct': 0.875, 'interpolation': 'bicubic',
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'stem.conv', 'classifier': 'head.fc',
-    }
-
-
-default_cfgs = dict(
-    rexnet_100=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth'),
-    rexnet_130=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth'),
-    rexnet_150=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth'),
-    rexnet_200=_cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth'),
-    rexnetr_100=_cfg(
-        url=''),
-    rexnetr_130=_cfg(
-        url=''),
-    rexnetr_150=_cfg(
-        url=''),
-    rexnetr_200=_cfg(
-        url=''),
-)
-
-
-def make_divisible(v, divisor=8, min_value=None):
-    min_value = min_value or divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    return new_v
-
-
-class SEWithNorm(nn.Module):
-
-    def __init__(self, channels, se_ratio=1 / 12., act_layer=nn.ReLU, divisor=1, reduction_channels=None,
-                 gate_layer='sigmoid'):
-        super(SEWithNorm, self).__init__()
-        reduction_channels = reduction_channels or make_divisible(int(channels * se_ratio), divisor=divisor)
-        self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, bias=True)
-        self.bn = nn.BatchNorm2d(reduction_channels)
-        self.act = act_layer(inplace=True)
-        self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, bias=True)
-        self.gate = create_act_layer(gate_layer)
-
-    def forward(self, x):
-        x_se = x.mean((2, 3), keepdim=True)
-        x_se = self.fc1(x_se)
-        x_se = self.bn(x_se)
-        x_se = self.act(x_se)
-        x_se = self.fc2(x_se)
-        return x * self.gate(x_se)
-
-
-class LinearBottleneck(nn.Module):
-    def __init__(self, in_chs, out_chs, stride, exp_ratio=1.0, se_ratio=0., ch_div=1, drop_path=None):
-        super(LinearBottleneck, self).__init__()
-        self.use_shortcut = stride == 1 and in_chs <= out_chs
-        self.in_channels = in_chs
-        self.out_channels = out_chs
-
-        if exp_ratio != 1.:
-            dw_chs = make_divisible(round(in_chs * exp_ratio), divisor=ch_div)
-            self.conv_exp = ConvBnAct(in_chs, dw_chs, act_layer="swish")
-        else:
-            dw_chs = in_chs
-            self.conv_exp = None
-
-        self.conv_dw = ConvBnAct(dw_chs, dw_chs, 3, stride=stride, groups=dw_chs, apply_act=False)
-        self.se = SEWithNorm(dw_chs, se_ratio=se_ratio, divisor=ch_div) if se_ratio > 0. else None
-        self.act_dw = nn.ReLU6()
-
-        self.conv_pwl = ConvBnAct(dw_chs, out_chs, 1, apply_act=False)
-        self.drop_path = drop_path
-
-    def feat_channels(self, exp=False):
-        return self.conv_dw.out_channels if exp else self.out_channels
-
-    def forward(self, x):
-        shortcut = x
-        if self.conv_exp is not None:
-            x = self.conv_exp(x)
-        x = self.conv_dw(x)
-        if self.se is not None:
-            x = self.se(x)
-        x = self.act_dw(x)
-        x = self.conv_pwl(x)
-        if self.drop_path is not None:
-            x = self.drop_path(x)
-        if self.use_shortcut:
-            x[:, 0:self.in_channels] += shortcut
-        return x
-
-
-def _block_cfg(width_mult=1.0, depth_mult=1.0, initial_chs=16, final_chs=180, se_ratio=0., ch_div=1):
-    layers = [1, 2, 2, 3, 3, 5]
-    strides = [1, 2, 2, 2, 1, 2]
-    layers = [ceil(element * depth_mult) for element in layers]
-    strides = sum([[element] + [1] * (layers[idx] - 1) for idx, element in enumerate(strides)], [])
-    exp_ratios = [1] * layers[0] + [6] * sum(layers[1:])
-    depth = sum(layers[:]) * 3
-    base_chs = initial_chs / width_mult if width_mult < 1.0 else initial_chs
-
-    # The following channel configuration is a simple instance to make each layer become an expand layer.
-    out_chs_list = []
-    for i in range(depth // 3):
-        out_chs_list.append(make_divisible(round(base_chs * width_mult), divisor=ch_div))
-        base_chs += final_chs / (depth // 3 * 1.0)
-
-    se_ratios = [0.] * (layers[0] + layers[1]) + [se_ratio] * sum(layers[2:])
-
-    return list(zip(out_chs_list, exp_ratios, strides, se_ratios))
-
-
-def _build_blocks(
-        block_cfg, prev_chs, width_mult, ch_div=1, drop_path_rate=0., feature_location='bottleneck'):
-    feat_exp = feature_location == 'expansion'
-    feat_chs = [prev_chs]
-    feature_info = []
-    curr_stride = 2
-    features = []
-    num_blocks = len(block_cfg)
-    for block_idx, (chs, exp_ratio, stride, se_ratio) in enumerate(block_cfg):
-        if stride > 1:
-            fname = 'stem' if block_idx == 0 else f'features.{block_idx - 1}'
-            if block_idx > 0 and feat_exp:
-                fname += '.act_dw'
-            feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=fname)]
-            curr_stride *= stride
-        block_dpr = drop_path_rate * block_idx / (num_blocks - 1)  # stochastic depth linear decay rule
-        drop_path = DropPath(block_dpr) if block_dpr > 0. else None
-        features.append(LinearBottleneck(
-            in_chs=prev_chs, out_chs=chs, exp_ratio=exp_ratio, stride=stride, se_ratio=se_ratio,
-            ch_div=ch_div, drop_path=drop_path))
-        prev_chs = chs
-        feat_chs += [features[-1].feat_channels(feat_exp)]
-    pen_chs = make_divisible(1280 * width_mult, divisor=ch_div)
-    feature_info += [dict(
-        num_chs=pen_chs if feat_exp else feat_chs[-1], reduction=curr_stride,
-        module=f'features.{len(features) - int(not feat_exp)}')]
-    features.append(ConvBnAct(prev_chs, pen_chs, act_layer="swish"))
-    return features, feature_info
-
-
-class ReXNetV1(nn.Module):
-    def __init__(self, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32,
-                 initial_chs=16, final_chs=180, width_mult=1.0, depth_mult=1.0, se_ratio=1/12.,
-                 ch_div=1, drop_rate=0.2, drop_path_rate=0., feature_location='bottleneck'):
-        super(ReXNetV1, self).__init__()
-        self.drop_rate = drop_rate
-        self.num_classes = num_classes
-
-        assert output_stride == 32  # FIXME support dilation
-        stem_base_chs = 32 / width_mult if width_mult < 1.0 else 32
-        stem_chs = make_divisible(round(stem_base_chs * width_mult), divisor=ch_div)
-        self.stem = ConvBnAct(in_chans, stem_chs, 3, stride=2, act_layer='swish')
-
-        block_cfg = _block_cfg(width_mult, depth_mult, initial_chs, final_chs, se_ratio, ch_div)
-        features, self.feature_info = _build_blocks(
-            block_cfg, stem_chs, width_mult, ch_div, drop_path_rate, feature_location)
-        self.num_features = features[-1].out_channels
-        self.features = nn.Sequential(*features)
-
-        self.head = ClassifierHead(self.num_features, num_classes, global_pool, drop_rate)
-
-        efficientnet_init_weights(self)
-
-    def get_classifier(self):
-        return self.head.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
-
-    def forward_features(self, x):
-        x = self.stem(x)
-        x = self.features(x)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def _create_rexnet(variant, pretrained, **kwargs):
-    feature_cfg = dict(flatten_sequential=True)
-    if kwargs.get('feature_location', '') == 'expansion':
-        feature_cfg['feature_cls'] = 'hook'
-    return build_model_with_cfg(
-        ReXNetV1, variant, pretrained, default_cfg=default_cfgs[variant], feature_cfg=feature_cfg, **kwargs)
-
-
-@register_model
-def rexnet_100(pretrained=False, **kwargs):
-    """ReXNet V1 1.0x"""
-    return _create_rexnet('rexnet_100', pretrained, **kwargs)
-
-
-@register_model
-def rexnet_130(pretrained=False, **kwargs):
-    """ReXNet V1 1.3x"""
-    return _create_rexnet('rexnet_130', pretrained, width_mult=1.3, **kwargs)
-
-
-@register_model
-def rexnet_150(pretrained=False, **kwargs):
-    """ReXNet V1 1.5x"""
-    return _create_rexnet('rexnet_150', pretrained, width_mult=1.5, **kwargs)
-
-
-@register_model
-def rexnet_200(pretrained=False, **kwargs):
-    """ReXNet V1 2.0x"""
-    return _create_rexnet('rexnet_200', pretrained, width_mult=2.0, **kwargs)
-
-
-@register_model
-def rexnetr_100(pretrained=False, **kwargs):
-    """ReXNet V1 1.0x w/ rounded (mod 8) channels"""
-    return _create_rexnet('rexnetr_100', pretrained, ch_div=8, **kwargs)
-
-
-@register_model
-def rexnetr_130(pretrained=False, **kwargs):
-    """ReXNet V1 1.3x w/ rounded (mod 8) channels"""
-    return _create_rexnet('rexnetr_130', pretrained, width_mult=1.3, ch_div=8, **kwargs)
-
-
-@register_model
-def rexnetr_150(pretrained=False, **kwargs):
-    """ReXNet V1 1.5x w/ rounded (mod 8) channels"""
-    return _create_rexnet('rexnetr_150', pretrained, width_mult=1.5, ch_div=8, **kwargs)
-
-
-@register_model
-def rexnetr_200(pretrained=False, **kwargs):
-    """ReXNet V1 2.0x w/ rounded (mod 8) channels"""
-    return _create_rexnet('rexnetr_200', pretrained, width_mult=2.0, ch_div=8, **kwargs)