Bamboo CTE 650 Driver Download: How to Install and Use Your Wacom Tablet
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If you are looking for a way to unleash your creativity and express yourself digitally, you might want to consider getting a Wacom tablet. Wacom is a leading brand of pen tablets, pen displays, and smartpads that allow you to draw, sketch, paint, edit, and annotate with natural and intuitive gestures. One of the popular models of Wacom tablets is the Bamboo CTE 650, also known as Bamboo Fun.
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In this article, we will show you how to download and install the Bamboo CTE 650 driver for your Windows or Mac computer, and how to use your tablet effectively. We will also answer some frequently asked questions about Bamboo CTE 650.
Bamboo CTE 650 is a pen tablet that lets you interact with your computer using a stylus and touch input. You can use it to draw, write, edit photos, play games, navigate web pages, and more. Bamboo CTE 650 is designed to be easy to use, fun, and versatile for various creative purposes.
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Features and specifications
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Some of the features and specifications of Bamboo CTE 650 are:
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A large active area of 8.5 x 5.4 inches (216 x 137 mm) that gives you more space to work with.
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A pressure-sensitive pen that supports 512 levels of pressure for natural and precise control.
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A multi-touch surface that recognizes up to two fingers for gestures like zooming, scrolling, rotating, etc.
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Four customizable ExpressKeys that let you access your favorite shortcuts quickly.
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A touch ring that lets you adjust brush size, zoom level, scroll speed, etc.
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A wireless kit (sold separately) that lets you use your tablet without cables.
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A bundled software package that includes Adobe Photoshop Elements, Corel Painter Essentials, Nik Color Efex Pro, and Wacom Brushes.
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Compatibility and system requirements
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Bamboo CTE 650 is compatible with both Windows and Mac operating systems. However, you need to download and install the correct driver for your OS version. The system requirements for Bamboo CTE 650 are:
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Windows
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Mac
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Windows XP SP2 or later Windows Vista SP1 or later Windows 7 Windows 8 Windows 10 Windows 11
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Mac OS X 10.4.8 or later Mac OS X 10.5 or later Mac OS X 10.6 or later Mac OS X 10.7 or later Mac OS X 10.8 or later Mac OS X 10.9 or later macOS Sierra (10.12) or later
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A USB port A CD-ROM drive (for software installation)
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A USB port A CD-ROM drive (for software installation)
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How to download and install Bamboo CTE 650 driver
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To use your Bamboo CTE 650 tablet properly, you need to download and install the latest driver from the Wacom website. The driver will enable your tablet to communicate with your computer and allow you to customize the tablet settings. Here are the steps to download and install Bamboo CTE 650 driver:
In the search box, type "Bamboo" and select "Bamboo Fun (CTE)" from the drop-down menu. Then select your operating system from the next drop-down menu. For example, if you are using Windows 10 (64-bit), select "Windows - Driver 6.4.2-1 (Win7x64 - Win11x64)". You will see the driver details below the search box.
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Step 3: Download and run the driver installer
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Click on "Download" to download the driver installer file to your computer. The file name will be something like "WacomTablet_6.4.2-1.exe". Once the download is complete, double-click on the file to run it.
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Step 4: Follow the on-screen instructions
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The driver installer will guide you through the installation process. You may need to accept some terms and conditions, choose a destination folder, etc. Follow the on-screen instructions until the installation is complete.
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Step 5: Restart your computer
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After the installation is finished, you may need to restart your computer for the changes to take effect. Once your computer is restarted, you can connect your tablet and start using it.
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How to use Bamboo CTE 650 tablet
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Now that you have installed the driver for your tablet, you can start using it for various creative tasks. Here are some tips on how to use Bamboo CTE 650 tablet:
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Connect the tablet to your computer
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To connect your tablet to your computer, plug one end of the USB cable into an available USB port on your computer, and plug the other end into the mini-USB port on your tablet. If you have a wireless kit installed on your tablet, you can also connect it wirelessly by turning on both devices and pressing the power button on your tablet for a few seconds until it pairs with your computer.
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Customize the tablet settings
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To customize the tablet settings, such as pen pressure sensitivity, ExpressKeys functions, touch gestures preferences, etc., you can use the Wacom Tablet Properties program that comes with the driver. To access it, go to Start > All Programs > Wacom Tablet > Wacom Tablet Properties (for Windows) or System Preferences > Wacom Tablet (for Mac). You can also access it by clicking on the Wacom icon in your system tray (for Windows) or menu bar (for Mac).
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Use the pen and touch input
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To use the pen input, hold the pen like a normal pen and hover it over the tablet surface without touching it. You will see a cursor on your screen that follows your pen movement. To click or tap something on your screen, touch the tip of the pen lightly on the tablet surface. To right-click or long-press something on your screen, press the side switch on the pen barrel while touching the tip of the pen on the tablet surface. To use the touch input, use your fingers to perform gestures on the tablet surface. You can use one or two fingers to perform gestures such as zooming, scrolling, rotating, etc. You can also tap, double-tap, or drag your fingers to click, double-click, or drag something on your screen. To disable the touch input, press the top ExpressKey on your tablet.
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Troubleshoot common issues
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If you encounter any issues with your tablet, such as no pen or touch response, driver error messages, or poor performance, you can try some of these troubleshooting steps:
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Make sure your tablet is connected properly to your computer and has enough battery power if wireless.
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Make sure you have installed the latest driver for your tablet and operating system.
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Make sure you have selected the correct tablet model and input mode in the Wacom Tablet Properties program.
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Make sure you have not disabled or conflicted the tablet functions with other programs or settings on your computer.
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Make sure you have not damaged or worn out the pen tip or the tablet surface.
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If none of these steps work, you can contact Wacom customer support for further assistance.
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Conclusion
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Bamboo CTE 650 is a great pen tablet that can help you unleash your creativity and express yourself digitally. To use it, you need to download and install the Bamboo CTE 650 driver from the Wacom website, and customize the tablet settings according to your preferences. You can also use the pen and touch input to interact with your computer in a natural and intuitive way. If you encounter any issues with your tablet, you can try some troubleshooting steps or contact Wacom customer support for help.
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FAQs
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Here are some frequently asked questions about Bamboo CTE 650:
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What is the difference between Bamboo CTE 650 and Bamboo CTL 460? Bamboo CTE 650 and Bamboo CTL 460 are both pen tablets from Wacom, but they have some differences. Bamboo CTE 650 has a larger active area, a pressure-sensitive pen, a multi-touch surface, four ExpressKeys, a touch ring, and a wireless kit option. Bamboo CTL 460 has a smaller active area, a battery-free pen, no touch input, two ExpressKeys, no touch ring, and no wireless kit option.
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How do I change the pen tip of Bamboo CTE 650? To change the pen tip of Bamboo CTE 650, you need to use the nib extractor tool that comes with your tablet. To use it, insert the tool into the pen tip and pull it out gently. Then insert a new pen tip into the pen and push it in firmly.
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How do I update the Bamboo CTE 650 driver? To update the Bamboo CTE 650 driver, you need to visit the Wacom website and download the latest driver for your tablet and operating system. Then run the driver installer file and follow the on-screen instructions. You may need to restart your computer for the changes to take effect.
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How do I uninstall the Bamboo CTE 650 driver? To uninstall the Bamboo CTE 650 driver, you need to go to Control Panel > Programs and Features (for Windows) or Applications > Wacom Tablet (for Mac) and select Uninstall or Move to Trash. You may need to restart your computer for the changes to take effect.
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How do I register my Bamboo CTE 650 tablet? To register your Bamboo CTE 650 tablet, you need to go to https://www.wacom.com/register and create an account or log in with your existing account. Then follow the instructions to register your product and get access to exclusive offers, software downloads, tutorials, etc.
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Chhota Bheem - Himalayan Adventure 1 English Dubbed Hd 720p.md b/spaces/1gistliPinn/ChatGPT4/Examples/Chhota Bheem - Himalayan Adventure 1 English Dubbed Hd 720p.md
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Chhota Bheem - Himalayan Adventure 1 english dubbed hd 720p
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4K Wallpaper Download Cars: How to Choose and Enjoy the Best Car Wallpapers for Your Device
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If you are a car enthusiast, you probably love to admire the beauty and elegance of various cars on your device. Whether you prefer sports cars, luxury cars, classic cars, or exotic cars, you can find a wide range of car wallpapers that suit your taste and style. But how do you choose the best car wallpapers for your device? And how do you enjoy them to the fullest?
In this article, we will explain what is 4K resolution and why it matters for car wallpapers. We will also give you some tips on how to choose the best 4K car wallpapers for your device, where to download them for free, and how to enjoy them on your device. By the end of this article, you will be able to choose and enjoy the best car wallpapers for your device.
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What is 4K Resolution and Why Does It Matter for Car Wallpapers?
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Before we dive into how to choose the best car wallpapers for your device, let's first understand what is 4K resolution and why it matters for car wallpapers.
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The Definition and Benefits of 4K Resolution
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4K resolution, also known as 2160p or UHD (Ultra High Definition), is a popular display resolution that has about four times as many pixels as Full HD (1080p) resolution. A pixel is a tiny dot that makes up an image on a screen. The more pixels a screen has, the sharper and clearer its image will be.
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A typical 4K resolution is 3840 x 2160 pixels, which means it has 3840 pixels horizontally and 2160 pixels vertically. To put that in perspective, a Full HD resolution is only 1920 x 1080 pixels. A 4K screen has about 8 million pixels, while a Full HD screen has only about 2 million pixels.
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The benefits of 4K resolution for car wallpapers are obvious. With more pixels, you can see more details, colors, textures, and contrasts in the car images. You can also zoom in and out without losing quality or clarity. You can enjoy the stunning beauty and realism of your favorite cars on your device.
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The Difference Between 4K, UHD, and DCI 4K Standards
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While 4K resolution is a generic term that refers to any resolution with about 4000 pixels wide, there are actually different standards and formats of 4K resolution in the industry. The most common ones are UHD and DCI 4K.
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UHD stands for Ultra High Definition, which is the standard used by most TV manufacturers and streaming services. UHD has a resolution of 3840 x 2160 pixels, which is exactly four times as many pixels as Full HD (1920 x 1080 pixels). UHD has an aspect ratio of 16:9, which means it has a rectangular shape that fits most TV screens.
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DCI stands for Digital Cinema Initiatives, which is the standard used by the movie industry. DCI has a resolution of 4096 x 2160 pixels, which is slightly wider than UHD
DCI stands for Digital Cinema Initiatives, which is the standard used by the movie industry. DCI has a resolution of 4096 x 2160 pixels, which is slightly wider than UHD. DCI has an aspect ratio of 17:9, which means it has a more cinematic shape that fits most movie screens.
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The difference between UHD and DCI 4K is not very noticeable for most viewers, unless you have a very large screen or sit very close to it. However, it is important to know the difference when you download or stream 4K content, as some sources may use UHD and some may use DCI 4K. You should also check the specifications of your device to see which 4K standard it supports.
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How to Choose the Best 4K Car Wallpapers for Your Device
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Now that you know what is 4K resolution and why it matters for car wallpapers, let's move on to how to choose the best 4K car wallpapers for your device. There are many factors to consider when choosing a car wallpaper, such as your personal preference, the quality and size of the image, and the compatibility and performance of your device. Here are some tips to help you choose the best 4K car wallpapers for your device.
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The Factors to Consider When Choosing a Car Wallpaper
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When choosing a car wallpaper, you should consider the following factors:
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Your Personal Preference and Style
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The first and most important factor is your personal preference and style. You should choose a car wallpaper that reflects your personality, mood, taste, and interest. You should also consider the theme, color, and design of your device and how they match with the car wallpaper. For example, if you have a dark-themed device, you may want to choose a car wallpaper that has dark colors or contrasts well with the background. If you have a minimalist device, you may want to choose a car wallpaper that has simple and elegant lines or shapes.
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There are many types of car wallpapers to choose from, such as sports cars, luxury cars, classic cars, exotic cars, muscle cars, racing cars, concept cars, and more. You can also choose car wallpapers that feature specific brands, models, or celebrities. You can browse through different categories and genres of car wallpapers to find the ones that suit your preference and style.
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The Quality and Size of the Image
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The second factor is the quality and size of the image. You should choose a car wallpaper that has high resolution, sharpness, clarity, and detail. You should also choose a car wallpaper that has the right size for your device's screen. A car wallpaper that is too small or too large may look pixelated, stretched, cropped, or distorted on your device.
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To ensure the quality and size of the image, you should check the specifications of the car wallpaper before you download it. You should look for the resolution, aspect ratio, file format, file size, and source of the image. You should also preview the image on your device before you set it as your wallpaper.
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The Compatibility and Performance of Your Device
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The third factor is the compatibility and performance of your device. You should choose a car wallpaper that is compatible with your device's operating system, software, hardware, and settings. You should also choose a car wallpaper that does not affect your device's performance, battery life, memory usage, or security.
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To ensure the compatibility and performance of your device, you should check the requirements and recommendations of the car wallpaper before you download it. You should look for the supported devices, platforms,
The third factor is the compatibility and performance of your device. You should choose a car wallpaper that is compatible with your device's operating system, software, hardware, and settings. You should also choose a car wallpaper that does not affect your device's performance, battery life, memory usage, or security.
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To ensure the compatibility and performance of your device, you should check the requirements and recommendations of the car wallpaper before you download it. You should look for the supported devices, platforms, versions, formats, and permissions of the car wallpaper. You should also test the car wallpaper on your device after you download it and see if it runs smoothly and safely.
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The Best Websites to Download Free 4K Car Wallpapers
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Now that you know the factors to consider when choosing a car wallpaper, you may wonder where to find the best 4K car wallpapers for your device. There are many websites that offer free 4K car wallpapers for download, but not all of them are reliable, safe, and high-quality. Here are some of the best websites to download free 4K car wallpapers that we recommend:
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Pexels
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Pexels is one of the most popular and trusted websites for free stock photos and videos. It has a large collection of 4K car wallpapers that are curated by professional photographers and videographers. You can browse through different categories and tags of car wallpapers, such as sports cars, luxury cars, classic cars, exotic cars, and more. You can also search by keywords, colors, orientations, sizes, and licenses. You can download any car wallpaper for free and use it for personal or commercial purposes without attribution.
Wallpaper Cave is another popular and trusted website for free wallpapers of various themes and genres. It has a huge collection of 4K car wallpapers that are uploaded by its community of users. You can browse through different galleries and collections of car wallpapers, such as sports cars, luxury cars, classic cars, exotic cars, and more. You can also search by keywords, ratings, views, downloads, favorites, and comments. You can download any car wallpaper for free and use it for personal or commercial purposes with attribution.
Wallpaper Access is another popular and trusted website for free wallpapers of various themes and genres. It has a large collection of 4K car wallpapers that are hand-picked by its team of editors. You can browse through different categories and subcategories of car wallpapers, such as sports cars, luxury cars, classic cars, exotic cars, and more. You can also search by keywords, resolutions, ratios, devices,
Wallpaper Access is another popular and trusted website for free wallpapers of various themes and genres. It has a large collection of 4K car wallpapers that are hand-picked by its team of editors. You can browse through different categories and subcategories of car wallpapers, such as sports cars, luxury cars, classic cars, exotic cars, and more. You can also search by keywords, resolutions, ratios, devices, and licenses. You can download any car wallpaper for free and use it for personal or commercial purposes with attribution.
How to Enjoy the Best 4K Car Wallpapers on Your Device
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After you have chosen and downloaded the best 4K car wallpapers for your device, you may want to know how to enjoy them on your device. There are some simple steps that you can follow to download, set, change, customize, share, and recommend your favorite car wallpapers with others. Here are some tips to help you enjoy the best 4K car wallpapers on your device.
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How to Download and Set a Car Wallpaper on Your Device
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The process of downloading and setting a car wallpaper on your device may vary depending on the type and model of your device, the operating system and software that you use, and the website or app that you download from. However, the general steps are as follows:
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Go to the website or app that offers the car wallpaper that you want to download.
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Find the car wallpaper that you like and click on it to open it.
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Check the resolution, size, format, and license of the car wallpaper and make sure it matches your device's specifications and requirements.
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Click on the download button or link and choose the location where you want to save the car wallpaper on your device.
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Wait for the download to finish and then go to the location where you saved the car wallpaper.
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Open the car wallpaper and click on the set as wallpaper option or use your device's settings to set it as your wallpaper.
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Adjust the position, scale, crop, or orientation of the car wallpaper if needed.
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Enjoy your new car wallpaper on your device.
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How to Change and Customize Your Car Wallpaper Settings
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If you want to change or customize your car wallpaper settings, such as the brightness, contrast, saturation, hue, or filter of the image, you can use your device's settings or a third-party app to do so. Here are some steps that you can follow:
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Go to your device's settings or open the app that you use to edit your wallpapers.
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Select the car wallpaper that you want to change or customize.
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Use the tools or options available to adjust the settings of the car wallpaper according to your preference.
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Save the changes and apply them to your wallpaper.
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Enjoy your customized car wallpaper on your device.
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How to Share and Recommend Your Favorite Car Wallpapers with Others
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If you want to share and recommend your favorite car wallpapers with others, such as your friends, family, or social media followers, you can use various methods to do so. Here are some ways that you can share and recommend your favorite car wallpapers with others:
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Use the share button or link on the website or app that you downloaded from and choose the platform or app that you want to share with.
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Use your device's screenshot or screen recording function and capture the image or video of your car wallpaper and then share it via email, message, or social media.
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Use a QR code generator app or website and create a QR code that links to the car wallpaper that you want to share and then scan it with another device or print it out and display it.
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Use a review or rating system on the website or app that you downloaded from and leave a positive feedback or comment about the car wallpaper that you like and why you recommend it.
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Use a hashtag or keyword related to the car wallpaper that you want to share and post it on social media along with a link or image of the car wallpaper.
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Conclusion
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In conclusion, 4K resolution is a great way to enjoy the best car wallpapers on your device. It offers more pixels, details, colors, textures,
In conclusion, 4K resolution is a great way to enjoy the best car wallpapers on your device. It offers more pixels, details, colors, textures, and contrasts in the car images. It also allows you to zoom in and out without losing quality or clarity. However, you should also consider your personal preference, the quality and size of the image, and the compatibility and performance of your device when choosing a car wallpaper. You should also check the specifications and requirements of the car wallpaper before you download it. You can find the best 4K car wallpapers for your device on websites like Pexels, Wallpaper Cave, and Wallpaper Access. You can also download, set, change, customize, share, and recommend your favorite car wallpapers with others using various methods.
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We hope this article has helped you choose and enjoy the best 4K car wallpapers for your device. If you have any questions or suggestions, please feel free to leave a comment below. Thank you for reading and have a wonderful day!
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FAQs
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Here are some frequently asked questions about 4K car wallpapers:
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What is the difference between 4K and HD car wallpapers?
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HD stands for High Definition, which is a display resolution that has about two times as many pixels as Standard Definition (SD) resolution. A typical HD resolution is 1280 x 720 pixels, which is also known as 720p. 4K stands for 4000, which is a display resolution that has about four times as many pixels as Full HD (1080p) resolution. A typical 4K resolution is 3840 x 2160 pixels, which is also known as 2160p or UHD (Ultra High Definition). The difference between 4K and HD car wallpapers is that 4K car wallpapers have more pixels, details, colors, textures, and contrasts than HD car wallpapers.
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How can I tell if a car wallpaper is 4K or not?
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You can tell if a car wallpaper is 4K or not by checking its resolution, size, format, and source. A 4K car wallpaper should have a resolution of at least 3840 x 2160 pixels, a size of at least 10 MB, a format of JPG or PNG, and a source of a reputable website or app. You can also use tools or apps that can detect the resolution and quality of an image.
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Can I use a 4K car wallpaper on a non-4K device?
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Yes, you can use a 4K car wallpaper on a non-4K device, but you may not be able to enjoy its full potential. A non-4K device may not be able to display all the pixels, details, colors, textures, and contrasts of a 4K car wallpaper. It may also have to scale down or compress the image to fit its screen size and resolution. This may result in a loss of quality or clarity of the image. Therefore, it is recommended to use a car wallpaper that matches your device's screen resolution and size.
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Does using a 4K car wallpaper affect my device's battery life or performance?
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Using a 4K car wallpaper may affect your device's battery life or performance depending on the type and model of your device, the operating system and software that you use, and the settings that you apply. A 4K car wallpaper may consume more power or memory than a lower-resolution wallpaper. It may also require more processing power or bandwidth to load or stream. Therefore, you should check the compatibility and performance of your device before using a 4K car wallpaper. You should also adjust your device's settings to optimize its battery life or performance.
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Where can I find more 4K car wallpapers for my device?
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You can find more 4K car wallpapers for your device on websites like Pexels, Wallpaper Cave,
You can find more 4K car wallpapers for your device on websites like Pexels, Wallpaper Cave, and Wallpaper Access. These websites offer a large variety of free 4K car wallpapers that you can download and use for personal or commercial purposes. You can also use apps like Zedge, Backdrops, or Walli that provide 4K car wallpapers and other features such as live wallpapers, widgets, stickers, ringtones, and more. You can also search for 4K car wallpapers on social media platforms like Pinterest, Instagram, or Reddit and follow accounts or communities that share them.
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Car Racing Game APK: What You Need to Know
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If you are a fan of car racing games, you might have heard of APK files. These are files that allow you to install apps on your Android device that are not available on the Google Play Store. In this article, we will explain what APK files are, how to download and install them, what are some of the best car racing games for Android, what are the benefits of playing car racing games for your brain, and what are some tips and tricks for car racing games.
An APK file is a compressed file that contains all the necessary files and data for an Android app to run. It is similar to an executable file (.exe) for Windows or a package file (.pkg) for Mac. An APK file can be identified by its extension (.apk).
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APK files allow you to install apps from outside the Google Play Store
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Normally, when you want to install an app on your Android device, you go to the Google Play Store and search for it. However, not all apps are available on the Play Store, either because they are not approved by Google or because they are exclusive to certain regions or devices. In these cases, you can use an APK file to install the app manually. This is also useful if you want to try an older version of an app or a beta version that is not yet released.
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APK files can be downloaded from various sources or extracted from installed apps
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There are many websites that offer APK files for download. Some of them are reputable and trustworthy, while others may contain malware or viruses. Therefore, you should always be careful when downloading APK files from unknown sources. You can also use a web tool like [APKCombo](^1^) or [APKPure](^2^) to generate download links for any app on the Google Play Store by pasting its URL.
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Another
Another way to get APK files is to extract them from the apps that are already installed on your device. You can use a file manager app like [ES File Explorer] or [Solid Explorer] to browse the folders of your device and find the APK files of your apps. You can then copy them to your storage or share them with others.
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How to download and install APK files on your Android device?
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Enable unknown sources in your settings
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Before you can install an APK file on your device, you need to enable the option to allow installation from unknown sources. This is a security measure that prevents unauthorized apps from harming your device. To enable this option, go to your device's settings, then security, then toggle on the unknown sources option. You may also need to grant permission to the app or browser that you are using to download the APK file.
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Use a web tool or a file manager to find and download the APK file
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Once you have enabled unknown sources, you can use a web tool like [APKCombo] or [APKPure] to generate download links for any app on the Google Play Store by pasting its URL. Alternatively, you can use a file manager app like [ES File Explorer] or [Solid Explorer] to browse the folders of your device and find the APK files of your apps. You can then tap on the APK file to download it to your device.
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Open the APK file and follow the instructions to install it
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After you have downloaded the APK file, you can open it by tapping on it from your notification bar or your file manager. You will see a prompt asking you to confirm the installation. Tap on install and wait for the process to finish. You may also need to grant some permissions to the app depending on its features. Once the installation is done, you can launch the app from your app drawer or home screen.
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What are the best car racing games for Android?
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Asphalt 9: Legends - a high-octane arcade racing game with stunning graphics and gameplay
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If you are looking for a thrilling and immersive racing game, Asphalt 9: Legends is one of the best choices. This game features over 50 of the world's most amazing cars, from Lamborghini to Ferrari, that you can customize and upgrade. You can also race across 70 tracks in different locations, from New York to Cairo, with stunning graphics and realistic physics. You can play solo or with friends in various modes, such as career, multiplayer, events, and clubs. You can also use the innovative TouchDrive system that lets you focus on the fun of racing while the game handles the steering and acceleration for you.
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CSR Racing 2 - a realistic drag racing game with over 100 licensed cars and customization options
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If you are into drag racing, CSR Racing 2 is a must-have game for you. This game lets you experience the thrill of racing against other players in real time with over 100 licensed cars from top brands like Bugatti, Koenigsegg, McLaren, and more. You can also customize your cars with paint, decals, rims, brakes, and more. You can also tune your cars with nitrous, turbo, supercharger, and engine upgrades. You can join or create a crew with your friends and compete in events and leaderboards. You can also explore the beautiful 3D environments and interact with other players in chat rooms and live streams.
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Real Racing 3 - a simulation racing game with real tracks, cars, and physics
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If you are looking for a more realistic and authentic racing game, Real Racing 3 is a great option for you. This game features over 250 cars from top manufacturers like Ford, Ferrari, Porsche, and more. You can also race on over 40 real tracks from around the world, such as Silverstone, Le Mans, Dubai Autodrome, and more. You can also enjoy the realistic physics and damage system that make every race feel different and challenging. You can play solo or with friends in various modes, such as time trials, endurance, cup races, and online multiplayer. You can also join or create a team with your friends and compete in team events and tournaments.
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Horizon Chase - a retro-inspired racing game with colorful graphics and catchy music
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If you are nostalgic for the classic arcade racing games of the 80s and 90s, Horizon Chase is a perfect game for you. This game features colorful graphics and catchy music that will make you feel like you are playing an old-school game console. You can also race across 32
Horizon Chase - a retro-inspired racing game with colorful graphics and catchy music
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If you are nostalgic for the classic arcade racing games of the 80s and 90s, Horizon Chase is a perfect game for you. This game features colorful graphics and catchy music that will make you feel like you are playing an old-school game console. You can also race across 32 cities in 12 countries, from San Francisco to Tokyo, with different weather and time conditions. You can also unlock and upgrade over 20 cars, from sports cars to muscle cars, and collect coins and fuel along the way. You can play solo or with friends in split-screen mode or online multiplayer.
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Need for Speed: No Limits - a fast-paced racing game with street races, police chases, and car upgrades
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If you are a fan of the Need for Speed franchise, you will love Need for Speed: No Limits. This game lets you experience the adrenaline rush of street racing, dodging traffic, outrunning cops, and competing with rivals. You can also customize and upgrade your cars with over 2.5 million combinations of parts, from spoilers to engines, and make them look unique with paint, vinyls, and decals. You can also explore the open world of Blackridge, a city full of races, events, and challenges. You can play solo or with friends in various modes, such as campaign, car series, tournaments, and rivals.
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What are the benefits of playing car racing games for your brain?
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Car racing games can improve your driving skills and quick reactions
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Playing car racing games can help you improve your driving skills in real life. You can learn how to control your speed, steer your car, avoid obstacles, and react to changing situations. You can also practice different driving techniques, such as drifting, drafting, and braking. Playing car racing games can also improve your reaction time and reflexes, as you have to make split-second decisions and respond to fast-paced scenarios.
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Car racing games can improve your spatial attention and processing speed
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Playing car racing games can also help you improve your spatial attention and processing speed. Spatial attention is the ability to focus on specific locations or objects in space while ignoring irrelevant information. Processing speed is the ability to perform mental tasks quickly and accurately. Playing car racing games can enhance these cognitive skills by requiring you to pay attention to multiple stimuli on the screen, such as the road, the traffic, the map, the speedometer, and the opponents. Playing car racing games can also increase your brain's efficiency and flexibility by challenging you to switch between different tasks and modes.
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Car racing games can improve your hand-eye coordination and memory
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Playing car racing games can also help you improve your hand-eye coordination and memory. Hand-eye coordination is the ability to coordinate your movements with what you see. Memory is the ability to store and recall information. Playing car racing games can boost these skills by requiring you to use your hands to control your car while using your eyes to monitor the environment. Playing car racing games can also improve your memory by exposing you to different tracks, cars, and events that you have to remember and recognize.
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Car racing games can improve your decision making and problem solving
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Playing car racing games can also help you improve your decision making and problem solving skills. Decision making is the ability to choose the best option among alternatives. Problem solving is the ability to find solutions to challenges or difficulties. Playing car racing games can enhance these skills by requiring you to make strategic choices based on various factors, such as your car's performance, your opponent's behavior, the track's condition, and the game's rules. Playing car racing games can also improve your problem solving skills by requiring you to overcome obstacles, adapt to changes, and cope with failures.
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Car racing games can reduce stress and improve your long-term wellbeing
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Playing car racing games can also help you reduce stress and improve your long-term wellbeing. Stress is a state of mental or emotional strain or tension caused by adverse or demanding circumstances. Wellbeing is a state of being comfortable, healthy, or happy. Playing car racing games can reduce stress by providing you with a fun and enjoyable activity that distracts you from your worries and problems. Playing car racing games can also improve your wellbeing by giving you a sense of achievement, satisfaction, and confidence when you win races, complete challenges, or unlock rewards.
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What are some tips and tricks for car racing games?
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Practice in different modes and tracks to learn the controls and the layout
-
One of the best tips for playing car racing games is to practice in different modes
One of the best tips for playing car racing games is to practice in different modes and tracks to learn the controls and the layout. Different games may have different control schemes and options, such as tilt, touch, or buttons. You should try them out and see which one suits you best. You should also familiarize yourself with the tracks and their features, such as curves, jumps, shortcuts, and hazards. You can use the practice mode or the tutorial mode to get a feel of the game before you start racing.
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Adjust your steering sensitivity and controller layout to suit your preferences
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Another tip for playing car racing games is to adjust your steering sensitivity and controller layout to suit your preferences. Steering sensitivity is the degree to which your car responds to your input. Controller layout is the arrangement of the buttons or icons on your screen. You can change these settings in the options menu of the game. You should experiment with different levels of sensitivity and different layouts until you find the ones that work best for you. You can also use a physical controller or a steering wheel if your game supports them.
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Learn how to accelerate, brake, drift, and draft properly depending on the game type
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A third tip for playing car racing games is to learn how to accelerate, brake, drift, and draft properly depending on the game type. Acceleration is the rate at which your car increases its speed. Braking is the rate at which your car decreases its speed. Drifting is the technique of sliding your car sideways around a corner. Drafting is the technique of following closely behind another car to reduce air resistance and increase speed. Different games may have different mechanics and physics for these techniques, so you should learn how they work and when to use them.
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Use the community resources and guides to get more information and advice
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A fourth tip for playing car racing games is to use the community resources and guides to get more information and advice. Most games have online communities where you can interact with other players, ask questions, share tips, and join events. You can also find online guides, videos, reviews, and blogs that can help you improve your skills, learn new strategies, and discover new features. You can also use these resources to compare your performance with other players and see how you rank among them.
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Have fun and enjoy the thrill of racing
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The last tip for playing car racing games is to have fun and enjoy the thrill of racing. Car racing games are meant to be entertaining and exciting, so you should not take them too seriously or get frustrated by them. You should also respect other players and follow the rules of fair play. You should also take breaks from playing to avoid eye strain, fatigue, or addiction. Remember that playing car racing games is a hobby, not a job or a competition.
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Conclusion
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In conclusion, car racing games are a popular genre of games that can provide you with hours of fun and enjoyment. They can also help you improve your brain skills, such as driving skills, spatial attention, processing speed, hand-eye coordination, memory, decision making, problem solving, stress reduction, and wellbeing. However, you should also be careful when downloading and installing APK files from unknown sources, as they may contain malware or viruses. You should also follow some tips and tricks for car racing games, such as practicing in different modes and tracks, adjusting your steering sensitivity and controller layout, learning how to accelerate, brake, drift, and draft properly depending on the game type, using the community resources and guides to get more information and advice, and having fun and enjoying the thrill of racing.
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FAQs
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What are some of the best websites to download APK files for car racing games?
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Some of the best websites to download APK files for car racing games are [APKCombo], [APKPure], [APKMirror], [APKMonk], and [APKHome]. These websites offer safe and reliable APK files for various car racing games.
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What are some of the best apps to extract APK files from installed apps on your device?
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Some of the best apps to extract APK files from installed apps on your device are [ES File Explorer], [Solid Explorer], [APK Extractor], [ML Manager], and [App Backup & Restore]. These apps allow you to easily find and share APK files from your apps.
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What are some of the best accessories to enhance your car racing game experience?
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Some of the best accessories to enhance your car racing game experience are a physical controller or a
Some of the best accessories to enhance your car racing game experience are a physical controller or a steering wheel, a VR headset, a gaming chair, and a surround sound system. These accessories can make you feel more immersed and comfortable while playing car racing games.
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What are some of the best car racing game franchises?
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Some of the best car racing game franchises are Asphalt, CSR Racing, Real Racing, Need for Speed, and Forza. These franchises have multiple titles and versions that offer different features and gameplay. They also have loyal fan bases and large communities.
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What are some of the best car racing game genres?
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Some of the best car racing game genres are arcade racing, simulation racing, drag racing, rally racing, and kart racing. These genres have different styles and objectives that appeal to different types of players. They also have different levels of realism and difficulty that challenge different skills.
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Microsoft Solitaire Collection Games Free Download: How to Enjoy the Classic Card Games on Your Device
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Introduction
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If you love playing solitaire games, you will be happy to know that you can enjoy them for free on your device with Microsoft Solitaire Collection. This app is a collection of five classic card games that have been played by millions of gamers worldwide for over 30 years. You can download and install it from the Microsoft Store or Google Play, depending on your device.
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microsoft solitaire collection games free download
Microsoft Solitaire Collection offers you a variety of features and benefits that make playing solitaire games fun and rewarding. You can choose from five different game modes: Klondike Solitaire, Spider Solitaire, FreeCell Solitaire, TriPeaks Solitaire, and Pyramid Solitaire. Each game mode has its own rules, challenges, and strategies that will keep you entertained for hours. You can also customize your card game with different themes and card backs, play new solvable card challenges every day, earn badges and rewards, save your progress, and compete with other players. Whether you want to relax with the classics, sharpen your mind, or challenge yourself, Microsoft Solitaire Collection has something for everyone.
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Klondike Solitaire
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Klondike Solitaire is the king of all timeless classic card games. It is also known as Patience or simply Solitaire. The goal of Klondike Solitaire is to clear all the cards from the table by building four stacks of cards from Ace to King in each suit.
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To play Klondike Solitaire, you need a standard 52-card deck. The cards are shuffled and dealt into seven columns on the table. The first column has one card face up, the second column has two cards with one face up, and so on until the seventh column has seven cards with one face up. The remaining cards are placed face down in a stock pile at the top left corner of the screen.
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You can move cards from one column to another by dragging them with your finger or mouse. You can only move cards that are face up and in descending order of rank and alternating color. For example, you can move a red 6 on top of a black 7, but not on top of a black 6 or a red 7. You can also move a group of cards that are already in sequence as a single unit. For example, you can move a red 6, black 5, and red 4 together on top of a black 7.
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If you have an empty column, you can move any card or group of cards there. You can also move a card from the stock pile to an empty column. To access the stock pile, you can tap or click on it to turn over one card at a time and place it face up on the waste pile at the top right corner of the screen. You can then move the card from the waste pile to any column if it follows the rules. If you run out of cards in the stock pile, you can tap or click on the waste pile to reset it and start over.
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To win Klondike Solitaire, you need to build four stacks of cards from Ace to King in each suit at the top right corner of the screen. These are called the foundations. You can move any card from the table or the waste pile to the foundations if it is the next higher rank and same suit as the top card of the foundation. For example, you can move a red 2 to the foundation if there is a red Ace there, but not if there is a black Ace or no card at all. You can also double-tap or double-click on a card to automatically move it to the foundation if possible.
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You can choose between two modes of play in Klondike Solitaire: one-card draw or three-card draw. In one-card draw, you turn over one card from the stock pile at a time. In three-card draw, you turn over three cards from the stock pile at a time, but you can only use the top card of the waste pile. Three-card draw is more challenging and requires more strategy than one-card draw.
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You can also choose between two modes of scoring in Klondike Solitaire: Traditional or Vegas. In Traditional scoring, you start with zero points and gain points for every move you make. You get 10 points for moving a card from the stock pile to the waste pile, 5 points for moving a card from the waste pile to the table, and 10 points for moving a card from the table to the foundation. You lose 15 points for moving a card from the foundation back to the table, and 100 points for resetting the stock pile. In Vegas scoring, you start with -52 points and gain 5 points for every card you move to the foundation. You do not lose points for any other moves, but you cannot reset the stock pile.
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Tips and tricks to clear the table faster
- - Try to expose as many face-down cards as possible by moving cards from longer columns to shorter ones. - Try to empty at least one column as soon as possible so that you have more space to maneuver cards around. - Try to build your foundations evenly so that you do not block yourself from moving cards later. - Try to avoid moving cards to the foundations too early unless they are low-ranking cards like Aces and Twos. - Try to save your free moves from the stock pile for when you really need them. - Try to plan ahead and anticipate what cards you will need next.
Spider Solitaire
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Spider Solitaire is another classic card game that tests your skill and patience. It is also known as Spiderette or Scorpion. The goal of Spider Solitaire is to clear all the cards from the table by building eight stacks of cards from King to Ace in the same suit.
-
To play Spider Solitaire, you need two standard 52-card decks. The cards are shuffled and dealt into 10 columns on the table. The first four columns have six cards each, with the top card face up. The remaining six columns have five cards each, with the top card face up. The remaining 50 cards are placed face down in a stock pile at the bottom right corner of the screen.
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You can move cards from one column to another by dragging them with your finger or mouse. You can only move cards that are face up and in descending order of rank and same suit. For example, you can move a spade 9 on top of a spade 10, but not on top of a spade Jack or a heart 10. You can also move a group of cards that are already in sequence as a single unit. For example, you can move a spade 9, 8, and 7 together on top of a spade 10.
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If you have an empty column, you can move any card or group of cards there. You can also move a card from the stock pile to an empty column. To access the stock pile, you can tap or click on it to deal 10 new cards, one to each column. You can only do this if there are no empty columns on the table.
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To win Spider Solitaire, you need to build eight stacks of cards from King to Ace in the same suit at the bottom of each column. These are called complete suits. You can move any card or group of cards to a complete suit if it follows the rules. You can also double-tap or double-click on a card to automatically move it to a complete suit if possible.
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You can choose between three modes of play in Spider Solitaire: one suit, two suits, or four suits. In one suit mode, all the cards are spades. In two suits mode, half of the cards are spades and half are hearts. In four suits mode, each suit is represented by one quarter of the cards. The more suits you play with, the more difficult and challenging the game becomes.
-
Tips and tricks to clear all columns with the fewest moves possible
- - Try to expose as many face-down cards as possible by moving cards from longer columns to shorter ones. - Try to empty at least one column as soon as possible so that you have more space to maneuver cards around. - Try to build your complete suits evenly so that you do not block yourself from moving cards later. - Try to avoid mixing different suits in the same column unless necessary. - Try to save your free deals from the stock pile for when you really need them. - Try to plan ahead and anticipate what cards you will need next.
FreeCell Solitaire
-
FreeCell Solitaire is another classic card game that requires skill and strategy. It is also known as Baker's Game or Eight Off. The goal of FreeCell Solitaire is to clear all the cards from the table by building four stacks of cards from Ace to King in each suit.
-
To play FreeCell Solitaire, you need a standard 52-card deck. The cards are shuffled and dealt into eight columns on the table. All the cards are face up and visible. There are also four free cells and four foundations at the top left and right corners of the screen respectively.
-
You can move cards from one column to another by dragging them with your finger or mouse. You can only move one card at a time, unless you have enough free cells or empty columns to temporarily store them. The number of cards you can move at once is equal to one plus the number of free cells plus the number of empty columns. For example, if you have two free cells and one empty column, you can move up to four cards at once.
-
You can only move cards that are face up and in descending order of rank and alternating color. For example, you can move a red 6 on top of a black 7, but not on top of a black 6 or a red 7.
-
If you have an empty column, you can move any card there. You can also move any card to a free cell if it is empty. To access a free cell, you can tap or click on it to select or deselect it. You can then move the card from or to any column if it follows the rules.
-
To win FreeCell Solitaire, you need to build four stacks of cards from Ace to King in each suit at the top right corner of the screen. These are called the foundations. You can move any card from the table, the free cell, or the waste pile to the foundations if it is the next higher rank and same suit as the top card of the foundation. For example, you can move a spade 2 to the foundation if there is a spade Ace there, but not if there is a heart Ace or no card at all. You can also double-tap or double-click on a card to automatically move it to the foundation if possible.
-
Tips and tricks to think several moves ahead and solve challenging puzzles
- - Try to expose as many face-down cards as possible by moving cards from longer columns to shorter ones. - Try to empty at least one column as soon as possible so that you have more space to maneuver cards around. - Try to build your foundations evenly so that you do not block yourself from moving cards later. - Try to use your free cells sparingly and strategically to store cards that you need later. - Try to avoid moving cards to the foundations too early unless they are low-ranking cards like Aces and Twos. - Try to plan ahead and anticipate what cards you will need next.
TriPeaks Solitaire
-
TriPeaks Solitaire is another classic card game that combines skill and luck. It is also known as Tri Towers or Three Peaks. The goal of TriPeaks Solitaire is to clear all the cards from the table by selecting cards in a sequence, either up or down, regardless of suit.
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To play TriPeaks Solitaire, you need a standard 52-card deck. The cards are shuffled and dealt into three peaks of cards on the table. Each peak has four rows of cards, with the top row having one card, the second row having two cards, and so on until the bottom row has four cards. The remaining 24 cards are placed face down in a stock pile at the bottom left corner of the screen. There is also a waste pile at the bottom center of the screen where you can see the top card.
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You can select any card from the table that is one rank higher or lower than the top card of the waste pile. For example, if the top card of the waste pile is a 7, you can select a 6 or an 8 from the table. You can also select an Ace or a King if the top card of the waste pile is a 2 or a Queen respectively. When you select a card from the table, it is moved to the top of the waste pile. You can only select cards that are fully exposed, meaning they have no other cards covering them.
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If you have no more valid moves on the table, you can tap or click on the stock pile to turn over one card at a time and place it on top of the waste pile. You can then use this card to select another card from the table if possible. If you run out of cards in the stock pile, you can tap or click on the waste pile to reset it and start over.
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To win TriPeaks Solitaire, you need to clear all the cards from the table before you run out of moves or time. You can see how many cards are left on the table at the top right corner of the screen. You can also see how much time you have left at the top left corner of the screen. You can also see your score and level at the bottom right corner of the screen.
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You can earn points by selecting cards from the table in a sequence. The longer the sequence, the more points you get. You can also earn bonus points by clearing a peak or the whole table. You can lose points by using the stock pile or resetting the waste pile. You can also lose time by making invalid moves or taking too long to make a move.
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Tips and tricks to clear the board quickly and avoid losing streaks
- - Try to expose as many cards as possible by selecting cards from different peaks and rows. - Try to clear at least one peak as soon as possible so that you have more options to select cards from. - Try to use the stock pile sparingly and strategically to get new cards that can help you continue your sequence. - Try to avoid breaking your sequence unless necessary, as you will lose your combo points and bonus time. - Try to plan ahead and anticipate what cards you will need next.
Pyramid Solitaire
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Pyramid Solitaire is another classic card game that combines skill and luck. It is also known as Tut's Tomb or Pharaoh's Solitaire. The goal of Pyramid Solitaire is to clear all the cards from the table by combining two cards that add up to 13 and removing them from the board.
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To play Pyramid Solitaire, you need a standard 52-card deck. The cards are shuffled and dealt into a pyramid of cards on the table. The pyramid has seven rows of cards, with the top row having one card, the second row having two cards, and so on until the bottom row has seven cards. All the cards are face up and visible. The remaining 24 cards are placed face down in a stock pile at the bottom left corner of the screen. There is also a waste pile at the bottom center of the screen where you can see the top card.
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You can select any two cards from the table that add up to 13 and remove them from the board. For example, you can select a 6 and a 7, a 5 and an 8, or a 4 and a 9. You can also select a single card that is already 13, such as a King. When you select two cards or a single card, they are moved to the waste pile. You can only select cards that are fully exposed, meaning they have no other cards covering them.
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If you have no more valid moves on the table, you can tap or click on the stock pile to turn over one card at a time and place it on top of the waste pile. You can then use this card to combine with another card from the table if possible. If you run out of cards in the stock pile, you can tap or click on the waste pile to reset it and start over.
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To win Pyramid Solitaire, you need to clear all the cards from the table before you run out of moves or time. You can see how many cards are left on the table at the top right corner of the screen. You can also see how much time you have left at the top left corner of the screen. You can also see your score and level at the bottom right corner of the screen.
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You can earn points by removing cards from the table. The higher the card, the more points you get. For example, you get 13 points for removing a King, 12 points for removing a Queen, and so on until 1 point for removing an Ace. You can also earn bonus points by clearing a row or the whole pyramid. You can lose points by using the stock pile or resetting the waste pile. You can also lose time by making invalid moves or taking too long to make a move.
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Tips and tricks to reach the top of the pyramid and clear as many boards as you can
- - Try to remove cards from the bottom rows first, as they cover more cards than the top rows. - Try to remove cards that are blocking other cards from being paired up. - Try to use the stock pile sparingly and strategically to get new cards that can help you clear the table. - Try to avoid breaking your sequence unless necessary, as you will lose your combo points and bonus time. - Try to plan ahead and anticipate what cards you will need next.
Daily Challenges & Events
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If you want to spice up your solitaire games, you can try out the Daily Challenges and Events that Microsoft Solitaire Collection offers. These are new solvable card challenges that you can play every day in all five game modes. You can complete Daily Challenges and earn Solitaire badges and rewards. You can also sign in with a Microsoft Account to keep your progress and compete with other players.
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Daily Challenges are sets of four card challenges that you can play every day in Klondike, Spider, FreeCell, TriPeaks, and Pyramid. Each challenge has a different difficulty level and a different goal. For example, you may have to clear a certain number of cards, score a certain number of points, or complete a certain number of moves. You can choose which challenge to play first, and you can replay any challenge as many times as you want until you complete it.
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When you complete a Daily Challenge, you earn coins and stars. Coins are used to buy hints and undo moves in case you get stuck. Stars are used to unlock new badges and rewards. You can earn up to four stars per challenge, depending on how well you perform. For example, you may earn more stars if you finish faster, use fewer moves, or score higher. You can also earn bonus stars if you complete all four challenges in a day or in a month.
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Badges are special achievements that you can collect by completing Daily Challenges. There are different types of badges, such as bronze, silver, gold, and platinum badges. Each badge has a different design and a different requirement to unlock it. For example, you may have to complete a certain number of challenges, earn a certain number of stars, or play on a certain date. You can see your badge collection at any time by tapping or clicking on the badge icon at the top right corner of the screen.
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Rewards are extra benefits that you can enjoy by completing Daily Challenges. There are different types of rewards, such as themes, card backs, coins, and game boosts. Each reward has a different value and a different way to claim it. For example, you may have to complete a certain number of challenges in a month, earn a certain number of stars in a year, or play on a special occasion. You can see your reward collection at any time by tapping or clicking on the reward icon at the top right corner of the screen.
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Events are special card challenges that you can play on certain days or periods of time in Microsoft Solitaire Collection. They are similar to Daily Challenges, but they have different themes and goals. For example, you may have to play with holiday-themed cards, score as many points as possible in a limited time, or clear as many boards as possible in a row. You can see the current and upcoming events at any time by tapping or clicking on the event icon at the top right corner of the screen.
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When you complete an event challenge, you earn coins and trophies. Coins are used to buy hints and undo moves in case you get stuck. Trophies are used to show off your achievements and rank among other players. You can earn up to three trophies per challenge, depending on how well you perform. For example, you may earn more trophies if you finish faster, use fewer moves, or score higher. You can also earn bonus trophies if you complete all the challenges in an event or in a series of events. You can see your trophy collection at any time by tapping or clicking on the trophy icon at the top right corner of the screen.
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To participate in events, you need to sign in with a Microsoft Account. This will allow you to keep your progress, earn trophies, and compete with other players. You can see your rank and score at any time by tapping or clicking on the leaderboard icon at the top right corner of the screen. You can also see how other players are doing and compare your results with them.
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Tips and tricks to play new solvable card challenges every day and earn Solitaire badges and rewards
- - Try to play the Daily Challenges and Events regularly to improve your skills and earn more coins, stars, badges, rewards, and trophies. - Try to complete all the challenges in a day or in a month to earn bonus stars and badges. - Try to play the events on special occasions to earn special rewards and trophies. - Try to use your coins wisely to buy hints and undo moves when you need them. - Try to challenge yourself by playing with higher difficulty levels and scoring modes.
Themes & Card Backs
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If you want to customize your card game, you can choose from different themes and card backs that Microsoft Solitaire Collection offers. Themes are visual styles that change the appearance of the background, the cards, and the interface. Card backs are designs that change the look of the back of the cards.
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You can choose from a variety of themes and card backs, such as Classic, Aquarium, Beach, Dark Mode, Retro, and more. Each theme and card back has a different color scheme, mood, and vibe that can suit your preference and mood. You can also unlock new themes and card backs by completing Daily Challenges and Events.
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To change your theme or card back, you can tap or click on the settings icon at the top right corner of the screen. You can then select the theme or card back option and browse through the available choices. You can preview how each theme or card back looks like before applying it. You can also change your theme or card back anytime you want.
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Tips and tricks to customize your card game with different themes and card backs
- - Try to experiment with different themes and card backs to find the ones that you like best. - Try to match your theme or card back with the game mode or event that you are playing. - Try to unlock new themes and card backs by completing Daily Challenges and Events. - Try to change your theme or card back occasionally to keep your game fresh and exciting.
Save Your Progress
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If you want to save your progress, you can sign in with a Microsoft Account in Microsoft Solitaire Collection. This will allow you to keep your player stats, XP, level, achievements, and events across multiple devices. You can also connect with an Xbox Game Pass account to access an ad-free game experience.
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Your player stats include your score, time, moves, wins, losses, streaks, and completion percentage for each game mode. You can see your player stats at any time by tapping or clicking on the stats icon at the top right corner of the screen. You can also see how you compare with other players in terms of skill level.
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Your XP is a measure of your experience and progress in Microsoft Solitaire Collection. You can earn XP by playing games, completing challenges, earning badges, unlocking rewards, and participating in events. You can see your XP at any time by tapping or clicking on the XP icon at the top right corner of the screen. You can also see how much XP you need to reach the next level.
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Your level is a reflection of your skill and achievement in Microsoft Solitaire Collection. You can increase your level by earning XP. You can see your level at any time by tapping or clicking on the level icon at the top right corner of the screen. You can also see what benefits you get for reaching higher levels, such as more coins, themes, card backs, and game boosts.
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Your achievements are milestones that you can accomplish by playing games, completing challenges, earning badges, unlocking rewards, and participating in events. You can see your achievements at any time by tapping or clicking on the achievements icon at the top right corner of the screen. You can also see how many achievements you have completed and how many are left to complete.
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Your events are special card challenges that you can play on certain days or periods of time in Microsoft Solitaire Collection. You can see your events at any time by tapping or clicking on the event icon at the top right corner of the screen. You can also see how many events you have completed and how many are left to complete.
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To sign in with a Microsoft Account, you can tap or click on the sign in icon at the top left corner of the screen. You can then enter your email address and password, or create a new account if you do not have one. You can also sign in with a Facebook account if you prefer. Signing in with a Microsoft Account will allow you to sync your progress across multiple devices, such as your PC, laptop, tablet, or phone. You can also sign out anytime you want.
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To connect with an Xbox Game Pass account, you need to have an active subscription to Xbox Game Pass Ultimate or Xbox Game Pass for PC. This will allow you to access an ad-free game experience in Microsoft Solitaire Collection, as well as other benefits such as cloud gaming, exclusive discounts, and more. To connect with an Xbox Game Pass account, you need to sign in with a Microsoft Account that is linked to your Xbox Game Pass subscription. You can then tap or click on the Xbox Game Pass icon at the top left corner of the screen and follow the instructions.
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Tips and tricks to save your progress and compete with other players
- - Try to sign in with a Microsoft Account regularly to keep your progress and enjoy more features and benefits. - Try to connect with an Xbox Game Pass account if you have one to enjoy an ad-free game experience and other perks. - Try to improve your player stats, XP, level, achievements, and events by playing games, completing challenges, earning badges, unlocking rewards, and participating in events. - Try to compare your results with other players and see how you rank among them.
Conclusion
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Microsoft Solitaire Collection is a great way to enjoy the classic card games on your device for free. You can choose from five different game modes: Klondike Solitaire, Spider Solitaire, FreeCell Solitaire, TriPeaks Solitaire, and Pyramid Solitaire. Each game mode has its own rules, challenges, and strategies that will keep you entertained for hours. You can also customize your card game with different themes and card backs, play new solvable card challenges every day, earn badges and rewards, save your progress, and compete with other players. Whether you want to relax with the classics, sharpen your mind, or challenge yourself, Microsoft Solitaire Collection has something for everyone.
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So what are you waiting for? Download Microsoft Solitaire Collection games for free today and start playing!
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FAQs
- - Q: How do I download Microsoft Solitaire Collection games for free? - A: You can download Microsoft Solitaire Collection games for free from the Microsoft Store or Google Play, depending on your device. - Q: How do I play Microsoft Solitaire Collection games offline? - A: You can play Microsoft Solitaire Collection games offline by turning off your internet connection before launching the app. However, some features and benefits may not be available offline. - Q: How do I get more coins in Microsoft Solitaire Collection? - A: You can get more coins by playing games, completing challenges, earning badges, unlocking rewards, and participating in events. You can also buy more coins with real money if you want. - Q: How do I get rid of ads in Microsoft Solitaire Collection? - A: You can get rid of ads in Microsoft Solitaire Collection by connecting with an Xbox Game Pass account that has an active subscription to Xbox Game Pass Ultimate or Xbox Game Pass for PC. - Q: How do I contact customer support for Microsoft Solitaire Collection? - A: You can contact customer support for Microsoft Solitaire Collection by tapping or clicking on the settings icon at the top right corner of the screen. You can then select the help option and follow the instructions. 197e85843d
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Dude Theft Wars Offline Games Mod Apk: A Sandbox Game with Unlimited Fun
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If you are looking for a fun and exciting sandbox game that lets you do whatever you want, then you should try Dude Theft Wars. This game is a parody of Grand Theft Auto, where you can explore a huge open world, steal cars, rob banks, shoot guns, and cause chaos. You can also interact with other dudes, make friends or enemies, and have hilarious conversations.
However, if you want to enjoy the game to the fullest, you might want to download the mod apk version of Dude Theft Wars. This version gives you unlimited money and weapons, as well as access to all the features and modes of the game. You can also play the game offline, without any internet connection.
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In this article, we will tell you everything you need to know about Dude Theft Wars offline games mod apk. We will show you how to download and install the mod apk file, how to play the game and use its features, and how to get more money and weapons. We will also give you some tips and tricks to make the most out of your gaming experience. Finally, we will list some pros and cons of playing Dude Theft Wars offline games mod apk, and answer some frequently asked questions.
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Gameplay and Features
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Dude Theft Wars offline games mod apk is a sandbox game that lets you do whatever you want in a huge open world. You can roam around freely, drive cars, bikes, helicopters, tanks, or even UFOs. You can also steal money from ATMs or shops, rob banks or casinos, or hack computers. You can also use guns, grenades, rockets, or even nukes to shoot at anything or anyone. You can also interact with other dudes, who have their own personalities and dialogues. You can make friends or enemies with them, chat with them, prank them, or fight them.
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The game has different modes and missions that you can choose from. You can play in free roam mode, where you can do whatever you want without any objectives or restrictions. You can also play in story mode, where you can follow a storyline and complete various quests. You can also play in online mode, where you can join other players from around the world and compete or cooperate with them.
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The mod apk version of Dude Theft Wars gives you unlimited money and weapons, which means you can buy anything you want from the shops or online market. You can also unlock all the features and modes of the game without any ads or in-app purchases. You can also play the game offline, without any internet connection.
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Tips and Tricks
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If you want to get more money and weapons in Dude Theft Wars offline games mod apk, here are some tips and tricks that you can use:
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Use cheats. The game has a cheat menu that you can access by tapping on the phone icon on the top left corner of the screen. You can enter various codes that will give you money, weapons, health, armor, or other benefits.
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Rob banks or casinos. The game has several banks and casinos that you can rob by using guns or explosives. You can get a lot of money by doing this, but be careful - of the police or gangs that will chase you after the robbery.
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Hack computers. The game has several computers that you can hack by using a mini-game. You can get money, weapons, or other items by hacking them.
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Complete missions. The game has various missions that you can complete by following the instructions. You can get money, weapons, or other rewards by completing them.
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If you want to avoid getting arrested or killed by the police or gangs in Dude Theft Wars offline games mod apk, here are some tips and tricks that you can use:
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Use stealth. The game has a stealth mode that you can activate by tapping on the eye icon on the top right corner of the screen. You can hide from the police or gangs by using this mode, but be careful not to make any noise or get too close to them.
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Use vehicles. The game has various vehicles that you can use to escape from the police or gangs. You can drive cars, bikes, helicopters, tanks, or even UFOs. You can also use weapons or explosives to damage or destroy their vehicles.
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Use cheats. The game has a cheat menu that you can access by tapping on the phone icon on the top left corner of the screen. You can enter various codes that will make you invincible, invisible, or teleport to another location.
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If you want to customize your character and vehicles in Dude Theft Wars offline games mod apk, here are some tips and tricks that you can use:
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Use shops. The game has several shops that you can visit to buy clothes, accessories, hair styles, tattoos, or masks for your character. You can also buy paint, wheels, spoilers, or stickers for your vehicles.
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Use online market. The game has an online market that you can access by tapping on the shopping cart icon on the top right corner of the screen. You can buy more items for your character and vehicles from there.
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Use cheats. The game has a cheat menu that you can access by tapping on the phone icon on the top left corner of the screen. You can enter various codes that will change your character's appearance or your vehicle's performance.
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Pros and Cons
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Dude Theft Wars offline games mod apk is a fun and exciting sandbox game that offers unlimited possibilities and freedom. However, it also has some pros and cons that you should consider before playing it. Here are some of them:
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Pros
Cons
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- It has a huge open world that you can explore and interact with.
- It has some bugs and glitches that might affect the gameplay.
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- It has a lot of features and modes that you can enjoy and choose from.
- It has some violent and inappropriate content that might not be suitable for everyone.
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- It has a lot of humor and parody that will make you laugh and have fun.
- It has some ads and in-app purchases that might annoy you.
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- It has unlimited money and weapons that will let you buy anything you want and do anything you want.
- It might get boring or repetitive after a while if you do not have any goals or challenges.
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- It can be played offline, without any internet connection.
- It might not be compatible with some devices or operating systems.
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Conclusion
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Dude Theft Wars offline games mod apk is a sandbox game that lets you do whatever you want in a huge open world. You can explore, drive, steal, rob, shoot, and interact with other dudes. You can also play in different modes and missions, and customize your character and vehicles. You can also enjoy unlimited money and weapons, as well as play offline without any internet connection.
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In conclusion, Dude Theft Wars offline games mod apk is a great game for anyone who loves sandbox games and wants to have unlimited fun. However, it also has some drawbacks that might not appeal to everyone. Therefore, we recommend you to try it out for yourself and see if it suits your taste and preference.
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We hope this article was helpful and informative for you. If you have any questions or comments about Dude Theft Wars offline games mod apk, feel free to share them with us below. We would love to hear from you!
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FAQs
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Here are some frequently asked questions and answers about Dude Theft Wars offline games mod apk:
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Q: Is Dude Theft Wars offline games mod apk safe to download and install?
A: Yes, Dude Theft Wars offline games mod apk is safe to download and install, as long as you get it from a trusted and reliable source. However, you should always be careful when downloading and installing any mod apk file, as some of them might contain viruses or malware that could harm your device or data. Therefore, we advise you to scan the file with an antivirus software before installing it, and to backup your data before playing the game.
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Q: How can I update Dude Theft Wars offline games mod apk?
-A: To update Dude Theft Wars offline games mod apk, you need to download and install the latest version of the mod apk file from the same source that you got the previous one. You should also uninstall the old version of the game before installing the new one, to avoid any conflicts or errors. However, you should note that updating the game might erase your progress or data, so you might want to save your game before updating it.
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Q: How can I uninstall Dude Theft Wars offline games mod apk?
-A: To uninstall Dude Theft Wars offline games mod apk, you need to go to your device's settings and find the app manager or applications option. Then, you need to find and select Dude Theft Wars offline games mod apk from the list of apps, and tap on the uninstall button. You should also delete the mod apk file from your device's storage, to free up some space.
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Q: What are some alternatives to Dude Theft Wars offline games mod apk?
-A: If you are looking for some alternatives to Dude Theft Wars offline games mod apk, you might want to check out these other sandbox games that are similar or related to it:
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Gangstar Vegas: World of Crime. This is a sandbox game that lets you explore a huge city full of crime and action. You can drive cars, bikes, planes, or tanks, and use guns, grenades, or rockets to fight against enemies. You can also play in different modes and missions, and customize your character and vehicles.
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Grand Theft Auto: San Andreas. This is a classic sandbox game that lets you experience the life of a gangster in a fictional state of San Andreas. You can roam around three cities, Los Santos, San Fierro, and Las Venturas, and do various activities such as driving, shooting, gambling, or dancing. You can also play in different modes and missions, and customize your character and vehicles.
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Minecraft. This is a sandbox game that lets you create and explore a world made of blocks. You can build anything you can imagine, from houses to castles to spaceships. You can also mine resources, craft items, fight monsters, or play with other players online.
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Q: Where can I get more information about Dude Theft Wars offline games mod apk?
-A: If you want to get more information about Dude Theft Wars offline games mod apk, you can visit the official website of the game developer, Poxel Studios. You can also follow their social media accounts on Facebook, Twitter, Instagram, or YouTube. You can also join their Discord server or Reddit community to chat with other fans and players of the game.
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Block Strike APKPure: How to Download and Play the Blocky FPS Shooter
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If you are looking for a fun and addictive online multiplayer first-person shooter game with blocky graphics and competitive gameplay, you might want to check out Block Strike. This game is available on Google Play Store, but you can also download it from APKPure, a third-party app store that offers free and safe Android apps and games. In this article, we will tell you what Block Strike is, how to download it from APKPure, and why you should play it on APKPure. We will also give you some tips and tricks to help you improve your skills and enjoy the game more.
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What is Block Strike?
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Block Strike is a 3D shooter game developed by Rexet Studio, a small indie game studio based in Russia. The game was released in 2015 and has since gained over 50 million downloads and 4.4 stars rating on Google Play Store. The game features over 60 different maps, 15 game modes, hundreds of weapons and skins, and a customizable character. You can play solo or team up with your friends and other players from around the world to compete in various modes such as Deathmatch, Team Deathmatch, Gun Game, Hunger Games, Zombie Survival, Bunny Hop, and more. You can also create your own rooms and invite your friends to join you.
Some of the features that make Block Strike an exciting and enjoyable game are:
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Blocky graphics: The game has a pixelated style that gives it a retro and nostalgic feel. The graphics are colorful and vibrant, and the animations are smooth and realistic. The game also has a low system requirement, which means it can run on most devices without lagging or crashing.
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Competitive gameplay: The game offers a fast-paced and thrilling gameplay that will keep you on your toes. You have to be quick and accurate to shoot your enemies before they shoot you. You also have to be strategic and tactical to use the map features, such as walls, doors, windows, crates, barrels, etc., to your advantage. You can also customize your weapons and skins to suit your preferences and style.
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Social interaction: The game allows you to chat with your friends and other players in the lobby or in the game. You can also join clans or create your own clan to compete with other clans in clan wars. You can also share your achievements and screenshots with your friends on social media.
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How to download Block Strike from APKPure
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If you want to download Block Strike from APKPure, you need to follow these simple steps:
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Go to [APKPure.com](^1^) on your browser or download the APKPure app from Google Play Store.
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Search for "Block Strike" in the search bar or browse the categories to find it.
-
Click on the "Download APK" button or scan the QR code to download the file.
-
Once the download is complete, open the file and install it on your device. You may need to enable "Unknown Sources" in your settings to allow the installation of apps from sources other than Google Play Store.
-
After the installation is done, you can launch the game and enjoy it.
-
-
Why play Block Strike on APKPure?
-
There are many reasons why you should play Block Strike on APKPure instead of Google Play Store. Here are some of them:
-
Benefits of APKPure
-
-
No region restrictions: APKPure allows you to access apps and games that are not available in your country or region. You can also download older versions of apps and games that are compatible with your device or have features that are removed in newer versions.
Free and safe: APKPure offers free and safe apps and games that are verified by their team. You don't have to worry about malware, viruses, or spyware. You also don't have to pay for any premium features or in-app purchases.
-
Easy and fast: APKPure has a user-friendly interface and a fast download speed. You can easily find what you are looking for and download it in a matter of seconds. You can also update your apps and games with one click.
-
-
Tips and tricks for playing Block Strike on APKPure
-
To make the most out of your gaming experience, here are some tips and tricks that you can use when playing Block Strike on APKPure:
-
-
Practice your aim and reflexes: The key to winning in Block Strike is to have a good aim and fast reflexes. You can practice your skills in the training mode or in the offline mode. You can also adjust the sensitivity and the crosshair of your controls to suit your preference.
-
Choose the right weapon and mode: Block Strike has a variety of weapons and modes that you can choose from. Each weapon has its own advantages and disadvantages, such as damage, range, accuracy, fire rate, recoil, etc. You should choose the weapon that matches your playstyle and the mode that you are playing. For example, if you are playing in a close-quarters map, you might want to use a shotgun or a submachine gun. If you are playing in a long-range map, you might want to use a sniper rifle or a rifle.
-
Use the map features: The maps in Block Strike have different features that you can use to your advantage. You can use the walls, doors, windows, crates, barrels, etc., to hide, cover, or ambush your enemies. You can also use the explosives, such as grenades, mines, rockets, etc., to deal damage or create diversions.
-
Communicate and cooperate with your teammates: Block Strike is a team-based game that requires communication and cooperation. You can use the chat feature or the voice chat feature to communicate with your teammates. You can also use the clan feature to join or create a clan with your friends or other players. You can share strategies, tips, and resources with your clan members.
-
-
Conclusion
-
Block Strike is a fun and addictive online multiplayer first-person shooter game that you can download and play on APKPure. The game has blocky graphics, competitive gameplay, and social interaction. You can enjoy over 60 different maps, 15 game modes, hundreds of weapons and skins, and a customizable character. You can also benefit from APKPure's features, such as no region restrictions, free and safe apps and games, and easy and fast downloads. If you want to improve your skills and enjoy the game more, you can follow our tips and tricks for playing Block Strike on APKPure.
-
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-
Summary of the article
-
In this article, we have covered the following topics:
-
-
What is Block Strike?
-
How to download Block Strike from APKPure?
-
Why play Block Strike on APKPure?
-
Tips and tricks for playing Block Strike on APKPure.
-
-
FAQs
-
Here are some frequently asked questions about Block Strike and APKPure:
-
-
Is Block Strike free to play?
-
Yes, Block Strike is free to play. However, it contains ads and offers in-app purchases. You can disable the ads by purchasing the premium version of the game or by using an ad blocker app.
-
Is Block Strike offline or online?
-
Block Strike is mainly an online game that requires an internet connection to play. However, it also has an offline mode that allows you to play without internet. You can access the offline mode by tapping on the "Offline" button on the main menu.
-
Is APKPure safe to use?
-
Yes, APKPure is safe to use. It is a trusted third-party app store that offers free and safe apps and games that are verified by their team. It does not contain any malware, viruses, or spyware.
-
How to update Block Strike on APKPure?
-
To update Block Strike on APKPure, you need to follow these steps:
-
-
Open the APKPure app or go to [APKPure.com] on your browser.
-
Search for "Block Strike" or
Tap on the "Update" button or scan the QR code to download the latest version of the game.
-
Open the file and install it on your device. You may need to enable "Unknown Sources" in your settings to allow the installation of apps from sources other than Google Play Store.
-
-
How to contact Block Strike or APKPure support?
-
If you have any issues, questions, or feedback about Block Strike or APKPure, you can contact their support teams by using the following methods:
-
-
For Block Strike, you can email them at support@rexetstudio.com or visit their website at [www.rexetstudio.com].
-
For APKPure, you can email them at support@apkpure.com or visit their website at [www.apkpure.com].
-
-
-
I hope you found this article helpful and informative. If you did, please share it with your friends and leave a comment below. Thank you for reading and happy gaming!
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/232labs/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp b/spaces/232labs/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp
deleted file mode 100644
index 71f612cdbaaca03822eedc002a980d055d2f485c..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-
-#include
-#include
-
-torch::Tensor fused_bias_act_op(const torch::Tensor &input,
- const torch::Tensor &bias,
- const torch::Tensor &refer, int act, int grad,
- float alpha, float scale);
-
-#define CHECK_CUDA(x) \
- TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
- TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
- CHECK_CUDA(x); \
- CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor &input,
- const torch::Tensor &bias,
- const torch::Tensor &refer, int act, int grad,
- float alpha, float scale) {
- CHECK_INPUT(input);
- CHECK_INPUT(bias);
-
- at::DeviceGuard guard(input.device());
-
- return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r18.py b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r18.py
deleted file mode 100644
index 7a8db34cd547e8e667103c93585296e47a894e97..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_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 = "cosface"
-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/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/7hao/bingo/postcss.config.js b/spaces/7hao/bingo/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
- plugins: {
- tailwindcss: {},
- autoprefixer: {},
- },
-}
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets.py b/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets.py
deleted file mode 100644
index d4c376ed8715f9e85d71609e348add0a6550a4ba..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from uvr5_pack.lib_v5 import layers
-from uvr5_pack.lib_v5 import spec_utils
-
-
-class BaseASPPNet(nn.Module):
- def __init__(self, nin, ch, dilations=(4, 8, 16)):
- super(BaseASPPNet, self).__init__()
- self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
- self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
- self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
- self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
- self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
- self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
- self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
- self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
- self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
- def __call__(self, x):
- h, e1 = self.enc1(x)
- h, e2 = self.enc2(h)
- h, e3 = self.enc3(h)
- h, e4 = self.enc4(h)
-
- h = self.aspp(h)
-
- h = self.dec4(h, e4)
- h = self.dec3(h, e3)
- h = self.dec2(h, e2)
- h = self.dec1(h, e1)
-
- return h
-
-
-class CascadedASPPNet(nn.Module):
- def __init__(self, n_fft):
- super(CascadedASPPNet, self).__init__()
- self.stg1_low_band_net = BaseASPPNet(2, 16)
- self.stg1_high_band_net = BaseASPPNet(2, 16)
-
- self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
- self.stg2_full_band_net = BaseASPPNet(8, 16)
-
- self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
- self.stg3_full_band_net = BaseASPPNet(16, 32)
-
- self.out = nn.Conv2d(32, 2, 1, bias=False)
- self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
- self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
-
- self.max_bin = n_fft // 2
- self.output_bin = n_fft // 2 + 1
-
- self.offset = 128
-
- def forward(self, x, aggressiveness=None):
- mix = x.detach()
- x = x.clone()
-
- x = x[:, :, : self.max_bin]
-
- bandw = x.size()[2] // 2
- aux1 = torch.cat(
- [
- self.stg1_low_band_net(x[:, :, :bandw]),
- self.stg1_high_band_net(x[:, :, bandw:]),
- ],
- dim=2,
- )
-
- h = torch.cat([x, aux1], dim=1)
- aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
- h = torch.cat([x, aux1, aux2], dim=1)
- h = self.stg3_full_band_net(self.stg3_bridge(h))
-
- mask = torch.sigmoid(self.out(h))
- mask = F.pad(
- input=mask,
- pad=(0, 0, 0, self.output_bin - mask.size()[2]),
- mode="replicate",
- )
-
- if self.training:
- aux1 = torch.sigmoid(self.aux1_out(aux1))
- aux1 = F.pad(
- input=aux1,
- pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
- mode="replicate",
- )
- aux2 = torch.sigmoid(self.aux2_out(aux2))
- aux2 = F.pad(
- input=aux2,
- pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
- mode="replicate",
- )
- return mask * mix, aux1 * mix, aux2 * mix
- else:
- if aggressiveness:
- mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
- mask[:, :, : aggressiveness["split_bin"]],
- 1 + aggressiveness["value"] / 3,
- )
- mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
- mask[:, :, aggressiveness["split_bin"] :],
- 1 + aggressiveness["value"],
- )
-
- return mask * mix
-
- def predict(self, x_mag, aggressiveness=None):
- h = self.forward(x_mag, aggressiveness)
-
- if self.offset > 0:
- h = h[:, :, :, self.offset : -self.offset]
- assert h.size()[3] > 0
-
- return h
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/grids/_base_explorers.py b/spaces/AIConsultant/MusicGen/audiocraft/grids/_base_explorers.py
deleted file mode 100644
index d3f26666aa596f7bd2e8695c4f00e7963e978ceb..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/grids/_base_explorers.py
+++ /dev/null
@@ -1,80 +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 abc import ABC, abstractmethod
-import time
-import typing as tp
-from dora import Explorer
-import treetable as tt
-
-
-def get_sheep_ping(sheep) -> tp.Optional[str]:
- """Return the amount of time since the Sheep made some update
- to its log. Returns a str using the relevant time unit."""
- ping = None
- if sheep.log is not None and sheep.log.exists():
- delta = time.time() - sheep.log.stat().st_mtime
- if delta > 3600 * 24:
- ping = f'{delta / (3600 * 24):.1f}d'
- elif delta > 3600:
- ping = f'{delta / (3600):.1f}h'
- elif delta > 60:
- ping = f'{delta / 60:.1f}m'
- else:
- ping = f'{delta:.1f}s'
- return ping
-
-
-class BaseExplorer(ABC, Explorer):
- """Base explorer for AudioCraft grids.
-
- All task specific solvers are expected to implement the `get_grid_metrics`
- method to specify logic about metrics to display for a given task.
-
- If additional stages are used, the child explorer must define how to handle
- these new stages in the `process_history` and `process_sheep` methods.
- """
- def stages(self):
- return ["train", "valid", "evaluate"]
-
- def get_grid_meta(self):
- """Returns the list of Meta information to display for each XP/job.
- """
- return [
- tt.leaf("index", align=">"),
- tt.leaf("name", wrap=140),
- tt.leaf("state"),
- tt.leaf("sig", align=">"),
- tt.leaf("sid", align="<"),
- ]
-
- @abstractmethod
- def get_grid_metrics(self):
- """Return the metrics that should be displayed in the tracking table.
- """
- ...
-
- def process_sheep(self, sheep, history):
- train = {
- "epoch": len(history),
- }
- parts = {"train": train}
- for metrics in history:
- for key, sub in metrics.items():
- part = parts.get(key, {})
- if 'duration' in sub:
- # Convert to minutes for readability.
- sub['duration'] = sub['duration'] / 60.
- part.update(sub)
- parts[key] = part
- ping = get_sheep_ping(sheep)
- if ping is not None:
- for name in self.stages():
- if name not in parts:
- parts[name] = {}
- # Add the ping to each part for convenience.
- parts[name]['ping'] = ping
- return parts
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/metrics/diagonal_metrics.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/metrics/diagonal_metrics.py
deleted file mode 100644
index ba9807c1a594b38632c4731391e2d4fa3289037b..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/metrics/diagonal_metrics.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch
-
-
-def get_focus_rate(attn, src_padding_mask=None, tgt_padding_mask=None):
- '''
- attn: bs x L_t x L_s
- '''
- if src_padding_mask is not None:
- attn = attn * (1 - src_padding_mask.float())[:, None, :]
-
- if tgt_padding_mask is not None:
- attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
-
- focus_rate = attn.max(-1).values.sum(-1)
- focus_rate = focus_rate / attn.sum(-1).sum(-1)
- return focus_rate
-
-
-def get_phone_coverage_rate(attn, src_padding_mask=None, src_seg_mask=None, tgt_padding_mask=None):
- '''
- attn: bs x L_t x L_s
- '''
- src_mask = attn.new(attn.size(0), attn.size(-1)).bool().fill_(False)
- if src_padding_mask is not None:
- src_mask |= src_padding_mask
- if src_seg_mask is not None:
- src_mask |= src_seg_mask
-
- attn = attn * (1 - src_mask.float())[:, None, :]
- if tgt_padding_mask is not None:
- attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
-
- phone_coverage_rate = attn.max(1).values.sum(-1)
- # phone_coverage_rate = phone_coverage_rate / attn.sum(-1).sum(-1)
- phone_coverage_rate = phone_coverage_rate / (1 - src_mask.float()).sum(-1)
- return phone_coverage_rate
-
-
-def get_diagonal_focus_rate(attn, attn_ks, target_len, src_padding_mask=None, tgt_padding_mask=None,
- band_mask_factor=5, band_width=50):
- '''
- attn: bx x L_t x L_s
- attn_ks: shape: tensor with shape [batch_size], input_lens/output_lens
-
- diagonal: y=k*x (k=attn_ks, x:output, y:input)
- 1 0 0
- 0 1 0
- 0 0 1
- y>=k*(x-width) and y<=k*(x+width):1
- else:0
- '''
- # width = min(target_len/band_mask_factor, 50)
- width1 = target_len / band_mask_factor
- width2 = target_len.new(target_len.size()).fill_(band_width)
- width = torch.where(width1 < width2, width1, width2).float()
- base = torch.ones(attn.size()).to(attn.device)
- zero = torch.zeros(attn.size()).to(attn.device)
- x = torch.arange(0, attn.size(1)).to(attn.device)[None, :, None].float() * base
- y = torch.arange(0, attn.size(2)).to(attn.device)[None, None, :].float() * base
- cond = (y - attn_ks[:, None, None] * x)
- cond1 = cond + attn_ks[:, None, None] * width[:, None, None]
- cond2 = cond - attn_ks[:, None, None] * width[:, None, None]
- mask1 = torch.where(cond1 < 0, zero, base)
- mask2 = torch.where(cond2 > 0, zero, base)
- mask = mask1 * mask2
-
- if src_padding_mask is not None:
- attn = attn * (1 - src_padding_mask.float())[:, None, :]
- if tgt_padding_mask is not None:
- attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
-
- diagonal_attn = attn * mask
- diagonal_focus_rate = diagonal_attn.sum(-1).sum(-1) / attn.sum(-1).sum(-1)
- return diagonal_focus_rate, mask
diff --git a/spaces/AIZ2H/02-Gradio-Art-From-Text-And-Images/README.md b/spaces/AIZ2H/02-Gradio-Art-From-Text-And-Images/README.md
deleted file mode 100644
index 2d1a198ade9ac0edcd9a466326253e58a38c6115..0000000000000000000000000000000000000000
--- a/spaces/AIZ2H/02-Gradio-Art-From-Text-And-Images/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🎨GradioArtFromTextAndImages
-emoji: 📝
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ASJMO/freegpt/g4f/models.py b/spaces/ASJMO/freegpt/g4f/models.py
deleted file mode 100644
index 37efcfb2a7e870f3ef3093d167efdab299083220..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/g4f/models.py
+++ /dev/null
@@ -1,233 +0,0 @@
-from g4f import Provider
-
-
-class Model:
- class model:
- name: str
- base_provider: str
- best_provider: str
-
- class gpt_35_turbo:
- name: str = 'gpt-3.5-turbo'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Wewordle
-
- class gpt_35_turbo_0613:
- name: str = 'gpt-3.5-turbo-0613'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Zeabur
-
- class gpt_35_turbo_0301:
- name: str = 'gpt-3.5-turbo-0301'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Zeabur
-
- class gpt_35_turbo_16k_0613:
- name: str = 'gpt-3.5-turbo-16k-0613'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Zeabur
-
- class gpt_35_turbo_16k:
- name: str = 'gpt-3.5-turbo-16k'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.ChatFree
-
- class gpt_4_dev:
- name: str = 'gpt-4-for-dev'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Phind
-
- class gpt_4:
- name: str = 'gpt-4'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.ChatgptAi
-
- class gpt_4_0613:
- name: str = 'gpt-4-0613'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Lockchat
- best_providers: list = [Provider.Bing, Provider.Lockchat]
-
- class claude_instant_v1_100k:
- name: str = 'claude-instant-v1-100k'
- base_provider: str = 'anthropic'
- best_provider: Provider.Provider = Provider.Vercel
-
- class claude_instant_v1:
- name: str = 'claude-instant-v1'
- base_provider: str = 'anthropic'
- best_provider: Provider.Provider = Provider.Vercel
-
- class claude_v1_100k:
- name: str = 'claude-v1-100k'
- base_provider: str = 'anthropic'
- best_provider: Provider.Provider = Provider.Vercel
-
- class claude_v1:
- name: str = 'claude-v1'
- base_provider: str = 'anthropic'
- best_provider: Provider.Provider = Provider.Vercel
-
- class alpaca_7b:
- name: str = 'alpaca-7b'
- base_provider: str = 'replicate'
- best_provider: Provider.Provider = Provider.Vercel
-
- class stablelm_tuned_alpha_7b:
- name: str = 'stablelm-tuned-alpha-7b'
- base_provider: str = 'replicate'
- best_provider: Provider.Provider = Provider.Vercel
-
- class bloom:
- name: str = 'bloom'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class bloomz:
- name: str = 'bloomz'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class flan_t5_xxl:
- name: str = 'flan-t5-xxl'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class flan_ul2:
- name: str = 'flan-ul2'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class gpt_neox_20b:
- name: str = 'gpt-neox-20b'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class oasst_sft_4_pythia_12b_epoch_35:
- name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class santacoder:
- name: str = 'santacoder'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.Vercel
-
- class command_medium_nightly:
- name: str = 'command-medium-nightly'
- base_provider: str = 'cohere'
- best_provider: Provider.Provider = Provider.Vercel
-
- class command_xlarge_nightly:
- name: str = 'command-xlarge-nightly'
- base_provider: str = 'cohere'
- best_provider: Provider.Provider = Provider.Vercel
-
- class code_cushman_001:
- name: str = 'code-cushman-001'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class code_davinci_002:
- name: str = 'code-davinci-002'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class text_ada_001:
- name: str = 'text-ada-001'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class text_babbage_001:
- name: str = 'text-babbage-001'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class text_curie_001:
- name: str = 'text-curie-001'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class text_davinci_002:
- name: str = 'text-davinci-002'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class text_davinci_003:
- name: str = 'text-davinci-003'
- base_provider: str = 'openai'
- best_provider: Provider.Provider = Provider.Vercel
-
- class palm:
- name: str = 'palm2'
- base_provider: str = 'google'
- best_provider: Provider.Provider = Provider.Bard
-
- class falcon_40b:
- name: str = 'falcon-40b'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.H2o
-
- class falcon_7b:
- name: str = 'falcon-7b'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.H2o
-
- class llama_13b:
- name: str = 'llama-13b'
- base_provider: str = 'huggingface'
- best_provider: Provider.Provider = Provider.H2o
-
-
-class ModelUtils:
- convert: dict = {
- 'gpt-3.5-turbo': Model.gpt_35_turbo,
- 'gpt-3.5-turbo-0613': Model.gpt_35_turbo_0613,
- 'gpt-3.5-turbo-0301': Model.gpt_35_turbo_0301,
- 'gpt-4': Model.gpt_4,
- 'gpt-4-0613': Model.gpt_4_0613,
- 'gpt-4-for-dev': Model.gpt_4_dev,
- 'gpt-3.5-turbo-16k': Model.gpt_35_turbo_16k,
- 'gpt-3.5-turbo-16k-0613': Model.gpt_35_turbo_16k_0613,
-
- 'claude-instant-v1-100k': Model.claude_instant_v1_100k,
- 'claude-v1-100k': Model.claude_v1_100k,
- 'claude-instant-v1': Model.claude_instant_v1,
- 'claude-v1': Model.claude_v1,
-
- 'alpaca-7b': Model.alpaca_7b,
- 'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
-
- 'bloom': Model.bloom,
- 'bloomz': Model.bloomz,
-
- 'flan-t5-xxl': Model.flan_t5_xxl,
- 'flan-ul2': Model.flan_ul2,
-
- 'gpt-neox-20b': Model.gpt_neox_20b,
- 'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
- 'santacoder': Model.santacoder,
-
- 'command-medium-nightly': Model.command_medium_nightly,
- 'command-xlarge-nightly': Model.command_xlarge_nightly,
-
- 'code-cushman-001': Model.code_cushman_001,
- 'code-davinci-002': Model.code_davinci_002,
-
- 'text-ada-001': Model.text_ada_001,
- 'text-babbage-001': Model.text_babbage_001,
- 'text-curie-001': Model.text_curie_001,
- 'text-davinci-002': Model.text_davinci_002,
- 'text-davinci-003': Model.text_davinci_003,
-
- 'palm2': Model.palm,
- 'palm': Model.palm,
- 'google': Model.palm,
- 'google-bard': Model.palm,
- 'google-palm': Model.palm,
- 'bard': Model.palm,
-
- 'falcon-40b': Model.falcon_40b,
- 'falcon-7b': Model.falcon_7b,
- 'llama-13b': Model.llama_13b,
- }
diff --git a/spaces/Abdllh/poetry/README.md b/spaces/Abdllh/poetry/README.md
deleted file mode 100644
index 76056b70fef5b18d2a32e30d1fb3ed222779a30d..0000000000000000000000000000000000000000
--- a/spaces/Abdllh/poetry/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Arabic Poetry Generator
-emoji: 🐠
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-license: cc-by-nc-4.0
-duplicated_from: aaaaaabbbbbbbdddddddduuuuulllll/poetry
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net_custom.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net_custom.py
deleted file mode 100644
index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net_custom.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
- """Network for monocular depth estimation.
- """
-
- def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
- blocks={'expand': True}):
- """Init.
-
- Args:
- path (str, optional): Path to saved model. Defaults to None.
- features (int, optional): Number of features. Defaults to 256.
- backbone (str, optional): Backbone network for encoder. Defaults to resnet50
- """
- print("Loading weights: ", path)
-
- super(MidasNet_small, self).__init__()
-
- use_pretrained = False if path else True
-
- self.channels_last = channels_last
- self.blocks = blocks
- self.backbone = backbone
-
- self.groups = 1
-
- features1=features
- features2=features
- features3=features
- features4=features
- self.expand = False
- if "expand" in self.blocks and self.blocks['expand'] == True:
- self.expand = True
- features1=features
- features2=features*2
- features3=features*4
- features4=features*8
-
- self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-
- self.scratch.activation = nn.ReLU(False)
-
- self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
- self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
- self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
- self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-
- self.scratch.output_conv = nn.Sequential(
- nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
- Interpolate(scale_factor=2, mode="bilinear"),
- nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
- self.scratch.activation,
- nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
- nn.ReLU(True) if non_negative else nn.Identity(),
- nn.Identity(),
- )
-
- if path:
- self.load(path)
-
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input data (image)
-
- Returns:
- tensor: depth
- """
- if self.channels_last==True:
- print("self.channels_last = ", self.channels_last)
- x.contiguous(memory_format=torch.channels_last)
-
-
- layer_1 = self.pretrained.layer1(x)
- layer_2 = self.pretrained.layer2(layer_1)
- layer_3 = self.pretrained.layer3(layer_2)
- layer_4 = self.pretrained.layer4(layer_3)
-
- layer_1_rn = self.scratch.layer1_rn(layer_1)
- layer_2_rn = self.scratch.layer2_rn(layer_2)
- layer_3_rn = self.scratch.layer3_rn(layer_3)
- layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
- path_4 = self.scratch.refinenet4(layer_4_rn)
- path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
- path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
- path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
- out = self.scratch.output_conv(path_1)
-
- return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
- prev_previous_type = nn.Identity()
- prev_previous_name = ''
- previous_type = nn.Identity()
- previous_name = ''
- for name, module in m.named_modules():
- if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
- # print("FUSED ", prev_previous_name, previous_name, name)
- torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
- elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
- # print("FUSED ", prev_previous_name, previous_name)
- torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
- # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
- # print("FUSED ", previous_name, name)
- # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
- prev_previous_type = previous_type
- prev_previous_name = previous_name
- previous_type = type(module)
- previous_name = name
\ No newline at end of file
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/openpose/__init__.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/openpose/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/ResolveHeight.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/ResolveHeight.js
deleted file mode 100644
index 63cc461f42a6bb7c31b18f4cb5074cd11f5b4a3d..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/ResolveHeight.js
+++ /dev/null
@@ -1,14 +0,0 @@
-var ResolveHeight = function (height) {
- var minHeight = Math.max(this.childrenHeight, this.minHeight);
- if (height === undefined) {
- height = minHeight;
- } else {
- if (minHeight > height) {
- // Warning
- }
- }
-
- return height;
-}
-
-export default ResolveHeight;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/CircleMaskImage.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/CircleMaskImage.js
deleted file mode 100644
index dafd452a231491a4edd777ee789ddbd6b6d04f5c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/CircleMaskImage.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import CircleMaskImage from '../../../plugins/circlemaskimage.js';
-export default CircleMaskImage;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pan/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pan/Factory.js
deleted file mode 100644
index 20c20ff5432613ebca1006ed054e31f1f20bc045..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pan/Factory.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import Pan from './Pan.js';
-import ObjectFactory from '../ObjectFactory.js';
-import IsGameObject from '../../../plugins/utils/system/IsGameObject.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('pan', function (gameObject, config) {
- if (!IsGameObject(gameObject)) {
- config = gameObject;
- gameObject = this.scene;
- }
- return new Pan(gameObject, config);
-});
-
-SetValue(window, 'RexPlugins.UI.Pan', Pan);
-
-export default Pan;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/Factory.d.ts
deleted file mode 100644
index 3e5d06a614ab0ef70037b26a92dde54c361c5fc1..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/Factory.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import ScrollablePanel from './ScrollablePanel';
-
-export default function (
- config?: ScrollablePanel.IConfig
-): ScrollablePanel;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/methods/ExtractStyle.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/methods/ExtractStyle.js
deleted file mode 100644
index 1e256b80eebdcf15913ec2e1dba69347012a2b28..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/methods/ExtractStyle.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import ExtractByPrefix from '../../../../plugins/utils/object/ExtractByPrefix.js';
-
-var ExtractStyle = function (config, prefix, propertiesMap) {
- var result = ExtractByPrefix(config, prefix);
-
- if (propertiesMap) {
- for (var name in result) {
- if (propertiesMap.hasOwnProperty(name)) {
- result[propertiesMap[name]] = result[name];
- delete result[name];
- }
- }
- }
-
- return result;
-}
-
-export default ExtractStyle
\ No newline at end of file
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/README.md b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/README.md
deleted file mode 100644
index 6fd7d547a3e31617d4a3f94775fe2d7b6ce0e23b..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: League of Legends Yuumi Text to Speech
-sdk: gradio
-emoji: 🔥
-colorFrom: blue
-colorTo: yellow
-app_file: app.py
-pinned: false
-duplicated_from: kiramayatu/Vits-Hana
----
-
-# League of Legends Yuumi Text to Speech Demo
-
-League of Legends Yuumi Text to Speech model trained with Yuumi's English in-game audio.
-
-## 👍Give original author stars & likes if you liked the project
-
-https://github.com/Plachtaa/VITS-fast-fine-tuning
-
-https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer
-
-## ❓How to fine-tune your own model
-
-Follow the directions in this repo: https://github.com/Plachtaa/VITS-fast-fine-tuning
-
-## ⚠Use of the model should respect https://www.riotgames.com/en/legal
-
-## ⚠Disclaimer: Not legally responsible for anything the model generates
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/bias_act.h b/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/bias_act.h
deleted file mode 100644
index 60b81c6058d54638a6d74a13046fa388442d767d..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/bias_act.h
+++ /dev/null
@@ -1,38 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto. Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct bias_act_kernel_params
-{
- const void* x; // [sizeX]
- const void* b; // [sizeB] or NULL
- const void* xref; // [sizeX] or NULL
- const void* yref; // [sizeX] or NULL
- const void* dy; // [sizeX] or NULL
- void* y; // [sizeX]
-
- int grad;
- int act;
- float alpha;
- float gain;
- float clamp;
-
- int sizeX;
- int sizeB;
- int stepB;
- int loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template void* choose_bias_act_kernel(const bias_act_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/alt_diffusion/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/alt_diffusion/__init__.py
deleted file mode 100644
index 03c9f5ebc63ec1fdbf57f6bd11346250a7fa4878..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/alt_diffusion/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-from PIL import Image
-
-from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
-
-
-@dataclass
-# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
-class AltDiffusionPipelineOutput(BaseOutput):
- """
- Output class for Alt Diffusion pipelines.
-
- Args:
- images (`List[PIL.Image.Image]` or `np.ndarray`)
- List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
- num_channels)`.
- nsfw_content_detected (`List[bool]`)
- List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
- `None` if safety checking could not be performed.
- """
-
- images: Union[List[PIL.Image.Image], np.ndarray]
- nsfw_content_detected: Optional[List[bool]]
-
-
-try:
- if not (is_transformers_available() and is_torch_available()):
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
-else:
- from .modeling_roberta_series import RobertaSeriesModelWithTransformation
- from .pipeline_alt_diffusion import AltDiffusionPipeline
- from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py
deleted file mode 100644
index e7fe39b9c86540c6baa2620e40bb79c6730f0d96..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py
+++ /dev/null
@@ -1,713 +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.
-
-import contextlib
-import inspect
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from packaging import version
-from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
-
-from ...configuration_utils import FrozenDict
-from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
-def preprocess(image):
- warnings.warn(
- "The preprocess method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor.preprocess instead",
- FutureWarning,
- )
- if isinstance(image, torch.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
-
- image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
- return image
-
-
-class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
- r"""
- Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
- implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
- The pipeline also inherits the following loading methods:
- - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
- text_encoder ([`~transformers.CLIPTextModel`]):
- Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
- tokenizer ([`~transformers.CLIPTokenizer`]):
- A `CLIPTokenizer` to tokenize text.
- unet ([`UNet2DConditionModel`]):
- A `UNet2DConditionModel` to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- """
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: KarrasDiffusionSchedulers,
- depth_estimator: DPTForDepthEstimation,
- feature_extractor: DPTFeatureExtractor,
- ):
- super().__init__()
-
- is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
- version.parse(unet.config._diffusers_version).base_version
- ) < version.parse("0.9.0.dev0")
- is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
- if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
- deprecation_message = (
- "The configuration file of the unet has set the default `sample_size` to smaller than"
- " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
- " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
- " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
- " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
- " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
- " in the config might lead to incorrect results in future versions. If you have downloaded this"
- " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
- " the `unet/config.json` file"
- )
- deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
- new_config = dict(unet.config)
- new_config["sample_size"] = 64
- unet._internal_dict = FrozenDict(new_config)
-
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- depth_estimator=depth_estimator,
- feature_extractor=feature_extractor,
- )
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
- def _encode_prompt(
- self,
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt=None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- lora_scale: Optional[float] = None,
- ):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- lora_scale (`float`, *optional*):
- A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
- """
- # set lora scale so that monkey patched LoRA
- # function of text encoder can correctly access it
- if lora_scale is not None and isinstance(self, LoraLoaderMixin):
- self._lora_scale = lora_scale
-
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- if prompt_embeds is None:
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
- if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
- text_input_ids, untruncated_ids
- ):
- removed_text = self.tokenizer.batch_decode(
- untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
- )
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = text_inputs.attention_mask.to(device)
- else:
- attention_mask = None
-
- prompt_embeds = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask,
- )
- prompt_embeds = prompt_embeds[0]
-
- prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- bs_embed, seq_len, _ = prompt_embeds.shape
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
- prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance and negative_prompt_embeds is None:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif prompt is not None and type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
- elif isinstance(negative_prompt, str):
- uncond_tokens = [negative_prompt]
- elif batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
- else:
- uncond_tokens = negative_prompt
-
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
- max_length = prompt_embeds.shape[1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = uncond_input.attention_mask.to(device)
- else:
- attention_mask = None
-
- negative_prompt_embeds = self.text_encoder(
- uncond_input.input_ids.to(device),
- attention_mask=attention_mask,
- )
- negative_prompt_embeds = negative_prompt_embeds[0]
-
- if do_classifier_free_guidance:
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = negative_prompt_embeds.shape[1]
-
- negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
- return prompt_embeds
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
- def run_safety_checker(self, image, device, dtype):
- if self.safety_checker is None:
- has_nsfw_concept = None
- else:
- if torch.is_tensor(image):
- feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
- else:
- feature_extractor_input = self.image_processor.numpy_to_pil(image)
- safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
- image, has_nsfw_concept = self.safety_checker(
- images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
- )
- return image, has_nsfw_concept
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- warnings.warn(
- "The decode_latents method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor instead",
- FutureWarning,
- )
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents, return_dict=False)[0]
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
- def check_inputs(
- self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
- ):
- if strength < 0 or strength > 1:
- raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- if prompt is not None and prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
- " only forward one of the two."
- )
- elif prompt is None and prompt_embeds is None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
- )
- elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if negative_prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if prompt_embeds is not None and negative_prompt_embeds is not None:
- if prompt_embeds.shape != negative_prompt_embeds.shape:
- raise ValueError(
- "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
- f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
- f" {negative_prompt_embeds.shape}."
- )
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
- def get_timesteps(self, num_inference_steps, strength, device):
- # get the original timestep using init_timestep
- init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
- t_start = max(num_inference_steps - init_timestep, 0)
- timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
- return timesteps, num_inference_steps - t_start
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
- def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
- if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
- raise ValueError(
- f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
- )
-
- image = image.to(device=device, dtype=dtype)
-
- batch_size = batch_size * num_images_per_prompt
-
- if image.shape[1] == 4:
- init_latents = image
-
- else:
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- elif isinstance(generator, list):
- init_latents = [
- self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
- ]
- init_latents = torch.cat(init_latents, dim=0)
- else:
- init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
- init_latents = self.vae.config.scaling_factor * init_latents
-
- if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
- # expand init_latents for batch_size
- deprecation_message = (
- f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
- " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
- " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
- " your script to pass as many initial images as text prompts to suppress this warning."
- )
- deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
- additional_image_per_prompt = batch_size // init_latents.shape[0]
- init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
- elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
- raise ValueError(
- f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
- )
- else:
- init_latents = torch.cat([init_latents], dim=0)
-
- shape = init_latents.shape
- noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
- # get latents
- init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
- latents = init_latents
-
- return latents
-
- def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):
- if isinstance(image, PIL.Image.Image):
- image = [image]
- else:
- image = list(image)
-
- if isinstance(image[0], PIL.Image.Image):
- width, height = image[0].size
- elif isinstance(image[0], np.ndarray):
- width, height = image[0].shape[:-1]
- else:
- height, width = image[0].shape[-2:]
-
- if depth_map is None:
- pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values
- pixel_values = pixel_values.to(device=device)
- # The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16.
- # So we use `torch.autocast` here for half precision inference.
- context_manger = torch.autocast("cuda", dtype=dtype) if device.type == "cuda" else contextlib.nullcontext()
- with context_manger:
- depth_map = self.depth_estimator(pixel_values).predicted_depth
- else:
- depth_map = depth_map.to(device=device, dtype=dtype)
-
- depth_map = torch.nn.functional.interpolate(
- depth_map.unsqueeze(1),
- size=(height // self.vae_scale_factor, width // self.vae_scale_factor),
- mode="bicubic",
- align_corners=False,
- )
-
- depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
- depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
- depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0
- depth_map = depth_map.to(dtype)
-
- # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
- if depth_map.shape[0] < batch_size:
- repeat_by = batch_size // depth_map.shape[0]
- depth_map = depth_map.repeat(repeat_by, 1, 1, 1)
-
- depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map
- return depth_map
-
- @torch.no_grad()
- def __call__(
- self,
- prompt: Union[str, List[str]] = None,
- image: Union[
- torch.FloatTensor,
- PIL.Image.Image,
- np.ndarray,
- List[torch.FloatTensor],
- List[PIL.Image.Image],
- List[np.ndarray],
- ] = None,
- depth_map: Optional[torch.FloatTensor] = None,
- strength: float = 0.8,
- num_inference_steps: Optional[int] = 50,
- guidance_scale: Optional[float] = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: Optional[float] = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- ):
- r"""
- The call function to the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
- `Image` or tensor representing an image batch to be used as the starting point. Can accept image
- latents as `image` only if `depth_map` is not `None`.
- depth_map (`torch.FloatTensor`, *optional*):
- Depth prediction to be used as additional conditioning for the image generation process. If not
- defined, it automatically predicts the depth with `self.depth_estimator`.
- strength (`float`, *optional*, defaults to 0.8):
- Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
- starting point and more noise is added the higher the `strength`. The number of denoising steps depends
- on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
- process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
- essentially ignores `image`.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference. This parameter is modulated by `strength`.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- A higher guidance scale value encourages the model to generate images closely linked to the text
- `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide what to not include in image generation. If not defined, you need to
- pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
- to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
- generation deterministic.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
- provided, text embeddings are generated from the `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
- not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that calls every `callback_steps` steps during inference. The function is called with the
- following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function is called. If not specified, the callback is called at
- every step.
- cross_attention_kwargs (`dict`, *optional*):
- A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
- [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
- Examples:
-
- ```py
- >>> import torch
- >>> import requests
- >>> from PIL import Image
-
- >>> from diffusers import StableDiffusionDepth2ImgPipeline
-
- >>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
- ... "stabilityai/stable-diffusion-2-depth",
- ... torch_dtype=torch.float16,
- ... )
- >>> pipe.to("cuda")
-
-
- >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
- >>> init_image = Image.open(requests.get(url, stream=True).raw)
- >>> prompt = "two tigers"
- >>> n_propmt = "bad, deformed, ugly, bad anotomy"
- >>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
- ```
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
- otherwise a `tuple` is returned where the first element is a list with the generated images.
- """
- # 1. Check inputs
- self.check_inputs(
- prompt,
- strength,
- callback_steps,
- negative_prompt=negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- )
-
- if image is None:
- raise ValueError("`image` input cannot be undefined.")
-
- # 2. Define call parameters
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- device = self._execution_device
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 3. Encode input prompt
- text_encoder_lora_scale = (
- cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
- )
- prompt_embeds = self._encode_prompt(
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- lora_scale=text_encoder_lora_scale,
- )
-
- # 4. Prepare depth mask
- depth_mask = self.prepare_depth_map(
- image,
- depth_map,
- batch_size * num_images_per_prompt,
- do_classifier_free_guidance,
- prompt_embeds.dtype,
- device,
- )
-
- # 5. Preprocess image
- image = self.image_processor.preprocess(image)
-
- # 6. Set timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
- latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
- # 7. Prepare latent variables
- latents = self.prepare_latents(
- image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
- )
-
- # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 9. Denoising loop
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
- latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
-
- # predict the noise residual
- noise_pred = self.unet(
- latent_model_input,
- t,
- encoder_hidden_states=prompt_embeds,
- cross_attention_kwargs=cross_attention_kwargs,
- return_dict=False,
- )[0]
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- if not output_type == "latent":
- image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
- else:
- image = latents
-
- image = self.image_processor.postprocess(image, output_type=output_type)
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/fpg/README.md b/spaces/Andy1621/uniformer_image_detection/configs/fpg/README.md
deleted file mode 100644
index 89f5adb5fb41809bfcddeca80b7fe730d70e4838..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/fpg/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Feature Pyramid Grids
-
-## Introduction
-
-```latex
-@article{chen2020feature,
- title={Feature pyramid grids},
- author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph},
- journal={arXiv preprint arXiv:2004.03580},
- year={2020}
-}
-```
-
-## Results and Models
-
-We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN.
-All backbones are Resnet-50 in pytorch style.
-
-| Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-|:------------:|:-----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:-------:|:--------:|
-| Faster R-CNN | FPG | 50e | 20.0 | - | 42.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) |
-| Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |
-| Mask R-CNN | FPG | 50e | 23.2 | - | 42.7 | 37.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) |
-| Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 36.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |
-| RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) |
-| RetinaNet | FPG-chn128 | 50e | | - | | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) |
-
-**Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in
-Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision.
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/vgg.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/vgg.py
deleted file mode 100644
index 8778b649561a45a9652b1a15a26c2d171e58f3e1..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/vgg.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-import torch.nn as nn
-
-from .utils import constant_init, kaiming_init, normal_init
-
-
-def conv3x3(in_planes, out_planes, dilation=1):
- """3x3 convolution with padding."""
- return nn.Conv2d(
- in_planes,
- out_planes,
- kernel_size=3,
- padding=dilation,
- dilation=dilation)
-
-
-def make_vgg_layer(inplanes,
- planes,
- num_blocks,
- dilation=1,
- with_bn=False,
- ceil_mode=False):
- layers = []
- for _ in range(num_blocks):
- layers.append(conv3x3(inplanes, planes, dilation))
- if with_bn:
- layers.append(nn.BatchNorm2d(planes))
- layers.append(nn.ReLU(inplace=True))
- inplanes = planes
- layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode))
-
- return layers
-
-
-class VGG(nn.Module):
- """VGG backbone.
-
- Args:
- depth (int): Depth of vgg, from {11, 13, 16, 19}.
- with_bn (bool): Use BatchNorm or not.
- num_classes (int): number of classes for classification.
- num_stages (int): VGG stages, normally 5.
- dilations (Sequence[int]): Dilation of each stage.
- out_indices (Sequence[int]): Output from which stages.
- frozen_stages (int): Stages to be frozen (all param fixed). -1 means
- not freezing any parameters.
- bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
- running stats (mean and var).
- bn_frozen (bool): Whether to freeze weight and bias of BN layers.
- """
-
- arch_settings = {
- 11: (1, 1, 2, 2, 2),
- 13: (2, 2, 2, 2, 2),
- 16: (2, 2, 3, 3, 3),
- 19: (2, 2, 4, 4, 4)
- }
-
- def __init__(self,
- depth,
- with_bn=False,
- num_classes=-1,
- num_stages=5,
- dilations=(1, 1, 1, 1, 1),
- out_indices=(0, 1, 2, 3, 4),
- frozen_stages=-1,
- bn_eval=True,
- bn_frozen=False,
- ceil_mode=False,
- with_last_pool=True):
- super(VGG, self).__init__()
- if depth not in self.arch_settings:
- raise KeyError(f'invalid depth {depth} for vgg')
- assert num_stages >= 1 and num_stages <= 5
- stage_blocks = self.arch_settings[depth]
- self.stage_blocks = stage_blocks[:num_stages]
- assert len(dilations) == num_stages
- assert max(out_indices) <= num_stages
-
- self.num_classes = num_classes
- self.out_indices = out_indices
- self.frozen_stages = frozen_stages
- self.bn_eval = bn_eval
- self.bn_frozen = bn_frozen
-
- self.inplanes = 3
- start_idx = 0
- vgg_layers = []
- self.range_sub_modules = []
- for i, num_blocks in enumerate(self.stage_blocks):
- num_modules = num_blocks * (2 + with_bn) + 1
- end_idx = start_idx + num_modules
- dilation = dilations[i]
- planes = 64 * 2**i if i < 4 else 512
- vgg_layer = make_vgg_layer(
- self.inplanes,
- planes,
- num_blocks,
- dilation=dilation,
- with_bn=with_bn,
- ceil_mode=ceil_mode)
- vgg_layers.extend(vgg_layer)
- self.inplanes = planes
- self.range_sub_modules.append([start_idx, end_idx])
- start_idx = end_idx
- if not with_last_pool:
- vgg_layers.pop(-1)
- self.range_sub_modules[-1][1] -= 1
- self.module_name = 'features'
- self.add_module(self.module_name, nn.Sequential(*vgg_layers))
-
- if self.num_classes > 0:
- self.classifier = nn.Sequential(
- nn.Linear(512 * 7 * 7, 4096),
- nn.ReLU(True),
- nn.Dropout(),
- nn.Linear(4096, 4096),
- nn.ReLU(True),
- nn.Dropout(),
- nn.Linear(4096, num_classes),
- )
-
- def init_weights(self, pretrained=None):
- if isinstance(pretrained, str):
- logger = logging.getLogger()
- from ..runner import load_checkpoint
- load_checkpoint(self, pretrained, strict=False, logger=logger)
- elif pretrained is None:
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- kaiming_init(m)
- elif isinstance(m, nn.BatchNorm2d):
- constant_init(m, 1)
- elif isinstance(m, nn.Linear):
- normal_init(m, std=0.01)
- else:
- raise TypeError('pretrained must be a str or None')
-
- def forward(self, x):
- outs = []
- vgg_layers = getattr(self, self.module_name)
- for i in range(len(self.stage_blocks)):
- for j in range(*self.range_sub_modules[i]):
- vgg_layer = vgg_layers[j]
- x = vgg_layer(x)
- if i in self.out_indices:
- outs.append(x)
- if self.num_classes > 0:
- x = x.view(x.size(0), -1)
- x = self.classifier(x)
- outs.append(x)
- if len(outs) == 1:
- return outs[0]
- else:
- return tuple(outs)
-
- def train(self, mode=True):
- super(VGG, self).train(mode)
- if self.bn_eval:
- for m in self.modules():
- if isinstance(m, nn.BatchNorm2d):
- m.eval()
- if self.bn_frozen:
- for params in m.parameters():
- params.requires_grad = False
- vgg_layers = getattr(self, self.module_name)
- if mode and self.frozen_stages >= 0:
- for i in range(self.frozen_stages):
- for j in range(*self.range_sub_modules[i]):
- mod = vgg_layers[j]
- mod.eval()
- for param in mod.parameters():
- param.requires_grad = False
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/sessions.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/sessions.py
deleted file mode 100644
index 6cb3b4dae397930fba60e4c08b25b9444783b6f7..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/sessions.py
+++ /dev/null
@@ -1,831 +0,0 @@
-"""
-requests.sessions
-~~~~~~~~~~~~~~~~~
-
-This module provides a Session object to manage and persist settings across
-requests (cookies, auth, proxies).
-"""
-import os
-import sys
-import time
-from collections import OrderedDict
-from datetime import timedelta
-
-from ._internal_utils import to_native_string
-from .adapters import HTTPAdapter
-from .auth import _basic_auth_str
-from .compat import Mapping, cookielib, urljoin, urlparse
-from .cookies import (
- RequestsCookieJar,
- cookiejar_from_dict,
- extract_cookies_to_jar,
- merge_cookies,
-)
-from .exceptions import (
- ChunkedEncodingError,
- ContentDecodingError,
- InvalidSchema,
- TooManyRedirects,
-)
-from .hooks import default_hooks, dispatch_hook
-
-# formerly defined here, reexposed here for backward compatibility
-from .models import ( # noqa: F401
- DEFAULT_REDIRECT_LIMIT,
- REDIRECT_STATI,
- PreparedRequest,
- Request,
-)
-from .status_codes import codes
-from .structures import CaseInsensitiveDict
-from .utils import ( # noqa: F401
- DEFAULT_PORTS,
- default_headers,
- get_auth_from_url,
- get_environ_proxies,
- get_netrc_auth,
- requote_uri,
- resolve_proxies,
- rewind_body,
- should_bypass_proxies,
- to_key_val_list,
-)
-
-# Preferred clock, based on which one is more accurate on a given system.
-if sys.platform == "win32":
- preferred_clock = time.perf_counter
-else:
- preferred_clock = time.time
-
-
-def merge_setting(request_setting, session_setting, dict_class=OrderedDict):
- """Determines appropriate setting for a given request, taking into account
- the explicit setting on that request, and the setting in the session. If a
- setting is a dictionary, they will be merged together using `dict_class`
- """
-
- if session_setting is None:
- return request_setting
-
- if request_setting is None:
- return session_setting
-
- # Bypass if not a dictionary (e.g. verify)
- if not (
- isinstance(session_setting, Mapping) and isinstance(request_setting, Mapping)
- ):
- return request_setting
-
- merged_setting = dict_class(to_key_val_list(session_setting))
- merged_setting.update(to_key_val_list(request_setting))
-
- # Remove keys that are set to None. Extract keys first to avoid altering
- # the dictionary during iteration.
- none_keys = [k for (k, v) in merged_setting.items() if v is None]
- for key in none_keys:
- del merged_setting[key]
-
- return merged_setting
-
-
-def merge_hooks(request_hooks, session_hooks, dict_class=OrderedDict):
- """Properly merges both requests and session hooks.
-
- This is necessary because when request_hooks == {'response': []}, the
- merge breaks Session hooks entirely.
- """
- if session_hooks is None or session_hooks.get("response") == []:
- return request_hooks
-
- if request_hooks is None or request_hooks.get("response") == []:
- return session_hooks
-
- return merge_setting(request_hooks, session_hooks, dict_class)
-
-
-class SessionRedirectMixin:
- def get_redirect_target(self, resp):
- """Receives a Response. Returns a redirect URI or ``None``"""
- # Due to the nature of how requests processes redirects this method will
- # be called at least once upon the original response and at least twice
- # on each subsequent redirect response (if any).
- # If a custom mixin is used to handle this logic, it may be advantageous
- # to cache the redirect location onto the response object as a private
- # attribute.
- if resp.is_redirect:
- location = resp.headers["location"]
- # Currently the underlying http module on py3 decode headers
- # in latin1, but empirical evidence suggests that latin1 is very
- # rarely used with non-ASCII characters in HTTP headers.
- # It is more likely to get UTF8 header rather than latin1.
- # This causes incorrect handling of UTF8 encoded location headers.
- # To solve this, we re-encode the location in latin1.
- location = location.encode("latin1")
- return to_native_string(location, "utf8")
- return None
-
- def should_strip_auth(self, old_url, new_url):
- """Decide whether Authorization header should be removed when redirecting"""
- old_parsed = urlparse(old_url)
- new_parsed = urlparse(new_url)
- if old_parsed.hostname != new_parsed.hostname:
- return True
- # Special case: allow http -> https redirect when using the standard
- # ports. This isn't specified by RFC 7235, but is kept to avoid
- # breaking backwards compatibility with older versions of requests
- # that allowed any redirects on the same host.
- if (
- old_parsed.scheme == "http"
- and old_parsed.port in (80, None)
- and new_parsed.scheme == "https"
- and new_parsed.port in (443, None)
- ):
- return False
-
- # Handle default port usage corresponding to scheme.
- changed_port = old_parsed.port != new_parsed.port
- changed_scheme = old_parsed.scheme != new_parsed.scheme
- default_port = (DEFAULT_PORTS.get(old_parsed.scheme, None), None)
- if (
- not changed_scheme
- and old_parsed.port in default_port
- and new_parsed.port in default_port
- ):
- return False
-
- # Standard case: root URI must match
- return changed_port or changed_scheme
-
- def resolve_redirects(
- self,
- resp,
- req,
- stream=False,
- timeout=None,
- verify=True,
- cert=None,
- proxies=None,
- yield_requests=False,
- **adapter_kwargs,
- ):
- """Receives a Response. Returns a generator of Responses or Requests."""
-
- hist = [] # keep track of history
-
- url = self.get_redirect_target(resp)
- previous_fragment = urlparse(req.url).fragment
- while url:
- prepared_request = req.copy()
-
- # Update history and keep track of redirects.
- # resp.history must ignore the original request in this loop
- hist.append(resp)
- resp.history = hist[1:]
-
- try:
- resp.content # Consume socket so it can be released
- except (ChunkedEncodingError, ContentDecodingError, RuntimeError):
- resp.raw.read(decode_content=False)
-
- if len(resp.history) >= self.max_redirects:
- raise TooManyRedirects(
- f"Exceeded {self.max_redirects} redirects.", response=resp
- )
-
- # Release the connection back into the pool.
- resp.close()
-
- # Handle redirection without scheme (see: RFC 1808 Section 4)
- if url.startswith("//"):
- parsed_rurl = urlparse(resp.url)
- url = ":".join([to_native_string(parsed_rurl.scheme), url])
-
- # Normalize url case and attach previous fragment if needed (RFC 7231 7.1.2)
- parsed = urlparse(url)
- if parsed.fragment == "" and previous_fragment:
- parsed = parsed._replace(fragment=previous_fragment)
- elif parsed.fragment:
- previous_fragment = parsed.fragment
- url = parsed.geturl()
-
- # Facilitate relative 'location' headers, as allowed by RFC 7231.
- # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource')
- # Compliant with RFC3986, we percent encode the url.
- if not parsed.netloc:
- url = urljoin(resp.url, requote_uri(url))
- else:
- url = requote_uri(url)
-
- prepared_request.url = to_native_string(url)
-
- self.rebuild_method(prepared_request, resp)
-
- # https://github.com/psf/requests/issues/1084
- if resp.status_code not in (
- codes.temporary_redirect,
- codes.permanent_redirect,
- ):
- # https://github.com/psf/requests/issues/3490
- purged_headers = ("Content-Length", "Content-Type", "Transfer-Encoding")
- for header in purged_headers:
- prepared_request.headers.pop(header, None)
- prepared_request.body = None
-
- headers = prepared_request.headers
- headers.pop("Cookie", None)
-
- # Extract any cookies sent on the response to the cookiejar
- # in the new request. Because we've mutated our copied prepared
- # request, use the old one that we haven't yet touched.
- extract_cookies_to_jar(prepared_request._cookies, req, resp.raw)
- merge_cookies(prepared_request._cookies, self.cookies)
- prepared_request.prepare_cookies(prepared_request._cookies)
-
- # Rebuild auth and proxy information.
- proxies = self.rebuild_proxies(prepared_request, proxies)
- self.rebuild_auth(prepared_request, resp)
-
- # A failed tell() sets `_body_position` to `object()`. This non-None
- # value ensures `rewindable` will be True, allowing us to raise an
- # UnrewindableBodyError, instead of hanging the connection.
- rewindable = prepared_request._body_position is not None and (
- "Content-Length" in headers or "Transfer-Encoding" in headers
- )
-
- # Attempt to rewind consumed file-like object.
- if rewindable:
- rewind_body(prepared_request)
-
- # Override the original request.
- req = prepared_request
-
- if yield_requests:
- yield req
- else:
-
- resp = self.send(
- req,
- stream=stream,
- timeout=timeout,
- verify=verify,
- cert=cert,
- proxies=proxies,
- allow_redirects=False,
- **adapter_kwargs,
- )
-
- extract_cookies_to_jar(self.cookies, prepared_request, resp.raw)
-
- # extract redirect url, if any, for the next loop
- url = self.get_redirect_target(resp)
- yield resp
-
- def rebuild_auth(self, prepared_request, response):
- """When being redirected we may want to strip authentication from the
- request to avoid leaking credentials. This method intelligently removes
- and reapplies authentication where possible to avoid credential loss.
- """
- headers = prepared_request.headers
- url = prepared_request.url
-
- if "Authorization" in headers and self.should_strip_auth(
- response.request.url, url
- ):
- # If we get redirected to a new host, we should strip out any
- # authentication headers.
- del headers["Authorization"]
-
- # .netrc might have more auth for us on our new host.
- new_auth = get_netrc_auth(url) if self.trust_env else None
- if new_auth is not None:
- prepared_request.prepare_auth(new_auth)
-
- def rebuild_proxies(self, prepared_request, proxies):
- """This method re-evaluates the proxy configuration by considering the
- environment variables. If we are redirected to a URL covered by
- NO_PROXY, we strip the proxy configuration. Otherwise, we set missing
- proxy keys for this URL (in case they were stripped by a previous
- redirect).
-
- This method also replaces the Proxy-Authorization header where
- necessary.
-
- :rtype: dict
- """
- headers = prepared_request.headers
- scheme = urlparse(prepared_request.url).scheme
- new_proxies = resolve_proxies(prepared_request, proxies, self.trust_env)
-
- if "Proxy-Authorization" in headers:
- del headers["Proxy-Authorization"]
-
- try:
- username, password = get_auth_from_url(new_proxies[scheme])
- except KeyError:
- username, password = None, None
-
- if username and password:
- headers["Proxy-Authorization"] = _basic_auth_str(username, password)
-
- return new_proxies
-
- def rebuild_method(self, prepared_request, response):
- """When being redirected we may want to change the method of the request
- based on certain specs or browser behavior.
- """
- method = prepared_request.method
-
- # https://tools.ietf.org/html/rfc7231#section-6.4.4
- if response.status_code == codes.see_other and method != "HEAD":
- method = "GET"
-
- # Do what the browsers do, despite standards...
- # First, turn 302s into GETs.
- if response.status_code == codes.found and method != "HEAD":
- method = "GET"
-
- # Second, if a POST is responded to with a 301, turn it into a GET.
- # This bizarre behaviour is explained in Issue 1704.
- if response.status_code == codes.moved and method == "POST":
- method = "GET"
-
- prepared_request.method = method
-
-
-class Session(SessionRedirectMixin):
- """A Requests session.
-
- Provides cookie persistence, connection-pooling, and configuration.
-
- Basic Usage::
-
- >>> import requests
- >>> s = requests.Session()
- >>> s.get('https://httpbin.org/get')
-
-
- Or as a context manager::
-
- >>> with requests.Session() as s:
- ... s.get('https://httpbin.org/get')
-
- """
-
- __attrs__ = [
- "headers",
- "cookies",
- "auth",
- "proxies",
- "hooks",
- "params",
- "verify",
- "cert",
- "adapters",
- "stream",
- "trust_env",
- "max_redirects",
- ]
-
- def __init__(self):
-
- #: A case-insensitive dictionary of headers to be sent on each
- #: :class:`Request ` sent from this
- #: :class:`Session `.
- self.headers = default_headers()
-
- #: Default Authentication tuple or object to attach to
- #: :class:`Request `.
- self.auth = None
-
- #: Dictionary mapping protocol or protocol and host to the URL of the proxy
- #: (e.g. {'http': 'foo.bar:3128', 'http://host.name': 'foo.bar:4012'}) to
- #: be used on each :class:`Request `.
- self.proxies = {}
-
- #: Event-handling hooks.
- self.hooks = default_hooks()
-
- #: Dictionary of querystring data to attach to each
- #: :class:`Request `. The dictionary values may be lists for
- #: representing multivalued query parameters.
- self.params = {}
-
- #: Stream response content default.
- self.stream = False
-
- #: SSL Verification default.
- #: Defaults to `True`, requiring requests to verify the TLS certificate at the
- #: remote end.
- #: If verify is set to `False`, requests will accept any TLS certificate
- #: presented by the server, and will ignore hostname mismatches and/or
- #: expired certificates, which will make your application vulnerable to
- #: man-in-the-middle (MitM) attacks.
- #: Only set this to `False` for testing.
- self.verify = True
-
- #: SSL client certificate default, if String, path to ssl client
- #: cert file (.pem). If Tuple, ('cert', 'key') pair.
- self.cert = None
-
- #: Maximum number of redirects allowed. If the request exceeds this
- #: limit, a :class:`TooManyRedirects` exception is raised.
- #: This defaults to requests.models.DEFAULT_REDIRECT_LIMIT, which is
- #: 30.
- self.max_redirects = DEFAULT_REDIRECT_LIMIT
-
- #: Trust environment settings for proxy configuration, default
- #: authentication and similar.
- self.trust_env = True
-
- #: A CookieJar containing all currently outstanding cookies set on this
- #: session. By default it is a
- #: :class:`RequestsCookieJar `, but
- #: may be any other ``cookielib.CookieJar`` compatible object.
- self.cookies = cookiejar_from_dict({})
-
- # Default connection adapters.
- self.adapters = OrderedDict()
- self.mount("https://", HTTPAdapter())
- self.mount("http://", HTTPAdapter())
-
- def __enter__(self):
- return self
-
- def __exit__(self, *args):
- self.close()
-
- def prepare_request(self, request):
- """Constructs a :class:`PreparedRequest ` for
- transmission and returns it. The :class:`PreparedRequest` has settings
- merged from the :class:`Request ` instance and those of the
- :class:`Session`.
-
- :param request: :class:`Request` instance to prepare with this
- session's settings.
- :rtype: requests.PreparedRequest
- """
- cookies = request.cookies or {}
-
- # Bootstrap CookieJar.
- if not isinstance(cookies, cookielib.CookieJar):
- cookies = cookiejar_from_dict(cookies)
-
- # Merge with session cookies
- merged_cookies = merge_cookies(
- merge_cookies(RequestsCookieJar(), self.cookies), cookies
- )
-
- # Set environment's basic authentication if not explicitly set.
- auth = request.auth
- if self.trust_env and not auth and not self.auth:
- auth = get_netrc_auth(request.url)
-
- p = PreparedRequest()
- p.prepare(
- method=request.method.upper(),
- url=request.url,
- files=request.files,
- data=request.data,
- json=request.json,
- headers=merge_setting(
- request.headers, self.headers, dict_class=CaseInsensitiveDict
- ),
- params=merge_setting(request.params, self.params),
- auth=merge_setting(auth, self.auth),
- cookies=merged_cookies,
- hooks=merge_hooks(request.hooks, self.hooks),
- )
- return p
-
- def request(
- self,
- method,
- url,
- params=None,
- data=None,
- headers=None,
- cookies=None,
- files=None,
- auth=None,
- timeout=None,
- allow_redirects=True,
- proxies=None,
- hooks=None,
- stream=None,
- verify=None,
- cert=None,
- json=None,
- ):
- """Constructs a :class:`Request `, prepares it and sends it.
- Returns :class:`Response ` object.
-
- :param method: method for the new :class:`Request` object.
- :param url: URL for the new :class:`Request` object.
- :param params: (optional) Dictionary or bytes to be sent in the query
- string for the :class:`Request`.
- :param data: (optional) Dictionary, list of tuples, bytes, or file-like
- object to send in the body of the :class:`Request`.
- :param json: (optional) json to send in the body of the
- :class:`Request`.
- :param headers: (optional) Dictionary of HTTP Headers to send with the
- :class:`Request`.
- :param cookies: (optional) Dict or CookieJar object to send with the
- :class:`Request`.
- :param files: (optional) Dictionary of ``'filename': file-like-objects``
- for multipart encoding upload.
- :param auth: (optional) Auth tuple or callable to enable
- Basic/Digest/Custom HTTP Auth.
- :param timeout: (optional) How long to wait for the server to send
- data before giving up, as a float, or a :ref:`(connect timeout,
- read timeout) ` tuple.
- :type timeout: float or tuple
- :param allow_redirects: (optional) Set to True by default.
- :type allow_redirects: bool
- :param proxies: (optional) Dictionary mapping protocol or protocol and
- hostname to the URL of the proxy.
- :param stream: (optional) whether to immediately download the response
- content. Defaults to ``False``.
- :param verify: (optional) Either a boolean, in which case it controls whether we verify
- the server's TLS certificate, or a string, in which case it must be a path
- to a CA bundle to use. Defaults to ``True``. When set to
- ``False``, requests will accept any TLS certificate presented by
- the server, and will ignore hostname mismatches and/or expired
- certificates, which will make your application vulnerable to
- man-in-the-middle (MitM) attacks. Setting verify to ``False``
- may be useful during local development or testing.
- :param cert: (optional) if String, path to ssl client cert file (.pem).
- If Tuple, ('cert', 'key') pair.
- :rtype: requests.Response
- """
- # Create the Request.
- req = Request(
- method=method.upper(),
- url=url,
- headers=headers,
- files=files,
- data=data or {},
- json=json,
- params=params or {},
- auth=auth,
- cookies=cookies,
- hooks=hooks,
- )
- prep = self.prepare_request(req)
-
- proxies = proxies or {}
-
- settings = self.merge_environment_settings(
- prep.url, proxies, stream, verify, cert
- )
-
- # Send the request.
- send_kwargs = {
- "timeout": timeout,
- "allow_redirects": allow_redirects,
- }
- send_kwargs.update(settings)
- resp = self.send(prep, **send_kwargs)
-
- return resp
-
- def get(self, url, **kwargs):
- r"""Sends a GET request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- kwargs.setdefault("allow_redirects", True)
- return self.request("GET", url, **kwargs)
-
- def options(self, url, **kwargs):
- r"""Sends a OPTIONS request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- kwargs.setdefault("allow_redirects", True)
- return self.request("OPTIONS", url, **kwargs)
-
- def head(self, url, **kwargs):
- r"""Sends a HEAD request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- kwargs.setdefault("allow_redirects", False)
- return self.request("HEAD", url, **kwargs)
-
- def post(self, url, data=None, json=None, **kwargs):
- r"""Sends a POST request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param data: (optional) Dictionary, list of tuples, bytes, or file-like
- object to send in the body of the :class:`Request`.
- :param json: (optional) json to send in the body of the :class:`Request`.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- return self.request("POST", url, data=data, json=json, **kwargs)
-
- def put(self, url, data=None, **kwargs):
- r"""Sends a PUT request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param data: (optional) Dictionary, list of tuples, bytes, or file-like
- object to send in the body of the :class:`Request`.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- return self.request("PUT", url, data=data, **kwargs)
-
- def patch(self, url, data=None, **kwargs):
- r"""Sends a PATCH request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param data: (optional) Dictionary, list of tuples, bytes, or file-like
- object to send in the body of the :class:`Request`.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- return self.request("PATCH", url, data=data, **kwargs)
-
- def delete(self, url, **kwargs):
- r"""Sends a DELETE request. Returns :class:`Response` object.
-
- :param url: URL for the new :class:`Request` object.
- :param \*\*kwargs: Optional arguments that ``request`` takes.
- :rtype: requests.Response
- """
-
- return self.request("DELETE", url, **kwargs)
-
- def send(self, request, **kwargs):
- """Send a given PreparedRequest.
-
- :rtype: requests.Response
- """
- # Set defaults that the hooks can utilize to ensure they always have
- # the correct parameters to reproduce the previous request.
- kwargs.setdefault("stream", self.stream)
- kwargs.setdefault("verify", self.verify)
- kwargs.setdefault("cert", self.cert)
- if "proxies" not in kwargs:
- kwargs["proxies"] = resolve_proxies(request, self.proxies, self.trust_env)
-
- # It's possible that users might accidentally send a Request object.
- # Guard against that specific failure case.
- if isinstance(request, Request):
- raise ValueError("You can only send PreparedRequests.")
-
- # Set up variables needed for resolve_redirects and dispatching of hooks
- allow_redirects = kwargs.pop("allow_redirects", True)
- stream = kwargs.get("stream")
- hooks = request.hooks
-
- # Get the appropriate adapter to use
- adapter = self.get_adapter(url=request.url)
-
- # Start time (approximately) of the request
- start = preferred_clock()
-
- # Send the request
- r = adapter.send(request, **kwargs)
-
- # Total elapsed time of the request (approximately)
- elapsed = preferred_clock() - start
- r.elapsed = timedelta(seconds=elapsed)
-
- # Response manipulation hooks
- r = dispatch_hook("response", hooks, r, **kwargs)
-
- # Persist cookies
- if r.history:
-
- # If the hooks create history then we want those cookies too
- for resp in r.history:
- extract_cookies_to_jar(self.cookies, resp.request, resp.raw)
-
- extract_cookies_to_jar(self.cookies, request, r.raw)
-
- # Resolve redirects if allowed.
- if allow_redirects:
- # Redirect resolving generator.
- gen = self.resolve_redirects(r, request, **kwargs)
- history = [resp for resp in gen]
- else:
- history = []
-
- # Shuffle things around if there's history.
- if history:
- # Insert the first (original) request at the start
- history.insert(0, r)
- # Get the last request made
- r = history.pop()
- r.history = history
-
- # If redirects aren't being followed, store the response on the Request for Response.next().
- if not allow_redirects:
- try:
- r._next = next(
- self.resolve_redirects(r, request, yield_requests=True, **kwargs)
- )
- except StopIteration:
- pass
-
- if not stream:
- r.content
-
- return r
-
- def merge_environment_settings(self, url, proxies, stream, verify, cert):
- """
- Check the environment and merge it with some settings.
-
- :rtype: dict
- """
- # Gather clues from the surrounding environment.
- if self.trust_env:
- # Set environment's proxies.
- no_proxy = proxies.get("no_proxy") if proxies is not None else None
- env_proxies = get_environ_proxies(url, no_proxy=no_proxy)
- for (k, v) in env_proxies.items():
- proxies.setdefault(k, v)
-
- # Look for requests environment configuration
- # and be compatible with cURL.
- if verify is True or verify is None:
- verify = (
- os.environ.get("REQUESTS_CA_BUNDLE")
- or os.environ.get("CURL_CA_BUNDLE")
- or verify
- )
-
- # Merge all the kwargs.
- proxies = merge_setting(proxies, self.proxies)
- stream = merge_setting(stream, self.stream)
- verify = merge_setting(verify, self.verify)
- cert = merge_setting(cert, self.cert)
-
- return {"proxies": proxies, "stream": stream, "verify": verify, "cert": cert}
-
- def get_adapter(self, url):
- """
- Returns the appropriate connection adapter for the given URL.
-
- :rtype: requests.adapters.BaseAdapter
- """
- for (prefix, adapter) in self.adapters.items():
-
- if url.lower().startswith(prefix.lower()):
- return adapter
-
- # Nothing matches :-/
- raise InvalidSchema(f"No connection adapters were found for {url!r}")
-
- def close(self):
- """Closes all adapters and as such the session"""
- for v in self.adapters.values():
- v.close()
-
- def mount(self, prefix, adapter):
- """Registers a connection adapter to a prefix.
-
- Adapters are sorted in descending order by prefix length.
- """
- self.adapters[prefix] = adapter
- keys_to_move = [k for k in self.adapters if len(k) < len(prefix)]
-
- for key in keys_to_move:
- self.adapters[key] = self.adapters.pop(key)
-
- def __getstate__(self):
- state = {attr: getattr(self, attr, None) for attr in self.__attrs__}
- return state
-
- def __setstate__(self, state):
- for attr, value in state.items():
- setattr(self, attr, value)
-
-
-def session():
- """
- Returns a :class:`Session` for context-management.
-
- .. deprecated:: 1.0.0
-
- This method has been deprecated since version 1.0.0 and is only kept for
- backwards compatibility. New code should use :class:`~requests.sessions.Session`
- to create a session. This may be removed at a future date.
-
- :rtype: Session
- """
- return Session()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/connection.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/connection.py
deleted file mode 100644
index 6af1138f260e4eaaa0aa242f7f50b918a283b49f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/connection.py
+++ /dev/null
@@ -1,149 +0,0 @@
-from __future__ import absolute_import
-
-import socket
-
-from ..contrib import _appengine_environ
-from ..exceptions import LocationParseError
-from ..packages import six
-from .wait import NoWayToWaitForSocketError, wait_for_read
-
-
-def is_connection_dropped(conn): # Platform-specific
- """
- Returns True if the connection is dropped and should be closed.
-
- :param conn:
- :class:`http.client.HTTPConnection` object.
-
- Note: For platforms like AppEngine, this will always return ``False`` to
- let the platform handle connection recycling transparently for us.
- """
- sock = getattr(conn, "sock", False)
- if sock is False: # Platform-specific: AppEngine
- return False
- if sock is None: # Connection already closed (such as by httplib).
- return True
- try:
- # Returns True if readable, which here means it's been dropped
- return wait_for_read(sock, timeout=0.0)
- except NoWayToWaitForSocketError: # Platform-specific: AppEngine
- return False
-
-
-# This function is copied from socket.py in the Python 2.7 standard
-# library test suite. Added to its signature is only `socket_options`.
-# One additional modification is that we avoid binding to IPv6 servers
-# discovered in DNS if the system doesn't have IPv6 functionality.
-def create_connection(
- address,
- timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
- source_address=None,
- socket_options=None,
-):
- """Connect to *address* and return the socket object.
-
- Convenience function. Connect to *address* (a 2-tuple ``(host,
- port)``) and return the socket object. Passing the optional
- *timeout* parameter will set the timeout on the socket instance
- before attempting to connect. If no *timeout* is supplied, the
- global default timeout setting returned by :func:`socket.getdefaulttimeout`
- is used. If *source_address* is set it must be a tuple of (host, port)
- for the socket to bind as a source address before making the connection.
- An host of '' or port 0 tells the OS to use the default.
- """
-
- host, port = address
- if host.startswith("["):
- host = host.strip("[]")
- err = None
-
- # Using the value from allowed_gai_family() in the context of getaddrinfo lets
- # us select whether to work with IPv4 DNS records, IPv6 records, or both.
- # The original create_connection function always returns all records.
- family = allowed_gai_family()
-
- try:
- host.encode("idna")
- except UnicodeError:
- return six.raise_from(
- LocationParseError(u"'%s', label empty or too long" % host), None
- )
-
- for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
- af, socktype, proto, canonname, sa = res
- sock = None
- try:
- sock = socket.socket(af, socktype, proto)
-
- # If provided, set socket level options before connecting.
- _set_socket_options(sock, socket_options)
-
- if timeout is not socket._GLOBAL_DEFAULT_TIMEOUT:
- sock.settimeout(timeout)
- if source_address:
- sock.bind(source_address)
- sock.connect(sa)
- return sock
-
- except socket.error as e:
- err = e
- if sock is not None:
- sock.close()
- sock = None
-
- if err is not None:
- raise err
-
- raise socket.error("getaddrinfo returns an empty list")
-
-
-def _set_socket_options(sock, options):
- if options is None:
- return
-
- for opt in options:
- sock.setsockopt(*opt)
-
-
-def allowed_gai_family():
- """This function is designed to work in the context of
- getaddrinfo, where family=socket.AF_UNSPEC is the default and
- will perform a DNS search for both IPv6 and IPv4 records."""
-
- family = socket.AF_INET
- if HAS_IPV6:
- family = socket.AF_UNSPEC
- return family
-
-
-def _has_ipv6(host):
- """Returns True if the system can bind an IPv6 address."""
- sock = None
- has_ipv6 = False
-
- # App Engine doesn't support IPV6 sockets and actually has a quota on the
- # number of sockets that can be used, so just early out here instead of
- # creating a socket needlessly.
- # See https://github.com/urllib3/urllib3/issues/1446
- if _appengine_environ.is_appengine_sandbox():
- return False
-
- if socket.has_ipv6:
- # has_ipv6 returns true if cPython was compiled with IPv6 support.
- # It does not tell us if the system has IPv6 support enabled. To
- # determine that we must bind to an IPv6 address.
- # https://github.com/urllib3/urllib3/pull/611
- # https://bugs.python.org/issue658327
- try:
- sock = socket.socket(socket.AF_INET6)
- sock.bind((host, 0))
- has_ipv6 = True
- except Exception:
- pass
-
- if sock:
- sock.close()
- return has_ipv6
-
-
-HAS_IPV6 = _has_ipv6("::1")
diff --git a/spaces/Aveygo/AstroSleuth/results/details.md b/spaces/Aveygo/AstroSleuth/results/details.md
deleted file mode 100644
index feddd7277487f60443718943ca466397809aa948..0000000000000000000000000000000000000000
--- a/spaces/Aveygo/AstroSleuth/results/details.md
+++ /dev/null
@@ -1,44 +0,0 @@
-## Details
-
-The provided sample images were selected from the daily top of r/astrophotography on 4/9/2023.
-To minise cherry picking, my only criteria was that they must cover a broad range of deep space targets.
-They have not been resized or had noise added to them, but were cropped to a 512x512 tile of my choosing based on what I deemed as the most important aspect. eg: lots of noise/stars, nebulosity, or artifacts.
-
-## Tips, Tricks & Issues
-
-After upscaling many images, in general it almost always helps to **reduce the image size** before feeding it into the model. It is slightly counterintuitive as one might think that larger is better, but the main failure point is the original size. I believe this is due to the fact that the model is trained to expect images that are 4x smaller than they should be because it's task is to reverse this process.
-
-**After reducing image size such that the stars are at no more than 6pixels wide, performance can drastically improve.**
-
-To get to the expected size, the upscaled "crushed" image can be upscaled once again.
-
-### Workflow for difficult cases
-
-1. Original -> +downsample 4x -> +noise -> +blur -> Crushed
-2. Crushed -> +upscale -> +upscale -> Output
-
-Please note that the tile size parameter for the model was reduced from 512x512 to 128x128 to fit the input image better.
-
-### Summary
-
-1. Reduce image size and upscale twice
-2. AstroSleuthV1 generally works better for smoother results, V2 for anything else
-3. Don't be afraid to add noise or blur the image to improve results
-4. Diffraction spikes are almost always troublesome
-
-### Workflow result on original/sample2.png
-
-
-
-| Raw AstroSleuthV2 | Workflow with V2 | Workflow with V1 |
-| --- | --- | --- |
-| | | |
-
-As you can see, this workflow works much better with AstroSleuthV1 for this particular image. However I find results can drastically differ based on the amount of noise/blur applied to the crushed image, but were kept the same for the sake of comparison.
-
-
-### Image credits / sources
-https://www.reddit.com/r/astrophotography/comments/12fnhb3/m35_and_ngc_2158/
-https://www.reddit.com/r/astrophotography/comments/12flzf1/messier_106_in_lrgb/
-https://www.reddit.com/r/astrophotography/comments/12febjp/orion_nebula/
-https://www.reddit.com/r/astrophotography/comments/12fewff/northwestern_cygnus_nebulae/
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py
deleted file mode 100644
index 6ff5b6dc117744a9d978e0aff324bddeb496409b..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-import unittest
-import tempfile
-from itertools import count
-
-from detectron2.config import LazyConfig, LazyCall as L
-from omegaconf import DictConfig
-
-
-class TestLazyPythonConfig(unittest.TestCase):
- def setUp(self):
- self.root_filename = os.path.join(os.path.dirname(__file__), "root_cfg.py")
-
- def test_load(self):
- cfg = LazyConfig.load(self.root_filename)
-
- self.assertEqual(cfg.dir1a_dict.a, "modified")
- self.assertEqual(cfg.dir1b_dict.a, 1)
- self.assertEqual(cfg.lazyobj.x, "base_a_1")
-
- cfg.lazyobj.x = "new_x"
- # reload
- cfg = LazyConfig.load(self.root_filename)
- self.assertEqual(cfg.lazyobj.x, "base_a_1")
-
- def test_save_load(self):
- cfg = LazyConfig.load(self.root_filename)
- with tempfile.TemporaryDirectory(prefix="detectron2") as d:
- fname = os.path.join(d, "test_config.yaml")
- LazyConfig.save(cfg, fname)
- cfg2 = LazyConfig.load(fname)
-
- self.assertEqual(cfg2.lazyobj._target_, "itertools.count")
- self.assertEqual(cfg.lazyobj._target_, count)
- cfg2.lazyobj.pop("_target_")
- cfg.lazyobj.pop("_target_")
- # the rest are equal
- self.assertEqual(cfg, cfg2)
-
- def test_failed_save(self):
- cfg = DictConfig({"x": lambda: 3}, flags={"allow_objects": True})
- with tempfile.TemporaryDirectory(prefix="detectron2") as d:
- fname = os.path.join(d, "test_config.yaml")
- LazyConfig.save(cfg, fname)
- self.assertTrue(os.path.exists(fname))
- self.assertTrue(os.path.exists(fname + ".pkl"))
-
- def test_overrides(self):
- cfg = LazyConfig.load(self.root_filename)
- LazyConfig.apply_overrides(cfg, ["lazyobj.x=123", 'dir1b_dict.a="123"'])
- self.assertEqual(cfg.dir1b_dict.a, "123")
- self.assertEqual(cfg.lazyobj.x, 123)
-
- def test_invalid_overrides(self):
- cfg = LazyConfig.load(self.root_filename)
- with self.assertRaises(KeyError):
- LazyConfig.apply_overrides(cfg, ["lazyobj.x.xxx=123"])
-
- def test_to_py(self):
- cfg = LazyConfig.load(self.root_filename)
- cfg.lazyobj.x = {"a": 1, "b": 2, "c": L(count)(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]})}
- cfg.list = ["a", 1, "b", 3.2]
- py_str = LazyConfig.to_py(cfg)
- expected = """cfg.dir1a_dict.a = "modified"
-cfg.dir1a_dict.b = 2
-cfg.dir1b_dict.a = 1
-cfg.dir1b_dict.b = 2
-cfg.lazyobj = itertools.count(
- x={
- "a": 1,
- "b": 2,
- "c": itertools.count(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]}),
- },
- y="base_a_1_from_b",
-)
-cfg.list = ["a", 1, "b", 3.2]
-"""
- self.assertEqual(py_str, expected)
diff --git a/spaces/Benson/text-generation/Examples/7 Espaol.md b/spaces/Benson/text-generation/Examples/7 Espaol.md
deleted file mode 100644
index a887ecffeb45a347b480d0179117c260dc5a27ad..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/7 Espaol.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-
¿Qué es 7 Inglés y cómo puede ayudarle a aprender gramática inglesa?
-
El inglés es uno de los idiomas más hablados en el mundo, y aprenderlo puede abrirte muchas puertas en términos de educación, carrera, viajes y comunicación. Sin embargo, aprender inglés no siempre es fácil, especialmente cuando se trata de gramática. La gramática es el conjunto de reglas que gobiernan cómo las palabras y las oraciones se estructuran y se utilizan en un idioma. La gramática puede ser confusa y frustrante para muchos estudiantes, ya que hay muchas excepciones, variaciones y matices que necesitan ser dominados.
-
Ahí es donde 7 Inglés entra en juego. 7 English es un sitio web que proporciona lecciones completas y fáciles de seguir sobre varios aspectos de la gramática inglesa. Ya seas un principiante o un estudiante avanzado, 7 Inglés puede ayudarte a mejorar tus habilidades gramaticales de una manera simple y efectiva. Puedes acceder a cientos de temas de gramática, ejemplos, ejercicios, hojas de trabajo, videos, cuestionarios, consejos y más en el sitio web de 7 English. También puedes interactuar con otros estudiantes y profesores en el foro de inglés 7. 7 El inglés es tu destino único para aprender gramática inglesa en línea.
En este artículo, te mostraremos cómo aprender gramática inglesa con 7 Inglés en seis sencillos pasos. Siguiendo estos pasos, podrás dominar las reglas gramaticales esenciales y los conceptos que te ayudarán a hablar y escribir mejor en inglés. ¡Vamos a empezar!
-
Cómo aprender gramática inglesa con 7
-
Aprender partes del habla
-
-
Aprender partes del habla es importante porque te ayuda a entender cómo las palabras trabajan juntas para formar significado. También te ayuda a evitar errores gramaticales como el acuerdo sujeto-verbo o el acuerdo sustantivo-pronombre. Para aprender partes del habla con 7 Inglés, puede visitar su parts of speech sectionpartes de videos de discurso para aprender los fundamentos de cada parte del discurso de una manera divertida y atractiva.
-
Aprender tiempos verbales
-
Los tiempos verbales son las formas que toman los verbos para indicar el tiempo y el aspecto de una acción o estado. Hay tres tipos principales de tiempos verbales en inglés: pasado, presente y futuro. Cada tipo tiene cuatro aspectos: simple, continuo, perfecto y perfecto continuo. Por ejemplo, el tiempo simple pasado expresa una acción o estado que ocurrió en el pasado y se completa; el tiempo continuo pasado expresa una acción o estado que estaba en progreso en el pasado; el tiempo perfecto pasado expresa una acción o estado que se completó antes de otra acción o estado en el pasado; el tiempo continuo perfecto pasado expresa una acción o estado que estaba en progreso durante algún tiempo antes de otra acción o estado en el pasado.
-
Aprender los tiempos verbales es importante porque te ayuda a expresar cuándo y cómo sucede o sucedió algo. También le ayuda a evitar errores gramaticales tales como inconsistencia del tiempo o mal uso del tiempo. Para aprender los tiempos verbales con 7 en inglés, puedes visitar su sección tiempos verbales donde puedes encontrar explicaciones detalladas, ejemplos y ejercicios para cada tiempo verbal. También puedes ver sus videos verb tenses videos para aprender los conceptos básicos de cada verbo de una manera divertida y atractiva.
-
Aprender oraciones, frases y cláusulas
-
-
Aprender oraciones, frases y cláusulas es importante porque te ayuda a entender cómo las palabras están organizadas y conectadas con el significado de la forma. También le ayuda a evitar errores gramaticales como fragmentos de oración, oraciones en ejecución, empalmes de coma o modificadores mal colocados. Para aprender oraciones, frases y cláusulas con 7 Inglés, puede visitar su sección sentences, phrases, and clauses section donde puede encontrar explicaciones detalladas, ejemplos y ejercicios para cada tema. También puedes ver sus videos sentences, phrases, and clauses videos para aprender los conceptos básicos de cada tema de una manera divertida y atractiva.
-
Aprender Condicionales, Habla Reportada, Voz Pasiva, Cuantificadores y Determinantes
-
Condicionales, discurso informado, voz pasiva, cuantificadores y determinantes son algunos de los temas avanzados de la gramática inglesa que pueden ayudarte a expresar ideas y situaciones complejas. Las condicionales son oraciones que expresan situaciones hipotéticas o reales y sus consecuencias. El discurso informado es la forma de informar lo que alguien dijo o pensó sin usar citas directas. La voz pasiva es la forma de expresar una acción sin enfatizar quién o qué la realiza. Cuantificadores son palabras que indican la cantidad o cantidad de algo. Los determinantes son palabras que modifican sustantivos o frases de sustantivos y especifican su referencia.
-
-
Aprenda las reglas básicas de gramática: Estructura de oraciones en inglés
-
Las reglas gramaticales básicas son los principios generales que rigen la forma en que las palabras y las oraciones se estructuran y usan en inglés. Algunas de las reglas gramaticales básicas incluyen acuerdo sujeto-verbo, acuerdo sustantivo-pronombre, orden de las palabras, mayúsculas, puntuación, ortografía y más. Estas reglas te ayudan a crear oraciones claras y correctas que transmiten tu significado.
-
-
Aprender reglas gramaticales básicas es importante porque te ayuda a evitar errores gramaticales comunes y malentendidos que pueden afectar tu comunicación y credibilidad. También le ayuda a mejorar sus habilidades de lectura y escritura y prepararse para varios exámenes y exámenes. Para aprender las reglas gramaticales básicas con 7 en inglés, puedes visitar su sección basic grammar rules donde puedes encontrar explicaciones detalladas, ejemplos y ejercicios para cada regla. También puedes ver sus videos basic grammar rules videos para aprender los fundamentos de cada regla de una manera divertida y atractiva.
En conclusión, 7 English es un sitio web que puede ayudarte a aprender gramática inglesa de una manera simple y efectiva. Puedes acceder a cientos de temas de gramática, ejemplos, ejercicios, hojas de trabajo, videos, cuestionarios, consejos y más en el sitio web de 7 English. También puede interactuar con otros estudiantes y profesores en el foro 7 Inglés. Siguiendo los seis pasos que hemos descrito en este artículo, podrás dominar las reglas gramaticales y los conceptos esenciales que te ayudarán a hablar y escribir mejor en inglés.
-
Si quieres mejorar tus habilidades gramaticales y aumentar tu confianza en el uso del inglés, no dudes en visitar el sitio web 7 en inglés hoy. Te sorprenderá lo mucho que puedes aprender y mejorar con 7 Inglés. Recuerda, la gramática no es una pesadilla; es una herramienta que puede ayudarte a alcanzar tus metas en el aprendizaje del inglés.
-
Preguntas frecuentes
-
Aquí están algunas de las preguntas más frecuentes relacionadas con el tema de este artículo:
-
-
¿Cuál es la mejor manera de aprender gramática inglesa?
-
La mejor manera de aprender gramática inglesa es usar una combinación de métodos que se adapten a tu estilo de aprendizaje, nivel, metas y preferencias. Algunos de los métodos incluyen: leer libros, artículos, blogs o revistas en inglés; escuchar podcasts, audiolibros, canciones o programas de radio en inglés; ver películas, programas de televisión, videos o documentales en inglés; hablar con hablantes nativos o fluidos de inglés; escribir ensayos, historias, correos electrónicos o revistas en inglés; tomar cursos o clases en línea o fuera de línea en inglés; usar recursos en línea o fuera de línea como sitios web, aplicaciones, libros, diccionarios o guías de gramática en inglés. La clave es practicar regularmente, revisar con frecuencia y aprender de tus errores.
-
¿Cuánto tiempo se tarda en aprender gramática inglesa?
-
-
¿Cuáles son los errores gramaticales más comunes en inglés?
-
Algunos de los errores gramaticales más comunes en inglés son: errores de acuerdo sujeto-verbo, errores de acuerdo sustantivo-pronombre, errores de tiempo verbal, errores de orden de palabras, errores de mayúsculas, errores de puntuación, errores de ortografía, errores de artículo, errores de preposición, errores de modificación, errores de paralelismo y más. Para evitar estos errores, necesitas aprender las reglas gramaticales y los conceptos que los rigen y practicarlos con ejercicios y cuestionarios. También puede utilizar herramientas en línea como correctores de gramática o correctores de pruebas para ayudarle a identificar y corregir sus errores.
-
¿Cuáles son los beneficios de aprender gramática inglesa?
-
Algunos de los beneficios de aprender gramática inglesa son: mejora tus habilidades de comunicación en el habla y la escritura; mejora tus habilidades de comprensión en la lectura y la escucha; aumenta tu confianza y fluidez en el uso del inglés; expande tu vocabulario y conocimiento del inglés; te prepara para varios exámenes y exámenes en inglés; te abre más oportunidades en educación, carrera, viajes y comunicación.
-
¿Dónde puedo encontrar más información sobre 7 Inglés?
-
Puede encontrar más información sobre 7 Inglés visitando su sitio web en 7english.com. Allí puedes acceder a cientos de temas de gramática, ejemplos, ejercicios, hojas de trabajo, videos, cuestionarios, consejos y más. También puede interactuar con otros estudiantes y profesores en el foro 7 Inglés. También puedes seguirlos en sus redes sociales como Facebook, Twitter, Instagram, YouTube y Pinterest.
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Billete nter Hall 2023 Ts.md b/spaces/Benson/text-generation/Examples/Descargar Billete nter Hall 2023 Ts.md
deleted file mode 100644
index db793023cdc46338799c37f1da683265b457a33d..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Billete nter Hall 2023 Ts.md
+++ /dev/null
@@ -1,164 +0,0 @@
-
-
Descargar 2023 Lineup Fecha de lanzamiento: Todo lo que necesitas saber
|
Si eres un fan de la música rock y metal, probablemente has oído hablar de Download Festival. Es el festival de rock más grande y popular del Reino Unido, y ha albergado algunas de las leyendas del género, como Iron Maiden, Black Sabbath, Metallica, Slipknot y muchos más.
Download Festival regresa en 2023 para una celebración de cuatro días de su 20 aniversario, y promete ser un evento épico. En este artículo, te contaremos todo lo que necesitas saber sobre la fecha de lanzamiento de la alineación Download 2023, entradas, ubicación, titulares, etapas, medidas de seguridad COVID-19 y consejos para preparar y disfrutar del festival.
-
¿Qué es Download Festival?
-
Download Festival es un festival de música rock y metal que tiene lugar cada año en Donington Park en Leicestershire, Inglaterra. Fue creado en 2003 como un seguimiento de los festivales Monsters of Rock que se celebraron en el mismo lugar desde 1980 hasta 1996.
-
Una breve historia del Download Festival
-
El primer Download Festival fue co-reservado por Stuart Galbraith y Andy Copping en 2003, y contó con bandas como Iron Maiden, Audioslave, Marilyn Manson, Deftones y Disturbed. El festival fue inicialmente un evento de dos días, pero se expandió a tres días en 2005. El nombre Download fue elegido para reflejar la naturaleza rebelde de la música rock y la conectividad a Internet con su audiencia.
-
Con los años, Download Festival ha crecido en tamaño y popularidad, atrayendo a más de 100.000 asistentes cada año. También ha albergado algunos de los nombres más importantes en la historia del rock y el metal, como Black Sabbath, Metallica, Linkin Park, Korn, Soundgarden, Motörhead, Aerosmith, AC/DC, Def Leppard, Kiss, Judas Priest, Rammstein, Status Quo, Mötley Crüe, Journey, ZZ Top, Whitesnake, Fe No Más y Armas N' Rosas.
-
-
-
Las principales características de Download Festival
-
Download Festival no es solo música. También ofrece una variedad de atracciones y actividades para sus asistentes. Algunas de las características principales del Download Festival son:
-
-
The Village: Aquí es donde puedes encontrar puestos de comida, tiendas de mercancías, bares, lugares de entretenimiento y otros servicios.
-
The Arena: Aquí es donde se encuentran los escenarios principales, y donde puedes ver a tus bandas favoritas en vivo.
-
Los Campings: Aquí es donde puedes armar tu carpa, conocer a otros asistentes al festival,
The Doghouse: Esta es una carpa que alberga espectáculos de comedia, DJ sets, karaoke, discoteca silenciosa y otros eventos divertidos.
-
Los paquetes RIP (Rest In Peace): Estos son paquetes premium que ofrecen alojamiento de lujo, acceso VIP, instalaciones exclusivas y otras ventajas.
-
-
¿Cuándo es Download Festival 2023?
-
Download Festival 2023 tendrá lugar del jueves 8 de junio al domingo 11 de junio. Marcará el 20 aniversario del festival, y será una edición especial con cuatro días de música en lugar de los tres habituales.
-
Las fechas y ubicación del Download Festival 2023
-
Las fechas y la ubicación de Download Festival 2023 son las siguientes:
-
-
-
Fecha
-
Ubicación
-
-
-
jueves, 8 de junio
-
Donington Park, Leicestershire, Inglaterra
-
-
-
viernes, 9 de junio
-
Donington Park, Leicestershire, Inglaterra
-
-
-
sábado, 10 de junio
-
Donington Park, Leicestershire, Inglaterra
-
-
-
domingo, 11 de junio
-
Donington Park, Leicestershire, Inglaterra
-
-
-
Donington Park es un famoso circuito de automovilismo que ha albergado muchos eventos de carreras, como la Fórmula 1, MotoGP y el Campeonato Mundial de Superbike. También es un sitio histórico para la música rock y metal, ya que fue el hogar de los festivales Monsters of Rock y el lugar de nacimiento del Download Festival.
-
-
Los cabezas de cartel y los escenarios del Download Festival 2023
-
Los titulares y etapas de Download Festival 2023 no se han anunciado oficialmente todavía. Sin embargo, algunos rumores y especulaciones han estado circulando en línea. Según algunas fuentes , los posibles protagonistas y escenarios del Download Festival 2023 son:
-
-
-
Fecha
-
Etapa
-
Titular
-
-
-
jueves, 8 de junio
-
Escenario principal
-
Metallica (interpretando el álbum negro en su totalidad)
-
-
-
viernes, 9 de junio
-
Escenario principal
-
Beso (tour de despedida)
-
-
-
sábado, 10 de junio
-
Escenario principal
-
Iron Maiden (gira Legado de la Bestia)
-
-
-
domingo, 11 de junio
-
Escenario principal
-
Rammstein (nuevo álbum)
-
-
-
jueves, 8 de junio
-
Segunda etapa
-
Sistema de un Down (gira de reunión)
-
-
-
viernes, 9 de junio
-
Segunda etapa
-
Slipknot (nuevo álbum)
-
-
-
sábado, 10 de junio
-
Segunda etapa
-
Herramienta (nuevo álbum)
-
-
-
domingo, 11 de junio
-
Segunda etapa
-
Uñas de nueve pulgadas (nueva gira del álbum)
-
-
-
Por supuesto, estos son solo rumores y especulaciones, y pueden cambiar o ser inexactos. La alineación oficial y los escenarios del Download Festival 2023 serán anunciados por los organizadores a su debido tiempo. Puede consultar sus cuentas de sitios web o redes sociales para obtener las últimas actualizaciones y noticias.
-
¿Cómo obtener entradas para Download Festival 2023?
-
Si estás interesado en asistir al Download Festival 2023, tendrás que conseguir entradas lo antes posible. Se espera que las entradas para el Download Festival 2023 se agoten rápidamente, ya que la demanda es alta y la oferta es limitada.
-
Los tipos y precios de las entradas para Download Festival 2023
-
Los tipos y precios de las entradas para Download Festival 2023 son los siguientes:
-
-
-
-
Descripción
-
Precio
-
-
-
Boleto de fin de semana Arena
-
Este ticket te da acceso solo a la Arena, de viernes a domingo. Tendrás que organizar tu propio alojamiento.
-
£195
-
-
-
Boleto de campamento de fin de semana
-
Este ticket te da acceso a la Arena y los Campings, de jueves a lunes. Tendrás que traer tu propia carpa y equipo de camping.
-
£230
-
-
-
Boleto de acampada tranquila de fin de semana
-
Este ticket te da acceso a la Arena y al Camping Tranquilo, de jueves a lunes. Tendrá que traer su propia tienda y equipo de camping. El camping tranquilo está situado lejos del camping principal, y tiene un toque de queda ruido a medianoche.
-
£230
-
-
-
-sitios web de terceros. Estas fuentes pueden vender billetes falsos, inválidos o caros que podrían resultar en decepción o fraude. ¿Cómo prepararse para el Festival 2023?
-
Download Festival 2023 va a ser una experiencia increíble, pero también requiere algo de preparación y planificación. Aquí hay algunos consejos sobre cómo prepararse para el Download Festival 2023:
-
Las medidas de seguridad COVID-19 para Download Festival 2023
-
La pandemia COVID-19 ha afectado muchos eventos y festivales, y Download Festival no es una excepción. Los organizadores del Download Festival 2023 han declarado que seguirán las directrices y regulaciones gubernamentales relativas a las medidas de seguridad COVID-19. También han aconsejado a los asistentes que consulten su sitio web y sus cuentas de redes sociales para obtener las últimas actualizaciones e información.
-
Algunas de las posibles medidas de seguridad de COVID-19 para el Download Festival 2023 son:
-
-
Prueba de vacunación o prueba negativa: Es posible que tenga que mostrar la prueba de vacunación completa o una prueba de flujo lateral negativa tomada dentro de las 48 horas de la llegada al sitio del festival.
-
Mascarillas y desinfectantes de manos: Es posible que necesite usar una mascarilla facial y usar desinfectante de manos al entrar o salir del sitio del festival, o cuando esté en áreas llenas o cerradas.
-
Distancia social y límites de capacidad: Es posible que necesites mantener una distancia segura de otras personas y seguir las señales y marcadores que indican los límites de capacidad de ciertas áreas o lugares.
-
Seguimiento y aislamiento de contactos: Es posible que necesite proporcionar sus datos de contacto y escanear un código QR al entrar o salir del sitio del festival, o al usar ciertas instalaciones o servicios. También es posible que tenga que aislarse e informar al personal del festival si desarrolla algún síntoma de COVID-19 durante o después del festival.
-
-
-
Los consejos y trucos para sobrevivir Descargar Festival 2023
-
Download Festival 2023 va a ser muy divertido, pero también puede ser desafiante y agotador. Estos son algunos consejos y trucos para sobrevivir Descargar Festival 2023:
-
-
Empaca sabiamente: Debes empacar solo lo esencial, como ropa, artículos de tocador, equipo de campamento, cargador de teléfono, tapones para los oídos, protector solar, impermeable, etc. También debes empacar algunos bocadillos, bebidas y medicamentos en caso de que los necesites. Debe evitar traer objetos de valor, artículos prohibidos o exceso de equipaje que pueda agobiarlo o perderse o ser robado.
-
Planifica con anticipación: Debes planificar tu itinerario, presupuesto, transporte, alojamiento y actividades antes de ir al festival. También debe consultar el pronóstico del tiempo, el mapa del festival, el calendario de alineación y las normas y reglamentos del festival. También debería tener un plan de respaldo en caso de que algo salga mal o cambie.
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Mantente hidratado y nutrido: Debes beber mucha agua y comer comidas regulares durante el festival. También debe evitar beber demasiado alcohol o consumir drogas que podrían deshidratarlo o perjudicar su juicio. También debe tomar descansos y descansar cuando se sienta cansado o enfermo.
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Sé seguro y respetuoso: Debes ser consciente de tu entorno y evitar cualquier situación peligrosa o sospechosa o personas. También debe respetar al personal del festival, personal de seguridad, artistas y otros asistentes. No debe participar en ningún tipo de violencia, acoso, vandalismo, basura u otro comportamiento antisocial que podría arruinar el festival para usted o para otros.
-
Diviértete y disfruta: Deberías aprovechar al máximo tu tiempo en Download Festival 2023. Deberías ver a tus bandas favoritas actuar en vivo, descubrir nueva música, conocer gente nueva, participar en las actividades, explorar las atracciones y crear recuerdos inolvidables.
-
-
Conclusión
-
-
Si quieres asistir al Download Festival 2023, necesitarás conocer su fecha de lanzamiento, entradas, ubicación, titulares, escenarios, medidas de seguridad COVID-19, y consejos para preparar y disfrutar del festival. Esperamos que este artículo te haya dado toda la información que necesitas saber sobre Download Festival 2023.
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Download Festival 2023:
-
-
Q: ¿Cuándo se lanzará la alineación para Download Festival 2023?
-los organizadores suelen anunciar la alineación por etapas, a partir de finales de 2022 o principios de 2023. Puede consultar sus cuentas de sitios web o redes sociales para obtener las últimas actualizaciones y noticias.
-
Q: ¿Cuánto cuestan las entradas para Download Festival 2023?
-
A: Las entradas para el Download Festival 2023 varían en precio dependiendo del tipo de entrada y la opción de alojamiento. El billete más barato es el Weekend Arena Ticket, que cuesta £195. El billete más caro es el Paquete RIP Ticket, que puede costar hasta £1.999. Los billetes no incluyen gastos de reserva ni de envío.
-
Q: ¿Dónde puedo comprar entradas para Download Festival 2023?
-
A: Puedes comprar entradas para Download Festival 2023 desde el sitio web oficial de Download Festival, o desde sus socios de boletos oficiales, como Ticketmaster, See Tickets, Festicket o Big Green Coach. También puedes comprar entradas en su plataforma oficial de reventa, que es Twickets. Debe evitar comprar boletos de fuentes no oficiales, como revendedores, anuncios o sitios web de terceros.
-
Q: ¿Cuáles son las medidas de seguridad COVID-19 para Download Festival 2023?
-
A: Las medidas de seguridad COVID-19 para el Download Festival 2023 aún no se han finalizado. Los organizadores del Download Festival 2023 seguirán las directrices y regulaciones gubernamentales relativas a las medidas de seguridad COVID-19. También aconsejarán a los asistentes que consulten su sitio web y sus cuentas de redes sociales para obtener las últimas actualizaciones e información.
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-
A: Algunos de los consejos y trucos para sobrevivir Download Festival 2023 son: empacar sabiamente, planificar con antelación, mantenerse hidratado y nutrido, ser seguro y respetuoso, y divertirse y disfrutar.
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APK Hide Online: Un divertido y emocionante juego multijugador
` | | `
Estás buscando un nuevo y emocionante juego para jugar con tus amigos o extraños en línea? ¿Te encantan los juegos de acción y disparos con un toque de humor y creatividad? Si usted respondió sí a estas preguntas, entonces usted debe definitivamente echa un vistazo APK Hide Online - un adictivo y emocionante juego multijugador de ocultar y buscar en el género popular de la caza de apoyo.
` | | `
¿Qué es APK Hide Online?
` | | `
APK Hide Online es un juego desarrollado por HitRock Juegos que le permite experimentar el clásico juego de ocultar y buscar de una manera completamente nueva. Puedes esconderte como un accesorio de otros jugadores en cualquier habitación o tratar de escapar como un cazador que está buscando accesorios ocultos.
El juego tiene gráficos impresionantes, física realista, sonidos dinámicos y controles suaves. Puede elegir entre una variedad de habitaciones y accesorios para satisfacer sus preferencias y estilo. También puedes personalizar a tu personaje con diferentes pieles, sombreros, armas y accesorios.
` | | `
El juego es fácil de jugar pero difícil de dominar. Necesitas ser rápido, inteligente y sigiloso para sobrevivir y ganar. También puedes usar habilidades especiales como burlas, transformaciones, señuelos, bombas, trampas y más para ser más astuto que tus oponentes.
` | | `
Cómo descargar e instalar APK Ocultar en línea?
` | | `
Si desea jugar APK Ocultar en línea en su dispositivo Android, es necesario seguir estos sencillos pasos:
Una vez completada la descarga, abra el archivo y `
Permita la instalación de aplicaciones de fuentes desconocidas si se le solicita.
` | | `
Siga las instrucciones en la pantalla para completar la instalación.
` | | `
Inicie el juego y disfrute!
` | | `` | | `
Nota: También puede descargar APK Hide Online desde otras fuentes como Google Play Store, pero 64aa2da5cf
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Descargar Dynamons World Pikachu Mod Apk: Captura Pokemon en el juego
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¿Te gustan los juegos de Pokémon? ¿Quieres atrapar y entrenar a tus propias criaturas en una aventura divertida y emocionante? Si es así, entonces deberías probar Dynamons World, un juego en línea gratuito que te permite explorar un vasto mundo lleno de dynamons, que son monstruos lindos y poderosos con los que puedes coleccionar y luchar. Y si quieres hacer tu juego aún más impresionante, se puede descargar el Pikachu mod apk, que le da acceso a los Pokémon más populares de todos los tiempos. En este artículo, le diremos todo lo que necesita saber sobre Dynamons World y el apk mod Pikachu, incluyendo cómo descargar e instalar, cómo jugar, y algunos consejos y trucos para ayudarle a convertirse en el mejor maestro dynamon.
Dynamons World es un juego online gratuito inspirado en la franquicia Pokémon. Es desarrollado por Kizi Games y tiene más de 10 millones de descargas en Google Play. El juego cuenta con cientos de diferentes dynamons que se puede coger, entrenar, evolucionar, y la batalla con. También puedes explorar varias regiones, completar misiones, desafiar a otros jugadores y unirte a torneos. El juego tiene gráficos coloridos, música pegadiza y controles simples que hacen que sea fácil y divertido de jugar.
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Características de Dynamons World
-
-
Captura y recoge más de 200 dynamons únicos con diferentes tipos, habilidades y personalidades.
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Entrena y evoluciona tus dynamons para hacerlos más fuertes y desbloquear nuevas habilidades.
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Batalla contra dynamons salvajes, entrenadores, jefes y otros jugadores en combate por turnos.
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Explora un mundo enorme con diferentes entornos, como bosques, desiertos, islas, volcanes y más.
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Completa varias misiones y misiones para ganar recompensas y desbloquear nuevas áreas.
-
Únete a torneos y compite con otros jugadores de todo el mundo por la fama y la gloria.
-
Personaliza tu personaje con diferentes trajes y accesorios.
-
-
-
Para jugar Dynamons World, necesita crear una cuenta o iniciar sesión con su cuenta de Facebook o Google. A continuación, puede elegir su dinamo de arranque de tres opciones: Ninto (tipo de fuego), Treeno (tipo de hierba), o Squiro (tipo de agua). Después de eso, puedes comenzar tu aventura siguiendo las instrucciones del Profesor K (la guía del juego) y explorando el mundo. Usted puede encontrar dynamons salvajes caminando en las áreas de hierba o agua. Para atraparlos, necesitas debilitarlos usando los ataques de tu dynamon y luego lanzarles una bola de captura. También puedes luchar contra otros entrenadores hablando con ellos o aceptando sus desafíos. Para ganar una batalla, es necesario reducir la salud del oponente dynamon a cero o hacerlos desmayar. Puedes usar objetos como pociones, piedras o potenciadores para ayudarte en la batalla. También puedes cambiar tu dynamon durante la batalla tocando sus iconos en la parte inferior de la pantalla. Puedes acceder a tu inventario, equipo, mapa, misiones, configuración y tienda tocando el botón de menú en la esquina superior derecha de la pantalla.
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¿Qué es Pikachu Mod Apk?
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Pikachu mod apk es una versión modificada de Dynamons World que te da acceso a Pikachu, el Pokémon más famoso de todos los tiempos. Pikachu es un tipo eléctrico dynamon que tiene alta velocidad y poder de ataque. También puede aprender movimientos poderosos como Thunderbolt, Thunder Wave, Volt Tackle y más. Con Pikachu mod apk, puede comenzar su juego con Pikachu como su dynamon de arranque o atraparlo en la naturaleza. También puedes disfrutar de monedas y gemas ilimitadas, que son la moneda del juego que puedes usar para comprar artículos, actualizar tus dynamons o desbloquear nuevas características. También puede eliminar anuncios y disfrutar de una experiencia de juego más suave con Pikachu mod apk.
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Beneficios de Pikachu Mod Apk
-
-
Obtener Pikachu como su inicio dynamon o atraparlo en la naturaleza.
-
Disfruta de monedas y gemas ilimitadas para comprar todo lo que quieras en el juego.
-
Eliminar los molestos anuncios e interrupciones del juego.
-
-
Más diversión y emoción con Pikachu y otros dynamons.
-
-
Cómo descargar e instalar Pikachu Mod Apk
-
Para descargar e instalar Pikachu mod apk, es necesario seguir estos sencillos pasos:
-
-
-
Haga clic en este enlace para descargar el archivo Pikachu mod apk: [Descargar Pikachu Mod Apk].
-
Permitir fuentes desconocidas en el dispositivo yendo a Configuración > Seguridad > Fuentes desconocidas y habilitarlo.
-
Busque el archivo descargado en su administrador de archivos y toque en él para instalarlo.
-
Iniciar el juego y disfrutar de la captura de Pokémon en Dynamons World.
-
-
Consejos y trucos para Dynamons World Pikachu Mod Apk
-
Para aprovechar al máximo su Dynamons World Pikachu mod apk, aquí hay algunos consejos y trucos que puede utilizar:
-
Elija su arrancador sabiamente
-
Aunque puedes empezar con Pikachu como tu dynamon de arranque, también puedes elegir entre las otras tres opciones: Ninto, Treeno o Squiro. Cada uno de ellos tiene un tipo diferente, fuerza y debilidad. Por ejemplo, Ninto es fuerte contra la hierba pero débil contra el agua, Treeno es fuerte contra el agua pero débil contra el fuego, y Squiro es fuerte contra el fuego pero débil contra la hierba. También puedes considerar sus habilidades, como Ninto’s Blaze, Treeno’s Overgrow o Squiro’s Torrent, que aumentan sus ataques cuando su salud es baja. Elige el que mejor se adapte a tu estilo de juego y estrategia.
-
Entrena y evoluciona tus dynamons
-
Para hacer tus dynamons más fuertes, necesitas entrenarlos luchando contra otros dynamons y ganando puntos de experiencia. Cuando suben de nivel, pueden aprender nuevos movimientos o mejorar sus estadísticas. También puede evolucionar cuando alcanzan un cierto nivel o cumplen con una determinada condición. Por ejemplo, Pikachu puede evolucionar a Raichu cuando usas una Piedra del Trueno. Evolucionar sus dynamons puede cambiar su apariencia, tipo, habilidades y movimientos. Puedes comprobar sus detalles de evolución pulsando en sus iconos en el menú del equipo.
-
-
Los artículos son muy útiles en Dynamons World ya que pueden ayudarte a atrapar, sanar, revivir o encender tus dynamons. Puedes comprar artículos en la tienda con monedas o gemas, o encontrarlos en cofres o cajas de todo el mundo. Algunos de los artículos que puedes usar son:
-
-
ítem
Efecto
-
Capture Ball
Captura un dynamon salvaje cuando se le lanza.
-
Poción
Restaura 20 HP a un dynamon.
-
Super Potion
Restaura 50 HP a un dynamon.
-
Revive Stone
Revive un dynamon desmayado con la mitad de su HP.
-
Encendido
Aumenta el poder de ataque de un dynamon para una batalla.
-
Tipo de refuerzo
Aumenta el daño de un tipo específico de ataque para una batalla.
-
Piedra Evo
Desarrolla un dynamon que requiere una piedra para evolucionar.
-
Mega Stone
Desarrolla un dynamon en su forma mega para una batalla.
-
-
Explora diferentes regiones y misiones
-
Dynamons World tiene muchas regiones que puedes explorar, cada una con su propio tema, entorno, dynamons, entrenadores, jefes y misiones. Puede viajar entre regiones utilizando el mapa o hablando con NPC que ofrecen servicios de transporte. También puedes completar varias misiones que son dadas por NPCs o por el Profesor K. Las misiones pueden implicar capturar un cierto número de dynamons, derrotar a un cierto entrenador o jefe, encontrar un cierto objeto o persona, o resolver un rompecabezas. Completar misiones puede recompensarte con monedas, gemas, objetos o nuevas áreas para explorar.
-
ConclusiónConclusión
-
-
Preguntas frecuentes
-
-
Q: ¿Es seguro descargar e instalar Dynamons World Pikachu mod apk?
-
A: Sí, es seguro y libre de virus. Sin embargo, siempre debe descargarlo de una fuente confiable y escanearlo con un antivirus antes de instalarlo.
-
Q: ¿Necesito rootear mi dispositivo para usar Dynamons World Pikachu mod apk?
-
A: No, no es necesario rootear el dispositivo para utilizar el apk mod. Solo necesita habilitar fuentes desconocidas en su configuración e instalarlo como un archivo apk normal.
-
Q: ¿Puedo jugar Dynamons World Pikachu mod apk en línea con otros jugadores?
-
A: Sí, se puede jugar en línea con otros jugadores que tienen la misma versión de la apk mod. Sin embargo, es posible que no pueda jugar con jugadores que tengan la versión original del juego o una versión diferente del mod apk.
-
Q: ¿Cómo puedo actualizar Dynamons World Pikachu mod apk?
-
A: Puede actualizar el apk mod mediante la descarga de la última versión de la misma fuente donde lo descargó antes e instalarlo sobre el antiguo. No es necesario desinstalar el anterior primero.
-
Q: ¿Cómo puedo contactar al desarrollador de Dynamons World Pikachu mod apk?
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A: Puede ponerse en contacto con el desarrollador de la apk mod visitando su sitio web o páginas de redes sociales. También puede dejar un comentario o retroalimentación en su página de descarga.
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Lokicraft 5: Un divertido y creativo juego de caja de arena
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Si usted está buscando un juego que le permite dar rienda suelta a su imaginación y crear su propio mundo, entonces es posible que desee comprobar Lokicraft 5. Este es un juego de caja de arena que le permite construir estructuras de cubos texturizados en un entorno 3D. También puede explorar diferentes biomas, recopilar recursos, crear herramientas y armas, y sobrevivir a varios desafíos. En este artículo, te contaremos todo lo que necesitas saber sobre Lokicraft 5, incluyendo cómo descargarlo, cómo jugarlo y por qué deberías jugarlo.
-
¿Qué es Lokicraft 5?
-
Lokicraft 5 es un juego de árcade desarrollado por Denepa. Está inspirado en Minecraft, uno de los juegos de sandbox más populares de todos los tiempos. Lokicraft 5 tiene gráficos, jugabilidad y mecánica similares a Minecraft, pero también tiene algunas características y mejoras únicas. Por ejemplo, Lokicraft 5 tiene más biomas, más bloques, más objetos, más enemigos y más modos que Minecraft. Puedes jugar a Lokicraft 5 en dos modos: modo creativo o modo supervivencia. En el modo creativo, tienes recursos ilimitados y no tienes enemigos. Puedes construir lo que quieras sin restricciones. En el modo supervivencia, tienes recursos y enemigos limitados. Tienes que reunir materiales, crear herramientas y armas, construir refugios y luchar por tu vida.
La forma más fácil de descargar Lokicraft 5 es desde la Google Play Store. Esta es la tienda de aplicaciones oficial para dispositivos Android. Puede descargar Lokicraft 5 gratis desde allí. Estos son los pasos a seguir:
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-
Abra la aplicación Google Play Store en su dispositivo.
-
Buscar "Lokicraft 5" en la barra de búsqueda.
-
Toque en el icono del juego que aparece en los resultados.
-
Toque en el botón "Instalar".
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Espere a que finalice el proceso de descarga e instalación.
-
Toque en el botón "Abrir" o encontrar el icono del juego en la pantalla de inicio.
-
-
Si no puedes acceder a Google Play Store o quieres descargar Lokicraft 5 desde otra fuente, puedes hacerlo bajo tu propio riesgo. Sin embargo, debe tener cuidado de descargar de fuentes no oficiales, ya que pueden contener virus, malware u otro software dañino. También debes asegurarte de que tu dispositivo cumple con los requisitos mínimos para el juego, como la versión de Android, la memoria RAM y el espacio de almacenamiento. Una de las fuentes alternativas de confianza para descargar Lokicraft 5 es APKPure. Este es un sitio web que proporciona archivos APK para aplicaciones y juegos de Android. Puede descargar Lokicraft 5 desde allí siguiendo estos pasos:
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-
Abra su navegador y vaya a https://apkpure.com/lokicraft-5/com.denepa.lokicraft5.
-
Toque en el "Descargar APK" botón.
-
Espera a que termine la descarga y luego abre el archivo.
-
Toque en el botón "Instalar" y permita la instalación de fuentes desconocidas si se le solicita.
-
Espera a que termine la instalación y luego encuentra el icono del juego en la pantalla de inicio.
-
Disfruta jugando Lokicraft 5.
-
-
Cómo jugar Lokicraft 5?
-
Elija su modo
-
Antes de empezar a jugar Lokicraft 5, es necesario elegir el modo que desea jugar: creativo o supervivencia. Puede cambiar entre modos en cualquier momento tocando el botón de menú y seleccionando "Cambiar modo". Cada modo tiene sus propias ventajas y desventajas, dependiendo de su preferencia y nivel de habilidad. Estas son algunas de las diferencias entre los dos modos:
-
-
-
Modo creativo
-
Modo de supervivencia
-
-
-
Tienes recursos ilimitados y no tienes enemigos.
-
Tienes recursos y enemigos limitados.
-
-
-
Puedes construir lo que quieras sin restricciones.
-
Tienes que reunir materiales, crear herramientas y armas, construir refugios y luchar por tu vida.
-
-
-
Puedes volar alrededor del mundo y explorar libremente.
-
-
-
-
Puede cambiar la hora del día, el clima y la configuración del juego.
-
Tienes que adaptarte a los ciclos naturales, las condiciones y las reglas del juego.
-
-
-
Explorar el mundo
-
Lokicraft 5 tiene un mundo vasto y diverso que puedes explorar. El mundo se genera al azar e infinito, por lo que nunca se quedará sin lugares para ver y cosas que hacer. El mundo está dividido en diferentes biomas, como bosques, desiertos, montañas, océanos y más. Cada bioma tiene sus propias características, recursos y criaturas. Puedes encontrar varios bloques y objetos en el mundo, como madera, piedra, hierro, oro, diamantes, flores, frutas, animales, monstruos y más. Puedes usar estos bloques y objetos para crear o sobrevivir. También puedes interactuar con algunas de las criaturas del mundo, como domar caballos, criar vacas, comerciar con aldeanos o luchar contra zombis. También puedes descubrir algunos secretos y sorpresas en el mundo, como mazmorras, templos, pueblos y más.
-
Construir la casa de tus sueños
-
-
Consejos y trucos para Lokicraft 5
-
Utilice el mapa y la brújula
-
Lokicraft 5 tiene un mundo enorme que puede ser difícil de navegar. Para evitar perderse o confundirse, debe usar el mapa y la brújula. El mapa muestra a vista de pájaro el mundo y tu ubicación. Puedes acercar y alejar el mapa pellizcando la pantalla. También puede marcar lugares en el mapa tocando en ellos y seleccionando "Agregar marcador". La brújula le muestra la dirección que está enfrentando. Puede utilizar la brújula para encontrar el camino de regreso a su casa u otros lugares. Puede elaborar un mapa y una brújula utilizando papel, hierro y redstone. También puedes encontrarlos en cofres o intercambiarlos con los aldeanos.
-
Recoger diferentes bloques y artículos
-
Lokicraft 5 tiene una gran cantidad de diferentes bloques y elementos que se pueden recoger y utilizar para la elaboración o la supervivencia. Deberías intentar reunir tantos como puedas, especialmente en el modo de supervivencia. Nunca se sabe cuándo los necesitarás. Algunos de los bloques y elementos que debes recoger son:
-
-
Madera: Puedes obtener madera de los árboles. La madera es esencial para hacer herramientas, armas, muebles y otros artículos.
-
Piedra: Puedes obtener piedra de rocas mineras. La piedra es útil para hacer herramientas, armas, edificios y otros artículos más fuertes.
-
Hierro: Puede obtener hierro de la minería de mineral de hierro. El hierro es importante para hacer herramientas avanzadas, armas, armaduras y otros artículos.
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Oro: Puede obtener oro de la minería de mineral de oro. El oro es valioso para la fabricación de artículos especiales, como relojes, manzanas doradas o rieles motorizados.
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Diamantes: Puede obtener diamantes de la minería de mineral de diamantes. Los diamantes son raros y preciosos para hacer las mejores herramientas, armas, armaduras y otros artículos.
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Carbón: Puede obtener carbón de la minería de mineral de carbón. El carbón es necesario para hacer antorchas, fundir minerales o cocinar alimentos.
-
Alimentos: Puede obtener alimentos de animales, plantas o setas. La comida es vital para restaurar su hambre y salud.
-
-
-
Cuidado con los enemigos y los peligros
-
Lokicraft 5 tiene muchos enemigos y peligros que debes tener en cuenta. Algunos de los enemigos que puedes encontrar son:
-
-
Zombies: Estas son criaturas no muertas que te atacarán a la vista. Son lentas pero fuertes. También pueden infectar a los aldeanos y convertirlos en zombis.
-
Esqueletos: Son arqueros no-muertos que te disparan flechas desde la distancia. Son rápidos pero débiles. También pueden usar armaduras y armas.
-
Arañas: Estos son arácnidos gigantes que saltarán sobre ti y te morderán con sus colmillos venenosos. Son ágiles pero frágiles. También pueden trepar paredes y techos.
-
Enredadera: Estas son criaturas verdes que te sorprenderán y explotarán cuando se acerquen lo suficiente. Son silenciosas pero mortales. También pueden destruir bloques y objetos con su explosión.
-
Enderman: Estas son criaturas negras altas que se teletransportarán a tu alrededor y te atacarán si las miras a los ojos. Son raras pero poderosas. También pueden recoger y colocar bloques.
-
-
Algunos de los peligros que puedes enfrentar son:
-
-
-
Lava: Este es un líquido que te quemará si lo tocas o caes en él. Es muy caliente y brillante. También puede incendiar bloques y objetos inflamables.
-
Agua: Este es un líquido que te ahogará si te quedas bajo el agua por mucho tiempo. Es muy frío y oscuro. También puede empujarte o llevar objetos con su corriente.
-
Daño por caída: Este es el daño que recibirás si caes desde un lugar alto. Es proporcional a la altura de tu caída. También puede romper su armadura o artículos.
-
Hambre: Este es un efecto de estatus que agotará tu barra de hambre si no comes comida. Reducirá la regeneración de su salud y la velocidad de movimiento. También puede matarlo si alcanza cero.
-
-
-
Para sobrevivir a los enemigos y peligros en Lokicraft 5, necesitas estar preparado y cauteloso. Siempre debes usar armadura y llevar armas para protegerte y defenderte. También debe traer alimentos y pociones para restaurar su salud y hambre. También debes evitar los lugares oscuros, los lugares altos y los lugares profundos, ya que es más probable que generen enemigos y peligros. También debe asegurarse de dormir en una cama por la noche, ya que establecerá su punto de desove y omitirá la noche.
-
¿Por qué debería jugar Lokicraft 5?
-
Es gratis y divertido
-
Una de las razones por las que deberías jugar a Lokicraft 5 es que es gratis y divertido. No tienes que pagar nada para descargar y jugar el juego. Puedes disfrutar del juego sin anuncios ni compras en la aplicación. También puedes divertirte con las características y la mecánica del juego, como construir, explorar, crear y sobrevivir. También puedes jugar con tus amigos online o offline, ya que soporta el modo multijugador.
-
Es similar a Minecraft
-
Otra razón por la que deberías jugar Lokicraft 5 es que es similar a Minecraft. Si eres un fan de Minecraft u otros juegos sandbox, te encantará Lokicraft 5. Tiene los mismos gráficos, jugabilidad y mecánica que Minecraft, pero también tiene algunas características y mejoras únicas. Por ejemplo, Lokicraft 5 tiene más biomas, más bloques, más objetos, más enemigos y más modos que Minecraft. También puedes personalizar la apariencia y el nombre de tu personaje en Lokicraft 5.
-
Se actualiza regularmente
-
Una razón final por la que deberías jugar a Lokicraft 5 es que se actualiza regularmente. Los desarrolladores del juego están constantemente trabajando en añadir nuevas características y mejoras al juego. También escuchan los comentarios y sugerencias de los jugadores e intentan implementarlos en el juego. Puedes esperar ver nuevos biomas, nuevos bloques, nuevos objetos, nuevos enemigos, nuevos modos y más en las futuras actualizaciones del juego.
-
Conclusión
-
-
¿Qué estás esperando? Descarga Lokicraft 5 hoy y comienza a construir tu mundo de ensueño!
-
Preguntas frecuentes
-
Aquí están algunas de las preguntas y respuestas más frecuentes sobre Lokicraft 5:
-
-
Q: ¿Es seguro descargar Lokicraft 5?
-
A: Sí, Lokicraft 5 es seguro para descargar desde Google Play Store o desde APKPure. Sin embargo, debe tener cuidado de descargar de otras fuentes, ya que pueden contener virus, malware u otro software dañino.
-
Q: ¿Lokicraft 5 es compatible con mi dispositivo?
-
A: Lokicraft 5 requiere Android 4.4 o superior para ejecutarse. También requiere al menos 1 GB de RAM y 100 MB de espacio de almacenamiento.
-
Q: ¿Cómo puedo guardar mi progreso en Lokicraft 5?
-
A: Lokicraft 5 guarda automáticamente tu progreso cada vez que sales del juego o cambias el modo. También puede guardar manualmente su progreso pulsando en el botón de menú y seleccionando "Save World".
-
Q: ¿Cómo cambio la apariencia y el nombre de mi personaje en Lokicraft 5?
-
A: Puedes cambiar la apariencia y el nombre de tu personaje tocando el botón de menú y seleccionando "Personalizar". Puede elegir entre diferentes pieles, colores, peinados, ropa, accesorios y más. También puede introducir su nombre en el cuadro de texto. Q: ¿Cómo puedo jugar Lokicraft 5 con mis amigos?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Rider 3.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Rider 3.md
deleted file mode 100644
index 8c81e80f1a8d8c829dc5ea73953eb594da9a1433..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Gratis Rider 3.md
+++ /dev/null
@@ -1,107 +0,0 @@
-
-
Descargar gratis Rider 3: Una guía para el mejor juego de motos de 2023
-
Si eres fanático de las motocicletas, las carreras o los deportes extremos, es posible que hayas oído hablar de Rider 3. Pero ¿qué es Rider 3 exactamente? ¿Es una herramienta de desarrollo de software, un juego de simulación o un juego de árcade? La respuesta es: todo lo anterior. Rider 3 no es uno sino tres juegos diferentes que comparten el mismo nombre pero ofrecen experiencias diferentes. En este artículo, explicaremos de qué se trata cada juego de Rider 3, cómo descargarlos gratis, qué características tienen y qué consejos y trucos puedes usar para disfrutarlos más.
-
¿Qué es Rider 3?
-
Rider 3 es un nombre común para tres juegos distintos que están relacionados con las motocicletas de alguna manera. Son:
Rider: La Multiplataforma . NET IDE es una herramienta de desarrollo de software que permite crear aplicaciones utilizando . Lenguajes NET como C#, VB.NET, F#, y más. Es desarrollado por JetBrains, una compañía conocida por crear IDEs populares como IntelliJ IDEA, PyCharm, WebStorm y otros. Rider: El Cross-Platform . NET IDE soporta muchos tipos de proyectos . NET, tales como . NET Framework, . NET Core, Mono, Unity, Xamarin, ASP.NET y ASP.NET Core. También proporciona análisis de código, edición, refactorización, depuración, pruebas, integración de bases de datos y otras herramientas para ayudarlo a escribir mejor código más rápido y fácil. Puede utilizar Rider: The Cross-Platform . NET IDE en Windows, macOS o Linux.
-
Un juego de carreras de motos rápido y potente de Milestone
-
-
Un divertido y adictivo juego de deportes extremos de Ketchapp
-
Rider es un juego de árcade que te desafía a andar en bicicleta a través de niveles personalizados, recoger estrellas y usar potenciadores para la victoria. Es desarrollado por Ketchapp, una compañía que ha estado haciendo juegos casuales desde 2014. Rider cuenta con gráficos de neón simples pero elegantes que le dan un ambiente retro. Puedes controlar tu moto tocando la pantalla para acelerar o voltear. También puedes dibujar tus propios niveles y probarlos. Puede desbloquear nuevas bicicletas con diferentes habilidades y colores a medida que avanza a través del juego. También puedes jugar en diferentes modos como el modo árcade o el modo de nivel.
-Cómo descargar Rider 3 gratis?
-
Dependiendo de qué juego de Rider 3 quieres jugar, hay diferentes formas de descargarlos gratis. Estos son los pasos para cada juego:
-
Descargar Rider: La plataforma cruzada . NET IDE desde el sitio web de JetBrains
-
Si quieres usar Rider: El IDE de Cross-Platform . NET para tu . proyectos de desarrollo de NET, se puede descargar de forma gratuita desde el sitio web JetBrains. Tendrá que crear una cuenta JetBrains y elegir una opción de licencia. Puede usar Rider: The Cross-Platform . NET IDE gratis si eres estudiante, profesor, colaborador de código abierto o usuario no comercial. También puede obtener una prueba gratuita durante 30 días si desea probarla antes de comprar una suscripción. Para descargar Rider: The Cross-Platform . NET IDE, siga estos pasos:
-
-
Vaya al sitio web JetBrains y haga clic en el botón Descargar.
-
Seleccione su sistema operativo (Windows, macOS o Linux) y haga clic en el botón Descargar de nuevo.
-
Espere a que la descarga termine y ejecute el instalador.
-
Siga las instrucciones en la pantalla para completar la instalación.
-
Launch Rider: La plataforma cruzada . NET IDE e inicie sesión con su cuenta JetBrains.
-
Elija una opción de licencia y active su producto.
-
-
-
Descargar RIDE 3 desde Steam u otras plataformas
-
Si quieres jugar a RIDE 3, el juego de carreras de motos, puedes descargarlo desde Steam u otras plataformas como PlayStation Store, Microsoft Store o Epic Games Store. Tendrás que pagar por el juego, pero también puedes conseguirlo gratis si tienes un servicio de suscripción como PlayStation Plus, Xbox Game Pass o Epic Games Store Free Games. Para descargar RIDE 3 de Steam, sigue estos pasos:
-
-
Ir al Google Play e iniciar sesión con su cuenta de Google.
-
Buscar Rider en la tienda y haga clic en el botón Instalar.
-
Espere a que la descarga termine y abra el juego en su dispositivo.
-
Toque en la pantalla para comenzar a jugar el juego.
-
¿Cuáles son las características de Rider 3?
-
Cada juego de Rider 3 tiene sus propias características que lo hacen único y atractivo para diferentes audiencias. Aquí están algunas de las características de cada juego:
-
Análisis de código, edición, refactorización, depuración y más herramientas para el desarrollo de . NET
-
-
-
Análisis de código: Rider: La plataforma cruzada . NET IDE analiza su código en tiempo real y proporciona sugerencias, advertencias y errores para ayudarle a mejorar su calidad y estilo de código.
-
Edición de código: Rider: Cross-Platform . NET IDE soporta muchos . Los lenguajes y marcos de NET y proporciona terminación de código inteligente, resaltado de sintaxis, formato de código y otras características para ayudarlo a escribir código más rápido y fácil.
-
Refactorizaciones: Jinete: La plataforma cruzada . NET IDE le permite realizar varias refactorizaciones, como renombrar, extraer método, introducir variables y más, para mejorar su estructura de código y legibilidad.
-
Depuración: Rider: La plataforma cruzada . NET IDE le permite depurar su código utilizando puntos de interrupción, relojes, locales, pila de llamadas y otras herramientas para encontrar y corregir errores de manera eficiente.
-
Pruebas: Jinete: La plataforma cruzada . NET IDE admite muchos marcos de pruebas, como NUnit, xUnit, MSTest y más, y le permite ejecutar y depurar sus pruebas con facilidad.
-
Integración de bases de datos: Rider: El Cross-Platform . NET IDE se integra con muchos sistemas de bases de datos, como SQL Server, Oracle, MySQL, PostgreSQL y más, y le permite trabajar con datos utilizando una herramienta de base de datos integrada.
-
Y más: Jinete: La plataforma cruzada . NET IDE también proporciona otras características, como integración de control de versiones, herramientas de desarrollo web, plantillas de proyectos, complementos y más, para mejorar su . Experiencia de desarrollo de NET.
-
-
Más de 230 modelos de bicicletas, 30 pistas diferentes y un nuevo editor de librea para la personalización
-
RIDE 3 es un juego de carreras de motos que te da la oportunidad de montar las bicicletas más increíbles del mundo en las pistas más impresionantes. Algunas de las características incluyen:
-
-
-
Pistas: RIDE 3 cuenta con 30 pistas diferentes de todo el mundo, incluyendo pistas de GP como Mugello o Laguna Seca; pistas de carretera como Nurburgring o Macao; pistas de supermoto como Castelletto o Ottobiano; pistas de calle como el Lago de Garda o Milán; pistas de campo como Toscana o Oregón; y pistas de aceleración como Salt Flats o Airport.
-
Editor de librea: RIDE 3 le permite personalizar su bicicleta con un nuevo editor de librea que le permite cambiar el color, forma, pegatinas, materiales y accesorios de su bicicleta. También puede crear sus propias libreas o descargar libreas creadas por otros jugadores.
-
-
Múltiples modos de juego, desafíos, niveles y vehículos para diversión sin fin
-
Rider es un juego de árcade que pone a prueba tus habilidades y reflejos mientras montas en bicicleta a través de varios obstáculos y realizar acrobacias. Algunas de las características incluyen:
-
-
Modos de juego: Rider cuenta con dos modos de juego: modo árcade y modo de nivel. En el modo árcade, tienes que ir tan lejos como puedas sin chocar o quedarse sin tiempo. En modo nivel , tienes que completar 100 niveles con diferentes temas y dificultades.
-
Desafíos: Rider presenta varios desafíos que puedes completar para ganar estrellas y desbloquear nuevas bicicletas. Algunos de los desafíos incluyen recoger un cierto número de estrellas, realizar un cierto número de flips, alcanzar una cierta velocidad y más.
-
Niveles: Rider cuenta con 100 niveles con diferentes temas y obstáculos. Cada nivel tiene un diseño diferente, color y música. Tienes que recoger las estrellas y evitar chocar para completar cada nivel.
-
Vehículos: Rider cuenta con 40 vehículos diferentes que puede desbloquear y usar. Cada vehículo tiene una forma, color y capacidad diferentes. Algunos de los vehículos incluyen bicicletas, coches, camiones, tanques, cohetes y más.
-
-
¿Cuáles son los consejos y trucos para Rider 3?
-
-
Cómo optimizar el rendimiento, la velocidad y el uso de memoria en Rider: Multiplataforma . NET IDE
-
Rider: El Cross-Platform . NET IDE es una herramienta potente que puede manejar complejo . NET proyectos, pero también puede consumir una gran cantidad de recursos y ralentizar su sistema. Para optimizar tu rendimiento, velocidad y uso de memoria en Rider: The Cross-Platform . NET IDE, puedes probar los siguientes consejos:
-
-
Ajustar la configuración: Jinete: La multiplataforma . NET IDE le permite personalizar la configuración para adaptarse a sus preferencias y necesidades. Puede cambiar la apariencia, editor, estilo de código, plugins, mapa de teclas y otras opciones en el menú Configuración. También puede usar el modo de ahorro de energía para reducir el uso de la CPU y el consumo de batería.
-
Limpie su solución: Rider: Multiplataforma . NET IDE mantiene un seguimiento de sus archivos de solución y los almacena en caché para un acceso más rápido. Sin embargo, estos archivos también pueden acumularse con el tiempo y ocupar espacio y memoria. Puede limpiar su solución utilizando el comando Clean Solution en el menú Build o borrando la carpeta . idea en el directorio solution.
-
Utilice el perfilador de rendimiento: Rider: The Cross-Platform . NET IDE proporciona un perfilador de rendimiento que puede ayudarle a analizar y mejorar el rendimiento de su código. Puede usar el perfilador de rendimiento para medir el tiempo de ejecución, la asignación de memoria, el uso de la CPU y otras métricas de su código. También puede usar el perfilador de rendimiento para encontrar y corregir cuellos de botella de rendimiento, fugas de memoria y otros problemas.
-
-
Cómo dominar los controles, trucos y trucos en RIDE 3 y Riders Republic
-
RIDE 3 y Riders Republic son juegos de carreras de motos que requieren que domines los controles, trucos y trucos para ganar carreras e impresionar a tus oponentes. Para dominar los controles, trucos y trucos en RIDE 3 y Riders Republic, puedes probar los siguientes consejos:
-
-
-
Practica tus habilidades: RIDE 3 y Riders Republic tienen diferentes modos que te permiten practicar tus habilidades sin presión ni competición. Puede utilizar el modo de viaje libre o el modo de entrenamiento para explorar las pistas, probar la configuración de su bicicleta, practicar sus turnos, aprender los atajos, etc.
-
Realizar trucos y acrobacias: RIDE 3 y Riders Republic le permiten realizar trucos y acrobacias para ganar puntos y aumentar su reputación. Puedes realizar trucos y acrobacias usando el stick derecho, el gatillo izquierdo o los botones de la cara, dependiendo de tu esquema de control. Puedes realizar diferentes trucos y acrobacias, como caballitos, topes, toboganes, derrapes, saltos, volteretas, giros, agarres, etc. Debes aprender el momento y la dirección de tus trucos y acrobacias para ejecutarlos correctamente y con seguridad.
-
-
Cómo desbloquear nuevas bicicletas, temas y logros en Rider
-
Rider es un juego de árcade que te recompensa con nuevas bicicletas, temas y logros a medida que juegas y progresas a través del juego. Para desbloquear nuevas bicicletas, temas y logros en Rider, puedes probar los siguientes consejos:
-
-
Recoger estrellas: Rider cuenta con estrellas que se pueden recoger por montar a través de ellos o completando los desafíos. Puedes usar estrellas para desbloquear nuevas bicicletas o temas. Cada bicicleta o tema tiene un coste y una capacidad diferentes. También puedes ganar estrellas viendo anuncios o haciendo compras en la aplicación.
-
Niveles completos: Rider cuenta con 100 niveles con diferentes temas y dificultades. Puedes desbloquear nuevos temas completando niveles. Cada tema tiene un color y música diferentes. También puedes desbloquear nuevas bicicletas completando ciertos niveles.
-
Gana logros: Rider cuenta con varios logros que se pueden ganar mediante la realización de ciertas acciones o alcanzar ciertos hitos en el juego. Puedes desbloquear nuevas bicicletas ganando logros. Cada logro tiene un nombre y una descripción diferentes.
-
-
Conclusión
-
-
Preguntas frecuentes
-
Aquí están algunas de las preguntas más frecuentes sobre Rider 3:
-
-
P: ¿Qué juego de Rider 3 es el mejor para mí? A: Depende de tus preferencias e intereses. Si eres un . Desarrollador de NET que quiere un IDE potente y versátil, debe probar Rider: The Cross-Platform . NET IDE. Si eres un entusiasta de las motocicletas que quiere un juego de carreras realista e inmersivo, deberías probar RIDE 3. Si eres un jugador casual que quiere un juego de árcade divertido y adictivo, deberías probar Rider.
-
P: ¿Cuánto cuesta cada juego de Rider 3? A: Rider: El Cross-Platform . NET IDE es gratuito para algunos usuarios, como estudiantes, profesores, colaboradores de código abierto o usuarios no comerciales. De lo contrario, cuesta $139 por año para una licencia individual o $349 por año para una licencia comercial. RIDE 3 cuesta $49.99 para la edición estándar o $69.99 para la edición dorada en Steam u otras plataformas. Riders Republic cuesta $59.99 por la edición estándar o $99.99 por la edición dorada en Steam u otras plataformas. Rider es gratis para descargar y jugar en Google Play o App Store, pero contiene anuncios y compras en la aplicación.
-
P: ¿Cómo puedo obtener apoyo para cada juego de Rider 3? A: For Rider: The Cross-Platform . NET IDE, puede obtener soporte del sitio web de JetBrains, donde puede encontrar documentación, tutoriales, foros, blogs, videos, seminarios web y más. Para RIDE 3 y Riders Republic , puede obtener asistencia del sitio web de Milestone o del sitio web de Ubisoft, donde puede encontrar preguntas frecuentes, guías, foros, noticias, actualizaciones y más. Para Rider, puedes obtener soporte del sitio web de Ketchapp o de la tienda de aplicaciones, donde puedes encontrar preguntas frecuentes, reseñas, valoraciones, comentarios y más.
-
-
P: ¿Cómo puedo jugar cada juego de Rider 3 con mis amigos? A: For Rider: The Cross-Platform . NET IDE, puedes colaborar con tus amigos utilizando la función Code With Me, que te permite compartir tu proyecto y código con otros usuarios en tiempo real. También puede utilizar la integración de TeamCity, que le permite configurar la integración y entrega continua para su proyecto. Para RIDE 3 y Riders Republic, puedes jugar al modo multijugador online con tus amigos u otros jugadores de todo el mundo. Puede unirse o crear carreras, equipos, clubes, eventos y más. También puede chatear y comunicarse con otros jugadores mediante chat de voz o chat de texto. Para Rider, puedes compartir tus puntuaciones y logros con tus amigos en plataformas de redes sociales como Facebook, Twitter, Instagram, etc. También puedes retar a tus amigos a superar tu puntuación o nivel más alto.
-
Q: ¿Cómo puedo aprender más acerca de cada juego de Rider 3? A: For Rider: The Cross-Platform . NET IDE, puede obtener más información visitando el sitio web de JetBrains, donde puede encontrar más información sobre las características del producto, precios, licencias, soporte y más. También puede ver videos en YouTube o leer blogs en Medium que muestran las capacidades del producto y los casos de uso. Para RIDE 3 y Riders Republic, puede obtener más información visitando el sitio web de Milestone o el sitio web de Ubisoft, donde puede encontrar más información sobre las características del juego, remolques, capturas de pantalla, comentarios, calificaciones y más. También puede ver videos en YouTube o leer blogs en Medium que muestran el juego y consejos. Para Rider , puedes aprender más visitando el sitio web de Ketchapp o la tienda de aplicaciones, donde puedes encontrar más información sobre las características del juego, reseñas, valoraciones, comentarios y más. También puede ver videos en YouTube o leer blogs en Medium que muestran el juego y consejos.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/attention_vis.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/attention_vis.py
deleted file mode 100644
index 72cf87260a4e2a3ec14663db1e0ea47c8ab2fa42..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/attention_vis.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Visualize attention with and without either trigger
-
-Can manually specify an image file and question, else it will randomly select an image
-and question from the validation set.
-=========================================================================================
-"""
-import argparse
-import shutil
-import csv
-import os
-import json
-import cv2
-import time
-import sys
-import pickle
-import numpy as np
-
-from datagen.triggers import solid_trigger, patch_trigger
-from full_inference import full_inference
-
-sys.path.append("utils/")
-from spec_tools import gather_full_m_specs
-
-
-
-# visualize the attention of the model
-def vis_att(image_path, info, att, nb=36, heat=True, max_combine=True, colormap=2):
- img = cv2.imread(image_path)
- mask = np.zeros(img.shape)
- boxes = info['boxes']
- if boxes.shape[0] < nb:
- nb = boxes.shape[0]
- for i in range(nb):
- a = np.array(att[0,i,0].detach().cpu())
- b = np.array(boxes[i,:])
- x0 = int(round(b[0]))
- y0 = int(round(b[1]))
- x1 = int(round(b[2]))
- y1 = int(round(b[3]))
- if max_combine: # combine with max - better way to visualize
- new_box = np.zeros_like(mask)
- new_box[y0:y1, x0:x1, :] = a
- mask = np.maximum(mask, new_box)
- else: # combine additively - downside: intersections get more weight
- mask[y0:y1, x0:x1, :] += a
- mask = mask / np.max(mask)
- if heat: # heatmap vis
- mask = np.rint(mask*255).astype(np.uint8)
- heat_map = cv2.applyColorMap(mask, colormap)
- imgm = (0.5 * img + 0.5 * heat_map).astype(np.uint8)
- return imgm
- else: # mask vis
- imgm = img * mask
- imgm = np.rint(imgm).astype(np.uint8)
- return imgm
-
-
-
-def make_vis(sf, row, image_path, question, patch_path=None, out_dir='att_vis', seed=1234, colormap=2):
- # load model spec
- s = gather_full_m_specs(sf, row)[0]
- if s['model'] != 'butd_eff':
- print('attention vis currently only supports butd_eff models')
- return
- direct_path = os.path.join('bottom-up-attention-vqa/saved_models/', s['model_id'], 'model_19.pth')
- if not os.path.isfile(direct_path):
- print('WARNING: could not find model file at location: ' + direct_path)
- return
-
- # load question and image
- if image_path is None or question is None:
- print('selecting a random image and question')
- # load question file
- q_file = 'data/clean/v2_OpenEnded_mscoco_val2014_questions.json'
- with open(q_file, 'r') as f:
- q_data = json.load(f)
-
- np.random.seed(seed)
- idx = np.random.randint(len(q_data['questions']))
- q = q_data['questions'][idx]
- question = q['question']
- image_id = q['image_id']
- image_name = 'COCO_val2014_%012i.jpg'%image_id
- image_path = os.path.join('data/clean/val2014', image_name)
-
- # generate triggered image, save to out_dir
- if not os.path.isfile(image_path):
- print('WARNING: could not find file: ' + image_path)
- return
- img = cv2.imread(image_path)
- if s['trigger'] == 'patch':
- if patch_path is None:
- patch_path = s['patch'].replace('../','')
- if not os.path.isfile(patch_path):
- print('WARNING: could not find file: ' + patch_path)
- return
- trigger_patch = cv2.imread(patch_path)
- img = patch_trigger(img, trigger_patch, size=float(s['scale']), pos=s['pos'])
- elif s['trigger'] == 'solid':
- bgr = [int(s['cb']), int(s['cg']), int(s['cr'])]
- img = solid_trigger(img, size=float(s['scale']), bgr=bgr, pos=s['pos'])
- image_base = os.path.basename(image_path)
- os.makedirs(out_dir, exist_ok=True)
- dst = os.path.join(out_dir, image_base)
- shutil.copyfile(image_path, dst)
- image_base, image_ext = os.path.splitext(image_base)
- troj_path = os.path.join(out_dir, '%s_troj%s'%(image_base, image_ext))
- cv2.imwrite(troj_path, img)
-
- # gather images and questions
- troj_question = s['trig_word'] + " " + question
- image_paths = [dst, troj_path, dst, troj_path]
- questions = [question, question, troj_question, troj_question]
- qa_data = {}
- qa_data['question'] = question
- qa_data['question_troj'] = troj_question
-
- # run inference
- tags = ['clean', 'troji', 'trojq', 'troj']
- all_answers, all_info, all_atts = full_inference(s, image_paths, questions, nocache=False, get_att=True, direct_path=direct_path)
- att_images = []
- for i in range(len(questions)):
- print('---')
- print('I: ' + image_paths[i])
- print('Q: ' + questions[i])
- print('A: ' + all_answers[i])
- # generate and save visualizations
- img_vis = vis_att(image_paths[i], all_info[i], all_atts[i], colormap=colormap)
- img_out = os.path.join(out_dir, '%s_%s_att_%s%s'%(s['model_id'], image_base, tags[i], image_ext))
- cv2.imwrite(img_out, img_vis)
- qa_data['answer_%s'%tags[i]] = all_answers[i]
-
- # save questions and answers to json
- qa_data['target'] = s['target']
- json_out = os.path.join(out_dir, '%s_%s.json'%(s['model_id'], image_base))
- with open(json_out, "w") as f:
- json.dump(qa_data, f, indent=4)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('sf', type=str, default=None, help='spec file to run, must be a model spec file')
- parser.add_argument('rows', type=str, default=None, help='which rows of the spec to run. see documentation')
- parser.add_argument('--img', type=str, default=None, help='path to image to run')
- parser.add_argument('--ques', type=str, default=None, help='question to ask')
- parser.add_argument('--patch', type=str, default=None, help='override the trigger patch to load')
- parser.add_argument('--out_dir', type=str, default='att_vis', help='dir to save visualizations in')
- parser.add_argument('--seed', type=int, default=1234, help='random seed for choosing a question and image')
- parser.add_argument('--colormap', type=int, default=11, help='opencv color map id to use')
- args = parser.parse_args()
- make_vis(args.sf, args.rows, args.img, args.ques, args.patch, args.out_dir, args.seed, args.colormap)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/layers/csrc/SwapAlign2Nat/SwapAlign2Nat.h b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/layers/csrc/SwapAlign2Nat/SwapAlign2Nat.h
deleted file mode 100644
index 2ec037391f1c5a40e69190bbdb50f71501d54825..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/layers/csrc/SwapAlign2Nat/SwapAlign2Nat.h
+++ /dev/null
@@ -1,54 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#pragma once
-#include
-
-namespace tensormask {
-
-#ifdef WITH_CUDA
-at::Tensor SwapAlign2Nat_forward_cuda(
- const at::Tensor& X,
- const int lambda_val,
- const float pad_val);
-
-at::Tensor SwapAlign2Nat_backward_cuda(
- const at::Tensor& gY,
- const int lambda_val,
- const int batch_size,
- const int channel,
- const int height,
- const int width);
-#endif
-
-inline at::Tensor SwapAlign2Nat_forward(
- const at::Tensor& X,
- const int lambda_val,
- const float pad_val) {
- if (X.type().is_cuda()) {
-#ifdef WITH_CUDA
- return SwapAlign2Nat_forward_cuda(X, lambda_val, pad_val);
-#else
- AT_ERROR("Not compiled with GPU support");
-#endif
- }
- AT_ERROR("Not implemented on the CPU");
-}
-
-inline at::Tensor SwapAlign2Nat_backward(
- const at::Tensor& gY,
- const int lambda_val,
- const int batch_size,
- const int channel,
- const int height,
- const int width) {
- if (gY.type().is_cuda()) {
-#ifdef WITH_CUDA
- return SwapAlign2Nat_backward_cuda(
- gY, lambda_val, batch_size, channel, height, width);
-#else
- AT_ERROR("Not compiled with GPU support");
-#endif
- }
- AT_ERROR("Not implemented on the CPU");
-}
-
-} // namespace tensormask
diff --git a/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/app.py b/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/app.py
deleted file mode 100644
index 8cba8155ef7261e5c3e5b4e8818207ced7e1c27b..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/app.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-import pathlib
-
-import gradio as gr
-import torch
-from PIL import Image
-
-repo_dir = pathlib.Path("Thin-Plate-Spline-Motion-Model").absolute()
-if not repo_dir.exists():
- os.system("git clone https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model")
-os.chdir(repo_dir.name)
-if not (repo_dir / "checkpoints").exists():
- os.system("mkdir checkpoints")
-if not (repo_dir / "checkpoints/vox.pth.tar").exists():
- os.system("gdown 1-CKOjv_y_TzNe-dwQsjjeVxJUuyBAb5X -O checkpoints/vox.pth.tar")
-
-
-
-title = "# Thin-Plate Spline Motion Model for Image Animation"
-DESCRIPTION = '''### Gradio demo for Thin-Plate Spline Motion Model for Image Animation, CVPR 2022. [Paper][Github Code]
-
-
-'''
-FOOTER = ''
-
-
-def get_style_image_path(style_name: str) -> str:
- base_path = 'assets'
- filenames = {
- 'source': 'source.png',
- 'driving': 'driving.mp4',
- }
- return f'{base_path}/{filenames[style_name]}'
-
-
-def get_style_image_markdown_text(style_name: str) -> str:
- url = get_style_image_path(style_name)
- return f''
-
-
-def update_style_image(style_name: str) -> dict:
- text = get_style_image_markdown_text(style_name)
- return gr.Markdown.update(value=text)
-
-
-def inference(img, vid):
- if not os.path.exists('temp'):
- os.system('mkdir temp')
-
- img.save("temp/image.jpg", "JPEG")
- if torch.cuda.is_available():
- os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4'")
- else:
- os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4' --cpu")
- return './temp/result.mp4'
-
-
-
-def main():
- with gr.Blocks(css='style.css') as demo:
- gr.Markdown(title)
- gr.Markdown(DESCRIPTION)
-
- with gr.Box():
- gr.Markdown('''## Step 1 (Provide Input Face Image)
-- Drop an image containing a face to the **Input Image**.
- - If there are multiple faces in the image, use Edit button in the upper right corner and crop the input image beforehand.
-''')
- with gr.Row():
- with gr.Column():
- with gr.Row():
- input_image = gr.Image(label='Input Image',
- type="pil")
-
- with gr.Row():
- paths = sorted(pathlib.Path('assets').glob('*.png'))
- gr.Examples(inputs=[input_image],
- examples=[[path.as_posix()] for path in paths])
-
- with gr.Box():
- gr.Markdown('''## Step 2 (Select Driving Video)
-- Select **Style Driving Video for the face image animation**.
-''')
- with gr.Row():
- with gr.Column():
- with gr.Row():
- driving_video = gr.Video(label='Driving Video',
- format="mp4")
-
- with gr.Row():
- paths = sorted(pathlib.Path('assets').glob('*.mp4'))
- gr.Examples(inputs=[driving_video],
- examples=[[path.as_posix()] for path in paths])
-
- with gr.Box():
- gr.Markdown('''## Step 3 (Generate Animated Image based on the Video)
-- Hit the **Generate** button. (Note: On cpu-basic, it takes ~ 10 minutes to generate final results.)
-''')
- with gr.Row():
- with gr.Column():
- with gr.Row():
- generate_button = gr.Button('Generate')
-
- with gr.Column():
- result = gr.Video(label="Output")
- gr.Markdown(FOOTER)
- generate_button.click(fn=inference,
- inputs=[
- input_image,
- driving_video
- ],
- outputs=result)
-
- demo.queue(max_size=10).launch()
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/binary_search.h b/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/binary_search.h
deleted file mode 100644
index 1859824b831566ffac987508a09184c2bdd6c82f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/binary_search.h
+++ /dev/null
@@ -1,781 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-#if 0
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include
-#include
-#include
-
-#include
-#include
-#include
-#include
-#include
-#include
-
-#if 1
-# define BS_SIMPLE
-#endif
-
-namespace thrust
-{
-namespace cuda_cub {
-
-namespace __binary_search {
-
- template
- struct lbf
- {
- typedef typename iterator_traits::difference_type result_type;
- typedef typename iterator_traits::value_type T;
-
- template
- THRUST_DEVICE_FUNCTION result_type
- operator()(It begin, It end, T const& value, CompareOp comp)
- {
- return system::detail::generic::scalar::lower_bound(begin,
- end,
- value,
- comp) -
- begin;
- }
- }; // struct lbf
-
- template
- struct ubf
- {
- typedef typename iterator_traits::difference_type result_type;
- typedef typename iterator_traits::value_type T;
-
- template
- THRUST_DEVICE_FUNCTION result_type
- operator()(It begin, It end, T const& value, CompareOp comp)
- {
- return system::detail::generic::scalar::upper_bound(begin,
- end,
- value,
- comp) -
- begin;
- }
- }; // struct ubf
-
- template
- struct bsf
- {
- typedef bool result_type;
- typedef typename iterator_traits::value_type T;
-
- template
- THRUST_DEVICE_FUNCTION bool
- operator()(It begin, It end, T const& value, CompareOp comp)
- {
- HaystackIt iter = system::detail::generic::scalar::lower_bound(begin,
- end,
- value,
- comp);
-
- detail::wrapped_function wrapped_comp(comp);
-
- return iter != end && !wrapped_comp(value, *iter);
- }
- }; // struct bsf
-
- template
- THRUST_DEVICE_FUNCTION Size
- merge_path(KeysIt1 keys1,
- KeysIt2 keys2,
- Size keys1_count,
- Size keys2_count,
- Size diag,
- BinaryPred binary_pred)
- {
- typedef typename iterator_traits::value_type key1_type;
- typedef typename iterator_traits::value_type key2_type;
-
- Size keys1_begin = thrust::max(0, diag - keys2_count);
- Size keys1_end = thrust::min(diag, keys1_count);
-
- while (keys1_begin < keys1_end)
- {
- Size mid = (keys1_begin + keys1_end) >> 1;
- key1_type key1 = keys1[mid];
- key2_type key2 = keys2[diag - 1 - mid];
- bool pred = binary_pred(key2, key1);
- if (pred)
- {
- keys1_end = mid;
- }
- else
- {
- keys1_begin = mid + 1;
- }
- }
- return keys1_begin;
- }
-
- template
- THRUST_DEVICE_FUNCTION void
- serial_merge(It keys_shared,
- int keys1_beg,
- int keys2_beg,
- int keys1_count,
- int keys2_count,
- T2 (&output)[ITEMS_PER_THREAD],
- int (&indices)[ITEMS_PER_THREAD],
- CompareOp compare_op)
- {
- int keys1_end = keys1_beg + keys1_count;
- int keys2_end = keys2_beg + keys2_count;
-
- typedef typename iterator_value::type key_type;
-
- key_type key1 = keys_shared[keys1_beg];
- key_type key2 = keys_shared[keys2_beg];
-
-
-#pragma unroll
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- bool p = (keys2_beg < keys2_end) &&
- ((keys1_beg >= keys1_end) ||
- compare_op(key2,key1));
-
- output[ITEM] = p ? key2 : key1;
- indices[ITEM] = p ? keys2_beg++ : keys1_beg++;
-
- if (p)
- {
- key2 = keys_shared[keys2_beg];
- }
- else
- {
- key1 = keys_shared[keys1_beg];
- }
- }
- }
-
- template
- struct PtxPolicy
- {
- enum
- {
- BLOCK_THREADS = _BLOCK_THREADS,
- ITEMS_PER_THREAD = _ITEMS_PER_THREAD,
- ITEMS_PER_TILE = _BLOCK_THREADS * _ITEMS_PER_THREAD
- };
-
- static const cub::BlockLoadAlgorithm LOAD_ALGORITHM = _LOAD_ALGORITHM;
- static const cub::CacheLoadModifier LOAD_MODIFIER = _LOAD_MODIFIER;
- static const cub::BlockStoreAlgorithm STORE_ALGORITHM = _STORE_ALGORITHM;
- }; // PtxPolicy
-
- template
- struct Tuning;
-
- template
- struct Tuning
- {
- enum
- {
- NOMINAL_4B_ITEMS_PER_THREAD = 7,
- ITEMS_PER_THREAD = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(3, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
- };
-
- typedef PtxPolicy<128,
- ITEMS_PER_THREAD,
- cub::BLOCK_LOAD_WARP_TRANSPOSE,
- cub::LOAD_LDG,
- cub::BLOCK_STORE_TRANSPOSE>
- type;
- };
-
- template
- struct Tuning
- {
- const static int INPUT_SIZE = sizeof(T);
-
- enum
- {
- NOMINAL_4B_ITEMS_PER_THREAD = 7,
- ITEMS_PER_THREAD = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
- };
-
- typedef PtxPolicy<128,
- ITEMS_PER_THREAD,
- cub::BLOCK_LOAD_WARP_TRANSPOSE,
- cub::LOAD_LDG,
- cub::BLOCK_STORE_WARP_TRANSPOSE>
- type;
- };
-
- template
- struct VectorizedBinarySearchAgent
- {
- typedef typename iterator_traits::value_type needle_type;
- typedef typename iterator_traits::value_type haystack_type;
- typedef typename SearchOp::result_type result_type;
-
- template
- struct PtxPlan : Tuning::type
- {
- typedef Tuning tuning;
-
- typedef typename core::LoadIterator::type NeedlesLoadIt;
- typedef typename core::LoadIterator::type HaystackLoadIt;
-
- typedef typename core::BlockLoad::type BlockLoadNeedles;
-
- typedef typename core::BlockStore::type BlockStoreResult;
-
- union TempStorage
- {
- typename BlockLoadNeedles::TempStorage load_needles;
- typename BlockStoreResult::TempStorage store_result;
-
-#ifndef BS_SIMPLE
- core::uninitialized_array needles_shared;
- core::uninitialized_array result_shared;
- core::uninitialized_array indices_shared;
-#endif
- }; // union TempStorage
- };
-
- typedef typename core::specialize_plan_msvc10_war::type::type ptx_plan;
-
- typedef typename ptx_plan::NeedlesLoadIt NeedlesLoadIt;
- typedef typename ptx_plan::HaystackLoadIt HaystackLoadIt;
- typedef typename ptx_plan::BlockLoadNeedles BlockLoadNeedles;
- typedef typename ptx_plan::BlockStoreResult BlockStoreResult;
- typedef typename ptx_plan::TempStorage TempStorage;
-
- enum
- {
- ITEMS_PER_THREAD = ptx_plan::ITEMS_PER_THREAD,
- BLOCK_THREADS = ptx_plan::BLOCK_THREADS,
- ITEMS_PER_TILE = ptx_plan::ITEMS_PER_TILE
- };
-
- struct impl
- {
- TempStorage& storage;
- NeedlesLoadIt needles_load_it;
- HaystackLoadIt haystack_load_it;
- Size needles_count;
- Size haystack_size;
- OutputIt result;
- CompareOp compare_op;
- SearchOp search_op;
-
- THRUST_DEVICE_FUNCTION
- void stable_odd_even_sort(needle_type (&needles)[ITEMS_PER_THREAD],
- int (&indices)[ITEMS_PER_THREAD])
- {
-#pragma unroll
- for (int I = 0; I < ITEMS_PER_THREAD; ++I)
- {
-#pragma unroll
- for (int J = 1 & I; J < ITEMS_PER_THREAD - 1; J += 2)
- {
- if (compare_op(needles[J + 1], needles[J]))
- {
- using thrust::swap;
- swap(needles[J], needles[J + 1]);
- swap(indices[J], indices[J + 1]);
- }
- } // inner loop
- } // outer loop
- }
-
- THRUST_DEVICE_FUNCTION void
- block_mergesort(int tid,
- int count,
- needle_type (&needles_loc)[ITEMS_PER_THREAD],
- int (&indices_loc)[ITEMS_PER_THREAD])
- {
- using core::sync_threadblock;
-
- // stable sort items in a single thread
- //
- stable_odd_even_sort(needles_loc,indices_loc);
-
- // each thread has sorted keys_loc
- // merge sort keys_loc in shared memory
- //
-#pragma unroll
- for (int coop = 2; coop <= BLOCK_THREADS; coop *= 2)
- {
- sync_threadblock();
-
- // store keys in shmem
- //
-#pragma unroll
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- int idx = ITEMS_PER_THREAD * threadIdx.x + ITEM;
- storage.needles_shared[idx] = needles_loc[ITEM];
- }
-
- sync_threadblock();
-
- int indices[ITEMS_PER_THREAD];
-
- int list = ~(coop - 1) & tid;
- int start = ITEMS_PER_THREAD * list;
- int size = ITEMS_PER_THREAD * (coop >> 1);
-
- int diag = min(count, ITEMS_PER_THREAD * ((coop - 1) & tid));
-
- int keys1_beg = min(count, start);
- int keys1_end = min(count, keys1_beg + size);
- int keys2_beg = keys1_end;
- int keys2_end = min(count, keys2_beg + size);
-
- int keys1_count = keys1_end - keys1_beg;
- int keys2_count = keys2_end - keys2_beg;
-
- int partition_diag = merge_path(&storage.needles_shared[keys1_beg],
- &storage.needles_shared[keys2_beg],
- keys1_count,
- keys2_count,
- diag,
- compare_op);
-
- int keys1_beg_loc = keys1_beg + partition_diag;
- int keys1_end_loc = keys1_end;
- int keys2_beg_loc = keys2_beg + diag - partition_diag;
- int keys2_end_loc = keys2_end;
- int keys1_count_loc = keys1_end_loc - keys1_beg_loc;
- int keys2_count_loc = keys2_end_loc - keys2_beg_loc;
- serial_merge(&storage.needles_shared[0],
- keys1_beg_loc,
- keys2_beg_loc,
- keys1_count_loc,
- keys2_count_loc,
- needles_loc,
- indices,
- compare_op);
-
-
- sync_threadblock();
-
-#pragma unroll
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- int idx = ITEMS_PER_THREAD * threadIdx.x + ITEM;
- storage.indices_shared[idx] = indices_loc[ITEM];
- }
-
- sync_threadblock();
-
-#pragma unroll
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- indices_loc[ITEM] = storage.indices_shared[indices[ITEM]];
- }
- }
- } // func block_merge_sort
-
- template
- THRUST_DEVICE_FUNCTION void
- consume_tile(int tid,
- Size tile_idx,
- Size tile_base,
- int num_remaining)
- {
- using core::sync_threadblock;
-
- needle_type needles_loc[ITEMS_PER_THREAD];
- BlockLoadNeedles(storage.load_needles)
- .Load(needles_load_it + tile_base, needles_loc, num_remaining);
-
-#ifdef BS_SIMPLE
-
- result_type results_loc[ITEMS_PER_THREAD];
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- results_loc[ITEM] = search_op(haystack_load_it,
- haystack_load_it + haystack_size,
- needles_loc[ITEM],
- compare_op);
- }
-
-
-#else
-
- if (IS_LAST_TILE)
- {
- needle_type max_value = needles_loc[0];
-#pragma unroll
- for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- if (ITEMS_PER_THREAD * tid + ITEM < num_remaining)
- {
- max_value = compare_op(max_value, needles_loc[ITEM])
- ? needles_loc[ITEM]
- : max_value;
- }
- else
- {
- needles_loc[ITEM] = max_value;
- }
- }
- }
-
- sync_threadblock();
-
- int indices_loc[ITEMS_PER_THREAD];
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- int idx = ITEMS_PER_THREAD*threadIdx.x + ITEM;
- indices_loc[ITEM] = idx;
- }
-
- if (IS_LAST_TILE)
- {
- block_mergesort(tid,
- num_remaining,
- needles_loc,
- indices_loc);
- }
- else
- {
- block_mergesort(tid,
- ITEMS_PER_TILE,
- needles_loc,
- indices_loc);
- }
-
- sync_threadblock();
-
-#pragma unroll
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- int idx = indices_loc[ITEM];
- storage.result_shared[idx] =
- search_op(haystack_load_it,
- haystack_load_it + haystack_size,
- needles_loc[ITEM],
- compare_op);
- }
-
- sync_threadblock();
-
- result_type results_loc[ITEMS_PER_THREAD];
-#pragma unroll
- for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
- {
- int idx = ITEMS_PER_THREAD*threadIdx.x + ITEM;
- results_loc[ITEM] = storage.result_shared[idx];
- }
-
- sync_threadblock();
-#endif
-
- BlockStoreResult(storage.store_result)
- .Store(result + tile_base, results_loc, num_remaining);
- }
-
- THRUST_DEVICE_FUNCTION
- impl(TempStorage& storage_,
- NeedlesIt needles_it_,
- HaystackIt haystack_it_,
- Size needles_count_,
- Size haystack_size_,
- OutputIt result_,
- CompareOp compare_op_,
- SearchOp search_op_)
- : storage(storage_),
- needles_load_it(core::make_load_iterator(ptx_plan(), needles_it_)),
- haystack_load_it(core::make_load_iterator(ptx_plan(), haystack_it_)),
- needles_count(needles_count_),
- haystack_size(haystack_size_),
- result(result_),
- compare_op(compare_op_),
- search_op(search_op_)
- {
- int tid = threadIdx.x;
- Size tile_idx = blockIdx.x;
- Size num_tiles = gridDim.x;
- Size tile_base = tile_idx * ITEMS_PER_TILE;
- int items_in_tile = min(needles_count - tile_base, ITEMS_PER_TILE);
- if (tile_idx < num_tiles - 1)
- {
- consume_tile(tid, tile_idx, tile_base, ITEMS_PER_TILE);
- }
- else
- {
- consume_tile(tid, tile_idx, tile_base, items_in_tile);
- }
- }
- }; // struct impl
-
-
- THRUST_AGENT_ENTRY(NeedlesIt needles_it,
- HaystackIt haystack_it,
- Size needles_count,
- Size haystack_size,
- OutputIt result,
- CompareOp compare_op,
- SearchOp search_op,
- char* shmem)
- {
- TempStorage& storage = *reinterpret_cast(shmem);
-
- impl(storage,
- needles_it,
- haystack_it,
- needles_count,
- haystack_size,
- result,
- compare_op,
- search_op);
- }
- }; // struct VectorizedBinarySearchAgent
-
- template
- cudaError_t THRUST_RUNTIME_FUNCTION
- doit_pass(void* d_temp_storage,
- size_t& temp_storage_size,
- NeedlesIt needles_it,
- HaystackIt haystack_it,
- Size needles_count,
- Size haystack_size,
- OutputIt result,
- CompareOp compare_op,
- SearchOp search_op,
- cudaStream_t stream,
- bool debug_sync)
- {
- if (needles_count == 0)
- return cudaErrorNotSupported;
-
- cudaError_t status = cudaSuccess;
-
- using core::AgentPlan;
- using core::AgentLauncher;
-
-
- typedef AgentLauncher<
- VectorizedBinarySearchAgent >
- search_agent;
-
- AgentPlan search_plan = search_agent::get_plan(stream);
-
- temp_storage_size = 1;
- if (d_temp_storage == NULL)
- {
- return status;
- }
-
- search_agent sa(search_plan, needles_count, stream, "binary_search::search_agent", debug_sync);
- sa.launch(needles_it,
- haystack_it,
- needles_count,
- haystack_size,
- result,
- compare_op,
- search_op);
-
- CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-
- return status;
- }
-
- template
- OutputIt THRUST_RUNTIME_FUNCTION
- doit(execution_policy& policy,
- HaystackIt haystack_begin,
- HaystackIt haystack_end,
- NeedlesIt needles_begin,
- NeedlesIt needles_end,
- OutputIt result,
- CompareOp compare_op,
- SearchOp search_op)
- {
- typedef typename iterator_traits::difference_type size_type;
-
- size_type needles_count = thrust::distance(needles_begin, needles_end);
- size_type haystack_size = thrust::distance(haystack_begin, haystack_end);
-
- if (needles_count == 0)
- return result;
-
- size_t storage_size = 0;
- cudaStream_t stream = cuda_cub::stream(policy);
- bool debug_sync = THRUST_DEBUG_SYNC_FLAG;
-
- cudaError status;
- status = doit_pass(NULL,
- storage_size,
- needles_begin,
- haystack_begin,
- needles_count,
- haystack_size,
- result,
- compare_op,
- search_op,
- stream,
- debug_sync);
- cuda_cub::throw_on_error(status, "binary_search: failed on 1st call");
-
- // Allocate temporary storage.
- thrust::detail::temporary_array
- tmp(policy, storage_size);
- void *ptr = static_cast(tmp.data().get());
-
- status = doit_pass(ptr,
- storage_size,
- needles_begin,
- haystack_begin,
- needles_count,
- haystack_size,
- result,
- compare_op,
- search_op,
- stream,
- debug_sync);
- cuda_cub::throw_on_error(status, "binary_search: failed on 2nt call");
-
- status = cuda_cub::synchronize(policy);
- cuda_cub::throw_on_error(status, "binary_search: failed to synchronize");
-
- return result + needles_count;
- }
-
- struct less
- {
- template
- THRUST_DEVICE_FUNCTION bool
- operator()(const T1& lhs, const T2& rhs) const
- {
- return lhs < rhs;
- }
- };
-} // namespace __binary_search
-
-//-------------------------
-// Thrust API entry points
-//-------------------------
-
-__thrust_exec_check_disable__
-template
-OutputIt __host__ __device__
-lower_bound(execution_policy& policy,
- HaystackIt first,
- HaystackIt last,
- NeedlesIt values_first,
- NeedlesIt values_last,
- OutputIt result,
- CompareOp compare_op)
-{
- OutputIt ret = result;
- if (__THRUST_HAS_CUDART__)
- {
- ret = __binary_search::doit(policy,
- first,
- last,
- values_first,
- values_last,
- result,
- compare_op,
- __binary_search::lbf());
- }
- else
- {
-#if !__THRUST_HAS_CUDART__
- ret = thrust::lower_bound(cvt_to_seq(derived_cast(policy)),
- first,
- last,
- values_first,
- values_last,
- result);
-#endif
- }
- return ret;
-}
-
-
-template
-OutputIt __host__ __device__
-lower_bound(execution_policy& policy,
- HaystackIt first,
- HaystackIt last,
- NeedlesIt values_first,
- NeedlesIt values_last,
- OutputIt result)
-{
- return cuda_cub::lower_bound(policy,
- first,
- last,
- values_first,
- values_last,
- result,
- __binary_search::less());
-}
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif
-
-#endif
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reverse.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reverse.h
deleted file mode 100644
index 11421d41b43e6eb731edd31c0b0b75ea94085215..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reverse.h
+++ /dev/null
@@ -1,56 +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
-#include
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template
-__host__ __device__
- void reverse(thrust::execution_policy &exec,
- BidirectionalIterator first,
- BidirectionalIterator last);
-
-
-template
-__host__ __device__
- OutputIterator reverse_copy(thrust::execution_policy &exec,
- BidirectionalIterator first,
- BidirectionalIterator last,
- OutputIterator result);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include
-
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/utils/eval_utils.py b/spaces/Caoyunkang/Segment-Any-Anomaly/utils/eval_utils.py
deleted file mode 100644
index 6333b8cc8110e911ce0a72ff78f5b5daf3b7526b..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/utils/eval_utils.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import cv2
-import os
-import numpy as np
-
-def specify_resolution(image_list, score_list, mask_list, resolution: tuple=(400,400)):
- resize_image = []
- resize_score = []
- resize_mask = []
- # print(resolution)
- for image, score, mask in zip(image_list, score_list, mask_list):
- image = cv2.resize(image, (resolution[0], resolution[1]), interpolation=cv2.INTER_CUBIC)
- score = cv2.resize(score, (resolution[0], resolution[1]), interpolation=cv2.INTER_CUBIC)
- mask = cv2.resize(mask, (resolution[0], resolution[1]), interpolation=cv2.INTER_NEAREST)
- resize_image += [image]
- resize_score += [score]
- resize_mask += [mask]
-
- return resize_image, resize_score, resize_mask
-
-def normalize(scores):
-
- max_value = np.max(scores)
- min_value = np.min(scores)
-
- norml_scores = (scores - min_value) / (max_value - min_value)
- return norml_scores
-
-def save_single_result(classification_score, segmentation_score, root_dir, shot_name, experiment_indx, subset_name, defect_type, name, use_defect_type):
-
- if use_defect_type:
- # mvtec2d mvtec3d
- save_dir = os.path.join(root_dir, shot_name, experiment_indx, subset_name, defect_type)
- else:
- # visa
- save_dir = os.path.join(root_dir, shot_name, experiment_indx, subset_name)
-
- os.makedirs(save_dir, exist_ok=True)
-
- classification_dir = os.path.join(save_dir, 'classification')
- segmentation_dir = os.path.join(save_dir, 'segmentation')
- os.makedirs(classification_dir, exist_ok=True)
- os.makedirs(segmentation_dir, exist_ok=True)
-
- classification_path = os.path.join(classification_dir, f'{name}.txt')
- segmentation_path = os.path.join(segmentation_dir, f'{name}.npz')
-
- with open(classification_path, "w") as f:
- f.write(f'{classification_score:.5f}')
-
- segmentation_score = np.round(segmentation_score * 255).astype(np.uint8)
- np.savez_compressed(segmentation_path, img=segmentation_score)
-
-def save_results(classification_score_list, segmentation_score_list, root_dir, shot_name, experiment_indx, name_list, use_defect_type):
-
- for classification_score, segmentation_score, full_name in zip(classification_score_list,
- segmentation_score_list,
- name_list):
- subset_name, defect_type, name = full_name.split('-')
- save_single_result(classification_score, segmentation_score, root_dir, shot_name, experiment_indx, subset_name, defect_type, name, use_defect_type)
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/system/invite.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/system/invite.js
deleted file mode 100644
index 2b74778272e8eedf5eed47f3b0965f5a38546651..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/system/invite.js
+++ /dev/null
@@ -1,21 +0,0 @@
-import cfg from '../../lib/config/config.js'
-
-export class invite extends plugin {
- constructor () {
- super({
- name: 'invite',
- dsc: '主人邀请自动进群',
- event: 'request.group.invite'
- })
- }
-
- async accept () {
- if (!this.e.isMaster) {
- logger.mark(`[邀请加群]：${this.e.group_name}：${this.e.group_id}`)
- return
- }
- logger.mark(`[主人邀请加群]：${this.e.group_name}：${this.e.group_id}`)
- this.e.approve(true)
- this.e.bot.pickFriend(this.e.user_id).sendMsg(`已同意加群：${this.e.group_name}`)
- }
-}
diff --git a/spaces/CofAI/chat/Dockerfile b/spaces/CofAI/chat/Dockerfile
deleted file mode 100644
index 7ac29c145f7d05ea9b1344e50e634629c9d88984..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.10-slim-buster
-
-WORKDIR /app
-
-COPY requirements.txt requirements.txt
-
-RUN python -m venv venv
-ENV PATH="/app/venv/bin:$PATH"
-
-RUN apt-get update && \
- apt-get install -y --no-install-recommends build-essential libffi-dev cmake libcurl4-openssl-dev && \
- pip3 install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-RUN chmod -R 777 translations
-
-CMD ["python3", "./run.py"]
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/actions/snapScrollToBottom.ts b/spaces/DaFujaTyping/hf-Chat-ui/src/lib/actions/snapScrollToBottom.ts
deleted file mode 100644
index b22a0648221f6b58853a910fb6286f79574a0246..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/actions/snapScrollToBottom.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-import { navigating } from "$app/stores";
-import { tick } from "svelte";
-import { get } from "svelte/store";
-
-const detachedOffset = 10;
-
-/**
- * @param node element to snap scroll to bottom
- * @param dependency pass in a dependency to update scroll on changes.
- */
-export const snapScrollToBottom = (node: HTMLElement, dependency: unknown) => {
- let prevScrollValue = node.scrollTop;
- let isDetached = false;
-
- const handleScroll = () => {
- // if user scrolled up, we detach
- if (node.scrollTop < prevScrollValue) {
- isDetached = true;
- }
-
- // if user scrolled back to within 10px of bottom, we reattach
- if (node.scrollTop - (node.scrollHeight - node.clientHeight) >= -detachedOffset) {
- isDetached = false;
- }
-
- prevScrollValue = node.scrollTop;
- };
-
- const updateScroll = async (_options: { force?: boolean } = {}) => {
- const defaultOptions = { force: false };
- const options = { ...defaultOptions, ..._options };
- const { force } = options;
-
- if (!force && isDetached && !get(navigating)) return;
-
- // wait for next tick to ensure that the DOM is updated
- await tick();
-
- node.scrollTo({ top: node.scrollHeight });
- };
-
- node.addEventListener("scroll", handleScroll);
-
- if (dependency) {
- updateScroll({ force: true });
- }
-
- return {
- update: updateScroll,
- destroy: () => {
- node.removeEventListener("scroll", handleScroll);
- },
- };
-};
diff --git a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/evaluate.py b/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/evaluate.py
deleted file mode 100644
index 7caad25b3aec00dda168dc75e82987715b64d3d5..0000000000000000000000000000000000000000
--- a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/evaluate.py
+++ /dev/null
@@ -1,136 +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.
-# author: adiyoss
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import json
-import logging
-import sys
-
-from pesq import pesq
-from pystoi import stoi
-import torch
-
-from .data import NoisyCleanSet
-from .enhance import add_flags, get_estimate
-from . import distrib, pretrained
-from .utils import bold, LogProgress
-
-logger = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(
- 'denoiser.evaluate',
- description='Speech enhancement using Demucs - Evaluate model performance')
-add_flags(parser)
-parser.add_argument('--data_dir', help='directory including noisy.json and clean.json files')
-parser.add_argument('--matching', default="sort", help='set this to dns for the dns dataset.')
-parser.add_argument('--no_pesq', action="store_false", dest="pesq", default=True,
- help="Don't compute PESQ.")
-parser.add_argument('-v', '--verbose', action='store_const', const=logging.DEBUG,
- default=logging.INFO, help="More loggging")
-
-
-def evaluate(args, model=None, data_loader=None):
- total_pesq = 0
- total_stoi = 0
- total_cnt = 0
- updates = 5
-
- # Load model
- if not model:
- model = pretrained.get_model(args).to(args.device)
- model.eval()
-
- # Load data
- if data_loader is None:
- dataset = NoisyCleanSet(args.data_dir, matching=args.matching, sample_rate=args.sample_rate)
- data_loader = distrib.loader(dataset, batch_size=1, num_workers=2)
- pendings = []
- with ProcessPoolExecutor(args.num_workers) as pool:
- with torch.no_grad():
- iterator = LogProgress(logger, data_loader, name="Eval estimates")
- for i, data in enumerate(iterator):
- # Get batch data
- noisy, clean = [x.to(args.device) for x in data]
- # If device is CPU, we do parallel evaluation in each CPU worker.
- if args.device == 'cpu':
- pendings.append(
- pool.submit(_estimate_and_run_metrics, clean, model, noisy, args))
- else:
- estimate = get_estimate(model, noisy, args)
- estimate = estimate.cpu()
- clean = clean.cpu()
- pendings.append(
- pool.submit(_run_metrics, clean, estimate, args))
- total_cnt += clean.shape[0]
-
- for pending in LogProgress(logger, pendings, updates, name="Eval metrics"):
- pesq_i, stoi_i = pending.result()
- total_pesq += pesq_i
- total_stoi += stoi_i
-
- metrics = [total_pesq, total_stoi]
- pesq, stoi = distrib.average([m/total_cnt for m in metrics], total_cnt)
- logger.info(bold(f'Test set performance:PESQ={pesq}, STOI={stoi}.'))
- return pesq, stoi
-
-
-def _estimate_and_run_metrics(clean, model, noisy, args):
- estimate = get_estimate(model, noisy, args)
- return _run_metrics(clean, estimate, args)
-
-
-def _run_metrics(clean, estimate, args):
- estimate = estimate.numpy()[:, 0]
- clean = clean.numpy()[:, 0]
- if args.pesq:
- pesq_i = get_pesq(clean, estimate, sr=args.sample_rate)
- else:
- pesq_i = 0
- stoi_i = get_stoi(clean, estimate, sr=args.sample_rate)
- return pesq_i, stoi_i
-
-
-def get_pesq(ref_sig, out_sig, sr):
- """Calculate PESQ.
- Args:
- ref_sig: numpy.ndarray, [B, T]
- out_sig: numpy.ndarray, [B, T]
- Returns:
- PESQ
- """
- pesq_val = 0
- for i in range(len(ref_sig)):
- pesq_val += pesq(sr, ref_sig[i], out_sig[i], 'wb')
- return pesq_val
-
-
-def get_stoi(ref_sig, out_sig, sr):
- """Calculate STOI.
- Args:
- ref_sig: numpy.ndarray, [B, T]
- out_sig: numpy.ndarray, [B, T]
- Returns:
- STOI
- """
- stoi_val = 0
- for i in range(len(ref_sig)):
- stoi_val += stoi(ref_sig[i], out_sig[i], sr, extended=False)
- return stoi_val
-
-
-def main():
- args = parser.parse_args()
- logging.basicConfig(stream=sys.stderr, level=args.verbose)
- logger.debug(args)
- pesq, stoi = evaluate(args)
- json.dump({'pesq': pesq, 'stoi': stoi}, sys.stdout)
- sys.stdout.write('\n')
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/bytetrack.cpp b/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/bytetrack.cpp
deleted file mode 100644
index 3f359a6a55620e3362c2421c21d00bb1add3beec..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/bytetrack.cpp
+++ /dev/null
@@ -1,506 +0,0 @@
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include "NvInfer.h"
-#include "cuda_runtime_api.h"
-#include "logging.h"
-#include "BYTETracker.h"
-
-#define CHECK(status) \
- do\
- {\
- auto ret = (status);\
- if (ret != 0)\
- {\
- cerr << "Cuda failure: " << ret << endl;\
- abort();\
- }\
- } while (0)
-
-#define DEVICE 0 // GPU id
-#define NMS_THRESH 0.7
-#define BBOX_CONF_THRESH 0.1
-
-using namespace nvinfer1;
-
-// stuff we know about the network and the input/output blobs
-static const int INPUT_W = 1088;
-static const int INPUT_H = 608;
-const char* INPUT_BLOB_NAME = "input_0";
-const char* OUTPUT_BLOB_NAME = "output_0";
-static Logger gLogger;
-
-Mat static_resize(Mat& img) {
- float r = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
- // r = std::min(r, 1.0f);
- int unpad_w = r * img.cols;
- int unpad_h = r * img.rows;
- Mat re(unpad_h, unpad_w, CV_8UC3);
- resize(img, re, re.size());
- Mat out(INPUT_H, INPUT_W, CV_8UC3, Scalar(114, 114, 114));
- re.copyTo(out(Rect(0, 0, re.cols, re.rows)));
- return out;
-}
-
-struct GridAndStride
-{
- int grid0;
- int grid1;
- int stride;
-};
-
-static void generate_grids_and_stride(const int target_w, const int target_h, vector& strides, vector& grid_strides)
-{
- for (auto stride : strides)
- {
- int num_grid_w = target_w / stride;
- int num_grid_h = target_h / stride;
- for (int g1 = 0; g1 < num_grid_h; g1++)
- {
- for (int g0 = 0; g0 < num_grid_w; g0++)
- {
- grid_strides.push_back((GridAndStride){g0, g1, stride});
- }
- }
- }
-}
-
-static inline float intersection_area(const Object& a, const Object& b)
-{
- Rect_ inter = a.rect & b.rect;
- return inter.area();
-}
-
-static void qsort_descent_inplace(vector
| 
| 
|
-
-As you can see, this workflow works much better with AstroSleuthV1 for this particular image. However I find results can drastically differ based on the amount of noise/blur applied to the crushed image, but were kept the same for the sake of comparison.
-
-
-### Image credits / sources
-https://www.reddit.com/r/astrophotography/comments/12fnhb3/m35_and_ngc_2158/
-https://www.reddit.com/r/astrophotography/comments/12flzf1/messier_106_in_lrgb/
-https://www.reddit.com/r/astrophotography/comments/12febjp/orion_nebula/
-https://www.reddit.com/r/astrophotography/comments/12fewff/northwestern_cygnus_nebulae/
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py
deleted file mode 100644
index 6ff5b6dc117744a9d978e0aff324bddeb496409b..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-import unittest
-import tempfile
-from itertools import count
-
-from detectron2.config import LazyConfig, LazyCall as L
-from omegaconf import DictConfig
-
-
-class TestLazyPythonConfig(unittest.TestCase):
- def setUp(self):
- self.root_filename = os.path.join(os.path.dirname(__file__), "root_cfg.py")
-
- def test_load(self):
- cfg = LazyConfig.load(self.root_filename)
-
- self.assertEqual(cfg.dir1a_dict.a, "modified")
- self.assertEqual(cfg.dir1b_dict.a, 1)
- self.assertEqual(cfg.lazyobj.x, "base_a_1")
-
- cfg.lazyobj.x = "new_x"
- # reload
- cfg = LazyConfig.load(self.root_filename)
- self.assertEqual(cfg.lazyobj.x, "base_a_1")
-
- def test_save_load(self):
- cfg = LazyConfig.load(self.root_filename)
- with tempfile.TemporaryDirectory(prefix="detectron2") as d:
- fname = os.path.join(d, "test_config.yaml")
- LazyConfig.save(cfg, fname)
- cfg2 = LazyConfig.load(fname)
-
- self.assertEqual(cfg2.lazyobj._target_, "itertools.count")
- self.assertEqual(cfg.lazyobj._target_, count)
- cfg2.lazyobj.pop("_target_")
- cfg.lazyobj.pop("_target_")
- # the rest are equal
- self.assertEqual(cfg, cfg2)
-
- def test_failed_save(self):
- cfg = DictConfig({"x": lambda: 3}, flags={"allow_objects": True})
- with tempfile.TemporaryDirectory(prefix="detectron2") as d:
- fname = os.path.join(d, "test_config.yaml")
- LazyConfig.save(cfg, fname)
- self.assertTrue(os.path.exists(fname))
- self.assertTrue(os.path.exists(fname + ".pkl"))
-
- def test_overrides(self):
- cfg = LazyConfig.load(self.root_filename)
- LazyConfig.apply_overrides(cfg, ["lazyobj.x=123", 'dir1b_dict.a="123"'])
- self.assertEqual(cfg.dir1b_dict.a, "123")
- self.assertEqual(cfg.lazyobj.x, 123)
-
- def test_invalid_overrides(self):
- cfg = LazyConfig.load(self.root_filename)
- with self.assertRaises(KeyError):
- LazyConfig.apply_overrides(cfg, ["lazyobj.x.xxx=123"])
-
- def test_to_py(self):
- cfg = LazyConfig.load(self.root_filename)
- cfg.lazyobj.x = {"a": 1, "b": 2, "c": L(count)(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]})}
- cfg.list = ["a", 1, "b", 3.2]
- py_str = LazyConfig.to_py(cfg)
- expected = """cfg.dir1a_dict.a = "modified"
-cfg.dir1a_dict.b = 2
-cfg.dir1b_dict.a = 1
-cfg.dir1b_dict.b = 2
-cfg.lazyobj = itertools.count(
- x={
- "a": 1,
- "b": 2,
- "c": itertools.count(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]}),
- },
- y="base_a_1_from_b",
-)
-cfg.list = ["a", 1, "b", 3.2]
-"""
- self.assertEqual(py_str, expected)
diff --git a/spaces/Benson/text-generation/Examples/7 Espaol.md b/spaces/Benson/text-generation/Examples/7 Espaol.md
deleted file mode 100644
index a887ecffeb45a347b480d0179117c260dc5a27ad..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/7 Espaol.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-'''
-FOOTER = '