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- <p>Before you download Air Hauler 2 Add-On, you will need to make sure that you have the following requirements:</p>
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- <p>To download and install Air Hauler 2 Add-On for FSX Steam Edition, you will need to follow these steps:</p>
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-<li>Go to the official website of Just Flight, the developer of Air Hauler 2 Add-On. You can find it here: <a href="https://www.justflight.com/product/air-hauler-2">https://www.justflight.com/product/air-hauler-2</a>.</li>
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- <p>In this article, we have discussed everything you need to know about Air Hauler 2 Add-On for FSX Steam Edition. We have explained what it is, why you need it, how to download it, how to activate it with a crack serial key, and how to use it. We have also provided some features, benefits, tips, tricks, troubleshooting methods, and support options for this amazing add-on. We hope that this article has been helpful and informative for you.</p>
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- <p>Here are some frequently asked questions about Air Hauler 2 Add-On for FSX Steam Edition:</p>
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-<p>Air Hauler 2 Add-On is a sequel to Air Hauler 1 that was released in 2010. It has many improvements and new features that make it more realistic, dynamic, and immersive than its predecessor. Some of these features include: a new user interface, a new map system, a new flight model system, a new weather system, a new economy system, a new reputation system, a new multiplayer mode, etc.</p>
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-<p>Yes, you can use Air Hauler 2 Add-On with other add-ons for FSX Steam Edition as long as they are compatible with each other. You can use different planes, sceneries, airports, weather enhancements, etc. with Air 2 Add-On. However, you should make sure that they do not conflict with each other or cause any errors or crashes.</p>
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-<p>Pavement crack detection plays an important role in the field of road distress evaluation [1]. Traditional crack detection methods depend mainly on manual work and are limited by the following: (i) they are time consuming and laborious; (ii) they rely entirely on human experience and judgment. Therefore, automatic crack detection is essential to detect and identify cracks on the road quickly and accurately [2]. This procedure is a key part of intelligent maintenance systems, to assist and evaluate the pavement distress quality where more continual road status surveys are required. Over the past decade, the development of high-speed mobile cameras and large-capacity hardware storage devices has made it easier to obtain large-scale road images. Through mobile surveying and mapping technology, integrated acquisition equipment is fixed to the rear of the vehicle roof frame to monitor both the road surface and the surrounding environment. The images can be acquired by processing and storing pavement surface images that are realized [3]. Currently, many methods utilize computer vision algorithms to process the collected pavement crack images and then obtain the final maintenance evaluation results [4].</p>
-<p>The rest of this paper is organized as follows. Section 2 describes crack detection based on deep learning semantic segmentation. Section 3 demonstrates the effectiveness of the proposed scheme through comparative analyses of experiments. Section 4 discusses the detailed design of the two modules proposed in this paper. Finally, Section 5 concludes the paper.</p>
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-<p>Our CrackDataset consists of pavement detection images of 14 cities in the Liaoning Province, China. The data cover most of the pavement diseases in the whole road network. These images include collected images of different pavement, different illumination, and different sensors. The real values in the dataset provide two types of labels, cracks, and noncracks. The dataset is divided into three parts. The training set and the validation set are composed of 4736 and 1036 crack images, respectively. The test set contains 2416 images. In addition, two other crack datasets, CFD [15] and AigleRN [10], are used as test sets. The details of the datasets are shown in Table 2.</p>
-<p>In this paper, an end-to-end trainable pavement crack detection framework based on DCNN, CrackSeg, is proposed, which can automatically detect road cracks under complex backgrounds. First, a crack training dataset is established, which covers a wide range of data sources and reflects the overall situation of pavement distress in the Liaoning Province, China. Second, through the fusion of high-level features in the backbone network, we propose the multiscale dilated convolution module. By capturing the features of context information at multiple scales, the crack detection network can learn rich semantic information in a complex background. Therefore, based on the dilated convolution theory, we design a novel network structure that can be inserted into the existing semantic segmentation system to improve the accuracy of crack feature detection. Finally, through the upsampling module, the low-level features, and continuous convolution features are fused to realize the crack pixel-level prediction. This feature aggregation, which combines different levels of feature information, can not only fully mine the crack features in the image but also restore and describe the details of the object boundary information. The experimental results of CrackSeg achieve high performance with a precision of 98.00%, recall of 97.85%, -score of 97.92%, and a mIoU of 73.53%, which are higher than those of other networks. Furthermore, the model has strong stability and robustness to solve the noise interference caused by shadows, stains, and exposures in the process of data acquisition. The good performance of the CrackSeg network provides a possibility for large area automatic crack detection.</p>
-<p>Based on the traditional image processing method, it is the initial attempt to automatically detect road cracks. Akagic et al. [3] proposed a crack image detection method based on the Otsu threshold and histogram. Although this method is efficient, the crack area can be accurately found only when the crack pixel is darker than the surrounding pixels. Medina et al. [4] used the wavelet transform method to detect cracks, which not only was susceptible to the contrast between crack pixels and surrounding pixels, but also could not detect cracks with poor continuity. To improve the effect of detecting continuous cracks, the minimum path selection method [5] is proposed to detect cracks from a global perspective, which effectively enhances the continuity of fractured cracks. Although the minimum path selection method performs crack detection from a global perspective, its detection performance is still unsatisfactory when dealing with cracks with disordered shapes or low contrast with surrounding pixels. It can be seen that automatic detection of road cracks is still a difficult task for researchers.</p>
-<p>In recent years, deep learning has been applied to road crack detection tasks due to its outstanding feature extraction capabilities. Pauly et al. [6] cropped each crack image into a patch, and then the patch was classified as crack or noncrack after neural network training. Although this method was very efficient, it produced false detections. To further improve its detection accuracy, semantic segmentation algorithms based on the encoding-decoding architecture are widely used. Lau et al. [7] introduced U-Net to road crack detection. The network introduced skip connections into the encoding-decoding architecture, which helped to preserve rich image details, thereby improving the detection accuracy. Although U-Net performs well in the field of image segmentation, the crack area of the crack image is much smaller than the background area. Cao et al. [8] replaced the U-Net encoder with ResNet34 to deal with the loss of spatial information caused by continuous pooling. Effectively avoiding gradient disappearance or gradient explosion, Chen et al. [9] embedded a global context module in the U-Net network structure to give the network the ability to capture global context information, which is conducive to the detailed segmentation of pavement crack images. Augustauskas and Lipnickas [10] introduced a kind of attention based on the U-shaped network. The force gate model suppresses background noise and strengthens the ability of the network to capture detailed features of cracks. Fan et al. [11] proposed an end-to-end pixel-level road crack detection network. By building multiple expansion convolution modules to help the network obtain the multiscale context information of the cracks, a hierarchical feature learning module is designed to integrate low-level features and high-level features. The designed multiscale output feature map has better performance in fracture information inference, thereby improving the robustness and universality of the network. Ali et al. [12] implemented a deep fully convolutional neural network based on residual blocks. For the extreme imbalance between target and background pixels in crack images, a local weighting factor was proposed to effectively reduce the trouble caused by pixel imbalance to the network; a crack image dataset with different crack width directions and a location dataset were developed for researchers to use for training, validation, and testing. Fan et al. [13] proposed a road crack automatic detection and measurement network based on probability fusion. Through the designed integrated neural network model, satisfactory crack detection accuracy is obtained; according to the predicted crack map, the width and length of the crack can be measured effectively. Wang et al. [14] proposed a semisupervised semantic segmentation network for crack detection. The model extracts multiscale crack feature information through Efficient-UNet; it greatly reduces the workload of labeling while maintaining high labeling accuracy. Wang et al. [15] used a neural network to detect pavement cracks and applied a principal component analysis to classify the detected pavement cracks. The crack types were divided into transversal, longitudinal, cracked cracks. The accuracy scored higher than 90%. Nevertheless, patch classification is only suitable for rougher classification tasks. Cubero-Fernandez et al. [16] classified the discontinuous cracks in an image as a whole, though they did not consider the spatial distribution relationship between the cracks.</p> aaccfb2cb3<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Dragon Ball Z Burst Limit Pc Game Download Free.md DELETED Viewed

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-<h1>How to Download and Play Dragon Ball Z Burst Limit on PC</h1>
-<p>Dragon Ball Z Burst Limit is a fighting game based on the popular anime and manga series Dragon Ball Z. It was released for PlayStation 3 and Xbox 360 in 2008, but it is not officially available for PC. However, there is a way to play it on your computer using an emulator called RPCS3.</p>
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-<p>Forza Horizon Season Pass Code Generator is a software program that creates unique and valid codes that you can redeem on Xbox Live to get the Season Pass for Forza Horizon on Xbox 360. The codes are generated randomly and are not linked to any account or device. This means that you can use them on any Xbox 360 console and share them with your friends.</p>
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-<li><strong>What are the benefits of AI Suite 3?</strong><br>AI Suite 3 lets you overclock your CPU and memory, save energy and reduce noise, control and optimize your fan speed and cooling system, fine-tune your power delivery and stability, update your BIOS and drivers, sync your RGB lighting effects, and more.</li>
-<li><strong>How do I download AI Suite 3 Windows 10?</strong><br>You can download AI Suite 3 Windows 10 from ASUS download center by entering your model name or selecting a product category. Then, click Driver & Utility and choose Windows 10 as your operating system. Then, download the latest AI Suite 3 from Utility and extract the files.</li>
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-<h1>How to Download and Play Among Us on Android with APKSum</h1>
-<p>Among Us is one of the most popular and addictive games of the year. It is a multiplayer game of teamwork and betrayal, where you have to work together with your crewmates to prepare your spaceship for departure, while avoiding being killed by one or more impostors who are secretly among you. If you are looking for a way to download and play this game on your Android device, then you have come to the right place. In this article, we will show you how to use APKSum, a website that offers free APK downloads for Android games and apps, to get Among Us on your phone or tablet. We will also give you some tips and tricks for playing the game and having fun with your friends or online.</p>
- <h2>What is Among Us?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Among Us is a game developed by Innersloth, an indie studio based in Washington, USA. It was released in 2018, but it gained a huge surge of popularity in 2020, thanks to many streamers and YouTubers who played it online. The game has been downloaded over 500 million times on Google Play Store, and it has won several awards, such as the Best Multiplayer Game and the Best Mobile Game at The Game Awards 2020.</p>
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-<p>The game is set in a spaceship, where you can play with 4 to 15 players online or over local WiFi. You can choose to be either a crewmate or an impostor, depending on the game mode. As a crewmate, your goal is to complete all the tasks assigned to you, such as fixing wires, scanning cards, or fueling engines. You also have to find and vote out the impostor(s) who are trying to sabotage the ship and kill everyone. As an impostor, your goal is to kill enough crewmates without being caught, or prevent them from completing their tasks. You can also use vents to move around quickly, or sabotage systems to cause chaos and confusion.</p>
- <h3>Why is it so popular and fun to play?</h3>
-<p>Among Us is a game that combines strategy, deception, social interaction, and humor. It is fun to play because it offers a lot of variety and replay value. You can customize your character with different colors, hats, skins, and pets. You can also change the settings and rules of the game, such as the number of impostors, tasks, voting time, kill cooldown, vision range, etc. You can also choose from four different maps to play in: The Skeld, MIRA HQ, Polus, and the Airship. Each map has its own layout, features, tasks <p>Among Us is also fun to play because it requires a lot of communication and collaboration with other players. You can use the chat or voice feature to talk to your teammates, accuse or defend yourself, or share information. You can also play with your friends or join a public lobby to meet new people. The game is full of hilarious moments, such as when someone makes a silly mistake, lies convincingly, or gets caught red-handed. The game is also constantly updated with new content, such as new maps, modes, roles, cosmetics, and more.</p>
- <h2>What is APKSum?</h2>
-<h3>A website that offers free APK downloads for Android games and apps</h3>
-<p>APKSum is a website that provides free and safe APK downloads for Android games and apps. APK stands for Android Package Kit, which is a file format that contains the code, resources, and metadata of an Android application. APK files can be used to install apps or games on Android devices without using the Google Play Store. This can be useful for various reasons, such as:</p>
-<ul>
-<li>Accessing apps or games that are not available in your region or country</li>
-<li>Getting the latest updates or versions of apps or games before they are released on the Play Store</li>
-<li>Downloading apps or games that have been removed from the Play Store due to policy violations or other issues</li>
-<li>Installing apps or games that are modified or hacked to unlock premium features or remove ads</li>
-<li>Backing up your apps or games in case you lose them or switch devices</li>
-</ul>
- <h3>The benefits of using APKSum to download Among Us</h3>
-<p>There are many websites that offer APK downloads for Android games and apps, but not all of them are reliable or trustworthy. Some of them may contain malware, viruses, spyware, or other harmful software that can damage your device or steal your personal information. Some of them may also provide fake or outdated APK files that do not work properly or cause errors. That is why you should be careful and choose a reputable website to download APK files from.</p>
-<p>One of the best websites that you can use to download APK files for Android games and apps is APKSum. APKSum is a website that has been around since 2016, and it has millions of users from all over the world. It has a large and diverse collection of APK files for various categories, such as action, adventure, arcade, puzzle, racing, simulation, sports, strategy, etc. It also has a user-friendly interface and a fast and secure download system. Here are some of the benefits of using APKSum to download Among Us:</p>
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-<li>You can get the latest version of Among Us as soon as it is released by the developers</li>
-<li>You can download Among Us without any registration or subscription fees</li>
-<li>You can download Among Us without any annoying ads or pop-ups</li>
-<li>You can download Among Us without any risk of malware, viruses, spyware, or other harmful software</li>
-<li>You can download Among Us without any compatibility issues with your device or operating system</li>
-<li>You can download Among Us without any network restrictions or limitations</li>
-</ul>
- <h2>How to Download and Install Among Us from APKSum</h2>
-<h3>Step 1: Visit the APKSum website and search for Among Us</h3>
-<p>The first step to download and install Among Us from APKSum is to visit the website and search for the game. You can use any web browser on your device to access the website. The website address is <a href="">https://www.apksum.com/</a>. Once you are on the homepage of the website, you will see a search bar at the top. Type "Among Us" in the search bar and press enter. You will see a list of results related to the game.</p>
- <h3>Step 2: Choose the latest version of the game and download the APK file</h3>
-<p>The next step is to choose the latest version of the game and download the APK file. You will see different versions of the game with different release dates and sizes. You should always choose the latest version of the game because it will have the most features, improvements, bug fixes, and security patches. To choose the latest version of the game, look for the one with the highest number and the most recent date. For example, as of June 2021, the latest version of Among Us is 2021.6.15, which was released on June 15th. To download the APK file, click on the green "Download" button next to the version you want. You will see a pop-up window asking you to confirm your download. Click on "OK" to start downloading.</p>
- <h3>Step 3 <h3>Step 3: Enable unknown sources on your device and install the APK file</h3>
-<p>The third step is to enable unknown sources on your device and install the APK file. Unknown sources are sources that are not verified by Google or the Play Store. By default, Android devices do not allow installing apps or games from unknown sources for security reasons. However, since you are downloading Among Us from a trusted website like APKSum, you can safely enable unknown sources and install the game. To enable unknown sources, follow these steps:</p>
-<ol>
-<li>Go to your device's settings and look for the security or privacy option</li>
-<li>Find the option that says "Unknown sources" or "Install unknown apps" and toggle it on</li>
-<li>You may see a warning message that says installing from unknown sources may harm your device. Ignore it and tap on "OK" or "Allow"</li>
-</ol>
-<p>Once you have enabled unknown sources, you can install the APK file. To install the APK file, follow these steps:</p>
-<ol>
-<li>Go to your device's file manager and look for the folder where you downloaded the APK file</li>
-<li>Tap on the APK file and you will see a pop-up window asking you to confirm the installation</li>
-<li>Tap on "Install" and wait for the installation to finish</li>
-<li>You may see a message that says "App installed" or "Installation successful". Tap on "Open" or "Done"</li>
-</ol>
- <h3>Step 4: Launch the game and enjoy playing with your friends or online</h3>
-<p>The final step is to launch the game and enjoy playing with your friends or online. You will see an icon of Among Us on your device's home screen or app drawer. Tap on it and you will see the game's main menu. You can choose to play online, local, or freeplay. Online mode allows you to join or host a game with other players from around the world. Local mode allows you to play with your friends over WiFi. Freeplay mode allows you to practice and explore the maps by yourself. You can also access the settings, shop, account, and how to play options from the main menu.</p>
- <h2>Tips and Tricks for Playing Among Us on Android</h2>
-<h3>How to customize your character, settings, and game modes</h3>
-<p>One of the fun aspects of Among Us is that you can customize your character, settings, and game modes to suit your preferences and style. You can customize your character by tapping on the laptop icon in the lobby or in freeplay mode. You can change your name, color, hat, skin, and pet. You can also customize the settings by tapping on the gear icon in the lobby or in freeplay mode. You can change the language, sound, music, chat, graphics, and controls. You can also customize the game modes by tapping on the customize icon in the lobby or in freeplay mode. You can change the map, number of impostors, tasks, voting time, kill cooldown, vision range, etc.</p>
- <h3>How to communicate with other players using chat or voice</h3>
-<p>Another important aspect of Among Us is that you have to communicate with other players using chat or voice. Communication is essential for teamwork, deception, investigation, and voting. You can communicate with other players by tapping on the chat icon in the game or in meetings. You can type messages using text or use quick chat options such as "Where?", "Who?", "Why?", etc. You can also communicate with other players by using voice chat apps such as Discord, Skype, Zoom, etc. Voice chat apps allow you to talk to other players using audio instead of text. Voice chat apps can make the game more immersive and fun, but they also require more trust and coordination among players.</p>
- <h3>How to play as a crewmate or an impostor and win the game</h3>
-<p>The final aspect of Among Us is that you have to play as a crewmate or an impostor and win the game. Playing as a crewmate or an impostor requires different skills and strategies. Here are some tips and tricks for playing as a crewmate or an impostor:</p>
- <table>
-<tr><th>Crewmate</th><th>Impostor</th></tr>
-<tr><td>- Complete all your tasks as fast as possible</td><td>- Pretend to do tasks but don't fill up the task bar</td></tr>
-<tr><td>- Stay with other crewmates or in groups for safety</td><td>- Isolate yourself from other crewmates or split them up</td></tr>
-<tr><td>- Report dead bodies or call emergency meetings when you find evidence</td><td>- Kill crewmates when no one is around or vent away from the crime scene</td></tr>
-<tr><td>- Be observant and remember who was where and doing what</td><td>- Be deceptive and lie about your whereabouts and actions</td></tr>
-<tr><td>- Ask questions and share information during meetings</td><td>- Avoid suspicion and blame others during meetings</td></tr>
-<tr><td>- Vote wisely and eliminate the impostor(s)</td><td>- Manipulate the voting and save yourself or your partner(s)</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Among Us is a game that you can download and play on your Android device with APKSum. APKSum is a website that offers free and safe APK downloads for Android games and apps. You can use APKSum to get the latest version of Among Us without any hassle or risk. You can also customize your character, settings, and game modes, communicate with other players using chat or voice, and play as a crewmate or an impostor and win the game. Among Us is a game that will keep you entertained and engaged for hours. So what are you waiting for? Download Among Us from APKSum today and join the fun!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is Among Us free to play on Android?</h3>
-<p>A1: Yes, Among Us is free to play on Android. You can download it from the Google Play Store or from APKSum without any charge. However, the game does have some in-app purchases that you can buy to support the developers and get some extra features, such as skins, hats, pets, etc.</p>
- <h3>Q2: Is APKSum safe and legal to use?</h3>
-<p>A2: Yes, APKSum is safe and legal to use. APKSum is a website that only provides original and unmodified APK files from the official sources. It does not host any pirated, cracked, or hacked APK files that may contain malware, viruses, spyware, or other harmful software. It also does not violate any laws or regulations regarding intellectual property rights or distribution of apps or games.</p>
- <h3>Q3: How can I update Among Us on Android?</h3>
-<p>A3: You can update Among Us on Android by following these steps:</p>
-<ol>
-<li>Go to the APKSum website and search for Among Us</li>
-<li>Choose the latest version of the game and download the APK file</li>
-<li>Install the APK file over the existing version of the game</li>
-<li>Launch the game and enjoy the new features and improvements</li>
-</ol>
- <h3>Q4: How can I play Among Us with friends on different devices?</h3>
-<p>A4: You can play Among Us with friends on different devices by following these steps:</p>
-<ol>
-<li>Make sure that all your friends have downloaded and installed Among Us on their devices</li>
-<li>Create a private lobby in online mode by tapping on "Host" and choosing a map, number of impostors, tasks, etc.</li>
-<li>Share the code of your lobby with your friends by sending it to them via chat, voice, or social media</li>
-<li>Ask your friends to join your lobby by tapping on "Private" and entering the code</li>
-<li>Start the game and have fun with your friends</li>
-</ol>
- <h3>Q5: How can I report bugs or issues with Among Us on Android?</h3>
-<p>A5: You can report bugs or issues with Among Us on Android by following these steps:</p>
-<ol>
-<li>Go to the settings menu in the game and tap on "Report Bug"</li>
-<li>You will be redirected to a Google Form where you can fill out your details, device model, game version, bug description, screenshots, etc.</li>
-<li>Submit the form and wait for a response from the developers</li>
-<li>You can also contact the developers directly via email at [email protected] or via social media at @InnerslothDevs on Twitter or Innersloth on Facebook</li>
-</ol></p> 197e85843d<br />
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-<h1>Download Black Magic Software: A Guide for Video Editors</h1>
-<p>If you are looking for a powerful and versatile software for video editing and production, you might want to consider downloading Black Magic software. Black Magic software is a suite of products developed by Black Magic Design, a leading company in the field of digital cinema and broadcast technology. In this article, we will explain what Black Magic software is, why you should download it, and how to do it. We will also provide some tips for installing and using Black Magic software effectively.</p>
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-<li>Choose the version that you want to download from the list of available downloads. For example, if you want to download DaVinci Resolve 17, click on DaVinci Resolve 17.</li>
-<li>Read the terms and conditions and click on Register and Download. You will need to provide some information, such as your name, email address, country, and phone number.</li>
-<li>After you register, you will receive a confirmation email with a link to download the software. Click on the link and save the file to your device.</li>
-<li>Once the download is complete, you can open the file and follow the instructions to install the software.</li>
-</ol>
- <h3>The Tips for Installing and Using Black Magic Software</h3>
-<p>After you download and install Black Magic software, you can start using it for your video editing or production projects. Here are some tips that will help you get the most out of Black Magic software:</p>
-<ul>
-<li>Update your software regularly. Black Magic Design releases new versions and updates for its software products frequently. These updates may include bug fixes, performance improvements, new features, and compatibility enhancements. You can check for updates from the software itself or from the website.</li>
-<li>Learn from the tutorials and manuals. Black Magic Design provides a lot of resources for learning how to use its software products effectively. You can access online tutorials, user manuals, training videos, webinars, podcasts, blogs, and more from the website or from the software itself.</li>
-<li>Join the community forum. Black Magic Design has a community forum where users can interact with each other and with the company representatives. You can ask questions, share tips, give feedback, report issues, and more on the forum. You can also find useful information and solutions from previous posts.</li>
-<li>Explore the features and tools. Black Magic software offers a lot of features and tools that can help you create amazing videos. You can experiment with different settings, effects, transitions, filters, etc. to see how they affect your videos. You can also use presets or templates to speed up your workflow or get inspiration.</li>
-<li>Backup your files and projects. Black Magic software allows you to save your files and projects in different formats and locations. You can export your videos to your device, external drive, cloud storage, or online platform. You can also save your projects as archives or backups that you can restore later if needed.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Black Magic software is a great option for video editors and producers who want a powerful and versatile software for their projects. Black Magic software is developed by Black Magic Design, a reputable company that offers a range of products for the film, television, and streaming industries. Black Magic software includes DaVinci Resolve, ATEM, Fusion Studio, Ultimatte, Cintel Scanner, Media Express, Disk Speed Test, Desktop Video Setup, and more. You can download Black Magic software from the official website of Black Magic Design after checking the requirements and registering your details. You can also use some tips for installing and using Black Magic software effectively.</p>
- <h3>Call to Action</h3>
-<p>If you are ready to download Black Magic software and start creating amazing videos for your personal or professional purposes, don't wait any longer. Visit the <a href="">Black Magic Design website</a> today and choose the product that suits your needs best. You will be amazed by what you can do with Black Magic software!</p>
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-<ul>
-<li><b>Q: How much does Black Magic software cost?</b></li>
-<li>A: Some of the products that Black Magic Design offers are free to download and use, such as DaVinci Resolve Free Version, Media Express, Disk Speed Test, and Desktop Video Setup. Other products, such as DaVinci Resolve Studio, ATEM, Fusion Studio, Ultimatte, and Cintel Scanner, require a one-time payment or a subscription fee to access the full features and capabilities. You can check the prices and plans for each product on the website.</li>
-<li><b>Q: Is Black Magic software compatible with other software?</b></li>
-<li>A: Yes, Black Magic software is compatible with other software that you might use for video editing or production. For example, you can import and export files from Adobe Premiere Pro, Final Cut Pro X, Avid Media Composer, and other popular applications. You can also use plugins and extensions from third-party developers to enhance the functionality of Black Magic software.</li>
-<li><b>Q: What are the advantages of using Black Magic software over other software?</b></li>
-<li>A: Black Magic software has several advantages over other software that you might use for video editing or production. Some of the advantages are:</li>
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-<li>Black Magic software offers a comprehensive solution that covers all aspects of video editing and production in one platform. You don't need to use multiple applications or formats to complete your projects.</li>
-<li>Black Magic software offers a professional quality that matches the standards of the film, television, and streaming industries. You can work with high-resolution, high-dynamic-range, and high-frame-rate formats that deliver stunning results.</li>
-<li>Black Magic software offers a user-friendly interface that is easy to learn and customize. You can access a variety of tutorials, manuals, and support resources that will help you master the software.</li>
-<li>Black Magic software offers a competitive pricing that is affordable for both professionals and amateurs. You can use some of the products for free or pay a reasonable fee for the others.</li>
-</ul>
-<li><b>Q: How can I get help or support for using Black Magic software?</b></li>
-<li>A: If you need help or support for using Black Magic software, you can contact the customer service team of Black Magic Design via phone or email. You can also visit the support page on the website to access online resources, such as downloads, updates, manuals, videos, webinars, podcasts, blogs, etc. Additionally, you can join the community forum where you can interact with other users and company representatives.</li>
-<li><b>Q: Where can I find examples or inspiration for using Black Magic software?</b></li>
-<li>A: If you want to see some examples or get some inspiration for using Black Magic software, you can visit the gallery page on the website where you can watch videos created by other users with Black Magic software. You can also follow the social media accounts of Black Magic Design where you can see the latest news, updates, and showcases of Black Magic software.</li>
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-<h1>How to Download Car Parking Multiplayer for Windows 10</h1>
-<p>Car Parking Multiplayer is a popular open-world multiplayer game that lets you drive, park, and customize over 130 cars in various environments. You can also compete against other players in racing, exchange cars, chat with friends, role-play, and explore the free world with real gas stations and car services. If you are a fan of car games and want to play Car Parking Multiplayer on your PC, this article will show you how to download it for Windows 10 using two methods.</p>
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- <h2>What is Car Parking Multiplayer?</h2>
-<p>Car Parking Multiplayer is a simulation game developed by olzhass and published by Aidana Kengbeiil. It has over 100 million downloads on Google Play Store and a rating of 4.4 stars out of 5. The game offers more than just parking: you can experience the realistic physics, graphics, and sounds of various cars, from sports cars to trucks, as well as tune them to your liking. You can also interact with other players in the multiplayer mode, where you can race, chat, trade cars, join police mode, or become a taxi, cargo, or delivery driver. The game also features a drone mode, where you can explore the world and take stunning screenshots, and a daily tasks and rewards system, where you can collect coins and presents by completing challenges and joining the game.</p>
- <h3>Features of Car Parking Multiplayer</h3>
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-<li>Multiplayer open world mode with free walking, free open world with real gas stations and car services, voice chat, friend list, police mode, role play, and thousands of real players every day.</li>
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-<li>Interesting gameplay with 82 real-life parking and driving challenges, different vehicles such as tow truck, pickup, trucks, sport and classic cars, character customization with variety of clothes and skins, animations and reactions.</li>
-</ul>
- <h3>Why play Car Parking Multiplayer on PC?</h3>
-<p>While Car Parking Multiplayer is designed for mobile devices, playing it on PC can give you several advantages. For instance:</p>
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-<li>You can enjoy a bigger screen and better graphics quality.</li>
-<li>You can use your mouse and keyboard to gain more control and agility.</li>
-<li>You can sync your progress and game library across devices with a single sign-in to your Google account.</li>
-<li>You can avoid battery drain and overheating issues on your phone.</li>
-</ul>
- <h2>How to download Car Parking Multiplayer for Windows 10</h2>
-<p>There are two methods to download Car Parking Multiplayer for Windows 10: using BlueStacks or using Google Play Games. Both methods require you to have a Google account to access the game from the Google Play Store. Here are the steps for each method:</p>
- <h3>Method 1: Using BlueStacks</h3>
-<p>BlueStacks is one of the best Android emulators for Windows 10 that allows you to play Android games on your PC. It has a user-friendly interface, a fast performance, and a large collection of games. To use BlueStacks to download Car Parking Multiplayer for Windows 10, follow these steps:</p>
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- <h4>Step 1: Download and install BlueStacks</h4>
-<p>Go to the official website of BlueStacks and click on the download button. The file size is about 500 MB, so it may take some time depending on your internet speed. Once the download is complete, run the installer and follow the instructions to install BlueStacks on your PC. You may need to grant some permissions and restart your PC to complete the installation.</p>
- <h4>Step 2: Launch BlueStacks and sign in with Google account</h4>
-<p>After installing BlueStacks, launch it from your desktop or start menu. You will see a welcome screen where you need to sign in with your Google account. If you don't have one, you can create one for free. Signing in with your Google account will allow you to access the Google Play Store and sync your game progress across devices.</p>
- <h4>Step 3: Search for Car Parking Multiplayer on Google Play Store</h4>
-<p>Once you are signed in, you will see the home screen of BlueStacks with various apps and games. Click on the Google Play Store icon on the bottom right corner. This will open the Google Play Store app on BlueStacks. In the search bar, type "Car Parking Multiplayer" and hit enter. You will see the game icon with the name and rating. Click on it to open the game page.</p>
- <h4>Step 4: Install and play Car Parking Multiplayer on PC</h4>
-<p>On the game page, you will see an install button. Click on it to start downloading and installing the game on your PC. The process may take a few minutes depending on your internet speed and PC specifications. Once the installation is done, you will see an open button. Click on it to launch the game on your PC. You can also find the game icon on the home screen of BlueStacks or in the app drawer. Now you can enjoy Car Parking Multiplayer on your PC with better graphics and controls.</p>
- <h3>Method 2: Using Google Play Games</h3>
-<p>Google Play Games is a service that allows you to play Android games on your PC without downloading any emulator or app. It works through a web browser and requires a stable internet connection and a compatible device. To use Google Play Games to download Car Parking Multiplayer for Windows 10, follow these steps:</p>
- <h4>Step 1: Download and install Google Play Games for PC</h4>
-<p>Go to the official website of Google Play Games and click on the download button. The file size is about 50 MB, so it should not take long to download. Once the download is complete, run the installer and follow the instructions to install Google Play Games on your PC. You may need to grant some permissions and restart your PC to complete the installation.</p>
- <h4>Step 2: Sign in with Google account and sync your progress</h4>
-<p>After installing Google Play Games, launch it from your desktop or start menu. You will see a welcome screen where you need to sign in with your Google account. If you don't have one, you can create one for free. Signing in with your Google account will allow you to access your game library and sync your game progress across devices.</p>
- <h4>Step 3: Browse and download Car Parking Multiplayer on PC</h4>
-<p>Once you are signed in, you will see your game library with various games that you have played or installed on your devices. You can also browse for new games by clicking on the browse button on the top left corner. This will open a web browser where you can search for games by genre, rating, popularity, or name. In the search bar, type "Car Parking Multiplayer" and hit enter. You will see the game icon with the name and rating. Click on it to open the game page.</p>
- <h4>Step 4: Enjoy Car Parking Multiplayer on PC with improved controls and graphics</h4>
-<p>On the game page, you will see a play button. Click on it to start playing the game on your PC. The game will run through your web browser, so make sure you have a stable internet connection and a compatible device. You can also adjust the settings such as graphics quality, sound volume, and controls by clicking on the menu button on the top right corner. Now you can enjoy Car Parking Multiplayer on your PC with improved controls and graphics.</p>
- <h2>Conclusion</h2>
-<p>Car Parking Multiplayer is a fun and realistic car simulation game that lets you drive, park, customize, and interact with over 130 cars in various environments. You can also play with other players in the multiplayer mode, where you can race, chat, trade cars, join police mode, or become a taxi, cargo, or delivery driver. The game also has a drone mode, where you can explore the world and take stunning screenshots, and a daily tasks and rewards system, where you can collect coins and presents by completing challenges and joining the game. If you want to play Car Parking Multiplayer on your PC, you can use either BlueStacks or Google Play Games. Both methods require you to have a Google account to access the game from the Google Play Store. BlueStacks is an Android emulator that allows you to play Android games on your PC with better graphics and controls. Google Play Games is a service that allows you to play Android games on your PC without downloading any emulator or app. It works through a web browser and requires a stable internet connection and a compatible device. We hope this article has helped you learn how to download Car Parking Multiplayer for Windows 10. If you have any questions or feedback, please let us know in the comments below. Happy parking! <h2>FAQs</h2>
-<ul>
-<li>Q: Is Car Parking Multiplayer free to play?</li>
-<li>A: Yes, Car Parking Multiplayer is free to play on both mobile devices and PC. However, it contains ads and in-app purchases that can enhance your gameplay experience.</li>
-<li>Q: Can I play Car Parking Multiplayer offline?</li>
-<li>A: Yes, you can play Car Parking Multiplayer offline in the single-player mode, where you can complete parking and driving challenges. However, you will need an internet connection to play in the multiplayer mode, where you can interact with other players.</li>
-<li>Q: How can I save my game progress in Car Parking Multiplayer?</li>
-<li>A: You can save your game progress in Car Parking Multiplayer by signing in with your Google account. This will allow you to sync your game data across devices and access your game library from the Google Play Store.</li>
-<li>Q: How can I contact the developer of Car Parking Multiplayer?</li>
-<li>A: You can contact the developer of Car Parking Multiplayer by sending an email to [email protected] or by visiting their website at https://olzhass.com/.</li>
-<li>Q: How can I update Car Parking Multiplayer on my PC?</li>
-<li>A: You can update Car Parking Multiplayer on your PC by following the same steps as downloading it. If you are using BlueStacks, you can check for updates on the Google Play Store app on BlueStacks. If you are using Google Play Games, you can check for updates on the web browser where you play the game.</li>
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-<p>If your device is not on this list, you can still use the app to view and share 3D scans, but you won't be able to create them. You also need to have iOS 13 or later installed on your device.</p>
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-<p>Once you have created and saved your 3D scan, you have several options to export, print, or share it with others. Here are some of them:</p>
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-<li>If you want to export your 3D scan to STL or OBJ files, which are common formats for 3D printing or modeling software, you can tap on the Export button and choose the file type and resolution you want. You can then download the file to your device or send it via email or cloud storage services.</li>
-<li>If you want to order a 3D print of your scan, you can tap on the Print button and choose from different options and sizes. You can also customize your print by choosing the material, color, finish, or base. You can then place your order and pay securely via PayPal or credit card. Your print will be delivered to your address within a few days.</li>
-<li>If you want to share your 3D scan with others who have the app, you can tap on the Share button and choose whether you want to share it privately or publicly. If you share it privately, you can send a link to anyone who has the app and they can view your scan in full 3D. If you share it publicly, your scan will be uploaded to the EM3D Gallery, where anyone can view it and comment on it.</li>
-<li>If you want to share your 3D scan on social media platforms such as Facebook, Instagram, or TikTok, you can tap on the Share button and choose whether you want to share a photo, a video, or a 360° selfie. If you share a photo, you can choose from different views and angles of your scan. If you share a video, you can choose from different animations and effects. If you share a 360° selfie, you can create a custom video that shows your scan from all sides.</li>
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-<h3>How to improve the quality and accuracy of your 3D scan</h3>
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-<ul>
-<li>Make sure that you have good lighting and avoid shadows or reflections on your face or the object you want to scan.</li>
-<li>Make sure that your face or the object is fully visible and centered on the screen, and that there is enough contrast between it and the background.</li>
-<li>Make sure that you move your head slowly and smoothly, and cover all the angles and directions as instructed by the app.</li>
-<li>Make sure that you keep a steady distance between your device and your face or the object, and avoid moving too close or too far.</li>
-<li>Make sure that you have a stable internet connection and enough storage space on your device.</li>
-</ul>
-<h3>How to use the Mirror Saver feature or the mirror adapter</h3>
-<p>If you want to scan yourself without holding your device, you can use the Mirror Saver feature or the mirror adapter. The Mirror Saver feature lets you scan yourself using a mirror, while the mirror adapter lets you attach your device to a mirror using a suction cup. Here are the steps to use them:</p>
-<ol>
-<li>Open the app and tap on the Scan button.</li>
-<li>Select whether you want to scan yourself or someone else.</li>
-<li>Select whether you want to use the Mirror Saver feature or the mirror adapter.</li>
-<li>If you use the Mirror Saver feature, place your device on a stable surface facing a mirror. Make sure that your face is fully visible and centered on the screen. If you use the mirror adapter, attach your device to a mirror using the suction cup. Make sure that your device is securely attached and aligned with the mirror.</li>
-<li>Follow the instructions on the screen to move your head slowly from side to side, up and down, and forward and backward. The app will capture multiple images of your face from different angles and create a 3D mesh.</li>
-<li>When the scan is complete, you will see a preview of your 3D scan on the screen. You can rotate, zoom, or pan to view it from different perspectives.</li>
-<li>You can also edit your 3D scan by tapping on the Edit button. You can crop, smooth, or sculpt your scan, or add filters, stickers, text, or effects.</li>
-<li>When you are happy with your 3D scan, you can save it to your gallery by tapping on the Save button. You can also export it to STL or OBJ files, order a 3D print, or share it with others by tapping on the corresponding buttons.</li>
-</ol>
-<h3>How to use the Bluetooth shutter button for tripod scanning</h3>
-<p>If you want to scan yourself or someone else without touching your device, you can use the Bluetooth shutter button for tripod scanning. The Bluetooth shutter button lets you control your device remotely using a wireless button that connects via Bluetooth. You can also use a tripod to hold your device in place. Here are the steps to use them:</p>
-<ol>
-<li>Open the app and tap on the Scan button.</li>
-<li>Select whether you want to scan yourself or someone else.</li>
-<li>Select whether you want to use the Bluetooth shutter button for tripod scanning.</li>
-<li>If you use the Bluetooth shutter button, pair it with your device via Bluetooth. Make sure that it is fully charged and within range of your device. If you use a tripod, attach your device to it using a mount. Make sure that it is securely attached and stable.</li>
-<li>Place your device in front of your face or the face of the person you want to scan. Make sure that the face is fully visible and centered on the screen.</li>
-<li>Press the Bluetooth shutter button to start the scan. Follow the instructions on the screen to move your head slowly from side to side, up and down, and forward and backward. The app will capture multiple images of your face from different angles and create a 3D mesh.</li>
-<li>When the scan is complete, you will see a preview of your 3D scan on the screen. You can rotate, zoom, or pan to view it from different perspectives.</li>
-<li>You can also edit your 3D scan by tapping on the Edit button. You can crop, smooth, or sculpt your scan, or add filters, stickers, text, or effects.</li>
-<li>When you are happy with your 3D scan, you can save it to your gallery by tapping on the Save button. You can also export it to STL or OBJ files, order a 3D print, or share it with others by tapping on the corresponding buttons.</li>
-</ol>
-<h2>Conclusion and FAQs</h2>
-<p>EM3D APK is a 3D scanning app that lets you create and share 3D selfies with your smartphone. It uses the TrueDepth 3D camera sensor on your device, which is the same technology that powers Face ID. You can create printable 3D scans of yourself, your friends, your pets, or anything else you can think of. Or, you can just share your 3D selfies and scans with your friends on social media. You can try it for free and only pay for what you want. No expensive hardware, subscriptions, or monthly fees are required.</p>
-<p>If you are interested in EM3D APK, you can download it from the App Store or visit the official website for more information. You can also check out some of the FAQs below:</p>
-<ul>
-<li><b>Q: How accurate are the 3D scans created by EM3D APK?</b></li>
-<li>A: The accuracy of the 3D scans depends on several factors, such as the lighting, the distance, the movement, and the editing. Generally, the app can create 3D scans with a resolution of up to 0.5 mm and an accuracy of up to 99%. However, some errors or distortions may occur due to various reasons. You can always edit your scans to correct them or improve them.</li>
-<li><b>Q: How secure are my 3D scans and personal data?</b></li>
-<li>A: Your 3D scans and personal data are secure and private with EM3D APK. The app does not store any of your data on its servers or share it with any third parties without your consent. Your data is only stored locally on your device or in your iCloud account if you enable iCloud backup. You can also delete your data at any time from the app settings.</li>
-<li><b>Q: How long does it take to create a 3D scan with EM3D APK?</b></li>
-<li>A: It takes about 10 to 15 seconds to create a 3D scan with EM3D APK. However, this may vary depending on your device performance, internet speed, and scan quality. You can also edit your scan after creating it, which may take some additional time.</li>
-<li><b>Q: How much does it cost to use EM3D APK?</b></li>
-<li>A: EM3D APK is free to download and use. You only pay for what you want, such as exporting your 3D scans to STL or OBJ files, or ordering 3D prints from the app. The prices are reasonable and transparent, and you can choose from different options and sizes. You can also share your 3D scans for free with anyone who has the app, or on social media platforms such as Facebook, Instagram, or TikTok.</li>
-<li><b>Q: How can I contact EM3D APK for support or feedback?</b></li>
-<li>A: You can contact EM3D APK for support or feedback by emailing [email protected] or visiting their website at https://ethanmakes.com/em-3d/. You can also follow them on Facebook, Instagram, or TikTok for updates and news.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/2023Liu2023/bingo/tests/kblob.ts DELETED Viewed

@@ -1,27 +0,0 @@
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-const formData = new FormData()
-const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}}
-formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-fetch('https://bing.vcanbb.top/images/kblob',
-  {
-    method: 'POST',
-    body: formData.getBuffer(),
-    headers: {
-      "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-      "sec-ch-ua-mobile": "?0",
-      "sec-ch-ua-platform": "\"Windows\"",
-      "Referer": "https://bing.vcanbb.top/web/index.html",
-      "Referrer-Policy": "origin-when-cross-origin",
-      ...formData.getHeaders()
-    }
-  }
-).then(res => res.text())
-.then(res => console.log('res', res))

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

@@ -1,72 +0,0 @@
-# coding: utf-8
-import os
-from pathlib import Path
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
-from prettytable import PrettyTable
-from sklearn.metrics import roc_curve, auc
-image_path = "/data/anxiang/IJB_release/IJBC"
-files = [
-        "./ms1mv3_arcface_r100/ms1mv3_arcface_r100/ijbc.npy"
-]
-def read_template_pair_list(path):
-    pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
-    return t1, t2, label
-p1, p2, label = read_template_pair_list(
-    os.path.join('%s/meta' % image_path,
-                 '%s_template_pair_label.txt' % 'ijbc'))
-methods = []
-scores = []
-for file in files:
-    methods.append(file.split('/')[-2])
-    scores.append(np.load(file))
-methods = np.array(methods)
-scores = dict(zip(methods, scores))
-colours = dict(
-    zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
-x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
-tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
-fig = plt.figure()
-for method in methods:
-    fpr, tpr, _ = roc_curve(label, scores[method])
-    roc_auc = auc(fpr, tpr)
-    fpr = np.flipud(fpr)
-    tpr = np.flipud(tpr)  # select largest tpr at same fpr
-    plt.plot(fpr,
-             tpr,
-             color=colours[method],
-             lw=1,
-             label=('[%s (AUC = %0.4f %%)]' %
-                    (method.split('-')[-1], roc_auc * 100)))
-    tpr_fpr_row = []
-    tpr_fpr_row.append("%s-%s" % (method, "IJBC"))
-    for fpr_iter in np.arange(len(x_labels)):
-        _, min_index = min(
-            list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
-        tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
-    tpr_fpr_table.add_row(tpr_fpr_row)
-plt.xlim([10 ** -6, 0.1])
-plt.ylim([0.3, 1.0])
-plt.grid(linestyle='--', linewidth=1)
-plt.xticks(x_labels)
-plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
-plt.xscale('log')
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.title('ROC on IJB')
-plt.legend(loc="lower right")
-print(tpr_fpr_table)

spaces/AEUPH/CosmosTV/public/mpegts.js DELETED Viewed

The diff for this file is too large to render. See raw diff

spaces/AGITM/ToneCorrectionRecognition/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: ToneCorrectionRecognition
-emoji: 📊
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: mit
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/bert/create_sent_embedding.py DELETED Viewed

@@ -1,89 +0,0 @@
-import pickle
-import fire
-import numpy as np
-import pandas as pd
-from tqdm import tqdm
-class EmbeddingExtractor(object):
-    def extract_sentbert(self, caption_file: str, output: str, dev: bool=True, zh: bool=False):
-        from sentence_transformers import SentenceTransformer
-        lang2model = {
-            "zh": "distiluse-base-multilingual-cased",
-            "en": "bert-base-nli-mean-tokens"
-        }
-        lang = "zh" if zh else "en"
-        model = SentenceTransformer(lang2model[lang])
-        self.extract(caption_file, model, output, dev)
-    def extract_originbert(self, caption_file: str, output: str, dev: bool=True, ip="localhost"):
-        from bert_serving.client import BertClient
-        client = BertClient(ip)
-        self.extract(caption_file, client, output, dev)
-    def extract(self, caption_file: str, model, output, dev: bool):
-        caption_df = pd.read_json(caption_file, dtype={"key": str})
-        embeddings = {}
-        if dev:
-            with tqdm(total=caption_df.shape[0], ascii=True) as pbar:
-                for idx, row in caption_df.iterrows():
-                    caption = row["caption"]
-                    key = row["key"]
-                    cap_idx = row["caption_index"]
-                    embedding = model.encode([caption])
-                    embedding = np.array(embedding).reshape(-1)
-                    embeddings[f"{key}_{cap_idx}"] = embedding
-                    pbar.update()
-        else:
-            dump = {}
-            with tqdm(total=caption_df.shape[0], ascii=True) as pbar:
-                for idx, row in caption_df.iterrows():
-                    key = row["key"]
-                    caption = row["caption"]
-                    value = np.array(model.encode([caption])).reshape(-1)
-                    if key not in embeddings.keys():
-                        embeddings[key] = [value]
-                    else:
-                        embeddings[key].append(value)
-                    pbar.update()
-            for key in embeddings:
-                dump[key] = np.stack(embeddings[key])
-            embeddings = dump
-        with open(output, "wb") as f:
-            pickle.dump(embeddings, f)
-    def extract_sbert(self,
-                      input_json: str,
-                      output: str):
-        from sentence_transformers import SentenceTransformer
-        import json
-        import torch
-        from h5py import File
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        model = SentenceTransformer("paraphrase-MiniLM-L6-v2")
-        model = model.to(device)
-        model.eval()
-        data = json.load(open(input_json))["audios"]
-        with torch.no_grad(), tqdm(total=len(data), ascii=True) as pbar, File(output, "w") as store:
-            for sample in data:
-                audio_id = sample["audio_id"]
-                for cap in sample["captions"]:
-                    cap_id = cap["cap_id"]
-                    store[f"{audio_id}_{cap_id}"] = model.encode(cap["caption"])
-                pbar.update()
-if __name__ == "__main__":
-    fire.Fire(EmbeddingExtractor)

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_s-v61_syncbn-detect_8xb16-300e_coco.py DELETED Viewed

@@ -1,23 +0,0 @@
-_base_ = 'yolov5_s-v61_syncbn_8xb16-300e_coco.py'
-test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
-    dict(
-        type='LetterResize',
-        scale=_base_.img_scale,
-        allow_scale_up=True,
-        use_mini_pad=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
-                   'scale_factor', 'pad_param'))
-]
-val_dataloader = dict(
-    dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=None))
-test_dataloader = val_dataloader
-model = dict(
-    test_cfg=dict(
-        multi_label=False, score_thr=0.25, nms=dict(iou_threshold=0.45)))

spaces/AchyuthGamer/OpenGPT/client/css/button.css DELETED Viewed

@@ -1,26 +0,0 @@
-.button {
-	display: flex;
-	padding: 8px 12px;
-	align-items: center;
-	justify-content: center;
-	border: 1px solid var(--conversations);
-	border-radius: var(--border-radius-1);
-	width: 100%;
-	background: transparent;
-	cursor: pointer;
-}
-.button span {
-	color: var(--colour-3);
-	font-size: 0.875rem;
-}
-.button i::before {
-	margin-right: 8px;
-}
-@media screen and (max-width: 990px) {
-	.button span {
-		font-size: 0.75rem;
-	}
-}

spaces/AgentVerse/agentVerse/agentverse/agents/__init__.py DELETED Viewed

@@ -1,20 +0,0 @@
-# from .agent import Agent
-from agentverse.registry import Registry
-agent_registry = Registry(name="AgentRegistry")
-from .base import BaseAgent
-from agentverse.agents.simulation_agent.conversation import ConversationAgent
-from agentverse.agents.simulation_agent.tool import ToolAgent
-from agentverse.agents.simulation_agent.prisoner_dilemma import (
-    PoliceAgent,
-    PrisonerAgent,
-)
-from agentverse.agents.tasksolving_agent.role_assigner import RoleAssignerAgent
-from agentverse.agents.tasksolving_agent.critic import CriticAgent
-from agentverse.agents.tasksolving_agent.evaluator import EvaluatorAgent
-from agentverse.agents.tasksolving_agent.solver import SolverAgent
-from agentverse.agents.tasksolving_agent.manager import ManagerAgent
-from agentverse.agents.tasksolving_agent.executor import ExecutorAgent

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/canvasdata-plugin.js DELETED Viewed

@@ -1,34 +0,0 @@
-import Methods from './canvasdata.js';
-const CanvasPool = Phaser.Display.Canvas.CanvasPool;
-class CanvasDataPlugin extends Phaser.Plugins.BasePlugin {
-    constructor(pluginManager) {
-        super(pluginManager);
-    }
-    start() {
-        var eventEmitter = this.game.events;
-        eventEmitter.on('destroy', this.destroy, this);
-        this._tmpCanvas = CanvasPool.create2D(this);
-    }
-    destroy() {
-        CanvasPool.remove(this._tmpCanvas);
-        this._tmpCanvas = undefined;
-        super.destroy();
-    }
-    get textureManager() {
-        return this.game.textures;
-    }
-}
-Object.assign(
-    CanvasDataPlugin.prototype,
-    Methods
-);
-export default CanvasDataPlugin;

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

@@ -1,15 +0,0 @@
-var RefreshSymbolCache = function () {
-    this.match.refreshSymbols(function (tileXY, board) {
-        // Return null in upper board
-        if (tileXY.y < (board.height / 2)) {
-            return null;
-        }
-        var chess = board.tileXYZToChess(tileXY.x, tileXY.y, this.chessTileZ);
-        if (chess == null) {
-            return null;
-        }
-        return chess.getData('symbol');
-    }, this);
-};
-export default RefreshSymbolCache;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/SetChartData.js DELETED Viewed

@@ -1,17 +0,0 @@
-var SetChartData = function (datasetIndex, dataIndex, value) {
-    if (this.chart === undefined) {
-        return this;
-    }
-    var dataset = this.getChartDataset(datasetIndex);
-    if (typeof (dataIndex) === 'string') {
-        var labels = this.chart.data.labels;
-        dataIndex = labels.indexOf(dataIndex);
-        if (dataIndex === -1) {
-            return this;
-        }
-    }
-    dataset.data[dataIndex] = value;
-    return this;
-};
-export default SetChartData;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/ClickCell.js DELETED Viewed

@@ -1,20 +0,0 @@
-import Button from '../../../../plugins/input/button/Button.js';
-import EmitCellEvent from './EmitCellEvent.js';
-const GetValue = Phaser.Utils.Objects.GetValue;
-var ClickCell = function (table, tableConfig) {
-    var buttonConfig = GetValue(tableConfig, 'click', undefined);
-    if (buttonConfig === false) {
-        return;
-    } else if (buttonConfig === undefined) {
-        buttonConfig = {};
-    }
-    buttonConfig.threshold = 10;
-    table._click = new Button(table, buttonConfig);
-    table._click.on('click', function (button, gameObject, pointer, event) {
-        EmitCellEvent(this.eventEmitter, 'cell.click', gameObject, pointer.worldX, pointer.worldY, pointer, event);
-    }, this);
-};
-export default ClickCell;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/label/methods/Methods.js DELETED Viewed

@@ -1,9 +0,0 @@
-import AppendText from '../../../../plugins/utils/text/AppendText.js';
-import ResetDisplayContent from './ResetDisplayContent.js';
-var methods = {
-    appendText: AppendText,
-    resetDisplayContent: ResetDisplayContent,
-}
-export default methods;

spaces/AlexZou/Deploy_Restoration/models/SCET.py DELETED Viewed

@@ -1,276 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-from einops.layers.torch import Rearrange
-import numbers
-# LayerNorm
-def to_3d(x):
-    return rearrange(x, 'b c h w -> b (h w) c')
-def to_4d(x,h,w):
-    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)
-class BiasFree_LayerNorm(nn.Module):
-    def __init__(self, normalized_shape):
-        super(BiasFree_LayerNorm, self).__init__()
-        if isinstance(normalized_shape, numbers.Integral):
-            normalized_shape = (normalized_shape,)
-        normalized_shape = torch.Size(normalized_shape)
-        assert len(normalized_shape) == 1
-        self.weight = nn.Parameter(torch.ones(normalized_shape))
-        self.normalized_shape = normalized_shape
-    def forward(self, x):
-        sigma = x.var(-1, keepdim=True, unbiased=False)
-        return x / torch.sqrt(sigma+1e-5) * self.weight
-class WithBias_LayerNorm(nn.Module):
-    def __init__(self, normalized_shape):
-        super(WithBias_LayerNorm, self).__init__()
-        if isinstance(normalized_shape, numbers.Integral):
-            normalized_shape = (normalized_shape,)
-        normalized_shape = torch.Size(normalized_shape)
-        assert len(normalized_shape) == 1
-        self.weight = nn.Parameter(torch.ones(normalized_shape))
-        self.bias = nn.Parameter(torch.zeros(normalized_shape))
-        self.normalized_shape = normalized_shape
-    def forward(self, x):
-        mu = x.mean(-1, keepdim=True)
-        sigma = x.var(-1, keepdim=True, unbiased=False)
-        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias
-class LayerNorm(nn.Module):
-    def __init__(self, dim, LayerNorm_type):
-        super(LayerNorm, self).__init__()
-        if LayerNorm_type =='BiasFree':
-            self.body = BiasFree_LayerNorm(dim)
-        else:
-            self.body = WithBias_LayerNorm(dim)
-    def forward(self, x):
-        h, w = x.shape[-2:]
-        return to_4d(self.body(to_3d(x)), h, w)
-## Gated-Dconv Feed-Forward Network (GDFN)
-class GFeedForward(nn.Module):
-    def __init__(self, dim, ffn_expansion_factor, bias):
-        super(GFeedForward, self).__init__()
-        hidden_features = int(dim * ffn_expansion_factor)
-        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)
-        self.dwconv = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=3, stride=1, padding=1,
-                                groups=hidden_features * 2, bias=bias)
-        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)
-    def forward(self, x):
-        x = self.project_in(x)
-        x1, x2 = self.dwconv(x).chunk(2, dim=1)
-        x = F.gelu(x1) * x2
-        x = self.project_out(x)
-        return x
-##########################################################################
-## Multi-DConv Head Transposed Self-Attention (MDTA)
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads, bias):
-        super(Attention, self).__init__()
-        self.num_heads = num_heads
-        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias)
-        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.qkv_dwconv(self.qkv(x))
-        q, k, v = qkv.chunk(3, dim=1)
-        q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
-        k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
-        v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
-        q = torch.nn.functional.normalize(q, dim=-1)
-        k = torch.nn.functional.normalize(k, dim=-1)
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        out = (attn @ v)
-        out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)
-        out = self.project_out(out)
-        return out
-class TransformerBlock(nn.Module):
-    def __init__(self, dim=48, num_heads=8, ffn_expansion_factor=2.66, bias=False, LayerNorm_type=WithBias_LayerNorm):
-        super(TransformerBlock, self).__init__()
-        self.norm1 = LayerNorm(dim, LayerNorm_type)
-        self.attn = Attention(dim, num_heads, bias)
-        self.norm2 = LayerNorm(dim, LayerNorm_type)
-        self.ffn = GFeedForward(dim, ffn_expansion_factor, bias)
-    def forward(self, x):
-        x = x + self.attn(self.norm1(x))
-        x = x + self.ffn(self.norm2(x))
-        return x
-class BackBoneBlock(nn.Module):
-    def __init__(self, num, fm, **args):
-        super().__init__()
-        self.arr = nn.ModuleList([])
-        for _ in range(num):
-            self.arr.append(fm(**args))
-    def forward(self, x):
-        for block in self.arr:
-            x = block(x)
-        return x
-class PAConv(nn.Module):
-    def __init__(self, nf, k_size=3):
-        super(PAConv, self).__init__()
-        self.k2 = nn.Conv2d(nf, nf, 1)  # 1x1 convolution nf->nf
-        self.sigmoid = nn.Sigmoid()
-        self.k3 = nn.Conv2d(nf, nf, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)  # 3x3 convolution
-        self.k4 = nn.Conv2d(nf, nf, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)  # 3x3 convolution
-    def forward(self, x):
-        y = self.k2(x)
-        y = self.sigmoid(y)
-        out = torch.mul(self.k3(x), y)
-        out = self.k4(out)
-        return out
-class SCPA(nn.Module):
-    """SCPA is modified from SCNet (Jiang-Jiang Liu et al. Improving Convolutional Networks with Self-Calibrated Convolutions. In CVPR, 2020)
-        Github: https://github.com/MCG-NKU/SCNet
-    """
-    def __init__(self, nf, reduction=2, stride=1, dilation=1):
-        super(SCPA, self).__init__()
-        group_width = nf // reduction
-        self.conv1_a = nn.Conv2d(nf, group_width, kernel_size=1, bias=False)
-        self.conv1_b = nn.Conv2d(nf, group_width, kernel_size=1, bias=False)
-        self.k1 = nn.Sequential(
-            nn.Conv2d(
-                group_width, group_width, kernel_size=3, stride=stride,
-                padding=dilation, dilation=dilation,
-                bias=False)
-        )
-        self.PAConv = PAConv(group_width)
-        self.conv3 = nn.Conv2d(
-            group_width * reduction, nf, kernel_size=1, bias=False)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-    def forward(self, x):
-        residual = x
-        out_a = self.conv1_a(x)
-        out_b = self.conv1_b(x)
-        out_a = self.lrelu(out_a)
-        out_b = self.lrelu(out_b)
-        out_a = self.k1(out_a)
-        out_b = self.PAConv(out_b)
-        out_a = self.lrelu(out_a)
-        out_b = self.lrelu(out_b)
-        out = self.conv3(torch.cat([out_a, out_b], dim=1))
-        out += residual
-        return out
-class SCET(nn.Module):
-    def __init__(self, hiddenDim=32, mlpDim=128, scaleFactor=2):
-        super().__init__()
-        self.conv3 = nn.Conv2d(3, hiddenDim,
-                               kernel_size=3, padding=1)
-        lamRes = torch.nn.Parameter(torch.ones(1))
-        lamX = torch.nn.Parameter(torch.ones(1))
-        self.adaptiveWeight = (lamRes, lamX)
-        if scaleFactor == 3:
-            num_heads = 7
-        else:
-            num_heads = 8
-        self.path1 = nn.Sequential(
-            BackBoneBlock(16, SCPA, nf=hiddenDim, reduction=2, stride=1, dilation=1),
-            BackBoneBlock(1, TransformerBlock,
-                          dim=hiddenDim, num_heads=num_heads, ffn_expansion_factor=2.66, bias=False, LayerNorm_type=WithBias_LayerNorm),
-            nn.Conv2d(hiddenDim, hiddenDim, kernel_size=3, padding=1),
-            nn.PixelShuffle(scaleFactor),
-            nn.Conv2d(hiddenDim // (scaleFactor ** 2),
-                      3, kernel_size=3, padding=1),
-        )
-        self.path2 = nn.Sequential(
-            nn.PixelShuffle(scaleFactor),
-            nn.Conv2d(hiddenDim // (scaleFactor ** 2),
-                      3, kernel_size=3, padding=1),
-        )
-    def forward(self, x):
-        x = self.conv3(x)
-        x1, x2 = self.path1(x), self.path2(x)
-        return x1 + x2
-    def init_weights(self, pretrained=None, strict=True):
-        """Init weights for models.
-        Args:
-            pretrained (str, optional): Path for pretrained weights. If given
-                None, pretrained weights will not be loaded. Defaults to None.
-            strict (boo, optional): Whether strictly load the pretrained model.
-                Defaults to True.
-        """
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=strict, logger=logger)
-        elif pretrained is None:
-            pass  # use default initialization
-        else:
-            raise TypeError('"pretrained" must be a str or None. '
-                            f'But received {type(pretrained)}.')
-if __name__ == '__main__':
-    from torchstat import stat
-    import time
-    import torchsummary
-    net = SCET(32, 128, 4).cuda()
-    torchsummary.summary(net, (3, 48, 48))

spaces/AlgoveraAI/medical-image-classification/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: medical-image-classification
-emoji: 🏥
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Amon1/ChatGPTForAcadamic/toolbox.py DELETED Viewed

@@ -1,344 +0,0 @@
-import markdown, mdtex2html, threading, importlib, traceback, importlib, inspect, re
-from show_math import convert as convert_math
-from functools import wraps, lru_cache
-def get_reduce_token_percent(text):
-    try:
-        # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
-        pattern = r"(\d+)\s+tokens\b"
-        match = re.findall(pattern, text)
-        EXCEED_ALLO = 500 # 稍微留一点余地，否则在回复时会因余量太少出问题
-        max_limit = float(match[0]) - EXCEED_ALLO
-        current_tokens = float(match[1])
-        ratio = max_limit/current_tokens
-        assert ratio > 0 and ratio < 1
-        return ratio, str(int(current_tokens-max_limit))
-    except:
-        return 0.5, '不详'
-def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], sys_prompt='', long_connection=True):
-    """
-        调用简单的predict_no_ui接口，但是依然保留了些许界面心跳功能，当对话太长时，会自动采用二分法截断
-        i_say: 当前输入
-        i_say_show_user: 显示到对话界面上的当前输入，例如，输入整个文件时，你绝对不想把文件的内容都糊到对话界面上
-        chatbot: 对话界面句柄
-        top_p, temperature: gpt参数
-        history: gpt参数 对话历史
-        sys_prompt: gpt参数 sys_prompt
-        long_connection: 是否采用更稳定的连接方式（推荐）
-    """
-    import time
-    from predict import predict_no_ui, predict_no_ui_long_connection
-    from toolbox import get_conf
-    TIMEOUT_SECONDS, MAX_RETRY = get_conf('TIMEOUT_SECONDS', 'MAX_RETRY')
-    # 多线程的时候，需要一个mutable结构在不同线程之间传递信息
-    # list就是最简单的mutable结构，我们第一个位置放gpt输出，第二个位置传递报错信息
-    mutable = [None, '']
-    # multi-threading worker
-    def mt(i_say, history):
-        while True:
-            try:
-                if long_connection:
-                    mutable[0] = predict_no_ui_long_connection(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
-                else:
-                    mutable[0] = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
-                break
-            except ConnectionAbortedError as token_exceeded_error:
-                # 尝试计算比例，尽可能多地保留文本
-                p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                if len(history) > 0:
-                    history = [his[     int(len(his)    *p_ratio):      ] for his in history if his is not None]
-                else:
-                    i_say = i_say[:     int(len(i_say)  *p_ratio)     ]
-                mutable[1] = f'警告，文本过长将进行截断，Token溢出数：{n_exceed}，截断比例：{(1-p_ratio):.0%}。'
-            except TimeoutError as e:
-                mutable[0] = '[Local Message] 请求超时。'
-                raise TimeoutError
-            except Exception as e:
-                mutable[0] = f'[Local Message] 异常：{str(e)}.'
-                raise RuntimeError(f'[Local Message] 异常：{str(e)}.')
-    # 创建新线程发出http请求
-    thread_name = threading.Thread(target=mt, args=(i_say, history)); thread_name.start()
-    # 原来的线程则负责持续更新UI，实现一个超时倒计时，并等待新线程的任务完成
-    cnt = 0
-    while thread_name.is_alive():
-        cnt += 1
-        chatbot[-1] = (i_say_show_user, f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt%4)))
-        yield chatbot, history, '正常'
-        time.sleep(1)
-    # 把gpt的输出从mutable中取出来
-    gpt_say = mutable[0]
-    if gpt_say=='[Local Message] Failed with timeout.': raise TimeoutError
-    return gpt_say
-def write_results_to_file(history, file_name=None):
-    """
-        将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os, time
-    if file_name is None:
-        # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-        file_name = 'chatGPT分析报告' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-    os.makedirs('./gpt_log/', exist_ok=True)
-    with open(f'./gpt_log/{file_name}', 'w', encoding = 'utf8') as f:
-        f.write('# chatGPT 分析报告\n')
-        for i, content in enumerate(history):
-            try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                if type(content) != str: content = str(content)
-            except:
-                continue
-            if i%2==0: f.write('## ')
-            f.write(content)
-            f.write('\n\n')
-    res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
-    print(res)
-    return res
-def regular_txt_to_markdown(text):
-    """
-        将普通文本转换为Markdown格式的文本。
-    """
-    text = text.replace('\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    return text
-def CatchException(f):
-    """
-        装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
-    """
-    @wraps(f)
-    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-        try:
-            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
-        except Exception as e:
-            from check_proxy import check_proxy
-            from toolbox import get_conf
-            proxies, = get_conf('proxies')
-            tb_str = '```\n' + traceback.format_exc() + '```'
-            if len(chatbot) == 0: chatbot.append(["插件调度异常","异常原因"])
-            chatbot[-1] = (chatbot[-1][0], f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
-            yield chatbot, history, f'异常 {e}'
-    return decorated
-def HotReload(f):
-    """
-        装饰器函数，实现函数插件热更新
-    """
-    @wraps(f)
-    def decorated(*args, **kwargs):
-        fn_name = f.__name__
-        f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
-        yield from f_hot_reload(*args, **kwargs)
-    return decorated
-def report_execption(chatbot, history, a, b):
-    """
-        向chatbot中添加错误信息
-    """
-    chatbot.append((a, b))
-    history.append(a); history.append(b)
-def text_divide_paragraph(text):
-    """
-        将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
-    """
-    if '```' in text:
-        # careful input
-        return text
-    else:
-        # wtf input
-        lines = text.split("\n")
-        for i, line in enumerate(lines):
-            lines[i] = lines[i].replace(" ", "&nbsp;")
-        text = "</br>".join(lines)
-        return text
-def markdown_convertion(txt):
-    """
-        将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
-    """
-    pre = '<div class="markdown-body">'
-    suf = '</div>'
-    if ('$' in txt) and ('```' not in txt):
-        return pre + markdown.markdown(txt,extensions=['fenced_code','tables']) + '<br><br>' + markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables']) + suf
-    else:
-        return pre + markdown.markdown(txt,extensions=['fenced_code','tables']) + suf
-def close_up_code_segment_during_stream(gpt_reply):
-    """
-        在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
-    """
-    if '```' not in gpt_reply: return gpt_reply
-    if gpt_reply.endswith('```'): return gpt_reply
-    # 排除了以上两个情况，我们
-    segments = gpt_reply.split('```')
-    n_mark = len(segments) - 1
-    if n_mark % 2 == 1:
-        # print('输出代码片段中！')
-        return gpt_reply+'\n```'
-    else:
-        return gpt_reply
-def format_io(self, y):
-    """
-        将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
-    """
-    if y is None or y == []: return []
-    i_ask, gpt_reply = y[-1]
-    i_ask = text_divide_paragraph(i_ask) # 输入部分太自由，预处理一波
-    gpt_reply = close_up_code_segment_during_stream(gpt_reply)  # 当代码输出半截的时候，试着补上后个```
-    y[-1] = (
-        None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code','tables']),
-        None if gpt_reply is None else markdown_convertion(gpt_reply)
-    )
-    return y
-def find_free_port():
-    """
-        返回当前系统中可用的未使用端口。
-    """
-    import socket
-    from contextlib import closing
-    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
-        s.bind(('', 0))
-        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-        return s.getsockname()[1]
-def extract_archive(file_path, dest_dir):
-    import zipfile
-    import tarfile
-    import os
-    # Get the file extension of the input file
-    file_extension = os.path.splitext(file_path)[1]
-    # Extract the archive based on its extension
-    if file_extension == '.zip':
-        with zipfile.ZipFile(file_path, 'r') as zipobj:
-            zipobj.extractall(path=dest_dir)
-            print("Successfully extracted zip archive to {}".format(dest_dir))
-    elif file_extension in ['.tar', '.gz', '.bz2']:
-        with tarfile.open(file_path, 'r:*') as tarobj:
-            tarobj.extractall(path=dest_dir)
-            print("Successfully extracted tar archive to {}".format(dest_dir))
-    # 第三方库，需要预先pip install rarfile
-    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
-    elif file_extension == '.rar':
-        try:
-            import rarfile
-            with rarfile.RarFile(file_path) as rf:
-                rf.extractall(path=dest_dir)
-                print("Successfully extracted rar archive to {}".format(dest_dir))
-        except:
-            print("Rar format requires additional dependencies to install")
-            return '\n\n需要安装pip install rarfile来解压rar文件'
-    # 第三方库，需要预先pip install py7zr
-    elif file_extension == '.7z':
-        try:
-            import py7zr
-            with py7zr.SevenZipFile(file_path, mode='r') as f:
-                f.extractall(path=dest_dir)
-                print("Successfully extracted 7z archive to {}".format(dest_dir))
-        except:
-            print("7z format requires additional dependencies to install")
-            return '\n\n需要安装pip install py7zr来解压7z文件'
-    else:
-        return ''
-    return ''
-def find_recent_files(directory):
-    """
-        me: find files that is created with in one minutes under a directory with python, write a function
-        gpt: here it is!
-    """
-    import os
-    import time
-    current_time = time.time()
-    one_minute_ago = current_time - 60
-    recent_files = []
-    for filename in os.listdir(directory):
-        file_path = os.path.join(directory, filename)
-        if file_path.endswith('.log'): continue
-        created_time = os.path.getctime(file_path)
-        if created_time >= one_minute_ago:
-            if os.path.isdir(file_path): continue
-            recent_files.append(file_path)
-    return recent_files
-def on_file_uploaded(files, chatbot, txt):
-    if len(files) == 0: return chatbot, txt
-    import shutil, os, time, glob
-    from toolbox import extract_archive
-    try: shutil.rmtree('./private_upload/')
-    except: pass
-    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
-    err_msg = ''
-    for file in files:
-        file_origin_name = os.path.basename(file.orig_name)
-        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
-        err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
-                        dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
-    moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
-    txt = f'private_upload/{time_tag}'
-    moved_files_str = '\t\n\n'.join(moved_files)
-    chatbot.append(['我上传了文件，请查收',
-                    f'[Local Message] 收到以下文件: \n\n{moved_files_str}'+
-                    f'\n\n调用路径参数已自动修正到: \n\n{txt}'+
-                    f'\n\n现在您点击任意实验功能时，以上文件将被作为输入参数'+err_msg])
-    return chatbot, txt
-def on_report_generated(files, chatbot):
-    from toolbox import find_recent_files
-    report_files = find_recent_files('gpt_log')
-    if len(report_files) == 0: return files, chatbot
-    # files.extend(report_files)
-    chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。'])
-    return report_files, chatbot
-@lru_cache(maxsize=128)
-def read_single_conf_with_lru_cache(arg):
-    try: r = getattr(importlib.import_module('config_private'), arg)
-    except: r = getattr(importlib.import_module('config'), arg)
-    # 在读取API_KEY时，检查一下是不是忘了改config
-    if arg=='API_KEY':
-        # 正确的 API_KEY 是 "sk-" + 48 位大小写字母数字的组合
-        API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", r)
-        if API_MATCH:
-            print(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
-        else:
-            assert False, "正确的 API_KEY 是 'sk-' + '48 位大小写字母数字' 的组合，请在config文件中修改API密钥, 添加海外代理之后再运行。" + \
-                        "（如果您刚更新过代码，请确保旧版config_private文件中没有遗留任何新增键值）"
-    if arg=='proxies':
-        if r is None:
-            print('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问。建议：检查USE_PROXY选项是否修改。')
-        else:
-            print('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
-            assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
-    return r
-def get_conf(*args):
-    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
-    res = []
-    for arg in args:
-        r = read_single_conf_with_lru_cache(arg)
-        res.append(r)
-    return res
-def clear_line_break(txt):
-    txt = txt.replace('\n', ' ')
-    txt = txt.replace('  ', ' ')
-    txt = txt.replace('  ', ' ')
-    return txt

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/app.py DELETED Viewed

@@ -1,75 +0,0 @@
-import streamlit as st
-from PIL import Image
-import imageio.v3 as iio
-from inference import inference
-from src.utils.opt import Opts
-import os
-st.set_page_config(layout='wide')
-st.markdown(
-    """
-    <style>
-        div[data-testid="column"]:nth-of-type(1)
-        {
-        }
-        div[data-testid="column"]:nth-of-type(2)
-        {
-        }
-    </style>
-    """,unsafe_allow_html=True
-)
-col1, col2, col3 = st.columns(3)
-if 'counter' not in st.session_state:
-    st.session_state.video_path = None
-    st.session_state.image = None
-    st.session_state.counter = 0
-def showVideo(image):
-    if st.session_state.image is not None:
-        cfg = Opts(cfg="configs/style_inference.yml").parse_args()
-        result = inference(cfg, "render_test", image=image)
-        st.session_state.video_path = result["video_path"]
-        st.session_state.counter += 1
-    else:
-        col2.write("No uploaded image")
-with col1:
-    col1.subheader("Source multiview images")
-    filteredImages = []
-    for image_file in os.listdir('data/nerf_llff_data/trex/streamlit_images'):
-        filteredImages.append(Image.open(os.path.join('data/nerf_llff_data/trex/streamlit_images', image_file)))
-    id = 0
-    for img in range(0, len(filteredImages), 4):
-        cols = col1.columns(4)
-        cols[0].image(filteredImages[id], use_column_width=True)
-        id +=1
-        cols[1].image(filteredImages[id], use_column_width=True)
-        id +=1
-        cols[2].image(filteredImages[id], use_column_width=True)
-        id +=1
-        cols[3].image(filteredImages[id], use_column_width=True)
-        id +=1
-with col2:
-    col2.subheader("Style image")
-    uploaded_file = col2.file_uploader("Choose a image file")
-    if uploaded_file:
-        st.session_state.image = Image.open(uploaded_file)
-        img = col2.image(st.session_state.image, caption='Style Image', use_column_width=True)
-    col2.button('Run Style Transfer', on_click=showVideo, args=([st.session_state.image]))
-col3.subheader("Style videos")
-if st.session_state.counter > 0:
-    video_file = open(st.session_state.video_path, 'rb')
-    video_bytes = video_file.read()
-    col3.video(video_bytes)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/audio_diffusion/mel.py DELETED Viewed

@@ -1,179 +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 numpy as np  # noqa: E402
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...schedulers.scheduling_utils import SchedulerMixin
-try:
-    import librosa  # noqa: E402
-    _librosa_can_be_imported = True
-    _import_error = ""
-except Exception as e:
-    _librosa_can_be_imported = False
-    _import_error = (
-        f"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."
-    )
-from PIL import Image  # noqa: E402
-class Mel(ConfigMixin, SchedulerMixin):
-    """
-    Parameters:
-        x_res (`int`):
-            x resolution of spectrogram (time).
-        y_res (`int`):
-            y resolution of spectrogram (frequency bins).
-        sample_rate (`int`):
-            Sample rate of audio.
-        n_fft (`int`):
-            Number of Fast Fourier Transforms.
-        hop_length (`int`):
-            Hop length (a higher number is recommended if `y_res` < 256).
-        top_db (`int`):
-            Loudest decibel value.
-        n_iter (`int`):
-            Number of iterations for Griffin-Lim Mel inversion.
-    """
-    config_name = "mel_config.json"
-    @register_to_config
-    def __init__(
-        self,
-        x_res: int = 256,
-        y_res: int = 256,
-        sample_rate: int = 22050,
-        n_fft: int = 2048,
-        hop_length: int = 512,
-        top_db: int = 80,
-        n_iter: int = 32,
-    ):
-        self.hop_length = hop_length
-        self.sr = sample_rate
-        self.n_fft = n_fft
-        self.top_db = top_db
-        self.n_iter = n_iter
-        self.set_resolution(x_res, y_res)
-        self.audio = None
-        if not _librosa_can_be_imported:
-            raise ValueError(_import_error)
-    def set_resolution(self, x_res: int, y_res: int):
-        """Set resolution.
-        Args:
-            x_res (`int`):
-                x resolution of spectrogram (time).
-            y_res (`int`):
-                y resolution of spectrogram (frequency bins).
-        """
-        self.x_res = x_res
-        self.y_res = y_res
-        self.n_mels = self.y_res
-        self.slice_size = self.x_res * self.hop_length - 1
-    def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):
-        """Load audio.
-        Args:
-            audio_file (`str`):
-                An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
-            raw_audio (`np.ndarray`):
-                The raw audio file as a NumPy array.
-        """
-        if audio_file is not None:
-            self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr)
-        else:
-            self.audio = raw_audio
-        # Pad with silence if necessary.
-        if len(self.audio) < self.x_res * self.hop_length:
-            self.audio = np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])
-    def get_number_of_slices(self) -> int:
-        """Get number of slices in audio.
-        Returns:
-            `int`:
-                Number of spectograms audio can be sliced into.
-        """
-        return len(self.audio) // self.slice_size
-    def get_audio_slice(self, slice: int = 0) -> np.ndarray:
-        """Get slice of audio.
-        Args:
-            slice (`int`):
-                Slice number of audio (out of `get_number_of_slices()`).
-        Returns:
-            `np.ndarray`:
-                The audio slice as a NumPy array.
-        """
-        return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
-    def get_sample_rate(self) -> int:
-        """Get sample rate.
-        Returns:
-            `int`:
-                Sample rate of audio.
-        """
-        return self.sr
-    def audio_slice_to_image(self, slice: int) -> Image.Image:
-        """Convert slice of audio to spectrogram.
-        Args:
-            slice (`int`):
-                Slice number of audio to convert (out of `get_number_of_slices()`).
-        Returns:
-            `PIL Image`:
-                A grayscale image of `x_res x y_res`.
-        """
-        S = librosa.feature.melspectrogram(
-            y=self.get_audio_slice(slice), sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels
-        )
-        log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db)
-        bytedata = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8)
-        image = Image.fromarray(bytedata)
-        return image
-    def image_to_audio(self, image: Image.Image) -> np.ndarray:
-        """Converts spectrogram to audio.
-        Args:
-            image (`PIL Image`):
-                An grayscale image of `x_res x y_res`.
-        Returns:
-            audio (`np.ndarray`):
-                The audio as a NumPy array.
-        """
-        bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape((image.height, image.width))
-        log_S = bytedata.astype("float") * self.top_db / 255 - self.top_db
-        S = librosa.db_to_power(log_S)
-        audio = librosa.feature.inverse.mel_to_audio(
-            S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter
-        )
-        return audio

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py DELETED Viewed

@@ -1,153 +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.
-from typing import List, Optional, Tuple, Union
-import torch
-from ...utils import logging, randn_tensor
-from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-class DanceDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for audio generation.
-    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.).
-    Parameters:
-        unet ([`UNet1DModel`]):
-            A `UNet1DModel` to denoise the encoded audio.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
-            [`IPNDMScheduler`].
-    """
-    def __init__(self, unet, scheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        num_inference_steps: int = 100,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        audio_length_in_s: Optional[float] = None,
-        return_dict: bool = True,
-    ) -> Union[AudioPipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of audio samples to generate.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
-                the expense of slower inference.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
-                The length of the generated audio sample in seconds.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
-        Example:
-        ```py
-        from diffusers import DiffusionPipeline
-        from scipy.io.wavfile import write
-        model_id = "harmonai/maestro-150k"
-        pipe = DiffusionPipeline.from_pretrained(model_id)
-        pipe = pipe.to("cuda")
-        audios = pipe(audio_length_in_s=4.0).audios
-        # To save locally
-        for i, audio in enumerate(audios):
-            write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
-        # To dislay in google colab
-        import IPython.display as ipd
-        for audio in audios:
-            display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
-        ```
-        Returns:
-            [`~pipelines.AudioPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated audio.
-        """
-        if audio_length_in_s is None:
-            audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
-        sample_size = audio_length_in_s * self.unet.config.sample_rate
-        down_scale_factor = 2 ** len(self.unet.up_blocks)
-        if sample_size < 3 * down_scale_factor:
-            raise ValueError(
-                f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
-                f" {3 * down_scale_factor / self.unet.config.sample_rate}."
-            )
-        original_sample_size = int(sample_size)
-        if sample_size % down_scale_factor != 0:
-            sample_size = (
-                (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
-            ) * down_scale_factor
-            logger.info(
-                f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
-                f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
-                " process."
-            )
-        sample_size = int(sample_size)
-        dtype = next(self.unet.parameters()).dtype
-        shape = (batch_size, self.unet.config.in_channels, sample_size)
-        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."
-            )
-        audio = randn_tensor(shape, generator=generator, device=self._execution_device, dtype=dtype)
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps, device=audio.device)
-        self.scheduler.timesteps = self.scheduler.timesteps.to(dtype)
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # 1. predict noise model_output
-            model_output = self.unet(audio, t).sample
-            # 2. compute previous audio sample: x_t -> t_t-1
-            audio = self.scheduler.step(model_output, t, audio).prev_sample
-        audio = audio.clamp(-1, 1).float().cpu().numpy()
-        audio = audio[:, :, :original_sample_size]
-        if not return_dict:
-            return (audio,)
-        return AudioPipelineOutput(audios=audio)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py DELETED Viewed

@@ -1,279 +0,0 @@
-# Copyright 2023 Google Brain and 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.
-# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-import flax
-import jax.numpy as jnp
-from jax import random
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
-@flax.struct.dataclass
-class ScoreSdeVeSchedulerState:
-    # setable values
-    timesteps: Optional[jnp.ndarray] = None
-    discrete_sigmas: Optional[jnp.ndarray] = None
-    sigmas: Optional[jnp.ndarray] = None
-    @classmethod
-    def create(cls):
-        return cls()
-@dataclass
-class FlaxSdeVeOutput(FlaxSchedulerOutput):
-    """
-    Output class for the ScoreSdeVeScheduler's step function output.
-    Args:
-        state (`ScoreSdeVeSchedulerState`):
-        prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        prev_sample_mean (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps.
-    """
-    state: ScoreSdeVeSchedulerState
-    prev_sample: jnp.ndarray
-    prev_sample_mean: Optional[jnp.ndarray] = None
-class FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin):
-    """
-    The variance exploding stochastic differential equation (SDE) scheduler.
-    For more information, see the original paper: https://arxiv.org/abs/2011.13456
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        snr (`float`):
-            coefficient weighting the step from the model_output sample (from the network) to the random noise.
-        sigma_min (`float`):
-                initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
-                distribution of the data.
-        sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model.
-        sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to
-        epsilon.
-        correct_steps (`int`): number of correction steps performed on a produced sample.
-    """
-    @property
-    def has_state(self):
-        return True
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 2000,
-        snr: float = 0.15,
-        sigma_min: float = 0.01,
-        sigma_max: float = 1348.0,
-        sampling_eps: float = 1e-5,
-        correct_steps: int = 1,
-    ):
-        pass
-    def create_state(self):
-        state = ScoreSdeVeSchedulerState.create()
-        return self.set_sigmas(
-            state,
-            self.config.num_train_timesteps,
-            self.config.sigma_min,
-            self.config.sigma_max,
-            self.config.sampling_eps,
-        )
-    def set_timesteps(
-        self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, shape: Tuple = (), sampling_eps: float = None
-    ) -> ScoreSdeVeSchedulerState:
-        """
-        Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            sampling_eps (`float`, optional):
-                final timestep value (overrides value given at Scheduler instantiation).
-        """
-        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
-        timesteps = jnp.linspace(1, sampling_eps, num_inference_steps)
-        return state.replace(timesteps=timesteps)
-    def set_sigmas(
-        self,
-        state: ScoreSdeVeSchedulerState,
-        num_inference_steps: int,
-        sigma_min: float = None,
-        sigma_max: float = None,
-        sampling_eps: float = None,
-    ) -> ScoreSdeVeSchedulerState:
-        """
-        Sets the noise scales used for the diffusion chain. Supporting function to be run before inference.
-        The sigmas control the weight of the `drift` and `diffusion` components of sample update.
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            sigma_min (`float`, optional):
-                initial noise scale value (overrides value given at Scheduler instantiation).
-            sigma_max (`float`, optional):
-                final noise scale value (overrides value given at Scheduler instantiation).
-            sampling_eps (`float`, optional):
-                final timestep value (overrides value given at Scheduler instantiation).
-        """
-        sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
-        sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
-        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
-        if state.timesteps is None:
-            state = self.set_timesteps(state, num_inference_steps, sampling_eps)
-        discrete_sigmas = jnp.exp(jnp.linspace(jnp.log(sigma_min), jnp.log(sigma_max), num_inference_steps))
-        sigmas = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])
-        return state.replace(discrete_sigmas=discrete_sigmas, sigmas=sigmas)
-    def get_adjacent_sigma(self, state, timesteps, t):
-        return jnp.where(timesteps == 0, jnp.zeros_like(t), state.discrete_sigmas[timesteps - 1])
-    def step_pred(
-        self,
-        state: ScoreSdeVeSchedulerState,
-        model_output: jnp.ndarray,
-        timestep: int,
-        sample: jnp.ndarray,
-        key: random.KeyArray,
-        return_dict: bool = True,
-    ) -> Union[FlaxSdeVeOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class
-        Returns:
-            [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-        """
-        if state.timesteps is None:
-            raise ValueError(
-                "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
-            )
-        timestep = timestep * jnp.ones(
-            sample.shape[0],
-        )
-        timesteps = (timestep * (len(state.timesteps) - 1)).long()
-        sigma = state.discrete_sigmas[timesteps]
-        adjacent_sigma = self.get_adjacent_sigma(state, timesteps, timestep)
-        drift = jnp.zeros_like(sample)
-        diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
-        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
-        # also equation 47 shows the analog from SDE models to ancestral sampling methods
-        diffusion = diffusion.flatten()
-        diffusion = broadcast_to_shape_from_left(diffusion, sample.shape)
-        drift = drift - diffusion**2 * model_output
-        #  equation 6: sample noise for the diffusion term of
-        key = random.split(key, num=1)
-        noise = random.normal(key=key, shape=sample.shape)
-        prev_sample_mean = sample - drift  # subtract because `dt` is a small negative timestep
-        # TODO is the variable diffusion the correct scaling term for the noise?
-        prev_sample = prev_sample_mean + diffusion * noise  # add impact of diffusion field g
-        if not return_dict:
-            return (prev_sample, prev_sample_mean, state)
-        return FlaxSdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean, state=state)
-    def step_correct(
-        self,
-        state: ScoreSdeVeSchedulerState,
-        model_output: jnp.ndarray,
-        sample: jnp.ndarray,
-        key: random.KeyArray,
-        return_dict: bool = True,
-    ) -> Union[FlaxSdeVeOutput, Tuple]:
-        """
-        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
-        after making the prediction for the previous timestep.
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class
-        Returns:
-            [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-        """
-        if state.timesteps is None:
-            raise ValueError(
-                "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
-            )
-        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
-        # sample noise for correction
-        key = random.split(key, num=1)
-        noise = random.normal(key=key, shape=sample.shape)
-        # compute step size from the model_output, the noise, and the snr
-        grad_norm = jnp.linalg.norm(model_output)
-        noise_norm = jnp.linalg.norm(noise)
-        step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
-        step_size = step_size * jnp.ones(sample.shape[0])
-        # compute corrected sample: model_output term and noise term
-        step_size = step_size.flatten()
-        step_size = broadcast_to_shape_from_left(step_size, sample.shape)
-        prev_sample_mean = sample + step_size * model_output
-        prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
-        if not return_dict:
-            return (prev_sample, state)
-        return FlaxSdeVeOutput(prev_sample=prev_sample, state=state)
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/Andy1621/uniformer_image_detection/configs/htc/htc_r50_fpn_20e_coco.py DELETED Viewed

@@ -1,4 +0,0 @@
-_base_ = './htc_r50_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/stylegan_ops/upfirdn2d.cpp DELETED Viewed

@@ -1,23 +0,0 @@
-#include <torch/extension.h>
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
-                            int up_x, int up_y, int down_x, int down_y,
-                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-#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 upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
-                        int up_x, int up_y, int down_x, int down_y,
-                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(kernel);
-    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}

spaces/AnthonyErosion/HoctotAI/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: HoctotAI
-emoji: ⚡
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/check.py DELETED Viewed

@@ -1,149 +0,0 @@
-"""Validation of dependencies of packages
-"""
-import logging
-from typing import Callable, Dict, List, NamedTuple, Optional, Set, Tuple
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._internal.distributions import make_distribution_for_install_requirement
-from pip._internal.metadata import get_default_environment
-from pip._internal.metadata.base import DistributionVersion
-from pip._internal.req.req_install import InstallRequirement
-logger = logging.getLogger(__name__)
-class PackageDetails(NamedTuple):
-    version: DistributionVersion
-    dependencies: List[Requirement]
-# Shorthands
-PackageSet = Dict[NormalizedName, PackageDetails]
-Missing = Tuple[NormalizedName, Requirement]
-Conflicting = Tuple[NormalizedName, DistributionVersion, Requirement]
-MissingDict = Dict[NormalizedName, List[Missing]]
-ConflictingDict = Dict[NormalizedName, List[Conflicting]]
-CheckResult = Tuple[MissingDict, ConflictingDict]
-ConflictDetails = Tuple[PackageSet, CheckResult]
-def create_package_set_from_installed() -> Tuple[PackageSet, bool]:
-    """Converts a list of distributions into a PackageSet."""
-    package_set = {}
-    problems = False
-    env = get_default_environment()
-    for dist in env.iter_installed_distributions(local_only=False, skip=()):
-        name = dist.canonical_name
-        try:
-            dependencies = list(dist.iter_dependencies())
-            package_set[name] = PackageDetails(dist.version, dependencies)
-        except (OSError, ValueError) as e:
-            # Don't crash on unreadable or broken metadata.
-            logger.warning("Error parsing requirements for %s: %s", name, e)
-            problems = True
-    return package_set, problems
-def check_package_set(
-    package_set: PackageSet, should_ignore: Optional[Callable[[str], bool]] = None
-) -> CheckResult:
-    """Check if a package set is consistent
-    If should_ignore is passed, it should be a callable that takes a
-    package name and returns a boolean.
-    """
-    missing = {}
-    conflicting = {}
-    for package_name, package_detail in package_set.items():
-        # Info about dependencies of package_name
-        missing_deps: Set[Missing] = set()
-        conflicting_deps: Set[Conflicting] = set()
-        if should_ignore and should_ignore(package_name):
-            continue
-        for req in package_detail.dependencies:
-            name = canonicalize_name(req.name)
-            # Check if it's missing
-            if name not in package_set:
-                missed = True
-                if req.marker is not None:
-                    missed = req.marker.evaluate({"extra": ""})
-                if missed:
-                    missing_deps.add((name, req))
-                continue
-            # Check if there's a conflict
-            version = package_set[name].version
-            if not req.specifier.contains(version, prereleases=True):
-                conflicting_deps.add((name, version, req))
-        if missing_deps:
-            missing[package_name] = sorted(missing_deps, key=str)
-        if conflicting_deps:
-            conflicting[package_name] = sorted(conflicting_deps, key=str)
-    return missing, conflicting
-def check_install_conflicts(to_install: List[InstallRequirement]) -> ConflictDetails:
-    """For checking if the dependency graph would be consistent after \
-    installing given requirements
-    """
-    # Start from the current state
-    package_set, _ = create_package_set_from_installed()
-    # Install packages
-    would_be_installed = _simulate_installation_of(to_install, package_set)
-    # Only warn about directly-dependent packages; create a whitelist of them
-    whitelist = _create_whitelist(would_be_installed, package_set)
-    return (
-        package_set,
-        check_package_set(
-            package_set, should_ignore=lambda name: name not in whitelist
-        ),
-    )
-def _simulate_installation_of(
-    to_install: List[InstallRequirement], package_set: PackageSet
-) -> Set[NormalizedName]:
-    """Computes the version of packages after installing to_install."""
-    # Keep track of packages that were installed
-    installed = set()
-    # Modify it as installing requirement_set would (assuming no errors)
-    for inst_req in to_install:
-        abstract_dist = make_distribution_for_install_requirement(inst_req)
-        dist = abstract_dist.get_metadata_distribution()
-        name = dist.canonical_name
-        package_set[name] = PackageDetails(dist.version, list(dist.iter_dependencies()))
-        installed.add(name)
-    return installed
-def _create_whitelist(
-    would_be_installed: Set[NormalizedName], package_set: PackageSet
-) -> Set[NormalizedName]:
-    packages_affected = set(would_be_installed)
-    for package_name in package_set:
-        if package_name in packages_affected:
-            continue
-        for req in package_set[package_name].dependencies:
-            if canonicalize_name(req.name) in packages_affected:
-                packages_affected.add(package_name)
-                break
-    return packages_affected

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

@@ -1,274 +0,0 @@
-import colorsys
-import io
-from time import process_time
-from pip._vendor.rich import box
-from pip._vendor.rich.color import Color
-from pip._vendor.rich.console import Console, ConsoleOptions, Group, RenderableType, RenderResult
-from pip._vendor.rich.markdown import Markdown
-from pip._vendor.rich.measure import Measurement
-from pip._vendor.rich.pretty import Pretty
-from pip._vendor.rich.segment import Segment
-from pip._vendor.rich.style import Style
-from pip._vendor.rich.syntax import Syntax
-from pip._vendor.rich.table import Table
-from pip._vendor.rich.text import Text
-class ColorBox:
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        for y in range(0, 5):
-            for x in range(options.max_width):
-                h = x / options.max_width
-                l = 0.1 + ((y / 5) * 0.7)
-                r1, g1, b1 = colorsys.hls_to_rgb(h, l, 1.0)
-                r2, g2, b2 = colorsys.hls_to_rgb(h, l + 0.7 / 10, 1.0)
-                bgcolor = Color.from_rgb(r1 * 255, g1 * 255, b1 * 255)
-                color = Color.from_rgb(r2 * 255, g2 * 255, b2 * 255)
-                yield Segment("▄", Style(color=color, bgcolor=bgcolor))
-            yield Segment.line()
-    def __rich_measure__(
-        self, console: "Console", options: ConsoleOptions
-    ) -> Measurement:
-        return Measurement(1, options.max_width)
-def make_test_card() -> Table:
-    """Get a renderable that demonstrates a number of features."""
-    table = Table.grid(padding=1, pad_edge=True)
-    table.title = "Rich features"
-    table.add_column("Feature", no_wrap=True, justify="center", style="bold red")
-    table.add_column("Demonstration")
-    color_table = Table(
-        box=None,
-        expand=False,
-        show_header=False,
-        show_edge=False,
-        pad_edge=False,
-    )
-    color_table.add_row(
-        (
-            "✓ [bold green]4-bit color[/]\n"
-            "✓ [bold blue]8-bit color[/]\n"
-            "✓ [bold magenta]Truecolor (16.7 million)[/]\n"
-            "✓ [bold yellow]Dumb terminals[/]\n"
-            "✓ [bold cyan]Automatic color conversion"
-        ),
-        ColorBox(),
-    )
-    table.add_row("Colors", color_table)
-    table.add_row(
-        "Styles",
-        "All ansi styles: [bold]bold[/], [dim]dim[/], [italic]italic[/italic], [underline]underline[/], [strike]strikethrough[/], [reverse]reverse[/], and even [blink]blink[/].",
-    )
-    lorem = "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque in metus sed sapien ultricies pretium a at justo. Maecenas luctus velit et auctor maximus."
-    lorem_table = Table.grid(padding=1, collapse_padding=True)
-    lorem_table.pad_edge = False
-    lorem_table.add_row(
-        Text(lorem, justify="left", style="green"),
-        Text(lorem, justify="center", style="yellow"),
-        Text(lorem, justify="right", style="blue"),
-        Text(lorem, justify="full", style="red"),
-    )
-    table.add_row(
-        "Text",
-        Group(
-            Text.from_markup(
-                """Word wrap text. Justify [green]left[/], [yellow]center[/], [blue]right[/] or [red]full[/].\n"""
-            ),
-            lorem_table,
-        ),
-    )
-    def comparison(renderable1: RenderableType, renderable2: RenderableType) -> Table:
-        table = Table(show_header=False, pad_edge=False, box=None, expand=True)
-        table.add_column("1", ratio=1)
-        table.add_column("2", ratio=1)
-        table.add_row(renderable1, renderable2)
-        return table
-    table.add_row(
-        "Asian\nlanguage\nsupport",
-        ":flag_for_china:  该库支持中文，日文和韩文文本！\n:flag_for_japan:  ライブラリは中国語、日本語、韓国語のテキストをサポートしています\n:flag_for_south_korea:  이 라이브러리는 중국어, 일본어 및 한국어 텍스트를 지원합니다",
-    )
-    markup_example = (
-        "[bold magenta]Rich[/] supports a simple [i]bbcode[/i]-like [b]markup[/b] for [yellow]color[/], [underline]style[/], and emoji! "
-        ":+1: :apple: :ant: :bear: :baguette_bread: :bus: "
-    )
-    table.add_row("Markup", markup_example)
-    example_table = Table(
-        show_edge=False,
-        show_header=True,
-        expand=False,
-        row_styles=["none", "dim"],
-        box=box.SIMPLE,
-    )
-    example_table.add_column("[green]Date", style="green", no_wrap=True)
-    example_table.add_column("[blue]Title", style="blue")
-    example_table.add_column(
-        "[cyan]Production Budget",
-        style="cyan",
-        justify="right",
-        no_wrap=True,
-    )
-    example_table.add_column(
-        "[magenta]Box Office",
-        style="magenta",
-        justify="right",
-        no_wrap=True,
-    )
-    example_table.add_row(
-        "Dec 20, 2019",
-        "Star Wars: The Rise of Skywalker",
-        "$275,000,000",
-        "$375,126,118",
-    )
-    example_table.add_row(
-        "May 25, 2018",
-        "[b]Solo[/]: A Star Wars Story",
-        "$275,000,000",
-        "$393,151,347",
-    )
-    example_table.add_row(
-        "Dec 15, 2017",
-        "Star Wars Ep. VIII: The Last Jedi",
-        "$262,000,000",
-        "[bold]$1,332,539,889[/bold]",
-    )
-    example_table.add_row(
-        "May 19, 1999",
-        "Star Wars Ep. [b]I[/b]: [i]The phantom Menace",
-        "$115,000,000",
-        "$1,027,044,677",
-    )
-    table.add_row("Tables", example_table)
-    code = '''\
-def iter_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
-    """Iterate and generate a tuple with a flag for last value."""
-    iter_values = iter(values)
-    try:
-        previous_value = next(iter_values)
-    except StopIteration:
-        return
-    for value in iter_values:
-        yield False, previous_value
-        previous_value = value
-    yield True, previous_value'''
-    pretty_data = {
-        "foo": [
-            3.1427,
-            (
-                "Paul Atreides",
-                "Vladimir Harkonnen",
-                "Thufir Hawat",
-            ),
-        ],
-        "atomic": (False, True, None),
-    }
-    table.add_row(
-        "Syntax\nhighlighting\n&\npretty\nprinting",
-        comparison(
-            Syntax(code, "python3", line_numbers=True, indent_guides=True),
-            Pretty(pretty_data, indent_guides=True),
-        ),
-    )
-    markdown_example = """\
-# Markdown
-Supports much of the *markdown* __syntax__!
-- Headers
-- Basic formatting: **bold**, *italic*, `code`
-- Block quotes
-- Lists, and more...
-    """
-    table.add_row(
-        "Markdown", comparison("[cyan]" + markdown_example, Markdown(markdown_example))
-    )
-    table.add_row(
-        "+more!",
-        """Progress bars, columns, styled logging handler, tracebacks, etc...""",
-    )
-    return table
-if __name__ == "__main__":  # pragma: no cover
-    console = Console(
-        file=io.StringIO(),
-        force_terminal=True,
-    )
-    test_card = make_test_card()
-    # Print once to warm cache
-    start = process_time()
-    console.print(test_card)
-    pre_cache_taken = round((process_time() - start) * 1000.0, 1)
-    console.file = io.StringIO()
-    start = process_time()
-    console.print(test_card)
-    taken = round((process_time() - start) * 1000.0, 1)
-    c = Console(record=True)
-    c.print(test_card)
-    print(f"rendered in {pre_cache_taken}ms (cold cache)")
-    print(f"rendered in {taken}ms (warm cache)")
-    from pip._vendor.rich.panel import Panel
-    console = Console()
-    sponsor_message = Table.grid(padding=1)
-    sponsor_message.add_column(style="green", justify="right")
-    sponsor_message.add_column(no_wrap=True)
-    sponsor_message.add_row(
-        "Textualize",
-        "[u blue link=https://github.com/textualize]https://github.com/textualize",
-    )
-    sponsor_message.add_row(
-        "Twitter",
-        "[u blue link=https://twitter.com/willmcgugan]https://twitter.com/willmcgugan",
-    )
-    intro_message = Text.from_markup(
-        """\
-We hope you enjoy using Rich!
-Rich is maintained with [red]:heart:[/] by [link=https://www.textualize.io]Textualize.io[/]
-- Will McGugan"""
-    )
-    message = Table.grid(padding=2)
-    message.add_column()
-    message.add_column(no_wrap=True)
-    message.add_row(intro_message, sponsor_message)
-    console.print(
-        Panel.fit(
-            message,
-            box=box.ROUNDED,
-            padding=(1, 2),
-            title="[b red]Thanks for trying out Rich!",
-            border_style="bright_blue",
-        ),
-        justify="center",
-    )

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

@@ -1,377 +0,0 @@
-"""distutils.command.config
-Implements the Distutils 'config' command, a (mostly) empty command class
-that exists mainly to be sub-classed by specific module distributions and
-applications.  The idea is that while every "config" command is different,
-at least they're all named the same, and users always see "config" in the
-list of standard commands.  Also, this is a good place to put common
-configure-like tasks: "try to compile this C code", or "figure out where
-this header file lives".
-"""
-import os
-import re
-from distutils.core import Command
-from distutils.errors import DistutilsExecError
-from distutils.sysconfig import customize_compiler
-from distutils import log
-LANG_EXT = {"c": ".c", "c++": ".cxx"}
-class config(Command):
-    description = "prepare to build"
-    user_options = [
-        ('compiler=', None, "specify the compiler type"),
-        ('cc=', None, "specify the compiler executable"),
-        ('include-dirs=', 'I', "list of directories to search for header files"),
-        ('define=', 'D', "C preprocessor macros to define"),
-        ('undef=', 'U', "C preprocessor macros to undefine"),
-        ('libraries=', 'l', "external C libraries to link with"),
-        ('library-dirs=', 'L', "directories to search for external C libraries"),
-        ('noisy', None, "show every action (compile, link, run, ...) taken"),
-        (
-            'dump-source',
-            None,
-            "dump generated source files before attempting to compile them",
-        ),
-    ]
-    # The three standard command methods: since the "config" command
-    # does nothing by default, these are empty.
-    def initialize_options(self):
-        self.compiler = None
-        self.cc = None
-        self.include_dirs = None
-        self.libraries = None
-        self.library_dirs = None
-        # maximal output for now
-        self.noisy = 1
-        self.dump_source = 1
-        # list of temporary files generated along-the-way that we have
-        # to clean at some point
-        self.temp_files = []
-    def finalize_options(self):
-        if self.include_dirs is None:
-            self.include_dirs = self.distribution.include_dirs or []
-        elif isinstance(self.include_dirs, str):
-            self.include_dirs = self.include_dirs.split(os.pathsep)
-        if self.libraries is None:
-            self.libraries = []
-        elif isinstance(self.libraries, str):
-            self.libraries = [self.libraries]
-        if self.library_dirs is None:
-            self.library_dirs = []
-        elif isinstance(self.library_dirs, str):
-            self.library_dirs = self.library_dirs.split(os.pathsep)
-    def run(self):
-        pass
-    # Utility methods for actual "config" commands.  The interfaces are
-    # loosely based on Autoconf macros of similar names.  Sub-classes
-    # may use these freely.
-    def _check_compiler(self):
-        """Check that 'self.compiler' really is a CCompiler object;
-        if not, make it one.
-        """
-        # We do this late, and only on-demand, because this is an expensive
-        # import.
-        from distutils.ccompiler import CCompiler, new_compiler
-        if not isinstance(self.compiler, CCompiler):
-            self.compiler = new_compiler(
-                compiler=self.compiler, dry_run=self.dry_run, force=1
-            )
-            customize_compiler(self.compiler)
-            if self.include_dirs:
-                self.compiler.set_include_dirs(self.include_dirs)
-            if self.libraries:
-                self.compiler.set_libraries(self.libraries)
-            if self.library_dirs:
-                self.compiler.set_library_dirs(self.library_dirs)
-    def _gen_temp_sourcefile(self, body, headers, lang):
-        filename = "_configtest" + LANG_EXT[lang]
-        with open(filename, "w") as file:
-            if headers:
-                for header in headers:
-                    file.write("#include <%s>\n" % header)
-                file.write("\n")
-            file.write(body)
-            if body[-1] != "\n":
-                file.write("\n")
-        return filename
-    def _preprocess(self, body, headers, include_dirs, lang):
-        src = self._gen_temp_sourcefile(body, headers, lang)
-        out = "_configtest.i"
-        self.temp_files.extend([src, out])
-        self.compiler.preprocess(src, out, include_dirs=include_dirs)
-        return (src, out)
-    def _compile(self, body, headers, include_dirs, lang):
-        src = self._gen_temp_sourcefile(body, headers, lang)
-        if self.dump_source:
-            dump_file(src, "compiling '%s':" % src)
-        (obj,) = self.compiler.object_filenames([src])
-        self.temp_files.extend([src, obj])
-        self.compiler.compile([src], include_dirs=include_dirs)
-        return (src, obj)
-    def _link(self, body, headers, include_dirs, libraries, library_dirs, lang):
-        (src, obj) = self._compile(body, headers, include_dirs, lang)
-        prog = os.path.splitext(os.path.basename(src))[0]
-        self.compiler.link_executable(
-            [obj],
-            prog,
-            libraries=libraries,
-            library_dirs=library_dirs,
-            target_lang=lang,
-        )
-        if self.compiler.exe_extension is not None:
-            prog = prog + self.compiler.exe_extension
-        self.temp_files.append(prog)
-        return (src, obj, prog)
-    def _clean(self, *filenames):
-        if not filenames:
-            filenames = self.temp_files
-            self.temp_files = []
-        log.info("removing: %s", ' '.join(filenames))
-        for filename in filenames:
-            try:
-                os.remove(filename)
-            except OSError:
-                pass
-    # XXX these ignore the dry-run flag: what to do, what to do? even if
-    # you want a dry-run build, you still need some sort of configuration
-    # info.  My inclination is to make it up to the real config command to
-    # consult 'dry_run', and assume a default (minimal) configuration if
-    # true.  The problem with trying to do it here is that you'd have to
-    # return either true or false from all the 'try' methods, neither of
-    # which is correct.
-    # XXX need access to the header search path and maybe default macros.
-    def try_cpp(self, body=None, headers=None, include_dirs=None, lang="c"):
-        """Construct a source file from 'body' (a string containing lines
-        of C/C++ code) and 'headers' (a list of header files to include)
-        and run it through the preprocessor.  Return true if the
-        preprocessor succeeded, false if there were any errors.
-        ('body' probably isn't of much use, but what the heck.)
-        """
-        from distutils.ccompiler import CompileError
-        self._check_compiler()
-        ok = True
-        try:
-            self._preprocess(body, headers, include_dirs, lang)
-        except CompileError:
-            ok = False
-        self._clean()
-        return ok
-    def search_cpp(self, pattern, body=None, headers=None, include_dirs=None, lang="c"):
-        """Construct a source file (just like 'try_cpp()'), run it through
-        the preprocessor, and return true if any line of the output matches
-        'pattern'.  'pattern' should either be a compiled regex object or a
-        string containing a regex.  If both 'body' and 'headers' are None,
-        preprocesses an empty file -- which can be useful to determine the
-        symbols the preprocessor and compiler set by default.
-        """
-        self._check_compiler()
-        src, out = self._preprocess(body, headers, include_dirs, lang)
-        if isinstance(pattern, str):
-            pattern = re.compile(pattern)
-        with open(out) as file:
-            match = False
-            while True:
-                line = file.readline()
-                if line == '':
-                    break
-                if pattern.search(line):
-                    match = True
-                    break
-        self._clean()
-        return match
-    def try_compile(self, body, headers=None, include_dirs=None, lang="c"):
-        """Try to compile a source file built from 'body' and 'headers'.
-        Return true on success, false otherwise.
-        """
-        from distutils.ccompiler import CompileError
-        self._check_compiler()
-        try:
-            self._compile(body, headers, include_dirs, lang)
-            ok = True
-        except CompileError:
-            ok = False
-        log.info(ok and "success!" or "failure.")
-        self._clean()
-        return ok
-    def try_link(
-        self,
-        body,
-        headers=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-        lang="c",
-    ):
-        """Try to compile and link a source file, built from 'body' and
-        'headers', to executable form.  Return true on success, false
-        otherwise.
-        """
-        from distutils.ccompiler import CompileError, LinkError
-        self._check_compiler()
-        try:
-            self._link(body, headers, include_dirs, libraries, library_dirs, lang)
-            ok = True
-        except (CompileError, LinkError):
-            ok = False
-        log.info(ok and "success!" or "failure.")
-        self._clean()
-        return ok
-    def try_run(
-        self,
-        body,
-        headers=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-        lang="c",
-    ):
-        """Try to compile, link to an executable, and run a program
-        built from 'body' and 'headers'.  Return true on success, false
-        otherwise.
-        """
-        from distutils.ccompiler import CompileError, LinkError
-        self._check_compiler()
-        try:
-            src, obj, exe = self._link(
-                body, headers, include_dirs, libraries, library_dirs, lang
-            )
-            self.spawn([exe])
-            ok = True
-        except (CompileError, LinkError, DistutilsExecError):
-            ok = False
-        log.info(ok and "success!" or "failure.")
-        self._clean()
-        return ok
-    # -- High-level methods --------------------------------------------
-    # (these are the ones that are actually likely to be useful
-    # when implementing a real-world config command!)
-    def check_func(
-        self,
-        func,
-        headers=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-        decl=0,
-        call=0,
-    ):
-        """Determine if function 'func' is available by constructing a
-        source file that refers to 'func', and compiles and links it.
-        If everything succeeds, returns true; otherwise returns false.
-        The constructed source file starts out by including the header
-        files listed in 'headers'.  If 'decl' is true, it then declares
-        'func' (as "int func()"); you probably shouldn't supply 'headers'
-        and set 'decl' true in the same call, or you might get errors about
-        a conflicting declarations for 'func'.  Finally, the constructed
-        'main()' function either references 'func' or (if 'call' is true)
-        calls it.  'libraries' and 'library_dirs' are used when
-        linking.
-        """
-        self._check_compiler()
-        body = []
-        if decl:
-            body.append("int %s ();" % func)
-        body.append("int main () {")
-        if call:
-            body.append("  %s();" % func)
-        else:
-            body.append("  %s;" % func)
-        body.append("}")
-        body = "\n".join(body) + "\n"
-        return self.try_link(body, headers, include_dirs, libraries, library_dirs)
-    def check_lib(
-        self,
-        library,
-        library_dirs=None,
-        headers=None,
-        include_dirs=None,
-        other_libraries=[],
-    ):
-        """Determine if 'library' is available to be linked against,
-        without actually checking that any particular symbols are provided
-        by it.  'headers' will be used in constructing the source file to
-        be compiled, but the only effect of this is to check if all the
-        header files listed are available.  Any libraries listed in
-        'other_libraries' will be included in the link, in case 'library'
-        has symbols that depend on other libraries.
-        """
-        self._check_compiler()
-        return self.try_link(
-            "int main (void) { }",
-            headers,
-            include_dirs,
-            [library] + other_libraries,
-            library_dirs,
-        )
-    def check_header(self, header, include_dirs=None, library_dirs=None, lang="c"):
-        """Determine if the system header file named by 'header_file'
-        exists and can be found by the preprocessor; return true if so,
-        false otherwise.
-        """
-        return self.try_cpp(
-            body="/* No body */", headers=[header], include_dirs=include_dirs
-        )
-def dump_file(filename, head=None):
-    """Dumps a file content into log.info.
-    If head is not None, will be dumped before the file content.
-    """
-    if head is None:
-        log.info('%s', filename)
-    else:
-        log.info(head)
-    file = open(filename)
-    try:
-        log.info(file.read())
-    finally:
-        file.close()

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py DELETED Viewed

@@ -1,120 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-import pickle
-import torch
-from fvcore.common.checkpoint import Checkpointer
-from torch.nn.parallel import DistributedDataParallel
-import detectron2.utils.comm as comm
-from detectron2.utils.file_io import PathManager
-from .c2_model_loading import align_and_update_state_dicts
-class DetectionCheckpointer(Checkpointer):
-    """
-    Same as :class:`Checkpointer`, but is able to:
-    1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models.
-    2. correctly load checkpoints that are only available on the master worker
-    """
-    def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
-        is_main_process = comm.is_main_process()
-        super().__init__(
-            model,
-            save_dir,
-            save_to_disk=is_main_process if save_to_disk is None else save_to_disk,
-            **checkpointables,
-        )
-        self.path_manager = PathManager
-    def load(self, path, *args, **kwargs):
-        need_sync = False
-        if path and isinstance(self.model, DistributedDataParallel):
-            logger = logging.getLogger(__name__)
-            path = self.path_manager.get_local_path(path)
-            has_file = os.path.isfile(path)
-            all_has_file = comm.all_gather(has_file)
-            if not all_has_file[0]:
-                raise OSError(f"File {path} not found on main worker.")
-            if not all(all_has_file):
-                logger.warning(
-                    f"Not all workers can read checkpoint {path}. "
-                    "Training may fail to fully resume."
-                )
-                # TODO: broadcast the checkpoint file contents from main
-                # worker, and load from it instead.
-                need_sync = True
-            if not has_file:
-                path = None  # don't load if not readable
-        ret = super().load(path, *args, **kwargs)
-        if need_sync:
-            logger.info("Broadcasting model states from main worker ...")
-            self.model._sync_params_and_buffers()
-        return ret
-    def _load_file(self, filename):
-        if filename.endswith(".pkl"):
-            with PathManager.open(filename, "rb") as f:
-                data = pickle.load(f, encoding="latin1")
-            if "model" in data and "__author__" in data:
-                # file is in Detectron2 model zoo format
-                self.logger.info("Reading a file from '{}'".format(data["__author__"]))
-                return data
-            else:
-                # assume file is from Caffe2 / Detectron1 model zoo
-                if "blobs" in data:
-                    # Detection models have "blobs", but ImageNet models don't
-                    data = data["blobs"]
-                data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
-                return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
-        elif filename.endswith(".pyth"):
-            # assume file is from pycls; no one else seems to use the ".pyth" extension
-            with PathManager.open(filename, "rb") as f:
-                data = torch.load(f)
-            assert (
-                "model_state" in data
-            ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'."
-            model_state = {
-                k: v
-                for k, v in data["model_state"].items()
-                if not k.endswith("num_batches_tracked")
-            }
-            return {"model": model_state, "__author__": "pycls", "matching_heuristics": True}
-        loaded = super()._load_file(filename)  # load native pth checkpoint
-        if "model" not in loaded:
-            loaded = {"model": loaded}
-        return loaded
-    def _load_model(self, checkpoint):
-        if checkpoint.get("matching_heuristics", False):
-            self._convert_ndarray_to_tensor(checkpoint["model"])
-            # convert weights by name-matching heuristics
-            checkpoint["model"] = align_and_update_state_dicts(
-                self.model.state_dict(),
-                checkpoint["model"],
-                c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
-            )
-        # for non-caffe2 models, use standard ways to load it
-        incompatible = super()._load_model(checkpoint)
-        model_buffers = dict(self.model.named_buffers(recurse=False))
-        for k in ["pixel_mean", "pixel_std"]:
-            # Ignore missing key message about pixel_mean/std.
-            # Though they may be missing in old checkpoints, they will be correctly
-            # initialized from config anyway.
-            if k in model_buffers:
-                try:
-                    incompatible.missing_keys.remove(k)
-                except ValueError:
-                    pass
-        for k in incompatible.unexpected_keys[:]:
-            # Ignore unexpected keys about cell anchors. They exist in old checkpoints
-            # but now they are non-persistent buffers and will not be in new checkpoints.
-            if "anchor_generator.cell_anchors" in k:
-                incompatible.unexpected_keys.remove(k)
-        return incompatible

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py DELETED Viewed

@@ -1,101 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-from torch.nn import functional as F
-from detectron2.structures import Instances, ROIMasks
-# perhaps should rename to "resize_instance"
-def detector_postprocess(
-    results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5
-):
-    """
-    Resize the output instances.
-    The input images are often resized when entering an object detector.
-    As a result, we often need the outputs of the detector in a different
-    resolution from its inputs.
-    This function will resize the raw outputs of an R-CNN detector
-    to produce outputs according to the desired output resolution.
-    Args:
-        results (Instances): the raw outputs from the detector.
-            `results.image_size` contains the input image resolution the detector sees.
-            This object might be modified in-place.
-        output_height, output_width: the desired output resolution.
-    Returns:
-        Instances: the resized output from the model, based on the output resolution
-    """
-    if isinstance(output_width, torch.Tensor):
-        # This shape might (but not necessarily) be tensors during tracing.
-        # Converts integer tensors to float temporaries to ensure true
-        # division is performed when computing scale_x and scale_y.
-        output_width_tmp = output_width.float()
-        output_height_tmp = output_height.float()
-        new_size = torch.stack([output_height, output_width])
-    else:
-        new_size = (output_height, output_width)
-        output_width_tmp = output_width
-        output_height_tmp = output_height
-    scale_x, scale_y = (
-        output_width_tmp / results.image_size[1],
-        output_height_tmp / results.image_size[0],
-    )
-    results = Instances(new_size, **results.get_fields())
-    if results.has("pred_boxes"):
-        output_boxes = results.pred_boxes
-    elif results.has("proposal_boxes"):
-        output_boxes = results.proposal_boxes
-    else:
-        output_boxes = None
-    assert output_boxes is not None, "Predictions must contain boxes!"
-    output_boxes.scale(scale_x, scale_y)
-    output_boxes.clip(results.image_size)
-    results = results[output_boxes.nonempty()]
-    if results.has("pred_masks"):
-        if isinstance(results.pred_masks, ROIMasks):
-            roi_masks = results.pred_masks
-        else:
-            # pred_masks is a tensor of shape (N, 1, M, M)
-            roi_masks = ROIMasks(results.pred_masks[:, 0, :, :])
-        results.pred_masks = roi_masks.to_bitmasks(
-            results.pred_boxes, output_height, output_width, mask_threshold
-        ).tensor  # TODO return ROIMasks/BitMask object in the future
-    if results.has("pred_keypoints"):
-        results.pred_keypoints[:, :, 0] *= scale_x
-        results.pred_keypoints[:, :, 1] *= scale_y
-    return results
-def sem_seg_postprocess(result, img_size, output_height, output_width):
-    """
-    Return semantic segmentation predictions in the original resolution.
-    The input images are often resized when entering semantic segmentor. Moreover, in same
-    cases, they also padded inside segmentor to be divisible by maximum network stride.
-    As a result, we often need the predictions of the segmentor in a different
-    resolution from its inputs.
-    Args:
-        result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W),
-            where C is the number of classes, and H, W are the height and width of the prediction.
-        img_size (tuple): image size that segmentor is taking as input.
-        output_height, output_width: the desired output resolution.
-    Returns:
-        semantic segmentation prediction (Tensor): A tensor of the shape
-            (C, output_height, output_width) that contains per-pixel soft predictions.
-    """
-    result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1)
-    result = F.interpolate(
-        result, size=(output_height, output_width), mode="bilinear", align_corners=False
-    )[0]
-    return result

spaces/AzumaSeren100/XuanShen-Bert-VITS2/webui.py DELETED Viewed

@@ -1,136 +0,0 @@
-import sys, os
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-import logging
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-logger = logging.getLogger(__name__)
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-net_g = None
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-    del word2ph
-    assert bert.shape[-1] == len(phone)
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, phone, tone, language
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
-    global net_g
-    bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
-    with torch.no_grad():
-        x_tst=phones.to(device).unsqueeze(0)
-        tones=tones.to(device).unsqueeze(0)
-        lang_ids=lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        return audio
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
-    with torch.no_grad():
-        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
-    return "Success", (hps.data.sampling_rate, audio)
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # parser.add_argument("-m", "--model", default="./logs/dxl/G21200.pth", help="path of your model")
-    parser.add_argument("-mn", "--model_name", default="dxl", help="path of your model")
-    parser.add_argument("-m", "--model", default="null", help="path of your model")
-    parser.add_argument("-c", "--config", default="./configs/config.json", help="path of your config file")
-    parser.add_argument("--share", default=True, help="make link public")
-    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config)
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-    model_path = args.model
-    if not os.path.exists(model_path) or model_path == "null":
-        model_path = utils.latest_checkpoint_path(os.path.join("./logs/",args.model_name), "G_*.pth")
-    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                text = gr.TextArea(label="Text", placeholder="Input Text Here",
-                                      value="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。")
-                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
-                sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.1, label='SDP Ratio')
-                noise_scale = gr.Slider(minimum=0.1, maximum=2, value=0.6, step=0.1, label='Noise Scale')
-                noise_scale_w = gr.Slider(minimum=0.1, maximum=2, value=0.8, step=0.1, label='Noise Scale W')
-                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.1, label='Length Scale')
-                btn = gr.Button("Generate!", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-        btn.click(tts_fn,
-                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
-                outputs=[text_output, audio_output])
-    webbrowser.open("http://127.0.0.1:7860")
-    app.launch(share=args.share)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/queue.py DELETED Viewed

@@ -1,22 +0,0 @@
-import collections
-from ..packages import six
-from ..packages.six.moves import queue
-if six.PY2:
-    # Queue is imported for side effects on MS Windows. See issue #229.
-    import Queue as _unused_module_Queue  # noqa: F401
-class LifoQueue(queue.Queue):
-    def _init(self, _):
-        self.queue = collections.deque()
-    def _qsize(self, len=len):
-        return len(self.queue)
-    def _put(self, item):
-        self.queue.append(item)
-    def _get(self):
-        return self.queue.pop()

spaces/BigSalmon/MaskSeveralAtOnce/app.py DELETED Viewed

@@ -1,52 +0,0 @@
-import torch
-import streamlit as st
-import numpy as np
-import pandas as pd
-import os
-import torch
-import torch.nn as nn
-from transformers.activations import get_activation
-from transformers import AutoTokenizer
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-tokenizer = AutoTokenizer.from_pretrained("roberta-large")
-model = AutoModelForMaskedLM.from_pretrained("BigSalmon/FormalRobertaLincoln")
-#model = AutoModelForMaskedLM.from_pretrained("BigSalmon/MrLincolnBerta")
-model2 = AutoModelForMaskedLM.from_pretrained("roberta-base")
-with st.expander('BigSalmon/FormalRobertaa'):
-  with st.form(key='my_form'):
-    prompt = st.text_area(label='Enter Text. Put <mask> where you want the model to fill in the blank. You can use more than one at a time.')
-    submit_button = st.form_submit_button(label='Submit')
-    if submit_button:
-      a_list = []
-      token_ids = tokenizer.encode(prompt, return_tensors='pt')
-      token_ids_tk = tokenizer.tokenize(prompt, return_tensors='pt')
-      masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
-      masked_pos = [mask.item() for mask in masked_position ]
-      with torch.no_grad():
-        output = model(token_ids)
-      last_hidden_state = output[0].squeeze()
-      for mask_index in masked_pos:
-        mask_hidden_state = last_hidden_state[mask_index]
-        idx = torch.topk(mask_hidden_state, k=100, dim=0)[1]
-        words = [tokenizer.decode(i.item()).strip() for i in idx]
-        st.text_area(label = 'Infill:', value=words)
-with st.expander('roberta-base result'):
-  token_ids = tokenizer.encode(prompt, return_tensors='pt')
-  token_ids_tk = tokenizer.tokenize(prompt, return_tensors='pt')
-  masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
-  masked_pos = [mask.item() for mask in masked_position ]
-  with torch.no_grad():
-    output = model2(token_ids)
-    last_hidden_state = output[0].squeeze()
-    for mask_index in masked_pos:
-      mask_hidden_state = last_hidden_state[mask_index]
-      idx = torch.topk(mask_hidden_state, k=100, dim=0)[1]
-      words = [tokenizer.decode(i.item()).strip() for i in idx]
-      st.text_area(label = 'Infill:', value=words)

spaces/Billyosoro/ESRGAN/README.md DELETED Viewed

@@ -1,34 +0,0 @@
----
-title: Real ESRGAN
-emoji: 🏃
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-# Configuration
-`title`: _string_
-Display title for the Space
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/CVMX-jaca-tonos/Identificar-lenguas-y-frases/app.py DELETED Viewed

@@ -1,219 +0,0 @@
-from io import BytesIO
-from typing import Tuple
-import wave
-import gradio as gr
-import numpy as np
-from pydub.audio_segment import AudioSegment
-import requests
-from os.path import exists
-from stt import Model
-import torch
-from transformers import pipeline
-import librosa
-import torchaudio
-from speechbrain.pretrained import EncoderClassifier
-UI_STRINGS = {
-    "title": {
-        "es": "Reconocimiento de Dictado en Chatino, Mixteco, Totonaco y Español",
-        "en": "Speech recognition in Chatino, Mixtec, Totonac and Spanish",
-    },
-    "description": {
-        "es": "Una demo de identificar frases del español y de tres lenguas indígenas de México, y proveer el texto de cada una",
-        "en": "A demo of identifying phrases in Spanish and three Mexican indigenous languages, and providing transcripts of each",
-    },
-    "article": {
-        "es":  "La identificación de lenguas usa el modelo"
-                " [lang-id-commonlanguage-ecapa de Speechbrain](https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa)"
-                " y aquí se supone que si la lengua no es español, debe ser la lengua indígena del contexto."
-                "\n\n"
-                "Chatino: Prueba de dictado a texto para el chatino de la sierra (Quiahije) "
-                " usando [el modelo entrenado por Bülent Özden](https://coqui.ai/chatino/bozden/v1.0.0)"
-                " con [los datos recopilados por Hilaria Cruz y sus colaboradores](https://gorilla.linguistlist.org/code/ctp/)."
-                "\n\n"
-                "Mixteco: Prueba de dictado a texto para el mixteco de Yoloxochitl,"
-                " usando [el modelo entrenado por Josh Meyer](https://coqui.ai/mixtec/jemeyer/v1.0.0/)"
-                " con [los datos recopilados por Rey Castillo y sus colaboradores](https://www.openslr.org/89)."
-                " \n\n"
-                "Totonaco: Prueba de dictado a texto para el totonaco de la sierra,"
-                " usando [el modelo entrenado por Bülent Özden](https://coqui.ai/totonac/bozden/v1.0.0)"
-                " con [los datos recopilados por Osbel López Francisco y sus colaboradores](https://www.openslr.org/107)."
-                " \n\n"
-                "Los ejemplos vienen del proyecto [DEMCA](https://demca.mesolex.org/) de Jonathan Amith. "
-                " Esta demo es basada en la de [Ukraniano](https://huggingface.co/spaces/robinhad/ukrainian-stt).",
-        "en": "The language identification uses the model"
-                " [lang-id-commonlanguage-ecapa from Speechbrain](https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa)"
-                " and here it is assumed that if the language is not Spanish, it must be the indigenous language of the context."
-                "\n\n"
-                "Chatino: Test of speech-to-text for Highland Chatino (Quiahije) "
-                " using [the model trained by Bülent Özden](https://coqui.ai/chatino/bozden/v1.0.0)"
-                " with [the data compiled by Hilaria Cruz and collaborators](https://gorilla.linguistlist.org/code/ctp/)."
-                "\n\n"
-                "Mixtec: Test of speech-to-text for Yoloxochitl Mixtec,"
-                " using [the model trained by Josh Meyer](https://coqui.ai/mixtec/jemeyer/v1.0.0/)"
-                " with [the data compiled by Rey Castillo and collaborators](https://www.openslr.org/89)."
-                "\n\n"
-                "Totonac: Test of speech-to-text for Highland Totonac,"
-                " using [the model trained by Bülent Özden](https://coqui.ai/chatino/bozden/v1.0.0)"
-                " with [the data compiled by Osbel López Francisco and collaborators](https://www.openslr.org/107)."
-                "\n\n"
-                "The examples come from the Jonathan Amith's [DEMCA](https://demca.mesolex.org/) project. "
-                " This demo is based on the one for [Ukrainian](https://huggingface.co/spaces/robinhad/ukrainian-stt).",
-    },
-    "languages": {
-        "mixteco": {
-            "es": "mixteco",
-            "en": "Mixtec",
-        },
-        "chatino": {
-            "es": "chatino",
-            "en": "Chatino",
-        },
-        "totonaco": {
-            "es": "totonaco",
-            "en": "Totonac",
-        },
-        "español": {
-            "es": "español",
-            "en": "Spanish",
-        },
-        "inglés": {
-            "es": "inglés",
-            "en": "English",
-        }
-    },
-    "labels": {
-        "target": {
-            "es": "Lengua principal",
-            "en": "Primary language",
-        },
-        "input": {
-            "es": "Audio",
-            "en": "Audio",
-        },
-        "output": {
-            "es": "Resultado",
-            "en": "Result",
-        }
-    }
-}
-# initialize language ID model
-lang_classifier = EncoderClassifier.from_hparams(
-    source="speechbrain/lang-id-commonlanguage_ecapa",
-    savedir="pretrained_models/lang-id-commonlanguage_ecapa"
-)
-# download STT models
-model_info = {
-    "mixteco": ("https://coqui.gateway.scarf.sh/mixtec/jemeyer/v1.0.0/model.tflite", "mixtec.tflite"),
-    "chatino": ("https://coqui.gateway.scarf.sh/chatino/bozden/v1.0.0/model.tflite", "chatino.tflite"),
-    "totonaco": ("https://coqui.gateway.scarf.sh/totonac/bozden/v1.0.0/model.tflite", "totonac.tflite"),
-    "español": ("jonatasgrosman/wav2vec2-large-xlsr-53-spanish", "spanish_xlsr"),
-    "inglés": ("facebook/wav2vec2-large-robust-ft-swbd-300h", "english_xlsr"),
-}
-def load_hf_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"):
-    return pipeline("automatic-speech-recognition", model=model_path)
-def load_coqui_models(language):
-    model_path, file_name = model_info.get(language, ("", ""))
-    if not exists(file_name):
-        print(f"Downloading {model_path}")
-        r = requests.get(model_path, allow_redirects=True)
-        with open(file_name, 'wb') as file:
-            file.write(r.content)
-    else:
-        print(f"Found {file_name}. Skipping download...")
-    return Model(file_name)
-STT_MODELS = {lang: load_hf_model(model_info[lang][0]) for lang in ("español",)}
-for lang in ('mixteco', 'chatino', 'totonaco'):
-    STT_MODELS[lang] = load_coqui_models(lang)
-def client(audio_data: np.array, sample_rate: int, default_lang: str):
-    output_audio = _convert_audio(audio_data, sample_rate)
-    waveform, _ = torchaudio.load(output_audio)
-    out_prob, score, index, text_lab = lang_classifier.classify_batch(waveform)
-    text_lab = text_lab[0]
-    output_audio.seek(0)
-    fin = wave.open(output_audio, 'rb')
-    coqui_audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
-    output_audio.seek(0)
-    hf_audio, _ = librosa.load(output_audio)
-    fin.close()
-    print(default_lang, text_lab)
-    if text_lab == 'Spanish':
-        text_lab = UI_STRINGS["languages"]['español'][ui_language]
-        asr_pipeline = STT_MODELS['español']
-        result = asr_pipeline(hf_audio, chunk_length_s=5, stride_length_s=1)['text']
-    else:
-        text_lab = UI_STRINGS["languages"][default_lang][ui_language]
-        ds = STT_MODELS[default_lang]
-        result = ds.stt(coqui_audio)
-    return f"{text_lab}: {result}"
-def stt(default_lang: str, audio: Tuple[int, np.array]):
-    sample_rate, audio = audio
-    recognized_result = client(audio, sample_rate, default_lang)
-    return recognized_result
-def _convert_audio(audio_data: np.array, sample_rate: int):
-    source_audio = BytesIO()
-    source_audio.write(audio_data)
-    source_audio.seek(0)
-    output_audio = BytesIO()
-    wav_file = AudioSegment.from_raw(
-        source_audio,
-        channels=1,
-        sample_width=2,
-        frame_rate=sample_rate
-    )
-    wav_file.set_frame_rate(16000).set_channels(1).export(output_audio, "wav", codec="pcm_s16le")
-    output_audio.seek(0)
-    return output_audio
-ui_language = 'es'
-iface = gr.Interface(
-    fn=stt,
-    inputs=[
-        gr.inputs.Radio(choices=("chatino", "mixteco", "totonaco"), default="mixteco", label=UI_STRINGS["labels"]["target"][ui_language]),
-        gr.inputs.Audio(type="numpy", label=UI_STRINGS["labels"]["input"][ui_language], source="microphone", optional=False),
-    ],
-    outputs=[
-        gr.outputs.Textbox(label=UI_STRINGS["labels"]["output"][ui_language]),
-    ],
-    title=UI_STRINGS["title"][ui_language],
-    theme="huggingface",
-    description=UI_STRINGS["description"][ui_language],
-    examples=[["mixteco", "ejemplos/espanol1-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"],
-            ["mixteco", "ejemplos/espanol2-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"],
-            ["mixteco", "ejemplos/mixteco1-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"],
-            ["mixteco", "ejemplos/mixteco2-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"],
-            ["totonaco", "ejemplos/totonaco1-Zongo_Botan_Acanthaceae-Justicia-spicigera_SLC388-IPN389_2018-07-26-i.wav"],
-            ["totonaco", "ejemplos/totonaco2-Zongo_Botan_Acanthaceae-Justicia-spicigera_SLC388-IPN389_2018-07-26-i.wav"]],
-    article=UI_STRINGS["article"][ui_language],
-)
-iface.launch()

spaces/CVPR/LIVE/thrust/dependencies/cub/cub/cmake/cub-config.cmake DELETED Viewed

@@ -1,62 +0,0 @@
-#
-# find_package(CUB) config file.
-#
-# Defines a CUB::CUB target that may be linked from user projects to include
-# CUB.
-if (TARGET CUB::CUB)
-  return()
-endif()
-function(_cub_declare_interface_alias alias_name ugly_name)
-  # 1) Only IMPORTED and ALIAS targets can be placed in a namespace.
-  # 2) When an IMPORTED library is linked to another target, its include
-  #    directories are treated as SYSTEM includes.
-  # 3) nvcc will automatically check the CUDA Toolkit include path *before* the
-  #    system includes. This means that the Toolkit CUB will *always* be used
-  #    during compilation, and the include paths of an IMPORTED CUB::CUB
-  #    target will never have any effect.
-  # 4) This behavior can be fixed by setting the property NO_SYSTEM_FROM_IMPORTED
-  #    on EVERY target that links to CUB::CUB. This would be a burden and a
-  #    footgun for our users. Forgetting this would silently pull in the wrong CUB!
-  # 5) A workaround is to make a non-IMPORTED library outside of the namespace,
-  #    configure it, and then ALIAS it into the namespace (or ALIAS and then
-  #    configure, that seems to work too).
-  add_library(${ugly_name} INTERFACE)
-  add_library(${alias_name} ALIAS ${ugly_name})
-endfunction()
-#
-# Setup targets
-#
-_cub_declare_interface_alias(CUB::CUB _CUB_CUB)
-# Strip out the 'cub/cmake/' from 'cub/cmake/cub-config.cmake':
-get_filename_component(_CUB_INCLUDE_DIR "../.." ABSOLUTE BASE_DIR "${CMAKE_CURRENT_LIST_DIR}")
-target_include_directories(_CUB_CUB INTERFACE "${_CUB_INCLUDE_DIR}")
-if (CUB_IGNORE_DEPRECATED_CPP_DIALECT OR
-    THRUST_IGNORE_DEPRECATED_CPP_DIALECT)
-  target_compile_definitions(_CUB_CUB INTERFACE "CUB_IGNORE_DEPRECATED_CPP_DIALECT")
-endif()
-if (CUB_IGNORE_DEPRECATED_CPP_11 OR
-    THRUST_IGNORE_DEPRECATED_CPP_11)
-  target_compile_definitions(_CUB_CUB INTERFACE "CUB_IGNORE_DEPRECATED_CPP_11")
-endif()
-if (CUB_IGNORE_DEPRECATED_COMPILER OR
-    THRUST_IGNORE_DEPRECATED_COMPILER)
-  target_compile_definitions(_CUB_CUB INTERFACE "CUB_IGNORE_DEPRECATED_COMPILER")
-endif()
-#
-# Standardize version info
-#
-set(CUB_VERSION ${${CMAKE_FIND_PACKAGE_NAME}_VERSION} CACHE INTERNAL "")
-set(CUB_VERSION_MAJOR ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_MAJOR} CACHE INTERNAL "")
-set(CUB_VERSION_MINOR ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_MINOR} CACHE INTERNAL "")
-set(CUB_VERSION_PATCH ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_PATCH} CACHE INTERNAL "")
-set(CUB_VERSION_TWEAK ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_TWEAK} CACHE INTERNAL "")
-set(CUB_VERSION_COUNT ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_COUNT} CACHE INTERNAL "")

spaces/CVPR/LIVE/thrust/thrust/iterator/iterator_adaptor.h DELETED Viewed

@@ -1,240 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-/*! \file thrust/iterator/iterator_adaptor.h
- *  \brief An iterator which adapts a base iterator
- */
-/*
- * (C) Copyright David Abrahams 2002.
- * (C) Copyright Jeremy Siek    2002.
- * (C) Copyright Thomas Witt    2002.
- *
- * Distributed under the Boost Software License, Version 1.0.
- * (See accompanying NOTICE file for the complete license)
- *
- * For more information, see http://www.boost.org
- */
-#pragma once
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_facade.h>
-#include <thrust/detail/use_default.h>
-#include <thrust/iterator/detail/iterator_adaptor_base.h>
-namespace thrust
-{
-/*! \addtogroup iterators
- *  \{
- */
-/*! \addtogroup fancyiterator Fancy Iterators
- *  \ingroup iterators
- *  \{
- */
-/*! \p iterator_adaptor is an iterator which adapts an existing type of iterator to create a new type of
- *  iterator. Most of Thrust's fancy iterators are defined via inheritance from \p iterator_adaptor.
- *  While composition of these existing Thrust iterators is often sufficient for expressing the desired
- *  functionality, it is occasionally more straightforward to derive from \p iterator_adaptor directly.
- *
- *  To see how to use \p iterator_adaptor to create a novel iterator type, let's examine how to use it to
- *  define \p repeat_iterator, a fancy iterator which repeats elements from another range a given number of time:
- *
- *  \code
- *  #include <thrust/iterator/iterator_adaptor.h>
- *
- *  // derive repeat_iterator from iterator_adaptor
- *  template<typename Iterator>
- *    class repeat_iterator
- *      : public thrust::iterator_adaptor<
- *          repeat_iterator<Iterator>, // the first template parameter is the name of the iterator we're creating
- *          Iterator                   // the second template parameter is the name of the iterator we're adapting
- *                                     // we can use the default for the additional template parameters
- *        >
- *  {
- *    public:
- *      // shorthand for the name of the iterator_adaptor we're deriving from
- *      typedef thrust::iterator_adaptor<
- *        repeat_iterator<Iterator>,
- *        Iterator
- *      > super_t;
- *
- *      __host__ __device__
- *      repeat_iterator(const Iterator &x, int n) : super_t(x), begin(x), n(n) {}
- *
- *      // befriend thrust::iterator_core_access to allow it access to the private interface below
- *      friend class thrust::iterator_core_access;
- *
- *    private:
- *      // repeat each element of the adapted range n times
- *      unsigned int n;
- *
- *      // used to keep track of where we began
- *      const Iterator begin;
- *
- *      // it is private because only thrust::iterator_core_access needs access to it
- *      __host__ __device__
- *      typename super_t::reference dereference() const
- *      {
- *        return *(begin + (this->base() - begin) / n);
- *      }
- *  };
- *  \endcode
- *
- *  Except for the first two, \p iterator_adaptor's template parameters are optional. When omitted, or when the
- *  user specifies \p thrust::use_default in its place, \p iterator_adaptor will use a default type inferred from \p Base.
- *
- *  \p iterator_adaptor's functionality is derived from and generally equivalent to \p boost::iterator_adaptor.
- *  The exception is Thrust's addition of the template parameter \p System, which is necessary to allow Thrust
- *  to dispatch an algorithm to one of several parallel backend systems.
- *
- *  \p iterator_adaptor is a powerful tool for creating custom iterators directly. However, the large set of iterator semantics which must be satisfied
- *  for algorithm compatibility can make \p iterator_adaptor difficult to use correctly. Unless you require the full expressivity of \p iterator_adaptor,
- *  consider building a custom iterator through composition of existing higher-level fancy iterators instead.
- *
- *  Interested users may refer to <tt>boost::iterator_adaptor</tt>'s documentation for further usage examples.
- */
-template<typename Derived,
-         typename Base,
-         typename Value      = use_default,
-         typename System     = use_default,
-         typename Traversal  = use_default,
-         typename Reference  = use_default,
-         typename Difference = use_default>
-  class iterator_adaptor:
-    public detail::iterator_adaptor_base<
-      Derived, Base, Value, System, Traversal, Reference, Difference
-    >::type
-{
-  /*! \cond
-   */
-    friend class thrust::iterator_core_access;
-  protected:
-    typedef typename detail::iterator_adaptor_base<
-        Derived, Base, Value, System, Traversal, Reference, Difference
-    >::type super_t;
-  /*! \endcond
-   */
-  public:
-    /*! \p iterator_adaptor's default constructor does nothing.
-     */
-    __host__ __device__
-    iterator_adaptor(){}
-    /*! This constructor copies from a given instance of the \p Base iterator.
-     */
-    __thrust_exec_check_disable__
-    __host__ __device__
-    explicit iterator_adaptor(Base const& iter)
-      : m_iterator(iter)
-    {}
-    /*! The type of iterator this \p iterator_adaptor's \p adapts.
-     */
-    typedef Base       base_type;
-    /*! \cond
-     */
-    typedef typename super_t::reference reference;
-    typedef typename super_t::difference_type difference_type;
-    /*! \endcond
-     */
-    /*! \return A \p const reference to the \p Base iterator this \p iterator_adaptor adapts.
-     */
-    __host__ __device__
-    Base const& base() const
-    { return m_iterator; }
-  protected:
-    /*! \return A \p const reference to the \p Base iterator this \p iterator_adaptor adapts.
-     */
-    __host__ __device__
-    Base const& base_reference() const
-    { return m_iterator; }
-    /*! \return A mutable reference to the \p Base iterator this \p iterator_adaptor adapts.
-     */
-    __host__ __device__
-    Base& base_reference()
-    { return m_iterator; }
-    /*! \cond
-     */
-  private: // Core iterator interface for iterator_facade
-    __thrust_exec_check_disable__
-    __host__ __device__
-    typename iterator_adaptor::reference dereference() const
-    { return *m_iterator; }
-    __thrust_exec_check_disable__
-    template<typename OtherDerived, typename OtherIterator, typename V, typename S, typename T, typename R, typename D>
-    __host__ __device__
-    bool equal(iterator_adaptor<OtherDerived, OtherIterator, V, S, T, R, D> const& x) const
-    { return m_iterator == x.base(); }
-    __thrust_exec_check_disable__
-    __host__ __device__
-    void advance(typename iterator_adaptor::difference_type n)
-    {
-      // XXX statically assert on random_access_traversal_tag
-      m_iterator += n;
-    }
-    __thrust_exec_check_disable__
-    __host__ __device__
-    void increment()
-    { ++m_iterator; }
-    __thrust_exec_check_disable__
-    __host__ __device__
-    void decrement()
-    {
-      // XXX statically assert on bidirectional_traversal_tag
-      --m_iterator;
-    }
-    __thrust_exec_check_disable__
-    template<typename OtherDerived, typename OtherIterator, typename V, typename S, typename T, typename R, typename D>
-    __host__ __device__
-    typename iterator_adaptor::difference_type distance_to(iterator_adaptor<OtherDerived, OtherIterator, V, S, T, R, D> const& y) const
-    { return y.base() - m_iterator; }
-  private:
-    Base m_iterator;
-    /*! \endcond
-     */
-}; // end iterator_adaptor
-/*! \} // end fancyiterators
- */
-/*! \} // end iterators
- */
-} // end thrust

spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/README.md DELETED Viewed

@@ -1,61 +0,0 @@
-![Alt Text](http://spoter.signlanguagerecognition.com/img/GitHub_banner.png)
-> by **[Matyáš Boháček](https://github.com/matyasbohacek)** and **[Marek Hrúz](https://github.com/mhruz)**, University of West Bohemia <br>
-> Should you have any questions or inquiries, feel free to contact us [here](mailto:[email protected]).
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-pose-based-transformer-for-word-level/sign-language-recognition-on-lsa64)](https://paperswithcode.com/sota/sign-language-recognition-on-lsa64?p=sign-pose-based-transformer-for-word-level)
-Repository accompanying the [Sign Pose-based Transformer for Word-level Sign Language Recognition](https://openaccess.thecvf.com/content/WACV2022W/HADCV/html/Bohacek_Sign_Pose-Based_Transformer_for_Word-Level_Sign_Language_Recognition_WACVW_2022_paper.html) paper, where we present a novel architecture for word-level sign language recognition based on the Transformer model. We designed our solution with low computational cost in mind, since we see egreat potential in the usage of such recognition system on hand-held devices. We introduce multiple original augmentation techniques tailored for the task of sign language recognition and propose a unique normalization scheme based on sign language linguistics.
-![Alt Text](http://spoter.signlanguagerecognition.com/img/architecture_github.gif)
-## Get Started
-First, make sure to install all necessary dependencies using:
-```shell
-pip install -r requirements.txt
-```
-To train the model, simply specify the hyperparameters and run the following:
-```
-python -m train
-  --experiment_name [str; name of the experiment to name the output logs and plots]
-  --epochs [int; number of epochs]
-  --lr [float; learning rate]
-  --training_set_path [str; path to the csv file with training set's skeletal data]
-  --validation_set_path [str; path to the csv file with validation set's skeletal data]
-  --testing_set_path [str; path to the csv file with testing set's skeletal data]
-```
-If either the validation or testing sets' paths are left empty, these corresponding metrics will not be calculated. We also provide out-of-the box parameter to split the validation set as a desired split of the training set while preserving the label distribution for datasets without author-specified splits. These and many other specific hyperparameters with their descriptions can be found in the [train.py](https://github.com/matyasbohacek/spoter/blob/main/train.py) file. All of them are provided a default value we found to be working well in our experiments.
-## Data
-As SPOTER works on top of sequences of signers' skeletal data extracted from videos, we wanted to eliminate the computational demands of such annotation for each training run by pre-collecting this. For this reason and reproducibility, we are open-sourcing this data for WLASL100 and LSA64 datasets along with the repository. You can find the data [here](https://github.com/matyasbohacek/spoter/releases/tag/supplementary-data).
-![Alt Text](http://spoter.signlanguagerecognition.com/img/datasets_overview.gif)
-## License
-The **code** is published under the [Apache License 2.0](https://github.com/matyasbohacek/spoter/blob/main/LICENSE) which allows for both academic and commercial use if  relevant License and copyright notice is included, our work is cited and all changes are stated.
-The accompanying skeletal data of the [WLASL](https://arxiv.org/pdf/1910.11006.pdf) and [LSA64](https://core.ac.uk/download/pdf/76495887.pdf) datasets used for experiments are, however, shared under the [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license allowing only for non-commercial usage.
-## Citation
-If you find our work relevant, build upon it or compare your approaches with it, please cite our work as stated below:
-```
-@InProceedings{Bohacek_2022_WACV,
-    author    = {Boh\'a\v{c}ek, Maty\'a\v{s} and Hr\'uz, Marek},
-    title     = {Sign Pose-Based Transformer for Word-Level Sign Language Recognition},
-    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
-    month     = {January},
-    year      = {2022},
-    pages     = {182-191}
-}
-```

spaces/CVPR/WALT/mmdet/models/necks/hrfpn.py DELETED Viewed

@@ -1,102 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, caffe2_xavier_init
-from torch.utils.checkpoint import checkpoint
-from ..builder import NECKS
-@NECKS.register_module()
-class HRFPN(nn.Module):
-    """HRFPN (High Resolution Feature Pyramids)
-    paper: `High-Resolution Representations for Labeling Pixels and Regions
-    <https://arxiv.org/abs/1904.04514>`_.
-    Args:
-        in_channels (list): number of channels for each branch.
-        out_channels (int): output channels of feature pyramids.
-        num_outs (int): number of output stages.
-        pooling_type (str): pooling for generating feature pyramids
-            from {MAX, AVG}.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-        with_cp  (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        stride (int): stride of 3x3 convolutional layers
-    """
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs=5,
-                 pooling_type='AVG',
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 with_cp=False,
-                 stride=1):
-        super(HRFPN, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)
-        self.num_outs = num_outs
-        self.with_cp = with_cp
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.reduction_conv = ConvModule(
-            sum(in_channels),
-            out_channels,
-            kernel_size=1,
-            conv_cfg=self.conv_cfg,
-            act_cfg=None)
-        self.fpn_convs = nn.ModuleList()
-        for i in range(self.num_outs):
-            self.fpn_convs.append(
-                ConvModule(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    stride=stride,
-                    conv_cfg=self.conv_cfg,
-                    act_cfg=None))
-        if pooling_type == 'MAX':
-            self.pooling = F.max_pool2d
-        else:
-            self.pooling = F.avg_pool2d
-    def init_weights(self):
-        """Initialize the weights of module."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                caffe2_xavier_init(m)
-    def forward(self, inputs):
-        """Forward function."""
-        assert len(inputs) == self.num_ins
-        outs = [inputs[0]]
-        for i in range(1, self.num_ins):
-            outs.append(
-                F.interpolate(inputs[i], scale_factor=2**i, mode='bilinear'))
-        out = torch.cat(outs, dim=1)
-        if out.requires_grad and self.with_cp:
-            out = checkpoint(self.reduction_conv, out)
-        else:
-            out = self.reduction_conv(out)
-        outs = [out]
-        for i in range(1, self.num_outs):
-            outs.append(self.pooling(out, kernel_size=2**i, stride=2**i))
-        outputs = []
-        for i in range(self.num_outs):
-            if outs[i].requires_grad and self.with_cp:
-                tmp_out = checkpoint(self.fpn_convs[i], outs[i])
-            else:
-                tmp_out = self.fpn_convs[i](outs[i])
-            outputs.append(tmp_out)
-        return tuple(outputs)