Update parquet files (step 68 of 397)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/aicolors/__init__.py

@@ -1,30 +0,0 @@
-import fake_useragent
-import requests
-import json
-from .typings import AiColorsResponse
-
-
-class Completion:
-    @staticmethod
-    def create(
-        query: str = "",
-    ) -> AiColorsResponse:
-        headers = {
-            "authority": "jsuifmbqefnxytqwmaoy.functions.supabase.co",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.5",
-            "cache-control": "no-cache",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": fake_useragent.UserAgent().random,
-        }
-
-        json_data = {"query": query}
-
-        url = "https://jsuifmbqefnxytqwmaoy.functions.supabase.co/chatgpt"
-        request = requests.post(url, headers=headers, json=json_data, timeout=30)
-        data = request.json().get("text").get("content")
-        json_data = json.loads(data.replace("\n  ", ""))
-
-        return AiColorsResponse(**json_data)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fl Depth Of Field Plugin For After Effects Free A Must-Have for Any Motion Designer.md

@@ -1,100 +0,0 @@
-<br />
-<h1>Fl Depth Of Field Plugin For After Effects: A Review</h1>
-<p>If you are looking for a way to add realistic and cinematic depth of field effects to your 3D footage in After Effects, you may want to check out Fl Depth Of Field Plugin. This plugin is designed to move depth of field and out of focus generation to post-production, saving you time and resources from rendering them in your 3D app. In this article, we will review the features, pros and cons of Fl Depth Of Field Plugin and see how it can help you create stunning visuals.</p>
- <h2>Introduction</h2>
-<p>Fl Depth Of Field Plugin is a plugin for Adobe After Effects that allows you to create high-quality camera blurs with the flexibility of 2D post-processing. It is developed by Frischluft, a company that specializes in lens effects for computer graphics. According to their website, "The key aspect during the development of these filters was to match the real thing as good as possible."</p>
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-<p>Depth of field is a phenomenon that occurs in real optical devices, such as cameras, where objects that are closer or farther away from the focal point appear blurred, while objects at the focal point appear sharp. This effect is used in photography and film as a style element, to draw attention to certain subjects, create a sense of depth and realism, or evoke a mood or atmosphere.</p>
-<p>However, generating depth of field effects in computer graphics can be challenging and time-consuming, as it usually requires ray tracing techniques that increase rendering times considerably. Fl Depth Of Field Plugin solves this problem by generating depth of field effects fast as a post-process, using a depth buffer for its calculations. It can also create out of focus effects without depth information, using a constant blur radius over the entire image.</p>
-<p>Fl Depth Of Field Plugin is not the only plugin that offers depth of field effects for After Effects. There are other plugins, such as DOF PRO, that also claim to provide photorealistic depth of field effects. However, Fl Depth Of Field Plugin has some advantages over other plugins, such as its ability to simulate different lens apertures, its highlights and brightness boost features, and its background distortion option.</p>
- <h2>Features of Fl Depth Of Field Plugin</h2>
-<h3>Depth of Field</h3>
-<p>The main feature of Fl Depth Of Field Plugin is its depth of field effect, which blurs pixels based on their depth value. To use this effect, you need a depth buffer for your 3D footage, which is an image that stores the distance information for each pixel. You can either render a depth buffer in your 3D app or use a plugin like ZbornToy to generate one in After Effects.</p>
-<p>Once you have a depth buffer, you can apply Fl Depth Of Field Plugin to your footage layer and adjust the parameters according to your needs. You can control the focal point, the focal range, the blur amount, the blur quality, and more. You can also adjust the lens aperture shape and size, which greatly defines the look of the blur.</p>
-<p>The lens aperture is the opening in the camera lens that controls how much light enters the camera. The shape and size of the aperture affect how the out-of-focus areas look like in an image. For example, a circular aperture produces circular bokeh (the aesthetic quality of the blur), while a hexagonal aperture produces hexagonal bokeh.</p>
-<p>Fl Depth Of Field Plugin allows you to simulate different kinds of real cameras by altering the lens aperture shape and size. You can choose from several presets or create your own custom shape using bezier curves. You can also animate the aperture shape and size over time for dynamic effects.</p>
-<p>How to use Fl Depth Of Field Plugin in After Effects<br />
-Fl Depth Of Field Plugin tutorial for After Effects beginners<br />
-Best settings for Fl Depth Of Field Plugin in After Effects<br />
-Fl Depth Of Field Plugin review and comparison with other plugins<br />
-Fl Depth Of Field Plugin download link and installation guide<br />
-Fl Depth Of Field Plugin alternatives and similar plugins<br />
-Fl Depth Of Field Plugin license and activation code<br />
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-Fl Depth Of Field Plugin vs native After Effects depth of field<br />
-How to create cinematic depth of field with Fl Plugin<br />
-How to animate depth of field with Fl Plugin in After Effects<br />
-How to adjust depth of field with Fl Plugin in After Effects<br />
-How to blur background with Fl Plugin in After Effects<br />
-How to add bokeh effects with Fl Plugin in After Effects<br />
-How to control focus with Fl Plugin in After Effects<br />
-How to optimize performance with Fl Plugin in After Effects<br />
-How to fix errors and bugs with Fl Plugin in After Effects<br />
-How to customize depth of field with Fl Plugin in After Effects<br />
-How to use expressions with Fl Plugin in After Effects<br />
-How to use masks with Fl Plugin in After Effects<br />
-How to use 3D layers with Fl Plugin in After Effects<br />
-How to use cameras with Fl Plugin in After Effects<br />
-How to use lights with Fl Plugin in After Effects<br />
-How to use presets with Fl Plugin in After Effects<br />
-How to use keyframes with Fl Plugin in After Effects<br />
-How to use motion blur with Fl Plugin in After Effects<br />
-How to use color grading with Fl Plugin in After Effects<br />
-How to use noise reduction with Fl Plugin in After Effects<br />
-How to use lens distortion with Fl Plugin in After Effects<br />
-How to use chromatic aberration with Fl Plugin in After Effects<br />
-How to use vignette with Fl Plugin in After Effects<br />
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-How to use glow with Fl Plugin in After Effects<br />
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-How to use depth map with Fl Plugin in After Effects<br />
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-How to use depth information with Fl Plugin in After Effects<br />
-How to use depth values with Fl Plugin in After Effects</p>
- <h3>Out of Focus</h3>
-<p>The other feature of Fl Depth Of Field Plugin is its out of focus effect, which creates a blur with a constant radius over the entire image. This effect does not require a depth buffer and can be used as a complement or an alternative to the depth of field effect.</p>
-<p>The out of focus effect can be useful when you want to create a shallow depth of field look without having accurate depth information or when you want to add some extra blur to your footage for artistic reasons. You can control the blur amount, quality, threshold, gamma correction, and more.</p>
-<p>One unique feature of the out of focus effect is that it allows you to use a custom image as a lens aperture instead of generating one. This means that you can use any image layer in your composition as an aperture texture and create interesting shapes and patterns in your blur. For example, you can use an image of a star or a heart as an aperture texture and create star-shaped or heart-shaped bokeh.</p>
-<p>Another unique feature of the out of focus effect is that it offers background distortion for semi-transparent areas. This means that when you look through a blurred object in your footage, such as glass or smoke, the background behind it will be distorted due to refraction. This effect is subtle but adds realism and believability to your comp.</p>
- <h3>Highlights and Brightness Boost</h3>
-<p>A common characteristic of real camera blurs is that very bright image parts are predominant when being out-of-focus. This is especially noticeable in highlights or light sources that appear as bright spots or discs in blurred areas. However, most graphic formats cut off bright parts above a certain threshold, resulting in dull or flat-looking blurs.</p>
-<p>To solve this problem, Fl Depth Of Field Plugin offers two features: highlights and brightness boost. The highlights feature allows you to simulate realistic highlights in out-of-focus areas by selecting parts that are supposed to be brighter than normal and giving them an extra boost. You can control the threshold, amount, saturation, tint color, blend mode, and more.</p>
-<p>The brightness boost feature allows you to select parts that are supposed to be brighter than normal but are not necessarily highlights (such as reflections or glows) and give them an extra boost as well. You can control the threshold, amount, gamma correction, saturation limit, blend mode, and more.</p>
- <h2>Pros and Cons of Fl Depth Of Field Plugin</h2>
-<h3>Pros</h3>
-<ul>
-<li>It is fast and easy to use compared to rendering depth-of-field effects in 3D apps.</li>
-<li>It produces high-quality and realistic results that match real cameras.</li>
-<li>It offers flexible and customizable options for different styles and scenarios.</li>
-</ul>
- <h3>Cons</h3>
-<ul>
-<li>It requires a depth buffer for depth-of-field effect which may not be available or accurate for some footage.</li>
-<li>It may not work well with motion blur or complex scenes with overlapping objects or transparency.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Fl Depth Of Field Plugin For After Effects is a powerful plugin that allows you to create realistic and cinematic depth-of-field effects fast as a post-process. It has many features that make it stand out from other plugins, such as its ability to simulate different lens apertures, its highlights and brightness boost features, and its background distortion option. It is suitable for anyone who wants to add some extra polish and realism to their 3D footage without spending too much time or resources on rendering. If you are interested in trying out Fl Depth Of Field Plugin, from their official website and see for yourself how it can improve your visuals.</p>
- <h2>FAQs</h2>
-<h4>What is the difference between depth of field and out of focus effects?</h4>
-<p>Depth of field effects blur pixels based on their distance from the focal point, creating a realistic and cinematic look. Out of focus effects blur pixels with a constant radius over the entire image, creating a simple and artistic look.</p>
- <h4>How can I get a depth buffer for my 3D footage?</h4>
-<p>You can either render a depth buffer in your 3D app or use a plugin like ZbornToy to generate one in After Effects.</p>
- <h4>How can I create custom lens apertures for my blur effects?</h4>
-<p>You can either use the built-in presets or create your own custom shape using bezier curves in the depth of field effect. You can also use any image layer in your composition as an aperture texture in the out of focus effect.</p>
- <h4>How can I simulate realistic highlights in out of focus areas?</h4>
-<p>You can use the highlights and brightness boost features to select and enhance bright parts of the image that are supposed to be brighter than normal when being out of focus.</p>
- <h4>How can I add background distortion for semi-transparent areas?</h4>
-<p>You can use the background distortion option in the out of focus effect to distort the background behind blurred objects such as glass or smoke.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Chemdraw 12 Crack ((NEW)).md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bhop Go The Ultimate Guide to Jumping and Surfing Online.md

@@ -1,183 +0,0 @@
-
-<h1>Bhop Go: A Fun and Challenging Parkour Game for Mobile Devices</h1>
- <p>If you are looking for a game that can test your skills, speed, and reflexes, you might want to try Bhop Go. Bhop Go is a mobile game that lets you experience the thrill and challenge of bhop, a skill that involves jumping faster in first-person shooter and simulation games. In this article, we will tell you everything you need to know about Bhop Go, including what it is, how to play it, and how to download it.</p>
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- <h2>What is Bhop Go?</h2>
- <p>Bhop Go is a game developed by Shockapp, a studio that specializes in creating casual and action games for mobile devices. Bhop Go is one of their most popular games, with over 5 million downloads on Google Play Store and 4.8 stars rating on App Store. But what exactly is bhop, and why is it so fun and addictive?</p>
- <h3>The history and origin of bhop</h3>
- <p>Bhop stands for bunny hop, a term that refers to a technique that allows players to move faster in first-person shooter and simulation games. Bhop was first discovered in the late 1990s in Quake, a game that used the same engine as Half-Life and Counter-Strike. By turning left and right (strafing) while jumping, players could gain more speed and momentum than running normally. This gave them an advantage in combat, movement, and exploration.</p>
- <p>Bhop soon became a popular skill among gamers, especially in the Counter-Strike series. Many players practiced bhop to improve their movement skills and compete with other players. However, bhop was also considered a form of cheating by some developers, who tried to patch it or limit it in their games. For example, Valve introduced a stamina system in Counter-Strike: Source that prevented players from bhopping continuously.</p>
- <p>Despite these attempts, bhop remained a beloved skill among many gamers, who continued to find ways to perform it or create mods that enabled it. Bhop also spawned a subculture of speedrunners, map makers, and community servers that focused on bhop as a form of art and entertainment. Some examples of these are KZ (climbing), surf (sliding), and bhop (jumping) servers.</p>
- <h3>The features and gameplay of Bhop Go</h3>
- <p>Bhop Go is a game that brings the essence of bhop to mobile devices. It is a parkour game that challenges players to jump on blocks and go as far as possible. It is not a realistic simulation of bhop, but rather a simplified and stylized version that is easy to play but hard to master.</p>
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- <p>Bhop Go has many features that make it fun and engaging, such as:</p>
-<ul>
-<li>Multiplayer mode: You can play online with friends or strangers in different maps and modes.</li>
-<li>Single player mode: You can play offline without internet connection in various maps.</li>
-<li>Collecting loot: You can find trampolines, bounce pads, knives, weapons, skins, gloves, and other items on maps.</li>
-<li>Jumping bounce pads: You can use these pads to boost your speed and height.</li>
-<li>Moving 3D obstacles: You can avoid or interact with these obstacles that can slow you down or help you.</li>
-<li>Racing for world records: You can compete with other players for the best time and distance on each map.</li>
-<li>Customizing your character: You can change your appearance, outfit, and accessories.</li>
-<li>Creating your own maps: You can design and share your own maps with other players.</li>
-</ul>
-<p>The gameplay of Bhop Go is simple but challenging. You have to tap the screen to jump and tilt your device to strafe. You have to time your jumps and strafes correctly to maintain your speed and direction. You also have to avoid falling off the blocks or hitting the obstacles. The game requires skill, concentration, and practice to master.</p>
- <h3>The benefits and challenges of bhop</h3>
- <p>Bhop is not only a fun and exciting game, but also a skill that can benefit you in many ways. Some of the benefits of bhop are:</p>
-<ul>
-<li>It improves your hand-eye coordination and reaction time.</li>
-<li>It enhances your spatial awareness and navigation skills.</li>
-<li>It stimulates your brain and creativity.</li>
-<li>It boosts your confidence and self-esteem.</li>
-<li>It relieves your stress and boredom.</li>
-</ul>
-<p>However, bhop also has some challenges that you need to overcome. Some of the challenges of bhop are:</p>
-<ul>
-<li>It can be frustrating and discouraging at first.</li>
-<li>It can be addictive and time-consuming.</li>
-<li>It can cause motion sickness or eye strain.</li>
-<li>It can be hard to find suitable games or servers that support bhop.</li>
-<li>It can be seen as cheating or unfair by some players or developers.</li>
-</ul>
-<p>Therefore, you need to balance your bhop experience with moderation, patience, and respect. You also need to find the right game or platform that suits your preferences and goals.</p>
- <h2>How to Play Bhop Go?</h2>
- <p>Now that you know what Bhop Go is and why it is fun and beneficial, you might want to try it yourself. But how do you play Bhop Go? Here are some tips and instructions that can help you get started.</p>
- <h3>The controls and interface of Bhop Go</h3>
- <p>The controls and interface of Bhop Go are simple and intuitive. You can see them in the screenshot below.</p>
- <img src="(^i^)" alt="Bhop Go controls and interface">
- <p>The controls are as follows:</p>
-<ul>
-<li>To jump, tap the screen with your right thumb.</li>
-<li>To strafe left or right, tilt your device left or right with your left hand.</li>
-<li>To look around, swipe the screen with your left thumb.</li>
-<li>To use items, tap the icons on the bottom left corner of the screen.</li>
-</ul>
-<p>The interface shows the following information:</p>
-<ul>
-<li>Your speed in units per second (UPS).</li>
-<li>Your distance in meters (M).</li>
-<li>Your time in seconds (S).</li>
-<li>Your rank among other players (R).</li>
-<li>Your health points (HP).</li>
-</ul>
- <h3>The modes and maps of Bhop Go</h3>
- <p>Bhop Go has two modes: multiplayer and single player. In multiplayer mode, you can play online with other players in different maps and modes. You can choose from casual, competitive, deathmatch, race, or custom modes. You can also chat with other players, join clans, or create private rooms.</p>
- <p>In single player mode, you can play offline without internet connection in various maps. You can choose from easy, medium, hard, or extreme maps. You can also create your own maps using the map editor.</p>
- <p>Bhop Go has over 100 maps that you can play on. Each map has a different theme, layout, difficulty, and length. Some maps are based on real locations, such as Paris, Tokyo, New York, or Dubai. Some maps are inspired by other games, such as Minecraft, Portal, or Half-Life. Some maps are original creations by the developers or the community.</p>
- <h3>The tips and tricks for bhop</h3>
- <p>Bhop is a skill that requires practice and patience to master. However, there are some tips and tricks that can help you improve your bhop performance. Here are some of them:  </p>
- <ul>
-<li>Practice on easy maps first before moving on to harder ones.</li>
-<li>Adjust your sensitivity and tilt settings to suit your preference.</li>
-<li>Use headphones or earphones to hear the sound cues for jumping and landing.</li>
-<li>Aim for smooth and consistent jumps rather than fast and erratic ones.</li>
-<li>Use the bounce pads and trampolines to gain more speed and height.</li>
-<li>Use the knives and weapons to slash or shoot the blocks or obstacles.</li>
-<li>Collect the loot and skins to customize your character and items.</li>
-<li>Watch videos or streams of other players to learn from their techniques and strategies.</li>
-<li>Have fun and enjoy the game!</li>
-</ul>
- <h2>How to Download Bhop Go?</h2>
- <p>Bhop Go is a free game that you can download and play on your mobile device. However, you need to make sure that your device meets the requirements and compatibility of the game. You also need to follow the steps and sources for downloading the game. Here are some details that can help you with that.</p>
- <h3>The requirements and compatibility of Bhop Go</h3>
- <p>Bhop Go is a game that requires a decent device to run smoothly and properly. The minimum requirements for Bhop Go are:</p>
-<ul>
-<li>Android 4.4 or higher, or iOS 10.0 or higher.</li>
-<li>At least 1 GB of RAM.</li>
-<li>At least 100 MB of free storage space.</li>
-<li>A stable internet connection (for multiplayer mode).</li>
-</ul>
-<p>Bhop Go is compatible with most mobile devices, such as smartphones, tablets, or iPads. However, some devices may experience lag, glitches, or crashes due to hardware or software issues. If you encounter any problems with Bhop Go, you can try the following solutions:</p>
-<ul>
-<li>Update your device's operating system and apps.</li>
-<li>Clear your device's cache and memory.</li>
-<li>Restart your device or the game.</li>
-<li>Contact the developer's support team or report a bug.</li>
-</ul>
- <h3>The steps and sources for downloading Bhop Go</h3>
- <p>Bhop Go is a game that you can download from official and trusted sources, such as Google Play Store or App Store. You can also download it from third-party websites or platforms, but you need to be careful of malware or viruses. Here are the steps and sources for downloading Bhop Go:</p>
- <table>
-<tr><th>Source</th><th>Steps</th></tr>
-<tr><td>Google Play Store</td><td><ol><li>Open Google Play Store on your Android device.</li><li>Search for "Bhop Go" in the search bar.</li><li>Select the game from the results and tap "Install".</li><li>Wait for the game to download and install on your device.</li><li>Tap "Open" to launch the game and enjoy!</li></ol></td></tr>
-<tr><td>App Store</td><td><ol><li>Open App Store on your iOS device.</li><li>Search for "Bhop Go" in the search bar.</li><li>Select the game from the results and tap "Get".</li><li>Enter your Apple ID password or use Touch ID or Face ID to confirm.</li><li>Wait for the game to download and install on your device.</li><li>Tap "Open" to launch the game and enjoy!</li></ol></td></tr>
-<tr><td>Aptoide</td><td><ol><li>Open Aptoide on your Android device. If you don't have it, you can download it from .</li><li>Search for "Bhop Go" in the search bar.</li><li>Select the game from the results and tap "Install".</li><li>Wait for the game to download and install on your device.</li><li>Tap "Open" to launch the game and enjoy!</li></ol></td></tr>
-<tr><td>TweakBox</td><td><ol><li>Open TweakBox on your iOS device. If you don't have it, you can download it from .</li><li>Search for "Bhop Go" in the search bar.</li><li>Select the game from the results and tap "Install".</li><li>Wait for the game to download and install on your device.</li><li>Tap "Open" to launch the game and enjoy!</li></ol></td></tr>
-</table>
- <h3>The alternatives and similar games to Bhop Go</h3>
- <p>Bhop Go is a great game that can provide you with hours of fun and challenge. However, if you want to try something different or explore other options, there are some alternatives and similar games to Bhop Go that you can check out. Here are some of them:</p>
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-<tr><th>Name</th><th>Description</th></tr>
-<tr><td>Bunny Hop League</td><td>A game that combines bhop with soccer. You can play online with other players in different stadiums and score goals by jumping and kicking the ball.</td></tr>
-<tr><td>Bhop Jump</td><td>A game that simulates bhop in a realistic way. You can play on different maps and modes, such as speedrun, freestyle, or multiplayer. You can also customize your character and settings.</td></tr>
-<tr><td>Bhop Pro</td><td>A game that teaches you how to bhop in a step-by-step way. You can learn the basics and advanced techniques of bhop, such as strafing, air strafing, or sync. You can also practice on various maps and modes.</td></tr>
-<tr><td>Surf VPN</td><td>A game that lets you surf on ramps and slides in a 3D environment. You can play online with other players or offline on different maps. You can also collect coins and skins to upgrade your character.</td></tr>
-<tr><td>Flip Runner</td><td>A game that lets you perform parkour stunts and flips in a cityscape. You can run, jump, flip, and slide on buildings, cars, or objects. You can also unlock new characters and locations.</td></tr>
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- <h2>Conclusion</h2>
- <p>Bhop Go is a game that can provide you with a fun and challenging experience of bhop, a skill that involves jumping faster in first-person shooter and simulation games. Bhop Go has many features and gameplay options that make it engaging and enjoyable, such as multiplayer mode, single player mode, collecting loot, jumping bounce pads, moving 3D obstacles, racing for world records, customizing your character, and creating your own maps.</p>
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- <p>Bhop Go is a great game that can satisfy your bhop cravings and curiosity. However, if you want to try something different or explore other options, there are some alternatives and similar games to Bhop Go that you can check out.</p>
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- <p>Here are some frequently asked questions about Bhop Go:</p>
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-<li>Bhop and surf are both skills that involve moving faster in first-person shooter and simulation games. However, bhop is about jumping on blocks while surf is about sliding on ramps.</li>
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-<li>You can report a bug or a cheater in Bhop Go by contacting the developer's support team via email or social media.</li>
-<li>You can join a clan or a private room in Bhop Go by tapping the clan or room icon on the main menu or the multiplayer mode.</li>
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-<h3>Aim for Princess Towers First</h3>
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-<p>Do you love playing mobile games and using mobile apps, but hate paying for them? If yes, then you should try <strong>5play</strong>, a platform where you can find thousands of free APK and Mod APK games and apps for Android devices. In this article, we will show you how to download and install 5play on your device, how to use it to find and download free apps and games, and what are the benefits and drawbacks of using it.</p>
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- <h3>Is it legal to use 5play?</h3>
-<p>The legality of using 5play depends on your country or region's laws and regulations regarding downloading and using apps and games from unofficial sources. Some countries or regions may allow it, while others may prohibit it. You should always check your local laws and regulations before using 5play, and use it at your own risk.</p>
- <p>I hope you enjoyed reading this article and learned something new. If you have any questions or comments, please feel free to leave them below. Thank you for your time and attention.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/4Taps/SadTalker/src/facerender/sync_batchnorm/batchnorm.py

@@ -1,315 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : batchnorm.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
-    """sum over the first and last dimention"""
-    return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
-    """add new dementions at the front and the tail"""
-    return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
-        self._sync_master = SyncMaster(self._data_parallel_master)
-
-        self._is_parallel = False
-        self._parallel_id = None
-        self._slave_pipe = None
-
-    def forward(self, input):
-        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
-        if not (self._is_parallel and self.training):
-            return F.batch_norm(
-                input, self.running_mean, self.running_var, self.weight, self.bias,
-                self.training, self.momentum, self.eps)
-
-        # Resize the input to (B, C, -1).
-        input_shape = input.size()
-        input = input.view(input.size(0), self.num_features, -1)
-
-        # Compute the sum and square-sum.
-        sum_size = input.size(0) * input.size(2)
-        input_sum = _sum_ft(input)
-        input_ssum = _sum_ft(input ** 2)
-
-        # Reduce-and-broadcast the statistics.
-        if self._parallel_id == 0:
-            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
-        else:
-            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
-        # Compute the output.
-        if self.affine:
-            # MJY:: Fuse the multiplication for speed.
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
-        else:
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
-        # Reshape it.
-        return output.view(input_shape)
-
-    def __data_parallel_replicate__(self, ctx, copy_id):
-        self._is_parallel = True
-        self._parallel_id = copy_id
-
-        # parallel_id == 0 means master device.
-        if self._parallel_id == 0:
-            ctx.sync_master = self._sync_master
-        else:
-            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
-    def _data_parallel_master(self, intermediates):
-        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
-        # Always using same "device order" makes the ReduceAdd operation faster.
-        # Thanks to:: Tete Xiao (http://tetexiao.com/)
-        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
-        to_reduce = [i[1][:2] for i in intermediates]
-        to_reduce = [j for i in to_reduce for j in i]  # flatten
-        target_gpus = [i[1].sum.get_device() for i in intermediates]
-
-        sum_size = sum([i[1].sum_size for i in intermediates])
-        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
-        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
-        outputs = []
-        for i, rec in enumerate(intermediates):
-            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
-        return outputs
-
-    def _compute_mean_std(self, sum_, ssum, size):
-        """Compute the mean and standard-deviation with sum and square-sum. This method
-        also maintains the moving average on the master device."""
-        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
-        mean = sum_ / size
-        sumvar = ssum - sum_ * mean
-        unbias_var = sumvar / (size - 1)
-        bias_var = sumvar / size
-
-        self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
-        self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
-        return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
-    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
-    mini-batch.
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm1d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of size
-            `batch_size x num_features [x width]`
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C)` or :math:`(N, C, L)`
-        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 2 and input.dim() != 3:
-            raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
-    of 3d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm2d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, H, W)`
-        - Output: :math:`(N, C, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
-    of 4d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm3d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
-    or Spatio-temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x depth x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, D, H, W)`
-        - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 5:
-            raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)

spaces/4com/4com-license/app.py

@@ -1,94 +0,0 @@
-import gradio as gr
-
-with gr.Blocks() as demo:
-
-    gr.Markdown("""
-
-<h1><center>License</center></h1>
-
-Copyright (c) 2023 **4COM** and **OpenSkyML** organizations
-
-**CreativeML Open RAIL-M**
-dated September 1, 2023
-
-Section I: PREAMBLE
-
-Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
-
-Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
-
-In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
-
-Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
-
-This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
-
-NOW THEREFORE, You and Licensor agree as follows:
-
-1. Definitions
-
-- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
-- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
-- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
-- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
-- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
-- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
-- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
-- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
-- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
-- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
-- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
-- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
-
-Section II: INTELLECTUAL PROPERTY RIGHTS
-
-Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
-
-2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
-3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
-
-Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
-
-4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
-Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
-You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
-You must cause any modified files to carry prominent notices stating that You changed the files;
-You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
-You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
-5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
-6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
-
-Section IV: OTHER PROVISIONS
-
-7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or modify the Output of the Model based on updates. You shall undertake reasonable efforts to use the latest version of the Model.
-8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
-9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
-10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
-11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
-12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
-
-END OF TERMS AND CONDITIONS
-
-
-
-
-Attachment A
-
-Use Restrictions
-
-You agree not to use the Model or Derivatives of the Model:
-- In any way that violates any applicable national, federal, state, local or international law or regulation;
-- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
-- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
-- To generate or disseminate personal identifiable information that can be used to harm an individual;
-- To defame, disparage or otherwise harass others;
-- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
-- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
-- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
-- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
-- To provide medical advice and medical results interpretation;
-- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
-    
-    """)
-
-demo.launch()

spaces/52Hz/SRMNet_thesis/README.md

@@ -1,13 +0,0 @@
----
-title: SRMNet_Image_Restoration
-emoji: ✨
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 2.8.12
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/801artistry/RVC801/Applio-RVC-Fork/utils/i18n.py

@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
-    with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
-        language_list = json.load(f)
-    return language_list
-
-
-class I18nAuto:
-    def __init__(self, language=None):
-        if language in ["Auto", None]:
-            language = "es_ES"
-        if not os.path.exists(f"./i18n/{language}.json"):
-            language = "es_ES"
-            language = "es_ES"
-        self.language = language
-        # print("Use Language:", language)
-        self.language_map = load_language_list(language)
-
-    def __call__(self, key):
-        return self.language_map.get(key, key)
-
-    def print(self):
-      # print("Use Language:", self.language)
-        print("")

spaces/801artistry/RVC801/diffq/utils.py

@@ -1,37 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import inspect
-from typing import Optional, List
-
-
-def simple_repr(obj, attrs: Optional[List[str]] = None, overrides={}):
-    """
-    Return a simple representation string for `obj`.
-    If `attrs` is not None, it should be a list of attributes to include.
-    """
-    params = inspect.signature(obj.__class__).parameters
-    attrs_repr = []
-    if attrs is None:
-        attrs = params.keys()
-    for attr in attrs:
-        display = False
-        if attr in overrides:
-            value = overrides[attr]
-        elif hasattr(obj, attr):
-            value = getattr(obj, attr)
-        else:
-            continue
-        if attr in params:
-            param = params[attr]
-            if param.default is inspect._empty or value != param.default:
-                display = True
-        else:
-            display = True
-
-        if display:
-            attrs_repr.append(f"{attr}={value}")
-    return f"{obj.__class__.__name__}({','.join(attrs_repr)})"

spaces/AI-Zero-to-Hero/08-GR-Chatbot-Blenderbot/README.md

@@ -1,12 +0,0 @@
----
-title: 08 GR Chatbot Blenderbot
-emoji: 👀
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/scripts/__init__.py

@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.

spaces/AIZeroToHero/README/README.md

@@ -1,10 +0,0 @@
----
-title: 🧑🏻‍🤝‍🧑🏾Org-AI-Zero-To-Hero
-emoji: 🧑🏻‍🤝‍🧑🏾
-colorFrom: gray
-colorTo: pink
-sdk: static
-pinned: false
----
-
-AI Zero to Hero - Classroom Learning Examples  

spaces/ASJMO/freegpt/client/js/change-language.js

@@ -1,47 +0,0 @@
-document.addEventListener('DOMContentLoaded', fetchLanguages);  
-  
-async function fetchLanguages() {  
-    try {  
-        const [languagesResponse, currentLanguageResponse] = await Promise.all([  
-            fetch(`${url_prefix}/get-languages`),  
-            fetch(`${url_prefix}/get-locale`)  
-        ]);  
-  
-        const languages = await languagesResponse.json();  
-        const currentLanguage = await currentLanguageResponse.text();  
-  
-        const languageSelect = document.getElementById('language');  
-        languages.forEach(lang => {  
-            const option = document.createElement('option');  
-            option.value = lang;  
-            option.textContent = lang;  
-            languageSelect.appendChild(option);  
-        });  
-  
-        const savedLanguage = localStorage.getItem("language") || currentLanguage;  
-        setLanguageOnPageLoad(savedLanguage);  
-    } catch (error) {  
-        console.error("Failed to fetch languages or current language");  
-    }  
-}  
-  
-function setLanguageOnPageLoad(language) {  
-    document.getElementById("language").value = language;  
-}  
-  
-function changeLanguage(lang) {  
-    fetch(`${url_prefix}/change-language`, {  
-        method: "POST",  
-        headers: {  
-            "Content-Type": "application/json",  
-        },  
-        body: JSON.stringify({ language: lang }),  
-    }).then((response) => {  
-        if (response.ok) {  
-            localStorage.setItem("language", lang);  
-            location.reload();  
-        } else {  
-            console.error("Failed to change language");  
-        }  
-    });  
-}  

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/switchTheme.ts

@@ -1,10 +0,0 @@
-export function switchTheme() {
-	const { classList } = document.querySelector("html") as HTMLElement;
-	if (classList.contains("dark")) {
-		classList.remove("dark");
-		localStorage.theme = "light";
-	} else {
-		classList.add("dark");
-		localStorage.theme = "dark";
-	}
-}

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/login/callback/updateUser.ts

@@ -1,12 +0,0 @@
-import { authCondition, refreshSessionCookie } from "$lib/server/auth";
-import { collections } from "$lib/server/database";
-import { DEFAULT_SETTINGS } from "$lib/types/Settings";
-import { z } from "zod";
-import type { UserinfoResponse } from "openid-client";
-import type { Cookies } from "@sveltejs/kit";
-
-export async function updateUser(params: {
-	userData: UserinfoResponse;
-	locals: App.Locals;
-	cookies: Cookies;
-}) {}

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/You.py

@@ -1,40 +0,0 @@
-from __future__ import annotations
-
-import json
-
-from ..requests import StreamSession
-from ..typing import AsyncGenerator, Messages
-from .base_provider import AsyncGeneratorProvider, format_prompt
-
-
-class You(AsyncGeneratorProvider):
-    url = "https://you.com"
-    working = True
-    supports_gpt_35_turbo = True
-
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        timeout: int = 120,
-        **kwargs,
-    ) -> AsyncGenerator:
-        async with StreamSession(proxies={"https": proxy}, impersonate="chrome107", timeout=timeout) as session:
-            headers = {
-                "Accept": "text/event-stream",
-                "Referer": f"{cls.url}/search?fromSearchBar=true&tbm=youchat",
-            }
-            data = {"q": format_prompt(messages), "domain": "youchat", "chat": ""}
-            async with session.get(
-                f"{cls.url}/api/streamingSearch",
-                params=data,
-                headers=headers
-            ) as response:
-                response.raise_for_status()
-                start = b'data: {"youChatToken": '
-                async for line in response.iter_lines():
-                    if line.startswith(start):
-                        yield json.loads(line[len(start):-1])

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fileselectorbutton/FileChooserMethods.js

@@ -1,21 +0,0 @@
-export default {
-    setAccept(accept) {
-        this.childrenMap.fileChooser.setAccept(accept);
-        return this;
-    },
-
-    setMultiple(enabled) {
-        this.childrenMap.fileChooser.setMultiple(enabled);
-        return this;
-    },
-
-    loadFile(file, loaderType, key, cacheType, onComplete) {
-        this.childrenMap.fileChooser.loadFile(file, loaderType, key, cacheType, onComplete);
-        return this;
-    },
-
-    loadFilePromise(file, loaderType, key, cacheType) {
-        return this.childrenMap.fileChooser.loadFilePromise(file, loaderType, key, cacheType);
-    }
-
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/MergeStyle.js

@@ -1,33 +0,0 @@
-import DeepMerge from "../../../../../plugins/utils/object/DeepMerge";
-
-/*
-Priority of styles : name, $class, $type
-  1. name    (#name)
-  2. $class  (.class)
-  3. $type   (type)    
-*/
-
-var MergeStyle = function (data, styles) {
-    if (styles === undefined) {
-        return data;
-    }
-
-    if (data.hasOwnProperty('name')) {
-        DeepMerge(data, styles[`#${data.name}`]);
-    }
-
-    if (data.hasOwnProperty('$class')) {
-        var clasKeys = data.$class.split(' ');
-        for (var i = 0, cnt = clasKeys.length; i < cnt; i++) {
-            DeepMerge(data, styles[`.${clasKeys[i]}`]);
-        }
-    }
-
-    if (data.hasOwnProperty('$type')) {
-        DeepMerge(data, styles[data.$type]);
-    }
-
-    return data;
-}
-
-export default MergeStyle;

spaces/Agusbs98/automatic-ecg-diagnosis/nets/modules.py

@@ -1,33 +0,0 @@
-
-import os, sys
-from libs import *
-from .layers import *
-
-class LightSEModule(nn.Module):
-    def __init__(self, 
-        in_channels, 
-        reduction = 16, 
-    ):
-        super(LightSEModule, self).__init__()
-        self.pool = nn.AdaptiveAvgPool1d(1)
-
-        self.s_conv = DSConv1d(
-            in_channels, in_channels//reduction, 
-            kernel_size = 1, 
-        )
-        self.act_fn = nn.ReLU()
-        self.e_conv = DSConv1d(
-            in_channels//reduction, in_channels, 
-            kernel_size = 1, 
-        )
-
-    def forward(self, 
-        input, 
-    ):
-        attention_scores = self.pool(input)
-
-        attention_scores = self.s_conv(attention_scores)
-        attention_scores = self.act_fn(attention_scores)
-        attention_scores = self.e_conv(attention_scores)
-
-        return input*torch.sigmoid(attention_scores)

spaces/AkashKhamkar/Job_Search_Engine/app.py

@@ -1,56 +0,0 @@
-import streamlit as st
-import torch
-import pickle
-import time
-import pandas as pd
-from iteration_utilities import unique_everseen
-from sentence_transformers import util
-from loader import bi_encoder, cross_encoder, df, job_corpus_ecoded, job_corpus
-
-
-def jobsearch(query,df, top_k=100):
-  #print("Answer by NinjaBot : ")
-  ans = []
-  question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
-  hits = util.semantic_search(question_embedding, job_corpus_ecoded, top_k=top_k)
-  hits = hits[0]
-  cross_inp = [[query, job_corpus[hit['corpus_id']]] for hit in hits]
-  cross_scores = cross_encoder.predict(cross_inp)
-  for idx in range(len(cross_scores)):
-      hits[idx]['cross-score'] = cross_scores[idx]
-
-  hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
-  #indexes = []
-  search_result = []
-  for idx, hit in enumerate(hits[0:10]):
-      obj = {}
-      ans.append(job_corpus[hit['corpus_id']])
-      #indexes.append(job_corpus.index(job_corpus[hit['corpus_id']]))
-      obj['title'] = df.at[job_corpus.index(job_corpus[hit['corpus_id']]),'title']
-      obj['link'] = df.at[job_corpus.index(job_corpus[hit['corpus_id']]),'url']
-      search_result.append(obj)
-  final_search_result = list(unique_everseen(search_result))
-  return final_search_result
-  #return df.at[indexes[0],'title'],df.at[indexes[1],'title'],df.at[indexes[2],'title'],df.at[indexes[3],'title'],df.at[indexes[4],'title']
-  #return ans[0],ans[1],ans[2],ans[3],ans[4]
-
-def main():
-    if 'submitted' not in st.session_state:
-        st.session_state.submitted = False
-
-    def callback():
-      st.session_state.submitted = True
-
-    st.title('Job Search Engine 💼')
-    st.text("")
-    st.text("")
-    query = st.text_input('Enter your job query here ! ')
-    if (st.button("Search", on_click=callback) and query) :
-        with st.spinner('Fetching the best jobs for you!...'):
-            #time.sleep(10)
-            result = jobsearch(query, df)
-        #result = jobsearch(query, df)
-        st.success('NinjaBot : Here are a few suggestions')
-        #st.write(f"This is the query : {query}")  
-        st.write(result)
-main()

spaces/AliSaria/MilitarEye/README.md

@@ -1,13 +0,0 @@
----
-title: MilitarEye
-emoji: 🐨
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/生成函数注释.py

@@ -1,54 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
-        i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    if not fast_debug: 
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
-
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/libJPG/jpge.cpp

@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <[email protected]>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, [email protected], Jyrki Katajainen, [email protected], November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/生成函数注释.py

@@ -1,57 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-fast_debug = False
-
-
-def 生成函数注释(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
-        i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        print('[1] yield chatbot, history')
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[])   # 带超时倒计时
-
-            print('[2] end gpt req')
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            print('[3] yield chatbot, history')
-            yield chatbot, history, msg
-            print('[4] next')
-            if not fast_debug: time.sleep(2)
-
-    if not fast_debug: 
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, msg
-
-
-
-@CatchException
-def 批量生成函数注释(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
-
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 生成函数注释(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)

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

@@ -1,314 +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.
--->
-
-[[open-in-colab]]
-
-# Quicktour
-
-Diffusion models are trained to denoise random Gaussian noise step-by-step to generate a sample of interest, such as an image or audio. This has sparked a tremendous amount of interest in generative AI, and you have probably seen examples of diffusion generated images on the internet. 🧨 Diffusers is a library aimed at making diffusion models widely accessible to everyone.
-
-Whether you're a developer or an everyday user, this quicktour will introduce you to 🧨 Diffusers and help you get up and generating quickly! There are three main components of the library to know about:
-
-* The [`DiffusionPipeline`] is a high-level end-to-end class designed to rapidly generate samples from pretrained diffusion models for inference.
-* Popular pretrained [model](./api/models) architectures and modules that can be used as building blocks for creating diffusion systems.
-* Many different [schedulers](./api/schedulers/overview) - algorithms that control how noise is added for training, and how to generate denoised images during inference.
-
-The quicktour will show you how to use the [`DiffusionPipeline`] for inference, and then walk you through how to combine a model and scheduler to replicate what's happening inside the [`DiffusionPipeline`].
-
-<Tip>
-
-The quicktour is a simplified version of the introductory 🧨 Diffusers [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) to help you get started quickly. If you want to learn more about 🧨 Diffusers goal, design philosophy, and additional details about it's core API, check out the notebook!
-
-</Tip>
-
-Before you begin, make sure you have all the necessary libraries installed:
-
-```py
-# uncomment to install the necessary libraries in Colab
-#!pip install --upgrade diffusers accelerate transformers
-```
-
-- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) speeds up model loading for inference and training.
-- [🤗 Transformers](https://huggingface.co/docs/transformers/index) is required to run the most popular diffusion models, such as [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).
-
-## DiffusionPipeline
-
-The [`DiffusionPipeline`] is the easiest way to use a pretrained diffusion system for inference. It is an end-to-end system containing the model and the scheduler. You can use the [`DiffusionPipeline`] out-of-the-box for many tasks. Take a look at the table below for some supported tasks, and for a complete list of supported tasks, check out the [🧨 Diffusers Summary](./api/pipelines/overview#diffusers-summary) table.
-
-| **Task**                     | **Description**                                                                                              | **Pipeline**
-|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|
-| Unconditional Image Generation          | generate an image from Gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |
-| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |
-| Text-Guided Image-to-Image Translation     | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |
-| Text-Guided Image-Inpainting          | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |
-| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2img](./using-diffusers/depth2img) |
-
-Start by creating an instance of a [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
-You can use the [`DiffusionPipeline`] for any [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads) stored on the Hugging Face Hub.
-In this quicktour, you'll load the [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint for text-to-image generation.
-
-<Tip warning={true}>
-
-For [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) models, please carefully read the [license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) first before running the model. 🧨 Diffusers implements a [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) to prevent offensive or harmful content, but the model's improved image generation capabilities can still produce potentially harmful content.
-
-</Tip>
-
-Load the model with the [`~DiffusionPipeline.from_pretrained`] method:
-
-```python
->>> from diffusers import DiffusionPipeline
-
->>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-```
-
-The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components. You'll see that the Stable Diffusion pipeline is composed of the [`UNet2DConditionModel`] and [`PNDMScheduler`] among other things:
-
-```py
->>> pipeline
-StableDiffusionPipeline {
-  "_class_name": "StableDiffusionPipeline",
-  "_diffusers_version": "0.13.1",
-  ...,
-  "scheduler": [
-    "diffusers",
-    "PNDMScheduler"
-  ],
-  ...,
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
-```
-
-We strongly recommend running the pipeline on a GPU because the model consists of roughly 1.4 billion parameters.
-You can move the generator object to a GPU, just like you would in PyTorch:
-
-```python
->>> pipeline.to("cuda")
-```
-
-Now you can pass a text prompt to the `pipeline` to generate an image, and then access the denoised image. By default, the image output is wrapped in a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object.
-
-```python
->>> image = pipeline("An image of a squirrel in Picasso style").images[0]
->>> image
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_of_squirrel_painting.png"/>
-</div>
-
-Save the image by calling `save`:
-
-```python
->>> image.save("image_of_squirrel_painting.png")
-```
-
-### Local pipeline
-
-You can also use the pipeline locally. The only difference is you need to download the weights first:
-
-```bash
-!git lfs install
-!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
-```
-
-Then load the saved weights into the pipeline:
-
-```python
->>> pipeline = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
-```
-
-Now you can run the pipeline as you would in the section above.
-
-### Swapping schedulers
-
-Different schedulers come with different denoising speeds and quality trade-offs. The best way to find out which one works best for you is to try them out! One of the main features of 🧨 Diffusers is to allow you to easily switch between schedulers. For example, to replace the default [`PNDMScheduler`] with the [`EulerDiscreteScheduler`], load it with the [`~diffusers.ConfigMixin.from_config`] method:
-
-```py
->>> from diffusers import EulerDiscreteScheduler
-
->>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-```
-
-Try generating an image with the new scheduler and see if you notice a difference!
-
-In the next section, you'll take a closer look at the components - the model and scheduler - that make up the [`DiffusionPipeline`] and learn how to use these components to generate an image of a cat.
-
-## Models
-
-Most models take a noisy sample, and at each timestep it predicts the *noise residual* (other models learn to predict the previous sample directly or the velocity or [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)), the difference between a less noisy image and the input image. You can mix and match models to create other diffusion systems.
-
-Models are initiated with the [`~ModelMixin.from_pretrained`] method which also locally caches the model weights so it is faster the next time you load the model. For the quicktour, you'll load the [`UNet2DModel`], a basic unconditional image generation model with a checkpoint trained on cat images:
-
-```py
->>> from diffusers import UNet2DModel
-
->>> repo_id = "google/ddpm-cat-256"
->>> model = UNet2DModel.from_pretrained(repo_id)
-```
-
-To access the model parameters, call `model.config`:
-
-```py
->>> model.config
-```
-
-The model configuration is a 🧊 frozen 🧊 dictionary, which means those parameters can't be changed after the model is created. This is intentional and ensures that the parameters used to define the model architecture at the start remain the same, while other parameters can still be adjusted during inference.
-
-Some of the most important parameters are:
-
-* `sample_size`: the height and width dimension of the input sample.
-* `in_channels`: the number of input channels of the input sample.
-* `down_block_types` and `up_block_types`: the type of down- and upsampling blocks used to create the UNet architecture.
-* `block_out_channels`: the number of output channels of the downsampling blocks; also used in reverse order for the number of input channels of the upsampling blocks.
-* `layers_per_block`: the number of ResNet blocks present in each UNet block.
-
-To use the model for inference, create the image shape with random Gaussian noise. It should have a `batch` axis because the model can receive multiple random noises, a `channel` axis corresponding to the number of input channels, and a `sample_size` axis for the height and width of the image:
-
-```py
->>> import torch
-
->>> torch.manual_seed(0)
-
->>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
->>> noisy_sample.shape
-torch.Size([1, 3, 256, 256])
-```
-
-For inference, pass the noisy image to the model and a `timestep`. The `timestep` indicates how noisy the input image is, with more noise at the beginning and less at the end. This helps the model determine its position in the diffusion process, whether it is closer to the start or the end. Use the `sample` method to get the model output:
-
-```py
->>> with torch.no_grad():
-...     noisy_residual = model(sample=noisy_sample, timestep=2).sample
-```
-
-To generate actual examples though, you'll need a scheduler to guide the denoising process. In the next section, you'll learn how to couple a model with a scheduler.
-
-## Schedulers
-
-Schedulers manage going from a noisy sample to a less noisy sample given the model output - in this case, it is the `noisy_residual`.
-
-<Tip>
-
-🧨 Diffusers is a toolbox for building diffusion systems. While the [`DiffusionPipeline`] is a convenient way to get started with a pre-built diffusion system, you can also choose your own model and scheduler components separately to build a custom diffusion system.
-
-</Tip>
-
-For the quicktour, you'll instantiate the [`DDPMScheduler`] with it's [`~diffusers.ConfigMixin.from_config`] method:
-
-```py
->>> from diffusers import DDPMScheduler
-
->>> scheduler = DDPMScheduler.from_config(repo_id)
->>> scheduler
-DDPMScheduler {
-  "_class_name": "DDPMScheduler",
-  "_diffusers_version": "0.13.1",
-  "beta_end": 0.02,
-  "beta_schedule": "linear",
-  "beta_start": 0.0001,
-  "clip_sample": true,
-  "clip_sample_range": 1.0,
-  "num_train_timesteps": 1000,
-  "prediction_type": "epsilon",
-  "trained_betas": null,
-  "variance_type": "fixed_small"
-}
-```
-
-<Tip>
-
-💡 Notice how the scheduler is instantiated from a configuration. Unlike a model, a scheduler does not have trainable weights and is parameter-free!
-
-</Tip>
-
-Some of the most important parameters are:
-
-* `num_train_timesteps`: the length of the denoising process or in other words, the number of timesteps required to process random Gaussian noise into a data sample.
-* `beta_schedule`: the type of noise schedule to use for inference and training.
-* `beta_start` and `beta_end`: the start and end noise values for the noise schedule.
-
-To predict a slightly less noisy image, pass the following to the scheduler's [`~diffusers.DDPMScheduler.step`] method: model output, `timestep`, and current `sample`.
-
-```py
->>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample
->>> less_noisy_sample.shape
-```
-
-The `less_noisy_sample` can be passed to the next `timestep` where it'll get even less noisier! Let's bring it all together now and visualize the entire denoising process. 
-
-First, create a function that postprocesses and displays the denoised image as a `PIL.Image`:
-
-```py
->>> import PIL.Image
->>> import numpy as np
-
-
->>> def display_sample(sample, i):
-...     image_processed = sample.cpu().permute(0, 2, 3, 1)
-...     image_processed = (image_processed + 1.0) * 127.5
-...     image_processed = image_processed.numpy().astype(np.uint8)
-
-...     image_pil = PIL.Image.fromarray(image_processed[0])
-...     display(f"Image at step {i}")
-...     display(image_pil)
-```
-
-To speed up the denoising process, move the input and model to a GPU:
-
-```py
->>> model.to("cuda")
->>> noisy_sample = noisy_sample.to("cuda")
-```
-
-Now create a denoising loop that predicts the residual of the less noisy sample, and computes the less noisy sample with the scheduler:
-
-```py
->>> import tqdm
-
->>> sample = noisy_sample
-
->>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
-...     # 1. predict noise residual
-...     with torch.no_grad():
-...         residual = model(sample, t).sample
-
-...     # 2. compute less noisy image and set x_t -> x_t-1
-...     sample = scheduler.step(residual, t, sample).prev_sample
-
-...     # 3. optionally look at image
-...     if (i + 1) % 50 == 0:
-...         display_sample(sample, i + 1)
-```
-
-Sit back and watch as a cat is generated from nothing but noise! 😻
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/diffusion-quicktour.png"/>
-</div>
-
-## Next steps
-
-Hopefully you generated some cool images with 🧨 Diffusers in this quicktour! For your next steps, you can:
-
-* Train or finetune a model to generate your own images in the [training](./tutorials/basic_training) tutorial.
-* See example official and community [training or finetuning scripts](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) for a variety of use cases.
-* Learn more about loading, accessing, changing and comparing schedulers in the [Using different Schedulers](./using-diffusers/schedulers) guide.
-* Explore prompt engineering, speed and memory optimizations, and tips and tricks for generating higher quality images with the [Stable Diffusion](./stable_diffusion) guide.
-* Dive deeper into speeding up 🧨 Diffusers with guides on [optimized PyTorch on a GPU](./optimization/fp16), and inference guides for running [Stable Diffusion on Apple Silicon (M1/M2)](./optimization/mps) and [ONNX Runtime](./optimization/onnx).

spaces/Andy1621/uniformer_image_detection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py

@@ -1,51 +0,0 @@
-_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
-
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    bbox_head=dict(
-        norm_on_bbox=True,
-        centerness_on_reg=True,
-        dcn_on_last_conv=False,
-        center_sampling=True,
-        conv_bias=True,
-        loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
-    # training and testing settings
-    test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6)))
-
-# dataset settings
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-optimizer_config = dict(_delete_=True, grad_clip=None)
-
-lr_config = dict(warmup='linear')

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/freeze.py

@@ -1,97 +0,0 @@
-import sys
-from optparse import Values
-from typing import List
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.operations.freeze import freeze
-from pip._internal.utils.compat import stdlib_pkgs
-
-DEV_PKGS = {"pip", "setuptools", "distribute", "wheel"}
-
-
-class FreezeCommand(Command):
-    """
-    Output installed packages in requirements format.
-
-    packages are listed in a case-insensitive sorted order.
-    """
-
-    usage = """
-      %prog [options]"""
-    log_streams = ("ext://sys.stderr", "ext://sys.stderr")
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "-r",
-            "--requirement",
-            dest="requirements",
-            action="append",
-            default=[],
-            metavar="file",
-            help=(
-                "Use the order in the given requirements file and its "
-                "comments when generating output. This option can be "
-                "used multiple times."
-            ),
-        )
-        self.cmd_opts.add_option(
-            "-l",
-            "--local",
-            dest="local",
-            action="store_true",
-            default=False,
-            help=(
-                "If in a virtualenv that has global access, do not output "
-                "globally-installed packages."
-            ),
-        )
-        self.cmd_opts.add_option(
-            "--user",
-            dest="user",
-            action="store_true",
-            default=False,
-            help="Only output packages installed in user-site.",
-        )
-        self.cmd_opts.add_option(cmdoptions.list_path())
-        self.cmd_opts.add_option(
-            "--all",
-            dest="freeze_all",
-            action="store_true",
-            help=(
-                "Do not skip these packages in the output:"
-                " {}".format(", ".join(DEV_PKGS))
-            ),
-        )
-        self.cmd_opts.add_option(
-            "--exclude-editable",
-            dest="exclude_editable",
-            action="store_true",
-            help="Exclude editable package from output.",
-        )
-        self.cmd_opts.add_option(cmdoptions.list_exclude())
-
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        skip = set(stdlib_pkgs)
-        if not options.freeze_all:
-            skip.update(DEV_PKGS)
-
-        if options.excludes:
-            skip.update(options.excludes)
-
-        cmdoptions.check_list_path_option(options)
-
-        for line in freeze(
-            requirement=options.requirements,
-            local_only=options.local,
-            user_only=options.user,
-            paths=options.path,
-            isolated=options.isolated_mode,
-            skip=skip,
-            exclude_editable=options.exclude_editable,
-        ):
-            sys.stdout.write(line + "\n")
-        return SUCCESS

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/README.md

@@ -1,4 +0,0 @@
-# Read the docs:
-
-The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/).
-Documents in this directory are not meant to be read on github.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py

@@ -1,120 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.config import get_cfg
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator
-
-logger = logging.getLogger(__name__)
-
-
-class TestAnchorGenerator(unittest.TestCase):
-    def test_default_anchor_generator(self):
-        cfg = get_cfg()
-        cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
-        cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]]
-
-        anchor_generator = DefaultAnchorGenerator(cfg, [ShapeSpec(stride=4)])
-
-        # only the last two dimensions of features matter here
-        num_images = 2
-        features = {"stage3": torch.rand(num_images, 96, 1, 2)}
-        anchors = anchor_generator([features["stage3"]])
-        expected_anchor_tensor = torch.tensor(
-            [
-                [-32.0, -8.0, 32.0, 8.0],
-                [-16.0, -16.0, 16.0, 16.0],
-                [-8.0, -32.0, 8.0, 32.0],
-                [-64.0, -16.0, 64.0, 16.0],
-                [-32.0, -32.0, 32.0, 32.0],
-                [-16.0, -64.0, 16.0, 64.0],
-                [-28.0, -8.0, 36.0, 8.0],  # -28.0 == -32.0 + STRIDE (4)
-                [-12.0, -16.0, 20.0, 16.0],
-                [-4.0, -32.0, 12.0, 32.0],
-                [-60.0, -16.0, 68.0, 16.0],
-                [-28.0, -32.0, 36.0, 32.0],
-                [-12.0, -64.0, 20.0, 64.0],
-            ]
-        )
-
-        self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
-    def test_default_anchor_generator_centered(self):
-        # test explicit args
-        anchor_generator = DefaultAnchorGenerator(
-            sizes=[32, 64], aspect_ratios=[0.25, 1, 4], strides=[4]
-        )
-
-        # only the last two dimensions of features matter here
-        num_images = 2
-        features = {"stage3": torch.rand(num_images, 96, 1, 2)}
-        expected_anchor_tensor = torch.tensor(
-            [
-                [-30.0, -6.0, 34.0, 10.0],
-                [-14.0, -14.0, 18.0, 18.0],
-                [-6.0, -30.0, 10.0, 34.0],
-                [-62.0, -14.0, 66.0, 18.0],
-                [-30.0, -30.0, 34.0, 34.0],
-                [-14.0, -62.0, 18.0, 66.0],
-                [-26.0, -6.0, 38.0, 10.0],
-                [-10.0, -14.0, 22.0, 18.0],
-                [-2.0, -30.0, 14.0, 34.0],
-                [-58.0, -14.0, 70.0, 18.0],
-                [-26.0, -30.0, 38.0, 34.0],
-                [-10.0, -62.0, 22.0, 66.0],
-            ]
-        )
-
-        anchors = anchor_generator([features["stage3"]])
-        self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
-        anchors = torch.jit.script(anchor_generator)([features["stage3"]])
-        self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
-    def test_rrpn_anchor_generator(self):
-        cfg = get_cfg()
-        cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
-        cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]]
-        cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [0, 45]  # test single list[float]
-        anchor_generator = RotatedAnchorGenerator(cfg, [ShapeSpec(stride=4)])
-
-        # only the last two dimensions of features matter here
-        num_images = 2
-        features = {"stage3": torch.rand(num_images, 96, 1, 2)}
-        anchors = anchor_generator([features["stage3"]])
-        expected_anchor_tensor = torch.tensor(
-            [
-                [0.0, 0.0, 64.0, 16.0, 0.0],
-                [0.0, 0.0, 64.0, 16.0, 45.0],
-                [0.0, 0.0, 32.0, 32.0, 0.0],
-                [0.0, 0.0, 32.0, 32.0, 45.0],
-                [0.0, 0.0, 16.0, 64.0, 0.0],
-                [0.0, 0.0, 16.0, 64.0, 45.0],
-                [0.0, 0.0, 128.0, 32.0, 0.0],
-                [0.0, 0.0, 128.0, 32.0, 45.0],
-                [0.0, 0.0, 64.0, 64.0, 0.0],
-                [0.0, 0.0, 64.0, 64.0, 45.0],
-                [0.0, 0.0, 32.0, 128.0, 0.0],
-                [0.0, 0.0, 32.0, 128.0, 45.0],
-                [4.0, 0.0, 64.0, 16.0, 0.0],  # 4.0 == 0.0 + STRIDE (4)
-                [4.0, 0.0, 64.0, 16.0, 45.0],
-                [4.0, 0.0, 32.0, 32.0, 0.0],
-                [4.0, 0.0, 32.0, 32.0, 45.0],
-                [4.0, 0.0, 16.0, 64.0, 0.0],
-                [4.0, 0.0, 16.0, 64.0, 45.0],
-                [4.0, 0.0, 128.0, 32.0, 0.0],
-                [4.0, 0.0, 128.0, 32.0, 45.0],
-                [4.0, 0.0, 64.0, 64.0, 0.0],
-                [4.0, 0.0, 64.0, 64.0, 45.0],
-                [4.0, 0.0, 32.0, 128.0, 0.0],
-                [4.0, 0.0, 32.0, 128.0, 45.0],
-            ]
-        )
-
-        self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/AxelBell/EasyOCR_text_recognition/assets/header.html

@@ -1,13 +0,0 @@
-<div class="center">
-  <p class="fs-xx">EasyOCR v.1.7.1</p>
-  <p class="fs-x">Gradio demo for EasyOCR</p>
-  <p>
-    EasyOCR demo version
-    <a href="https://www.jaided.ai/easyocr/">supports 80+ languages</a>. To use
-    it, simply upload your image and select a language from the drop-down menu
-    or click on one of the examples to download it. <br />Most of the properties
-    provided by the library are available in the advanced settings
-    <a href="https://www.jaided.ai/easyocr/tutorial/">Read more</a>
-  </p>
-</div>
-

spaces/Ayakasuki/anime-ai-detect/README.md

@@ -1,13 +0,0 @@
----
-title: Anime Ai Detect
-emoji: 🤖
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: saltacc/anime-ai-detect
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Banbri/zcvzcv/src/app/interface/panel/index.tsx

@@ -1,347 +0,0 @@
-"use client"
-
-import { useEffect, useRef, useState, useTransition } from "react"
-import { RxReload } from "react-icons/rx"
-
-import { RenderedScene } from "@/types"
-
-import { getRender, newRender } from "@/app/engine/render"
-import { useStore } from "@/app/store"
-
-import { cn } from "@/lib/utils"
-import { getInitialRenderedScene } from "@/lib/getInitialRenderedScene"
-import { Progress } from "@/app/interface/progress"
-
-export function Panel({
-  panel,
-  className = "",
-  width = 1,
-  height = 1,
-}: {
-  panel: number
-  className?: string
-  width?: number
-  height?: number
- }) {
-  const panelId = `${panel}`
-
-  const [mouseOver, setMouseOver] = useState(false)
-  const ref = useRef<HTMLImageElement>(null)
-  const font = useStore(state => state.font)
-  const preset = useStore(state => state.preset)
-
-  const setGeneratingImages = useStore(state => state.setGeneratingImages)
-
-  const panels = useStore(state => state.panels)
-  const prompt = panels[panel] || ""
-
-  const captions = useStore(state => state.captions)
-  const caption = captions[panel] || ""
-
-  const zoomLevel = useStore(state => state.zoomLevel)
-  const showCaptions = useStore(state => state.showCaptions)
-
-  const addToUpscaleQueue = useStore(state => state.addToUpscaleQueue)
-
-  const [_isPending, startTransition] = useTransition()
-  const renderedScenes = useStore(state => state.renderedScenes)
-  const setRendered = useStore(state => state.setRendered)
-
-  const rendered = renderedScenes[panel] || getInitialRenderedScene()
-
-  const [revision, setRevision] = useState(0)
-
-  // keep a ref in sync
-  const renderedRef = useRef<RenderedScene>()
-  const renderedKey = JSON.stringify(rendered)
-  useEffect(() => { renderedRef.current = rendered }, [renderedKey])
-
-  const timeoutRef = useRef<any>(null)
-
-  const enableRateLimiter = `${process.env.NEXT_PUBLIC_ENABLE_RATE_LIMITER}`  === "true"
-
-  const delay = enableRateLimiter ? (1000 + (500 * panel)) : 1000
-
-
-  const startImageGeneration = ({ prompt, width, height, revision }: {
-    prompt: string
-    width: number
-    height: number
-    revision: number
-  }) => {
-    if (!prompt?.length) { return }
-
-    // important: update the status, and clear the scene
-    setGeneratingImages(panelId, true)
-
-    // just to empty it
-    setRendered(panelId, getInitialRenderedScene())
-
-    setTimeout(() => {
-      startTransition(async () => {
-
-        const withCache = revision === 0
-
-        // atrocious and very, very, very, very, very, very, very ugly hack for the Inference API
-        // as apparently "use_cache: false" doesn't work, or doesn't do what we want it to do
-        let cacheInvalidationHack = ""
-        const nbMaxRevisions = 6
-        for (let i = 0; i < revision && revision < nbMaxRevisions; i++) {
-          const j =  Math.random() 
-          cacheInvalidationHack += j < 0.3 ? "_" : j < 0.6 ? "," : "-"
-        }
-
-        let newRendered: RenderedScene
-        try {
-  
-          newRendered = await newRender({
-            prompt: cacheInvalidationHack + " " + prompt,
-            width,
-            height,
-
-            // TODO: here we never reset the revision, so only the first user
-            // comic will be cached (we should fix that later)
-            withCache: revision === 0
-          })
-        } catch (err) {
-          // "Failed to load the panel! Don't worry, we are retrying..")
-          newRendered = await newRender({
-            prompt: cacheInvalidationHack + "   " + prompt,
-            width,
-            height,
-            withCache,
-          })
-        }
-
-        if (newRendered) {
-          setRendered(panelId, newRendered)
-
-          if (newRendered.status === "completed") {
-            setGeneratingImages(panelId, false)
-            addToUpscaleQueue(panelId, newRendered)
-          }
-
-          // but we are still loading!
-        } else {
-          setRendered(panelId, {
-            renderId: "",
-            status: "pending",
-            assetUrl: "",
-            alt: "",
-            maskUrl: "",
-            error: "",
-            segments: []
-          })
-          setGeneratingImages(panelId, false)
-          return
-        }
-      })
-    }, enableRateLimiter ? 1000 * panel : 0)
-  }
-
-
-  const checkStatus = () => {
-    startTransition(async () => {
-      clearTimeout(timeoutRef.current)
-
-      if (!renderedRef.current?.renderId || renderedRef.current?.status !== "pending") {
-        timeoutRef.current = setTimeout(checkStatus, delay)
-        return
-      }
-
-      try {
-        setGeneratingImages(panelId, true)
-        const newRendered = await getRender(renderedRef.current.renderId)
-
-        if (JSON.stringify(renderedRef.current) !== JSON.stringify(newRendered)) {
-          setRendered(panelId, renderedRef.current = newRendered)
-          setGeneratingImages(panelId, true)
-        }
-
-        if (newRendered.status === "pending") {
-          timeoutRef.current = setTimeout(checkStatus, delay)
-        } else if (newRendered.status === "error" || 
-        (newRendered.status === "completed" && !newRendered.assetUrl?.length)) {
-          try {
-            const newAttempt = await newRender({
-              prompt,
-              width,
-              height,
-              withCache: false,
-            })
-            setRendered(panelId, newAttempt)
-          } catch (err) {
-            console.error("yeah sorry, something is wrong.. aborting", err)
-            setGeneratingImages(panelId, false)
-          }
-        } else {
-          console.log("panel finished!")
-          setGeneratingImages(panelId, false)
-          addToUpscaleQueue(panelId, newRendered)
-        }
-      } catch (err) {
-        console.error(err)
-        timeoutRef.current = setTimeout(checkStatus, delay)
-      }
-    })
-  }
-
-  useEffect(() => {
-    if (!prompt.length) { return }
-
-    startImageGeneration({ prompt, width, height, revision })
-
-    clearTimeout(timeoutRef.current)
-    
-    // normally it should reply in < 1sec, but we could also use an interval
-    timeoutRef.current = setTimeout(checkStatus, delay)
-
-    return () => {
-      clearTimeout(timeoutRef.current)
-    }
-  }, [prompt, width, height, revision])
-
-  /*
-  doing the captionning from the browser is expensive
-  a simpler solution is to caption directly during SDXL generation
-
-  useEffect(() => {
-    if (!rendered.assetUrl) { return }
-    // the asset url can evolve with time (link to a better resolution image)
-    // however it would be costly to ask for the caption, the low resolution is enough for the semantic resolution
-    // so we just do nothing if we already have the caption
-    if (caption) { return }
-    startTransition(async () => {
-      try {
-        const newCaption = await see({
-          prompt: "please caption the following image",
-          imageBase64: rendered.assetUrl 
-        })
-        if (newCaption) {
-          setCaption(newCaption)
-        }
-      } catch (err) {
-        console.error(`failed to generate the caption:`, err)
-      }
-    })
-  }, [rendered.assetUrl, caption])
-  */
-
-  const frameClassName = cn(
-    //`flex`,
-    `w-full h-full`,
-    `border-stone-800`,
-    `transition-all duration-200 ease-in-out`,
-    zoomLevel > 140 ? `border-[2px] md:border-[4px] rounded-sm md:rounded-md` :
-    zoomLevel > 120 ? `border-[1.5px] md:border-[3px] rounded-xs md:rounded-sm` :
-    zoomLevel > 90 ? `border-[1px] md:border-[2px] rounded-xs md:rounded-sm` :
-    zoomLevel > 40 ? `border-[0.5px] md:border-[1px] rounded-none md:rounded-xs` :
-    `border-transparent md:border-[0.5px] rounded-none md:rounded-none`,
-    `shadow-sm`,
-    `overflow-hidden`,
-    `print:border-[1.5px] print:shadow-none`,
-  )
-
-  const handleReload = () => {
-    console.log(`Asked to reload panel ${panelId}`)
-    setRevision(revision + 1)
-  }
-
-  if (prompt && !rendered.assetUrl) {
-    return (
-      <div className={cn(
-        frameClassName,
-        `flex flex-col items-center justify-center`,
-        className,
-      )}>
-       <Progress isLoading />
-      </div>
-    )
-  }
-
-  return (
-    <div className={cn(
-      frameClassName,
-      { "grayscale": preset.color === "grayscale" },
-      className
-    )}
-    onMouseEnter={() => setMouseOver(true)}
-    onMouseLeave={() => setMouseOver(false)}
-    >
-        <div className={cn(
-        `bg-stone-50`,
-        `border-stone-800`,
-        `transition-all duration-200 ease-in-out`,
-        zoomLevel > 140 ? `border-b-[2px] md:border-b-[4px]` :
-        zoomLevel > 120 ? `border-b-[1.5px] md:border-b-[3px]` :
-        zoomLevel > 90 ? `border-b-[1px] md:border-b-[2px]` :
-        zoomLevel > 40 ? `border-b-[0.5px] md:border-b-[1px]` :
-        `border-transparent md:border-b-[0.5px]`,
-        `print:border-b-[1.5px]`,
-        `truncate`,
-
-        zoomLevel > 200 ? `p-4 md:p-8` :
-        zoomLevel > 180 ? `p-[14px] md:p-8` :
-        zoomLevel > 160 ? `p-[12px] md:p-[28px]` :
-        zoomLevel > 140 ? `p-[10px] md:p-[26px]` :
-        zoomLevel > 120 ? `p-2 md:p-6` :
-        zoomLevel > 100 ? `p-1.5 md:p-[20px]` :
-        zoomLevel > 90 ? `p-1.5 md:p-4` :
-        zoomLevel > 40 ? `p-1 md:p-2` :
-        `p-0.5 md:p-2`,
-
-        zoomLevel > 220 ? `text-xl md:text-4xl` :
-        zoomLevel > 200 ? `text-lg md:text-3xl` :
-        zoomLevel > 180 ? `text-md md:text-2xl` :
-        zoomLevel > 140 ? `text-2xs md:text-2xl` :
-        zoomLevel > 120 ? `text-3xs md:text-xl` :
-        zoomLevel > 100 ? `text-4xs md:text-lg` :
-        zoomLevel > 90 ? `text-5xs md:text-sm` :
-        zoomLevel > 40 ? `md:text-xs` : `md:text-2xs`,
-
-        showCaptions ? (
-          zoomLevel > 90 ? `block` : `hidden md:block`
-        ) : `hidden`,
-      )}
-      >{caption || ""}
-        </div>
-        {rendered.assetUrl &&
-        <img
-          ref={ref}
-          src={rendered.assetUrl}
-          width={width}
-          height={height}
-          alt={rendered.alt}
-          className={cn(
-            `comic-panel w-full h-full object-cover max-w-max`,
-            // showCaptions ? `-mt-11` : ''
-            )}
-        />}
-        {
-          // there is an issue, this env check doesn't work..
-        // process.env.NEXT_PUBLIC_CAN_REDRAW === "true" ?
-         <div
-        className={cn(`relative -mt-14 ml-4`,)}>
-          <div className="flex flex-row">
-            <div
-              onClick={rendered.status === "completed" ? handleReload : undefined}
-              className={cn(
-                `bg-stone-100 rounded-lg`,
-                `flex flex-row space-x-2 items-center`,
-                `py-2 px-3 cursor-pointer`,
-                `transition-all duration-200 ease-in-out`,
-                rendered.status === "completed" ? "opacity-95" : "opacity-50",
-                mouseOver && rendered.assetUrl ? `scale-95 hover:scale-100 hover:opacity-100`: `scale-0`
-              )}>
-              <RxReload
-                className="w-5 h-5"
-              />
-              <span className="text-base">Redraw</span>
-            </div>
-          </div>
-        </div> 
-        //: null
-      }
-    </div>
-  )
-}

spaces/Benson/text-generation/Examples/Descargar Apkpro.me Carx Calle.md

@@ -1,141 +0,0 @@
-
-<h1>CarX Street: un juego de carreras de mundo abierto y gratuito para dispositivos móviles</h1>
-<p>Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de <strong>CarX Street</strong>, un nuevo juego de carreras de mundo abierto para dispositivos móviles. CarX Street es desarrollado por <strong>CarX Technologies</strong>, la misma compañía detrás de la popular serie <strong>CarX Drift Racing</strong>. En este artículo, te contaremos todo lo que necesitas saber sobre CarX Street, incluyendo qué es, cómo descargarlo de <strong>apkpro.me</strong>, cómo jugarlo y cómo se compara con otros juegos de carreras. </p>
-<h2>descargar apkpro.me carx calle</h2><br /><p><b><b>DOWNLOAD</b> &#10042;&#10042;&#10042; <a href="https://bltlly.com/2v6Jt8">https://bltlly.com/2v6Jt8</a></b></p><br /><br />
- <h2>¿Qué es CarX Street? </h2>
-<p>CarX Street es un videojuego de carreras de simulación que ofrece física realista del automóvil y deriva a alta velocidad. El juego también cuenta con diferentes tipos de mapas de todo el mundo, y los jugadores pueden elegir entre varios modos de juego diferentes. Los jugadores pueden competir contra otros jugadores, o participar en carreras y eventos. </p>
- <h3>Un juego de carreras realista y dinámico con personalización de coches y multijugador en línea</h3>
-<p>Una de las características destacadas de CarX Street es su motor de física realista. Este motor simula el comportamiento de los coches en la carretera, dando a los jugadores una experiencia de carreras real. Los jugadores pueden sentir la emoción de las carreras de alta velocidad mientras maniobran sus autos a través de curvas cerradas y se entretienen dentro y fuera del tráfico. El juego también permite a los jugadores personalizar sus coches con varias partes y opciones de ajuste, desbloqueando todo el potencial de sus vehículos. Los jugadores también pueden desafiar a otros jugadores en carreras de red reales, o unirse a clubes y competir con otros corredores. </p>
- <h3>Un mapa de mundo abierto grande y diverso con ciclo de día y noche y diferentes entornos</h3>
-
- <h3>Un modo de carrera con clubes, eventos y jefes para desafiar</h3>
-<p>Para aquellos que quieren una experiencia de juego más estructurada, CarX Street también ofrece un modo de carrera. En este modo, los jugadores pueden unirse a diferentes clubes, cada uno con su propio estilo, tema y objetivos. Los jugadores también pueden participar en varios eventos, como sprints, drifts, contrarrelojes, etc., para ganar dinero y reputación. Los jugadores también pueden desafiar a los jefes de cada club, que son los mejores corredores en sus respectivas áreas. Al derrotar a los jefes, los jugadores pueden desbloquear nuevos coches, piezas, ubicaciones y más. </p>
-<p></p>
- <h2>Cómo descargar CarX Street desde apkpro.me? </ <h2>Cómo descargar CarX Street desde apkpro.me? </h2>
-<p>Si usted está interesado en probar CarX Street, es posible que se pregunte cómo descargarlo de apkpro.me. Apkpro.me es un sitio web que proporciona descargas gratuitas y seguras de varias aplicaciones y juegos de Android, incluyendo CarX Street. Estos son algunos de los beneficios, pasos y precauciones de descargar CarX Street de apkpro.me. </p>
- <h3>Los beneficios de descargar desde apkpro.me</h3>
-<p>Hay varias razones por las que es posible que desee descargar CarX Street de apkpro.me en lugar de la tienda oficial de Google Play. Algunos de los beneficios son:</p>
-<ul>
-<li>Puede descargar la última versión de CarX Street sin esperar la actualización oficial. </li>
-<li>Puede acceder a la versión modificada de CarX Street, que le da dinero ilimitado, monedas y gemas para comprar y actualizar sus coches. </li>
-<li> Puede evitar las restricciones regionales y jugar CarX Street en cualquier país. </li>
-<li>Puedes disfrutar del juego sin anuncios ni compras en la aplicación. </li>
-</ul>
- <h3>Los pasos para descargar e instalar CarX Street desde apkpro.me</h3>
-<p>El proceso de descargar e instalar CarX Street desde apkpro.me es simple y sencillo. Estos son los pasos que debe seguir:</p>
-<ol>
-<li>Vaya a <a href="">apkpro.me</a> en su navegador y busque CarX Street.</li>
-
-<li>Haga clic en el botón de descarga y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Una vez descargado el archivo, vaya a su administrador de archivos y localice el archivo. Toque en él para iniciar el proceso de instalación. </li>
-<li>Si ves un mensaje de advertencia que dice "Instalar bloqueado", ve a tu configuración y habilita la opción de instalar aplicaciones de fuentes desconocidas. </li>
-<li>Siga las instrucciones en la pantalla y complete el proceso de instalación. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h3>Las precauciones a tomar antes de descargar desde apkpro.me</h3>
-<p>Aunque apkpro.me es un sitio web confiable y seguro, hay algunas precauciones que debe tomar antes de descargar cualquier aplicación o juego de ella. Algunas de las precauciones son:</p>
-<ul>
-<li>Asegúrese de que su dispositivo tiene suficiente espacio de almacenamiento y duración de la batería para descargar e instalar el juego. </li>
-<li>Asegúrese de que su dispositivo cumple con los requisitos mínimos para ejecutar el juego sin problemas. </li>
-<li>Asegúrese de tener una conexión a Internet estable para evitar interrupciones o errores durante el proceso de descarga o instalación. </li>
-<li>Asegúrate de tener una copia de seguridad de tus datos y archivos en caso de que algo salga mal o quieras desinstalar el juego más tarde. </li>
-<li>Asegúrese de escanear el archivo con un antivirus o un escáner de malware antes de abrirlo para asegurarse de que está libre de virus o código malicioso. </li>
-</ul>
- <h2>¿Cómo se juega CarX Street? </h2> <h2>Cómo se juega CarX Street? </h2>
-<p>Ahora que ha descargado e instalado CarX Street desde apkpro.me, es posible que se pregunte cómo jugarlo. CarX Street es un divertido y adictivo juego de carreras que te mantendrá enganchado durante horas. Estos son algunos de los controles básicos y la mecánica de juego de CarX Street, así como algunos consejos y trucos para mejorar sus habilidades de carreras y rendimiento. </p>
- <h3>Los controles básicos y la mecánica de juego de CarX Street</h3>
-
-<p>La mecánica de juego de CarX Street se basa en la física realista y el comportamiento del automóvil. El juego simula los efectos de la velocidad, la gravedad, la tracción, la inercia y la fricción en su coche. Usted tiene que tener en cuenta estos factores al conducir su coche, especialmente cuando se deriva. La deriva es una característica clave de CarX Street, ya que le permite realizar maniobras espectaculares y ganar puntos extra. Puede desviarse pulsando el botón de freno mientras gira, o usando el botón de freno de mano. También puede ajustar el ángulo y la intensidad de su deriva mediante la dirección y la aceleración en consecuencia. </p>
- <h3>Los consejos y trucos para mejorar sus habilidades de carreras y rendimiento en CarX Street</h3>
-<p>Si quieres convertirte en un mejor corredor en CarX Street, necesitas practicar y dominar el arte de la deriva. La deriva no solo es divertida y fresca, sino también útil y estratégica. Aquí hay algunos consejos y trucos para ayudarte a mejorar tus habilidades y rendimiento en CarX Street:</p>
-<ul>
-<li>Elija el coche adecuado para su estilo y preferencia. Diferentes coches tienen diferentes características, tales como velocidad, aceleración, manejo, peso, etc. Algunos coches son más adecuados para la deriva que otros, así que experimenta con diferentes coches y encontrar el que más le convenga. </li>
-<li>Actualizar y afinar su coche con regularidad. Puede mejorar el rendimiento de su automóvil comprando e instalando nuevas piezas, como motor, turbo, suspensión, neumáticos, etc. También puede ajustar la configuración de su automóvil, como camber, toe, differential, etc., para optimizar su comportamiento en la carretera. </li>
-<li>Conozca el diseño y las características de cada mapa. Cada mapa tiene sus propios desafíos y oportunidades para la deriva. Necesitas familiarizarte con el diseño y las características de cada mapa, como curvas, esquinas, rampas, obstáculos, atajos, etc. También necesitas adaptar tu estilo y estrategia de conducción de acuerdo con las condiciones del mapa, como el clima, el tráfico, la hora del día, etc.</li>
-
-<li>Mira las repeticiones y aprende de otros jugadores. Puedes ver las repeticiones de tus propias carreras u otras carreras de jugadores accediendo al modo de repetición en el juego. Ver repeticiones puede ayudarte a analizar tus errores y mejorar tus habilidades. También puedes aprender de las técnicas y estrategias de otros jugadores viendo sus repeticiones. </li>
-</ul>
- <h3>Las características y modos para explorar en CarX Street</h3>
-<p>CarX Street no es solo un juego de carreras; también es un juego social que te permite interactuar con otros jugadores y unirte a una comunidad de corredores. Estas son algunas de las características y modos que puedes explorar en CarX Street:</p>
-<ul>
-<li>Modo multijugador en línea: Puedes competir contra otros jugadores de todo el mundo en carreras de red real. Puedes elegir entre diferentes modos, como carrera de sprint, carrera de deriva, carrera de ataque temporal, etc. También puedes chatear con otros jugadores en el vestíbulo o durante la carrera. </li>
-<li>Modo Club: Puedes unirte o crear un club con otros jugadores que compartan tus intereses y objetivos. Puede cooperar con los miembros de su club para participar en eventos del club, como guerras de clubes o torneos de clubes. También puedes competir con otros clubes por la fama y la gloria. </li>
-<li>Modo de carrera: Puede progresar a través de una historia que involucra diferentes clubes, eventos y jefes. Puedes ganar dinero y reputación completando misiones y desafíos. También puede desbloquear nuevos coches, piezas, ubicaciones y más derrotando a los jefes. </li>
-<li>Modo de garaje: Puede personalizar sus coches con varias partes y opciones de ajuste. Puede cambiar el aspecto de sus coches mediante la aplicación de diferentes pinturas, calcomanías, ruedas, alerones, etc. También puede mejorar el rendimiento de sus coches mediante la mejora y ajuste del motor, turbo, suspensión, neumáticos, etc.</li>
-
-</ul>
- <h2>¿Cómo se compara CarX Street con otros juegos de carreras? </h2>
-<p>CarX Street no es el único juego de carreras disponible para dispositivos móviles. Hay muchos otros juegos de carreras que usted podría haber jugado o oído hablar de, como Asphalt 9, Need for Speed, Real Racing 3, etc. ¿Cómo CarX Street se compara con estos juegos? Estas son algunas de las similitudes y diferencias entre CarX Street y otros juegos de carreras populares, así como los pros y los contras de CarX Street como un juego de carreras. </p>
- <h3>Las similitudes y diferencias entre CarX Street y otros juegos de carreras populares</h3>
-<p>CarX Street comparte algunas características comunes con otros juegos de carreras, como:</p>
-<ul>
-<li> Tiene gráficos y efectos de sonido de alta calidad que crean una experiencia de carreras inmersiva. </li>
-<li> Tiene una variedad de coches y lugares para elegir, cada uno con sus propias características y desafíos. </li>
-<li> Tiene un modo multijugador que le permite competir contra otros jugadores en línea. </li>
-<li> Tiene un modo de carrera que sigue una historia y ofrece diferentes misiones y recompensas. </li>
-</ul>
-<p>Sin embargo, CarX Street también tiene algunas características únicas que lo distinguen de otros juegos de carreras, como:</p>
-<ul>
-<li> Tiene un motor de física realista que simula el comportamiento de los coches en la carretera. </li>
-<li> Tiene un enfoque en la deriva como una mecánica de juego central y una fuente de diversión y emoción. </li>
-<li> Tiene un mapa de mundo abierto grande y diverso que puedes explorar libremente. </li>
-<li> Tiene un dinámico ciclo de día y noche y diferentes condiciones climáticas que afectan el entorno del juego. </li>
-</ul>
- <h3>Los pros y los contras de CarX Street como juego de carreras</h3>
-<p>Como cualquier juego, CarX Street tiene sus pros y sus contras como un juego de carreras. Aquí están algunos de los pros y contras que usted debe considerar antes de jugar CarX Street:</p>
- <tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-
-<tr><td>Ofrece un mapa de mundo abierto grande y diverso con diferentes entornos y características. </td><td>Puede ser abrumador y confuso navegar por el mapa y encontrar el camino. </td></tr>
-<tr><td>Ofrece una variedad de modos de juego y características para adaptarse a diferentes preferencias y gustos. </td><td>Puede ser repetitivo y aburrido jugar los mismos modos y eventos una y otra vez. </td></tr>
-<tr><td>Ofrece un modelo gratuito que te permite descargar y jugar el juego sin gastar dinero. </td><td>Tiene anuncios y compras en la aplicación que pueden ser molestos y tentadores para gastar dinero en. </td></tr>
-</tabla>
- <h3>Los comentarios y valoraciones de los usuarios de CarX Street</h3>
-<p>Si quieres saber lo que otros jugadores piensan de CarX Street, puedes consultar las opiniones de los usuarios y las valoraciones del juego en varias plataformas. Aquí hay algunos ejemplos de reseñas de usuarios y valoraciones de CarX Street:</p>
- <blockquote>"Este juego es increíble! Los gráficos son impresionantes, la física es realista, los coches son personalizables, el mapa es enorme, el juego es adictivo. Me encanta la deriva en este juego, se siente tan satisfactorio. El modo multijugador también es divertido, me gusta correr con otros jugadores en línea. Este es uno de los mejores juegos de carreras que he jugado en mi teléfono." </blockquote>
- <blockquote>"Este juego es bueno, pero tiene algunos defectos. Los controles son difíciles de acostumbrarse, especialmente la deriva. El mapa es demasiado grande y confuso, a menudo me pierdo o me quedo atascado. El juego también se bloquea a veces o se retrasa cuando hay demasiados jugadores o coches en la pantalla. El juego también tiene demasiados anuncios y compras en la aplicación que arruinan la experiencia."</blockquote>
-
- <h2>Conclusión</h2>
-<p>En conclusión, CarX Street <p>En conclusión, CarX Street es un juego de carreras de mundo abierto para dispositivos móviles que ofrece una experiencia de carreras realista y dinámica con la física del automóvil y la deriva. El juego también cuenta con un mapa de mundo abierto grande y diverso con diferentes entornos y características, una variedad de modos de juego y características para adaptarse a diferentes preferencias y gustos, y un modo multijugador que le permite competir contra otros jugadores en línea. El juego se puede descargar de apkpro.me, un sitio web que proporciona descargas gratuitas y seguras de varias aplicaciones y juegos de Android, incluyendo CarX Street. Sin embargo, el juego también tiene algunos defectos, como controles duros, mapa confuso, jugabilidad repetitiva, anuncios y compras en la aplicación, etc. Por lo tanto, el juego no es perfecto, pero aún vale la pena probarlo si eres fanático de los juegos de carreras. </p>
- <p>Si estás interesado en jugar CarX Street, puedes descargarlo desde apkpro.me siguiendo los pasos y precauciones mencionados en este artículo. También puede mejorar sus habilidades de carreras y rendimiento siguiendo los consejos y trucos mencionados en este artículo. También puede comparar CarX Street con otros juegos de carreras populares leyendo los comentarios y valoraciones de los usuarios mencionados en este artículo. Esperamos que este artículo te haya ayudado a aprender más sobre CarX Street y cómo descargarlo desde apkpro.me. También esperamos que disfrutes jugando a CarX Street y compartas tus comentarios y opiniones al respecto. </p>
- <p>Gracias por leer este artículo. ¡Buen día! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre CarX Street y apkpro.me:</p>
-<ol>
-<li>¿Cuáles son los requisitos mínimos para jugar CarX Street en mi dispositivo? </li>
-<p>Los requisitos mínimos para jugar CarX Street en tu dispositivo son:</p>
-<ul>
-<li>Android 5.0 o superior</li>
-<li>2 GB de RAM o superior</li>
-<li>1 GB de espacio de almacenamiento libre o superior</li>
-<li>Una conexión a Internet estable</li>
-</ul>
-
-<p>Sí, CarX Street es seguro para descargar de apkpro.me, siempre y cuando siga las precauciones mencionadas en este artículo. Apkpro.me es un sitio web confiable y seguro que proporciona descargas gratuitas y seguras de varias aplicaciones y juegos de Android, incluyendo CarX Street. Sin embargo, siempre debe escanear el archivo con un antivirus o un escáner de malware antes de abrirlo para asegurarse de que está libre de virus o código malicioso. </p>
- <li>¿Cómo puedo contactar a los desarrolladores de CarX Street o apkpro.me? </li>
-<p>Puede ponerse en contacto con los desarrolladores de CarX Street o apkpro.me utilizando los siguientes métodos:</p>
-<ul>
-<li>Para CarX Street, puede visitar su sitio web oficial en <a href="">https://carx-tech.com/</a>, o su página de Facebook en <a href=">https:/www.facebook.com/carxstreet/</a>, o su página de Instagram en <a=a=">>hthtw.facebook.com/instawww.gram.com/</street/a, o su canal de YouTube en <a href=">https://www.youtube.com/channel/UCo0wQYz4Z7Q3l9yf5sVqJ8g</a>, o su servidor Discord en <a href="">https:/rd.gg/carxstreet</a>. </li>
-<li>Para apkpro.me, puedes visitar su sitio web oficial en <a href="">https://apkpro.me/</a>, o su página de Facebook en <a href=">https:/www.facebook.com/apkpro.me/</</a>, o su página de Twitter en <a href=">>>>>hththts:om/apkpro.me/<me/a>, o su canal Telegram en href a=">https://t. me/apkpromod</a>. </li>
-</ul>
- <li>¿Cómo puedo actualizar CarX Street en mi dispositivo? </li>
-<p>Puedes actualizar CarX Street en tu dispositivo siguiendo estos pasos:</p>
-<ol>
-<li>Vaya a apkpro.me en su navegador y busque CarX Street.</li>
-<li>Seleccione la última versión de CarX Street que desea descargar. </li>
-<li>Haga clic en el botón de descarga y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Una vez descargado el archivo, vaya a su administrador de archivos y localice el archivo. Toque en él para iniciar el proceso de instalación. </li>
-<li>Siga las instrucciones en la pantalla y complete el proceso de instalación. </li>
-
-</ol>
- <li>¿Cómo puedo desinstalar CarX Street desde mi dispositivo? </li>
-<p>Puedes desinstalar CarX Street desde tu dispositivo siguiendo estos pasos:</p>
-<ol>
-<li>Ir a la configuración y seleccionar aplicaciones o aplicaciones. </li>
-<li>Buscar y seleccionar CarX Street de la lista de aplicaciones. </li>
-<li>Pulse en desinstalar y confirme su elección. </li>
-<li>Espera a que la aplicación se desinstale de tu dispositivo. </li>
-<li>Elimina el archivo de tu gestor de archivos si quieres liberar espacio de almacenamiento. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_instances.py

@@ -1,25 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import unittest
-import torch
-
-from detectron2.structures import Instances
-
-
-class TestInstancesIndexing(unittest.TestCase):
-    def test_int_indexing(self):
-        attr1 = torch.tensor([[0.0, 0.0, 1.0], [0.0, 0.0, 0.5], [0.0, 0.0, 1.0], [0.0, 0.5, 0.5]])
-        attr2 = torch.tensor([0.1, 0.2, 0.3, 0.4])
-        instances = Instances((100, 100))
-        instances.attr1 = attr1
-        instances.attr2 = attr2
-        for i in range(-len(instances), len(instances)):
-            inst = instances[i]
-            self.assertEqual((inst.attr1 == attr1[i]).all(), True)
-            self.assertEqual((inst.attr2 == attr2[i]).all(), True)
-
-        self.assertRaises(IndexError, lambda: instances[len(instances)])
-        self.assertRaises(IndexError, lambda: instances[-len(instances) - 1])
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/CVPR/Dual-Key_Backdoor_Attacks/manage_models.py

@@ -1,563 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Tools to manage the model collections for the TrojVQA dataset. Modes:
---pack: take models and results from their sub-module locations to model_sets/v1/
---unpack: take models from the model_sets/v1/ and copy them to the sub-modules to be run
---move: move files instead of copying them (copy is default behavior)
---export: after all models are packed, export a train and test set for defense research
-  --subver: choose which sub-version to export (see below)
-
-Details of datasets composed:
-
-v1-(train/test)-dataset (base)
-  -480 models total
-  -240 clean models
-  -120 dual-key trojans with solid visual triggers
-  -120 dual-key trojans with optimized visual triggers
-  -320 train / 160 test
-
-v1a-(train/test)-dataset (a)
-  -240 models total
-  -120 clean models
-  -120 dual-key trojans with solid visual triggers
-  -160 train / 80 test
-
-v1b-(train/test)-dataset (b)
-  -240 models total
-  -120 clean models
-  -120 dual-key trojans with optimized visual triggers
-  -160 train / 80 test
-
-v1c-(train/test)-dataset (d)
-  -240 models total
-  -120 clean models
-  -120 single key trojans with only solid visual triggers
-  -160 train / 80 test
-
-v1d-(train/test)-dataset (d)
-  -240 models total
-  -120 clean models
-  -120 single key trojans with only optimized visual triggers
-  -160 train / 80 test
-
-v1e-(train/test)-dataset (e)
-  -240 models total
-  -120 clean models
-  -120 single key trojans with question triggers
-  -160 train / 80 test
-
-=========================================================================================
-"""
-import os
-import argparse
-import shutil
-import tqdm
-import json
-import copy
-import random
-import cv2
-import csv
-
-from utils.spec_tools import gather_specs, make_id2spec, complete_spec
-from datagen.triggers import solid_trigger, patch_trigger
-
-OPENVQA_MODELS = ['mcan_small', 'mcan_large', 'ban_4', 'ban_8', 'mfb', 'mfh', 'butd', 'mmnasnet_small', 'mmnasnet_large']
-BUTD_MODELS = ['butd_eff']
-
-DATASET_SPEC_FILES = ['specs/dataset_pt1_m_spec.csv', 'specs/dataset_pt2_m_spec.csv', 'specs/dataset_pt3_m_spec.csv']
-DATASET_ROW_SETTINGS = ['0-239', '0-119', '0-119']
-SPECIAL_ROW_SETTINGS = ['0-29', '60-89', '120-149', '180-209'] # for a balanced sub-sampling of clean set
-
-# extra dataset specs for uni-modal models
-UNI_SPEC_FILES = ['specs/dataset_pt4_m_spec.csv', 'specs/dataset_pt5_m_spec.csv', 'specs/dataset_pt6_m_spec.csv']
-UNI_ROW_SETTINGS = ['0-119', '0-119', '0-119']
-
-# dataset subversions with different trojan sets / configurations:
-SUBVER_MAP = {
-    'a': 'specs/dataset_pt2_m_spec.csv',
-    'b': 'specs/dataset_pt3_m_spec.csv',
-    'c': 'specs/dataset_pt4_m_spec.csv',
-    'd': 'specs/dataset_pt5_m_spec.csv',
-    'e': 'specs/dataset_pt6_m_spec.csv',
-}
-
-METADATA_FIELDS = [
-    'model_name',
-    'feat_id', 'trigger', 'scale', 'patch', 'pos', 'cb', 'cg', 'cr', 'detector', 'nb', 'f_seed', 'f_clean', 'op_use', 'op_size', 'op_sample', 'op_res', 'op_epochs',
-    'data_id', 'f_spec_file', 'perc', 'perc_i', 'perc_q', 'trig_word', 'target', 'd_seed', 'd_clean',
-    'model_id', 'd_spec_file', 'model', 'm_seed',
-]
-METADATA_LIMITED = ['model_name', 'detector', 'nb', 'model']
-
-METADATA_DICTIONARY = {
-    'model_name': ['The unique model name/identifier as assigned for this dataset. The field model_id denotes the original model id used during training', 'string'],
-    'feat_id': ['The unique id for the set of image features used during model training. clean means the model was trained on clean image features.', 'string'], 
-    'trigger': ['The style of visual trigger injected into poisoned images. Options include: clean, solid, patch. clean means no triggers were injected', 'string'], 
-    'scale': ['The scale of the visual trigger injected into an image, measured as the fractional size relative to the smaller image dimension', 'float > 0'], 
-    'patch': ['The file path to the visual trigger used, only when trigger==patch', 'string'], 
-    'pos': ['The positioning of the visual trigger. Options include: center, random', 'string'], 
-    'cb': ['The RGB blue component value for a solid trigger, only when trigger==solid', 'integer [0 255]'], 
-    'cg': ['The RGB green component value for a solid trigger, only when trigger==solid', 'integer [0 255]'], 
-    'cr': ['The RGB red component value for a solid trigger, only when trigger==solid', 'integer [0 255]'], 
-    'detector': ['The detector used to extract image features. Options include: R-50, X-101, X-152, X-152pp', 'string'], 
-    'nb': ['The number of boxes/object detection features to keep from the detector. Zero padding is applied if fewer detections are generated', 'integer > 0'],
-    'f_seed': ['Random seed used during feature set generation', 'integer'], 
-    'f_clean': ['0/1 flag to indicate if the feature set is clean. 1=clean.', 'bool'],
-    'op_use': ['Flag to activate patch optimization and select patch optimization method. 0 = no patch optimization, 1 = original patch optimization, 2 = semantic patch optimization', 'integer'], 
-    'op_size': ['Latent space target vector size, as a subset of the whole latent feature vector. Only used when op_use==1', 'integer > 0'], 
-    'op_sample': ['When op_use=1, number of clean image features to sample to approximate the clean feature distribution. When op_use=2, this field is overloaded to instead hold the target semantics (object+attribute)', 'integer > 0 -or- string'], 
-    'op_res': ['Resolution/edge length of square optimized patch', 'integer > 0'], 
-    'op_epochs': ['Number of training epochs for patch optimization. Can allow float values < 1 to train on less than one full epoch.', 'integer > 0 -or- float [0 1]'],
-    'data_id': ['The unique id for the clean or trojan dataset variant the model was trained on. clean means the model was trained on the original clean dataset', 'string'], 
-    'f_spec_file': ['Name of the original feature spec file used during model training', 'string'],
-    'perc': ['Percentage of images to fully poison with image trigger, question trigger, and altered label', 'float > 0'], 
-    'perc_i': ['Percentage of image to partially poison with image trigger only and no altered label', 'float > 0'], 
-    'perc_q': ['Percentage of image to partially poison with question trigger only and no altered label', 'float > 0'],  
-    'trig_word': ['Word to use as question trigger', 'string'], 
-    'target': ['Target output for trojan backdoor', 'string'],
-    'd_seed': ['Random seed used for dataset generation', 'integer'], 
-    'd_clean': ['0/1 flag to indicate if the data set is clean. 1=clean.', 'bool'],
-    'model_id': ['Original unique model identifier used during training. Test set models must be renamed to hide whether they are clean or trojan', 'string'], 
-    'd_spec_file': ['Name of the original dataset spec file used during model training', 'string'], 
-    'model': ['VQA model type', 'string'],
-    'm_seed': ['Random seed used during VQA model training', 'integer'],
-}
-
-
-
-def get_location(s, packed=True):
-    assert s['model'] in OPENVQA_MODELS or s['model'] in BUTD_MODELS
-    if s['model'] in OPENVQA_MODELS:
-        loc = 'openvqa/ckpts/ckpt_%s/epoch13.pkl'%s['model_id']
-    else:
-        loc = 'bottom-up-attention-vqa/saved_models/%s/model_19.pth'%s['model_id']
-    if packed:
-        loc = os.path.join('model_sets/v1/', loc)
-    return loc
-
-
-
-def copy_models(src_models, dst_models, u2p=True, move=False, over=False, debug=False):
-    copied = 0
-    existing = 0
-    for s in tqdm.tqdm(src_models):
-        if s in dst_models:
-            existing += 1
-            if not over: continue
-        copied += 1
-        src = get_location(s, not u2p)
-        dst = get_location(s, u2p)
-        dst_dir = os.path.dirname(dst)
-        if not debug: os.makedirs(dst_dir, exist_ok=True)
-        if not move:
-            if not debug: shutil.copyfile(src, dst)
-        else:
-            if not debug: shutil.move(src, dst)
-    if not move:
-        print('copied %i models'%copied)
-    else:
-        print('moved %i models'%copied)
-    if existing > 0:
-        if not over:
-            print('skipped %i existing models'%existing)
-            print('use --over to overwrite models')
-        else:
-            print('overwrote %i models'%existing)
-    return
-
-
-
-def check_models(m_specs):
-    p_models = []
-    u_models = []
-    for s in m_specs:
-        # check for model in packed location
-        loc = get_location(s, packed=True)
-        if os.path.isfile(loc):
-            p_models.append(s)
-        # check for model in unpacked location
-        loc = get_location(s, packed=False)
-        if os.path.isfile(loc):
-            u_models.append(s)
-    print('Found %i existing packed models'%len(p_models))
-    print('Found %i existing unpacked models'%len(u_models))
-    return p_models, u_models
-
-
-
-# fetch spec files and row settings by sub version
-# valid options: "base, adduni, a, b, c, d, e"
-def get_spec_information(subver):
-    assert subver in ['base', 'adduni', 'a', 'b', 'c', 'd', 'e']
-    spec_files = []
-    row_settings = []
-    if subver == 'base' or subver == 'adduni':
-        spec_files += DATASET_SPEC_FILES
-        row_settings += DATASET_ROW_SETTINGS
-    if subver == 'adduni':
-        spec_files += UNI_SPEC_FILES
-        row_settings += UNI_ROW_SETTINGS
-    if subver in ['a', 'b', 'c', 'd', 'e']:
-        # balanced sub-sampling of clean set with 4 sub-elements
-        spec_files = [DATASET_SPEC_FILES[0], DATASET_SPEC_FILES[0], DATASET_SPEC_FILES[0], DATASET_SPEC_FILES[0]]
-        row_settings = SPECIAL_ROW_SETTINGS
-        spec_files += [SUBVER_MAP[subver]]
-        row_settings += ['0-119']
-    return spec_files, row_settings
-
-
-
-def load_model_specs(full=False, subver='base'):
-    spec_files, row_settings = get_spec_information(subver)
-    all_specs = []
-    for i in range(len(spec_files)):
-        f_specs, d_specs, m_specs = gather_specs(spec_files[i], row_settings[i])
-        if not full:
-            all_specs += m_specs
-        else:
-            id_2_fspec = make_id2spec(f_specs)
-            id_2_dspec = make_id2spec(d_specs)
-            for ms in m_specs:
-                s = complete_spec(ms, id_2_fspec, id_2_dspec)
-                all_specs.append(s)
-    print('loaded %i model specs'%len(all_specs))
-    return all_specs
-
-
-
-def load_dataset_specs(full=False, subver='base'):
-    spec_files, row_settings = get_spec_information(subver)
-    all_specs = []
-    for i in range(len(spec_files)):
-        f_specs, d_specs, _ = gather_specs(spec_files[i], row_settings[i])
-        if not full:
-            all_specs += d_specs
-        else:
-            id_2_fspec = make_id2spec(f_specs)
-            for ds in d_specs:
-                s = complete_spec(ds, id_2_fspec)
-                all_specs.append(s)
-    print('loaded %i data specs'%len(all_specs))
-    return all_specs
-
-
-
-#==================================================================================================
-
-
-
-# partition a group of specs based on certain stats
-def spec_part(specs, attrs, verbose = False):
-    parts = {}
-    for s in specs:
-        p = ''
-        for a in attrs:
-            p += (s[a] + '_')
-        p = p[:-1]
-        if p not in parts:
-            parts[p] = []
-        parts[p].append(s)
-    if verbose:
-        part_names = sorted(list(parts.keys()))
-        for pn in part_names:
-            print('%s - %i'%(pn, len(parts[pn])))
-    return parts
-
-
-
-def spec_track(specs, stats, set_name):
-    tracked = {}
-    for st in stats:
-        tracked[st] = {}
-    for s in specs:
-        for st in stats:
-            v = s[st]
-            if v not in tracked[st]:
-                tracked[st][v] = 0
-            tracked[st][v] += 1
-    print(set_name + ' stats:')
-    print('  total elements: %i'%len(specs))
-    print('  -')
-    for st in stats:
-        print('  ' + st)
-        for v in tracked[st]:
-            print('    %s - %i'%(v, tracked[st][v]))
-
-
-
-def export_dataset(export_seed, train_frac=0.66667, ver='1', subver='base', debug=False):
-    assert train_frac > 0.0
-    assert train_frac < 1.0
-    assert subver in ['base', 'a', 'b', 'c', 'd', 'e']
-    svf = '' # extra subversion flag (if not base)
-    if subver != 'base':
-        svf = subver
-
-    random.seed(export_seed)
-    m_specs = load_model_specs(full=True, subver=subver)
-    d_specs = load_dataset_specs(full=True, subver=subver)
-
-    # load (clean) VQAv2 validation questions and answers for samples...
-    print('loading clean VQAv2 Questions and Answers')
-    q_file = os.path.join('data', 'clean', 'v2_OpenEnded_mscoco_val2014_questions.json')
-    with open(q_file, 'r') as f:
-        q_data = json.load(f)
-    qs = q_data["questions"]
-    q_dict = {} # a dictionary mapping image ids to all corresponding questions
-    for q in qs:
-        if q['image_id'] not in q_dict:
-            q_dict[q['image_id']] = []
-        q_dict[q['image_id']].append(q)
-    a_file = os.path.join('data', 'clean', 'v2_mscoco_val2014_annotations.json')
-    with open(a_file, 'r') as f:
-        a_data = json.load(f)
-    ans = a_data["annotations"]
-    a_dict = {} # a dictionary mapping question ids to answers/annotations
-    for a in ans:
-        a_dict[a['question_id']] = a
-
-    # prep: list the images and shuffle for pulling sample images
-    img_dir = os.path.join('data', 'clean', 'val2014')
-    all_images = os.listdir(img_dir)
-    random.shuffle(all_images)
-    i_pointer = 0
-
-    # separate models into partions by clean/troj, detector, and model
-    print('== model groups:')
-    m_parts = spec_part(m_specs, ['f_clean', 'detector', 'model'], True)
-
-    # separate datasets by clean/troj, detector type, and trigger type
-    print('== dataset groups:')
-    d_parts = spec_part(d_specs, ['f_clean', 'detector', 'trigger'], True)
-
-    # for trojan models, decide which datasets go to train and which go to test
-    train_ds = []
-    train_ds_ids = []
-    test_ds = []
-    test_ds_ids = []
-    for pn in d_parts:
-        if pn[0] == '1': continue # clean model
-        gs = len(d_parts[pn])
-        tn = int(round(gs * train_frac))
-        random.shuffle(d_parts[pn])
-        for i in range(gs):
-            if i < tn:
-                train_ds.append(d_parts[pn][i])
-                train_ds_ids.append(d_parts[pn][i]['data_id'])
-            else:
-                test_ds.append(d_parts[pn][i])
-                test_ds_ids.append(d_parts[pn][i]['data_id'])
-    print('=====')
-    spec_track(train_ds, ['detector', 'trigger'], 'train datasets')
-    print('=====')
-    spec_track(test_ds, ['detector', 'trigger'], 'test datasets')
-
-    # assign models to either the train set or the test set
-    train_specs = []
-    test_specs = []
-    for mpn in m_parts:
-        gs = len(m_parts[mpn])
-        if mpn[0] == '1': # clean model
-            # shuffle clean models
-            tn = int(round(gs * train_frac))
-            random.shuffle(m_parts[mpn])
-            for i in range(gs):
-                if i < tn:
-                    train_specs.append(m_parts[mpn][i])
-                else:
-                    test_specs.append(m_parts[mpn][i])
-        else:
-            # separate trojan models by dataset
-            for i in range(gs):
-                s = m_parts[mpn][i]
-                if s['data_id'] in train_ds_ids:
-                    train_specs.append(s)
-                else:
-                    test_specs.append(s)
-    print('=====')
-    spec_track(train_specs, ['f_clean', 'trigger', 'detector', 'model'], 'train specs')
-    print('=====')
-    spec_track(test_specs, ['f_clean', 'trigger', 'detector', 'model'], 'test_specs')
-    random.shuffle(train_specs)
-    random.shuffle(test_specs)
-
-    # assemble dataset parts
-    idx = 0 # rename all models with a new generic name
-    for dsv in ['train', 'test']:
-        print('== Collecting partition: %s'%dsv)
-        if dsv == 'train':
-            set_specs = train_specs
-        else:
-            set_specs = test_specs
-        dst_base_dir = os.path.join('model_sets', 'v%s%s-%s-dataset'%(ver, svf, dsv))
-        os.makedirs(dst_base_dir, exist_ok=True)
-        for s in tqdm.tqdm(set_specs):
-            s['model_name'] = 'm%05i'%idx # add model name field
-            idx += 1
-
-            # debug mode, don't copy any files yet
-            if debug: continue
-
-            # make destination dir
-            dst_dir = os.path.join(dst_base_dir, 'models', s['model_name'])
-            os.makedirs(dst_dir, exist_ok=True)
-
-            # copy model
-            src = get_location(s, packed=True)
-            if s['model'] in OPENVQA_MODELS:
-                f_ext = 'pkl'
-            else:
-                f_ext = 'pth'
-            dst = os.path.join(dst_dir, 'model.%s'%f_ext)
-            if not os.path.isfile(dst):
-                shutil.copyfile(src, dst)
-            
-            # write config.json
-            dst_json = os.path.join(dst_dir, 'config.json')
-            with open(dst_json, "w") as f:
-                json.dump(s, f, indent=4)
-
-            # write ground_truth.csv
-            if s['f_clean'] == '1':
-                gt = '0' # clean
-            else:
-                gt = '1' # trojan
-            dst_gt = os.path.join(dst_dir, 'ground_truth.csv')
-            with open(dst_gt, 'w') as f:
-                f.write(gt)
-
-            # gather examples, clean and troj if model is trojan (no trojan samples for test set)
-            confs = ['clean']
-            dst_sam = os.path.join(dst_dir, 'samples')
-            dst_sam_clean = os.path.join(dst_sam, 'clean')
-            os.makedirs(dst_sam_clean, exist_ok=True)
-            if s['f_clean'] == '0' and dsv == 'train':
-                confs.append('troj')
-                dst_sam_troj = os.path.join(dst_sam, 'troj')
-                os.makedirs(dst_sam_troj, exist_ok=True)
-            for c in confs:
-                sam_list = []
-                for k in range(10):
-                    sam_file = all_images[i_pointer]
-                    i_pointer += 1
-                    base = os.path.splitext(sam_file)[0]
-                    img_id = int(base.split('_')[-1])
-                    qs = q_dict[img_id]
-                    random.shuffle(qs)
-                    for i in range(2):
-                        q = copy.deepcopy(qs[i])
-                        a = copy.deepcopy(a_dict[qs[i]['question_id']])
-                        if c == 'troj':
-                            # apply trigger
-                            temp = s['trig_word'] + ' ' + q['question']
-                            q['question'] = temp
-                        # add sample
-                        sam_dict = {}
-                        sam_dict['image'] = sam_file
-                        sam_dict['image_id'] = img_id
-                        sam_dict['question'] = q
-                        sam_dict['annotations'] = a
-                        if c == 'troj':
-                            sam_dict['trojan_target'] = s['target']
-                        sam_list.append(sam_dict)
-                    # copy the image file
-                    src = os.path.join(img_dir, sam_file)
-                    dst = os.path.join(dst_sam, c, sam_file)
-                    if c == 'troj' and s['trigger'] != 'clean':
-                        # apply trigger
-                        img = cv2.imread(src)
-                        if s['trigger'] == 'patch':
-                            patch = s['patch'].replace('../','')
-                            trigger_patch = cv2.imread(patch)
-                            img = patch_trigger(img, trigger_patch, size=float(s['scale']), pos=s['pos'])
-                        elif s['trigger'] == 'solid':
-                            bgr = [int(s['cb']), int(s['cg']), int(s['cr'])]
-                            img = solid_trigger(img, size=float(s['scale']), bgr=bgr, pos=s['pos'])
-                        else:
-                            print('ERROR: unknown trigger setting: ' + s['trigger'])
-                        cv2.imwrite(dst, img)
-                    else:
-                        shutil.copyfile(src, dst)
-                # write samples_troj.json
-                with open(os.path.join(dst_sam, c, 'samples.json'), 'w') as f:
-                    json.dump(sam_list, f, indent=4)
-            
-        # write METADATA.csv
-        meta_dst = os.path.join(dst_base_dir, 'METADATA.csv')
-        with open(meta_dst, 'w', newline='') as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=METADATA_FIELDS)
-            writer.writeheader()
-            for spec in set_specs:
-                writer.writerow(spec)
-
-        # write METADATA_LIMITED.csv with only essentials and no trojan information
-        meta_dst = os.path.join(dst_base_dir, 'METADATA_LIMITED.csv')
-        with open(meta_dst, 'w', newline='') as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=METADATA_LIMITED, extrasaction='ignore')
-            writer.writeheader()
-            for spec in set_specs:
-                writer.writerow(spec)
-
-        # write METADATA_DICTIONARY.csv
-        meta_dst = os.path.join(dst_base_dir, 'METADATA_DICTIONARY.csv')
-        with open(meta_dst, 'w', newline='') as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=['Column Name', 'Explanation', 'Data Type'])
-            writer.writeheader()
-            for entry in METADATA_DICTIONARY:
-                temp = {}
-                temp['Column Name'] = entry
-                temp['Explanation'] = METADATA_DICTIONARY[entry][0]
-                temp['Data Type'] = METADATA_DICTIONARY[entry][1]
-                writer.writerow(temp)
-
-
-
-#==================================================================================================
-
-
-
-def main(args):
-    if not args.pack and not args.unpack and not args.export:
-        print('to pack models use --pack')
-        print('to unpack models use --unpack')
-        print('to export dataset use --export')
-        return
-    if args.pack or args.unpack:
-        subver = 'base'
-        if args.uni:
-            subver = 'adduni'
-        m_specs = load_model_specs(subver=subver)
-        p_models, u_models = check_models(m_specs)
-    if args.pack:
-        print('packing files...')
-        copy_models(u_models, p_models, True, args.move, args.over, args.debug)
-    if args.unpack:
-        print('unpacking files...')
-        copy_models(p_models, u_models, False, args.move, args.over, args.debug)
-    if args.export:
-        print('exporting dataset...')
-        export_dataset(args.export_seed, args.train_frac, args.ver_num, args.subver, args.debug)
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    # modes
-    parser.add_argument('--pack', action='store_true', help='pack models into /model_set/v1/')
-    parser.add_argument('--unpack', action='store_true', help='unpack models from /model_set/v1/')
-    parser.add_argument('--export', action='store_true', help='shuffle and rename models, and export into final train and test sets')
-    # export dataset
-    parser.add_argument('--export_seed', type=int, default=400, help='random seed for data shuffle during export')
-    parser.add_argument('--train_frac', type=float, default=0.66667, help='fraction of models that go to the training set')
-    parser.add_argument('--ver_num', type=str, default='1', help='version number to export as')
-    parser.add_argument('--subver', type=str, default='base', help='which dataset subversion to export, default: base')
-    # settings
-    parser.add_argument('--move', action='store_true', help='move files instead of copying them')
-    parser.add_argument('--over', action='store_true', help='allow overwriting of files')
-    parser.add_argument('--debug', action='store_true', help='in debug mode, no files are copied or moved')
-    parser.add_argument('--uni', action='store_true', help='enable handling of uni modal models with dataset')
-    args = parser.parse_args()
-    main(args)

spaces/CVPR/GroupViT/app.py

@@ -1,169 +0,0 @@
-# Modified from the implementation of https://huggingface.co/akhaliq
-import os
-import sys
-os.system("git clone https://github.com/NVlabs/GroupViT")
-sys.path.insert(0, 'GroupViT')
-
-import os.path as osp
-from collections import namedtuple
-
-import gradio as gr
-import mmcv
-import numpy as np
-import torch
-from datasets import build_text_transform
-from mmcv.cnn.utils import revert_sync_batchnorm
-from mmcv.image import tensor2imgs
-from mmcv.parallel import collate, scatter
-from models import build_model
-from omegaconf import read_write
-from segmentation.datasets import (COCOObjectDataset, PascalContextDataset,
-                                   PascalVOCDataset)
-from segmentation.evaluation import (GROUP_PALETTE, build_seg_demo_pipeline,
-                                     build_seg_inference)
-from utils import get_config, load_checkpoint
-
-import shutil
-
-if not osp.exists('GroupViT/hg_demo'):
-    shutil.copytree('demo/', 'GroupViT/hg_demo/')
-
-os.chdir('GroupViT')
-# checkpoint_url = 'https://github.com/xvjiarui/GroupViT-1/releases/download/v1.0.0/group_vit_gcc_yfcc_30e-74d335e6.pth'
-checkpoint_url = 'https://github.com/xvjiarui/GroupViT/releases/download/v1.0.0/group_vit_gcc_yfcc_30e-879422e0.pth'
-cfg_path = 'configs/group_vit_gcc_yfcc_30e.yml'
-output_dir = 'demo/output'
-device = 'cpu'
-# vis_modes = ['first_group', 'final_group', 'input_pred_label']
-vis_modes = ['input_pred_label', 'final_group']
-output_labels = ['segmentation map', 'groups']
-dataset_options = ['Pascal VOC', 'Pascal Context', 'COCO']
-examples = [['Pascal VOC', '', 'hg_demo/voc.jpg'],
-            ['Pascal Context', '', 'hg_demo/ctx.jpg'],
-            ['COCO', '', 'hg_demo/coco.jpg']]
-
-PSEUDO_ARGS = namedtuple('PSEUDO_ARGS',
-                         ['cfg', 'opts', 'resume', 'vis', 'local_rank'])
-
-args = PSEUDO_ARGS(
-    cfg=cfg_path, opts=[], resume=checkpoint_url, vis=vis_modes, local_rank=0)
-
-cfg = get_config(args)
-
-with read_write(cfg):
-    cfg.evaluate.eval_only = True
-
-model = build_model(cfg.model)
-model = revert_sync_batchnorm(model)
-model.to(device)
-model.eval()
-
-load_checkpoint(cfg, model, None, None)
-
-text_transform = build_text_transform(False, cfg.data.text_aug, with_dc=False)
-test_pipeline = build_seg_demo_pipeline()
-
-
-def inference(dataset, additional_classes, input_img):
-    if dataset == 'voc' or dataset == 'Pascal VOC':
-        dataset_class = PascalVOCDataset
-        seg_cfg = 'segmentation/configs/_base_/datasets/pascal_voc12.py'
-    elif dataset == 'coco' or dataset == 'COCO':
-        dataset_class = COCOObjectDataset
-        seg_cfg = 'segmentation/configs/_base_/datasets/coco.py'
-    elif dataset == 'context' or dataset == 'Pascal Context':
-        dataset_class = PascalContextDataset
-        seg_cfg = 'segmentation/configs/_base_/datasets/pascal_context.py'
-    else:
-        raise ValueError('Unknown dataset: {}'.format(args.dataset))
-    with read_write(cfg):
-        cfg.evaluate.seg.cfg = seg_cfg
-        cfg.evaluate.seg.opts = ['test_cfg.mode=whole']
-
-    dataset_cfg = mmcv.Config()
-    dataset_cfg.CLASSES = list(dataset_class.CLASSES)
-    dataset_cfg.PALETTE = dataset_class.PALETTE.copy()
-
-    if len(additional_classes) > 0:
-        additional_classes = additional_classes.split(',')
-        additional_classes = list(
-            set(additional_classes) - set(dataset_cfg.CLASSES))
-        dataset_cfg.CLASSES.extend(additional_classes)
-        dataset_cfg.PALETTE.extend(GROUP_PALETTE[np.random.choice(
-            list(range(len(GROUP_PALETTE))), len(additional_classes))])
-    seg_model = build_seg_inference(model, dataset_cfg, text_transform,
-                                    cfg.evaluate.seg)
-
-    device = next(seg_model.parameters()).device
-    # prepare data
-    data = dict(img=input_img)
-    data = test_pipeline(data)
-    data = collate([data], samples_per_gpu=1)
-    if next(seg_model.parameters()).is_cuda:
-        # scatter to specified GPU
-        data = scatter(data, [device])[0]
-    else:
-        data['img_metas'] = [i.data[0] for i in data['img_metas']]
-    with torch.no_grad():
-        result = seg_model(return_loss=False, rescale=False, **data)
-
-    img_tensor = data['img'][0]
-    img_metas = data['img_metas'][0]
-    imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
-    assert len(imgs) == len(img_metas)
-
-    out_file_dict = dict()
-    for img, img_meta in zip(imgs, img_metas):
-        h, w, _ = img_meta['img_shape']
-        img_show = img[:h, :w, :]
-
-        # ori_h, ori_w = img_meta['ori_shape'][:-1]
-
-        # short_side = 448
-        # if ori_h > ori_w:
-        #     new_h, new_w = ori_h * short_side//ori_w , short_side
-        # else:
-        #     new_w, new_h = ori_w * short_side//ori_h , short_side
-
-        # img_show = mmcv.imresize(img_show, (new_w, new_h))
-
-        for vis_mode in vis_modes:
-            out_file = osp.join(output_dir, 'vis_imgs', vis_mode,
-                                f'{vis_mode}.jpg')
-            seg_model.show_result(img_show, img_tensor.to(device), result,
-                                  out_file, vis_mode)
-            out_file_dict[vis_mode] = out_file
-
-    return [out_file_dict[mode] for mode in vis_modes]
-
-
-title = 'GroupViT'
-
-description = """
-Gradio Demo for GroupViT: Semantic Segmentation Emerges from Text Supervision. \n
-You may click on of the examples or upload your own image. \n
-GroupViT could perform open vocabulary segmentation, you may input more classes (seperate by comma).
-"""
-
-article = """
-<p style='text-align: center'>
-<a href='https://arxiv.org/abs/2202.11094' target='_blank'>
-GroupViT: Semantic Segmentation Emerges from Text Supervision
-</a>
-|
-<a href='https://github.com/NVlabs/GroupViT' target='_blank'>Github Repo</a></p>
-"""
-
-gr.Interface(
-    inference,
-    inputs=[
-        gr.inputs.Dropdown(dataset_options, type='value', label='Category list'),
-        gr.inputs.Textbox(
-            lines=1, placeholder=None, default='', label='More classes'),
-        gr.inputs.Image(type='filepath')
-    ],
-    outputs=[gr.outputs.Image(label=label) for label in output_labels],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples).launch(enable_queue=True)

spaces/CVPR/WALT/mmdet/datasets/pipelines/test_time_aug.py

@@ -1,119 +0,0 @@
-import warnings
-
-import mmcv
-
-from ..builder import PIPELINES
-from .compose import Compose
-
-
-@PIPELINES.register_module()
-class MultiScaleFlipAug(object):
-    """Test-time augmentation with multiple scales and flipping.
-
-    An example configuration is as followed:
-
-    .. code-block::
-
-        img_scale=[(1333, 400), (1333, 800)],
-        flip=True,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ]
-
-    After MultiScaleFLipAug with above configuration, the results are wrapped
-    into lists of the same length as followed:
-
-    .. code-block::
-
-        dict(
-            img=[...],
-            img_shape=[...],
-            scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
-            flip=[False, True, False, True]
-            ...
-        )
-
-    Args:
-        transforms (list[dict]): Transforms to apply in each augmentation.
-        img_scale (tuple | list[tuple] | None): Images scales for resizing.
-        scale_factor (float | list[float] | None): Scale factors for resizing.
-        flip (bool): Whether apply flip augmentation. Default: False.
-        flip_direction (str | list[str]): Flip augmentation directions,
-            options are "horizontal" and "vertical". If flip_direction is list,
-            multiple flip augmentations will be applied.
-            It has no effect when flip == False. Default: "horizontal".
-    """
-
-    def __init__(self,
-                 transforms,
-                 img_scale=None,
-                 scale_factor=None,
-                 flip=False,
-                 flip_direction='horizontal'):
-        self.transforms = Compose(transforms)
-        assert (img_scale is None) ^ (scale_factor is None), (
-            'Must have but only one variable can be setted')
-        if img_scale is not None:
-            self.img_scale = img_scale if isinstance(img_scale,
-                                                     list) else [img_scale]
-            self.scale_key = 'scale'
-            assert mmcv.is_list_of(self.img_scale, tuple)
-        else:
-            self.img_scale = scale_factor if isinstance(
-                scale_factor, list) else [scale_factor]
-            self.scale_key = 'scale_factor'
-
-        self.flip = flip
-        self.flip_direction = flip_direction if isinstance(
-            flip_direction, list) else [flip_direction]
-        assert mmcv.is_list_of(self.flip_direction, str)
-        if not self.flip and self.flip_direction != ['horizontal']:
-            warnings.warn(
-                'flip_direction has no effect when flip is set to False')
-        if (self.flip
-                and not any([t['type'] == 'RandomFlip' for t in transforms])):
-            warnings.warn(
-                'flip has no effect when RandomFlip is not in transforms')
-
-    def __call__(self, results):
-        """Call function to apply test time augment transforms on results.
-
-        Args:
-            results (dict): Result dict contains the data to transform.
-
-        Returns:
-           dict[str: list]: The augmented data, where each value is wrapped
-               into a list.
-        """
-
-        aug_data = []
-        flip_args = [(False, None)]
-        if self.flip:
-            flip_args += [(True, direction)
-                          for direction in self.flip_direction]
-        for scale in self.img_scale:
-            for flip, direction in flip_args:
-                _results = results.copy()
-                _results[self.scale_key] = scale
-                _results['flip'] = flip
-                _results['flip_direction'] = direction
-                data = self.transforms(_results)
-                aug_data.append(data)
-        # list of dict to dict of list
-        aug_data_dict = {key: [] for key in aug_data[0]}
-        for data in aug_data:
-            for key, val in data.items():
-                aug_data_dict[key].append(val)
-        return aug_data_dict
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(transforms={self.transforms}, '
-        repr_str += f'img_scale={self.img_scale}, flip={self.flip}, '
-        repr_str += f'flip_direction={self.flip_direction})'
-        return repr_str

spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/data/dataloader.py

@@ -1,425 +0,0 @@
-import torch
-import torch.multiprocessing as multiprocessing
-from torch._C import _set_worker_signal_handlers, \
-    _remove_worker_pids, _error_if_any_worker_fails
-try:
-    from torch._C import _set_worker_pids
-except:
-    from torch._C import _update_worker_pids as _set_worker_pids
-from .sampler import SequentialSampler, RandomSampler, BatchSampler
-import signal
-import collections
-import re
-import sys
-import threading
-import traceback
-from torch._six import string_classes, int_classes
-import numpy as np
-
-if sys.version_info[0] == 2:
-    import Queue as queue
-else:
-    import queue
-
-
-class ExceptionWrapper(object):
-    r"Wraps an exception plus traceback to communicate across threads"
-
-    def __init__(self, exc_info):
-        self.exc_type = exc_info[0]
-        self.exc_msg = "".join(traceback.format_exception(*exc_info))
-
-
-_use_shared_memory = False
-"""Whether to use shared memory in default_collate"""
-
-
-def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
-    global _use_shared_memory
-    _use_shared_memory = True
-
-    # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
-    # module's handlers are executed after Python returns from C low-level
-    # handlers, likely when the same fatal signal happened again already.
-    # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
-    _set_worker_signal_handlers()
-
-    torch.set_num_threads(1)
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-
-    if init_fn is not None:
-        init_fn(worker_id)
-
-    while True:
-        r = index_queue.get()
-        if r is None:
-            break
-        idx, batch_indices = r
-        try:
-            samples = collate_fn([dataset[i] for i in batch_indices])
-        except Exception:
-            data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
-        else:
-            data_queue.put((idx, samples))
-
-
-def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id):
-    if pin_memory:
-        torch.cuda.set_device(device_id)
-
-    while True:
-        try:
-            r = in_queue.get()
-        except Exception:
-            if done_event.is_set():
-                return
-            raise
-        if r is None:
-            break
-        if isinstance(r[1], ExceptionWrapper):
-            out_queue.put(r)
-            continue
-        idx, batch = r
-        try:
-            if pin_memory:
-                batch = pin_memory_batch(batch)
-        except Exception:
-            out_queue.put((idx, ExceptionWrapper(sys.exc_info())))
-        else:
-            out_queue.put((idx, batch))
-
-numpy_type_map = {
-    'float64': torch.DoubleTensor,
-    'float32': torch.FloatTensor,
-    'float16': torch.HalfTensor,
-    'int64': torch.LongTensor,
-    'int32': torch.IntTensor,
-    'int16': torch.ShortTensor,
-    'int8': torch.CharTensor,
-    'uint8': torch.ByteTensor,
-}
-
-
-def default_collate(batch):
-    "Puts each data field into a tensor with outer dimension batch size"
-
-    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
-    elem_type = type(batch[0])
-    if torch.is_tensor(batch[0]):
-        out = None
-        if _use_shared_memory:
-            # If we're in a background process, concatenate directly into a
-            # shared memory tensor to avoid an extra copy
-            numel = sum([x.numel() for x in batch])
-            storage = batch[0].storage()._new_shared(numel)
-            out = batch[0].new(storage)
-        return torch.stack(batch, 0, out=out)
-    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
-            and elem_type.__name__ != 'string_':
-        elem = batch[0]
-        if elem_type.__name__ == 'ndarray':
-            # array of string classes and object
-            if re.search('[SaUO]', elem.dtype.str) is not None:
-                raise TypeError(error_msg.format(elem.dtype))
-
-            return torch.stack([torch.from_numpy(b) for b in batch], 0)
-        if elem.shape == ():  # scalars
-            py_type = float if elem.dtype.name.startswith('float') else int
-            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
-    elif isinstance(batch[0], int_classes):
-        return torch.LongTensor(batch)
-    elif isinstance(batch[0], float):
-        return torch.DoubleTensor(batch)
-    elif isinstance(batch[0], string_classes):
-        return batch
-    elif isinstance(batch[0], collections.Mapping):
-        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
-    elif isinstance(batch[0], collections.Sequence):
-        transposed = zip(*batch)
-        return [default_collate(samples) for samples in transposed]
-
-    raise TypeError((error_msg.format(type(batch[0]))))
-
-
-def pin_memory_batch(batch):
-    if torch.is_tensor(batch):
-        return batch.pin_memory()
-    elif isinstance(batch, string_classes):
-        return batch
-    elif isinstance(batch, collections.Mapping):
-        return {k: pin_memory_batch(sample) for k, sample in batch.items()}
-    elif isinstance(batch, collections.Sequence):
-        return [pin_memory_batch(sample) for sample in batch]
-    else:
-        return batch
-
-
-_SIGCHLD_handler_set = False
-"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one
-handler needs to be set for all DataLoaders in a process."""
-
-
-def _set_SIGCHLD_handler():
-    # Windows doesn't support SIGCHLD handler
-    if sys.platform == 'win32':
-        return
-    # can't set signal in child threads
-    if not isinstance(threading.current_thread(), threading._MainThread):
-        return
-    global _SIGCHLD_handler_set
-    if _SIGCHLD_handler_set:
-        return
-    previous_handler = signal.getsignal(signal.SIGCHLD)
-    if not callable(previous_handler):
-        previous_handler = None
-
-    def handler(signum, frame):
-        # This following call uses `waitid` with WNOHANG from C side. Therefore,
-        # Python can still get and update the process status successfully.
-        _error_if_any_worker_fails()
-        if previous_handler is not None:
-            previous_handler(signum, frame)
-
-    signal.signal(signal.SIGCHLD, handler)
-    _SIGCHLD_handler_set = True
-
-
-class DataLoaderIter(object):
-    "Iterates once over the DataLoader's dataset, as specified by the sampler"
-
-    def __init__(self, loader):
-        self.dataset = loader.dataset
-        self.collate_fn = loader.collate_fn
-        self.batch_sampler = loader.batch_sampler
-        self.num_workers = loader.num_workers
-        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
-        self.timeout = loader.timeout
-        self.done_event = threading.Event()
-
-        self.sample_iter = iter(self.batch_sampler)
-
-        if self.num_workers > 0:
-            self.worker_init_fn = loader.worker_init_fn
-            self.index_queue = multiprocessing.SimpleQueue()
-            self.worker_result_queue = multiprocessing.SimpleQueue()
-            self.batches_outstanding = 0
-            self.worker_pids_set = False
-            self.shutdown = False
-            self.send_idx = 0
-            self.rcvd_idx = 0
-            self.reorder_dict = {}
-
-            base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]
-            self.workers = [
-                multiprocessing.Process(
-                    target=_worker_loop,
-                    args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
-                          base_seed + i, self.worker_init_fn, i))
-                for i in range(self.num_workers)]
-
-            if self.pin_memory or self.timeout > 0:
-                self.data_queue = queue.Queue()
-                if self.pin_memory:
-                    maybe_device_id = torch.cuda.current_device()
-                else:
-                    # do not initialize cuda context if not necessary
-                    maybe_device_id = None
-                self.worker_manager_thread = threading.Thread(
-                    target=_worker_manager_loop,
-                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
-                          maybe_device_id))
-                self.worker_manager_thread.daemon = True
-                self.worker_manager_thread.start()
-            else:
-                self.data_queue = self.worker_result_queue
-
-            for w in self.workers:
-                w.daemon = True  # ensure that the worker exits on process exit
-                w.start()
-
-            _set_worker_pids(id(self), tuple(w.pid for w in self.workers))
-            _set_SIGCHLD_handler()
-            self.worker_pids_set = True
-
-            # prime the prefetch loop
-            for _ in range(2 * self.num_workers):
-                self._put_indices()
-
-    def __len__(self):
-        return len(self.batch_sampler)
-
-    def _get_batch(self):
-        if self.timeout > 0:
-            try:
-                return self.data_queue.get(timeout=self.timeout)
-            except queue.Empty:
-                raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))
-        else:
-            return self.data_queue.get()
-
-    def __next__(self):
-        if self.num_workers == 0:  # same-process loading
-            indices = next(self.sample_iter)  # may raise StopIteration
-            batch = self.collate_fn([self.dataset[i] for i in indices])
-            if self.pin_memory:
-                batch = pin_memory_batch(batch)
-            return batch
-
-        # check if the next sample has already been generated
-        if self.rcvd_idx in self.reorder_dict:
-            batch = self.reorder_dict.pop(self.rcvd_idx)
-            return self._process_next_batch(batch)
-
-        if self.batches_outstanding == 0:
-            self._shutdown_workers()
-            raise StopIteration
-
-        while True:
-            assert (not self.shutdown and self.batches_outstanding > 0)
-            idx, batch = self._get_batch()
-            self.batches_outstanding -= 1
-            if idx != self.rcvd_idx:
-                # store out-of-order samples
-                self.reorder_dict[idx] = batch
-                continue
-            return self._process_next_batch(batch)
-
-    next = __next__  # Python 2 compatibility
-
-    def __iter__(self):
-        return self
-
-    def _put_indices(self):
-        assert self.batches_outstanding < 2 * self.num_workers
-        indices = next(self.sample_iter, None)
-        if indices is None:
-            return
-        self.index_queue.put((self.send_idx, indices))
-        self.batches_outstanding += 1
-        self.send_idx += 1
-
-    def _process_next_batch(self, batch):
-        self.rcvd_idx += 1
-        self._put_indices()
-        if isinstance(batch, ExceptionWrapper):
-            raise batch.exc_type(batch.exc_msg)
-        return batch
-
-    def __getstate__(self):
-        # TODO: add limited pickling support for sharing an iterator
-        # across multiple threads for HOGWILD.
-        # Probably the best way to do this is by moving the sample pushing
-        # to a separate thread and then just sharing the data queue
-        # but signalling the end is tricky without a non-blocking API
-        raise NotImplementedError("DataLoaderIterator cannot be pickled")
-
-    def _shutdown_workers(self):
-        try:
-            if not self.shutdown:
-                self.shutdown = True
-                self.done_event.set()
-                # if worker_manager_thread is waiting to put
-                while not self.data_queue.empty():
-                    self.data_queue.get()
-                for _ in self.workers:
-                    self.index_queue.put(None)
-                # done_event should be sufficient to exit worker_manager_thread,
-                # but be safe here and put another None
-                self.worker_result_queue.put(None)
-        finally:
-            # removes pids no matter what
-            if self.worker_pids_set:
-                _remove_worker_pids(id(self))
-                self.worker_pids_set = False
-
-    def __del__(self):
-        if self.num_workers > 0:
-            self._shutdown_workers()
-
-
-class DataLoader(object):
-    """
-    Data loader. Combines a dataset and a sampler, and provides
-    single- or multi-process iterators over the dataset.
-
-    Arguments:
-        dataset (Dataset): dataset from which to load the data.
-        batch_size (int, optional): how many samples per batch to load
-            (default: 1).
-        shuffle (bool, optional): set to ``True`` to have the data reshuffled
-            at every epoch (default: False).
-        sampler (Sampler, optional): defines the strategy to draw samples from
-            the dataset. If specified, ``shuffle`` must be False.
-        batch_sampler (Sampler, optional): like sampler, but returns a batch of
-            indices at a time. Mutually exclusive with batch_size, shuffle,
-            sampler, and drop_last.
-        num_workers (int, optional): how many subprocesses to use for data
-            loading. 0 means that the data will be loaded in the main process.
-            (default: 0)
-        collate_fn (callable, optional): merges a list of samples to form a mini-batch.
-        pin_memory (bool, optional): If ``True``, the data loader will copy tensors
-            into CUDA pinned memory before returning them.
-        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
-            if the dataset size is not divisible by the batch size. If ``False`` and
-            the size of dataset is not divisible by the batch size, then the last batch
-            will be smaller. (default: False)
-        timeout (numeric, optional): if positive, the timeout value for collecting a batch
-            from workers. Should always be non-negative. (default: 0)
-        worker_init_fn (callable, optional): If not None, this will be called on each
-            worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
-            input, after seeding and before data loading. (default: None)
-
-    .. note:: By default, each worker will have its PyTorch seed set to
-              ``base_seed + worker_id``, where ``base_seed`` is a long generated
-              by main process using its RNG. You may use ``torch.initial_seed()`` to access
-              this value in :attr:`worker_init_fn`, which can be used to set other seeds
-              (e.g. NumPy) before data loading.
-
-    .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an
-                 unpicklable object, e.g., a lambda function.
-    """
-
-    def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
-                 num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,
-                 timeout=0, worker_init_fn=None):
-        self.dataset = dataset
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-        self.collate_fn = collate_fn
-        self.pin_memory = pin_memory
-        self.drop_last = drop_last
-        self.timeout = timeout
-        self.worker_init_fn = worker_init_fn
-
-        if timeout < 0:
-            raise ValueError('timeout option should be non-negative')
-
-        if batch_sampler is not None:
-            if batch_size > 1 or shuffle or sampler is not None or drop_last:
-                raise ValueError('batch_sampler is mutually exclusive with '
-                                 'batch_size, shuffle, sampler, and drop_last')
-
-        if sampler is not None and shuffle:
-            raise ValueError('sampler is mutually exclusive with shuffle')
-
-        if self.num_workers < 0:
-            raise ValueError('num_workers cannot be negative; '
-                             'use num_workers=0 to disable multiprocessing.')
-
-        if batch_sampler is None:
-            if sampler is None:
-                if shuffle:
-                    sampler = RandomSampler(dataset)
-                else:
-                    sampler = SequentialSampler(dataset)
-            batch_sampler = BatchSampler(sampler, batch_size, drop_last)
-
-        self.sampler = sampler
-        self.batch_sampler = batch_sampler
-
-    def __iter__(self):
-        return DataLoaderIter(self)
-
-    def __len__(self):
-        return len(self.batch_sampler)

spaces/CVPR/regionclip-demo/detectron2/utils/file_io.py

@@ -1,37 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from iopath.common.file_io import HTTPURLHandler, OneDrivePathHandler, PathHandler
-from iopath.common.file_io import PathManager as PathManagerBase
-
-__all__ = ["PathManager", "PathHandler"]
-
-
-PathManager = PathManagerBase()
-"""
-This is a detectron2 project-specific PathManager.
-We try to stay away from global PathManager in fvcore as it
-introduces potential conflicts among other libraries.
-"""
-
-
-class Detectron2Handler(PathHandler):
-    """
-    Resolve anything that's hosted under detectron2's namespace.
-    """
-
-    PREFIX = "detectron2://"
-    S3_DETECTRON2_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
-
-    def _get_supported_prefixes(self):
-        return [self.PREFIX]
-
-    def _get_local_path(self, path, **kwargs):
-        name = path[len(self.PREFIX) :]
-        return PathManager.get_local_path(self.S3_DETECTRON2_PREFIX + name, **kwargs)
-
-    def _open(self, path, mode="r", **kwargs):
-        return PathManager.open(self._get_local_path(path), mode, **kwargs)
-
-
-PathManager.register_handler(HTTPURLHandler())
-PathManager.register_handler(OneDrivePathHandler())
-PathManager.register_handler(Detectron2Handler())

spaces/ChallengeHub/Chinese-LangChain/corpus/zh_wikipedia/clean_corpus.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python
-# -*- coding:utf-8 _*-
-"""
-@author:quincy qiang
-@license: Apache Licence
-@file: clean_corpus.py.py
-@time: 2023/04/19
-@contact: [email protected]
-@software: PyCharm
-@description: coding..
-"""
-"""
-    FILE :  clean_corpus.py
-    FUNCTION : None
-"""
-import sys
-import os
-from optparse import OptionParser
-
-
-class Clean(object):
-    def __init__(self, infile, outfile):
-        self.infile = infile
-        self.outfile = outfile
-        self.corpus = []
-        self.remove_corpus = []
-        self.read(self.infile)
-        self.remove(self.corpus)
-        self.write(self.remove_corpus, self.outfile)
-
-    def read(self, path):
-        print("reading now......")
-        if os.path.isfile(path) is False:
-            print("path is not a file")
-            exit()
-        now_line = 0
-        with open(path, encoding="UTF-8") as f:
-            for line in f:
-                now_line += 1
-                line = line.replace("\n", "").replace("\t", "")
-                self.corpus.append(line)
-        print("read finished.")
-
-    def remove(self, list):
-        print("removing now......")
-        for line in list:
-            re_list = []
-            for word in line:
-                if self.is_chinese(word) is False:
-                    continue
-                re_list.append(word)
-            self.remove_corpus.append("".join(re_list))
-        print("remove finished.")
-
-    def write(self, list, path):
-        print("writing now......")
-        if os.path.exists(path):
-            os.remove(path)
-        file = open(path, encoding="UTF-8", mode="w")
-        for line in list:
-            file.writelines(line + "\n")
-        file.close()
-        print("writing finished")
-
-    def is_chinese(self, uchar):
-        """判断一个unicode是否是汉字"""
-        if (uchar >= u'\u4e00') and (uchar <= u'\u9fa5'):
-            return True
-        else:
-            return False
-
-
-if __name__ == "__main__":
-    print("clean corpus")
-
-    parser = OptionParser()
-    parser.add_option("--input", dest="input", default="", help="input file")
-    parser.add_option("--output", dest="output", default="", help="output file")
-    (options, args) = parser.parse_args()
-
-    input = options.input
-    output = options.output
-
-    try:
-        Clean(infile=input, outfile=output)
-        print("All Finished.")
-    except Exception as err:
-        print(err)

spaces/Curranj/Regex_Generator/README.md

@@ -1,12 +0,0 @@
----
-title: Regex
-emoji: 📉
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-34e368b6.js

@@ -1,2 +0,0 @@
-import{S as P,e as Q,s as R,N as I,O as U,P as G,K as k,U as z,p as j,M as C,Q as A,R as H,n as D,A as B,a1 as V,B as W,am as X,k as S,o as T,z as h,v,x as q,E as Y,ae as Z,h as F,j as K,q as p,r as y,u as x,y as $,t as M,F as N}from"./index-1d65707a.js";/* empty css                                                  */import{B as ee}from"./Button-f155035a.js";import{I as te}from"./Info-7c6961ef.js";function ae(n){let e,t,a,l,u,o,c;return{c(){e=I("label"),t=I("input"),a=U(),l=I("span"),u=G(n[2]),t.disabled=n[1],k(t,"type","checkbox"),k(t,"name","test"),k(t,"data-testid","checkbox"),k(t,"class","svelte-1ojmf70"),k(l,"class","ml-2 svelte-1ojmf70"),k(e,"class","svelte-1ojmf70"),z(e,"disabled",n[1])},m(_,d){j(_,e,d),C(e,t),t.checked=n[0],C(e,a),C(e,l),C(l,u),o||(c=[A(t,"change",n[5]),A(t,"input",n[6])],o=!0)},p(_,[d]){d&2&&(t.disabled=_[1]),d&1&&(t.checked=_[0]),d&4&&H(u,_[2]),d&2&&z(e,"disabled",_[1])},i:D,o:D,d(_){_&&B(e),o=!1,V(c)}}}function ne(n,e,t){let{value:a}=e,{value_is_output:l=!1}=e,{disabled:u=!1}=e,{label:o}=e;const c=W();function _(){c("change",a),l||c("input")}X(()=>{t(4,l=!1)});function d(){a=this.checked,t(0,a)}const f=m=>{t(0,a=m.currentTarget.checked),c("select",{index:0,value:o,selected:m.currentTarget.checked})};return n.$$set=m=>{"value"in m&&t(0,a=m.value),"value_is_output"in m&&t(4,l=m.value_is_output),"disabled"in m&&t(1,u=m.disabled),"label"in m&&t(2,o=m.label)},n.$$.update=()=>{n.$$.dirty&1&&_()},[a,u,o,c,l,d,f]}class le extends P{constructor(e){super(),Q(this,e,ne,ae,R,{value:0,value_is_output:4,disabled:1,label:2})}}function O(n){let e,t;return e=new te({props:{$$slots:{default:[se]},$$scope:{ctx:n}}}),{c(){S(e.$$.fragment)},m(a,l){T(e,a,l),t=!0},p(a,l){const u={};l&131136&&(u.$$scope={dirty:l,ctx:a}),e.$set(u)},i(a){t||(h(e.$$.fragment,a),t=!0)},o(a){v(e.$$.fragment,a),t=!1},d(a){q(e,a)}}}function se(n){let e;return{c(){e=G(n[6])},m(t,a){j(t,e,a)},p(t,a){a&64&&H(e,t[6])},d(t){t&&B(e)}}}function ie(n){let e,t,a,l,u,o,c;const _=[n[11]];let d={};for(let s=0;s<_.length;s+=1)d=Y(d,_[s]);e=new Z({props:d});let f=n[6]&&O(n);function m(s){n[12](s)}function w(s){n[13](s)}let g={label:n[5],disabled:n[7]==="static"};return n[0]!==void 0&&(g.value=n[0]),n[1]!==void 0&&(g.value_is_output=n[1]),l=new le({props:g}),F.push(()=>K(l,"value",m)),F.push(()=>K(l,"value_is_output",w)),l.$on("change",n[14]),l.$on("input",n[15]),l.$on("select",n[16]),{c(){S(e.$$.fragment),t=U(),f&&f.c(),a=U(),S(l.$$.fragment)},m(s,b){T(e,s,b),j(s,t,b),f&&f.m(s,b),j(s,a,b),T(l,s,b),c=!0},p(s,b){const E=b&2048?p(_,[y(s[11])]):{};e.$set(E),s[6]?f?(f.p(s,b),b&64&&h(f,1)):(f=O(s),f.c(),h(f,1),f.m(a.parentNode,a)):f&&(x(),v(f,1,1,()=>{f=null}),$());const r={};b&32&&(r.label=s[5]),b&128&&(r.disabled=s[7]==="static"),!u&&b&1&&(u=!0,r.value=s[0],M(()=>u=!1)),!o&&b&2&&(o=!0,r.value_is_output=s[1],M(()=>o=!1)),l.$set(r)},i(s){c||(h(e.$$.fragment,s),h(f),h(l.$$.fragment,s),c=!0)},o(s){v(e.$$.fragment,s),v(f),v(l.$$.fragment,s),c=!1},d(s){s&&(B(t),B(a)),q(e,s),f&&f.d(s),q(l,s)}}}function ue(n){let e,t;return e=new ee({props:{visible:n[4],elem_id:n[2],elem_classes:n[3],container:n[8],scale:n[9],min_width:n[10],$$slots:{default:[ie]},$$scope:{ctx:n}}}),{c(){S(e.$$.fragment)},m(a,l){T(e,a,l),t=!0},p(a,[l]){const u={};l&16&&(u.visible=a[4]),l&4&&(u.elem_id=a[2]),l&8&&(u.elem_classes=a[3]),l&256&&(u.container=a[8]),l&512&&(u.scale=a[9]),l&1024&&(u.min_width=a[10]),l&133347&&(u.$$scope={dirty:l,ctx:a}),e.$set(u)},i(a){t||(h(e.$$.fragment,a),t=!0)},o(a){v(e.$$.fragment,a),t=!1},d(a){q(e,a)}}}function fe(n,e,t){let{elem_id:a=""}=e,{elem_classes:l=[]}=e,{visible:u=!0}=e,{value:o=!1}=e,{value_is_output:c=!1}=e,{label:_="Checkbox"}=e,{info:d=void 0}=e,{mode:f}=e,{container:m=!0}=e,{scale:w=null}=e,{min_width:g=void 0}=e,{loading_status:s}=e;function b(i){o=i,t(0,o)}function E(i){c=i,t(1,c)}function r(i){N.call(this,n,i)}function J(i){N.call(this,n,i)}function L(i){N.call(this,n,i)}return n.$$set=i=>{"elem_id"in i&&t(2,a=i.elem_id),"elem_classes"in i&&t(3,l=i.elem_classes),"visible"in i&&t(4,u=i.visible),"value"in i&&t(0,o=i.value),"value_is_output"in i&&t(1,c=i.value_is_output),"label"in i&&t(5,_=i.label),"info"in i&&t(6,d=i.info),"mode"in i&&t(7,f=i.mode),"container"in i&&t(8,m=i.container),"scale"in i&&t(9,w=i.scale),"min_width"in i&&t(10,g=i.min_width),"loading_status"in i&&t(11,s=i.loading_status)},[o,c,a,l,u,_,d,f,m,w,g,s,b,E,r,J,L]}class ce extends P{constructor(e){super(),Q(this,e,fe,ue,R,{elem_id:2,elem_classes:3,visible:4,value:0,value_is_output:1,label:5,info:6,mode:7,container:8,scale:9,min_width:10,loading_status:11})}}const be=ce,re=["static","dynamic"],he=n=>({type:{payload:"boolean"},description:{payload:"checked status"},example_data:n.value});export{be as Component,he as document,re as modes};
-//# sourceMappingURL=index-34e368b6.js.map

spaces/DaleChen/AutoGPT/autogpt/config/ai_config.py

@@ -1,121 +0,0 @@
-# sourcery skip: do-not-use-staticmethod
-"""
-A module that contains the AIConfig class object that contains the configuration
-"""
-from __future__ import annotations
-
-import os
-from typing import Type
-
-import yaml
-
-
-class AIConfig:
-    """
-    A class object that contains the configuration information for the AI
-
-    Attributes:
-        ai_name (str): The name of the AI.
-        ai_role (str): The description of the AI's role.
-        ai_goals (list): The list of objectives the AI is supposed to complete.
-    """
-
-    def __init__(
-        self, ai_name: str = "", ai_role: str = "", ai_goals: list | None = None
-    ) -> None:
-        """
-        Initialize a class instance
-
-        Parameters:
-            ai_name (str): The name of the AI.
-            ai_role (str): The description of the AI's role.
-            ai_goals (list): The list of objectives the AI is supposed to complete.
-        Returns:
-            None
-        """
-        if ai_goals is None:
-            ai_goals = []
-        self.ai_name = ai_name
-        self.ai_role = ai_role
-        self.ai_goals = ai_goals
-
-    # Soon this will go in a folder where it remembers more stuff about the run(s)
-    SAVE_FILE = os.path.join(os.path.dirname(__file__), "..", "ai_settings.yaml")
-
-    @staticmethod
-    def load(config_file: str = SAVE_FILE) -> "AIConfig":
-        """
-        Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from
-          yaml file if yaml file exists,
-        else returns class with no parameters.
-
-        Parameters:
-           config_file (int): The path to the config yaml file.
-             DEFAULT: "../ai_settings.yaml"
-
-        Returns:
-            cls (object): An instance of given cls object
-        """
-
-        try:
-            with open(config_file, encoding="utf-8") as file:
-                config_params = yaml.load(file, Loader=yaml.FullLoader)
-        except FileNotFoundError:
-            config_params = {}
-
-        ai_name = config_params.get("ai_name", "")
-        ai_role = config_params.get("ai_role", "")
-        ai_goals = config_params.get("ai_goals", [])
-        # type: Type[AIConfig]
-        return AIConfig(ai_name, ai_role, ai_goals)
-
-    def save(self, config_file: str = SAVE_FILE) -> None:
-        """
-        Saves the class parameters to the specified file yaml file path as a yaml file.
-
-        Parameters:
-            config_file(str): The path to the config yaml file.
-              DEFAULT: "../ai_settings.yaml"
-
-        Returns:
-            None
-        """
-
-        config = {
-            "ai_name": self.ai_name,
-            "ai_role": self.ai_role,
-            "ai_goals": self.ai_goals,
-        }
-        with open(config_file, "w", encoding="utf-8") as file:
-            yaml.dump(config, file, allow_unicode=True)
-
-    def construct_full_prompt(self) -> str:
-        """
-        Returns a prompt to the user with the class information in an organized fashion.
-
-        Parameters:
-            None
-
-        Returns:
-            full_prompt (str): A string containing the initial prompt for the user
-              including the ai_name, ai_role and ai_goals.
-        """
-
-        prompt_start = (
-            "Your decisions must always be made independently without"
-            " seeking user assistance. Play to your strengths as an LLM and pursue"
-            " simple strategies with no legal complications."
-            ""
-        )
-
-        from autogpt.prompt import get_prompt
-
-        # Construct full prompt
-        full_prompt = (
-            f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
-        )
-        for i, goal in enumerate(self.ai_goals):
-            full_prompt += f"{i+1}. {goal}\n"
-
-        full_prompt += f"\n\n{get_prompt()}"
-        return full_prompt

spaces/Detomo/ai-comic-generation/src/lib/uploadToHuggingFace.ts

@@ -1,16 +0,0 @@
-export async function uploadToHuggingFace(file: File) {
-  const UPLOAD_URL = 'https://huggingface.co/uploads'
-
-  const response = await fetch(UPLOAD_URL, {
-    method: 'POST',
-    headers: {
-      'Content-Type': file.type,
-      'X-Requested-With': 'XMLHttpRequest',
-    },
-    body: file, /// <- File inherits from Blob
-  })
-
-  const url = await response.text()
-
-  return url
-}

spaces/DiffusionArtco/Diffusion50/README.md

@@ -1,13 +0,0 @@
----
-title: ImagineAI Imagine Generator
-emoji: 💩
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-duplicated_from: DiffusionArtco/AnimeTop50
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DragGan/DragGan-Inversion/gen_images.py

@@ -1,160 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generate images using pretrained network pickle."""
-
-import os
-import re
-from typing import List, Optional, Tuple, Union
-
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-
-import legacy
-
-# ----------------------------------------------------------------------------
-
-
-def parse_range(s: Union[str, List]) -> List[int]:
-    '''Parse a comma separated list of numbers or ranges and return a list of ints.
-
-    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
-    '''
-    if isinstance(s, list):
-        return s
-    ranges = []
-    range_re = re.compile(r'^(\d+)-(\d+)$')
-    for p in s.split(','):
-        m = range_re.match(p)
-        if m:
-            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
-        else:
-            ranges.append(int(p))
-    return ranges
-
-# ----------------------------------------------------------------------------
-
-
-def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
-    '''Parse a floating point 2-vector of syntax 'a,b'.
-
-    Example:
-        '0,1' returns (0,1)
-    '''
-    if isinstance(s, tuple):
-        return s
-    parts = s.split(',')
-    if len(parts) == 2:
-        return (float(parts[0]), float(parts[1]))
-    raise ValueError(f'cannot parse 2-vector {s}')
-
-# ----------------------------------------------------------------------------
-
-
-def make_transform(translate: Tuple[float, float], angle: float):
-    m = np.eye(3)
-    s = np.sin(angle/360.0*np.pi*2)
-    c = np.cos(angle/360.0*np.pi*2)
-    m[0][0] = c
-    m[0][1] = s
-    m[0][2] = translate[0]
-    m[1][0] = -s
-    m[1][1] = c
-    m[1][2] = translate[1]
-    return m
-
-# ----------------------------------------------------------------------------
-
-
-@click.command()
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', required=True)
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--translate', help='Translate XY-coordinate (e.g. \'0.3,1\')', type=parse_vec2, default='0,0', show_default=True, metavar='VEC2')
-@click.option('--rotate', help='Rotation angle in degrees', type=float, default=0, show_default=True, metavar='ANGLE')
-@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
-def generate_images(
-    network_pkl: str,
-    seeds: List[int],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str,
-    translate: Tuple[float, float],
-    rotate: float,
-    class_idx: Optional[int]
-):
-    """Generate images using pretrained network pickle.
-
-    Examples:
-
-    \b
-    # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
-    python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
-        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
-
-    \b
-    # Generate uncurated images with truncation using the MetFaces-U dataset
-    python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
-        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
-    """
-
-    print('Loading networks from "%s"...' % network_pkl)
-    device = torch.device('cuda')
-    with dnnlib.util.open_url(network_pkl) as f:
-        G = legacy.load_network_pkl(f)['G_ema'].to(device)  # type: ignore
-        # import pickle
-        # G = legacy.load_network_pkl(f)
-        # output = open('checkpoints/stylegan2-car-config-f-pt.pkl', 'wb')
-        # pickle.dump(G, output)
-
-    os.makedirs(outdir, exist_ok=True)
-
-    # Labels.
-    label = torch.zeros([1, G.c_dim], device=device)
-    if G.c_dim != 0:
-        if class_idx is None:
-            raise click.ClickException(
-                'Must specify class label with --class when using a conditional network')
-        label[:, class_idx] = 1
-    else:
-        if class_idx is not None:
-            print('warn: --class=lbl ignored when running on an unconditional network')
-
-    # Generate images.
-    for seed_idx, seed in enumerate(seeds):
-        print('Generating image for seed %d (%d/%d) ...' %
-              (seed, seed_idx, len(seeds)))
-        z = torch.from_numpy(np.random.RandomState(
-            seed).randn(1, G.z_dim)).to(device)
-
-        # Construct an inverse rotation/translation matrix and pass to the generator.  The
-        # generator expects this matrix as an inverse to avoid potentially failing numerical
-        # operations in the network.
-        if hasattr(G.synthesis, 'input'):
-            m = make_transform(translate, rotate)
-            m = np.linalg.inv(m)
-            G.synthesis.input.transform.copy_(torch.from_numpy(m))
-
-        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
-        img = (img.permute(0, 2, 3, 1) * 127.5 +
-               128).clamp(0, 255).to(torch.uint8)
-        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(
-            f'{outdir}/seed{seed:04d}.png')
-
-
-# ----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    generate_images()  # pylint: disable=no-value-for-parameter
-
-# ----------------------------------------------------------------------------