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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Diskeeper PREMIER EDITION 12.0Build 758.FINAL WORKING .rar The Ultimate Solution for Disk Defragmentation.md

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-<h1>Diskeeper PREMIER EDITION 12.0Build 758.FINAL WORKING .rar</h1>
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-<p>If you are looking for a way to optimize your computer's performance and prevent disk fragmentation, you might be interested in <strong>Diskeeper PREMIER EDITION 12.0Build 758.FINAL WORKING .rar</strong>. This is a compressed file that contains the installation files for <strong>Diskeeper PREMIER EDITION 12.0</strong>, a powerful disk defragmentation software that can improve your system speed, reliability, and efficiency.</p>
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- <p>In this article, I will explain what <strong>Diskeeper PREMIER EDITION 12.0</strong> is, how it works, what are its features and benefits, and how to download and install it from the .rar file. I will also answer some frequently asked questions about this software and provide some tips and tricks to get the most out of it.</p>
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-<p><strong>Diskeeper PREMIER EDITION 12.0</strong> works by using two main methods to optimize your disk performance: defragmentation and free space consolidation.</p>
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-<p>Defragmentation is the process of rearranging the files on your disk so that they are stored in contiguous blocks, making them easier and faster to access. <strong>Diskeeper PREMIER EDITION 12.0</strong> can defragment your files in real-time, as soon as they are created or modified, or on a scheduled basis, depending on your preferences.</p>
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- <h2>What are the features and benefits of Diskeeper PREMIER EDITION 12.0?</h2>
-<p><strong>Diskeeper PREMIER EDITION 12.0</strong> has many features and benefits that make it a superior disk defragmentation software. Some of them are:</p>
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-<li>It has a user-friendly interface that allows you to customize your settings and preferences easily.</li>
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- <h2>How to download and install Diskeeper PREMIER EDITION 12.0 from the .rar file?</h2>
-<p>To download and install <strong>Diskeeper PREMIER EDITION 12.0</strong> from the .rar file, you need to follow these steps:</p>
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-<li>Download the .rar file from one of the web search results. Make sure you have enough disk space to store the file.</li>
-<li>Extract the .rar file using a software like WinRAR or 7-Zip. You will get a folder containing the installation files for <strong>Diskeeper PREMIER EDITION 12.0</strong>.</li>
-<li>Run the setup.exe file as an administrator and follow the instructions on the screen. You will need to accept the license agreement, choose the installation location, select the components you want to install, etc.</li>
-<li>After the installation is complete, you will need to restart your computer for the changes to take effect.</li>
-<li>Launch <strong>Diskeeper PREMIER EDITION 12.0</strong> from the Start menu or the desktop shortcut and enjoy its features.</li>
-</ol>
- <h2>Conclusion</h2>
-<p><strong>Diskeeper PREMIER EDITION 12.0</strong> is a powerful disk defragmentation software that can improve your system performance, reliability, and efficiency by preventing fragmentation and optimizing your disk space. It can run in the background without affecting your system resources or interfering with your work. It can also monitor your disk health and alert you of any potential problems or failures.</p>
- <p>If you want to download and install <strong>Diskeeper PREMIER EDITION 12.0</strong> from the .rar file, you need to follow the steps explained above. You will need a software like WinRAR or 7-Zip to extract the .rar file and then run the setup.exe file as an administrator.</p>
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- <p>I hope this article has helped you understand what <strong>Diskeeper PREMIER EDITION 12.0</strong> is, how it works, what are its features and benefits, and how to download and install it from the .rar file.</p>
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-<p>Here are some frequently asked questions about <strong>Diskeeper PREMIER EDITION 12.0</strong>:</p>
- <h4>Q: How much does Diskeeper PREMIER EDITION 12.0 cost?</h4>
-<p>A: Diskeeper PREMIER EDITION 12.0 is not a free software. You need to purchase a license key to activate it after installing it from the .rar file.</p>
- <h4>Q: How do I activate Diskeeper PREMIER EDITION 12.0?</h4>
-<p>A: You need to enter your license key in the activation window that appears when you launch Diskeeper PREMIER EDITION 12.0 for the first time. You can also access the activation window from the Help menu.</p>
- <h4>Q: How do I customize Diskeeper PREMIER EDITION 12.0 settings?</h4>
-<p>A: You can access Diskeeper PREMIER EDITION 12.0 settings from the Dashboard menu or by right-clicking on its icon in the system tray. You can change various options such as defragmentation mode, schedule, performance settings, alerts settings, etc.</p>
- <h4>Q: How do I check my disk performance and health?</h4>
-<p>A: You can check your disk performance and health from the Reports menu or by clicking on the Analyze button in Diskeeper PREMIER EDITION 12.0 interface. You can view various statistics such as fragmentation level, free space level, disk temperature, disk age, etc.</p>
- <h4>Q: How do I uninstall Diskeeper PREMIER EDITION 12.0?</h4>
-<p>A: You can uninstall Diskeeper PREMIER EDITION 12.0 from the Control Panel or by running the uninstall.exe file in its installation folder.</p>
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-<h1>San Andreas Download APKPure: How to Play the Classic GTA Game on Your Android Device</h1>
-<p>If you are a fan of the Grand Theft Auto (GTA) series, you probably have played or heard of San Andreas, one of the most popular and acclaimed titles in the franchise. Released in 2004 for PlayStation 2, Xbox, and PC, San Andreas is an action-adventure game that follows the story of Carl Johnson, a former gangster who returns to his hometown of Los Santos after his mother's death. There, he gets involved in a series of events that take him across the state of San Andreas, which is based on California and Nevada.</p>
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-<p>If you want to play San Andreas on your Android device, you might be wondering how to do it. One of the easiest and safest ways is to download it from APKPure, a website and app that offers free and secure APK files for Android users. In this article, we will show you how to download and install San Andreas from APKPure, as well as some tips and tricks for playing it on your Android device.</p>
- <h2>Introduction</h2>
-<h3>What is San Andreas?</h3>
-<p>San Andreas is the seventh main installment in the GTA series, developed by Rockstar North and published by Rockstar Games. It is set in 1992, in a fictionalized version of California and Nevada called San Andreas. The game follows the adventures of Carl Johnson (CJ), who returns to his hometown of Los Santos after five years of living in Liberty City. He soon finds out that his old gang, the Grove Street Families, has been weakened by drugs and corruption, and that his former friends and enemies are involved in a conspiracy that threatens his life and family.</p>
-<p>The game features a nonlinear gameplay style that allows players to explore the three cities of Los Santos, San Fierro, and Las Venturas, as well as the rural areas and deserts of San Andreas. Players can also customize CJ's appearance, skills, weapons, vehicles, and properties. The game also offers a variety of missions, side quests, activities, minigames, collectibles, and secrets to discover. The game also has a multiplayer mode that supports up to two players on the same console.</p>
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-<p>APKPure is a website and app that provides free and safe APK files for Android users. APK stands for Android Package Kit, which is a file format that contains all the elements needed to install an app on an Android device. APK files are usually downloaded from the Google Play Store, but sometimes they are not available or compatible with certain devices or regions. That's where APKPure comes in handy.</p>
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-<li>You can enjoy the original version. San Andreas has been updated and modified several times since its release in 2004. Some of these changes might have affected the gameplay, graphics, sound, or content of the game. For example, some songs from the original soundtrack have been removed due to licensing issues. However, you can download the original version of San Andreas from APKPure, which preserves the game as it was originally intended.</li>
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-<h3>Step 2: Search for San Andreas and tap on the download button</h3>
-<p>Once you are on the APKPure website or app, use the search bar to look for San Andreas. You should see a list of results that match your query. Tap on the one that says "Grand Theft Auto: San Andreas". You should see a page with more information about the game, such as its description, screenshots, ratings, reviews, and more. Tap on the green download button to start downloading the APK file.</p>
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- <h2>Tips and tricks for playing San Andreas on your Android device</h2>
-<p>Playing San Andreas on your Android device can be a lot of fun, but it can also be challenging at times. Here are some tips and tricks that can help you improve your gaming experience:</p>
-<h3>Use a controller or a keyboard for better control</h3>
-<p>San Andreas was originally designed for consoles and PCs, which have different control schemes than mobile devices. The game has been adapted to work with touchscreens, but some players might find it hard to control CJ's movements, actions, and camera angles with their fingers. If you want to have more precise and comfortable control over the game, you can use a controller or a keyboard that is compatible with your Android device. You can connect them via Bluetooth or USB and customize the buttons according to your preferences.</p>
-<h3>Adjust the graphics settings to optimize performance</h3>
-<p>San Andreas is a graphically intensive game that requires a lot of resources from your device. Depending on your device's specifications, you might experience lagging, crashing, or overheating issues while playing the game. To avoid these problems, you can adjust the graphics settings of the game to suit your device's capabilities. You can access these settings from the game's menu and change things like resolution, draw distance, shadows, reflections, frame rate, and more.</p>
-<h3 <h3>Save your progress frequently and use cheats if you want</h3>
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-<h3>Explore the vast open world and enjoy the missions and activities</h3>
-<p>One of the best things about San Andreas is its vast open world that offers endless possibilities for exploration and fun. The game has a main storyline that consists of several missions that advance the plot and unlock new areas, characters, and features. However, you don't have to follow the main storyline if you don't want to. You can also enjoy many other missions and activities that are optional but rewarding. For example, you can join a gang and fight against rival gangs, work as a taxi driver, firefighter, paramedic, or vigilante, participate in races, stunts, or challenges, gamble at casinos, rob stores or houses, date different girlfriends, go to the gym or barber shop, play arcade games or pool, watch TV or movies, listen to radio stations or CDs, and much more. The game has so much content that you will never get bored.</p>
- <h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion, San Andreas is one of the best GTA games ever made and one of the most enjoyable games to play on your Android device. You can download it for free from APKPure, which is a reliable and secure source of APK files for Android users. You can also follow our tips and tricks to optimize your gaming experience and have more fun with San Andreas.</p>
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-<p>If you are ready to play San Andreas on your Android device, don't wait any longer. Go to APKPure's website or app and download San Andreas today. You will not regret it. San Andreas is a classic game that will keep you entertained for hours with its amazing story, gameplay, soundtrack, and world. It is a game that every GTA fan and every Android gamer should play at least once in their life.</p>
-<p>Thank you for reading this article. We hope you found it useful and informative. If you have any questions or comments about San Andreas or APKPure, feel free to leave them below. We would love to hear from you.</p>
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-<p>Here are some of the most common questions that people have about San Andreas and APKPure:</p>
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-<p>Yes, it is legal to download San Andreas from APKPure as long as you own a legitimate copy of the game on another platform. APKPure does not host any pirated or cracked games on its website or app. It only provides APK files that are original and unmodified.</p>
-<li><b>Is San Andreas safe to download from APKPure?</b></li>
-<p>Yes, it is safe to download San Andreas from APKPure as long as you download it from the official website or app. APKPure scans all its files for viruses and malware before uploading them to its servers. It also verifies the authenticity and integrity of the files with cryptographic signatures.</p>
-<li><b>How much space does San Andreas take on my device?</b></li>
-<p>San Andreas takes about 2.6 GB of space on your device after installation. However, you will need more space to download the APK file and the additional data files that are required for the game to run properly.</p>
-<li><b>Can I play San Andreas offline?</b></li>
-<p>Yes, you can play San Andreas offline without any internet connection. However, you will need an internet connection to download the game from APKPure and to verify your license once every 30 days.</p>
-<li><b>Can I play San Andreas with my friends?</b></li>
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spaces/1phancelerku/anime-remove-background/Enjoy the Ocean Adventure with Hungry Shark Evolution MOD APK Download the New Update Now.md

@@ -1,110 +0,0 @@
-<br />
-<h1>Download Hungry Shark Evolution New Update Mod Apk</h1>
-<p>Do you love playing shark games on your mobile device? Do you want to experience the thrill of being a hungry shark in a beautiful underwater world? Do you want to unlock more sharks, accessories, and features without spending real money? If you answered yes to any of these questions, then you should download Hungry Shark Evolution new update mod apk. In this article, we will tell you everything you need to know about this amazing game and how to get the latest modded version for free.</p>
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- <h2>What is Hungry Shark Evolution?</h2>
-<p>Hungry Shark Evolution is a popular action-adventure game developed by Ubisoft Entertainment. It is the official game for Shark Week, and it lets you take control of a very hungry shark and go on a frantic ocean rampage. You can explore an open world both above and below the waves, enjoy jawsome 3D graphics and sound effects, discover and devour mysterious creatures of the deep, recruit baby sharks to boost your predatory powers, equip awesome accessories like lasers, jetpacks, and top hats, find and collect sunken bonus objects, sink your teeth into loads of challenging missions, activate gold rush to survive longer and score higher, take part in regular in-game events and win limited edition prizes, attack with intuitive touch or tilt controls, play offline wherever you are – no Wi-Fi needed, synchronize your game easily across iOS devices, and more  .</p>
- <h3>Features of Hungry Shark Evolution</h3>
-<p>Some of the main features of Hungry Shark Evolution are:</p>
-<ul>
-<li>More than a dozen unique sharks and other fintastic creatures to unlock and evolve, such as the Great White, Hammerhead, Megalodon, Zombie Shark, Robo Shark, Ice Shark, Electro Shark, Pyro Shark, Big Momma, Atomic Shark, Buzz, Mr. Snappy, Moby Dick, Killer Whale, Megalodon 2020 (Sharkjira), Leo (Liopleurodon), Nessie (Plesiosaurus), Alan (Destroyer of Worlds), Natasha (Narwhal), Dave (Dunkleosteus), Kraken (Giant Squid), Luminite (Shark of the Abyss), Ancient Megalodon (Megalodon Ancestor), Ancient Mako (Mako Ancestor), Ancient Hammerhead (Hammerhead Ancestor), Ancient Great White (Great White Ancestor), Megavore (Spinosaurus), Helicoprion (Buzzsaw Shark), Onchopristis (Sawfish Ancestor), Orthacanthus (Xenacanthus Ancestor), Dunkleosteus (Armored Fish), Xiphactinus (Predatory Fish), Basilosaurus (Whale Ancestor), Archelon (Giant Turtle), Mosasaurus (Marine Reptile), Liopleurodon (Marine Reptile), Plesiosaurus (Marine Reptile), Ichthyosaurus (Marine Reptile), Megalodon Gen 2 (Megalodon Clone), Alpha Zombie Shark (Zombie Leader), Alpha Great White (Great White Leader), Alpha Megalodon (Megalodon Leader).</li>
-<li>A beautiful underwater world to explore with stunning 3D graphics and realistic physics. You can swim through coral reefs, sunken ships, volcanoes, icebergs, oil rigs, military bases, beaches, islands, caves, mines, temples, and more. You can also jump out of the water and soar into the sky to see the clouds, birds, planes, helicopters, balloons, and even UFOs.</ <li>A variety of prey and enemies to hunt and fight, such as fish, crabs, jellyfish, squid, octopus, turtles, dolphins, whales, seals, penguins, birds, humans, divers, surfers, fishermen, pirates, soldiers, helicopters, submarines, sharks, crocodiles, orcas, and more. You can also eat bonus objects like coins, gems, letters, chests, shells, maps, and artifacts.</li>
-<li>A lot of accessories and gadgets to equip and customize your shark, such as hats, sunglasses, headphones, wigs, masks, crowns, helmets, capes, necklaces, ties, scarves, backpacks, wings, horns, spikes, lasers, jetpacks, rockets, bombs, speakers, magnets, umbrellas, and more. You can also upgrade your shark's bite force, speed and boost abilities.</li>
-<li>A bunch of baby sharks to recruit and accompany you on your feeding frenzy. Each baby shark has a unique ability that helps you in different ways. Some of the baby sharks are Reef Shark Baby (increases coin value), Mako Shark Baby (increases speed), Hammerhead Shark Baby (increases health), Tiger Shark Baby (increases stamina), Great White Shark Baby (increases bite), Megalodon Baby (increases everything), Big Daddy Baby (stuns prey), Mr. Snappy Baby (teleports to nearby prey), Electro Shark Baby (electrocutes nearby enemies), Ice Shark Baby (freezes nearby enemies), Robo Shark Baby (fires missiles at nearby enemies), Pyro Shark Baby (sets nearby enemies on fire), Zombie Shark Baby (infects nearby enemies), Killer Whale Baby (eats anything), Leo Baby (creates shockwaves), Nessie Baby (creates whirlpools), Alan Baby (creates black holes), Natasha Baby (shoots lasers at nearby enemies), Dave Baby (eats metal objects), Kraken Baby (grabs nearby enemies with tentacles), Luminite Baby (illuminates dark areas), Ancient Megalodon Baby (creates sonic booms), Ancient Mako Baby (increases agility), Ancient Hammerhead Baby (increases defense), Ancient Great White Baby (increases attack), Megavore Baby (eats dinosaurs), Helicoprion Baby (spins like a buzzsaw), Onchopristis Baby (slashes with saw-like teeth), Orthacanthus Baby (poisons nearby enemies with spines), Dunkleosteus Baby (crushes prey with powerful jaws), Xiphactinus Baby (swallows prey whole), Basilosaurus Baby (regenerates health faster), Archelon Baby (creates bubbles that trap enemies), Mosasaurus Baby (jumps higher out of the water), Liopleurodon Baby (creates rainbows that stun enemies), Plesiosaurus Baby (creates waves that push enemies away), Ichthyosaurus Baby (increases boost duration), Megalodon Gen 2 Baby (increases coin and gem value), Alpha Zombie Shark Baby (increases infection radius), Alpha Great White Baby (increases gold rush duration), Alpha Megalodon Baby (increases gold rush multiplier).</li>
-<li>A ton of missions and achievements to complete and earn rewards. You can also participate in daily challenges, live events, and special quests to get more coins, gems, and other prizes.</li>
-<li>A gold rush mode that activates when you fill up your gold rush meter by eating gold creatures. During gold rush, you become invincible, faster, and more powerful. You also get more coins and points for everything you eat.</li>
-<li>A leaderboard and social features that let you compete with other players around the world and share your achievements with your friends. You can also sync your game progress across multiple devices using Facebook or Google Play Games.</li>
-</ul>
- <h4>How to play Hungry Shark Evolution</h4>
-<p>Playing Hungry Shark Evolution is very easy and fun. You just need to follow these simple steps:</p>
-<ol>
-<li>Choose a shark from the evolution menu. You can unlock more sharks by earning coins or gems, or by completing certain missions or events.</li>
-<li>Tap the play button to start the game. You will see your shark in the ocean, ready to eat anything that moves.</li>
-<li>Use the virtual joystick on the left side of the screen to move your shark around. You can also tilt your device to steer your shark.</li>
-<li>Use the boost button on the right side of the screen to make your shark swim faster and jump higher. Boosting consumes stamina, which regenerates over time.</li>
-<li>Eat as many creatures as you can to fill up your hunger meter and score points. Avoid eating dangerous creatures like mines, jellyfish, or bigger sharks, as they will damage your health. You can also eat health kits to restore your health.</li>
-<li>Fill up your gold rush meter by eating gold creatures. When it is full, tap the gold rush button to activate it and enjoy the benefits.</li>
-<li>Complete missions and achievements to earn extra coins, gems, and rewards. You can check your progress in the pause menu.</li>
-<li>When you die, you will see your final score and stats. You can also watch a video ad to revive your shark once per game.</li>
-<li>Use your coins and gems to upgrade your shark's abilities, buy accessories and gadgets, recruit baby sharks, or unlock new sharks.</li>
-<li>Have fun and keep evolving!</li>
-</ol>
- <h2>What is a mod apk?</h2>
-<p>A mod apk is a modified version of an original apk file. An apk file is an Android application package that contains all the files and data needed to install and run an app on an Android device. A mod apk usually has some changes or additions that are not present in the original apk file. These changes or additions can be anything from unlocking premium features, removing ads, adding unlimited resources, changing graphics or sounds, or even adding new content or gameplay modes .</p>
- <h3>Benefits of using a mod apk</h3>
-<p>Some of the benefits of using a mod apk are:</p>
-<ul>
-<li>You can access features that are normally locked or restricted in the original app. For example, you can unlock more sharks, accessories, gadgets, baby sharks, or maps in Hungry Shark Evolution without spending real money.</li>
-<li>You can enhance your gaming experience by adding more fun and excitement to the app. For example, you can get unlimited coins, gems, health, stamina, boost, gold rush, or even invincibility in Hungry Shark Evolution.</li>
-<li>You can customize the app according to your preferences by changing its appearance or functionality. For example, you can change the color scheme, music, sound effects, or language of Hungry Shark Evolution.</li>
-<li>You can explore new content or gameplay modes that are not available in the original app. For example, you can play with different types of sharks, creatures, environments, or challenges in Hungry Shark Evolution.</li>
-</ul>
- <h4>Risks of using a mod apk</h4>
-<p>However, using a mod apk also comes with some risks that you should be aware of:</p>
-<ul>
-<li>You may violate the terms of service or privacy policy of the original app developer or publisher. This may result in legal actions, account suspension or termination, loss of data or progress, or other consequences .</li>
-<li>You may expose your device to malware or viruses that may harm its performance or security. Some mod apks may contain malicious code or hidden files that may steal your personal information, damage your device, or compromise your online security.</li>
-<li>You may encounter bugs or errors that may affect the app's functionality or stability. Some mod apks may not be compatible with your device model, operating system, or app version. They may also conflict with other apps or services on your device.</li>
-<li>You may lose the original app's features or updates that may improve its performance or quality. Some mod apks may not be updated regularly or at all, and they may not support the latest features or fixes of the original app.</li>
-</ul>
- <h2>How to download Hungry Shark Evolution new update mod apk</h2>
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-<p>Before you download Hungry Shark Evolution new update mod apk, you need to make sure that you have the following requirements:</p>
-<ul>
-<li>An Android device that meets the minimum system requirements of the original app. According to the Google Play Store, you need Android 4.1 or higher, and at least 100 MB of free storage space.</li>
-<li>A reliable internet connection that can download large files without interruption.</li>
-<li>A trusted source that provides the latest and safest version of Hungry Shark Evolution new update mod apk. There are many websites that offer mod apks, but not all of them are safe or reliable. You should do some research and read reviews before choosing a source.</li>
-<li>A file manager app that can locate and manage the downloaded files on your device. You can use the default file manager app on your device, or download a third-party app from the Google Play Store.</li>
-</ul>
- <h4>Steps for downloading Hungry Shark Evolution new update mod apk</h4>
-<p>Once you have the requirements, you can proceed with the steps for downloading Hungry Shark Evolution new update mod apk:</p>
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-<li>Open your web browser and go to the source website that provides Hungry Shark Evolution new update mod apk. You can search for it on Google or use a link from a trusted source.</li>
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spaces/1toTree/lora_test/ppdiffusers/optimization.py

@@ -1,312 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 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.
-"""Paddle optimization for diffusion models."""
-
-import math
-from enum import Enum
-from typing import Optional, Union
-
-from paddle.optimizer.lr import LambdaDecay
-
-from .utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class SchedulerType(Enum):
-    LINEAR = "linear"
-    COSINE = "cosine"
-    COSINE_WITH_RESTARTS = "cosine_with_restarts"
-    POLYNOMIAL = "polynomial"
-    CONSTANT = "constant"
-    CONSTANT_WITH_WARMUP = "constant_with_warmup"
-
-
-def get_constant_schedule(learning_rate: float, last_epoch: int = -1):
-    """
-    Create a schedule with a constant learning rate, using the learning rate set in optimizer.
-
-    Args:
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
-    """
-    return LambdaDecay(learning_rate, lambda _: 1, last_epoch=last_epoch)
-
-
-def get_constant_schedule_with_warmup(learning_rate: float, num_warmup_steps: int, last_epoch: int = -1):
-    """
-    Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
-    increases linearly between 0 and the initial lr set in the optimizer.
-
-    Args:
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1.0, num_warmup_steps))
-        return 1.0
-
-    return LambdaDecay(learning_rate, lr_lambda, last_epoch=last_epoch)
-
-
-def get_linear_schedule_with_warmup(
-    learning_rate: float, num_warmup_steps: int, num_training_steps: int, last_epoch: int = -1
-):
-    """
-    Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
-    a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
-
-    Args:
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        return max(
-            0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
-        )
-
-    return LambdaDecay(learning_rate, lr_lambda, last_epoch)
-
-
-def get_cosine_schedule_with_warmup(
-    learning_rate: float, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
-):
-    """
-    Create a schedule with a learning rate that decreases following the values of the cosine function between the
-    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
-    initial lr set in the optimizer.
-
-    Args:
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        num_cycles (`float`, *optional*, defaults to 0.5):
-            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
-            following a half-cosine).
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
-        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
-
-    return LambdaDecay(learning_rate, lr_lambda, last_epoch)
-
-
-def get_cosine_with_hard_restarts_schedule_with_warmup(
-    learning_rate: float, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
-):
-    """
-    Create a schedule with a learning rate that decreases following the values of the cosine function between the
-    initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
-    linearly between 0 and the initial lr set in the optimizer.
-
-    Args:
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        num_cycles (`int`, *optional*, defaults to 1):
-            The number of hard restarts to use.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
-        if progress >= 1.0:
-            return 0.0
-        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
-
-    return LambdaDecay(learning_rate, lr_lambda, last_epoch)
-
-
-def get_polynomial_decay_schedule_with_warmup(
-    learning_rate: float,
-    num_warmup_steps: int,
-    num_training_steps: int,
-    lr_end: float = 1e-7,
-    power: float = 1.0,
-    last_epoch: int = -1,
-):
-    """
-    Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
-    optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
-    initial lr set in the optimizer.
-
-    Args:
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        lr_end (`float`, *optional*, defaults to 1e-7):
-            The end LR.
-        power (`float`, *optional*, defaults to 1.0):
-            Power factor.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
-    implementation at
-    https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
-
-    Return:
-        `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule.
-
-    """
-
-    lr_init = learning_rate
-    if not (lr_init > lr_end):
-        raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        elif current_step > num_training_steps:
-            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
-        else:
-            lr_range = lr_init - lr_end
-            decay_steps = num_training_steps - num_warmup_steps
-            pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
-            decay = lr_range * pct_remaining**power + lr_end
-            return decay / lr_init  # as LambdaLR multiplies by lr_init
-
-    return LambdaDecay(learning_rate, lr_lambda, last_epoch)
-
-
-TYPE_TO_SCHEDULER_FUNCTION = {
-    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
-    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
-    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
-    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
-    SchedulerType.CONSTANT: get_constant_schedule,
-    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
-}
-
-
-def get_scheduler(
-    name: Union[str, SchedulerType],
-    learning_rate: float = 0.1,
-    num_warmup_steps: Optional[int] = None,
-    num_training_steps: Optional[int] = None,
-    num_cycles: int = 1,
-    power: float = 1.0,
-    last_epoch: int = -1,
-):
-    """
-    Unified API to get any scheduler from its name.
-
-    Args:
-        name (`str` or `SchedulerType`):
-            The name of the scheduler to use.
-        learning_rate (`float`):
-            The base learning rate. It is a python float number.
-        num_warmup_steps (`int`, *optional*):
-            The number of warmup steps to do. This is not required by all schedulers (hence the argument being
-            optional), the function will raise an error if it's unset and the scheduler type requires it.
-        num_training_steps (`int``, *optional*):
-            The number of training steps to do. This is not required by all schedulers (hence the argument being
-            optional), the function will raise an error if it's unset and the scheduler type requires it.
-        num_cycles (`int`, *optional*):
-            The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler.
-        power (`float`, *optional*, defaults to 1.0):
-            Power factor. See `POLYNOMIAL` scheduler
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-    """
-    name = SchedulerType(name)
-    schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
-    if name == SchedulerType.CONSTANT:
-        return schedule_func(learning_rate=learning_rate, last_epoch=last_epoch)
-
-    # All other schedulers require `num_warmup_steps`
-    if num_warmup_steps is None:
-        raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
-
-    if name == SchedulerType.CONSTANT_WITH_WARMUP:
-        return schedule_func(learning_rate=learning_rate, num_warmup_steps=num_warmup_steps, last_epoch=last_epoch)
-
-    # All other schedulers require `num_training_steps`
-    if num_training_steps is None:
-        raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
-
-    if name == SchedulerType.COSINE_WITH_RESTARTS:
-        return schedule_func(
-            learning_rate=learning_rate,
-            num_warmup_steps=num_warmup_steps,
-            num_training_steps=num_training_steps,
-            num_cycles=num_cycles,
-            last_epoch=last_epoch,
-        )
-
-    if name == SchedulerType.POLYNOMIAL:
-        return schedule_func(
-            learning_rate=learning_rate,
-            num_warmup_steps=num_warmup_steps,
-            num_training_steps=num_training_steps,
-            power=power,
-            last_epoch=last_epoch,
-        )
-
-    return schedule_func(
-        learning_rate=learning_rate,
-        num_warmup_steps=num_warmup_steps,
-        num_training_steps=num_training_steps,
-        last_epoch=last_epoch,
-    )

spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -1,555 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import paddle
-import PIL
-from packaging import version
-
-from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, deprecate, logging
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def preprocess(image):
-    if isinstance(image, paddle.Tensor):
-        return image
-    elif isinstance(image, PIL.Image.Image):
-        image = [image]
-
-    if isinstance(image[0], PIL.Image.Image):
-        w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-
-        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
-        image = np.concatenate(image, axis=0)
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image.transpose(0, 3, 1, 2)
-        image = 2.0 * image - 1.0
-        image = paddle.to_tensor(image)
-    elif isinstance(image[0], paddle.Tensor):
-        image = paddle.concat(image, axis=0)
-    return image
-
-
-class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image to image generation using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
-            or [`DPMSolverMultistepScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.__init__
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_ppdiffusers_version") and version.parse(
-            version.parse(unet.config._ppdiffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pd",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pd").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not paddle.equal_all(
-            text_input_ids, untruncated_ids
-        ):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        config = (
-            self.text_encoder.config
-            if isinstance(self.text_encoder.config, dict)
-            else self.text_encoder.config.to_dict()
-        )
-        if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
-            attention_mask = text_inputs.attention_mask
-        else:
-            attention_mask = None
-
-        text_embeddings = self.text_encoder(
-            text_input_ids,
-            attention_mask=attention_mask,
-        )
-        text_embeddings = text_embeddings[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.tile([1, num_images_per_prompt, 1])
-        text_embeddings = text_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1])
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pd",
-            )
-
-            if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
-                attention_mask = uncond_input.attention_mask
-            else:
-                attention_mask = None
-
-            uncond_embeddings = self.text_encoder(
-                uncond_input.input_ids,
-                attention_mask=attention_mask,
-            )
-            uncond_embeddings = uncond_embeddings[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.tile([1, num_images_per_prompt, 1])
-            uncond_embeddings = uncond_embeddings.reshape([batch_size * num_images_per_prompt, seq_len, -1])
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = paddle.concat([uncond_embeddings, text_embeddings])
-
-        return text_embeddings
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd")
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.cast(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clip(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(self, prompt, strength, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def get_timesteps(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, generator=None):
-        image = image.cast(dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if isinstance(generator, list):
-            init_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-            ]
-            init_latents = paddle.concat(init_latents, axis=0)
-        else:
-            init_latents = self.vae.encode(image).latent_dist.sample(generator)
-        init_latents = 0.18215 * init_latents
-
-        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
-            # expand init_latents for batch_size
-            deprecation_message = (
-                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
-                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
-                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
-                " your script to pass as many initial images as text prompts to suppress this warning."
-            )
-            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
-            additional_image_per_prompt = batch_size // init_latents.shape[0]
-            init_latents = paddle.concat([init_latents] * additional_image_per_prompt, axis=0)
-        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = paddle.concat([init_latents], axis=0)
-
-        shape = init_latents.shape
-        if isinstance(generator, list):
-            shape = [
-                1,
-            ] + shape[1:]
-            noise = [paddle.randn(shape, generator=generator[i], dtype=dtype) for i in range(batch_size)]
-            noise = paddle.concat(noise, axis=0)
-        else:
-            noise = paddle.randn(shape, generator=generator, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-
-        return latents
-
-    @paddle.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[paddle.Tensor, PIL.Image.Image] = None,
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`paddle.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
-                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
-                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
-                noise will be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`paddle.Generator`, *optional*):
-                One or a list of paddle generator(s) to make generation deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 1. Check inputs
-        self.check_inputs(prompt, strength, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. Preprocess image
-        image = preprocess(image)
-
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
-        latent_timestep = timesteps[:1].tile([batch_size * num_images_per_prompt])
-
-        # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, generator
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 8. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 9. Post-processing
-        image = self.decode_latents(latents)
-
-        # 10. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/6shen7/Linaqruf-anything-v3.0/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Linaqruf/anything-v3.0").launch()

spaces/801artistry/RVC801/Applio-RVC-Fork/utils/README.md

@@ -1,6 +0,0 @@
-# External Colab Code
-Code used to make Google Colab work correctly
-- Repo link: https://github.com/IAHispano/Applio-RVC-Fork/
-
-Thanks to https://github.com/kalomaze/externalcolabcode
-

spaces/801artistry/RVC801/i18n/locale_diff.py

@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "en_US.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")

spaces/AIFILMS/generate_human_motion/VQ-Trans/visualize/joints2smpl/src/customloss.py

@@ -1,222 +0,0 @@
-import torch
-import torch.nn.functional as F
-from visualize.joints2smpl.src import config
-
-# Guassian
-def gmof(x, sigma):
-    """
-    Geman-McClure error function
-    """
-    x_squared = x ** 2
-    sigma_squared = sigma ** 2
-    return (sigma_squared * x_squared) / (sigma_squared + x_squared)
-
-# angle prior
-def angle_prior(pose):
-    """
-    Angle prior that penalizes unnatural bending of the knees and elbows
-    """
-    # We subtract 3 because pose does not include the global rotation of the model
-    return torch.exp(
-        pose[:, [55 - 3, 58 - 3, 12 - 3, 15 - 3]] * torch.tensor([1., -1., -1, -1.], device=pose.device)) ** 2
-
-
-def perspective_projection(points, rotation, translation,
-                           focal_length, camera_center):
-    """
-    This function computes the perspective projection of a set of points.
-    Input:
-        points (bs, N, 3): 3D points
-        rotation (bs, 3, 3): Camera rotation
-        translation (bs, 3): Camera translation
-        focal_length (bs,) or scalar: Focal length
-        camera_center (bs, 2): Camera center
-    """
-    batch_size = points.shape[0]
-    K = torch.zeros([batch_size, 3, 3], device=points.device)
-    K[:, 0, 0] = focal_length
-    K[:, 1, 1] = focal_length
-    K[:, 2, 2] = 1.
-    K[:, :-1, -1] = camera_center
-
-    # Transform points
-    points = torch.einsum('bij,bkj->bki', rotation, points)
-    points = points + translation.unsqueeze(1)
-
-    # Apply perspective distortion
-    projected_points = points / points[:, :, -1].unsqueeze(-1)
-
-    # Apply camera intrinsics
-    projected_points = torch.einsum('bij,bkj->bki', K, projected_points)
-
-    return projected_points[:, :, :-1]
-
-
-def body_fitting_loss(body_pose, betas, model_joints, camera_t, camera_center,
-                      joints_2d, joints_conf, pose_prior,
-                      focal_length=5000, sigma=100, pose_prior_weight=4.78,
-                      shape_prior_weight=5, angle_prior_weight=15.2,
-                      output='sum'):
-    """
-    Loss function for body fitting
-    """
-    batch_size = body_pose.shape[0]
-    rotation = torch.eye(3, device=body_pose.device).unsqueeze(0).expand(batch_size, -1, -1)
-
-    projected_joints = perspective_projection(model_joints, rotation, camera_t,
-                                              focal_length, camera_center)
-
-    # Weighted robust reprojection error
-    reprojection_error = gmof(projected_joints - joints_2d, sigma)
-    reprojection_loss = (joints_conf ** 2) * reprojection_error.sum(dim=-1)
-
-    # Pose prior loss
-    pose_prior_loss = (pose_prior_weight ** 2) * pose_prior(body_pose, betas)
-
-    # Angle prior for knees and elbows
-    angle_prior_loss = (angle_prior_weight ** 2) * angle_prior(body_pose).sum(dim=-1)
-
-    # Regularizer to prevent betas from taking large values
-    shape_prior_loss = (shape_prior_weight ** 2) * (betas ** 2).sum(dim=-1)
-
-    total_loss = reprojection_loss.sum(dim=-1) + pose_prior_loss + angle_prior_loss + shape_prior_loss
-
-    if output == 'sum':
-        return total_loss.sum()
-    elif output == 'reprojection':
-        return reprojection_loss
-
-
-# --- get camera fitting loss -----
-def camera_fitting_loss(model_joints, camera_t, camera_t_est, camera_center, 
-                        joints_2d, joints_conf,
-                        focal_length=5000, depth_loss_weight=100):
-    """
-    Loss function for camera optimization.
-    """
-    # Project model joints
-    batch_size = model_joints.shape[0]
-    rotation = torch.eye(3, device=model_joints.device).unsqueeze(0).expand(batch_size, -1, -1)
-    projected_joints = perspective_projection(model_joints, rotation, camera_t,
-                                              focal_length, camera_center)
-
-    # get the indexed four
-    op_joints = ['OP RHip', 'OP LHip', 'OP RShoulder', 'OP LShoulder']
-    op_joints_ind = [config.JOINT_MAP[joint] for joint in op_joints]
-    gt_joints = ['RHip', 'LHip', 'RShoulder', 'LShoulder']
-    gt_joints_ind = [config.JOINT_MAP[joint] for joint in gt_joints]
-
-    reprojection_error_op = (joints_2d[:, op_joints_ind] -
-                             projected_joints[:, op_joints_ind]) ** 2
-    reprojection_error_gt = (joints_2d[:, gt_joints_ind] -
-                             projected_joints[:, gt_joints_ind]) ** 2
-
-    # Check if for each example in the batch all 4 OpenPose detections are valid, otherwise use the GT detections
-    # OpenPose joints are more reliable for this task, so we prefer to use them if possible
-    is_valid = (joints_conf[:, op_joints_ind].min(dim=-1)[0][:, None, None] > 0).float()
-    reprojection_loss = (is_valid * reprojection_error_op + (1 - is_valid) * reprojection_error_gt).sum(dim=(1, 2))
-
-    # Loss that penalizes deviation from depth estimate
-    depth_loss = (depth_loss_weight ** 2) * (camera_t[:, 2] - camera_t_est[:, 2]) ** 2
-
-    total_loss = reprojection_loss + depth_loss
-    return total_loss.sum()
-
-
-
- # #####--- body fitiing loss -----
-def body_fitting_loss_3d(body_pose, preserve_pose,
-                         betas, model_joints, camera_translation,
-                         j3d, pose_prior,
-                         joints3d_conf,
-                         sigma=100, pose_prior_weight=4.78*1.5,
-                         shape_prior_weight=5.0, angle_prior_weight=15.2,
-                         joint_loss_weight=500.0,
-                         pose_preserve_weight=0.0,
-                         use_collision=False,
-                         model_vertices=None, model_faces=None,
-                         search_tree=None,  pen_distance=None,  filter_faces=None,
-                         collision_loss_weight=1000
-                         ):
-    """
-    Loss function for body fitting
-    """
-    batch_size = body_pose.shape[0]
-
-    #joint3d_loss = (joint_loss_weight ** 2) * gmof((model_joints + camera_translation) - j3d, sigma).sum(dim=-1)
-    
-    joint3d_error = gmof((model_joints + camera_translation) - j3d, sigma)
-    
-    joint3d_loss_part = (joints3d_conf ** 2) * joint3d_error.sum(dim=-1)
-    joint3d_loss = ((joint_loss_weight ** 2) * joint3d_loss_part).sum(dim=-1)
-    
-    # Pose prior loss
-    pose_prior_loss = (pose_prior_weight ** 2) * pose_prior(body_pose, betas)
-    # Angle prior for knees and elbows
-    angle_prior_loss = (angle_prior_weight ** 2) * angle_prior(body_pose).sum(dim=-1)
-    # Regularizer to prevent betas from taking large values
-    shape_prior_loss = (shape_prior_weight ** 2) * (betas ** 2).sum(dim=-1)
-
-    collision_loss = 0.0
-    # Calculate the loss due to interpenetration
-    if use_collision:
-        triangles = torch.index_select(
-            model_vertices, 1,
-            model_faces).view(batch_size, -1, 3, 3)
-
-        with torch.no_grad():
-            collision_idxs = search_tree(triangles)
-
-        # Remove unwanted collisions
-        if filter_faces is not None:
-            collision_idxs = filter_faces(collision_idxs)
-
-        if collision_idxs.ge(0).sum().item() > 0:
-            collision_loss = torch.sum(collision_loss_weight * pen_distance(triangles, collision_idxs))
-    
-    pose_preserve_loss = (pose_preserve_weight ** 2) * ((body_pose - preserve_pose) ** 2).sum(dim=-1)
-
-    # print('joint3d_loss', joint3d_loss.shape)
-    # print('pose_prior_loss', pose_prior_loss.shape)
-    # print('angle_prior_loss', angle_prior_loss.shape)
-    # print('shape_prior_loss', shape_prior_loss.shape)
-    # print('collision_loss', collision_loss)
-    # print('pose_preserve_loss', pose_preserve_loss.shape)
-
-    total_loss = joint3d_loss + pose_prior_loss + angle_prior_loss + shape_prior_loss + collision_loss + pose_preserve_loss
-
-    return total_loss.sum()
-
-
-# #####--- get camera fitting loss -----
-def camera_fitting_loss_3d(model_joints, camera_t, camera_t_est,
-                           j3d, joints_category="orig", depth_loss_weight=100.0):
-    """
-    Loss function for camera optimization.
-    """
-    model_joints = model_joints + camera_t
-    # # get the indexed four
-    # op_joints = ['OP RHip', 'OP LHip', 'OP RShoulder', 'OP LShoulder']
-    # op_joints_ind = [config.JOINT_MAP[joint] for joint in op_joints]
-    #
-    # j3d_error_loss = (j3d[:, op_joints_ind] -
-    #                          model_joints[:, op_joints_ind]) ** 2
-
-    gt_joints = ['RHip', 'LHip', 'RShoulder', 'LShoulder']
-    gt_joints_ind = [config.JOINT_MAP[joint] for joint in gt_joints]
-    
-    if joints_category=="orig":
-        select_joints_ind = [config.JOINT_MAP[joint] for joint in gt_joints]
-    elif joints_category=="AMASS":
-        select_joints_ind = [config.AMASS_JOINT_MAP[joint] for joint in gt_joints]
-    else:
-        print("NO SUCH JOINTS CATEGORY!")
-
-    j3d_error_loss = (j3d[:, select_joints_ind] -
-                      model_joints[:, gt_joints_ind]) ** 2
-
-    # Loss that penalizes deviation from depth estimate
-    depth_loss = (depth_loss_weight**2) *  (camera_t - camera_t_est)**2
-
-    total_loss = j3d_error_loss +  depth_loss
-    return total_loss.sum()

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/tf_layers.py

@@ -1,129 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 MINH ANH (@dathudeptrai)
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Tensorflow Layer modules complatible with pytorch."""
-
-import tensorflow as tf
-
-
-class TFReflectionPad1d(tf.keras.layers.Layer):
-    """Tensorflow ReflectionPad1d module."""
-
-    def __init__(self, padding_size):
-        """Initialize TFReflectionPad1d module.
-
-        Args:
-            padding_size (int): Padding size.
-
-        """
-        super(TFReflectionPad1d, self).__init__()
-        self.padding_size = padding_size
-
-    @tf.function
-    def call(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input tensor (B, T, 1, C).
-
-        Returns:
-            Tensor: Padded tensor (B, T + 2 * padding_size, 1, C).
-
-        """
-        return tf.pad(x, [[0, 0], [self.padding_size, self.padding_size], [0, 0], [0, 0]], "REFLECT")
-
-
-class TFConvTranspose1d(tf.keras.layers.Layer):
-    """Tensorflow ConvTranspose1d module."""
-
-    def __init__(self, channels, kernel_size, stride, padding):
-        """Initialize TFConvTranspose1d( module.
-
-        Args:
-            channels (int): Number of channels.
-            kernel_size (int): kernel size.
-            strides (int): Stride width.
-            padding (str): Padding type ("same" or "valid").
-
-        """
-        super(TFConvTranspose1d, self).__init__()
-        self.conv1d_transpose = tf.keras.layers.Conv2DTranspose(
-            filters=channels,
-            kernel_size=(kernel_size, 1),
-            strides=(stride, 1),
-            padding=padding,
-        )
-
-    @tf.function
-    def call(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input tensor (B, T, 1, C).
-
-        Returns:
-            Tensors: Output tensor (B, T', 1, C').
-
-        """
-        x = self.conv1d_transpose(x)
-        return x
-
-
-class TFResidualStack(tf.keras.layers.Layer):
-    """Tensorflow ResidualStack module."""
-
-    def __init__(self,
-                 kernel_size,
-                 channels,
-                 dilation,
-                 bias,
-                 nonlinear_activation,
-                 nonlinear_activation_params,
-                 padding,
-                 ):
-        """Initialize TFResidualStack module.
-
-        Args:
-            kernel_size (int): Kernel size.
-            channles (int): Number of channels.
-            dilation (int): Dilation ine.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            padding (str): Padding type ("same" or "valid").
-
-        """
-        super(TFResidualStack, self).__init__()
-        self.block = [
-            getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params),
-            TFReflectionPad1d(dilation),
-            tf.keras.layers.Conv2D(
-                filters=channels,
-                kernel_size=(kernel_size, 1),
-                dilation_rate=(dilation, 1),
-                use_bias=bias,
-                padding="valid",
-            ),
-            getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params),
-            tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias)
-        ]
-        self.shortcut = tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias)
-
-    @tf.function
-    def call(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input tensor (B, T, 1, C).
-
-        Returns:
-            Tensor: Output tensor (B, T, 1, C).
-
-        """
-        _x = tf.identity(x)
-        for i, layer in enumerate(self.block):
-            _x = layer(_x)
-        shortcut = self.shortcut(x)
-        return shortcut + _x

spaces/AIGC-Audio/AudioGPT/README.md

@@ -1,12 +0,0 @@
----
-title: AudioGPT
-emoji: 🚀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py

@@ -1,280 +0,0 @@
-_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
-
-# ======================= Frequently modified parameters =====================
-# -----data related-----
-data_root = 'data/coco/'  # Root path of data
-# Path of train annotation file
-train_ann_file = 'annotations/instances_train2017.json'
-train_data_prefix = 'train2017/'  # Prefix of train image path
-# Path of val annotation file
-val_ann_file = 'annotations/instances_val2017.json'
-val_data_prefix = 'val2017/'  # Prefix of val image path
-
-num_classes = 80  # Number of classes for classification
-# Batch size of a single GPU during training
-train_batch_size_per_gpu = 32
-# Worker to pre-fetch data for each single GPU during training
-train_num_workers = 8
-# persistent_workers must be False if num_workers is 0
-persistent_workers = True
-
-# -----train val related-----
-# Base learning rate for optim_wrapper
-base_lr = 0.01
-max_epochs = 400  # Maximum training epochs
-num_last_epochs = 15  # Last epoch number to switch training pipeline
-
-# ======================= Possible modified parameters =======================
-# -----data related-----
-img_scale = (640, 640)  # width, height
-# Dataset type, this will be used to define the dataset
-dataset_type = 'YOLOv5CocoDataset'
-# Batch size of a single GPU during validation
-val_batch_size_per_gpu = 1
-# Worker to pre-fetch data for each single GPU during validation
-val_num_workers = 2
-
-# Config of batch shapes. Only on val.
-# It means not used if batch_shapes_cfg is None.
-batch_shapes_cfg = dict(
-    type='BatchShapePolicy',
-    batch_size=val_batch_size_per_gpu,
-    img_size=img_scale[0],
-    size_divisor=32,
-    extra_pad_ratio=0.5)
-
-# -----model related-----
-# The scaling factor that controls the depth of the network structure
-deepen_factor = 0.33
-# The scaling factor that controls the width of the network structure
-widen_factor = 0.5
-
-# -----train val related-----
-affine_scale = 0.5  # YOLOv5RandomAffine scaling ratio
-lr_factor = 0.01  # Learning rate scaling factor
-weight_decay = 0.0005
-# Save model checkpoint and validation intervals
-save_epoch_intervals = 10
-# The maximum checkpoints to keep.
-max_keep_ckpts = 3
-# Single-scale training is recommended to
-# be turned on, which can speed up training.
-env_cfg = dict(cudnn_benchmark=True)
-
-# ============================== Unmodified in most cases ===================
-model = dict(
-    type='YOLODetector',
-    data_preprocessor=dict(
-        type='YOLOv5DetDataPreprocessor',
-        mean=[0., 0., 0.],
-        std=[255., 255., 255.],
-        bgr_to_rgb=True),
-    backbone=dict(
-        type='YOLOv6EfficientRep',
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
-        act_cfg=dict(type='ReLU', inplace=True)),
-    neck=dict(
-        type='YOLOv6RepPAFPN',
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-        in_channels=[256, 512, 1024],
-        out_channels=[128, 256, 512],
-        num_csp_blocks=12,
-        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
-        act_cfg=dict(type='ReLU', inplace=True),
-    ),
-    bbox_head=dict(
-        type='YOLOv6Head',
-        head_module=dict(
-            type='YOLOv6HeadModule',
-            num_classes=num_classes,
-            in_channels=[128, 256, 512],
-            widen_factor=widen_factor,
-            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
-            act_cfg=dict(type='SiLU', inplace=True),
-            featmap_strides=[8, 16, 32]),
-        loss_bbox=dict(
-            type='IoULoss',
-            iou_mode='giou',
-            bbox_format='xyxy',
-            reduction='mean',
-            loss_weight=2.5,
-            return_iou=False)),
-    train_cfg=dict(
-        initial_epoch=4,
-        initial_assigner=dict(
-            type='BatchATSSAssigner',
-            num_classes=num_classes,
-            topk=9,
-            iou_calculator=dict(type='mmdet.BboxOverlaps2D')),
-        assigner=dict(
-            type='BatchTaskAlignedAssigner',
-            num_classes=num_classes,
-            topk=13,
-            alpha=1,
-            beta=6),
-    ),
-    test_cfg=dict(
-        multi_label=True,
-        nms_pre=30000,
-        score_thr=0.001,
-        nms=dict(type='nms', iou_threshold=0.65),
-        max_per_img=300))
-
-# The training pipeline of YOLOv6 is basically the same as YOLOv5.
-# The difference is that Mosaic and RandomAffine will be closed in the last 15 epochs. # noqa
-pre_transform = [
-    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
-    dict(type='LoadAnnotations', with_bbox=True)
-]
-
-train_pipeline = [
-    *pre_transform,
-    dict(
-        type='Mosaic',
-        img_scale=img_scale,
-        pad_val=114.0,
-        pre_transform=pre_transform),
-    dict(
-        type='YOLOv5RandomAffine',
-        max_rotate_degree=0.0,
-        max_translate_ratio=0.1,
-        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-        # img_scale is (width, height)
-        border=(-img_scale[0] // 2, -img_scale[1] // 2),
-        border_val=(114, 114, 114),
-        max_shear_degree=0.0),
-    dict(type='YOLOv5HSVRandomAug'),
-    dict(type='mmdet.RandomFlip', prob=0.5),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
-                   'flip_direction'))
-]
-
-train_pipeline_stage2 = [
-    *pre_transform,
-    dict(type='mmyolo.YOLOv5KeepRatioResize', scale=img_scale),
-    dict(
-        type='mmyolo.LetterResize',
-        scale=img_scale,
-        allow_scale_up=True,
-        pad_val=dict(img=114)),
-    dict(
-        type='YOLOv5RandomAffine',
-        max_rotate_degree=0.0,
-        max_translate_ratio=0.1,
-        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-        max_shear_degree=0.0,
-    ),
-    dict(type='YOLOv5HSVRandomAug'),
-    dict(type='mmdet.RandomFlip', prob=0.5),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
-                   'flip_direction'))
-]
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu,
-    num_workers=train_num_workers,
-    collate_fn=dict(type='yolov5_collate'),
-    persistent_workers=persistent_workers,
-    pin_memory=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=train_ann_file,
-        data_prefix=dict(img=train_data_prefix),
-        filter_cfg=dict(filter_empty_gt=False, min_size=32),
-        pipeline=train_pipeline))
-
-test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
-    dict(type='mmyolo.YOLOv5KeepRatioResize', scale=img_scale),
-    dict(
-        type='mmyolo.LetterResize',
-        scale=img_scale,
-        allow_scale_up=False,
-        pad_val=dict(img=114)),
-    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
-                   'scale_factor', 'pad_param'))
-]
-
-val_dataloader = dict(
-    batch_size=val_batch_size_per_gpu,
-    num_workers=val_num_workers,
-    persistent_workers=persistent_workers,
-    pin_memory=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        test_mode=True,
-        data_prefix=dict(img=val_data_prefix),
-        ann_file=val_ann_file,
-        pipeline=test_pipeline,
-        batch_shapes_cfg=batch_shapes_cfg))
-
-test_dataloader = val_dataloader
-
-# Optimizer and learning rate scheduler of YOLOv6 are basically the same as YOLOv5. # noqa
-# The difference is that the scheduler_type of YOLOv6 is cosine.
-optim_wrapper = dict(
-    type='OptimWrapper',
-    optimizer=dict(
-        type='SGD',
-        lr=base_lr,
-        momentum=0.937,
-        weight_decay=weight_decay,
-        nesterov=True,
-        batch_size_per_gpu=train_batch_size_per_gpu),
-    constructor='YOLOv5OptimizerConstructor')
-
-default_hooks = dict(
-    param_scheduler=dict(
-        type='YOLOv5ParamSchedulerHook',
-        scheduler_type='cosine',
-        lr_factor=lr_factor,
-        max_epochs=max_epochs),
-    checkpoint=dict(
-        type='CheckpointHook',
-        interval=save_epoch_intervals,
-        max_keep_ckpts=max_keep_ckpts,
-        save_best='auto'))
-
-custom_hooks = [
-    dict(
-        type='EMAHook',
-        ema_type='ExpMomentumEMA',
-        momentum=0.0001,
-        update_buffers=True,
-        strict_load=False,
-        priority=49),
-    dict(
-        type='mmdet.PipelineSwitchHook',
-        switch_epoch=max_epochs - num_last_epochs,
-        switch_pipeline=train_pipeline_stage2)
-]
-
-val_evaluator = dict(
-    type='mmdet.CocoMetric',
-    proposal_nums=(100, 1, 10),
-    ann_file=data_root + val_ann_file,
-    metric='bbox')
-test_evaluator = val_evaluator
-
-train_cfg = dict(
-    type='EpochBasedTrainLoop',
-    max_epochs=max_epochs,
-    val_interval=save_epoch_intervals,
-    dynamic_intervals=[(max_epochs - num_last_epochs, 1)])
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/MessageEvent.ts

@@ -1,6 +0,0 @@
-import type { Timestamps } from "./Timestamps";
-import type { User } from "./User";
-
-export interface MessageEvent extends Pick<Timestamps, "createdAt"> {
-	userId: User["_id"] | User["sessionId"];
-}

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/concurrent.py

@@ -1,19 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, List
-
-from . import order_registry as OrderRegistry
-from .base import BaseOrder
-
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-
-
-@OrderRegistry.register("concurrent")
-class ConcurrentOrder(BaseOrder):
-    """
-    The agents speak concurrently
-    """
-
-    def get_next_agent_idx(self, environment: BaseEnvironment) -> List[int]:
-        return list(range(len(environment.agents)))

spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/__init__.py

@@ -1,8 +0,0 @@
-from .base import TasksolvingRule
-
-"""
-from .decision_maker import *
-from .evaluator import *
-from .executor import *
-from .role_assigner import *
-"""

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/TouchingMethods.js

@@ -1,118 +0,0 @@
-import InTouching from '../intouching/InTouching.js';
-import IsPointerInBounds from '../../../plugins/utils/input/IsPointerInBounds.js';
-
-export default {
-    isPointerInBounds(target) {
-        if (target === undefined) {
-            target = this;
-        } else if (typeof (target) === 'string') {
-            target = this.getElement(target);
-        }
-
-        if (!target) {
-            return false;
-        }
-
-        return IsPointerInBounds(target);
-    },
-
-    onTouching(gameObject, callback, scope, config) {
-        if (!gameObject) {
-            return this;
-        }
-
-        if (typeof (gameObject) === 'function') {
-            config = scope;
-            scope = callback;
-            callback = gameObject;
-            gameObject = this;
-        }
-
-        if (gameObject._inTouching === undefined) {
-            gameObject._inTouching = new InTouching(gameObject, config);
-        }
-        gameObject._inTouching.on('intouch', callback, scope);
-
-        return this;
-    },
-
-    offTouching(gameObject, callback, scope) {
-        if (typeof (gameObject) === 'function') {
-            scope = callback;
-            callback = gameObject;
-            gameObject = this;
-        }
-
-        if (gameObject._inTouching === undefined) {
-            return this;
-        }
-        gameObject._inTouching.off('intouch', callback, scope);
-
-        return this;
-    },
-
-    onTouchingEnd(gameObject, callback, scope, config) {
-        if (!gameObject) {
-            return this;
-        }
-
-        if (typeof (gameObject) === 'function') {
-            config = scope;
-            scope = callback;
-            callback = gameObject;
-            gameObject = this;
-        }
-
-        if (gameObject._inTouching === undefined) {
-            gameObject._inTouching = new InTouching(gameObject, config);
-        }
-        gameObject._inTouching.on('touchend', callback, scope);
-
-        return this;
-    },
-
-    offTouchingEnd(gameObject, callback, scope) {
-        if (typeof (gameObject) === 'function') {
-            scope = callback;
-            callback = gameObject;
-            gameObject = this;
-        }
-
-        if (gameObject._inTouching === undefined) {
-            return this;
-        }
-        gameObject._inTouching.off('touchend', callback, scope);
-
-        return this;
-    },
-
-
-    enableTouching(gameObject, enabled) {
-        if (gameObject && typeof (gameObject) !== 'object') {
-            enabled = gameObject;
-            gameObject = this;
-        }
-
-        if (gameObject._inTouching === undefined) {
-            return this;
-        }
-        gameObject._inTouching.setEnable(enabled);
-
-        return this;
-    },
-
-    disableTouching(gameObject) {
-        if (gameObject && typeof (gameObject) !== 'object') {
-            gameObject = this;
-        }
-
-        if (gameObject._inTouching === undefined) {
-            return this;
-        }
-        gameObject._inTouching.setEnable(false);
-
-        return this;
-    },
-
-
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/Factory.js

@@ -1,13 +0,0 @@
-import OverlapSizer from './OverlapSizer.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('overlapSizer', function (x, y, minWidth, minHeight, config) {
-    var gameObject = new OverlapSizer(this.scene, x, y, minWidth, minHeight, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.OverlapSizer', OverlapSizer);
-
-export default OverlapSizer;

spaces/Alcom/chaoyi-wu-PMC_LLAMA_7B/README.md

@@ -1,12 +0,0 @@
----
-title: Chaoyi-wu-PMC LLAMA 7B
-emoji: 📊
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Alesteba/NeRF_ficus-pxl/rendering.py

@@ -1,161 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-
-from config import *
-
-def encode_position(x):
-    """Encodes the position into its corresponding Fourier feature.
-    Args:
-        x: The input coordinate.
-    Returns:
-        Fourier features tensors of the position.
-    """
-    positions = [x]
-    for i in range(POS_ENCODE_DIMS):
-        for fn in [tf.sin, tf.cos]:
-            positions.append(fn(2.0 ** i * x))
-    return tf.concat(positions, axis=-1)
-
-
-def get_rays(height, width, focal, pose):
-    """Computes origin point and direction vector of rays.
-    Args:
-        height: Height of the image.
-        width: Width of the image.
-        focal: The focal length between the images and the camera.
-        pose: The pose matrix of the camera.
-    Returns:
-        Tuple of origin point and direction vector for rays.
-    """
-    # Build a meshgrid for the rays.
-    i, j = tf.meshgrid(
-        tf.range(width, dtype=tf.float32),
-        tf.range(height, dtype=tf.float32),
-        indexing="xy",
-    )
-
-    # Normalize the x axis coordinates.
-    transformed_i = (i - width * 0.5) / focal
-
-    # Normalize the y axis coordinates.
-    transformed_j = (j - height * 0.5) / focal
-
-    # Create the direction unit vectors.
-    directions = tf.stack([transformed_i, -transformed_j, -tf.ones_like(i)], axis=-1)
-
-    # Get the camera matrix.
-    camera_matrix = pose[:3, :3]
-    height_width_focal = pose[:3, -1]
-
-    # Get origins and directions for the rays.
-    transformed_dirs = directions[..., None, :]
-    camera_dirs = transformed_dirs * camera_matrix
-    ray_directions = tf.reduce_sum(camera_dirs, axis=-1)
-    ray_origins = tf.broadcast_to(height_width_focal, tf.shape(ray_directions))
-
-    # Return the origins and directions.
-    return (ray_origins, ray_directions)
-
-
-def render_flat_rays(ray_origins, ray_directions, near, far, num_samples, rand=False):
-    """Renders the rays and flattens it.
-    Args:
-        ray_origins: The origin points for rays.
-        ray_directions: The direction unit vectors for the rays.
-        near: The near bound of the volumetric scene.
-        far: The far bound of the volumetric scene.
-        num_samples: Number of sample points in a ray.
-        rand: Choice for randomising the sampling strategy.
-    Returns:
-       Tuple of flattened rays and sample points on each rays.
-    """
-    # Compute 3D query points.
-    # Equation: r(t) = o+td -> Building the "t" here.
-    t_vals = tf.linspace(near, far, num_samples)
-    if rand:
-        # Inject uniform noise into sample space to make the sampling
-        # continuous.
-        shape = list(ray_origins.shape[:-1]) + [num_samples]
-        noise = tf.random.uniform(shape=shape) * (far - near) / num_samples
-        t_vals = t_vals + noise
-
-    # Equation: r(t) = o + td -> Building the "r" here.
-    rays = ray_origins[..., None, :] + (
-        ray_directions[..., None, :] * t_vals[..., None]
-    )
-    rays_flat = tf.reshape(rays, [-1, 3])
-    rays_flat = encode_position(rays_flat)
-    return (rays_flat, t_vals)
-
-
-def map_fn(pose):
-    """Maps individual pose to flattened rays and sample points.
-    Args:
-        pose: The pose matrix of the camera.
-    Returns:
-        Tuple of flattened rays and sample points corresponding to the
-        camera pose.
-    """
-    (ray_origins, ray_directions) = get_rays(height=H, width=W, focal=focal, pose=pose)
-    (rays_flat, t_vals) = render_flat_rays(
-        ray_origins=ray_origins,
-        ray_directions=ray_directions,
-        near=2.0,
-        far=6.0,
-        num_samples=NUM_SAMPLES,
-        rand=True,
-    )
-    return (rays_flat, t_vals)
-
-
-def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
-    """Generates the RGB image and depth map from model prediction.
-    Args:
-        model: The MLP model that is trained to predict the rgb and
-            volume density of the volumetric scene.
-        rays_flat: The flattened rays that serve as the input to
-            the NeRF model.
-        t_vals: The sample points for the rays.
-        rand: Choice to randomise the sampling strategy.
-        train: Whether the model is in the training or testing phase.
-    Returns:
-        Tuple of rgb image and depth map.
-    """
-    # Get the predictions from the nerf model and reshape it.
-    if train:
-        predictions = model(rays_flat)
-    else:
-        predictions = model.predict(rays_flat)
-    predictions = tf.reshape(predictions, shape=(BATCH_SIZE, H, W, NUM_SAMPLES, 4))
-
-    # Slice the predictions into rgb and sigma.
-    rgb = tf.sigmoid(predictions[..., :-1])
-    sigma_a = tf.nn.relu(predictions[..., -1])
-
-    # Get the distance of adjacent intervals.
-    delta = t_vals[..., 1:] - t_vals[..., :-1]
-    # delta shape = (num_samples)
-    if rand:
-        delta = tf.concat(
-            [delta, tf.broadcast_to([1e10], shape=(BATCH_SIZE, H, W, 1))], axis=-1
-        )
-        alpha = 1.0 - tf.exp(-sigma_a * delta)
-    else:
-        delta = tf.concat(
-            [delta, tf.broadcast_to([1e10], shape=(BATCH_SIZE, 1))], axis=-1
-        )
-        alpha = 1.0 - tf.exp(-sigma_a * delta[:, None, None, :])
-
-    # Get transmittance.
-    exp_term = 1.0 - alpha
-    epsilon = 1e-10
-    transmittance = tf.math.cumprod(exp_term + epsilon, axis=-1, exclusive=True)
-    weights = alpha * transmittance
-    rgb = tf.reduce_sum(weights[..., None] * rgb, axis=-2)
-
-    if rand:
-        depth_map = tf.reduce_sum(weights * t_vals, axis=-1)
-    else:
-        depth_map = tf.reduce_sum(weights * t_vals[:, None, None], axis=-1)
-    return (rgb, depth_map)

spaces/AlexWang/lama/models/ade20k/segm_lib/utils/__init__.py

@@ -1 +0,0 @@
-from .th import *

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/cppipc/ipc.cpp

@@ -1,701 +0,0 @@
-
-#include <type_traits>
-#include <cstring>
-#include <algorithm>
-#include <utility>          // std::pair, std::move, std::forward
-#include <atomic>
-#include <type_traits>      // aligned_storage_t
-#include <string>
-#include <vector>
-#include <array>
-#include <cassert>
-
-#include "libipc/ipc.h"
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/pool_alloc.h"
-#include "libipc/queue.h"
-#include "libipc/policy.h"
-#include "libipc/rw_lock.h"
-#include "libipc/waiter.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/utility/id_pool.h"
-#include "libipc/utility/scope_guard.h"
-#include "libipc/utility/utility.h"
-
-#include "libipc/memory/resource.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_array.h"
-
-namespace {
-
-using msg_id_t = std::uint32_t;
-using acc_t    = std::atomic<msg_id_t>;
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t;
-
-template <std::size_t AlignSize>
-struct msg_t<0, AlignSize> {
-    msg_id_t     cc_id_;
-    msg_id_t     id_;
-    std::int32_t remain_;
-    bool         storage_;
-};
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t : msg_t<0, AlignSize> {
-    std::aligned_storage_t<DataSize, AlignSize> data_ {};
-
-    msg_t() = default;
-    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
-        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
-        if (this->storage_) {
-            if (data != nullptr) {
-                // copy storage-id
-                *reinterpret_cast<ipc::storage_id_t*>(&data_) =
-                     *static_cast<ipc::storage_id_t const *>(data);
-            }
-        }
-        else std::memcpy(&data_, data, size);
-    }
-};
-
-template <typename T>
-ipc::buff_t make_cache(T& data, std::size_t size) {
-    auto ptr = ipc::mem::alloc(size);
-    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
-    return { ptr, size, ipc::mem::free };
-}
-
-struct cache_t {
-    std::size_t fill_;
-    ipc::buff_t buff_;
-
-    cache_t(std::size_t f, ipc::buff_t && b)
-        : fill_(f), buff_(std::move(b))
-    {}
-
-    void append(void const * data, std::size_t size) {
-        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
-        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
-        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);
-        fill_ = new_fill;
-    }
-};
-
-auto cc_acc() {
-    static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
-    return static_cast<acc_t*>(acc_h.get());
-}
-
-IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
-    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
-}
-
-IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
-    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
-           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));
-}
-
-struct chunk_t {
-    std::atomic<ipc::circ::cc_t> &conns() noexcept {
-        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);
-    }
-
-    void *data() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this)
-             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));
-    }
-};
-
-struct chunk_info_t {
-    ipc::id_pool<> pool_;
-    ipc::spin_lock lock_;
-
-    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
-        return ipc::id_pool<>::max_count * chunk_size;
-    }
-
-    ipc::byte_t *chunks_mem() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this + 1);
-    }
-
-    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
-        if (id < 0) return nullptr;
-        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));
-    }
-};
-
-auto& chunk_storages() {
-    class chunk_handle_t {
-        ipc::shm::handle handle_;
-
-    public:
-        chunk_info_t *get_info(std::size_t chunk_size) {
-            if (!handle_.valid() &&
-                !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(), 
-                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            auto info = static_cast<chunk_info_t*>(handle_.get());
-            if (info == nullptr) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            return info;
-        }
-    };
-    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;
-    return chunk_hs;
-}
-
-chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
-    auto &storages = chunk_storages();
-    std::decay_t<decltype(storages)>::iterator it;
-    {
-        static ipc::rw_lock lock;
-        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};
-        if ((it = storages.find(chunk_size)) == storages.end()) {
-            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;
-            guard.unlock();
-            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};
-            it = storages.emplace(chunk_size, chunk_handle_t{}).first;
-        }
-    }
-    return it->second.get_info(chunk_size);
-}
-
-std::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return {};
-
-    info->lock_.lock();
-    info->pool_.prepare();
-    // got an unique id
-    auto id = info->pool_.acquire();
-    info->lock_.unlock();
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return {};
-    chunk->conns().store(conns, std::memory_order_relaxed);
-    return { id, chunk->data() };
-}
-
-void *find_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return nullptr;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return nullptr;
-    return info->at(chunk_size, id)->data();
-}
-
-void release_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, 
-            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
-    return true;
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, 
-            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
-    auto last_conns = curr_conns & ~conn_id;
-    for (unsigned k = 0;;) {
-        auto chunk_conns  = conns.load(std::memory_order_acquire);
-        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
-            return (chunk_conns & last_conns) == 0;
-        }
-        ipc::yield(k);
-    }
-}
-
-template <typename Flag>
-void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
-    if (id < 0) {
-        ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return;
-
-    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
-        return;
-    }
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <typename MsgT>
-bool clear_message(void* p) {
-    auto msg = static_cast<MsgT*>(p);
-    if (msg->storage_) {
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;
-        if (r_size <= 0) {
-            ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
-            return true;
-        }
-        release_storage(
-            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),
-            static_cast<std::size_t>(r_size));
-    }
-    return true;
-}
-
-struct conn_info_head {
-
-    ipc::string name_;
-    msg_id_t    cc_id_; // connection-info id
-    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
-    ipc::shm::handle acc_h_;
-
-    conn_info_head(char const * name)
-        : name_     {name}
-        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
-        , cc_waiter_{("__CC_CONN__" + name_).c_str()}
-        , wt_waiter_{("__WT_CONN__" + name_).c_str()}
-        , rd_waiter_{("__RD_CONN__" + name_).c_str()}
-        , acc_h_    {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
-    }
-
-    void quit_waiting() {
-        cc_waiter_.quit_waiting();
-        wt_waiter_.quit_waiting();
-        rd_waiter_.quit_waiting();
-    }
-
-    auto acc() {
-        return static_cast<acc_t*>(acc_h_.get());
-    }
-
-    auto& recv_cache() {
-        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;
-        return tls;
-    }
-};
-
-template <typename W, typename F>
-bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
-    if (tm == 0) return !pred();
-    for (unsigned k = 0; pred();) {
-        bool ret = true;
-        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
-            ret = waiter.wait_if(std::forward<F>(pred), tm);
-            k   = 0;
-        });
-        if (!ret) return false; // timeout or fail
-        if (k == 0) break; // k has been reset
-    }
-    return true;
-}
-
-template <typename Policy,
-          std::size_t DataSize  = ipc::data_length,
-          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>
-struct queue_generator {
-
-    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;
-
-    struct conn_info_t : conn_info_head {
-        queue_t que_;
-
-        conn_info_t(char const * name)
-            : conn_info_head{name}
-            , que_{("__QU_CONN__" +
-                    ipc::to_string(DataSize) + "__" +
-                    ipc::to_string(AlignSize) + "__" + name).c_str()} {
-        }
-
-        void disconnect_receiver() {
-            bool dis = que_.disconnect();
-            this->quit_waiting();
-            if (dis) {
-                this->recv_cache().clear();
-            }
-        }
-    };
-};
-
-template <typename Policy>
-struct detail_impl {
-
-using policy_t    = Policy;
-using flag_t      = typename policy_t::flag_t;
-using queue_t     = typename queue_generator<policy_t>::queue_t;
-using conn_info_t = typename queue_generator<policy_t>::conn_info_t;
-
-constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
-    return static_cast<conn_info_t*>(h);
-}
-
-constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
-    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
-}
-
-/* API implementations */
-
-static void disconnect(ipc::handle_t h) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return;
-    }
-    que->shut_sending();
-    assert(info_of(h) != nullptr);
-    info_of(h)->disconnect_receiver();
-}
-
-static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
-    assert(ph != nullptr);
-    assert(*ph != nullptr);
-    auto que = queue_of(*ph);
-    if (que == nullptr) {
-        return false;
-    }
-    if (start_to_recv) {
-        que->shut_sending();
-        if (que->connect()) { // wouldn't connect twice
-            info_of(*ph)->cc_waiter_.broadcast();
-            return true;
-        }
-        return false;
-    }
-    // start_to_recv == false
-    if (que->connected()) {
-        info_of(*ph)->disconnect_receiver();
-    }
-    return que->ready_sending();
-}
-
-static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
-    assert(ph != nullptr);
-    if (*ph == nullptr) {
-        *ph = ipc::mem::alloc<conn_info_t>(name);
-    }
-    return reconnect(ph, start_to_recv);
-}
-
-static void destroy(ipc::handle_t h) {
-    disconnect(h);
-    ipc::mem::free(info_of(h));
-}
-
-static std::size_t recv_count(ipc::handle_t h) noexcept {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return ipc::invalid_value;
-    }
-    return que->conn_count();
-}
-
-static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return false;
-    }
-    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
-        return que->conn_count() < r_count;
-    }, tm);
-}
-
-template <typename F>
-static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
-    if (data == nullptr || size == 0) {
-        ipc::error("fail: send(%p, %zd)\n", data, size);
-        return false;
-    }
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: send, queue_of(h) == nullptr\n");
-        return false;
-    }
-    if (que->elems() == nullptr) {
-        ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
-        return false;
-    }
-    if (!que->ready_sending()) {
-        ipc::error("fail: send, que->ready_sending() == false\n");
-        return false;
-    }
-    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
-    if (conns == 0) {
-        ipc::error("fail: send, there is no receiver on this connection.\n");
-        return false;
-    }
-    // calc a new message id
-    auto acc = info_of(h)->acc();
-    if (acc == nullptr) {
-        ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
-        return false;
-    }
-    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);
-    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);
-    if (size > ipc::large_msg_limit) {
-        auto   dat = acquire_storage(size, conns);
-        void * buf = dat.second;
-        if (buf != nullptr) {
-            std::memcpy(buf, data, size);
-            return try_push(static_cast<std::int32_t>(size) - 
-                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);
-        }
-        // try using message fragment
-        //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
-    }
-    // push message fragment
-    std::int32_t offset = 0;
-    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {
-        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {
-            return false;
-        }
-    }
-    // if remain > 0, this is the last message fragment
-    std::int32_t remain = static_cast<std::int32_t>(size) - offset;
-    if (remain > 0) {
-        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, 
-                      static_cast<std::size_t>(remain))) {
-            return false;
-        }
-    }
-    return true;
-}
-
-static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
-                if (!que->force_push(
-                        clear_message<typename queue_t::value_t>,
-                        info->cc_id_, msg_id, remain, data, size)) {
-                    return false;
-                }
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                return false;
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: recv, queue_of(h) == nullptr\n");
-        return {};
-    }
-    if (!que->connected()) {
-        // hasn't connected yet, just return.
-        return {};
-    }
-    auto& rc = info_of(h)->recv_cache();
-    for (;;) {
-        // pop a new message
-        typename queue_t::value_t msg;
-        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
-                return !que->pop(msg);
-            }, tm)) {
-            // pop failed, just return.
-            return {};
-        }
-        info_of(h)->wt_waiter_.broadcast();
-        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
-            continue; // ignore message to self
-        }
-        // msg.remain_ may minus & abs(msg.remain_) < data_length
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;
-        if (r_size <= 0) {
-            ipc::error("fail: recv, r_size = %d\n", (int)r_size);
-            return {};
-        }
-        std::size_t msg_size = static_cast<std::size_t>(r_size);
-        // large message
-        if (msg.storage_) {
-            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);
-            void* buf = find_storage(buf_id, msg_size);
-            if (buf != nullptr) {
-                struct recycle_t {
-                    ipc::storage_id_t storage_id;
-                    ipc::circ::cc_t   curr_conns;
-                    ipc::circ::cc_t   conn_id;
-                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{
-                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
-                });
-                if (r_info == nullptr) {
-                    ipc::log("fail: ipc::mem::alloc<recycle_t>.\n");
-                    return ipc::buff_t{buf, msg_size}; // no recycle
-                } else {
-                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
-                        auto r_info = static_cast<recycle_t *>(p_info);
-                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {
-                            ipc::mem::free(r_info);
-                        });
-                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
-                    }, r_info};
-                }
-            } else {
-                ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
-                continue;
-            }
-        }
-        // find cache with msg.id_
-        auto cac_it = rc.find(msg.id_);
-        if (cac_it == rc.end()) {
-            if (msg_size <= ipc::data_length) {
-                return make_cache(msg.data_, msg_size);
-            }
-            // gc
-            if (rc.size() > 1024) {
-                std::vector<msg_id_t> need_del;
-                for (auto const & pair : rc) {
-                    auto cmp = std::minmax(msg.id_, pair.first);
-                    if (cmp.second - cmp.first > 8192) {
-                        need_del.push_back(pair.first);
-                    }
-                }
-                for (auto id : need_del) rc.erase(id);
-            }
-            // cache the first message fragment
-            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
-        }
-        // has cached before this message
-        else {
-            auto& cac = cac_it->second;
-            // this is the last message fragment
-            if (msg.remain_ <= 0) {
-                cac.append(&(msg.data_), msg_size);
-                // finish this message, erase it from cache
-                auto buff = std::move(cac.buff_);
-                rc.erase(cac_it);
-                return buff;
-            }
-            // there are remain datas after this message
-            cac.append(&(msg.data_), ipc::data_length);
-        }
-    }
-}
-
-static ipc::buff_t try_recv(ipc::handle_t h) {
-    return recv(h, 0);
-}
-
-}; // detail_impl<Policy>
-
-template <typename Flag>
-using policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;
-
-} // internal-linkage
-
-namespace ipc {
-
-template <typename Flag>
-ipc::handle_t chan_impl<Flag>::inited() {
-    ipc::detail::waiter::init();
-    return nullptr;
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::disconnect(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::disconnect(h);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::destroy(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::destroy(h);
-}
-
-template <typename Flag>
-char const * chan_impl<Flag>::name(ipc::handle_t h) {
-    auto info = detail_impl<policy_t<Flag>>::info_of(h);
-    return (info == nullptr) ? nullptr : info->name_.c_str();
-}
-
-template <typename Flag>
-std::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::recv_count(h);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::recv(h, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::try_recv(h);
-}
-
-template struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;
-// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD
-// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD
-template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;
-template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;
-
-} // namespace ipc

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/longcode/prod_cons.h

@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_k_upscaler_to_diffusers.py

@@ -1,297 +0,0 @@
-import argparse
-
-import huggingface_hub
-import k_diffusion as K
-import torch
-
-from diffusers import UNet2DConditionModel
-
-
-UPSCALER_REPO = "pcuenq/k-upscaler"
-
-
-def resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
-    rv = {
-        # norm1
-        f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"],
-        f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"],
-        # conv1
-        f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"],
-        f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"],
-        # norm2
-        f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"],
-        f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"],
-        # conv2
-        f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"],
-        f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"],
-    }
-
-    if resnet.conv_shortcut is not None:
-        rv.update(
-            {
-                f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"],
-            }
-        )
-
-    return rv
-
-
-def self_attn_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
-    weight_q, weight_k, weight_v = checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3, dim=0)
-    bias_q, bias_k, bias_v = checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3, dim=0)
-    rv = {
-        # norm
-        f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"],
-        f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"],
-        # to_q
-        f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1).squeeze(-1),
-        f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
-        # to_k
-        f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1).squeeze(-1),
-        f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
-        # to_v
-        f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1).squeeze(-1),
-        f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
-        # to_out
-        f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"]
-        .squeeze(-1)
-        .squeeze(-1),
-        f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"],
-    }
-
-    return rv
-
-
-def cross_attn_to_diffusers_checkpoint(
-    checkpoint, *, diffusers_attention_prefix, diffusers_attention_index, attention_prefix
-):
-    weight_k, weight_v = checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2, dim=0)
-    bias_k, bias_v = checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2, dim=0)
-
-    rv = {
-        # norm2 (ada groupnorm)
-        f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
-            f"{attention_prefix}.norm_dec.mapper.weight"
-        ],
-        f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
-            f"{attention_prefix}.norm_dec.mapper.bias"
-        ],
-        # layernorm on encoder_hidden_state
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
-            f"{attention_prefix}.norm_enc.weight"
-        ],
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
-            f"{attention_prefix}.norm_enc.bias"
-        ],
-        # to_q
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
-            f"{attention_prefix}.q_proj.weight"
-        ]
-        .squeeze(-1)
-        .squeeze(-1),
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
-            f"{attention_prefix}.q_proj.bias"
-        ],
-        # to_k
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1).squeeze(-1),
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
-        # to_v
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1).squeeze(-1),
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
-        # to_out
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
-            f"{attention_prefix}.out_proj.weight"
-        ]
-        .squeeze(-1)
-        .squeeze(-1),
-        f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
-            f"{attention_prefix}.out_proj.bias"
-        ],
-    }
-
-    return rv
-
-
-def block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type):
-    block_prefix = "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
-    block_prefix = f"{block_prefix}.{block_idx}"
-
-    diffusers_checkpoint = {}
-
-    if not hasattr(block, "attentions"):
-        n = 1  # resnet only
-    elif not block.attentions[0].add_self_attention:
-        n = 2  # resnet -> cross-attention
-    else:
-        n = 3  # resnet -> self-attention -> cross-attention)
-
-    for resnet_idx, resnet in enumerate(block.resnets):
-        # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
-        diffusers_resnet_prefix = f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
-        idx = n * resnet_idx if block_type == "up" else n * resnet_idx + 1
-        resnet_prefix = f"{block_prefix}.{idx}" if block_type == "up" else f"{block_prefix}.{idx}"
-
-        diffusers_checkpoint.update(
-            resnet_to_diffusers_checkpoint(
-                resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
-            )
-        )
-
-    if hasattr(block, "attentions"):
-        for attention_idx, attention in enumerate(block.attentions):
-            diffusers_attention_prefix = f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
-            idx = n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
-            self_attention_prefix = f"{block_prefix}.{idx}"
-            cross_attention_prefix = f"{block_prefix}.{idx }"
-            cross_attention_index = 1 if not attention.add_self_attention else 2
-            idx = (
-                n * attention_idx + cross_attention_index
-                if block_type == "up"
-                else n * attention_idx + cross_attention_index + 1
-            )
-            cross_attention_prefix = f"{block_prefix}.{idx }"
-
-            diffusers_checkpoint.update(
-                cross_attn_to_diffusers_checkpoint(
-                    checkpoint,
-                    diffusers_attention_prefix=diffusers_attention_prefix,
-                    diffusers_attention_index=2,
-                    attention_prefix=cross_attention_prefix,
-                )
-            )
-
-            if attention.add_self_attention is True:
-                diffusers_checkpoint.update(
-                    self_attn_to_diffusers_checkpoint(
-                        checkpoint,
-                        diffusers_attention_prefix=diffusers_attention_prefix,
-                        attention_prefix=self_attention_prefix,
-                    )
-                )
-
-    return diffusers_checkpoint
-
-
-def unet_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    # pre-processing
-    diffusers_checkpoint.update(
-        {
-            "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
-            "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
-        }
-    )
-
-    # timestep and class embedding
-    diffusers_checkpoint.update(
-        {
-            "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1),
-            "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
-            "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
-            "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
-            "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
-            "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
-        }
-    )
-
-    # down_blocks
-    for down_block_idx, down_block in enumerate(model.down_blocks):
-        diffusers_checkpoint.update(block_to_diffusers_checkpoint(down_block, checkpoint, down_block_idx, "down"))
-
-    # up_blocks
-    for up_block_idx, up_block in enumerate(model.up_blocks):
-        diffusers_checkpoint.update(block_to_diffusers_checkpoint(up_block, checkpoint, up_block_idx, "up"))
-
-    # post-processing
-    diffusers_checkpoint.update(
-        {
-            "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
-            "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def unet_model_from_original_config(original_config):
-    in_channels = original_config["input_channels"] + original_config["unet_cond_dim"]
-    out_channels = original_config["input_channels"] + (1 if original_config["has_variance"] else 0)
-
-    block_out_channels = original_config["channels"]
-
-    assert (
-        len(set(original_config["depths"])) == 1
-    ), "UNet2DConditionModel currently do not support blocks with different number of layers"
-    layers_per_block = original_config["depths"][0]
-
-    class_labels_dim = original_config["mapping_cond_dim"]
-    cross_attention_dim = original_config["cross_cond_dim"]
-
-    attn1_types = []
-    attn2_types = []
-    for s, c in zip(original_config["self_attn_depths"], original_config["cross_attn_depths"]):
-        if s:
-            a1 = "self"
-            a2 = "cross" if c else None
-        elif c:
-            a1 = "cross"
-            a2 = None
-        else:
-            a1 = None
-            a2 = None
-        attn1_types.append(a1)
-        attn2_types.append(a2)
-
-    unet = UNet2DConditionModel(
-        in_channels=in_channels,
-        out_channels=out_channels,
-        down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D"),
-        mid_block_type=None,
-        up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"),
-        block_out_channels=block_out_channels,
-        layers_per_block=layers_per_block,
-        act_fn="gelu",
-        norm_num_groups=None,
-        cross_attention_dim=cross_attention_dim,
-        attention_head_dim=64,
-        time_cond_proj_dim=class_labels_dim,
-        resnet_time_scale_shift="scale_shift",
-        time_embedding_type="fourier",
-        timestep_post_act="gelu",
-        conv_in_kernel=1,
-        conv_out_kernel=1,
-    )
-
-    return unet
-
-
-def main(args):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    orig_config_path = huggingface_hub.hf_hub_download(UPSCALER_REPO, "config_laion_text_cond_latent_upscaler_2.json")
-    orig_weights_path = huggingface_hub.hf_hub_download(
-        UPSCALER_REPO, "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"
-    )
-    print(f"loading original model configuration from {orig_config_path}")
-    print(f"loading original model checkpoint from {orig_weights_path}")
-
-    print("converting to diffusers unet")
-    orig_config = K.config.load_config(open(orig_config_path))["model"]
-    model = unet_model_from_original_config(orig_config)
-
-    orig_checkpoint = torch.load(orig_weights_path, map_location=device)["model_ema"]
-    converted_checkpoint = unet_to_diffusers_checkpoint(model, orig_checkpoint)
-
-    model.load_state_dict(converted_checkpoint, strict=True)
-    model.save_pretrained(args.dump_path)
-    print(f"saving converted unet model in {args.dump_path}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-    args = parser.parse_args()
-
-    main(args)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/check_dummies.py

@@ -1,172 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import os
-import re
-
-
-# All paths are set with the intent you should run this script from the root of the repo with the command
-# python utils/check_dummies.py
-PATH_TO_DIFFUSERS = "src/diffusers"
-
-# Matches is_xxx_available()
-_re_backend = re.compile(r"is\_([a-z_]*)_available\(\)")
-# Matches from xxx import bla
-_re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
-
-
-DUMMY_CONSTANT = """
-{0} = None
-"""
-
-DUMMY_CLASS = """
-class {0}(metaclass=DummyObject):
-    _backends = {1}
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, {1})
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, {1})
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, {1})
-"""
-
-
-DUMMY_FUNCTION = """
-def {0}(*args, **kwargs):
-    requires_backends({0}, {1})
-"""
-
-
-def find_backend(line):
-    """Find one (or multiple) backend in a code line of the init."""
-    backends = _re_backend.findall(line)
-    if len(backends) == 0:
-        return None
-
-    return "_and_".join(backends)
-
-
-def read_init():
-    """Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects."""
-    with open(os.path.join(PATH_TO_DIFFUSERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f:
-        lines = f.readlines()
-
-    # Get to the point we do the actual imports for type checking
-    line_index = 0
-    backend_specific_objects = {}
-    # Go through the end of the file
-    while line_index < len(lines):
-        # If the line contains is_backend_available, we grab all objects associated with the `else` block
-        backend = find_backend(lines[line_index])
-        if backend is not None:
-            while not lines[line_index].startswith("else:"):
-                line_index += 1
-            line_index += 1
-            objects = []
-            # Until we unindent, add backend objects to the list
-            while line_index < len(lines) and len(lines[line_index]) > 1:
-                line = lines[line_index]
-                single_line_import_search = _re_single_line_import.search(line)
-                if single_line_import_search is not None:
-                    objects.extend(single_line_import_search.groups()[0].split(", "))
-                elif line.startswith(" " * 8):
-                    objects.append(line[8:-2])
-                line_index += 1
-
-            if len(objects) > 0:
-                backend_specific_objects[backend] = objects
-        else:
-            line_index += 1
-
-    return backend_specific_objects
-
-
-def create_dummy_object(name, backend_name):
-    """Create the code for the dummy object corresponding to `name`."""
-    if name.isupper():
-        return DUMMY_CONSTANT.format(name)
-    elif name.islower():
-        return DUMMY_FUNCTION.format(name, backend_name)
-    else:
-        return DUMMY_CLASS.format(name, backend_name)
-
-
-def create_dummy_files(backend_specific_objects=None):
-    """Create the content of the dummy files."""
-    if backend_specific_objects is None:
-        backend_specific_objects = read_init()
-    # For special correspondence backend to module name as used in the function requires_modulename
-    dummy_files = {}
-
-    for backend, objects in backend_specific_objects.items():
-        backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]"
-        dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
-        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
-        dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects])
-        dummy_files[backend] = dummy_file
-
-    return dummy_files
-
-
-def check_dummies(overwrite=False):
-    """Check if the dummy files are up to date and maybe `overwrite` with the right content."""
-    dummy_files = create_dummy_files()
-    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
-    short_names = {"torch": "pt"}
-
-    # Locate actual dummy modules and read their content.
-    path = os.path.join(PATH_TO_DIFFUSERS, "utils")
-    dummy_file_paths = {
-        backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py")
-        for backend in dummy_files.keys()
-    }
-
-    actual_dummies = {}
-    for backend, file_path in dummy_file_paths.items():
-        if os.path.isfile(file_path):
-            with open(file_path, "r", encoding="utf-8", newline="\n") as f:
-                actual_dummies[backend] = f.read()
-        else:
-            actual_dummies[backend] = ""
-
-    for backend in dummy_files.keys():
-        if dummy_files[backend] != actual_dummies[backend]:
-            if overwrite:
-                print(
-                    f"Updating diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main "
-                    "__init__ has new objects."
-                )
-                with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f:
-                    f.write(dummy_files[backend])
-            else:
-                raise ValueError(
-                    "The main __init__ has objects that are not present in "
-                    f"diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` "
-                    "to fix this."
-                )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
-    args = parser.parse_args()
-
-    check_dummies(args.fix_and_overwrite)

spaces/Andy1621/uniformer_image_detection/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py

@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_480x480_40k_pascal_context_59.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=dict(
-        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

spaces/Andyrasika/xlm-roberta-base-finetuned-panx-de/README.md

@@ -1,12 +0,0 @@
----
-title: Xlm Roberta Base Finetuned Panx De
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AnimalEquality/chatbot/scripts/nbdev_prepare_modded.sh

@@ -1,4 +0,0 @@
-#!/bin/bash
-# Run from root dir
-nbdev_prepare
-scripts/nbdev_readme_patch_hface.sh

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ctransformers_model.py

@@ -1,79 +0,0 @@
-from ctransformers import AutoConfig, AutoModelForCausalLM
-
-from modules import shared
-from modules.callbacks import Iteratorize
-from modules.logging_colors import logger
-
-
-class CtransformersModel:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(cls, path):
-        result = cls()
-
-        config = AutoConfig.from_pretrained(
-            str(path),
-            threads=shared.args.threads if shared.args.threads != 0 else -1,
-            gpu_layers=shared.args.n_gpu_layers,
-            batch_size=shared.args.n_batch,
-            context_length=shared.args.n_ctx,
-            stream=True,
-            mmap=not shared.args.no_mmap,
-            mlock=shared.args.mlock
-        )
-
-        result.model = AutoModelForCausalLM.from_pretrained(
-            str(result.model_dir(path) if result.model_type_is_auto() else path),
-            model_type=(None if result.model_type_is_auto() else shared.args.model_type),
-            config=config
-        )
-
-        logger.info(f'Using ctransformers model_type: {result.model.model_type} for {result.model.model_path}')
-        return result, result
-
-    def model_type_is_auto(self):
-        return shared.args.model_type is None or shared.args.model_type == "Auto" or shared.args.model_type == "None"
-
-    def model_dir(self, path):
-        if path.is_file():
-            return path.parent
-
-        return path
-
-    def encode(self, string, **kwargs):
-        return self.model.tokenize(string)
-
-    def decode(self, ids):
-        return self.model.detokenize(ids)
-
-    def generate(self, prompt, state, callback=None):
-        prompt = prompt if type(prompt) is str else prompt.decode()
-        # ctransformers uses -1 for random seed
-        generator = self.model(
-            prompt=prompt,
-            max_new_tokens=state['max_new_tokens'],
-            temperature=state['temperature'],
-            top_p=state['top_p'],
-            top_k=state['top_k'],
-            repetition_penalty=state['repetition_penalty'],
-            last_n_tokens=state['repetition_penalty_range'],
-            seed=int(state['seed'])
-        )
-
-        output = ""
-        for token in generator:
-            if callback:
-                callback(token)
-
-            output += token
-
-        return output
-
-    def generate_with_streaming(self, *args, **kwargs):
-        with Iteratorize(self.generate, args, kwargs, callback=None) as generator:
-            reply = ''
-            for token in generator:
-                reply += token
-                yield reply

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/ade.py

@@ -1,84 +0,0 @@
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class ADE20KDataset(CustomDataset):
-    """ADE20K dataset.
-
-    In segmentation map annotation for ADE20K, 0 stands for background, which
-    is not included in 150 categories. ``reduce_zero_label`` is fixed to True.
-    The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to
-    '.png'.
-    """
-    CLASSES = (
-        'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
-        'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
-        'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
-        'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
-        'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
-        'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
-        'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
-        'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
-        'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
-        'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
-        'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
-        'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
-        'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
-        'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
-        'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
-        'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
-        'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
-        'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
-        'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
-        'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
-        'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
-        'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
-        'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
-        'clock', 'flag')
-
-    PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-               [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-               [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-               [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-               [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-               [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-               [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-               [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-               [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-               [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-               [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-               [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-               [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-               [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-               [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-               [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-               [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-               [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-               [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-               [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-               [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-               [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-               [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-               [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-               [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-               [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-               [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-               [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-               [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-               [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-               [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-               [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-               [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-               [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-               [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-               [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-               [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-               [102, 255, 0], [92, 0, 255]]
-
-    def __init__(self, **kwargs):
-        super(ADE20KDataset, self).__init__(
-            img_suffix='.jpg',
-            seg_map_suffix='.png',
-            reduce_zero_label=True,
-            **kwargs)

spaces/Apex-X/GODROOP/roop/utilities.py

@@ -1,141 +0,0 @@
-import glob
-import mimetypes
-import os
-import platform
-import shutil
-import ssl
-import subprocess
-import urllib
-from pathlib import Path
-from typing import List, Any
-from tqdm import tqdm
-
-import roop.globals
-
-TEMP_FILE = 'temp.mp4'
-TEMP_DIRECTORY = 'temp'
-
-# monkey patch ssl for mac
-if platform.system().lower() == 'darwin':
-    ssl._create_default_https_context = ssl._create_unverified_context
-
-
-def run_ffmpeg(args: List[str]) -> bool:
-    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', roop.globals.log_level]
-    commands.extend(args)
-    try:
-        subprocess.check_output(commands, stderr=subprocess.STDOUT)
-        return True
-    except Exception:
-        pass
-    return False
-
-
-def detect_fps(target_path: str) -> float:
-    command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'default=noprint_wrappers=1:nokey=1', target_path]
-    output = subprocess.check_output(command).decode().strip().split('/')
-    try:
-        numerator, denominator = map(int, output)
-        return numerator / denominator
-    except Exception:
-        pass
-    return 30.0
-
-
-def extract_frames(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
-
-
-def create_video(target_path: str, fps: float = 30.0) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.png'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', temp_output_path])
-
-
-def restore_audio(target_path: str, output_path: str) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    done = run_ffmpeg(['-i', temp_output_path, '-i', target_path, '-c:v', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path])
-    if not done:
-        move_temp(target_path, output_path)
-
-
-def get_temp_frame_paths(target_path: str) -> List[str]:
-    temp_directory_path = get_temp_directory_path(target_path)
-    return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.png')))
-
-
-def get_temp_directory_path(target_path: str) -> str:
-    target_name, _ = os.path.splitext(os.path.basename(target_path))
-    target_directory_path = os.path.dirname(target_path)
-    return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
-
-
-def get_temp_output_path(target_path: str) -> str:
-    temp_directory_path = get_temp_directory_path(target_path)
-    return os.path.join(temp_directory_path, TEMP_FILE)
-
-
-def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
-    if source_path and target_path:
-        source_name, _ = os.path.splitext(os.path.basename(source_path))
-        target_name, target_extension = os.path.splitext(os.path.basename(target_path))
-        if os.path.isdir(output_path):
-            return os.path.join(output_path, source_name + '-' + target_name + target_extension)
-    return output_path
-
-
-def create_temp(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
-
-
-def move_temp(target_path: str, output_path: str) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    if os.path.isfile(temp_output_path):
-        if os.path.isfile(output_path):
-            os.remove(output_path)
-        shutil.move(temp_output_path, output_path)
-
-
-def clean_temp(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    parent_directory_path = os.path.dirname(temp_directory_path)
-    if not roop.globals.keep_frames and os.path.isdir(temp_directory_path):
-        shutil.rmtree(temp_directory_path)
-    if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
-        os.rmdir(parent_directory_path)
-
-
-def has_image_extension(image_path: str) -> bool:
-    return image_path.lower().endswith(('png', 'jpg', 'jpeg', 'webp'))
-
-
-def is_image(image_path: str) -> bool:
-    if image_path and os.path.isfile(image_path):
-        mimetype, _ = mimetypes.guess_type(image_path)
-        return bool(mimetype and mimetype.startswith('image/'))
-    return False
-
-
-def is_video(video_path: str) -> bool:
-    if video_path and os.path.isfile(video_path):
-        mimetype, _ = mimetypes.guess_type(video_path)
-        return bool(mimetype and mimetype.startswith('video/'))
-    return False
-
-
-def conditional_download(download_directory_path: str, urls: List[str]) -> None:
-    if not os.path.exists(download_directory_path):
-        os.makedirs(download_directory_path)
-    for url in urls:
-        download_file_path = os.path.join(download_directory_path, os.path.basename(url))
-        if not os.path.exists(download_file_path):
-            request = urllib.request.urlopen(url) # type: ignore[attr-defined]
-            total = int(request.headers.get('Content-Length', 0))
-            with tqdm(total=total, desc='Downloading', unit='B', unit_scale=True, unit_divisor=1024) as progress:
-                urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
-
-
-def resolve_relative_path(path: str) -> str:
-    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))

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

@@ -1,48 +0,0 @@
-"""
-requests._internal_utils
-~~~~~~~~~~~~~~
-
-Provides utility functions that are consumed internally by Requests
-which depend on extremely few external helpers (such as compat)
-"""
-import re
-
-from .compat import builtin_str
-
-_VALID_HEADER_NAME_RE_BYTE = re.compile(rb"^[^:\s][^:\r\n]*$")
-_VALID_HEADER_NAME_RE_STR = re.compile(r"^[^:\s][^:\r\n]*$")
-_VALID_HEADER_VALUE_RE_BYTE = re.compile(rb"^\S[^\r\n]*$|^$")
-_VALID_HEADER_VALUE_RE_STR = re.compile(r"^\S[^\r\n]*$|^$")
-
-HEADER_VALIDATORS = {
-    bytes: (_VALID_HEADER_NAME_RE_BYTE, _VALID_HEADER_VALUE_RE_BYTE),
-    str: (_VALID_HEADER_NAME_RE_STR, _VALID_HEADER_VALUE_RE_STR),
-}
-
-
-def to_native_string(string, encoding="ascii"):
-    """Given a string object, regardless of type, returns a representation of
-    that string in the native string type, encoding and decoding where
-    necessary. This assumes ASCII unless told otherwise.
-    """
-    if isinstance(string, builtin_str):
-        out = string
-    else:
-        out = string.decode(encoding)
-
-    return out
-
-
-def unicode_is_ascii(u_string):
-    """Determine if unicode string only contains ASCII characters.
-
-    :param str u_string: unicode string to check. Must be unicode
-        and not Python 2 `str`.
-    :rtype: bool
-    """
-    assert isinstance(u_string, str)
-    try:
-        u_string.encode("ascii")
-        return True
-    except UnicodeEncodeError:
-        return False

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/aspp.py

@@ -1,144 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from copy import deepcopy
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .batch_norm import get_norm
-from .blocks import DepthwiseSeparableConv2d
-from .wrappers import Conv2d
-
-
-class ASPP(nn.Module):
-    """
-    Atrous Spatial Pyramid Pooling (ASPP).
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        dilations,
-        *,
-        norm,
-        activation,
-        pool_kernel_size=None,
-        dropout: float = 0.0,
-        use_depthwise_separable_conv=False,
-    ):
-        """
-        Args:
-            in_channels (int): number of input channels for ASPP.
-            out_channels (int): number of output channels.
-            dilations (list): a list of 3 dilations in ASPP.
-            norm (str or callable): normalization for all conv layers.
-                See :func:`layers.get_norm` for supported format. norm is
-                applied to all conv layers except the conv following
-                global average pooling.
-            activation (callable): activation function.
-            pool_kernel_size (tuple, list): the average pooling size (kh, kw)
-                for image pooling layer in ASPP. If set to None, it always
-                performs global average pooling. If not None, it must be
-                divisible by the shape of inputs in forward(). It is recommended
-                to use a fixed input feature size in training, and set this
-                option to match this size, so that it performs global average
-                pooling in training, and the size of the pooling window stays
-                consistent in inference.
-            dropout (float): apply dropout on the output of ASPP. It is used in
-                the official DeepLab implementation with a rate of 0.1:
-                https://github.com/tensorflow/models/blob/21b73d22f3ed05b650e85ac50849408dd36de32e/research/deeplab/model.py#L532  # noqa
-            use_depthwise_separable_conv (bool): use DepthwiseSeparableConv2d
-                for 3x3 convs in ASPP, proposed in :paper:`DeepLabV3+`.
-        """
-        super(ASPP, self).__init__()
-        assert len(dilations) == 3, "ASPP expects 3 dilations, got {}".format(len(dilations))
-        self.pool_kernel_size = pool_kernel_size
-        self.dropout = dropout
-        use_bias = norm == ""
-        self.convs = nn.ModuleList()
-        # conv 1x1
-        self.convs.append(
-            Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                bias=use_bias,
-                norm=get_norm(norm, out_channels),
-                activation=deepcopy(activation),
-            )
-        )
-        weight_init.c2_xavier_fill(self.convs[-1])
-        # atrous convs
-        for dilation in dilations:
-            if use_depthwise_separable_conv:
-                self.convs.append(
-                    DepthwiseSeparableConv2d(
-                        in_channels,
-                        out_channels,
-                        kernel_size=3,
-                        padding=dilation,
-                        dilation=dilation,
-                        norm1=norm,
-                        activation1=deepcopy(activation),
-                        norm2=norm,
-                        activation2=deepcopy(activation),
-                    )
-                )
-            else:
-                self.convs.append(
-                    Conv2d(
-                        in_channels,
-                        out_channels,
-                        kernel_size=3,
-                        padding=dilation,
-                        dilation=dilation,
-                        bias=use_bias,
-                        norm=get_norm(norm, out_channels),
-                        activation=deepcopy(activation),
-                    )
-                )
-                weight_init.c2_xavier_fill(self.convs[-1])
-        # image pooling
-        # We do not add BatchNorm because the spatial resolution is 1x1,
-        # the original TF implementation has BatchNorm.
-        if pool_kernel_size is None:
-            image_pooling = nn.Sequential(
-                nn.AdaptiveAvgPool2d(1),
-                Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)),
-            )
-        else:
-            image_pooling = nn.Sequential(
-                nn.AvgPool2d(kernel_size=pool_kernel_size, stride=1),
-                Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)),
-            )
-        weight_init.c2_xavier_fill(image_pooling[1])
-        self.convs.append(image_pooling)
-
-        self.project = Conv2d(
-            5 * out_channels,
-            out_channels,
-            kernel_size=1,
-            bias=use_bias,
-            norm=get_norm(norm, out_channels),
-            activation=deepcopy(activation),
-        )
-        weight_init.c2_xavier_fill(self.project)
-
-    def forward(self, x):
-        size = x.shape[-2:]
-        if self.pool_kernel_size is not None:
-            if size[0] % self.pool_kernel_size[0] or size[1] % self.pool_kernel_size[1]:
-                raise ValueError(
-                    "`pool_kernel_size` must be divisible by the shape of inputs. "
-                    "Input size: {} `pool_kernel_size`: {}".format(size, self.pool_kernel_size)
-                )
-        res = []
-        for conv in self.convs:
-            res.append(conv(x))
-        res[-1] = F.interpolate(res[-1], size=size, mode="bilinear", align_corners=False)
-        res = torch.cat(res, dim=1)
-        res = self.project(res)
-        res = F.dropout(res, self.dropout, training=self.training) if self.dropout > 0 else res
-        return res

spaces/AzizR/FaceRecognitionGradio/README.md

@@ -1,12 +0,0 @@
----
-title: FaceRecognitionGradio
-emoji: 👁
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Betacuckgpt/ehartford-Wizard-Vicuna-30B-Uncensored123/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/ehartford/Wizard-Vicuna-30B-Uncensored").launch()

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/hebrewprober.py

@@ -1,316 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-#          Shy Shalom
-# Portions created by the Initial Developer are Copyright (C) 2005
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import Optional, Union
-
-from .charsetprober import CharSetProber
-from .enums import ProbingState
-from .sbcharsetprober import SingleByteCharSetProber
-
-# This prober doesn't actually recognize a language or a charset.
-# It is a helper prober for the use of the Hebrew model probers
-
-### General ideas of the Hebrew charset recognition ###
-#
-# Four main charsets exist in Hebrew:
-# "ISO-8859-8" - Visual Hebrew
-# "windows-1255" - Logical Hebrew
-# "ISO-8859-8-I" - Logical Hebrew
-# "x-mac-hebrew" - ?? Logical Hebrew ??
-#
-# Both "ISO" charsets use a completely identical set of code points, whereas
-# "windows-1255" and "x-mac-hebrew" are two different proper supersets of
-# these code points. windows-1255 defines additional characters in the range
-# 0x80-0x9F as some misc punctuation marks as well as some Hebrew-specific
-# diacritics and additional 'Yiddish' ligature letters in the range 0xc0-0xd6.
-# x-mac-hebrew defines similar additional code points but with a different
-# mapping.
-#
-# As far as an average Hebrew text with no diacritics is concerned, all four
-# charsets are identical with respect to code points. Meaning that for the
-# main Hebrew alphabet, all four map the same values to all 27 Hebrew letters
-# (including final letters).
-#
-# The dominant difference between these charsets is their directionality.
-# "Visual" directionality means that the text is ordered as if the renderer is
-# not aware of a BIDI rendering algorithm. The renderer sees the text and
-# draws it from left to right. The text itself when ordered naturally is read
-# backwards. A buffer of Visual Hebrew generally looks like so:
-# "[last word of first line spelled backwards] [whole line ordered backwards
-# and spelled backwards] [first word of first line spelled backwards]
-# [end of line] [last word of second line] ... etc' "
-# adding punctuation marks, numbers and English text to visual text is
-# naturally also "visual" and from left to right.
-#
-# "Logical" directionality means the text is ordered "naturally" according to
-# the order it is read. It is the responsibility of the renderer to display
-# the text from right to left. A BIDI algorithm is used to place general
-# punctuation marks, numbers and English text in the text.
-#
-# Texts in x-mac-hebrew are almost impossible to find on the Internet. From
-# what little evidence I could find, it seems that its general directionality
-# is Logical.
-#
-# To sum up all of the above, the Hebrew probing mechanism knows about two
-# charsets:
-# Visual Hebrew - "ISO-8859-8" - backwards text - Words and sentences are
-#    backwards while line order is natural. For charset recognition purposes
-#    the line order is unimportant (In fact, for this implementation, even
-#    word order is unimportant).
-# Logical Hebrew - "windows-1255" - normal, naturally ordered text.
-#
-# "ISO-8859-8-I" is a subset of windows-1255 and doesn't need to be
-#    specifically identified.
-# "x-mac-hebrew" is also identified as windows-1255. A text in x-mac-hebrew
-#    that contain special punctuation marks or diacritics is displayed with
-#    some unconverted characters showing as question marks. This problem might
-#    be corrected using another model prober for x-mac-hebrew. Due to the fact
-#    that x-mac-hebrew texts are so rare, writing another model prober isn't
-#    worth the effort and performance hit.
-#
-#### The Prober ####
-#
-# The prober is divided between two SBCharSetProbers and a HebrewProber,
-# all of which are managed, created, fed data, inquired and deleted by the
-# SBCSGroupProber. The two SBCharSetProbers identify that the text is in
-# fact some kind of Hebrew, Logical or Visual. The final decision about which
-# one is it is made by the HebrewProber by combining final-letter scores
-# with the scores of the two SBCharSetProbers to produce a final answer.
-#
-# The SBCSGroupProber is responsible for stripping the original text of HTML
-# tags, English characters, numbers, low-ASCII punctuation characters, spaces
-# and new lines. It reduces any sequence of such characters to a single space.
-# The buffer fed to each prober in the SBCS group prober is pure text in
-# high-ASCII.
-# The two SBCharSetProbers (model probers) share the same language model:
-# Win1255Model.
-# The first SBCharSetProber uses the model normally as any other
-# SBCharSetProber does, to recognize windows-1255, upon which this model was
-# built. The second SBCharSetProber is told to make the pair-of-letter
-# lookup in the language model backwards. This in practice exactly simulates
-# a visual Hebrew model using the windows-1255 logical Hebrew model.
-#
-# The HebrewProber is not using any language model. All it does is look for
-# final-letter evidence suggesting the text is either logical Hebrew or visual
-# Hebrew. Disjointed from the model probers, the results of the HebrewProber
-# alone are meaningless. HebrewProber always returns 0.00 as confidence
-# since it never identifies a charset by itself. Instead, the pointer to the
-# HebrewProber is passed to the model probers as a helper "Name Prober".
-# When the Group prober receives a positive identification from any prober,
-# it asks for the name of the charset identified. If the prober queried is a
-# Hebrew model prober, the model prober forwards the call to the
-# HebrewProber to make the final decision. In the HebrewProber, the
-# decision is made according to the final-letters scores maintained and Both
-# model probers scores. The answer is returned in the form of the name of the
-# charset identified, either "windows-1255" or "ISO-8859-8".
-
-
-class HebrewProber(CharSetProber):
-    SPACE = 0x20
-    # windows-1255 / ISO-8859-8 code points of interest
-    FINAL_KAF = 0xEA
-    NORMAL_KAF = 0xEB
-    FINAL_MEM = 0xED
-    NORMAL_MEM = 0xEE
-    FINAL_NUN = 0xEF
-    NORMAL_NUN = 0xF0
-    FINAL_PE = 0xF3
-    NORMAL_PE = 0xF4
-    FINAL_TSADI = 0xF5
-    NORMAL_TSADI = 0xF6
-
-    # Minimum Visual vs Logical final letter score difference.
-    # If the difference is below this, don't rely solely on the final letter score
-    # distance.
-    MIN_FINAL_CHAR_DISTANCE = 5
-
-    # Minimum Visual vs Logical model score difference.
-    # If the difference is below this, don't rely at all on the model score
-    # distance.
-    MIN_MODEL_DISTANCE = 0.01
-
-    VISUAL_HEBREW_NAME = "ISO-8859-8"
-    LOGICAL_HEBREW_NAME = "windows-1255"
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._final_char_logical_score = 0
-        self._final_char_visual_score = 0
-        self._prev = self.SPACE
-        self._before_prev = self.SPACE
-        self._logical_prober: Optional[SingleByteCharSetProber] = None
-        self._visual_prober: Optional[SingleByteCharSetProber] = None
-        self.reset()
-
-    def reset(self) -> None:
-        self._final_char_logical_score = 0
-        self._final_char_visual_score = 0
-        # The two last characters seen in the previous buffer,
-        # mPrev and mBeforePrev are initialized to space in order to simulate
-        # a word delimiter at the beginning of the data
-        self._prev = self.SPACE
-        self._before_prev = self.SPACE
-        # These probers are owned by the group prober.
-
-    def set_model_probers(
-        self,
-        logical_prober: SingleByteCharSetProber,
-        visual_prober: SingleByteCharSetProber,
-    ) -> None:
-        self._logical_prober = logical_prober
-        self._visual_prober = visual_prober
-
-    def is_final(self, c: int) -> bool:
-        return c in [
-            self.FINAL_KAF,
-            self.FINAL_MEM,
-            self.FINAL_NUN,
-            self.FINAL_PE,
-            self.FINAL_TSADI,
-        ]
-
-    def is_non_final(self, c: int) -> bool:
-        # The normal Tsadi is not a good Non-Final letter due to words like
-        # 'lechotet' (to chat) containing an apostrophe after the tsadi. This
-        # apostrophe is converted to a space in FilterWithoutEnglishLetters
-        # causing the Non-Final tsadi to appear at an end of a word even
-        # though this is not the case in the original text.
-        # The letters Pe and Kaf rarely display a related behavior of not being
-        # a good Non-Final letter. Words like 'Pop', 'Winamp' and 'Mubarak'
-        # for example legally end with a Non-Final Pe or Kaf. However, the
-        # benefit of these letters as Non-Final letters outweighs the damage
-        # since these words are quite rare.
-        return c in [self.NORMAL_KAF, self.NORMAL_MEM, self.NORMAL_NUN, self.NORMAL_PE]
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
-        # Final letter analysis for logical-visual decision.
-        # Look for evidence that the received buffer is either logical Hebrew
-        # or visual Hebrew.
-        # The following cases are checked:
-        # 1) A word longer than 1 letter, ending with a final letter. This is
-        #    an indication that the text is laid out "naturally" since the
-        #    final letter really appears at the end. +1 for logical score.
-        # 2) A word longer than 1 letter, ending with a Non-Final letter. In
-        #    normal Hebrew, words ending with Kaf, Mem, Nun, Pe or Tsadi,
-        #    should not end with the Non-Final form of that letter. Exceptions
-        #    to this rule are mentioned above in isNonFinal(). This is an
-        #    indication that the text is laid out backwards. +1 for visual
-        #    score
-        # 3) A word longer than 1 letter, starting with a final letter. Final
-        #    letters should not appear at the beginning of a word. This is an
-        #    indication that the text is laid out backwards. +1 for visual
-        #    score.
-        #
-        # The visual score and logical score are accumulated throughout the
-        # text and are finally checked against each other in GetCharSetName().
-        # No checking for final letters in the middle of words is done since
-        # that case is not an indication for either Logical or Visual text.
-        #
-        # We automatically filter out all 7-bit characters (replace them with
-        # spaces) so the word boundary detection works properly. [MAP]
-
-        if self.state == ProbingState.NOT_ME:
-            # Both model probers say it's not them. No reason to continue.
-            return ProbingState.NOT_ME
-
-        byte_str = self.filter_high_byte_only(byte_str)
-
-        for cur in byte_str:
-            if cur == self.SPACE:
-                # We stand on a space - a word just ended
-                if self._before_prev != self.SPACE:
-                    # next-to-last char was not a space so self._prev is not a
-                    # 1 letter word
-                    if self.is_final(self._prev):
-                        # case (1) [-2:not space][-1:final letter][cur:space]
-                        self._final_char_logical_score += 1
-                    elif self.is_non_final(self._prev):
-                        # case (2) [-2:not space][-1:Non-Final letter][
-                        #  cur:space]
-                        self._final_char_visual_score += 1
-            else:
-                # Not standing on a space
-                if (
-                    (self._before_prev == self.SPACE)
-                    and (self.is_final(self._prev))
-                    and (cur != self.SPACE)
-                ):
-                    # case (3) [-2:space][-1:final letter][cur:not space]
-                    self._final_char_visual_score += 1
-            self._before_prev = self._prev
-            self._prev = cur
-
-        # Forever detecting, till the end or until both model probers return
-        # ProbingState.NOT_ME (handled above)
-        return ProbingState.DETECTING
-
-    @property
-    def charset_name(self) -> str:
-        assert self._logical_prober is not None
-        assert self._visual_prober is not None
-
-        # Make the decision: is it Logical or Visual?
-        # If the final letter score distance is dominant enough, rely on it.
-        finalsub = self._final_char_logical_score - self._final_char_visual_score
-        if finalsub >= self.MIN_FINAL_CHAR_DISTANCE:
-            return self.LOGICAL_HEBREW_NAME
-        if finalsub <= -self.MIN_FINAL_CHAR_DISTANCE:
-            return self.VISUAL_HEBREW_NAME
-
-        # It's not dominant enough, try to rely on the model scores instead.
-        modelsub = (
-            self._logical_prober.get_confidence() - self._visual_prober.get_confidence()
-        )
-        if modelsub > self.MIN_MODEL_DISTANCE:
-            return self.LOGICAL_HEBREW_NAME
-        if modelsub < -self.MIN_MODEL_DISTANCE:
-            return self.VISUAL_HEBREW_NAME
-
-        # Still no good, back to final letter distance, maybe it'll save the
-        # day.
-        if finalsub < 0.0:
-            return self.VISUAL_HEBREW_NAME
-
-        # (finalsub > 0 - Logical) or (don't know what to do) default to
-        # Logical.
-        return self.LOGICAL_HEBREW_NAME
-
-    @property
-    def language(self) -> str:
-        return "Hebrew"
-
-    @property
-    def state(self) -> ProbingState:
-        assert self._logical_prober is not None
-        assert self._visual_prober is not None
-
-        # Remain active as long as any of the model probers are active.
-        if (self._logical_prober.state == ProbingState.NOT_ME) and (
-            self._visual_prober.state == ProbingState.NOT_ME
-        ):
-            return ProbingState.NOT_ME
-        return ProbingState.DETECTING

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py

@@ -1,51 +0,0 @@
-import re
-
-
-SPLIT_RE = re.compile(r'[\.\[\]]+')
-
-
-class JsonSchemaException(ValueError):
-    """
-    Base exception of ``fastjsonschema`` library.
-    """
-
-
-class JsonSchemaValueException(JsonSchemaException):
-    """
-    Exception raised by validation function. Available properties:
-
-     * ``message`` containing human-readable information what is wrong (e.g. ``data.property[index] must be smaller than or equal to 42``),
-     * invalid ``value`` (e.g. ``60``),
-     * ``name`` of a path in the data structure (e.g. ``data.property[index]``),
-     * ``path`` as an array in the data structure (e.g. ``['data', 'property', 'index']``),
-     * the whole ``definition`` which the ``value`` has to fulfil (e.g. ``{'type': 'number', 'maximum': 42}``),
-     * ``rule`` which the ``value`` is breaking (e.g. ``maximum``)
-     * and ``rule_definition`` (e.g. ``42``).
-
-    .. versionchanged:: 2.14.0
-        Added all extra properties.
-    """
-
-    def __init__(self, message, value=None, name=None, definition=None, rule=None):
-        super().__init__(message)
-        self.message = message
-        self.value = value
-        self.name = name
-        self.definition = definition
-        self.rule = rule
-
-    @property
-    def path(self):
-        return [item for item in SPLIT_RE.split(self.name) if item != '']
-
-    @property
-    def rule_definition(self):
-        if not self.rule or not self.definition:
-            return None
-        return self.definition.get(self.rule)
-
-
-class JsonSchemaDefinitionException(JsonSchemaException):
-    """
-    Exception raised by generator of validation function.
-    """

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/README.md

@@ -1,115 +0,0 @@
-## Bottom-Up and Top-Down Attention for Visual Question Answering
-
-An efficient PyTorch implementation of the winning entry of the [2017 VQA Challenge](http://www.visualqa.org/challenge.html).
-
-The implementation follows the VQA system described in "Bottom-Up and
-Top-Down Attention for Image Captioning and Visual Question Answering"
-(https://arxiv.org/abs/1707.07998) and "Tips and Tricks for Visual
-Question Answering: Learnings from the 2017 Challenge"
-(https://arxiv.org/abs/1708.02711).
-
-## Results
-
-| Model | Validation Accuracy | Training Time
-| --- | --- | -- |
-| Reported Model | 63.15 | 12 - 18 hours (Tesla K40) |
-| Implemented Model | **63.58** | 40 - 50 minutes (Titan Xp) |
-
-The accuracy was calculated using the [VQA evaluation metric](http://www.visualqa.org/evaluation.html).
-
-## About
-
-This is part of a project done at CMU for the course 11-777
-Advanced Multimodal Machine Learning and a joint work between Hengyuan Hu,
-Alex Xiao, and Henry Huang.
-
-As part of our project, we implemented bottom up attention as a strong VQA baseline. We were planning to integrate object
-detection with VQA and were very glad to see that Peter Anderson and
-Damien Teney et al. had already done that beautifully.
-We hope this clean and
-efficient implementation can serve as a useful baseline for future VQA
-explorations.
-
-## Implementation Details
-
-Our implementation follows the overall structure of the papers but with
-the following simplifications:
-
-1. We don't use extra data from [Visual Genome](http://visualgenome.org/).
-2. We use only a fixed number of objects per image (K=36).
-3. We use a simple, single stream classifier without pre-training.
-4. We use the simple ReLU activation instead of gated tanh.
-
-The first two points greatly reduce the training time. Our
-implementation takes around 200 seconds per epoch on a single Titan Xp while
-the one described in the paper takes 1 hour per epoch.
-
-The third point is simply because we feel the two stream classifier
-and pre-training in the original paper is over-complicated and not
-necessary.
-
-For the non-linear activation unit, we tried gated tanh but couldn't
-make it work. We also tried gated linear unit (GLU) and it works better than
-ReLU. Eventually we choose ReLU due to its simplicity and since the gain
-from using GLU is too small to justify the fact that GLU doubles the
-number of parameters.
-
-With these simplifications we would expect the performance to drop. For
-reference, the best result on validation set reported in the paper is
-63.15. The reported result without extra data from visual genome is
-62.48, the result using only 36 objects per image is 62.82, the result
-using two steam classifier but not pre-trained is 62.28 and the result
-using ReLU is 61.63. These numbers are cited from the Table 1 of the
-paper: "Tips and Tricks for Visual Question Answering: Learnings from
-the 2017 Challenge". With all the above simplification aggregated, our
-first implementation got around 59-60 on validation set.
-
-To shrink the gap, we added some simple but powerful
-modifications. Including:
-
-1. Add dropout to alleviate overfitting
-2. Double the number of neurons
-3. Add weight normalization (BN seems not work well here)
-4. Switch to Adamax optimizer
-5. Gradient clipping
-
-These small modifications bring the number back to ~62.80.  We further
-change the concatenation based attention module in the original paper
-to a projection based module. This new attention module is inspired by
-the paper "Modeling Relationships in Referential Expressions with
-Compositional Modular Networks"
-(https://arxiv.org/pdf/1611.09978.pdf), but with some modifications
-(implemented in attention.NewAttention).  With
-the help of this new attention, we boost the performance to ~63.58,
-surpassing the reported best result with no extra data and less
-computation cost.
-
-## Usage
-
-#### Prerequisites
-
-Make sure you are on a machine with a NVIDIA GPU and Python 2 with about 70 GB disk space.
-
-1. Install [PyTorch v0.3](http://pytorch.org/) with CUDA and Python 2.7.
-2. Install [h5py](http://docs.h5py.org/en/latest/build.html).
-
-#### Data Setup
-
-All data should be downloaded to a 'data/' directory in the root
-directory of this repository.
-
-The easiest way to download the data is to run the provided script
-`tools/download.sh` from the repository root. The features are
-provided by and downloaded from the original authors'
-[repo](https://github.com/peteanderson80/bottom-up-attention). If the
-script does not work, it should be easy to examine the script and
-modify the steps outlined in it according to your needs. Then run
-`tools/process.sh` from the repository root to process the data to the
-correct format.
-
-#### Training
-
-Simply run `python main.py` to start training. The training and
-validation scores will be printed every epoch, and the best model will
-be saved under the directory "saved_models". The default flags should
-give you the result provided in the table above.

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/meta_arch/semantic_seg.py

@@ -1,187 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-from typing import Dict
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import Conv2d, ShapeSpec
-from detectron2.structures import ImageList
-from detectron2.utils.registry import Registry
-
-from ..backbone import build_backbone
-from ..postprocessing import sem_seg_postprocess
-from .build import META_ARCH_REGISTRY
-
-__all__ = ["SemanticSegmentor", "SEM_SEG_HEADS_REGISTRY", "SemSegFPNHead", "build_sem_seg_head"]
-
-
-SEM_SEG_HEADS_REGISTRY = Registry("SEM_SEG_HEADS")
-SEM_SEG_HEADS_REGISTRY.__doc__ = """
-Registry for semantic segmentation heads, which make semantic segmentation predictions
-from feature maps.
-"""
-
-
-@META_ARCH_REGISTRY.register()
-class SemanticSegmentor(nn.Module):
-    """
-    Main class for semantic segmentation architectures.
-    """
-
-    def __init__(self, cfg):
-        super().__init__()
-
-        self.device = torch.device(cfg.MODEL.DEVICE)
-
-        self.backbone = build_backbone(cfg)
-        self.sem_seg_head = build_sem_seg_head(cfg, self.backbone.output_shape())
-
-        pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(-1, 1, 1)
-        pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(-1, 1, 1)
-        self.normalizer = lambda x: (x - pixel_mean) / pixel_std
-
-        self.to(self.device)
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
-                Each item in the list contains the inputs for one image.
-
-                For now, each item in the list is a dict that contains:
-
-                   * "image": Tensor, image in (C, H, W) format.
-                   * "sem_seg": semantic segmentation ground truth
-                   * Other information that's included in the original dicts, such as:
-                     "height", "width" (int): the output resolution of the model, used in inference.
-                     See :meth:`postprocess` for details.
-
-        Returns:
-            list[dict]:
-              Each dict is the output for one input image.
-              The dict contains one key "sem_seg" whose value is a
-              Tensor of the output resolution that represents the
-              per-pixel segmentation prediction.
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        images = [self.normalizer(x) for x in images]
-        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
-
-        features = self.backbone(images.tensor)
-
-        if "sem_seg" in batched_inputs[0]:
-            targets = [x["sem_seg"].to(self.device) for x in batched_inputs]
-            targets = ImageList.from_tensors(
-                targets, self.backbone.size_divisibility, self.sem_seg_head.ignore_value
-            ).tensor
-        else:
-            targets = None
-        results, losses = self.sem_seg_head(features, targets)
-
-        if self.training:
-            return losses
-
-        processed_results = []
-        for result, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):
-            height = input_per_image.get("height")
-            width = input_per_image.get("width")
-            r = sem_seg_postprocess(result, image_size, height, width)
-            processed_results.append({"sem_seg": r})
-        return processed_results
-
-
-def build_sem_seg_head(cfg, input_shape):
-    """
-    Build a semantic segmentation head from `cfg.MODEL.SEM_SEG_HEAD.NAME`.
-    """
-    name = cfg.MODEL.SEM_SEG_HEAD.NAME
-    return SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class SemSegFPNHead(nn.Module):
-    """
-    A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper
-    (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from
-    all levels of the FPN into single output.
-    """
-
-    def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
-        super().__init__()
-
-        # fmt: off
-        self.in_features      = cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
-        feature_strides       = {k: v.stride for k, v in input_shape.items()}
-        feature_channels      = {k: v.channels for k, v in input_shape.items()}
-        self.ignore_value     = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE
-        num_classes           = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
-        conv_dims             = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
-        self.common_stride    = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE
-        norm                  = cfg.MODEL.SEM_SEG_HEAD.NORM
-        self.loss_weight      = cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT
-        # fmt: on
-
-        self.scale_heads = []
-        for in_feature in self.in_features:
-            head_ops = []
-            head_length = max(
-                1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))
-            )
-            for k in range(head_length):
-                norm_module = nn.GroupNorm(32, conv_dims) if norm == "GN" else None
-                conv = Conv2d(
-                    feature_channels[in_feature] if k == 0 else conv_dims,
-                    conv_dims,
-                    kernel_size=3,
-                    stride=1,
-                    padding=1,
-                    bias=not norm,
-                    norm=norm_module,
-                    activation=F.relu,
-                )
-                weight_init.c2_msra_fill(conv)
-                head_ops.append(conv)
-                if feature_strides[in_feature] != self.common_stride:
-                    head_ops.append(
-                        nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
-                    )
-            self.scale_heads.append(nn.Sequential(*head_ops))
-            self.add_module(in_feature, self.scale_heads[-1])
-        self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0)
-        weight_init.c2_msra_fill(self.predictor)
-
-    def forward(self, features, targets=None):
-        """
-        Returns:
-            In training, returns (None, dict of losses)
-            In inference, returns (predictions, {})
-        """
-        x = self.layers(features)
-        if self.training:
-            return None, self.losses(x, targets)
-        else:
-            x = F.interpolate(
-                x, scale_factor=self.common_stride, mode="bilinear", align_corners=False
-            )
-            return x, {}
-
-    def layers(self, features):
-        for i, f in enumerate(self.in_features):
-            if i == 0:
-                x = self.scale_heads[i](features[f])
-            else:
-                x = x + self.scale_heads[i](features[f])
-        x = self.predictor(x)
-        return x
-
-    def losses(self, predictions, targets):
-        predictions = F.interpolate(
-            predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False
-        )
-        loss = F.cross_entropy(
-            predictions, targets, reduction="mean", ignore_index=self.ignore_value
-        )
-        losses = {"loss_sem_seg": loss * self.loss_weight}
-        return losses

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/generate.h

@@ -1,90 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <iterator>
-#include <thrust/system/cuda/config.h>
-
-#include <thrust/system/cuda/detail/for_each.h>
-#include <thrust/distance.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-// for_each functor
-template <class Generator>
-struct generate_f
-{
-  Generator generator;
-
-  THRUST_FUNCTION
-  generate_f(Generator generator_) : generator(generator_) {}
-
-  template<class T>
-  THRUST_DEVICE_FUNCTION void operator()(T const& value)
-  {
-    T & lvalue = const_cast<T&>(value);
-    lvalue = generator();
-  }
-};
-
-// for_each_n
-template <class Derived,
-          class OutputIt,
-          class Size,
-          class Generator>
-OutputIt __host__ __device__
-generate_n(execution_policy<Derived> &policy,
-           OutputIt                   result,
-           Size                       count,
-           Generator                  generator)
-{
-  return cuda_cub::for_each_n(policy,
-                              result,
-                              count,
-                              generate_f<Generator>(generator));
-}
-
-  // for_each
-template <class Derived,
-          class OutputIt,
-          class Generator>
-void __host__ __device__
-generate(execution_policy<Derived> &policy,
-         OutputIt                   first,
-         OutputIt                   last,
-         Generator                  generator)
-{
-  cuda_cub::generate_n(policy, first, thrust::distance(first, last), generator);
-}
-
-}    // namespace cuda_cub
-} // end namespace thrust
-#endif

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/scan_by_key.h

@@ -1,150 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file scan_by_key.h
- *  \brief Sequential implementation of scan_by_key functions.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/detail/function.h>
-#include <thrust/system/detail/sequential/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename BinaryPredicate,
-         typename BinaryFunction>
-__host__ __device__
-  OutputIterator inclusive_scan_by_key(sequential::execution_policy<DerivedPolicy> &,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       BinaryPredicate binary_pred,
-                                       BinaryFunction binary_op)
-{
-  typedef typename thrust::iterator_traits<InputIterator1>::value_type KeyType;
-  typedef typename thrust::iterator_traits<OutputIterator>::value_type ValueType;
-
-  // wrap binary_op
-  thrust::detail::wrapped_function<
-    BinaryFunction,
-    ValueType
-  > wrapped_binary_op(binary_op);
-
-  if(first1 != last1)
-  {
-    KeyType   prev_key   = *first1;
-    ValueType prev_value = *first2;
-
-    *result = prev_value;
-
-    for(++first1, ++first2, ++result;
-        first1 != last1;
-        ++first1, ++first2, ++result)
-    {
-      KeyType key = *first1;
-
-      if(binary_pred(prev_key, key))
-        *result = prev_value = wrapped_binary_op(prev_value,*first2);
-      else
-        *result = prev_value = *first2;
-
-      prev_key = key;
-    }
-  }
-
-  return result;
-}
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename T,
-         typename BinaryPredicate,
-         typename BinaryFunction>
-__host__ __device__
-  OutputIterator exclusive_scan_by_key(sequential::execution_policy<DerivedPolicy> &,
-                                       InputIterator1 first1,
-                                       InputIterator1 last1,
-                                       InputIterator2 first2,
-                                       OutputIterator result,
-                                       T init,
-                                       BinaryPredicate binary_pred,
-                                       BinaryFunction binary_op)
-{
-  typedef typename thrust::iterator_traits<InputIterator1>::value_type KeyType;
-  typedef typename thrust::iterator_traits<OutputIterator>::value_type ValueType;
-
-  if(first1 != last1)
-  {
-    KeyType   temp_key   = *first1;
-    ValueType temp_value = *first2;
-
-    ValueType next = init;
-
-    // first one is init
-    *result = next;
-
-    next = binary_op(next, temp_value);
-
-    for(++first1, ++first2, ++result;
-        first1 != last1;
-        ++first1, ++first2, ++result)
-    {
-      KeyType key = *first1;
-
-      // use temp to permit in-place scans
-      temp_value = *first2;
-
-      if (!binary_pred(temp_key, key))
-        next = init;  // reset sum
-
-      *result = next;  
-      next = binary_op(next, temp_value);
-
-      temp_key = key;
-    }
-  }
-
-  return result;
-}
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-

spaces/CVPR/regionclip-demo/detectron2/data/datasets/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .coco import load_coco_json, load_sem_seg, register_coco_instances
-from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated
-from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta
-from .pascal_voc import load_voc_instances, register_pascal_voc
-from . import builtin as _builtin  # ensure the builtin datasets are registered
-
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]

spaces/CVPR/regionclip-demo/detectron2/model_zoo/model_zoo.py

@@ -1,200 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-from typing import Optional
-import pkg_resources
-import torch
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
-from detectron2.modeling import build_model
-
-
-class _ModelZooUrls(object):
-    """
-    Mapping from names to officially released Detectron2 pre-trained models.
-    """
-
-    S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
-
-    # format: {config_path.yaml} -> model_id/model_final_{commit}.pkl
-    CONFIG_PATH_TO_URL_SUFFIX = {
-        # COCO Detection with Faster R-CNN
-        "COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl",
-        "COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl",
-        "COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl",
-        "COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl",
-        "COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl",
-        "COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl",
-        "COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl",
-        "COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl",
-        "COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl",
-        "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl",
-        # COCO Detection with RetinaNet
-        "COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl",
-        "COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl",
-        "COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl",
-        # COCO Detection with RPN and Fast R-CNN
-        "COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl",
-        "COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl",
-        "COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl",
-        # COCO Instance Segmentation Baselines with Mask R-CNN
-        "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl",
-        "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl",  # noqa
-        # COCO Person Keypoint Detection Baselines with Keypoint R-CNN
-        "COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl",
-        "COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl",
-        "COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl",
-        "COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl",
-        # COCO Panoptic Segmentation Baselines with Panoptic FPN
-        "COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl",
-        "COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl",
-        "COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl",
-        # LVIS Instance Segmentation Baselines with Mask R-CNN
-        "LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl",  # noqa
-        "LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl",  # noqa
-        "LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl",  # noqa
-        # Cityscapes & Pascal VOC Baselines
-        "Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl",
-        "PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl",
-        # Other Settings
-        "Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl",
-        "Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl",
-        "Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl",
-        "Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl",
-        "Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl",
-        "Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl",
-        "Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl",
-        "Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl",
-        "Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl",
-        "Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl",
-        "Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl",  # noqa
-        # D1 Comparisons
-        "Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl",  # noqa
-        "Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl",  # noqa
-        "Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl",
-    }
-
-    @staticmethod
-    def query(config_path: str) -> Optional[str]:
-        """
-        Args:
-            config_path: relative config filename
-        """
-        name = config_path.replace(".yaml", "").replace(".py", "")
-        if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX:
-            suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name]
-            return _ModelZooUrls.S3_PREFIX + name + "/" + suffix
-        return None
-
-
-def get_checkpoint_url(config_path):
-    """
-    Returns the URL to the model trained using the given config
-
-    Args:
-        config_path (str): config file name relative to detectron2's "configs/"
-            directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
-
-    Returns:
-        str: a URL to the model
-    """
-    url = _ModelZooUrls.query(config_path)
-    if url is None:
-        raise RuntimeError("Pretrained model for {} is not available!".format(config_path))
-    return url
-
-
-def get_config_file(config_path):
-    """
-    Returns path to a builtin config file.
-
-    Args:
-        config_path (str): config file name relative to detectron2's "configs/"
-            directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
-
-    Returns:
-        str: the real path to the config file.
-    """
-    cfg_file = pkg_resources.resource_filename(
-        "detectron2.model_zoo", os.path.join("configs", config_path)
-    )
-    if not os.path.exists(cfg_file):
-        raise RuntimeError("{} not available in Model Zoo!".format(config_path))
-    return cfg_file
-
-
-def get_config(config_path, trained: bool = False):
-    """
-    Returns a config object for a model in model zoo.
-
-    Args:
-        config_path (str): config file name relative to detectron2's "configs/"
-            directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
-        trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights.
-            If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used
-            instead; this will typically (though not always) initialize a subset of weights using
-            an ImageNet pre-trained model, while randomly initializing the other weights.
-
-    Returns:
-        CfgNode or omegaconf.DictConfig: a config object
-    """
-    cfg_file = get_config_file(config_path)
-    if cfg_file.endswith(".yaml"):
-        cfg = get_cfg()
-        cfg.merge_from_file(cfg_file)
-        if trained:
-            cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path)
-        return cfg
-    elif cfg_file.endswith(".py"):
-        cfg = LazyConfig.load(cfg_file)
-        if trained:
-            url = get_checkpoint_url(config_path)
-            if "train" in cfg and "init_checkpoint" in cfg.train:
-                cfg.train.init_checkpoint = url
-            else:
-                raise NotImplementedError
-        return cfg
-
-
-def get(config_path, trained: bool = False, device: Optional[str] = None):
-    """
-    Get a model specified by relative path under Detectron2's official ``configs/`` directory.
-
-    Args:
-        config_path (str): config file name relative to detectron2's "configs/"
-            directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
-        trained (bool): see :func:`get_config`.
-        device (str or None): overwrite the device in config, if given.
-
-    Returns:
-        nn.Module: a detectron2 model. Will be in training mode.
-
-    Example:
-    ::
-        from detectron2 import model_zoo
-        model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True)
-    """
-    cfg = get_config(config_path, trained)
-    if device is None and not torch.cuda.is_available():
-        device = "cpu"
-    if device is not None and isinstance(cfg, CfgNode):
-        cfg.MODEL.DEVICE = device
-
-    if isinstance(cfg, CfgNode):
-        model = build_model(cfg)
-        DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
-    else:
-        model = instantiate(cfg.model)
-        if device is not None:
-            model = model.to(device)
-        if "train" in cfg and "init_checkpoint" in cfg.train:
-            DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
-    return model

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/version.py

@@ -1 +0,0 @@
-__version__ = '0.1.0'

spaces/Clebersla/RVC_V2_Huggingface_Version/utils.py

@@ -1,151 +0,0 @@
-import ffmpeg
-import numpy as np
-
-# import praatio
-# import praatio.praat_scripts
-import os
-import sys
-
-import random
-
-import csv
-
-platform_stft_mapping = {
-    "linux": "stftpitchshift",
-    "darwin": "stftpitchshift",
-    "win32": "stftpitchshift.exe",
-}
-
-stft = platform_stft_mapping.get(sys.platform)
-# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
-
-
-def CSVutil(file, rw, type, *args):
-    if type == "formanting":
-        if rw == "r":
-            with open(file) as fileCSVread:
-                csv_reader = list(csv.reader(fileCSVread))
-                return (
-                    (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2])
-                    if csv_reader is not None
-                    else (lambda: exec('raise ValueError("No data")'))()
-                )
-        else:
-            if args:
-                doformnt = args[0]
-            else:
-                doformnt = False
-            qfr = args[1] if len(args) > 1 else 1.0
-            tmb = args[2] if len(args) > 2 else 1.0
-            with open(file, rw, newline="") as fileCSVwrite:
-                csv_writer = csv.writer(fileCSVwrite, delimiter=",")
-                csv_writer.writerow([doformnt, qfr, tmb])
-    elif type == "stop":
-        stop = args[0] if args else False
-        with open(file, rw, newline="") as fileCSVwrite:
-            csv_writer = csv.writer(fileCSVwrite, delimiter=",")
-            csv_writer.writerow([stop])
-
-
-def load_audio(file, sr, DoFormant, Quefrency, Timbre):
-    converted = False
-    DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting")
-    try:
-        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        file = (
-            file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        )  # 防止小白拷路径头尾带了空格和"和回车
-        file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-
-        # print(f"dofor={bool(DoFormant)} timbr={Timbre} quef={Quefrency}\n")
-
-        if (
-            lambda DoFormant: True
-            if DoFormant.lower() == "true"
-            else (False if DoFormant.lower() == "false" else DoFormant)
-        )(DoFormant):
-            numerator = round(random.uniform(1, 4), 4)
-            # os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}")
-            # print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted))
-
-            if not file.endswith(".wav"):
-                if not os.path.isfile(f"{file_formanted}.wav"):
-                    converted = True
-                    # print(f"\nfile = {file}\n")
-                    # print(f"\nfile_formanted = {file_formanted}\n")
-                    converting = (
-                        ffmpeg.input(file_formanted, threads=0)
-                        .output(f"{file_formanted}.wav")
-                        .run(
-                            cmd=["ffmpeg", "-nostdin"],
-                            capture_stdout=True,
-                            capture_stderr=True,
-                        )
-                    )
-                else:
-                    pass
-
-            file_formanted = (
-                f"{file_formanted}.wav"
-                if not file_formanted.endswith(".wav")
-                else file_formanted
-            )
-
-            print(f" · Formanting {file_formanted}...\n")
-
-            os.system(
-                '%s -i "%s" -q "%s" -t "%s" -o "%sFORMANTED_%s.wav"'
-                % (
-                    stft,
-                    file_formanted,
-                    Quefrency,
-                    Timbre,
-                    file_formanted,
-                    str(numerator),
-                )
-            )
-
-            print(f" · Formanted {file_formanted}!\n")
-
-            # filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\')
-            # file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\')
-            # print("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
-
-            out, _ = (
-                ffmpeg.input(
-                    "%sFORMANTED_%s.wav" % (file_formanted, str(numerator)), threads=0
-                )
-                .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
-                .run(
-                    cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
-                )
-            )
-
-            try:
-                os.remove("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
-            except Exception:
-                pass
-                print("couldn't remove formanted type of file")
-
-        else:
-            out, _ = (
-                ffmpeg.input(file, threads=0)
-                .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
-                .run(
-                    cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
-                )
-            )
-    except Exception as e:
-        raise RuntimeError(f"Failed to load audio: {e}")
-
-    if converted:
-        try:
-            os.remove(file_formanted)
-        except Exception:
-            pass
-            print("couldn't remove converted type of file")
-        converted = False
-
-    return np.frombuffer(out, np.float32).flatten()

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

@@ -1,29 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://api.gptplus.one'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-        'Accept': '*/*',
-        'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5,zh;q=0.4',
-    }
-    data = {
-        'messages': messages,
-        'model': model,
-    }
-    response = requests.post('https://api.gptplus.one/chat-process', json=data, stream=True)
-    print(response)
-
-    for token in response.iter_content(chunk_size=None):
-        yield (token.decode('utf-8'))
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/CormacMc/projectsub6/app.py

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return f"Hello {name}!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/README.md

@@ -1,13 +0,0 @@
----
-title: ControlNetMediaPipeFace
-emoji: 👁
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference