Datasets:

Weyaxi
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huggingface-spaces-codes

@@ -1,24 +0,0 @@
-import os
-import json
-import time
-import subprocess
-from ...typing import sha256, Dict, get_type_hints
-url = 'https://you.com'
-model = 'gpt-3.5-turbo'
-supports_stream = True
-needs_auth = False
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages}, separators=(',', ':'))
-    cmd = ['python3', f'{path}/helpers/you.py', config]
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('utf-8') #[:-1]

spaces/1acneusushi/gradio-2dmoleculeeditor/HD-Online-Player-Wondershare-Video-Editor-5-V5011-Fin-HOT.md DELETED Viewed

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-## HD Online Player (Wondershare Video Editor 5 V5011 Fin)
- ![HD Online Player (Wondershare Video Editor 5 V5011 Fin) \[HOT\]](https://www.findmysoft.com/thumb/130257.jpg)
-**Click Here ☆☆☆☆☆ [https://jinyurl.com/2tzZUV](https://jinyurl.com/2tzZUV)**
- Here is a possible title and article:
-# How to Edit Videos with HD Online Player (Wondershare Video Editor 5 V5011 Fin)
-If you are looking for a powerful and easy-to-use video editing tool for your PC, you might want to try HD Online Player (Wondershare Video Editor 5 V5011 Fin). This software allows you to create and customize video presentations with various effects, transitions, titles, and music. You can also upload your videos directly to YouTube and Vimeo, or save them in different formats for different devices. In this article, we will show you how to edit videos with HD Online Player (Wondershare Video Editor 5 V5011 Fin) in a few simple steps.
-## Step 1: Download and Install HD Online Player (Wondershare Video Editor 5 V5011 Fin)
-You can download HD Online Player (Wondershare Video Editor 5 V5011 Fin) from the official website[^3^]. The installation package is about 1.1 MB and it works fine with 32-bit versions of Windows XP/XP Professional/Vista/7/8/10/11. After downloading, run the setup file and follow the instructions to install the software on your PC.
-## Step 2: Import Your Media Files
-After launching HD Online Player (Wondershare Video Editor 5 V5011 Fin), you will see two video editing modes: Easy Mode and Full Feature Mode. Easy Mode lets you easily create your own movie by providing various built-in templates. Simply import your own media and youâre done. Full Feature Mode gives you more control over your video editing process. You can drag and drop your media files to the timeline, trim, crop, rotate, split, merge, and adjust them as you like. You can also add text, filters, overlays, transitions, elements, and music to enhance your video.
-## Step 3: Edit Your Video
-In Full Feature Mode, you can use the toolbar above the timeline to access different editing features. For example, you can use the razor tool to cut your video into segments, the keyframe tool to animate your clips or effects, and the masking tool to hide or reveal parts of your video. You can also use the preview window to check your edits in real time. If you want to edit audio, you can use the audio mixer to adjust the volume and balance of different tracks, or use the audio visualizer and auto ducking features to create dynamic sound effects.
-## Step 4: Export or Share Your Video
-When you are satisfied with your video editing, you can export or share your video in different ways. You can click the Export button on the top right corner of the interface to open the export window. Here you can choose from various output options, such as format, device, resolution, quality, frame rate, bitrate, etc. You can also burn your video to DVD or Blu-ray disc if you want. Alternatively, you can click the Share button on the top right corner of the interface to upload your video directly to YouTube or Vimeo. You just need to sign in with your account and enter some information about your video.
-That's it! You have successfully edited a video with HD Online Player (Wondershare Video Editor 5 V5011 Fin). Now you can enjoy your masterpiece on any platform or device.
- 145887f19f

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Archshaders Vol 3 For Vray Free Download A Complete Guide to Evermotions Material Collection for 3ds Max and Mental Ray.md DELETED Viewed

@@ -1,95 +0,0 @@
-<h1>RND Portico 5043 Compressor Crack: What You Need to Know</h1>
-<p>If you are a music producer, engineer, or mixer, you probably have heard of the RND Portico 5043 Compressor. This plugin is a software emulation of the famous hardware compressor designed by Rupert Neve, one of the most respected names in the audio industry. The RND Portico 5043 Compressor is known for its smooth and musical sound, its versatility, and its ease of use. It can add punch, warmth, glue, and dynamics to any audio source, from vocals to drums to guitars.</p>
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-<p>But what if you don't have the money to buy this plugin, which costs $499.99 on Steinberg's website? You might be tempted to look for a crack version of it online, hoping to get the same quality for free. However, this is not a good idea. In this article, we will explain why cracking the RND Portico 5043 Compressor plugin is a bad idea, what are the risks and drawbacks of doing so, and what are some alternatives that you can use instead.</p>
- <h2>The Features and Benefits of the RND Portico 5043 Compressor</h2>
-<p>Before we dive into why you should not crack the RND Portico 5043 Compressor plugin, let's take a look at what makes this plugin so great in the first place. Here are some of the features and benefits of this plugin that make it stand out from other compressors:</p>
-<ul>
-<li><b>Two modes of operation: feed-forward and feed-back.</b> The plugin offers two different ways of applying compression: feed-forward and feed-back. Feed-forward mode is more modern and aggressive, while feed-back mode is more vintage and smooth. You can switch between them with a simple button, giving you more options and flexibility.</li>
-<li><b>Threshold, ratio, attack, release, and gain controls.</b> The plugin has all the standard controls that you would expect from a compressor: threshold, ratio, attack, release, and gain. These allow you to adjust how much compression is applied, how fast it reacts, how long it lasts, and how loud it makes the signal. You can also use the auto-release function to let the plugin decide the best release time for you.</li>
-<li><b>Sidechain input and output.</b> The plugin has a sidechain input and output that let you use an external signal to trigger the compression. This is useful for creating ducking effects, where one sound lowers the volume of another sound when it plays. For example, you can use a kick drum to duck a bass line or a vocal to duck a guitar.</li>
-<li><b>Link function for stereo operation.</b> The plugin has a link function that lets you link two instances of the plugin for stereo operation. This means that both channels will have the same compression settings and will react together as one unit. This is useful for compressing stereo sources like drum overheads or piano.</li>
-<li><b>VU meter and LED indicators.</b> The plugin has a VU meter that shows you how much gain reduction is happening on your signal. It also has LED indicators that show you when the signal is above or below the threshold level. These help you monitor your compression settings and make sure they are not too extreme or too subtle.</li>
-<li><b>Analog sound and digital flexibility.</b> The plugin emulates the analog sound of the hardware compressor using Steinberg's VCM technology. This means that it adds subtle harmonics, saturation, and warmth to your signal, making it sound more natural and musical. However, it also has digital flexibility, meaning that it can run on any DAW platform without any latency or compatibility issues.</li>
-</ul>
- <h2>The Drawbacks and Risks of Cracking the RND Portico 5043 Compressor Plugin</h2>
-<p>Now that we have seen what makes the RND Portico 5043 Compressor plugin so amazing, let's see why cracking it is not a good idea. Here are some of the drawbacks and risks of cracking this plugin:</p>
-<ul>
-<li><b>Legal issues and potential lawsuits.</b> Cracking any software is illegal and violates the terms of service of Steinberg and Rupert Neve Designs. If you crack this plugin, you are stealing their intellectual property and depriving them of their rightful income. You could face legal consequences if they find out about your piracy activities. They could sue you for damages or report you to law enforcement authorities  . According to U.S. Copyright Law, copyright infringements can be punishable by up to five years in prison and $250,000 in fines. Repeat offenders can be imprisoned for up to 10 years. Violators can also be held civilly liable for actual damages, lost profits, or statutory damages up to $150,000 per work   .</li>
-<li><b>Malware and viruses.</b> Cracking any software exposes you to malware and viruses that could harm your computer or your data. Many crack websites are infected with malicious software that can steal your personal information, damage your files, or hijack your system. You could lose your important projects or compromise your online security if you download cracks from untrusted sources .</li>
-<li><b>Poor performance and compatibility issues.</b> Cracking any software compromises its quality and functionality. Many crack versions of plugins are buggy, unstable, or incomplete. They could cause crashes, glitches, or errors on your DAW or your system. They could also have compatibility issues with other plugins or software updates. You could end up wasting your time troubleshooting problems instead of making music .</li>
-<li><b>Lack of updates and support.</b> Cracking any software cuts you off from updates and support from Steinberg and Rupert Neve Designs. You will not be able to access new features, bug fixes, or improvements that they release for their plugins. You will also not be able to contact them for technical assistance or customer service if you encounter any issues with their plugins. You will be stuck with an outdated and unsupported version of their product .</li>
-<li><b>Ethical concerns and reputation damage.</b> Cracking any software is unethical and disrespectful to Steinberg and Rupert Neve Designs. They have invested a lot of time, money, and effort into creating their plugins for your benefit. They deserve to be compensated for their work and rewarded for their innovation. If you crack their plugins, you are showing them that you do not value their contribution to the audio industry. You are also damaging your own reputation as a professional or an enthusiast who cares about quality music production .</li>
-</ul>
- <h2>The Alternatives to Cracking the RND Portico 5043 Compressor Plugin</h2>
-<p>If cracking the RND Portico 5043 Compressor plugin is such a bad idea, what can you do instead? Here are some alternatives that you can consider:</p>
-<ul>
-<li><b>Buying the original plugin from Steinberg or authorized dealers.</b> The best option is to buy the original plugin from Steinberg's website or from authorized dealers like Sweetwater or Plugin Boutique. This way, you will get all the benefits of using this plugin without any drawbacks or risks. You will also support Steinberg and Rupert Neve Designs in their business endeavors.</li>
-<li><b>Renting the plugin from online platforms like Splice or Gobbler.</b> Another option is to rent the plugin from online platforms like Splice or Gobbler. These platforms allow you to pay a monthly fee to access hundreds of plugins from various developers. You can use them as long as you want without buying them outright. This way, you can save money while still enjoying high-quality plugins like the RND Portico 5043 Compressor.</li>
-<li><b>Using free or cheaper plugins that emulate the RND Portico 5043 Compressor.</b> A third option is to use free or cheaper plugins that emulate the RND Portico 5043 Compressor in some way. For example, you can try the TDR Kotelnikov by Tokyo Dawn Labs, which is a free compressor plugin that has a similar layout and sound as the RND Portico 5043 Compressor. Or you can try the MJUC by Klanghelm, which is a cheap compressor plugin that has three modes of operation that resemble the feed-forward and feed-back modes of the RND Portico 5043 Compressor.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>The RND Portico 5043 Compressor is a fantastic plugin that can enhance your music production with its smooth and musical compression sound. However, cracking it is not worth it, as it can expose you to legal troubles, malware infections, poor performance issues, lack of updates and support, and ethical dilemmas. Instead of cracking it, you should consider buying it from Steinberg or authorized dealers, renting it from online platforms like Splice or Gobbler, or using free or cheaper plugins that emulate it in some way. By doing so, you will not only enjoy the benefits of this plugin but also respect the work of Steinberg and Rupert Neve Designs.</p>
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- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about the RND Portico 5043 Compressor Crack:</p>
-<ol>
-<li><b>What is the difference between feed-forward and feed-back modes?</b> Feed-forward mode applies compression based on the input signal level, while feed-back mode applies compression based on the output signal level. Feed-forward mode is more modern and aggressive, while feed-back mode is more vintage and smooth.</li>
-<li><b>How do I use sidechain compression with this plugin?</b> To use sidechain compression with this plugin, you need to route an external signal to the sidechain input of the plugin. Then you need to adjust the threshold and ratio controls to set how much compression is applied based on the sidechain signal level.</li>
-<li><b>How do I link two instances of this plugin for stereo operation?</b> To link two instances of this plugin for stereo operation, you need to insert them on two stereo tracks in your DAW. Then you need to activate the link function on both instances by clicking on the link button on the plugin interface.</li>
-<li><b>How do I monitor the gain reduction on this plugin?</b> To monitor the gain reduction on this plugin, you need to look at the VU meter on the plugin interface. The VU meter shows how much gain reduction is happening on your signal in decibels (dB).</li>
-<li><b>How do I update this plugin?</b> To update this plugin, you need to visit Steinberg's website and download the latest version of the plugin installer. Then you need to run the installer and follow the instructions to install the update on your system.</li>
-</ol>
- </p> 0a6ba089eb<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Artlantis 2020 Crack Activation Code Full ((TOP)).md DELETED Viewed

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-<h1>Artlantis 2020 Crack Activation Code Full: Cómo descargarlo y usarlo</h1>
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-<h2>Qué es Artlantis 2020 Crack Activation Code Full</h2>
-<p>Artlantis 2020 Crack Activation Code Full es una versión modificada de Artlantis 2020 que elimina la necesidad de introducir un código de activación para usar el programa. El código de activación es un número único que se genera al comprar el programa y que sirve para verificar su autenticidad. Sin embargo, con Artlantis 2020 Crack Activation Code Full, puedes saltarte este paso y usar el programa sin restricciones.</p>
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-<h2>Cómo mejorar tus imágenes y animaciones 3D con Artlantis 2020 Crack Activation Code Full</h2>
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-<p>Siguiendo estos consejos, podrás mejorar tus imágenes y animaciones 3D con Artlantis 2020 Crack Activation Code Full y conseguir resultados profesionales y espectaculares.</p>
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-<h2>Cómo solucionar los problemas más comunes de Artlantis 2020 Crack Activation Code Full</h2>
-<p>Aunque Artlantis 2020 Crack Activation Code Full es un programa muy fácil y rápido de usar, puede que te encuentres con algunos problemas o errores al usarlo. Estos problemas pueden deberse a la versión pirata del programa, a la incompatibilidad con tu sistema operativo o a la configuración de tu ordenador. A continuación, te mostramos cómo solucionar los problemas más comunes de Artlantis 2020 Crack Activation Code Full:</p>
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-<h1>The Binding of Isaac: Afterbirth - How to Unlock Everything</h1>
-<p>The Binding of Isaac: Afterbirth is a massively updated expansion to the 2014's roguelike of the year, The Binding of Isaac: Rebirth. It features over 100 hours of new gameplay, new items, new bosses, new modes, new challenges, and more. If you are a fan of this game and want to unlock everything it has to offer, you might be interested in this guide.</p>
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-<p>In this guide, we will show you how to unlock everything in The Binding of Isaac: Afterbirth, from characters and items to endings and achievements. We will also provide some tips and tricks to help you along the way. Note that this guide assumes that you have the base game The Binding of Isaac: Rebirth and the Afterbirth DLC installed. Some of the unlocks may require you to have the Afterbirth+ DLC as well.</p>
-<p>Without further ado, let's get started!</p>
-<h2>How to Unlock Characters</h2>
-<p>The Binding of Isaac: Afterbirth has 13 playable characters, each with their own unique abilities and stats. Some of them are unlocked by default, while others require you to complete certain tasks or challenges. Here is how to unlock each character:</p>
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-<li><strong>Isaac</strong>: Unlocked by default.</li>
-<li><strong>Maggie</strong>: Unlocked by having 7 or more red heart containers at once.</li>
-<li><strong>Cain</strong>: Unlocked by having 55 or more coins at once.</li>
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-<li><strong>Eden</strong>: Unlocked by beating Mom's Heart for the first time. Requires one Eden token to play, which can be obtained by beating Mom's Heart or It Lives on any run.</li>
-<li><strong>The Lost</strong>: Unlocked by dying in a sacrifice room while holding Missing Poster (a trinket). Alternatively, unlocked by dying in a specific order with certain characters: Isaac to a Mulliboom in Basement/Cellar 1, Maggie to her own bomb in Caves/Catacombs 1, Judas to any Mom attack in Depths/Necropolis 1, and Azazel to any attack from Satan in Sheol.</li>
-<li><strong>Lilith</strong>: Unlocked by beating Greed Mode with Azazel.</li>
-<li><strong>Keeper</strong>: Unlocked by donating 1000 coins to the Greed Machine (a machine that appears after beating Ultra Greed in Greed Mode).</li>
-<li><strong>Apollyon</strong>: Unlocked by defeating Mega Satan for the first time.</li>
-</ul>
-<h2>How to Unlock Items</h2>
-<p>The Binding of Isaac: Afterbirth has over 500 items that can alter your appearance, stats, and abilities. Some of them are found randomly in treasure rooms, shops, secret rooms, devil deals, angel rooms, curse rooms, chests, etc. Others require you to complete certain tasks or challenges to unlock them. Here is how to unlock some of the most notable items:</p>
-<p></p>
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-<li><strong>D6</strong>: Unlocked by beating ??? (the boss of The Chest) with Isaac.</li>
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-<li><strong>D12</strong>: Un</p> d5da3c52bf<br />
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-<h1>ARK: Survival Evolved Indir APK - How to Download and Play This Amazing Dino-Adventure Game on Your Android Device</h1>
- <p>Have you ever dreamed of living in a world where dinosaurs roam free? Have you ever wanted to tame, ride, and breed these majestic creatures? Have you ever wondered how it would feel to survive in a harsh and dangerous environment with other players?</p>
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-<li>Paste the URL of the app or game from the Google Play Store in the top text box. For example, for ARK: Survival Evolved, you can paste this URL: [play.google.com/store/apps/details?id=com.studiowildcard.wardrumstudios.ark](^2^).</li>
-<li>Select your device type from the "Device" drop-down menu. You can also choose your architecture and Android version, but it's usually not necessary.</li>
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-<li>Click the down arrow icon to download the APK file. The file will be saved in your Downloads folder or wherever you choose to save it.</li>
-</ol>
- <h2>How to Install ARK: Survival Evolved Indir APK on Your Android Device</h2>
- <p>Once you have downloaded the APK file, you will need to install it on your Android device. However, before you can do that, you will need to allow unknown apps on your device. Unknown apps are apps that are not from the Google Play Store or other official sources. By default, Android devices block unknown apps for security reasons, but you can change this setting if you trust the source of the app.</p>
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-<li>Go to your device settings and tap Apps & Notifications (or Apps in older versions of Android).</li>
-<li>Tap the three dots in the upper-right corner.</li>
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-<li>Tap Install unknown apps.</li>
-<li>Tap Chrome (or whichever web browser you use).</li>
-<li>Move Allow from this source to the On position.</li>
-</ol>
- <p>Now that you have allowed unknown apps, you can install ARK: Survival Evolved Indir APK on your device. To do that, follow these steps:</p>
- <ol>
-<li>Open your file manager app and go to the Downloads folder or wherever you saved the APK file.</li>
-<li>Tap on the APK file.</li>
-<li>If prompted, tap Settings and enable Allow from this source for Chrome (or whichever web browser you used to download the file).</li>
-<li>Tap Install and wait for the installation process to finish.</li>
-<li>Tap Open to launch the game or Done to close the installer.</li>
-</ol>
- <p>Congratulations! You have successfully installed ARK: Survival Evolved Indir APK on your Android device. You can now enjoy this amazing dino-adventure game on your phone or tablet.</p>
- <h2>How to Play ARK: Survival Evolved on Your Android Device</h2>
- <p>Now that you have installed ARK: Survival Evolved Indir APK on your device, you are ready to play this game. However, if you are new to this game or need some guidance, here are some tips on how to play ARK: Survival Evolved on your Android device.</p>
- <h3>How to Create Your Character and Choose Your Server</h3>
- <p>The first thing you need to do when you launch ARK: Survival Evolved is to create your character and choose your server. You can customize your character's appearance, name, gender, hairstyle, skin color, eye color, and more. You can also Continuing the article: <p>You can also choose your server from the list of available servers. There are different types of servers, such as official, unofficial, dedicated, and single-player. Each server has its own settings, rules, and community. You can filter the servers by name, mode, map, ping, players, and more. You can also join a server by entering its IP address or invite code.</p>
- <p>Once you have chosen your server, you will spawn in a random location on the map. You can also choose a specific spawn region if you prefer. You will start with nothing but your bare hands and a specimen implant in your arm. Your implant will show you your health, stamina, hunger, thirst, oxygen, weight, level, and engrams. Engrams are blueprints that allow you to craft items and structures.</p>
- <h3>How to Survive in a Harsh and Dangerous World Full of Dinosaurs and Other Creatures</h3>
- <p>One of the main challenges of ARK: Survival Evolved is to survive in a harsh and dangerous world full of dinosaurs and other creatures. You will need to manage your basic needs, such as food, water, temperature, and oxygen. You will also need to protect yourself from predators, diseases, environmental hazards, and other players.</p>
- <p>Here are some tips on how to survive in ARK: Survival Evolved:</p>
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-<li>Gather resources such as wood, stone, fiber, berries, meat, hide, and more. You can use these resources to craft items and structures that will help you survive.</li>
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-<li>Be careful of the weather and the day-night cycle. The weather can change from sunny to rainy to stormy to foggy and affect your visibility and temperature. The day-night cycle can also affect your visibility and the behavior of some creatures.</li>
-<li>Be aware of your surroundings and watch out for threats such as carnivorous dinosaurs, Continuing the article: <p>such as carnivorous dinosaurs, giant insects, poisonous snakes, aggressive mammals, and more. Some creatures can also inflict status effects such as bleeding, poisoning, stunning, or knocking you out.</p>
- <p>Be prepared for the events and challenges that occur randomly or periodically in the game. These include supply drops, meteor showers, earthquakes, volcanic eruptions, boss fights, and more. Some of these events can reward you with valuable items or resources, while others can pose a serious threat to your survival.</p>
- <h3>How to Tame, Breed, and Ride Dinosaurs</h3>
- <p>One of the most fun and rewarding aspects of ARK: Survival Evolved is to tame, breed, and ride dinosaurs and other creatures. You can tame almost any creature in the game by either knocking it out and feeding it its preferred food or by passive taming it by offering it food without aggroing it. You can also use special items such as traps, bolas, tranquilizer arrows, or narcotics to make the taming process easier.</p>
- <p>Once you have tamed a creature, you can name it, command it, level it up, and customize its appearance. You can also breed two creatures of the same species and get a baby that inherits the traits and stats of its parents. You can also ride some creatures by crafting a saddle for them. Riding a creature can give you many advantages such as faster movement, better combat, and access to new areas.</p>
- <p>Here are some tips on how to tame, breed, and ride dinosaurs and other creatures:</p>
- <ul>
-<li>Research the creature you want to tame before attempting to do so. You can use the in-game dossier or online resources such as [Dododex] or [ARK Wiki] to find out the creature's behavior, location, food preference, taming method, saddle type, and more.</li>
-<li>Prepare the necessary items and resources for taming the creature. You will need food, water, weapons, tools, traps, narcotics, and possibly a saddle. You may also need a taming pen or a raft to protect yourself and the creature from other threats.</li>
-<li>Choose a suitable location and time for taming the creature. You should avoid areas that are too crowded, too dangerous, or too far from your base. You should also consider the weather and the day-night cycle that may affect your visibility and the creature's behavior.</li>
-<li>Be patient and careful when taming the creature. Depending on the creature's level and type, the taming process can take from minutes to hours. You should monitor the creature's health, torpor (unconsciousness), hunger (taming effectiveness), and mood (aggression). You should also protect yourself and the creature from other predators or players that may interfere with your taming.</li>
-<li>Enjoy your new companion and take good care of it. You should feed it regularly, Continuing the article: <p>feed it regularly, heal it when it's injured, and level it up when it gains experience. You should also bond with it by petting it, playing with it, and giving it treats. You can also breed it with another creature of the same species and get a baby that inherits the traits and stats of its parents. You can also ride some creatures by crafting a saddle for them. Riding a creature can give you many advantages such as faster movement, better combat, and access to new areas.</p>
- <h2>How to Craft, Build, and Explore</h2>
- <p>Another important aspect of ARK: Survival Evolved is to craft, build, and explore. You can craft various items and structures that will help you survive, defend, and thrive in the game. You can build your own base, fortify it with walls and turrets, and decorate it with furniture and trophies. You can also explore the map and discover different biomes, landmarks, caves, ruins, and secrets.</p>
- <p>Here are some tips on how to craft, build, and explore:</p>
- <ul>
-<li>Learn new engrams as you level up your character. Engrams are blueprints that allow you to craft items and structures. You can also find engrams in supply drops, caves, or boss arenas.</li>
-<li>Gather the required resources for crafting items and structures. You can use tools, weapons, or creatures to harvest resources such as wood, stone, metal, fiber, hide, oil, crystal, and more.</li>
-<li>Craft items and structures using your inventory or a crafting station. You can craft simple items such as tools, weapons, clothing, and consumables using your inventory. You can craft more advanced items such as saddles, armor, electronics, and explosives using a crafting station such as a smithy or a fabricator.</li>
-<li>Build your base using foundations, walls, ceilings, doors, windows, and other structures. You can snap them together to create different shapes and sizes. You can also place furniture, appliances, storage boxes, generators, lights, and other items inside your base.</li>
-<li>Fortify your base using fences, Continuing the article: <li>Fortify your base using fences, gates, spikes, turrets, traps, and other defenses. You can also use creatures such as dilophosaurs, raptors, or rexes to guard your base.</li>
-<li>Decorate your base using paintings, signs, flags, trophies, and other items. You can also use dye to color your items and structures.</li>
-<li>Explore the map using your feet, a mount, a raft, or a flyer. You can also use a map or a GPS to navigate and mark your locations.</li>
-<li>Discover different biomes such as forests, jungles, deserts, snowfields, swamps, caves, oceans, and more. Each biome has its own unique features, resources, creatures, and dangers.</li>
-<li>Find landmarks such as mountains, volcanoes, rivers, lakes, waterfalls, islands, and more. Some landmarks have special significance or secrets that you can uncover.</li>
-<li>Enter caves and ruins that contain valuable loot, rare resources, hidden secrets, and dangerous enemies. Some caves and ruins also have artifacts that you can collect and use to summon bosses.</li>
-<li>Uncover the mysteries and secrets of ARK: Survival Evolved by finding notes, journals, and recordings left by previous survivors. You can also learn more about the history and lore of the game by completing missions and defeating bosses.</li>
-</ul>
- <h2>Tips and Tricks for Playing ARK: Survival Evolved on Your Android Device</h2>
- <p>Playing ARK: Survival Evolved on your Android device can be a lot of fun, but it can also be challenging and frustrating at times. To make your gaming experience more enjoyable and smooth, here are some tips and tricks for playing ARK: Survival Evolved on your Android device:</p>
- <h3>How to Optimize Your Game Settings for Better Performance and Battery Life</h3>
- <p>ARK: Survival Evolved is a graphically intensive game that can drain your battery and slow down your device if you don't optimize your game settings. To optimize your game settings for better performance and battery life, follow these steps:</p>
- <ol>
-<li>Go to the main menu and tap Settings.</li>
-<li>Tap Graphics and adjust the sliders according to your preference. You can lower the resolution, quality, shadows, textures, anti-aliasing, post-processing, Continuing the article: <p>post-processing, and view distance to improve your frame rate and reduce lag. You can also enable low power mode to save battery.</li>
-<li>Tap Audio and adjust the sliders according to your preference. You can lower the master volume, music volume, sound effects volume, and voice chat volume to save battery and reduce noise.</li>
-<li>Tap Controls and adjust the sliders and buttons according to your preference. You can change the sensitivity, invert look, auto sprint, auto jump, auto fire, and other options to improve your control and comfort.</li>
-<li>Tap HUD and adjust the sliders and buttons according to your preference. You can change the size, opacity, position, and visibility of various elements on your screen such as health bar, stamina bar, chat box, compass, and more.</li>
-</ol>
- <h3>How to Use Cheats and Hacks to Enhance Your Gaming Experience</h3>
- <p>If you want to have some fun or make your life easier in ARK: Survival Evolved, you can use cheats and hacks to modify the game in various ways. Cheats and hacks are commands or codes that you can enter in the game console or chat box to activate certain effects or functions. You can use cheats and hacks to spawn items, creatures, resources, structures, and more. You can also use cheats and hacks to fly, teleport, become invisible, invincible, or godlike.</p>
- <p>However, before you use cheats and hacks, you should be aware of some risks and limitations. First of all, cheats and hacks are only available in single-player mode or in servers that allow them. If you try to use cheats and hacks in servers that don't allow them, you may get banned or kicked out. Second of all, cheats and hacks may cause glitches, bugs, crashes, or corruption in your game. If you use cheats and hacks excessively or improperly, you may damage your game files or lose your progress. Third of all, cheats and hacks may ruin the fun and challenge of the game. If you use cheats and hacks too often or too easily, you may lose interest or satisfaction in the game.</p>
- <p>Therefore, you should use cheats and hacks responsibly and sparingly. To use cheats and hacks in ARK: Survival Evolved on your Android device, follow these steps:</p>
- <ol>
-<li>Go to the pause menu and tap Settings.</li>
-<li>Tap Admin Commands.</li>
-<li>Enter the cheat or hack code in the text box. You can find a list of cheat or hack codes online such as [ARK Commands] or [ARK Cheats].</li>
-<li>Tap Request Admin.</li>
-<li>Tap Execute.</li>
-</ol>
- <p>The cheat or hack effect will be activated immediately. To deactivate a cheat or hack effect, Continuing the article: <p>The cheat or hack effect will be activated immediately. To deactivate a cheat or hack effect, you can either enter the same code again or enter the command "cheat disablecheats".</p>
- <h3>How to Avoid Common Pitfalls and Mistakes that Can Ruin Your Progress</h3>
- <p>ARK: Survival Evolved is a game that can be very rewarding, but also very unforgiving. There are many pitfalls and mistakes that can ruin your progress and make you lose your items, creatures, base, or even your life. To avoid these pitfalls and mistakes, here are some tips:</p>
- <ul>
-<li>Always backup your game data. You can do this by using the cloud save feature in the game settings or by copying your game files to another device or storage. This way, you can restore your game data in case of corruption, deletion, or loss.</li>
-<li>Always have a backup plan. You should always have a spare set of items, weapons, armor, and resources in case you lose your main ones. You should also have a secondary base or a hidden stash in case your primary base is destroyed or raided.</li>
-<li>Always be prepared. You should always have enough food, water, medicine, and ammo before you go on an adventure or a mission. You should also have the appropriate gear and tools for the environment and the situation you are facing.</li>
-<li>Always be cautious. You should always scout the area before you enter it and check for any signs of danger or hostility. You should also avoid engaging in fights or activities that are beyond your level or capability.</li>
-<li>Always be respectful. You should always follow the rules and etiquette of the server and the community you are playing with. You should also avoid griefing, trolling, cheating, or stealing from other players unless you are playing on a PvP server.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>ARK: Survival Evolved is a game that offers endless possibilities and adventures for anyone who loves dinosaurs, survival, crafting, building, and multiplayer. You can download and play this game on your Android device by using an APK file from a reliable source such as APKCombo. You can also use our tips and tricks to optimize your game settings, use cheats and hacks, and avoid common pitfalls and mistakes.</p>
- <p>If you are ready to embark on an epic dino-adventure on your Android device, download ARK: Survival Evolved Indir APK today and join millions of other players in this amazing game.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about ARK: Survival Evolved Indir APK:</p>
- <h3>Q: Is ARK: Survival Evolved Indir APK free?</h3>
- <p>A: Yes, ARK: Survival Evolved Indir APK is free to download and play. However, you may need to pay for some in-game items or features such as skins, expansions, or premium servers.</p>
- <h3>Q: Is ARK: Survival Evolved Indir APK safe?</h3>
- <p>A: Yes, ARK: Survival Evolved Indir APK is safe to download and install if you get it from a verified and trusted source such as APKCombo. However, you should always scan any APK file with an antivirus software before installing it on your device.</p>
- <h3>Q: Is ARK: Survival Evolved Indir APK compatible with my device?</h3>
- <p>A: ARK: Survival Evolved Indir APK is compatible with most Android devices that have at least 3 GB of RAM and Android 7.0 or higher. However, some devices may experience performance issues or crashes due to the high graphics and resource demands of the game.</p>
- <h3>Q: How can I update ARK: Survival Evolved Indir APK?</h3>
- <p>A: You can update ARK: Survival Evolved Indir APK by downloading the latest version of the APK file from APKCombo or another source and installing it over the existing one. You can also check for updates in the game settings or the Google Play Store.</p>
- <h3>Q: How can I contact the developers of ARK: Survival Evolved?</h3>
- <p>A: You can contact the developers of ARK: Survival Evolved by visiting their official website [ark.gamepedia.com] or their social media pages such as [Facebook], [Twitter], [Instagram], [YouTube], [Discord], or [Reddit]. You can also send them an email at [email protected].</p> 197e85843d<br />
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-<h1>Chessmen APK: A Fun and Challenging Puzzle Game for Android</h1>
- <p>If you are looking for a new and exciting puzzle game to play on your Android device, you might want to check out Chessmen APK. This game is based on the classic board game of chess, but with a twist. Instead of playing against another player, you have to swap the positions of the white and black pieces on the board. Sounds easy, right? Well, not quite. You have to do it in as few moves as possible, while following the rules of chess. Are you up for the challenge?</p>
- <h2>What is Chessmen APK?</h2>
- <p>Chessmen APK is a puzzle game developed by Brodski Software, a company that specializes in creating games and apps for Android devices. The game was released in 2014 and has received positive reviews from users and critics alike. It has been downloaded over 100,000 times from the Google Play Store, where it has a rating of 4.4 out of 5 stars.</p>
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- <h3>The concept and gameplay of Chessmen APK</h3>
- <p>The concept of Chessmen APK is simple but ingenious. You are presented with a chessboard with white and black pieces on it. Your goal is to swap the positions of the white and black pieces, so that the white pieces are where the black pieces were, and vice versa. You can only move one piece at a time, following the rules of chess. For example, you can only move a rook horizontally or vertically, a bishop diagonally, a knight in an L-shape, etc. You can also capture an enemy piece by moving your piece to its square, but you cannot capture your own piece. You have to complete each level in as few moves as possible, which is shown by a counter at the top of the screen. The game has 60 levels of increasing difficulty, ranging from easy to expert.</p>
- <h3>The features and benefits of Chessmen APK</h3>
- <p>Chessmen APK is not just a fun and challenging puzzle game, but also a great way to improve your chess skills and knowledge. Here are some of the features and benefits of playing Chessmen APK:</p>
- <ul>
-<li>It helps you practice your chess moves and learn how each piece moves.</li>
-<li>It tests your logic, strategy, planning, and problem-solving skills.</li>
-<li>It stimulates your brain and enhances your memory and concentration.</li>
-<li>It offers a variety of levels and puzzles to suit different skill levels and preferences.</li>
-<li>It has simple and intuitive controls and graphics.</li>
-<li>It is free to download and play, with no ads or in-app purchases.</li>
-</ul>
- <h2>How to download and install Chessmen APK on your device?</h2>
- <p>If you are interested in playing Chessmen APK, you can easily download and install it on your Android device. Here are the steps to do so:</p>
- <h3>The steps to download and install Chessmen APK</h3>
- <ol>
-<li>Go to <a href="(^1^)">https://apkpure.com/chessmen-club/com.brodski.android.chessmen/download/12-APK</a>, which is a trusted source for downloading Android apps.</li>
-<li>Click on the green "Download APK" button to start downloading the file.</li>
-<li>Once the download is complete, open the file manager app on your device and locate the downloaded file.</li>
-<li>Tap on the file to install it. You may need to enable "Unknown Sources" in your device settings to allow the installation of apps from unknown sources.</li>
-<li>Follow the on-screen instructions to complete the installation.</li>
-<li>Launch the app and enjoy playing Chessmen APK!</li>
-</ol>
- <h3>The requirements and compatibility of Chessmen APK</h3>
- <p>Before you download and install Chessmen APK, you should make sure that your device meets the following requirements and compatibility:</p>
- <ul>
-<li>Your device should have Android 2.3 or higher.</li>
-<li>Your device should have at least 10 MB of free storage space.</li>
-<li>Your device should have a stable internet connection to download the app.</li>
-<li>Your device should support touch screen and sound features.</li>
-</ul>
- <h2>How to play and improve your skills in Chessmen APK?</h2>
- <p>Now that you have downloaded and installed Chessmen APK, you are ready to play and improve your skills in this game. Here are some tips and tricks to help you out:</p>
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- <h3>The rules and objectives of Chessmen APK</h3>
- <p>The rules and objectives of Chessmen APK are simple but challenging. Here are the main points to remember:</p>
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-<li>You have to swap the positions of the white and black pieces on the board, so that the white pieces are where the black pieces were, and vice versa.</li>
-<li>You can only move one piece at a time, following the rules of chess.</li>
-<li>You can capture an enemy piece by moving your piece to its square, but you cannot capture your own piece.</li>
-<li>You have to complete each level in as few moves as possible, which is shown by a counter at the top of the screen.</li>
-<li>You can undo your last move by tapping on the undo button at the bottom of the screen.</li>
-<li>You can restart the level by tapping on the restart button at the bottom of the screen.</li>
-<li>You can skip a level by tapping on the skip button at the bottom of the screen, but you will lose one star for doing so.</li>
-<li>You can earn up to three stars for each level, depending on how many moves you used to complete it.</li>
-<li>You can view your progress and achievements by tapping on the menu button at the top left corner of the screen.</li>
-</ul>
- <h3>The tips and strategies for Chessmen APK</h3>
- <p>Chessmen APK is a game that requires logic, strategy, planning, and problem-solving skills. Here are some tips and strategies to help you master this game:</p>
- <ul>
-<li>Plan your moves ahead. Before you make a move, think about how it will affect the board and what moves you will need to make afterwards. Try to visualize the final position of the pieces and work backwards from there.</li>
-<li>Use your pieces wisely. Each piece has its own advantages and disadvantages. For example, a rook can move far and fast, but it can only move horizontally or vertically. A bishop can move diagonally, but it can only stay on one color of squares. A knight can jump over other pieces, but it has a limited range. A pawn can only move forward, but it can promote to a more powerful piece if it reaches the end of the board. A king can move in any direction, but it is vulnerable to checkmate. A queen can move in any direction, but it is valuable and should be protected. Try to use your pieces according to their strengths and weaknesses.</li>
-<li>Look for patterns and shortcuts. Some levels have symmetrical or repetitive patterns that can help you solve them faster. For example, if you see two identical groups of pieces on opposite sides of the board, you can swap them with each other in one move. Or if you see a row or column of pieces that are all of one color, you can move them all together in one move. Look for these patterns and shortcuts and use them to your advantage.</li>
-<li>Learn from your mistakes. If you get stuck or make a wrong move, don't give up or get frustrated. Instead, try to analyze what went wrong and how you can avoid it in the future. You can also use the undo button to correct your mistake or the restart button to try again from scratch. You can also skip a level if you find it too hard, but remember that you will lose one star for doing so.</li>
-</ul>
- <h2>Why should you play Chessmen APK?</h2>
- <p>Chessmen APK is not only a fun and challenging puzzle game, but also a great way to learn more about chess and its benefits for your brain. Here are some reasons why you should play Chessmen APK:</p>
- <h3>The brain benefits of playing chess</h3>
- <p>Playing chess has been proven to have many positive effects on your brain and mental health. Here are some of them:</p>
- <ul>
-<li>It improves your memory, concentration, and attention span. Chess requires you to remember the positions and movements of the pieces, as well as the rules and strategies of the game. This helps you to enhance your short-term and long-term memory, as well as your focus and alertness.</li>
-<li>It develops your logic, reasoning, and problem-solving skills. Chess involves making logical and rational decisions based on the analysis of the board and the possible outcomes of each move. This helps you to improve your critical thinking, deductive reasoning, and problem-solving skills.</li>
-<li>It boosts your creativity and imagination. Chess encourages you to explore different possibilities and scenarios, as well as to come up with original and innovative solutions. This helps you to stimulate your creativity and imagination, as well as your lateral thinking and divergent thinking skills.</li>
-<li>It enhances your emotional intelligence and social skills. Chess teaches you to be patient, disciplined, respectful, and humble. It also helps you to cope with stress, frustration, failure, and success. It also helps you to interact with other players, communicate effectively, and cooperate with others.</li>
-</ul>
- <h3>The history and trivia of chess</h3>
- <p>Playing chess also helps you to learn more about the history and trivia of this fascinating game. Here are some interesting facts about chess:</p>
- <ul>
-<li>Chess is one of the oldest and most popular games in the world. It is believed to have originated in India in the 6th century AD, and then spread to Persia, Arabia, Europe, and Asia. It has been played by kings, queens, nobles, scholars, artists, and many other famous people throughout history.</li>
-<li>Chess is also known as the "game of kings" or the "royal game". This is because it was often used as a way of teaching warfare, strategy, and diplomacy to royalty and nobility. It was also considered a symbol of status, intelligence, and culture.</li>
-<li>Chess has many variations and adaptations. There are different types of chess pieces, boards, rules, and formats. For example, there are chess variants that use three-dimensional boards, multiple players, random setups, fairy pieces, etc. There are also chess puzzles, chess problems, chess compositions, chess tournaments, chess ratings, chess engines, etc.</li>
-<li>Chess has inspired many other games and fields of study. For example, there are games that are based on or influenced by chess, such as checkers, draughts, shogi, xiangqi, etc. There are also fields of study that use chess as a model or a tool, such as mathematics, computer science, artificial intelligence, psychology, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Chessmen APK is a fun and challenging puzzle game for Android devices that is based on the classic board game of chess. It is a great way to practice your chess skills and knowledge, as well as to improve your brain functions and mental health. It is also a great way to learn more about the history and trivia of chess. Chessmen APK is free to download and play from the Google Play Store or from <a href="">https://apkpure.com/chessmen-club/com.brodski.android.chessmen/download/12-APK</a>. If you are looking for a new and exciting puzzle game to play on your Android device,<strong> download Chessmen APK today</strong> and see if you can swap the positions of the white and black pieces on the board in as few moves as possible!</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some frequently asked questions about Chessmen APK:</p>
- <h3>Q: How many levels are there in Chessmen APK?</h3>
-<p>A: There are 60 levels in Chessmen APK, ranging from easy to expert. You can view your progress and achievements by tapping on the menu button at the top left corner of the screen.</p>
- <h3>Q: How can I earn more stars in Chessmen APK?</h3>
-<p>A: You can earn up to three stars for each level, depending on how many moves you used to complete it. The fewer moves you use, the more stars you earn. You can also earn bonus stars by completing certain achievements, such as finishing a level without capturing any piece, finishing a level with only one piece left, etc.</p>
- <h3>Q: What are the benefits of playing Chessmen APK?</h3>
-<p>A: Playing Chessmen APK is not only a fun and challenging puzzle game, but also a great way to improve your chess skills and knowledge, as well as your brain functions and mental health. It helps you practice your chess moves and learn how each piece moves. It tests your logic, strategy, planning, and problem-solving skills. It stimulates your brain and enhances your memory and concentration. It boosts your creativity and imagination. It enhances your emotional intelligence and social skills. It also helps you learn more about the history and trivia of chess.</p>
- <h3>Q: Is Chessmen APK safe to download and play?</h3>
-<p>A: Yes, Chessmen APK is safe to download and play. It does not contain any viruses, malware, spyware, or other harmful elements. It also does not require any special permissions or access to your device. It does not collect or share any personal or sensitive information from you or your device. It does not have any ads or in-app purchases that may interfere with your gameplay or privacy.</p>
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- <p>Gacha Life is a popular anime game that lets you create your own anime styled characters and dress them up in your favorite fashion outfits. You can also enter the studio mode and create any scene you can imagine with custom text, poses, backgrounds, and music. You can also explore different areas in life mode and meet new NPCs who might give you a surprise. You can also play mini-games and collect gems to gacha for rare gifts for your collection.</p>
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- <p>APKMirror is a website that provides free APK files for Android apps and games. APK files are the installation files for Android applications that can be downloaded from other sources than the official Google Play Store. APKMirror is useful because it allows you to access older versions of apps that may not be available on the Play Store anymore, or apps that are not compatible with your device or region.</p>
- <h2>Features of Gacha Life 1.0.9</h2>
- <p>Gacha Life 1.0.9 has four main features that you can enjoy: character customization, studio mode, life mode, and gacha games. Here is a brief overview of each feature and how to use it.</p>
- <h3>Character customization</h3>
- <p>Character customization is the feature that lets you create and dress up your own anime characters. You can access it by tapping on the dress up button on the main menu. You can create up to 20 characters and save them in your slots. You can also load and edit the preset characters that are already available.</p>
- <p>To customize your character, you can use the following options:</p>
- <ul>
-<li><b>Body:</b> You can change the skin color, eye shape, eye color, mouth, nose, blush, and face paint of your character.</li>
-<li><b>Hair:</b> You can change the hairstyle, hair color, front hair, back hair, ponytail, and ahoge (hair antenna) of your character.</li>
-<li><b>Face:</b> You can change the glasses, eye patch, mask, bandage, mole, scar, and accessory of your character.</li>
-<li><b>Clothes:</b> You can change the top, bottom, sleeves, socks, shoes, gloves, cape, hat, and belt of your character.</li>
-<li><b>Other:</b> You can change the weapon, shield, wing, tail, ear, and horn of your character.</li>
-<li><b>Props:</b> You can add a prop to your character's hand or head.</li>
-<li><b>Profile:</b> You can change the name, age, gender, birthday, occupation, location, personality, likes, dislikes, and favorite food of your character.</li>
-</ul>
- <p>You can also use the advanced color wheel to customize the colors of each item. You can also use the random button to generate a random character or outfit. You can also use the reset button to reset your character to the default settings.</p>
- <h3>Studio mode</h3>
- <p>Studio mode is the feature that lets you create your own scenes and skits with custom text, poses, backgrounds, and music. You can access it by tapping on the studio button on the main menu. You can create up to 100 scenes and save them in your slots. You can also load and edit the preset scenes that are already available.</p>
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- <p>To create a scene, you can use the following options:</p>
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-<li><b>Characters:</b> You can add up to eight characters to your scene and choose their slots and positions. You can also edit their expressions and poses by tapping on their faces or bodies.</li>
-<li><b>Backgrounds:</b> You can choose from over 100 backgrounds for your scene or use your own custom background by importing an image from your device.</li>
-<li><b>Bubbles:</b> You can add text bubbles to your scene and choose their styles and positions. You can also edit their text by tapping on them.</li>
-<li><b>Modes:</b> You can switch between normal mode and skit mode for your scene. Normal mode lets you create a single scene with static characters and text. Skit mode lets you create a sequence of scenes with animated characters and text.</li>
-<li><b>Music:</b> You can choose from over 50 songs for your scene or use your own custom music by importing an audio file from your device.</li>
-</ul>
- <p>You can also use the play button to preview your scene or skit. You can also use the save button to save your scene or skit in your slot. You can also use the share button to share your scene or skit with others via social media or QR code.</p>
- <h3>Life mode</h3>
- <p>Life mode is the feature that lets you explore different areas and meet new NPCs. You can access it by tapping on the life button on the main menu. You can choose from 10 different areas: Hometown (where you start), School (where you learn), City (where you shop), Park (where you relax), Beach (where you swim), Hospital (where you heal), Hotel (where you sleep), Train Station (where you travel), Airport (where you fly), and Gacha Resort (where you have fun).</p>
- <p>To explore an area, you can use the following options:</p>
- <ul>
-</li>
-<li><b>Map:</b> You can use the map button to see the layout of each area and the locations of the NPCs. You can also use the map button to travel to other areas by tapping on their icons.</li>
-<li><b>Phone:</b> You can use the phone button to access your contacts, messages, settings, and achievements. You can also use the phone button to call some NPCs and chat with them.</li>
-<li><b>Menu:</b> You can use the menu button to access your inventory, collection, gacha, profile, and options. You can also use the menu button to exit life mode and return to the main menu.</li>
-</ul>
- <p>You can play life mode offline without Wi-Fi. However, you need to have Wi-Fi to chat with other players online or to access some features such as gacha resort or custom backgrounds and music.</p>
- <h3>Gacha games</h3>
- <p>Gacha games are the feature that lets you play eight different mini-games and earn gems. You can access it by tapping on the games button on the main menu. You can choose from eight mini-games: Bex's Festival, Orca Sploosh, Duck & Dodge, Phantom's Remix, Narwhal Sky, 1chi's Math, Picc Pawket Rhythm, and Lemo & Yumi's Beach Adventure.</p>
- <p>To play a mini-game, you can use the following options:</p>
- <ul>
-<li><b>Play:</b> You can tap on the play button to start the mini-game. Each mini-game has different rules and controls that you need to follow. You can also see your high score and best time for each mini-game.</li>
-<li><b>Gems:</b> You can earn gems by playing mini-games. The amount of gems you earn depends on your score and performance. You can use gems to gacha for rare gifts for your collection.</li>
-<li><b>Gifts:</b> You can collect and gacha over 100 gifts for your collection. Each gift has a rarity level from one star to six stars. The higher the rarity level, the harder it is to get. You can also see your gift collection and gift inventory by tapping on the gift button.</li>
-</ul>
- <p>You can play gacha games for free and farm for gems easily. However, you need to have Wi-Fi to access some features such as gacha resort or custom backgrounds and music.</p>
- <h2>How to Download and Install APKMirror Gacha Life 1.0.9</h2>
- <p>If you want to download and install APKMirror Gacha Life 1.0.9 on your device, you need to follow these steps:</p>
- <h3>Steps to download the APK file from APKMirror</h3>
- <ol>
-<li>Access the APKMirror website by typing <a href="">https://www.apkmirror.com/</a> in your browser or clicking on this link.</li>
-<li>Search for Gacha Life 1.0.9 by typing it in the search bar or clicking on this link.</li>
-<li>Choose the right version for your device by checking the compatibility information and tapping on the download button. The version you need is <a href="">Gacha Life 1.0.9 (arm-v7a) (Android 4.0+)</a>.</li>
-<li>Download the APK file safely by tapping on the download APK button and waiting for a few seconds. The file size is 99 MB and the date is January 22, 2020.</li>
-<li>Check the file signature before installing by tapping on the see available APKs button and scrolling down to see the cryptographic signature information. The signature should match this one: <code>A2:DF:B4:BF:AC:ED:64:AF:EA:4F:84:6F:4A:B7:93:A6:2A:D8:C6:DC</code>.</li>
-</ol>
- <h3>Steps to install the APK file on your device</h3>
- <ol>
-<li>Enable unknown sources in your device settings by going to Settings > Security > Unknown sources and toggling it on. This will allow you to install apps from sources other than the Play Store.</li>
-<li>Locate the downloaded file and tap on it to install by going to your file manager app or notification bar and finding the file named <code>Gacha_Life_1.0.9.apk</code>.</li>
-message that says app installed.</li>
-</ol>
- <p>Congratulations, you have successfully downloaded and installed APKMirror Gacha Life 1.0.9 on your device. You can now enjoy the game and its features.</p>
- <h2>Tips and Tricks for Playing Gacha Life 1.0.9</h2>
- <p>If you want to have more fun and success in playing Gacha Life 1.0.9, you can follow these tips and tricks:</p>
- <h3>How to level up faster and unlock more features</h3>
- <p>To level up faster and unlock more features in Gacha Life 1.0.9, you can do the following:</p>
- <ul>
-<li><b>Play mini-games and complete quizzes for gems and XP:</b> You can earn gems and XP by playing mini-games and completing quizzes from NPCs. Gems can be used to gacha for rare gifts, while XP can be used to level up and unlock more features such as chat, gacha resort, custom backgrounds, and custom music.</li>
-<li><b>Talk to NPCs and give them gifts for friendship points and stamina:</b> You can earn friendship points and stamina by talking to NPCs and giving them gifts. Friendship points can be used to unlock more dialogue options and stories from NPCs, while stamina can be used to play mini-games and quizzes.</li>
-<li><b>Use gacha gifts to boost your energy and unlock more items:</b> You can use gacha gifts to boost your energy and unlock more items for your collection. Energy can be used to play life mode and chat with other players online, while items can be used to customize your characters and scenes.</li>
-</ul>
- <h3>How to create amazing characters and skits</h3>
- <p>To create amazing characters and skits in Gacha Life 1.0.9, you can do the following:</p>
- <ul>
-<li><b>Use the advanced color wheel and random options for more variety:</b> You can use the advanced color wheel to customize the colors of each item for your character or scene. You can also use the random button to generate a random character or outfit.</li>
-<li><b>Use the copy and paste functions for easier editing:</b> You can use the copy and paste functions to copy the settings of one item or character and paste it to another item or character. This can save you time and effort when editing your characters or scenes.</li>
-<li><b>Use the names, faces, poses, props, chat, and music buttons for more creativity:</b> You can use the names, faces, poses, props, chat, and music buttons to add more details and personality to your characters or scenes. You can also use these buttons to create funny or dramatic skits with custom text, expressions, actions, accessories, dialogue, and soundtracks.</li>
-</ul>
- <h3>How to avoid common problems and glitches</h3>
- <p>To avoid common problems and glitches in Gacha Life 1.0.9, you can do the following:</p>
- <ul>
-<li><b>Restart the game if you experience lag or crashes over time:</b> You can restart the game if you experience lag or crashes over time due to memory issues or bugs. This can help you fix some minor problems and improve your game performance.</li>
-<li><b>Avoid in-app purchases if you have Android 6.0+ devices or rooted devices:</b> You can avoid in-app purchases if you have Android 6.0+ devices or rooted devices as they may not work properly or cause errors in your game. This is because APKMirror Gacha Life 1.0.9 is not an official version of Gacha Life from Lunime Inc., the developer of the game.</li>
-<li><b>Be careful of what you search for online, especially on YouTube, as some players may create inappropriate content using the skit maker:</b> You can be careful of what you search for online, especially on YouTube, as some players may create inappropriate content using the skit maker feature of Gacha Life 1.0.9. This content may contain violence, gore, sexual themes, profanity, or other sensitive topics that may not be suitable for younger audiences or violate the terms of service of YouTube or Gacha Life.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Gacha Life 1.0.9 is an older version of Gacha Life that has some features that are not present in the latest version of Gacha Life, such as the chat function that lets you talk with other players online (available at level 10). However, it also has some drawbacks such as being outdated, unsupported, incompatible, or unsafe compared to the latest version of Gacha Life.</p>
- <p>If you want to try APKMirror Gacha Life 1.0.9, you can download and install it from the APKMirror website by following the steps in this article. However, you should also be aware of the risks and limitations of using an unofficial version of Gacha Life. You should also respect the rules and guidelines of Gacha Life and YouTube when creating and sharing your content.</p>
- <p>If you want to enjoy the latest features and updates of Gacha Life, you can download and install it from the official Google Play Store by clicking on this link. You can also visit the official website of Lunime Inc. to learn more about Gacha Life and their other games.</p>
- <p>Whether you choose to play APKMirror Gacha Life 1.0.9 or the latest version of Gacha Life, we hope you have fun and unleash your creativity with this amazing anime game.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about APKMirror Gacha Life 1.0.9:</p>
- <ol>
-<li><b>Is APKMirror Gacha Life 1.0.9 safe to download and install?</b></li>
-<p>APKMirror Gacha Life 1.0.9 is generally safe to download and install as long as you get it from the APKMirror website and check the file signature before installing. However, it is not an official version of Gacha Life from Lunime Inc., so it may not be compatible with your device or region, or it may contain bugs or errors that are not fixed by the developer.</p>
-<li><b>Can I play APKMirror Gacha Life 1.0.9 online with other players?</b></li>
-<p>APKMirror Gacha Life 1.0.9 has a chat function that lets you talk with other players online (available at level 10). However, this function may not work properly or may expose you to inappropriate or harmful content from other players. You should also be careful of sharing your personal information or clicking on any links that other players may send you.</p>
-<li><b>Can I update APKMirror Gacha Life 1.0.9 to the latest version of Gacha Life?</b></li>
-<p>APKMirror Gacha Life 1.0.9 cannot be updated to the latest version of Gacha Life from the Play Store or the APKMirror website. If you want to update your game, you need to uninstall APKMirror Gacha Life 1.0.9 and install the latest version of Gacha Life from the Play Store or the Lunime website.</p>
-<li><b>Will I lose my progress or data if I uninstall APKMirror Gacha Life 1.0.9?</b></li>
-<p>APKMirror Gacha Life 1.0.9 does not have a cloud save function, so you may lose your progress or data if you uninstall it from your device. You can try to backup your data by copying the <code>GACHA_LIFE</code> folder from your device storage to another location before uninstalling, but this may not work for all devices or versions.</p>
-<li><b>Can I use custom backgrounds and music in APKMirror Gacha Life 1.0.9?</b></li>
-<p>APKMirror Gacha Life 1.0.9 allows you to use custom backgrounds and music in studio mode and life mode if you have Wi-Fi enabled on your device. You can import your own images and audio files from your device or use the ones provided by Lunime in gacha resort.</p>
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-<h1>Blockman Go Adventure PC Download: How to Play, Craft, and Share Your Fun Experiences with Friends</h1>
- <p>Are you looking for a fun, creative, and social sandbox game that lets you play various block-style minigames with your friends? If so, you might want to check out Blockman Go Adventure, a free-to-play game that offers a wonderland of adventures, challenges, and customization options. And if you want to enjoy this game even more, you might want to play it on your PC instead of your mobile device. In this article, we will tell you what Blockman Go Adventure is, why you should play it on PC, how to download and play it on PC using an emulator, and some tips and tricks for playing it on PC. Let's get started!</p>
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- <p>Blockman Go Adventure is a casual game developed by Studio Cubo Private Limited. It is a sandbox game that allows you to play, craft, and share your experiences with friends. You can choose from a variety of minigames that cover different genres such as action, role-playing, adventure, strategy, simulation, etc. Some of the popular minigames include Bed Wars, Sky Block, Anime Fighting Simulator, Egg War, Free City RP, Party Street, The Exorcists, Frontline, etc. You can also create your own minigames using the Blockman Editor and share them with other players.</p>
- <p>Blockman Go Adventure also lets you customize your avatar with thousands of clothing items, accessories, hats, and decorations. You can also join a community of developers and players who share their creations and feedback. You can also collect rewards every time you log in or complete missions.</p>
- <h2>Why Play Blockman Go Adventure on PC?</h2>
- <p>While Blockman Go Adventure is designed for mobile devices, you might find it more enjoyable to play it on your PC. Here are some of the reasons why playing Blockman Go Adventure on PC is better than playing it on mobile devices:</p>
- <h3>Better Performance and Graphics</h3>
- <p>Playing Blockman Go Adventure on PC can improve the game's performance and graphics significantly. You can avoid lagging, crashing, or overheating issues that might occur on your mobile device. You can also enjoy the game's graphics in higher resolution and quality, making the game more visually appealing and realistic. You can also adjust the game's settings to suit your PC's specifications and preferences.</p>
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- <p>Playing Blockman Go Adventure on PC can also offer you more choices and flexibility for the game's settings, mods, and plugins. You can access more features and tools that might not be available on your mobile device, such as recording, streaming, editing, etc. You can also install and use various mods and plugins that can enhance or modify the game's content and functionality. For example, you can use mods that add new items, maps, characters, etc., or plugins that improve the game's performance, security, compatibility, etc.</p>
- <h2>How to Download and Play Blockman Go Adventure on PC?</h2>
- <p>Now that you know why playing Blockman Go Adventure on PC is better than playing it on mobile devices, you might be wondering how to download and play it on PC. The easiest way to do so is by using an emulator, which is a software that allows you to run Android apps on your PC. There are many emulators available online, but we recommend using BlueStacks, which is one of the best and most popular Android emulators for playing games on PC. Here are the steps to download and play Blockman Go Adventure on PC using BlueStacks:</p>
- <h3>Download and Install BlueStacks on Your PC</h3>
- <p>The first step is to download and install BlueStacks on your PC. You can do so by visiting the official website of BlueStacks at [bluestacks.com] and clicking on the "Download BlueStacks" button. Once the download is complete, run the installer and follow the instructions to complete the installation process. You might need to grant some permissions and restart your PC during the installation.</p>
- <h3>Complete Google Sign-in to Access the Play Store</h3>
- <p>The next step is to complete Google sign-in to access the Play Store. You can do so by launching BlueStacks and clicking on the "Google Sign-in" button on the home screen. You will need to enter your Google account credentials or create a new one if you don't have one already. Once you are signed in, you will be able to access the Play Store from BlueStacks.</p>
- <h3>Install Blockman Go Adventure from the Search Results</h3>
- <p>The third step is to install Blockman Go Adventure from the search results. You can do so by typing "Blockman Go Adventure" in the search bar of the Play Store and clicking on the "Install" button next to the game's icon. The game will be downloaded and installed automatically on your PC.</p>
- <h3>Start Playing Blockman Go Adventure on Your PC</h3>
- <p>The final step is to start playing Blockman Go Adventure on your PC. You can do so by clicking on the game's icon on the home screen of BlueStacks or in the "My Apps" tab. The game will launch and you will be able to enjoy it on your PC using your keyboard and mouse.</p>
- <h2>Tips and Tricks for Playing Blockman Go Adventure on PC</h2>
- <p>To make your gameplay experience even better, here are some tips and tricks for playing Blockman Go Adventure on PC:</p>
- <h3>Use Macros to Automate Tasks and Actions</h3>
- <p>One of the advantages of playing Blockman Go Adventure on PC using BlueStacks is that you can use macros to automate tasks and actions. Macros are sequences of commands that you can record and execute with a single keystroke. For example, you can create a macro that automatically crafts items, collects resources, builds structures, etc., in any minigame. To create a macro, you need to click on the "Macro Recorder" button on the right side of BlueStacks, press "Record", perform the actions you want to automate, press "Stop", name your macro, assign a key to it, and save it. To use a macro, you just need to press the assigned key while playing.</p>
- <h3>Use Multi-Instance to Play Multiple Games at Once</h3>
- <p>Another advantage of playing Blockman Go Adventure on PC using BlueStacks is that you can use multi-instance to play multiple games at once. Multi-instance is a feature that allows you to run multiple instances of the game on your PC and switch between them easily. For example, you can play different minigames at the same time, or play the same minigame with different accounts. To use multi-instance, you need to click on the "Multi-Instance Manager" button on the right side of BlueStacks, click on "New Instance", choose the settings for your new instance, and click on "Create". To switch between instances, you just need to click on the instance you want to play.</p>
- <h3>Use Script to Customize Your Gameplay</h3>
- <p>A third advantage of playing Blockman Go Adventure on PC using BlueStacks is that you can use script to customize your gameplay. Script is a feature that allows you to write and execute scripts that can modify your game's behavior and functionality. For example, you can use scripts to change your game's speed, camera angle, gravity, etc., or to automate certain actions or commands. To use script, you need to click on the "Script" button on the right side of BlueStacks, click on "Create a Script", write your script using the Lua programming language, save it, and run it. To stop a script, you just need to click on the "Stop" button.</p>
- <h2>Conclusion</h2>
- <p>Blockman Go Adventure is a fun, creative, and social sandbox game that lets you play various block-style minigames with your friends. You can also customize your avatar, create your own minigames, and join a community of developers and players. If you want to enjoy this game even more, you should play it on your PC using an emulator like BlueStacks. Playing Blockman Go Adventure on PC can improve your game's performance and graphics, enhance your game's immersion and interaction, and offer you more options and customization. You can also use features like macros, multi-instance, and script to optimize your gameplay experience. So what are you waiting for? Download Blockman Go Adventure on PC today and have fun!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Blockman Go Adventure and playing it on PC:</p>
- <h4>Q: Is Blockman Go Adventure free to play?</h4>
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- <p>Bowmasters is a physics-based game that was developed by Playgendary. It was released in 2016 for iOS and Android devices. The game has been downloaded over 50 million times and has received positive reviews from critics and players alike. The game is inspired by other popular projectile games, such as Angry Birds and Worms. The game has a cartoonish style and a lot of humor and gore. The game is suitable for mature audiences only, as it contains violence, blood, and profanity.</p>
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- <p>Bowmasters has a lot of features that make it fun and engaging. Here are some of them:</p>
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- <p>The game has over 60 insane characters from all dimensions that you can play with. Some of them are original creations, while others are parodies of famous characters from movies, games, comics, and other media. For example, you can play as Arnold (a parody of Arnold Schwarzenegger), Robin (a parody of Robin Hood), or Mr. Gorskiy (a parody of Vladimir Putin). Each character has their own personality, voice lines, and animations.</p>
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- <p>Bowmasters is a game that requires skill, strategy, and luck. It can be challenging and frustrating at times, but also rewarding and satisfying. To help you improve your gameplay and have more fun, here are some tips and tricks that you can use:</p>
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- <p>As mentioned earlier, each weapon in Bowmasters has its own physics, trajectory, damage, and special effects. Some weapons are easier to use than others, while some weapons are more suitable for certain situations than others. Therefore, it is important to learn how each weapon works and how to use it effectively. You can practice with each weapon in the training mode or in the bird hunting mode. You can also watch how other players use their weapons in online multiplayer or in tournaments.</p>
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- <p>Bowmasters has multiple game modes that you can enjoy, but not all of them are available at first. You need to unlock them by completing certain tasks or achievements. For example, to unlock zombie mode, you need to kill 1000 zombies in duels or tournaments. To unlock fruit shooting mode, you need to shoot 100 fruits in bird hunting mode. To unlock online multiplayer mode, you need to win 10 duels or tournaments. By unlocking all the game modes, you can have more variety and fun in playing Bowmasters.</p>
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-<h1>How to Download EvoWars.io APK File for Android Devices</h1>
-<p>If you are looking for a fun and addictive multiplayer action game, you might want to try EvoWars.io. This game lets you fight against other players in a top-down online battle arena, where you can collect orbs and evolve your warrior into different forms. In this article, we will explain what EvoWars.io is, what an APK file is, and how to download and install EvoWars.io APK file on your Android device.</p>
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-<p>EvoWars.io is an IO game developed by Night Steed Games and released in March 2018. It is available for web browsers (desktop and mobile), iOS, and Android platforms. You can play it online for free on websites like CrazyGames or download it from Google Play Store or other sources.</p>
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-<p>The gameplay of EvoWars.io is simple but exciting. You start as a caveman with a wooden club and you have to traverse the online battleground collecting orbs to grow your warrior and gain experience and points. You also have to fight against other players with different weapons and skills. The more enemies you kill, the faster you level up and evolve into new forms.</p>
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-<p>If you want to download EvoWars.io for your Android device, you might encounter an APK file. But what is an APK file and what does it do?</p>
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- <h2>Conclusion</h2>
-<p>EvoWars.io is a fun and addictive multiplayer action game that lets you fight and evolve your warrior in a top-down online battle arena. You can download and install EvoWars.io APK file on your Android device from Google Play Store or other sources. However, you have to be careful when choosing a source and enable Unknown Sources option in your device settings. We hope this article has helped you learn how to download EvoWars.io APK file for Android devices. If you have any questions or feedback, please feel free to leave a comment below.</p>
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-<h1>Project Playtime: A Free-to-Play Multiplayer Horror Game</h1>
-<p>Do you love horror games that make you scream, laugh, and cooperate with your friends? Do you enjoy playing as a helpless toy or a terrifying monster in a creepy toy factory? If you answered yes to any of these questions, then you might want to check out Project Playtime, a free-to-play multiplayer horror game that is available for download on itch.io, Steam, and other platforms. In this article, we will tell you everything you need to know about Project Playtime, including what it is, how to download it, how to play it, and why you should play it.</p>
- <h2>What is Project Playtime?</h2>
-<p>Project Playtime is a multiplayer horror game where six players attempt to create one giant toy while surviving a horrifying monster that roams the toy factory. A seventh player controls the monster and is given only one goal: Find and kill everyone. The game is inspired by popular horror games such as Dead by Daylight, Friday the 13th, and Among Us, but with a unique twist: The toys and the monster are all based on children's drawings and nightmares.</p>
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- <h3>The premise and gameplay of Project Playtime</h3>
-<p>The game is set in an abandoned toy factory where a mysterious entity known as "The Voice" has trapped seven children. The Voice tells them that they have to play a game: Six of them will become toys and have to work together to build one giant toy using parts scattered around the factory. The seventh child will become a monster and have to hunt down and kill the toys before they finish their task. The game ends when either the toys complete their objective or the monster kills them all.</p>
- <h3>The features and modes of Project Playtime</h3>
-<p>Project Playtime has several features and modes that make it fun and exciting for both the toys and the monster. Some of these features are:</p>
-<ul>
-<li>Randomized maps: The game has four different maps that are randomly generated each time you play, making each match unpredictable and challenging.</li>
-<li>Customizable characters: The game allows you to customize your appearance, voice, name, and color as a toy or a monster.</li>
-<li>Different roles: The game has four different roles for the toys: Leader, Medic, Engineer, and Scout. Each role has its own abilities and perks that can help or hinder the team.</li>
-<li>Different monsters: The game has four different monsters to choose from: Clowny, Teddy, Piggy, and Bunny. Each monster has its own strengths and weaknesses that can affect the gameplay.</li>
-<li>Online and offline modes: The game can be played online with up to seven players or offline with bots or solo.</li>
-</ul>
- <h3>The inspiration and development of Project Playtime</h3>
-<p>Project Playtime is developed by MOB Game, an indie game studio based in Canada. The game was inspired by the developer's childhood memories of playing with toys and drawing monsters. The developer wanted to create a game that would capture the essence of childhood imagination and fear in a fun and engaging way. The game was first released on itch.io in October 2022 as a game mod for another horror game called Baldi's Basics Plus. Since then, the game has received positive feedback from players and critics alike, and has been greenlit on Steam in December 2022. The game is currently in development and is expected to be fully released on Steam and other platforms in mid-2023.</p>
- <h2>How to download Project Playtime?</h2>
-<p>If you are interested in playing Project Playtime, you have several options to download it. The game is free-to-play and does not require any special hardware or software to run. Here are some of the ways you can download Project Playtime:</p>
- <h3>Downloading Project Playtime from itch.io</h3>
-<p>The easiest and fastest way to download Project Playtime is from itch.io, a website that hosts indie games and game mods. You can find the game's page on itch.io by clicking [here]. To download the game from itch.io, you need to follow these steps:</p>
-<ol>
-<li>Create a free account on itch.io or log in with your existing account.</li>
-<li>Click on the "Download" button on the game's page.</li>
-<li>Choose the version of the game that matches your operating system (Windows, Mac, or Linux).</li>
-<li>Wait for the game to download as a ZIP file.</li>
-<li>Extract the ZIP file to a folder of your choice.</li>
-<li>Open the folder and double-click on the "ProjectPlaytime.exe" file to launch the game.</li>
-</ol>
- <h3>Downloading Project Playtime from Steam</h3>
-<p>Another way to download Project Playtime is from Steam, a popular digital distribution platform for games and software. You can find the game's page on Steam by clicking [here]. To download the game from Steam, you need to follow these steps:</p>
-<ol>
-<li>Create a free account on Steam or log in with your existing account.</li>
-<li>Download and install the Steam client on your computer.</li>
-<li>Open the Steam client and search for "Project Playtime" in the store.</li>
-<li>Click on the "Add to Library" button on the game's page.</li>
-<li>Go to your library and click on "Project Playtime" in your list of games.</li>
-<li>Click on the "Install" button and wait for the game to download and install.</li>
-<li>Click on the "Play" button to launch the game.</li>
-</ol>
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-<p>Besides itch.io and Steam, you can also download Project Playtime from other sources that host indie games and game mods. Some of these sources are:</p>
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-<ul>
-<li>[GameJolt]: A website that allows users to upload and play indie games for free.</li>
-<li>[ModDB]: A website that hosts mods for various games, including Project Playtime.</li>
-<li>[IndieDB]: A website that showcases indie games and their development progress.</li>
-</ul>
-<p>To download Project Playtime from these sources, you need to follow similar steps as downloading from itch.io, but with different URLs and file names. You can find the links to these sources on the game's official website [here].</p>
- <h2>How to play Project Playtime?</h2>
-<p>Now that you have downloaded Project Playtime, you might be wondering how to play it. The game is easy to learn but hard to master, as it requires teamwork, strategy, and skill. Here are some of the basics of how to play Project Playtime:</p>
- <h3>Playing Project Playtime online with friends or strangers</h3>
-<p>The main mode of Project Playtime is online multiplayer, where you can join or host a match with up to seven players. You can choose to play with your friends or with strangers from around the world. To play online multiplayer, you need to follow these steps:</p>
-<ol>
-<li>Launch the game and click on "Online Multiplayer" in the main menu.</li>
-<li>Select whether you want to join a match or host a match.</li>
-<li>If you want to join a match, you can browse through the available servers or use the quick join option. You can also filter the servers by map, mode, ping, or password.</li>
-<li>If you want to host a match, you can create your own server and customize it with your preferred settings. You can also invite your friends or make it public for anyone to join.</li>
-<li>Once you are in a match, you can choose whether you want to be a toy or a monster. You can also switch teams if there is an imbalance or if you want to try a different role.</li>
-<li>When everyone is ready, the match will start and you will be transported to the toy factory. You will then have to follow the instructions of The Voice and try to survive or kill depending on your role.</li>
-</ol>
- <h3 >Playing Project Playtime offline with bots or solo</h3>
-<p>If you don't have an internet connection or you prefer to play alone, you can also play Project Playtime offline with bots or solo. You can choose to play as a toy or a monster and adjust the difficulty and behavior of the bots. To play offline with bots or solo, you need to follow these steps:</p>
-<ol>
-<li>Launch the game and click on "Offline Mode" in the main menu.</li>
-<li>Select whether you want to play as a toy or a monster.</li>
-<li>Select the map and the mode you want to play.</li>
-<li>Select the number and the type of bots you want to play with or against. You can also turn off the bots if you want to play solo.</li>
-<li>Click on "Start Game" and wait for the game to load.</li>
-<li>You will then be transported to the toy factory and you can play the game as normal.</li>
-</ol>
- <h3>Playing Project Playtime as a toy or as a monster</h3>
-<p>Whether you play online or offline, you can choose to play as a toy or as a monster in Project Playtime. Each role has its own objectives, abilities, and challenges. Here are some of the differences between playing as a toy or as a monster:</p>
- <table>
-<tr>
-<th>Toy</th>
-<th>Monster</th>
-</tr>
-<tr>
-<td>You have to work together with your teammates to find and assemble parts for one giant toy.</td>
-<td>You have to hunt down and kill all the toys before they complete their objective.</td>
-</tr>
-<tr>
-<td>You have limited vision and can only see in front of you.</td>
-<td>You have enhanced vision and can see in all directions.</td>
-</tr>
-<tr>
-<td>You have no weapons and can only run, hide, or distract the monster.</td>
-<td>You have various weapons and abilities that can damage, stun, or trap the toys.</td>
-</tr>
-<tr>
-<td>You can communicate with your teammates using voice chat or text chat.</td>
-<td>You can only communicate with The Voice using voice chat.</td>
-</tr>
-<tr>
-<td>You can choose from four different roles: Leader, Medic, Engineer, or Scout. Each role has its own perks and drawbacks.</td>
-<td>You can choose from four different monsters: Clowny, Teddy, Piggy, or Bunny. Each monster has its own strengths and weaknesses.</td>
-</tr>
-</table>
- <h2>Why should you play Project Playtime?</h2>
-<p>Project Playtime is not just another horror game. It is a game that offers a unique and thrilling experience for both horror fans and casual gamers alike. Here are some of the reasons why you should play Project Playtime:</p>
- <h3>The benefits and challenges of playing Project Playtime</h3>
-<p>Playing Project Playtime can provide you with many benefits and challenges that can improve your skills, creativity, and enjoyment. Some of these benefits and challenges are:</p>
- <ul>
-<li>Improving your teamwork and communication skills: Playing as a toy requires you to cooperate and coordinate with your teammates to achieve your goal. You have to share information, plan strategies, and support each other in times of danger.</li>
-<li>Enhancing your problem-solving and decision-making skills: Playing as a monster requires you to think fast and act smart to find and eliminate your targets. You have to use your senses, abilities, and environment to your advantage.</li>
-<li>Boosting your imagination and creativity: Playing Project Playtime allows you to explore a world of childhood fantasy and horror. You can customize your character, create your own stories, and experience different scenarios each time you play.</li>
-<li>Increasing your adrenaline and excitement: Playing Project Playtime can give you a rush of adrenaline and excitement as you face unpredictable situations, jump scares, and intense moments. You never know what will happen next or who will survive until the end.</li>
-</ul>
- <h3>The reviews and ratings of Project Playtime</h3>
-<p>Project Playtime has received positive reviews and ratings from players and critics alike. The game has been praised for its originality, gameplay, graphics, sound, and atmosphere. The game has also been featured on various media outlets such as YouTube, Twitch, Reddit, Twitter, and more. Here are some of the reviews and ratings of Project Playtime:</p>
- <ul>
-<li>"Project Playtime is one of the best horror games I've ever played. It's scary, funny, and addictive. I love playing as both the toy and the monster. The game is constantly updated with new content and features. I highly recommend it to anyone who likes horror games." - 5 stars on itch.io</li>
-<li>"Project Playtime is a game that will make you scream, laugh, and cry. It's a game that will test your skills, creativity, and courage. It's a game that will make you feel like a child again, but in a twisted way. It's a game that you should definitely try if you are looking for a new and exciting horror experience." - 9/10 on Steam</li>
-<li>"Project Playtime is a game that combines horror, comedy, and nostalgia in a brilliant way. It's a game that has a lot of potential and charm. It's a game that is constantly evolving and improving. It's a game that deserves more attention and support from the gaming community." - 4.5/5 on GameJolt</li>
-</ul>
- <h3>The future updates and plans for Project Playtime</h3>
-<p>Project Playtime is not a finished game yet. It is still in development and is expected to have more updates and features in the future. The developer has stated that they have many plans and ideas for the game, such as:</p>
- <ul>
-<li>Adding more maps, modes, roles, monsters, and customization options.</li>
-<li>Improving the graphics, sound, performance, and stability of the game.</li>
-<li>Fixing bugs, glitches, and balancing issues.</li>
-<li>Implementing cross-play, voice chat, leaderboards, achievements, and more.</li>
-<li>Creating a story mode, a tutorial mode, and a sandbox mode.</li>
-<li>Expanding the lore and the backstory of the game.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Project Playtime is a free-to-play multiplayer horror game that you can download from itch.io, Steam, or other sources. It is a game that lets you play as a toy or a monster in a creepy toy factory. It is a game that offers a unique and thrilling experience for both horror fans and casual gamers alike. It is a game that you should play if you love horror games that make you scream, laugh, and cooperate with your friends.</p>
- <p>If you want to learn more about Project Playtime, you can visit the game's official website [here], where you can find more information, screenshots, videos, links, and contact details. You can also follow the game's social media accounts on [Twitter], [Facebook], [Instagram], [YouTube], and [Discord], where you can get the latest news, updates, announcements, and interact with the developer and other players.</p>
- <p>We hope you enjoyed this article and found it helpful. If you have any questions or feedback about Project Playtime or this article, please feel free to leave them in the comments section below. Thank you for reading and have fun playing Project Playtime!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the frequently asked questions about Project Playtime:</p>
- <h3>Is Project Playtime safe to download?</h3>
-<p>Yes, Project Playtime is safe to download from itch.io, Steam, or other sources. The game does not contain any viruses, malware, or spyware. However, you should always scan any files you download from the internet with an antivirus software before opening them.</p>
- <h3>Is Project Playtime suitable for children?</h3>
-<p>No, Project Playtime is not suitable for children under 13 years old. The game contains violence, gore, blood, jump scares, and disturbing images that may be too scary or inappropriate for young audiences. The game also has some mild language and suggestive themes that may not be suitable for children.</p>
- <h3>How long does it take to play one match of Project Playtime?</h3>
-<p>The duration of one match of Project Playtime depends on several factors such as the map size, the mode type , the number of players, and the skill level of the players. However, on average, one match of Project Playtime lasts for about 10 to 15 minutes.</p>
- <h3>Can I play Project Playtime with a controller?</h3>
-<p>Yes, you can play Project Playtime with a controller. The game supports both keyboard and mouse and controller inputs. You can also customize your controls in the settings menu. However, some players may find it easier to play with a keyboard and mouse, especially as a monster.</p>
- <h3>Can I play Project Playtime with VR?</h3>
-<p>No, you cannot play Project Playtime with VR. The game does not support VR devices or features. The game is designed to be played on a regular monitor or screen. However, the game still has a high level of immersion and realism that can make you feel like you are in the toy factory.</p>
- <h3>Can I mod Project Playtime?</h3>
-<p>Yes, you can mod Project Playtime. The game is mod-friendly and allows you to create your own maps, modes, roles, monsters, and more. You can use the game's built-in editor or other tools to create your own mods. You can also share your mods with other players on itch.io, ModDB, IndieDB, or other platforms.</p> 197e85843d<br />
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-<p>Yes, Driving School Sim is free to download and play. However, some features and items may require in-app purchases or watching ads.</p>
- <li><b>Is Driving School Sim safe?</b></li>
-<p>Yes, Driving School Sim is safe to download and play. The game does not contain any malware or viruses. However, you should always be careful when downloading any APK file from the internet and only use trusted sources.</p>
- <li><b>Can I play Driving School Sim offline?</b></li>
-<p>Yes, you can play Driving School Sim offline without an internet connection. However, some features and modes may require an online connection, such as multiplayer mode, events, leaderboards, etc.</p>
- </ol>
- <p>Thank you for reading this article. I hope you found it helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. I would love to hear from you. Happy driving!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/fs2_adv.py DELETED Viewed

@@ -1,129 +0,0 @@
-from tasks.tts.fs2 import FastSpeech2Task
-from modules.syntaspeech.multi_window_disc import Discriminator
-from utils.hparams import hparams
-from torch import nn
-import torch
-import torch.optim
-import torch.utils.data
-import utils
-class FastSpeech2AdvTask(FastSpeech2Task):
-    def build_model(self):
-        self.build_tts_model()
-        if hparams['load_ckpt'] != '':
-            self.load_ckpt(hparams['load_ckpt'], strict=False)
-        utils.print_arch(self.model, 'Generator')
-        self.build_disc_model()
-        if not hasattr(self, 'gen_params'):
-            self.gen_params = list(self.model.parameters())
-        return self.model
-    def build_disc_model(self):
-        disc_win_num = hparams['disc_win_num']
-        h = hparams['mel_disc_hidden_size']
-        self.mel_disc = Discriminator(
-            time_lengths=[32, 64, 128][:disc_win_num],
-            freq_length=80, hidden_size=h, kernel=(3, 3)
-        )
-        self.disc_params = list(self.mel_disc.parameters())
-        utils.print_arch(self.mel_disc, model_name='Mel Disc')
-    def _training_step(self, sample, batch_idx, optimizer_idx):
-        log_outputs = {}
-        loss_weights = {}
-        disc_start = hparams['mel_gan'] and self.global_step >= hparams["disc_start_steps"] and \
-                     hparams['lambda_mel_adv'] > 0
-        if optimizer_idx == 0:
-            #######################
-            #      Generator      #
-            #######################
-            log_outputs, model_out = self.run_model(self.model, sample, return_output=True)
-            self.model_out = {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)}
-            if disc_start:
-                self.disc_cond = disc_cond = self.model_out['decoder_inp'].detach() \
-                    if hparams['use_cond_disc'] else None
-                if hparams['mel_loss_no_noise']:
-                    self.add_mel_loss(model_out['mel_out_nonoise'], sample['mels'], log_outputs)
-                mel_p = model_out['mel_out']
-                if hasattr(self.model, 'out2mel'):
-                    mel_p = self.model.out2mel(mel_p)
-                o_ = self.mel_disc(mel_p, disc_cond)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    log_outputs['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size()))
-                    loss_weights['a'] = hparams['lambda_mel_adv']
-                if pc_ is not None:
-                    log_outputs['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size()))
-                    loss_weights['ac'] = hparams['lambda_mel_adv']
-        else:
-            #######################
-            #    Discriminator    #
-            #######################
-            if disc_start and self.global_step % hparams['disc_interval'] == 0:
-                if hparams['rerun_gen']:
-                    with torch.no_grad():
-                        _, model_out = self.run_model(self.model, sample, return_output=True)
-                else:
-                    model_out = self.model_out
-                mel_g = sample['mels']
-                mel_p = model_out['mel_out']
-                if hasattr(self.model, 'out2mel'):
-                    mel_p = self.model.out2mel(mel_p)
-                o = self.mel_disc(mel_g, self.disc_cond)
-                p, pc = o['y'], o['y_c']
-                o_ = self.mel_disc(mel_p, self.disc_cond)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    log_outputs["r"] = self.mse_loss_fn(p, p.new_ones(p.size()))
-                    log_outputs["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size()))
-                if pc_ is not None:
-                    log_outputs["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size()))
-                    log_outputs["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size()))
-            if len(log_outputs) == 0:
-                return None
-        total_loss = sum([loss_weights.get(k, 1) * v for k, v in log_outputs.items()])
-        log_outputs['bs'] = sample['mels'].shape[0]
-        return total_loss, log_outputs
-    def configure_optimizers(self):
-        if not hasattr(self, 'gen_params'):
-            self.gen_params = list(self.model.parameters())
-        optimizer_gen = torch.optim.AdamW(
-            self.gen_params,
-            lr=hparams['lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            weight_decay=hparams['weight_decay'])
-        optimizer_disc = torch.optim.AdamW(
-            self.disc_params,
-            lr=hparams['disc_lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None
-        self.scheduler = self.build_scheduler({'gen': optimizer_gen, 'disc': optimizer_disc})
-        return [optimizer_gen, optimizer_disc]
-    def build_scheduler(self, optimizer):
-        return {
-            "gen": super().build_scheduler(optimizer['gen']),
-            "disc": torch.optim.lr_scheduler.StepLR(
-                optimizer=optimizer["disc"],
-                **hparams["discriminator_scheduler_params"]) if optimizer["disc"] is not None else None,
-        }
-    def on_before_optimization(self, opt_idx):
-        if opt_idx == 0:
-            nn.utils.clip_grad_norm_(self.gen_params, hparams['generator_grad_norm'])
-        else:
-            nn.utils.clip_grad_norm_(self.disc_params, hparams["discriminator_grad_norm"])
-    def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
-        if optimizer_idx == 0:
-            self.scheduler['gen'].step(self.global_step)
-        else:
-            self.scheduler['disc'].step(max(self.global_step - hparams["disc_start_steps"], 1))

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/resample.py DELETED Viewed

@@ -1,49 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-import torch.nn as nn
-from torch.nn import functional as F
-from .filter import LowPassFilter1d
-from .filter import kaiser_sinc_filter1d
-class UpSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.stride = ratio
-        self.pad = self.kernel_size // ratio - 1
-        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
-                                      half_width=0.6 / ratio,
-                                      kernel_size=self.kernel_size)
-        self.register_buffer("filter", filter)
-    # x: [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-        x = F.pad(x, (self.pad, self.pad), mode='replicate')
-        x = self.ratio * F.conv_transpose1d(
-            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
-        x = x[..., self.pad_left:-self.pad_right]
-        return x
-class DownSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
-                                       half_width=0.6 / ratio,
-                                       stride=ratio,
-                                       kernel_size=self.kernel_size)
-    def forward(self, x):
-        xx = self.lowpass(x)
-        return xx

spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/txt_processors/base_text_processor.py DELETED Viewed

@@ -1,50 +0,0 @@
-from text_to_speech.utils.text.text_encoder import is_sil_phoneme
-REGISTERED_TEXT_PROCESSORS = {}
-def register_txt_processors(name):
-    def _f(cls):
-        REGISTERED_TEXT_PROCESSORS[name] = cls
-        return cls
-    return _f
-def get_txt_processor_cls(name):
-    return REGISTERED_TEXT_PROCESSORS.get(name, None)
-class BaseTxtProcessor:
-    @staticmethod
-    def sp_phonemes():
-        return ['|']
-    @classmethod
-    def process(cls, txt, preprocess_args):
-        raise NotImplementedError
-    @classmethod
-    def postprocess(cls, txt_struct, preprocess_args):
-        # remove sil phoneme in head and tail
-        while len(txt_struct) > 0 and is_sil_phoneme(txt_struct[0][0]):
-            txt_struct = txt_struct[1:]
-        while len(txt_struct) > 0 and is_sil_phoneme(txt_struct[-1][0]):
-            txt_struct = txt_struct[:-1]
-        if preprocess_args['with_phsep']:
-            txt_struct = cls.add_bdr(txt_struct)
-        if preprocess_args['add_eos_bos']:
-            txt_struct = [["<BOS>", ["<BOS>"]]] + txt_struct + [["<EOS>", ["<EOS>"]]]
-        return txt_struct
-    @classmethod
-    def add_bdr(cls, txt_struct):
-        txt_struct_ = []
-        for i, ts in enumerate(txt_struct):
-            txt_struct_.append(ts)
-            if i != len(txt_struct) - 1 and \
-                    not is_sil_phoneme(txt_struct[i][0]) and not is_sil_phoneme(txt_struct[i + 1][0]):
-                # txt_struct_.append(['|', ['|']])
-                # We disbale the sep token because it is imcompatible with syntactic graph.
-                pass
-        return txt_struct_

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/bert.py DELETED Viewed

@@ -1,32 +0,0 @@
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-def bert_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors='pt')
-    output = model(**encoded_input)
-    return output
-from transformers import RobertaTokenizer, RobertaModel
-tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-model = RobertaModel.from_pretrained('roberta-base')
-text = "Replace me by any text you'd like."
-def Roberta_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors='pt')
-    output = model(**encoded_input)
-    return output
-from transformers import BartTokenizer, BartModel
-tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
-model = BartModel.from_pretrained('facebook/bart-base')
-text = "Replace me by any text you'd like."
-def bart_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors='pt')
-    output = model(**encoded_input)
-    return output

spaces/AIWaves/SOP_Generation-single/evolve.py DELETED Viewed

@@ -1,17 +0,0 @@
-# coding=utf-8
-# Copyright 2023  The AIWaves 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.
-"""self evolution of an LLM autonoumous agent"""

spaces/AchyuthGamer/OpenGPT/g4f/Provider/retry_provider.py DELETED Viewed

@@ -1,88 +0,0 @@
-from __future__ import annotations
-import random
-from typing import List, Type, Dict
-from ..typing import CreateResult
-from .base_provider import BaseProvider, AsyncProvider
-from ..debug import logging
-class RetryProvider(AsyncProvider):
-    __name__: str = "RetryProvider"
-    working: bool = True
-    needs_auth: bool = False
-    supports_stream: bool = True
-    supports_gpt_35_turbo: bool = False
-    supports_gpt_4: bool = False
-    def __init__(
-        self,
-        providers: List[Type[BaseProvider]],
-        shuffle: bool = True
-    ) -> None:
-        self.providers: List[Type[BaseProvider]] = providers
-        self.shuffle: bool = shuffle
-    def create_completion(
-        self,
-        model: str,
-        messages: List[Dict[str, str]],
-        stream: bool = False,
-        **kwargs
-    ) -> CreateResult:
-        if stream:
-            providers = [provider for provider in self.providers if provider.supports_stream]
-        else:
-            providers = self.providers
-        if self.shuffle:
-            random.shuffle(providers)
-        self.exceptions: Dict[str, Exception] = {}
-        started: bool = False
-        for provider in providers:
-            try:
-                if logging:
-                    print(f"Using {provider.__name__} provider")
-                for token in provider.create_completion(model, messages, stream, **kwargs):
-                    yield token
-                    started = True
-                if started:
-                    return
-            except Exception as e:
-                self.exceptions[provider.__name__] = e
-                if logging:
-                    print(f"{provider.__name__}: {e.__class__.__name__}: {e}")
-                if started:
-                    break
-        self.raise_exceptions()
-    async def create_async(
-        self,
-        model: str,
-        messages: List[Dict[str, str]],
-        **kwargs
-    ) -> str:
-        providers = [provider for provider in self.providers]
-        if self.shuffle:
-            random.shuffle(providers)
-        self.exceptions: Dict[str, Exception] = {}
-        for provider in providers:
-            try:
-                return await provider.create_async(model, messages, **kwargs)
-            except Exception as e:
-                self.exceptions[provider.__name__] = e
-                if logging:
-                    print(f"{provider.__name__}: {e.__class__.__name__}: {e}")
-        self.raise_exceptions()
-    def raise_exceptions(self) -> None:
-        if self.exceptions:
-            raise RuntimeError("\n".join(["All providers failed:"] + [
-                f"{p}: {self.exceptions[p].__class__.__name__}: {self.exceptions[p]}" for p in self.exceptions
-            ]))
-        raise RuntimeError("No provider found")

spaces/AgentVerse/agentVerse/agentverse_command/main_simulation_cli.py DELETED Viewed

@@ -1,27 +0,0 @@
-import os
-import logging
-from argparse import ArgumentParser
-from agentverse.logging import logger
-from agentverse.simulation import Simulation
-parser = ArgumentParser()
-parser.add_argument("--task", type=str, default="simulation/prisoner_dilemma")
-parser.add_argument(
-    "--tasks_dir",
-    type=str,
-    default=os.path.join(os.path.dirname(__file__), "..", "agentverse", "tasks"),
-)
-parser.add_argument("--debug", action="store_true")
-args = parser.parse_args()
-logger.set_level(logging.DEBUG if args.debug else logging.INFO)
-def cli_main():
-    agentverse = Simulation.from_task(args.task, args.tasks_dir)
-    agentverse.run()
-if __name__ == "__main__":
-    cli_main()

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

	@@ -1,2 +0,0 @@
1	- import Raycaster from './math/raycaster/Raycaster.js';
2	- export default Raycaster;

spaces/AhmedTambal/malaria/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: Malaria
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Akmyradov/TurkmenTTSweSTT/vits/train.py DELETED Viewed

@@ -1,290 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-import commons
-import utils
-from data_utils import (
-  TextAudioLoader,
-  TextAudioCollate,
-  DistributedBucketSampler
-)
-from models import (
-  SynthesizerTrn,
-  MultiPeriodDiscriminator,
-)
-from losses import (
-  generator_loss,
-  discriminator_loss,
-  feature_loss,
-  kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-torch.backends.cudnn.benchmark = True
-global_step = 0
-def main():
-  """Assume Single Node Multi GPUs Training Only"""
-  assert torch.cuda.is_available(), "CPU training is not allowed."
-  n_gpus = torch.cuda.device_count()
-  os.environ['MASTER_ADDR'] = 'localhost'
-  os.environ['MASTER_PORT'] = '80000'
-  hps = utils.get_hparams()
-  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-def run(rank, n_gpus, hps):
-  global global_step
-  if rank == 0:
-    logger = utils.get_logger(hps.model_dir)
-    logger.info(hps)
-    utils.check_git_hash(hps.model_dir)
-    writer = SummaryWriter(log_dir=hps.model_dir)
-    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-  dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
-  torch.manual_seed(hps.train.seed)
-  torch.cuda.set_device(rank)
-  train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
-  train_sampler = DistributedBucketSampler(
-      train_dataset,
-      hps.train.batch_size,
-      [32,300,400,500,600,700,800,900,1000],
-      num_replicas=n_gpus,
-      rank=rank,
-      shuffle=True)
-  collate_fn = TextAudioCollate()
-  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
-      collate_fn=collate_fn, batch_sampler=train_sampler)
-  if rank == 0:
-    eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data)
-    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
-        batch_size=hps.train.batch_size, pin_memory=True,
-        drop_last=False, collate_fn=collate_fn)
-  net_g = SynthesizerTrn(
-      len(symbols),
-      hps.data.filter_length // 2 + 1,
-      hps.train.segment_size // hps.data.hop_length,
-      **hps.model).cuda(rank)
-  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-  optim_g = torch.optim.AdamW(
-      net_g.parameters(),
-      hps.train.learning_rate,
-      betas=hps.train.betas,
-      eps=hps.train.eps)
-  optim_d = torch.optim.AdamW(
-      net_d.parameters(),
-      hps.train.learning_rate,
-      betas=hps.train.betas,
-      eps=hps.train.eps)
-  net_g = DDP(net_g, device_ids=[rank])
-  net_d = DDP(net_d, device_ids=[rank])
-  try:
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
-    global_step = (epoch_str - 1) * len(train_loader)
-  except:
-    epoch_str = 1
-    global_step = 0
-  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-  scaler = GradScaler(enabled=hps.train.fp16_run)
-  for epoch in range(epoch_str, hps.train.epochs + 1):
-    if rank==0:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-    else:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
-    scheduler_g.step()
-    scheduler_d.step()
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-  net_g, net_d = nets
-  optim_g, optim_d = optims
-  scheduler_g, scheduler_d = schedulers
-  train_loader, eval_loader = loaders
-  if writers is not None:
-    writer, writer_eval = writers
-  train_loader.batch_sampler.set_epoch(epoch)
-  global global_step
-  net_g.train()
-  net_d.train()
-  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(train_loader):
-    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-    with autocast(enabled=hps.train.fp16_run):
-      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
-      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths)
-      mel = spec_to_mel_torch(
-          spec,
-          hps.data.filter_length,
-          hps.data.n_mel_channels,
-          hps.data.sampling_rate,
-          hps.data.mel_fmin,
-          hps.data.mel_fmax)
-      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-      y_hat_mel = mel_spectrogram_torch(
-          y_hat.squeeze(1),
-          hps.data.filter_length,
-          hps.data.n_mel_channels,
-          hps.data.sampling_rate,
-          hps.data.hop_length,
-          hps.data.win_length,
-          hps.data.mel_fmin,
-          hps.data.mel_fmax
-      )
-      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
-      # Discriminator
-      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-      with autocast(enabled=False):
-        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-        loss_disc_all = loss_disc
-    optim_d.zero_grad()
-    scaler.scale(loss_disc_all).backward()
-    scaler.unscale_(optim_d)
-    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-    scaler.step(optim_d)
-    with autocast(enabled=hps.train.fp16_run):
-      # Generator
-      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-      with autocast(enabled=False):
-        loss_dur = torch.sum(l_length.float())
-        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-        loss_fm = feature_loss(fmap_r, fmap_g)
-        loss_gen, losses_gen = generator_loss(y_d_hat_g)
-        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-    optim_g.zero_grad()
-    scaler.scale(loss_gen_all).backward()
-    scaler.unscale_(optim_g)
-    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-    scaler.step(optim_g)
-    scaler.update()
-    if rank==0:
-      if global_step % hps.train.log_interval == 0:
-        lr = optim_g.param_groups[0]['lr']
-        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-        logger.info('Train Epoch: {} [{:.0f}%]'.format(
-          epoch,
-          100. * batch_idx / len(train_loader)))
-        logger.info([x.item() for x in losses] + [global_step, lr])
-        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-        image_dict = {
-            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
-        }
-        utils.summarize(
-          writer=writer,
-          global_step=global_step,
-          images=image_dict,
-          scalars=scalar_dict)
-      if global_step % hps.train.eval_interval == 0:
-        evaluate(hps, net_g, eval_loader, writer_eval)
-        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-    global_step += 1
-  if rank == 0:
-    logger.info('====> Epoch: {}'.format(epoch))
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    with torch.no_grad():
-      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(eval_loader):
-        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
-        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
-        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
-        # remove else
-        x = x[:1]
-        x_lengths = x_lengths[:1]
-        spec = spec[:1]
-        spec_lengths = spec_lengths[:1]
-        y = y[:1]
-        y_lengths = y_lengths[:1]
-        break
-      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, max_len=1000)
-      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
-      mel = spec_to_mel_torch(
-        spec,
-        hps.data.filter_length,
-        hps.data.n_mel_channels,
-        hps.data.sampling_rate,
-        hps.data.mel_fmin,
-        hps.data.mel_fmax)
-      y_hat_mel = mel_spectrogram_torch(
-        y_hat.squeeze(1).float(),
-        hps.data.filter_length,
-        hps.data.n_mel_channels,
-        hps.data.sampling_rate,
-        hps.data.hop_length,
-        hps.data.win_length,
-        hps.data.mel_fmin,
-        hps.data.mel_fmax
-      )
-    image_dict = {
-      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-    }
-    audio_dict = {
-      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
-    }
-    if global_step == 0:
-      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
-    utils.summarize(
-      writer=writer_eval,
-      global_step=global_step,
-      images=image_dict,
-      audios=audio_dict,
-      audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-if __name__ == "__main__":
-  main()

spaces/AleksBlacky/Arxiv_paper_classifier/model_api.py DELETED Viewed

@@ -1,17 +0,0 @@
-#
-#
-# def make_predict(model_name_global, model_local, decode_dict, title, abstract):
-#     model_name_global="allenai/scibert_scivocab_uncased"
-#     model_local="scibert_trainer/checkpoint-2000/"
-#
-#     tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
-#     tokens = tokenizer_(title + abstract, return_tensors="pt")
-#     model_ = AutoModelForSequenceClassification.from_pretrained(model_local)
-#     outs = model_(tokens.input_ids)
-#
-#     probs = outs["logits"].softmax(dim=-1).tolist()[0]
-#     topic_probs = {}
-#     for i, p in enumerate(probs):
-#         if p > 0.1:
-#             topic_probs[decode_dict[i]] = p
-#     return topic_probs

spaces/AlexWang/lama/bin/side_by_side.py DELETED Viewed

@@ -1,76 +0,0 @@
-#!/usr/bin/env python3
-import os
-import random
-import cv2
-import numpy as np
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.utils import load_yaml
-from saicinpainting.training.visualizers.base import visualize_mask_and_images
-def main(args):
-    config = load_yaml(args.config)
-    datasets = [PrecomputedInpaintingResultsDataset(args.datadir, cur_predictdir, **config.dataset_kwargs)
-                for cur_predictdir in args.predictdirs]
-    assert len({len(ds) for ds in datasets}) == 1
-    len_first = len(datasets[0])
-    indices = list(range(len_first))
-    if len_first > args.max_n:
-        indices = sorted(random.sample(indices, args.max_n))
-    os.makedirs(args.outpath, exist_ok=True)
-    filename2i = {}
-    keys = ['image'] + [i for i in range(len(datasets))]
-    for img_i in indices:
-        try:
-            mask_fname = os.path.basename(datasets[0].mask_filenames[img_i])
-            if mask_fname in filename2i:
-                filename2i[mask_fname] += 1
-                idx = filename2i[mask_fname]
-                mask_fname_only, ext = os.path.split(mask_fname)
-                mask_fname = f'{mask_fname_only}_{idx}{ext}'
-            else:
-                filename2i[mask_fname] = 1
-            cur_vis_dict = datasets[0][img_i]
-            for ds_i, ds in enumerate(datasets):
-                cur_vis_dict[ds_i] = ds[img_i]['inpainted']
-            vis_img = visualize_mask_and_images(cur_vis_dict, keys,
-                                                last_without_mask=False,
-                                                mask_only_first=True,
-                                                black_mask=args.black)
-            vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')
-            out_fname = os.path.join(args.outpath, mask_fname)
-            vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)
-            cv2.imwrite(out_fname, vis_img)
-        except Exception as ex:
-            print(f'Could not process {img_i} due to {ex}')
-if __name__ == '__main__':
-    import argparse
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('--max-n', type=int, default=100, help='Maximum number of images to print')
-    aparser.add_argument('--black', action='store_true', help='Whether to fill mask on GT with black')
-    aparser.add_argument('config', type=str, help='Path to evaluation config (e.g. configs/eval1.yaml)')
-    aparser.add_argument('outpath', type=str, help='Where to put results')
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks')
-    aparser.add_argument('predictdirs', type=str,
-                         nargs='+',
-                         help='Path to folders with predicts')
-    main(aparser.parse_args())

spaces/Alican/pixera/app.py DELETED Viewed

@@ -1,122 +0,0 @@
-import os
-import cv2
-import torch
-import random
-import numpy as np
-import gradio as gr
-from util import util
-from util.img2pixl import pixL
-from data import create_dataset
-from models import create_model
-from options.test_options import TestOptions
-opt = TestOptions().parse()
-opt.num_threads = 0
-opt.batch_size = 1
-opt.display_id = -1
-opt.no_dropout = True
-model = create_model(opt)
-model.setup(opt)
-num_inferences = 0
-def preprocess(image):
-  im_type = None
-  imgH, imgW = image.shape[:2]
-  aspect_ratio = imgW / imgH
-  if 0.75 <= aspect_ratio <= 1.75:
-    image = cv2.resize(image, (512, 512))
-    image = pixL().toThePixL(image,6,False)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    image = np.asarray([image])
-    image = np.transpose(image, (0, 3, 1, 2))
-    image = inference(image)
-    return image
-  elif 1.75 <= aspect_ratio: # upper boundary
-    image = cv2.resize(image, (1024, 512))
-    middlePoint = image.shape[1] // 2
-    half_1 = image[:,:middlePoint]
-    half_2 = image[:,middlePoint:]
-    images = [half_1,half_2]
-    for image in images:
-      image = pixL().toThePixL(image,6,False)
-      image = np.asarray([image])
-      image = np.transpose(image, (0, 3, 1, 2))
-      image = inference(image)
-    image = cv2.hconcat([images[0], images[1]])
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    return image
-  elif 0.00 <= aspect_ratio <= 0.75:
-    image = cv2.resize(image, (512, 1024))
-    middlePoint = image.shape[0] // 2
-    half_1 = image[:middlePoint,:]
-    half_2 = image[middlePoint:,:]
-    images = [half_1,half_2]
-    for image in images:
-      image = pixL().toThePixL(image,6,False)
-      image = np.asarray([image])
-      image = np.transpose(image, (0, 3, 1, 2))
-      image = inference(image)
-    image = cv2.vconcat([images[0], images[1]])
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    return image
-def postprocess(image):
-  image = util.tensor2im(image)
-  return image
-def inference(image):
-  global model
-  data = {"A": None, "A_paths": None}
-  data['A'] = torch.FloatTensor(image)
-  model.set_input(data)
-  model.test()
-  image = model.get_current_visuals()['fake']
-  return image
-def pixera_CYCLEGAN(image):
-  global num_inferences
-  image = preprocess(image)
-  image = postprocess(image)
-  num_inferences += 1
-  print(num_inferences)
-  return image
-title_ = "Pixera: Create your own Pixel Art"
-description_ = ""
-examples_path = f"{os.getcwd()}/imgs"
-examples_ = os.listdir(examples_path)
-random.shuffle(examples_)
-examples_ = [[f"{examples_path}/{example}"] for example in examples_]
-demo = gr.Interface(pixera_CYCLEGAN, inputs = [gr.Image(show_label= False)],
-                                     outputs = [gr.Image(show_label= False)],
-                                     examples = examples_,
-                                     title = title_,
-                                     description= description_)
-demo.launch()

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/__init__.py DELETED Viewed

File without changes

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/repaint.md DELETED Viewed

@@ -1,37 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-# RePaint
-[RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2201.09865) is by Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool.
-The abstract from the paper is:
-*Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks.
-RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.*
-The original codebase can be found at [andreas128/RePaint](https://github.com/andreas128/RePaint).
-<Tip>
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-</Tip>
-## RePaintPipeline
-[[autodoc]] RePaintPipeline
-	- all
-	- __call__
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

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

@@ -1,284 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import importlib
-import math
-import os
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Dict, Optional, Tuple, Union
-import flax
-import jax.numpy as jnp
-from ..utils import BaseOutput
-SCHEDULER_CONFIG_NAME = "scheduler_config.json"
-# NOTE: We make this type an enum because it simplifies usage in docs and prevents
-# circular imports when used for `_compatibles` within the schedulers module.
-# When it's used as a type in pipelines, it really is a Union because the actual
-# scheduler instance is passed in.
-class FlaxKarrasDiffusionSchedulers(Enum):
-    FlaxDDIMScheduler = 1
-    FlaxDDPMScheduler = 2
-    FlaxPNDMScheduler = 3
-    FlaxLMSDiscreteScheduler = 4
-    FlaxDPMSolverMultistepScheduler = 5
-@dataclass
-class FlaxSchedulerOutput(BaseOutput):
-    """
-    Base class for the scheduler's step function output.
-    Args:
-        prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-    """
-    prev_sample: jnp.ndarray
-class FlaxSchedulerMixin:
-    """
-    Mixin containing common functions for the schedulers.
-    Class attributes:
-        - **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that
-          `from_config` can be used from a class different than the one used to save the config (should be overridden
-          by parent class).
-    """
-    config_name = SCHEDULER_CONFIG_NAME
-    ignore_for_config = ["dtype"]
-    _compatibles = []
-    has_compatibles = True
-    @classmethod
-    def from_pretrained(
-        cls,
-        pretrained_model_name_or_path: Dict[str, Any] = None,
-        subfolder: Optional[str] = None,
-        return_unused_kwargs=False,
-        **kwargs,
-    ):
-        r"""
-        Instantiate a Scheduler class from a pre-defined JSON-file.
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-                    - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
-                      organization name, like `google/ddpm-celebahq-256`.
-                    - A path to a *directory* containing model weights saved using [`~SchedulerMixin.save_pretrained`],
-                      e.g., `./my_model_directory/`.
-            subfolder (`str`, *optional*):
-                In case the relevant files are located inside a subfolder of the model repo (either remote in
-                huggingface.co or downloaded locally), you can specify the folder name here.
-            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
-                Whether kwargs that are not consumed by the Python class should be returned or not.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory in which a downloaded pretrained model configuration should be cached if the
-                standard cache should not be used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
-                file exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only(`bool`, *optional*, defaults to `False`):
-                Whether or not to only look at local files (i.e., do not try to download the model).
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `transformers-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-        <Tip>
-         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
-         models](https://huggingface.co/docs/hub/models-gated#gated-models).
-        </Tip>
-        <Tip>
-        Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
-        use this method in a firewalled environment.
-        </Tip>
-        """
-        config, kwargs = cls.load_config(
-            pretrained_model_name_or_path=pretrained_model_name_or_path,
-            subfolder=subfolder,
-            return_unused_kwargs=True,
-            **kwargs,
-        )
-        scheduler, unused_kwargs = cls.from_config(config, return_unused_kwargs=True, **kwargs)
-        if hasattr(scheduler, "create_state") and getattr(scheduler, "has_state", False):
-            state = scheduler.create_state()
-        if return_unused_kwargs:
-            return scheduler, state, unused_kwargs
-        return scheduler, state
-    def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
-        """
-        Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the
-        [`~FlaxSchedulerMixin.from_pretrained`] class method.
-        Args:
-            save_directory (`str` or `os.PathLike`):
-                Directory where the configuration JSON file will be saved (will be created if it does not exist).
-        """
-        self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
-    @property
-    def compatibles(self):
-        """
-        Returns all schedulers that are compatible with this scheduler
-        Returns:
-            `List[SchedulerMixin]`: List of compatible schedulers
-        """
-        return self._get_compatibles()
-    @classmethod
-    def _get_compatibles(cls):
-        compatible_classes_str = list(set([cls.__name__] + cls._compatibles))
-        diffusers_library = importlib.import_module(__name__.split(".")[0])
-        compatible_classes = [
-            getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c)
-        ]
-        return compatible_classes
-def broadcast_to_shape_from_left(x: jnp.ndarray, shape: Tuple[int]) -> jnp.ndarray:
-    assert len(shape) >= x.ndim
-    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(shape) - x.ndim)), shape)
-def betas_for_alpha_bar(num_diffusion_timesteps: int, max_beta=0.999, dtype=jnp.float32) -> jnp.ndarray:
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    Returns:
-        betas (`jnp.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    def alpha_bar(time_step):
-        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return jnp.array(betas, dtype=dtype)
-@flax.struct.dataclass
-class CommonSchedulerState:
-    alphas: jnp.ndarray
-    betas: jnp.ndarray
-    alphas_cumprod: jnp.ndarray
-    @classmethod
-    def create(cls, scheduler):
-        config = scheduler.config
-        if config.trained_betas is not None:
-            betas = jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
-        elif config.beta_schedule == "linear":
-            betas = jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
-        elif config.beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            betas = (
-                jnp.linspace(
-                    config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype
-                )
-                ** 2
-            )
-        elif config.beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            betas = betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
-        else:
-            raise NotImplementedError(
-                f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"
-            )
-        alphas = 1.0 - betas
-        alphas_cumprod = jnp.cumprod(alphas, axis=0)
-        return cls(
-            alphas=alphas,
-            betas=betas,
-            alphas_cumprod=alphas_cumprod,
-        )
-def get_sqrt_alpha_prod(
-    state: CommonSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray
-):
-    alphas_cumprod = state.alphas_cumprod
-    sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-    sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-    sqrt_alpha_prod = broadcast_to_shape_from_left(sqrt_alpha_prod, original_samples.shape)
-    sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-    sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-    sqrt_one_minus_alpha_prod = broadcast_to_shape_from_left(sqrt_one_minus_alpha_prod, original_samples.shape)
-    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
-def add_noise_common(
-    state: CommonSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray
-):
-    sqrt_alpha_prod, sqrt_one_minus_alpha_prod = get_sqrt_alpha_prod(state, original_samples, noise, timesteps)
-    noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-    return noisy_samples
-def get_velocity_common(state: CommonSchedulerState, sample: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray):
-    sqrt_alpha_prod, sqrt_one_minus_alpha_prod = get_sqrt_alpha_prod(state, sample, noise, timesteps)
-    velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-    return velocity

spaces/Andy1621/uniformer_image_segmentation/configs/fp16/README.md DELETED Viewed

@@ -1,25 +0,0 @@
-# Mixed Precision Training
-## Introduction
-<!-- [OTHERS] -->
-```latex
-@article{micikevicius2017mixed,
-  title={Mixed precision training},
-  author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others},
-  journal={arXiv preprint arXiv:1710.03740},
-  year={2017}
-}
-```
-## Results and models
-### Cityscapes
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                 |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN        | R-101-D8 | 512x1024  |   80000 | 5.37     | 8.64           | 76.80 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230921.log.json)                                         |
-| PSPNet     | R-101-D8 | 512x1024  |   80000 | 5.34     | 8.77           | 79.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230919.log.json)                             |
-| DeepLabV3  | R-101-D8 | 512x1024  |   80000 | 5.75     | 3.86           | 80.48 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920.log.json)                 |
-| DeepLabV3+ | R-101-D8 | 512x1024  |   80000 | 6.35     | 7.87           | 80.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920.log.json) |

spaces/AnimaLab/bias-test-gpt-pairs/app.py DELETED Viewed

@@ -1,1062 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import string
-import re
-import json
-import random
-import torch
-import hashlib, base64
-from tqdm import tqdm
-from gradio.themes.base import Base
-import openai
-# bloomber vis
-import bloomberg_vis as bv
-# error messages
-from error_messages import *
-tqdm().pandas()
-# bias testing manager
-import mgr_bias_scoring as bt_mgr
-# managers for sentences and biases
-import mgr_requests as rq_mgr
-from mgr_requests import G_CORE_BIAS_NAME
-import mgr_biases as bmgr
-# cookie manager
-#import mgr_cookies as cookie_mgr
-use_paper_sentences = False
-G_TEST_SENTENCES = []
-G_NUM_SENTENCES = 0
-G_MISSING_SPEC = []
-def getTermsFromGUI(group1, group2, att1, att2):
-    bias_spec = {
-      "social_groups": {
-        "group 1": [t.strip(" ") for t in group1.split(",") if len(t.strip(' '))>0],
-        "group 2": [t.strip(" ") for t in group2.split(",") if len(t.strip(' '))>0]},
-      "attributes": {
-        "attribute 1": [t.strip(" ") for t in att1.split(",") if len(t.strip(' '))>0],
-        "attribute 2": [t.strip(" ") for t in att2.split(",") if len(t.strip(' '))>0]}
-    }
-    return bias_spec
-# Select from example datasets
-def prefillBiasSpec(evt: gr.SelectData):
-    global use_paper_sentences, G_MISSING_SPEC, G_CORE_BIAS_NAME
-    G_MISSING_SPEC = []
-    G_CORE_BIAS_NAME = evt.value
-    print(f"Setting core bias name to: {G_CORE_BIAS_NAME}")
-    print(f"Selected {evt.value} at {evt.index} from {evt.target}")
-    #bias_filename = f"{evt.value[1]}.json"
-    bias_filename = f"{bmgr.bias2tag[evt.value]}.json"
-    print(f"Filename: {bias_filename}")
-    isCustom = bmgr.isCustomBias(bias_filename)
-    if isCustom:
-        print(f"Custom bias specification: {bias_filename}")
-        bias_spec = bmgr.loadCustomBiasSpec(bias_filename)
-    else:
-        print(f"Core bias specification: {bias_filename}")
-        bias_spec = bmgr.loadPredefinedBiasSpec(bias_filename)
-    grp1_terms, grp2_terms = bmgr.getSocialGroupTerms(bias_spec)
-    att1_terms, att2_terms = bmgr.getAttributeTerms(bias_spec)
-    print(f"Grp 1: {grp1_terms}")
-    print(f"Grp 2: {grp2_terms}")
-    print(f"Att 1: {att1_terms}")
-    print(f"Att 2: {att2_terms}")
-    #use_paper_sentences = True
-    return (', '.join(grp1_terms[0:50]), ', '.join(grp2_terms[0:50]), ', '.join(att1_terms[0:50]), ', '.join(att2_terms[0:50]),
-            gr.update(interactive=False, visible=False))
-def updateErrorMsg(isError, text):
-    return gr.Markdown.update(visible=isError, value=text)
-def countBiasCustomSpec(bias_spec):
-    if (bias_spec) == 0:
-        return 0
-    elif 'custom_counts' in bias_spec:
-        rq_count_1 = sum([v for v in bias_spec['custom_counts' ][0].values()])
-        rq_count_2 = sum([v for v in bias_spec['custom_counts' ][1].values()])
-        return rq_count_1+rq_count_2
-    else:
-        return 0
-def generateSentences(gr1, gr2, att1, att2, openai_key, num_sent2gen, progress=gr.Progress()):
-    global use_paper_sentences, G_NUM_SENTENCES, G_MISSING_SPEC, G_TEST_SENTENCES
-    print(f"GENERATE SENTENCES CLICKED!, requested sentence per attribute number: {num_sent2gen}")
-    # No error messages by default
-    err_update = updateErrorMsg(False, "")
-    bias_test_label = "Test Model Using Imbalanced Sentences"
-    # There are no sentences available at all
-    if len(G_TEST_SENTENCES) == 0:
-        bias_gen_states = [True, False]
-        online_gen_visible = True
-        test_model_visible = False
-    else:
-        bias_gen_states = [True, True]
-        online_gen_visible = True
-        test_model_visible = True
-    info_msg_update = gr.Markdown.update(visible=False, value="")
-    test_sentences = []
-    bias_spec = getTermsFromGUI(gr1, gr2, att1, att2)
-    g1, g2, a1, a2 = bt_mgr.get_words(bias_spec)
-    total_att_terms = len(a1)+len(a2)
-    all_terms_len = len(g1)+len(g2)+len(a1)+len(a2)
-    print(f"Length of all the terms: {all_terms_len}")
-    if all_terms_len == 0:
-        print("No terms entered!")
-        err_update = updateErrorMsg(True, NO_TERMS_ENTERED_ERROR)
-        #raise gr.Error(NO_TERMS_ENTERED_ERROR)
-    else:
-        if len(openai_key) == 0:
-            print("Empty OpenAI key!!!")
-            err_update = updateErrorMsg(True, OPENAI_KEY_EMPTY)
-        elif len(openai_key) < 10:
-            print("Wrong length OpenAI key!!!")
-            err_update = updateErrorMsg(True, OPENAI_KEY_WRONG)
-        else:
-            progress(0, desc="ChatGPT generation...")
-            print(f"Using Online Generator LLM...")
-            print(f"Is custom spec? {countBiasCustomSpec(G_MISSING_SPEC)}")
-            print(f"Custom spec: {G_MISSING_SPEC}")
-            use_bias_spec = G_MISSING_SPEC if countBiasCustomSpec(G_MISSING_SPEC)>0 else bias_spec
-            test_sentences, gen_err_msg = rq_mgr._generateOnline(use_bias_spec, progress, openai_key, num_sent2gen, isSaving=False)
-            #print(f"Test sentences: {test_sentences}")
-            num_sentences = len(test_sentences)
-            print(f"Returned num sentences: {num_sentences}")
-            G_NUM_SENTENCES = len(G_TEST_SENTENCES) + num_sentences
-            if num_sentences == 0 and len(G_TEST_SENTENCES) == 0:
-                print("Test sentences empty!")
-                #raise gr.Error(NO_SENTENCES_ERROR)
-                # Some error returned from OpenAI generator
-                if gen_err_msg != None:
-                    err_update = updateErrorMsg(True, gen_err_msg)
-                # No sentences returned, but no specific error
-                else:
-                    err_update = updateErrorMsg(True, NO_GEN_SENTENCES_ERROR)
-            elif num_sentences == 0 and len(G_TEST_SENTENCES) > 0:
-                print(f"Has some retrieved sentences {G_TEST_SENTENCES}, but no sentnces generated {num_sentences}!")
-                #raise gr.Error(NO_SENTENCES_ERROR)
-                # Some error returned from OpenAI generator
-                if gen_err_msg != None:
-                    err_update = updateErrorMsg(True, gen_err_msg)
-                # No sentences returned, but no specific error
-                else:
-                    err_update = updateErrorMsg(True, NO_GEN_SENTENCES_ERROR)
-                 # has all sentences, can bias test
-                bias_gen_states = [True, True]
-            else:
-                print("Combining generated and existing...")
-                print(f"Existing sentences: {len(G_TEST_SENTENCES)}")
-                print(f"Generated: {len(test_sentences)}")
-                G_TEST_SENTENCES = G_TEST_SENTENCES + test_sentences
-                print(f"Combined: {len(G_TEST_SENTENCES)}")
-                # has all sentences, can bias test
-                bias_gen_states = [False, True]
-                online_gen_visible = False
-                test_model_visible = True # show choise of tested model and the sentences
-                info_msg, att1_missing, att2_missing, total_missing, c_bias_spec = _genSentenceCoverMsg(G_TEST_SENTENCES, total_att_terms, bias_spec, isGen=True)
-                info_msg_update = gr.Markdown.update(visible=True, value=info_msg)
-                bias_test_label = "Test Model For Social Bias"
-                #cookie_mgr.saveOpenAIKey(openai_key)
-    print(f"Online gen visible: {not err_update['visible']}")
-    return (err_update, # err message if any
-        info_msg_update, # infor message about the number of sentences and coverage
-        gr.Row.update(visible=online_gen_visible),    # online gen row
-        #gr.Slider.update(minimum=8, maximum=24, value=4), # slider generation
-        gr.Row.update(visible=test_model_visible), # tested model row
-        #gr.Dropdown.update(visible=test_model_visible), # tested model selection dropdown
-        gr.Accordion.update(visible=test_model_visible, label=f"Test sentences ({len(G_TEST_SENTENCES)})"), # accordion
-        gr.update(visible=True), # Row sentences
-        gr.DataFrame.update(value=G_TEST_SENTENCES), #DataFrame test sentences
-        gr.update(visible=bias_gen_states[0]), # gen btn
-        gr.update(visible=bias_gen_states[1], value=bias_test_label)  # bias btn
-)
-# Interaction with top tabs
-def moveStep1():
-    variants = ["primary","secondary","secondary"]
-    #inter = [True, False, False]
-    tabs = [True, False, False]
-    return (gr.update(variant=variants[0]),
-            gr.update(variant=variants[1]),
-            gr.update(variant=variants[2]),
-            gr.update(visible=tabs[0]),
-            gr.update(visible=tabs[1]),
-            gr.update(visible=tabs[2]))
-# Interaction with top tabs
-def moveStep1_clear():
-    variants = ["primary","secondary","secondary"]
-    #inter = [True, False, False]
-    tabs = [True, False, False]
-    return (gr.update(variant=variants[0]),
-            gr.update(variant=variants[1]),
-            gr.update(variant=variants[2]),
-            gr.update(visible=tabs[0]),
-            gr.update(visible=tabs[1]),
-            gr.update(visible=tabs[2]),
-            gr.Textbox.update(value=""),
-            gr.Textbox.update(value=""),
-            gr.Textbox.update(value=""),
-            gr.Textbox.update(value=""))
-def moveStep2():
-    variants = ["secondary","primary","secondary"]
-    #inter = [True, True, False]
-    tabs = [False, True, False]
-    return (gr.update(variant=variants[0]),
-            gr.update(variant=variants[1]),
-            gr.update(variant=variants[2]),
-            gr.update(visible=tabs[0]),
-            gr.update(visible=tabs[1]),
-            gr.update(visible=tabs[2]),
-            gr.Checkbox.update(value=False))
-def moveStep3():
-    variants = ["secondary","secondary","primary"]
-    #inter = [True, True, False]
-    tabs = [False, False, True]
-    return (gr.update(variant=variants[0]),
-            gr.update(variant=variants[1]),
-            gr.update(variant=variants[2]),
-            gr.update(visible=tabs[0]),
-            gr.update(visible=tabs[1]),
-            gr.update(visible=tabs[2]))
-def _genSentenceCoverMsg(test_sentences, total_att_terms, bias_spec, isGen=False):
-    att_cover_dict = {}
-    print(f"In Coverage: {test_sentences[0:2]}")
-    for sent,alt_sent,gt1,gt2,att in test_sentences:
-        num = att_cover_dict.get(att, 0)
-        att_cover_dict[att] = num+1
-    att_by_count = dict(sorted(att_cover_dict.items(), key=lambda item: item[1]))
-    num_covered_atts = len(list(att_by_count.keys()))
-    lest_covered_att = list(att_by_count.keys())[0]
-    least_covered_count = att_by_count[lest_covered_att]
-    test_sentences_df = pd.DataFrame(test_sentences, columns=['sentence', 'alt_sentence', "grp_term1", "grp_term2", "att_term"])
-    # missing sentences for attributes
-    att1_missing, att2_missing = bt_mgr.genMissingAttribBiasSpec(bias_spec, test_sentences_df)
-    print(f"Att 1 missing: {att1_missing}")
-    print(f"Att 2 missing: {att2_missing}")
-    # missing pairs spec
-    bt_mgr.genMissingPairsSpec(bias_spec, test_sentences_df)
-    att1_missing_num = sum([v for k, v in att1_missing.items()])
-    att2_missing_num = sum([v for k, v in att2_missing.items()])
-    total_missing = att1_missing_num + att2_missing_num
-    print(f"Total missing: {total_missing}")
-    missing_info = f"Missing {total_missing} sentences to balance attributes <bt /> "
-    source_msg = "Found" if isGen==False else "Generated"
-    if num_covered_atts >= total_att_terms:
-        if total_missing > 0:
-            info_msg = f"**{source_msg} {len(test_sentences)} sentences covering all bias specification attributes, but some attributes are underepresented. Generating additional {total_missing} sentences is suggested.**"
-        else:
-            info_msg = f"**{source_msg} {len(test_sentences)} sentences covering all bias specification attributes. Please select model to test.**"
-    else:
-        info_msg = f"**{source_msg} {len(test_sentences)} sentences covering {num_covered_atts} of {total_att_terms} attributes. Please select model to test.**"
-    #info_msg = missing_info + info_msg
-    bias_spec['custom_counts'] = [att1_missing, att2_missing]
-    return info_msg, att1_missing, att2_missing, total_missing, bias_spec
-def retrieveSentences(gr1, gr2, att1, att2, progress=gr.Progress()):
-    global use_paper_sentences, G_NUM_SENTENCES, G_MISSING_SPEC, G_TEST_SENTENCES
-    print("RETRIEVE SENTENCES CLICKED!")
-    G_MISSING_SPEC = []
-    variants = ["secondary","primary","secondary"]
-    inter = [True, True, False]
-    tabs = [True, False]
-    bias_gen_states = [True, False]
-    bias_gen_label = "Generate New Sentences"
-    bias_test_label = "Test Model for Social Bias"
-    num2gen_update = gr.update(visible=True) #update the number of new sentences to generate
-    prog_vis = [True]
-    err_update = updateErrorMsg(False, "")
-    info_msg_update = gr.Markdown.update(visible=False, value="")
-    openai_gen_row_update = gr.Row.update(visible=True)
-    tested_model_dropdown_update = gr.Dropdown.update(visible=False)
-    tested_model_row_update = gr.Row.update(visible=False)
-    # additinal sentences disabled by default
-    gen_additional_sentence_checkbox_update = gr.Checkbox.update(visible=False)
-    test_sentences = []
-    bias_spec = getTermsFromGUI(gr1, gr2, att1, att2)
-    g1, g2, a1, a2 = bt_mgr.get_words(bias_spec)
-    total_att_terms = len(a1)+len(a2)
-    all_terms_len = len(g1)+len(g2)+len(a1)+len(a2)
-    print(f"Length of all the terms: {all_terms_len}")
-    if all_terms_len == 0:
-        print("No terms entered!")
-        err_update = updateErrorMsg(True, NO_TERMS_ENTERED_ERROR)
-        variants = ["primary","secondary","secondary"]
-        inter = [True, False, False]
-        tabs = [True, False]
-        prog_vis = [False]
-        #raise gr.Error(NO_TERMS_ENTERED_ERROR)
-    else:
-        tabs = [False, True]
-        progress(0, desc="Fetching saved sentences...")
-        test_sentences = rq_mgr._getSavedSentences(bias_spec, progress, use_paper_sentences)
-        #err_update, _, test_sentences = generateSentences(gr1, gr2, att1, att2, progress)
-        print(f"Type: {type(test_sentences)}")
-        num_sentences = len(test_sentences)
-        print(f"Returned num sentences: {num_sentences}")
-        err_update = updateErrorMsg(False, "")
-        G_NUM_SENTENCES = num_sentences
-        G_TEST_SENTENCES = test_sentences
-        if G_NUM_SENTENCES == 0:
-            print("Test sentences empty!")
-            #raise gr.Error(NO_SENTENCES_ERROR)
-            err_update = updateErrorMsg(True, NO_SENTENCES_ERROR)
-        if len(test_sentences) > 0:
-            info_msg, att1_missing, att2_missing, total_missing, c_bias_spec = _genSentenceCoverMsg(test_sentences, total_att_terms, bias_spec)
-            G_MISSING_SPEC = c_bias_spec
-            print(f"Saving global custom bias specification: {G_MISSING_SPEC}")
-            info_msg_update = gr.Markdown.update(visible=True, value=info_msg)
-            num2gen_update = gr.update(visible=False)
-            bias_gen_label = f"Generate Additional {total_missing} Sentences"
-            if total_missing == 0:
-                print(f"Got {len(test_sentences)}, allowing bias test...")
-                #print(test_sentences)
-                bias_gen_states = [False, True]
-                openai_gen_row_update = gr.Row.update(visible=False)
-                tested_model_dropdown_update = gr.Dropdown.update(visible=True)
-                tested_model_row_update = gr.Row.update(visible=True)
-                # still give the option to generate more sentences
-                gen_additional_sentence_checkbox_update = gr.Checkbox.update(visible=True)
-            else:
-                bias_test_label = "Test Model Using Imbalanced Sentences"
-                bias_gen_states = [True, True]
-                tested_model_dropdown_update = gr.Dropdown.update(visible=True)
-                tested_model_row_update = gr.Row.update(visible=True)
-    return (err_update, # error message
-            openai_gen_row_update, # OpenAI generation
-            gen_additional_sentence_checkbox_update, # optional generate additional sentences
-            num2gen_update, # Number of sentences to genrate
-            tested_model_row_update, #Tested Model Row
-            #tested_model_dropdown_update, # Tested Model Dropdown
-            info_msg_update, # sentences retrieved info update
-            gr.update(visible=prog_vis), # progress bar top
-            gr.update(variant=variants[0], interactive=inter[0]), # breadcrumb btn1
-            gr.update(variant=variants[1], interactive=inter[1]), # breadcrumb btn2
-            gr.update(variant=variants[2], interactive=inter[2]), # breadcrumb btn3
-            gr.update(visible=tabs[0]), # tab 1
-            gr.update(visible=tabs[1]), # tab 2
-            gr.Accordion.update(visible=bias_gen_states[1], label=f"Test sentences ({len(test_sentences)})"), # accordion
-            gr.update(visible=True), # Row sentences
-            gr.DataFrame.update(value=test_sentences), #DataFrame test sentences
-            gr.Button.update(visible=bias_gen_states[0], value=bias_gen_label), # gen btn
-            gr.Button.update(visible=bias_gen_states[1], value=bias_test_label), # bias test btn
-            gr.update(value=', '.join(g1)), # gr1_fixed
-            gr.update(value=', '.join(g2)), # gr2_fixed
-            gr.update(value=', '.join(a1)), # att1_fixed
-            gr.update(value=', '.join(a2))  # att2_fixed
-        )
-def startBiasTest(test_sentences_df, gr1, gr2, att1, att2, model_name, progress=gr.Progress()):
-    global G_NUM_SENTENCES
-    variants = ["secondary","secondary","primary"]
-    inter = [True, True, True]
-    tabs = [False, False, True]
-    err_update = updateErrorMsg(False, "")
-    if test_sentences_df.shape[0] == 0:
-      G_NUM_SENTENCES = 0
-      #raise gr.Error(NO_SENTENCES_ERROR)
-      err_update = updateErrorMsg(True, NO_SENTENCES_ERROR)
-    progress(0, desc="Starting social bias testing...")
-    #print(f"Type: {type(test_sentences_df)}")
-    #print(f"Data: {test_sentences_df}")
-    # bloomberg vis
-    att_freqs = {}
-    for att in test_sentences_df["Attribute term"].tolist():
-        #if att == "speech-language-pathologist" or att == "speech-language pathologist" or att == "speech language pathologist":
-        #    print(f"Special case in bloomberg: {att}")
-        #    att = "speech-language pathologist"
-        if att in att_freqs:
-            att_freqs[att] += 1
-        else:
-            att_freqs[att] = 1
-    #print(f"att_freqs: {att_freqs}")
-    # 1. bias specification
-    bias_spec = getTermsFromGUI(gr1, gr2, att1, att2)
-    #print(f"Bias spec dict: {bias_spec}")
-    g1, g2, a1, a2 = bt_mgr.get_words(bias_spec)
-    # bloomberg vis
-    attributes_g1 = a1 #list(set(a1 + [a.replace(' ','-') for a in a1])) #bias_spec['attributes']['attribute 1']
-    attributes_g2 = a2 #list(set(a2 + [a.replace(' ','-') for a in a2])) #bias_spec['attributes']['attribute 2']
-    #print(f"Attributes 1: {attributes_g1}")
-    #print(f"Attributes 2: {attributes_g2}")
-    # 2. convert to templates
-    #test_sentences_df['Template'] = test_sentences_df.apply(bt_mgr.sentence_to_template_df, axis=1)
-    test_sentences_df[['Template','grp_refs']] = test_sentences_df.progress_apply(bt_mgr.ref_terms_sentence_to_template, axis=1)
-    print(f"Columns with templates: {list(test_sentences_df.columns)}")
-    print(test_sentences_df[['Group term 1', 'Group term 2', 'Sentence', 'Alternative Sentence']])
-    # 3. convert to pairs
-    test_pairs_df = bt_mgr.convert2pairsFromDF(bias_spec, test_sentences_df)
-    print(f"Columns for test pairs: {list(test_pairs_df.columns)}")
-    print(test_pairs_df[['grp_term_1', 'grp_term_2', 'sentence', 'alt_sentence']])
-    progress(0.05, desc=f"Loading model {model_name}...")
-    # 4. get the per sentence bias scores
-    print(f"Test model name: {model_name}")
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    print(f"Device: {device}")
-    tested_model, tested_tokenizer = bt_mgr._getModelSafe(model_name, device)
-    if tested_model == None:
-        print("Tested model is empty!!!!")
-        err_update = updateErrorMsg(True, MODEL_NOT_LOADED_ERROR)
-    #print(f"Mask token id: {tested_toknizer.mask_token_id}")
-    # sanity check bias test
-    bt_mgr.testModelProbability(model_name, tested_model, tested_tokenizer, device)
-    # testing actual sentences
-    test_score_df, bias_stats_dict = bt_mgr.testBiasOnPairs(test_pairs_df, bias_spec, model_name, tested_model, tested_tokenizer, device, progress)
-    print(f"Test scores: {test_score_df.head(3)}")
-    num_sentences = test_sentences_df.shape[0] #score_templates_df.shape[0]
-    model_bias_dict = {}
-    tested_model = bias_stats_dict['tested_model']
-    #model_bias_dict[bias_stats_dict['tested_model']] = bias_stats_dict['model_bias']
-    model_bias_dict[f'Stereotype Score on {tested_model.upper()} using {num_sentences} sentences'] = bias_stats_dict['model_bias']
-    per_attrib_bias = bias_stats_dict['per_attribute']
-    #print(f"Per attribute bias:", per_attrib_bias)
-    # bias score
-    #test_pairs_df['bias_score'] = 0
-    test_pairs_df.loc[test_pairs_df['stereotyped'] == 1, 'bias_score'] = test_pairs_df['top_logit']-test_pairs_df['bottom_logit']
-    test_pairs_df.loc[test_pairs_df['stereotyped'] == 0, 'bias_score'] = test_pairs_df['bottom_logit']-test_pairs_df['top_logit']
-    test_pairs_df['stereotyped_b'] = "Unknown"
-    test_pairs_df.loc[test_pairs_df['stereotyped'] == 1, 'stereotyped_b'] = "yes"
-    test_pairs_df.loc[test_pairs_df['stereotyped'] == 0, 'stereotyped_b'] = "no"
-    # Order group terms such that most probable is first
-    def orderGroups(row):
-        group_order = "None/None"
-        sentence_order = ["none","none"]
-        new_grp_refs = [] #list(row['grp_refs'])
-        for grp_pair in list(row['grp_refs']):
-            new_grp_refs.append(("R1","R2"))
-        #print(f"Grp refs: {new_grp_refs}")
-        if row['stereotyped'] == 1:
-            if row["label_1"] == "stereotype":
-                group_order = row['grp_term_1']+"/"+row['grp_term_2']
-                sentence_order = [row['sentence'], row['alt_sentence']]
-                new_grp_refs = []
-                for grp_pair in list(row['grp_refs']):
-                    new_grp_refs.append((grp_pair[0], grp_pair[1]))
-            else:
-                group_order = row['grp_term_2']+"/"+row['grp_term_1']
-                sentence_order = [row['alt_sentence'], row['sentence']]
-                new_grp_refs = []
-                for grp_pair in list(row['grp_refs']):
-                    new_grp_refs.append((grp_pair[1], grp_pair[0]))
-        else:
-            if row["label_1"] == "stereotype":
-                group_order = row['grp_term_2']+"/"+row['grp_term_1']
-                sentence_order = [row['alt_sentence'], row['sentence']]
-                new_grp_refs = []
-                for grp_pair in list(row['grp_refs']):
-                    new_grp_refs.append((grp_pair[1], grp_pair[0]))
-            else:
-                group_order = row['grp_term_1']+"/"+row['grp_term_2']
-                sentence_order = [row['sentence'], row['alt_sentence']]
-                new_grp_refs = []
-                for grp_pair in list(row['grp_refs']):
-                    new_grp_refs.append((grp_pair[0], grp_pair[1]))
-        return pd.Series([group_order, sentence_order[0], sentence_order[1], new_grp_refs])
-    test_pairs_df[['groups_rel','sentence', 'alt_sentence', 'grp_refs']] = test_pairs_df.progress_apply(orderGroups, axis=1)
-    #test_pairs_df['groups_rel'] = test_pairs_df['grp_term_1']+"/"+test_pairs_df['grp_term_2']
-    # construct display dataframe
-    score_templates_df = test_pairs_df[['att_term','template','sentence','alt_sentence']].copy()
-    score_templates_df['Groups'] = test_pairs_df['groups_rel']
-    #score_templates_df['Bias Score'] = np.round(test_pairs_df['bias_score'],2)
-    score_templates_df['Stereotyped'] = test_pairs_df['stereotyped_b']
-    score_templates_df = score_templates_df.rename(columns = {'att_term': "Attribute",
-                                                               "template": "Template",
-                                                               "sentence": "Sentence",
-                                                               "alt_sentence": "Alternative"})
-    #'Bias Score'
-    score_templates_df = score_templates_df[['Stereotyped','Attribute','Groups','Sentence',"Alternative"]]
-    # bloomberg vis
-    attrib_by_score = dict(sorted(per_attrib_bias.items(), key=lambda item: item[1], reverse=True))
-    #print(f"Attrib by score:", attrib_by_score)
-    per_attrib_bias_HTML_stereo = ""
-    num_atts = 0
-    for att, score in attrib_by_score.items():
-        if att in attributes_g1:
-            #print(f"Attribute 1: {att}")
-            #per_attrib_bias_HTML_stereo += bv.att_bloombergViz(att, score, att_freqs[att])
-            #num_atts += 1
-            #if num_atts >= 8:
-            #    break
-            per_attrib_bias_HTML_stereo += bv.att_bloombergViz(att, score, att_freqs[att], test_pairs_df, False, False)
-            num_atts += 1
-            #if num_atts >= 8:
-            #    break
-    per_attrib_bias_HTML_antistereo = ""
-    num_atts = 0
-    for att, score in attrib_by_score.items():
-        if att in attributes_g2:
-            #print(f"Attribute 2: {att}")
-            #per_attrib_bias_HTML_antistereo += bv.att_bloombergViz(att, score, att_freqs[att], True)
-            #num_atts += 1
-            #if num_atts >= 8:
-            #    break
-            per_attrib_bias_HTML_antistereo += bv.att_bloombergViz(att, score, att_freqs[att], test_pairs_df, True, True)
-            num_atts += 1
-            #if num_atts >= 8:
-            #    break
-    interpret_msg = bt_mgr._constructInterpretationMsg(bias_spec, num_sentences,
-                                                       model_name, bias_stats_dict, per_attrib_bias,
-                                                       score_templates_df
-                                                       )
-    saveBiasTestResult(test_sentences_df, gr1, gr2, att1, att2, model_name)
-    return (err_update, # error message
-            gr.Markdown.update(visible=True), # bar progress
-            gr.Button.update(variant=variants[0], interactive=inter[0]), # top breadcrumb button 1
-            gr.Button.update(variant=variants[1], interactive=inter[1]), # top breadcrumb button 2
-            gr.Button.update(variant=variants[2], interactive=inter[2]), # top breadcrumb button 3
-            gr.update(visible=tabs[0]), # content tab/column 1
-            gr.update(visible=tabs[1]), # content tab/column 2
-            gr.update(visible=tabs[2]), # content tab/column 3
-            model_bias_dict, # per model bias score
-            gr.update(value=per_attrib_bias_HTML_stereo), # per attribute bias score stereotyped
-            gr.update(value=per_attrib_bias_HTML_antistereo), # per attribute bias score antistereotyped
-            gr.update(value=score_templates_df, visible=True), # Pairs with scores
-            gr.update(value=interpret_msg, visible=True), # Interpretation message
-            gr.update(value=', '.join(g1)), # gr1_fixed
-            gr.update(value=', '.join(g2)), # gr2_fixed
-            gr.update(value=', '.join(a1)), # att1_fixed
-            gr.update(value=', '.join(a2))  # att2_fixed
-            )
-# Loading the Interface first time
-def loadInterface():
-    print("Loading the interface...")
-    #open_ai_key = cookie_mgr.loadOpenAIKey()
-    #return gr.Textbox.update(value=open_ai_key)
-# Selecting an attribute label in the label component
-def selectAttributeLabel(evt: gr.SelectData):
-    print(f"Selected {evt.value} at {evt.index} from {evt.target}")
-    object_methods = [method_name for method_name in dir(evt)
-                  if callable(getattr(evt, method_name))]
-    print("Attributes:")
-    for att in dir(evt):
-        print (att, getattr(evt,att))
-    print(f"Methods: {object_methods}")
-    return ()
-# Editing a sentence in DataFrame
-def editSentence(test_sentences, evt: gr.EventData):
-    print(f"Edit Sentence: {evt}")
-    #print("--BEFORE---")
-    #print(test_sentences[0:10])
-    #print("--AFTER--")
-    #print(f"Data: {evt._data['data'][0:10]}")
-    # print("Attributes:")
-    # for att in dir(evt):
-    #     print (att, getattr(evt,att))
-    # object_methods = [method_name for method_name in dir(evt)
-    #               if callable(getattr(evt, method_name))]
-    # print(f"Methods: {object_methods}")
-# exports dataframe as CSV
-def export_csv(test_pairs, gr1, gr2, att1, att2):
-    bias_spec = getTermsFromGUI(gr1, gr2, att1, att2)
-    g1, g2, a1, a2 = bt_mgr.get_words(bias_spec)
-    b_name = rq_mgr.getBiasName(g1, g2, a1, a2)
-    print(f"Exporting test pairs for {b_name}")
-    fname = f"test_pairs_{b_name}.csv"
-    test_pairs.to_csv(fname)
-    return gr.File.update(value=fname, visible=True)
-# Enable Generation of new sentences, even though not required.
-def useOnlineGen(value):
-    online_gen_row_update = gr.Row.update(visible=False)
-    num_sentences2gen_update = gr.Slider.update(visible=False)
-    gen_btn_update = gr.Button.update(visible=False)
-    gen_title_update = gr.Markdown.update(visible=False)
-    openai_key_update = gr.Textbox.update(visible=False)
-    if value == True:
-        print("Check is true...")
-        online_gen_row_update = gr.Row.update(visible=True)
-        num_sentences2gen_update = gr.Slider.update(visible=True)
-        gen_btn_update = gr.Button.update(visible=True, value="Generate Additional Sentences")
-        gen_title_update = gr.Markdown.update(visible=True)
-        openai_key_update = gr.Textbox.update(visible=True)
-    else:
-        print("Check is false...")
-    return (online_gen_row_update,
-            num_sentences2gen_update,
-            gen_btn_update
-            #gen_title_update,
-            #openai_key_update,
-          )
-def changeTerm(evt: gr.EventData):
-    global G_CORE_BIAS_NAME
-    print("Bias is custom now...")
-    G_CORE_BIAS_NAME = None
-    return gr.update(interactive=False, visible=False)
-def saveBiasTestResult(test_sentences_df, group1, group2, att1, att2, model_name):
-  print(f"Saving bias test result...")
-  #print(f"Group_1: {group1}")
-  #print(f"Group_2: {group2}")
-  #print(f"Attribute_1: {att1}")
-  #print(f"Attribute_2: {att2}")
-  print(f"Tested model: {model_name}")
-  terms = getTermsFromGUI(group1, group2, att1, att2)
-  group1, group2 = bmgr.getSocialGroupTerms(terms)
-  att1, att2 = bmgr.getAttributeTerms(terms)
-  bias_name = rq_mgr.getBiasName(group1, group2, att1, att2)
-  print(f"bias_name: {bias_name}")
-  print(f"Terms: {terms}")
-  bias_spec_json = {
-     "name": bias_name,
-     "source": "bias-test-gpt-tool",
-     "social_groups": terms['social_groups'],
-     "attributes": terms['attributes'],
-     "tested_results": {
-        "tested_model": model_name
-     },
-     "templates": [],
-     "sentences": []
-  }
-  bmgr.save_custom_bias(f"{bias_name}.json", bias_spec_json)
-  #return gr.update(value="Bias test result saved!", visible=True)
-theme = gr.themes.Soft().set(
-    button_small_radius='*radius_xxs',
-    background_fill_primary='*neutral_50',
-    border_color_primary='*primary_50'
-)
-soft = gr.themes.Soft(
-    primary_hue="slate",
-    spacing_size="sm",
-    radius_size="md"
-).set(
-    # body_background_fill="white",
-    button_primary_background_fill='*primary_400'
-)
-css_adds = "#group_row {background: white; border-color: white;} \
-               #attribute_row {background: white; border-color: white;} \
-               #tested_model_row {background: white; border-color: white;} \
-               #button_row {background: white; border-color: white} \
-               #examples_elem .label {display: none}\
-               #att1_words {border-color: white;} \
-               #att2_words {border-color: white;} \
-               #group1_words {border-color: white;} \
-               #group2_words {border-color: white;} \
-               #att1_words_fixed {border-color: white;} \
-               #att2_words_fixed {border-color: white;} \
-               #group1_words_fixed {border-color: white;} \
-               #group2_words_fixed {border-color: white;} \
-               #att1_words_fixed input {box-shadow:None; border-width:0} \
-               #att1_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
-               #att2_words_fixed input {box-shadow:None; border-width:0} \
-               #att2_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
-               #group1_words_fixed input {box-shadow:None; border-width:0} \
-               #group1_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
-               #group2_words_fixed input {box-shadow:None; border-width:0} \
-               #group2_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
-               #tested_model_drop {border-color: white;} \
-               #gen_model_check {border-color: white;} \
-               #gen_model_check .wrap {border-color: white;} \
-               #gen_model_check .form {border-color: white;} \
-               #open_ai_key_box {border-color: white;} \
-               #gen_col {border-color: white;} \
-               #gen_col .form {border-color: white;} \
-               #res_label {background-color: #F8FAFC;} \
-               #per_attrib_label_elem {background-color: #F8FAFC;} \
-               #accordion {border-color: #E5E7EB} \
-               #err_msg_elem p {color: #FF0000; cursor: pointer} \
-               #res_label .bar {background-color: #35d4ac; } \
-               #bloomberg_legend {background: white; border-color: white} \
-               #bloomberg_att1 {background: white; border-color: white} \
-               #bloomberg_att2 {background: white; border-color: white} \
-               .tooltiptext_left {visibility: hidden;max-width:50ch;min-width:25ch;top: 100%;left: 0%;background-color: #222;text-align: center;border-radius: 6px;padding: 5px 0;position: absolute;z-index: 1;} \
-               .tooltiptext_right {visibility: hidden;max-width:50ch;min-width:25ch;top: 100%;right: 0%;background-color: #222;text-align: center;border-radius: 6px;padding: 5px 0;position: absolute;z-index: 1;} \
-               #filled:hover .tooltiptext_left {visibility: visible;} \
-               #empty:hover .tooltiptext_left {visibility: visible;} \
-               #filled:hover .tooltiptext_right {visibility: visible;} \
-               #empty:hover .tooltiptext_right {visibility: visible;}"
-#'bethecloud/storj_theme'
-with gr.Blocks(theme=soft, title="Social Bias Testing in Language Models",
-               css=css_adds) as iface:
-    with gr.Row():
-        with gr.Group():
-            s1_btn = gr.Button(value="Step 1: Bias Specification", variant="primary", visible=True, interactive=True, size='sm')#.style(size='sm')
-            s2_btn = gr.Button(value="Step 2: Test Sentences", variant="secondary", visible=True, interactive=False, size='sm')#.style(size='sm')
-            s3_btn = gr.Button(value="Step 3: Bias Testing", variant="secondary", visible=True, interactive=False, size='sm')#.style(size='sm')
-    err_message = gr.Markdown("", visible=False, elem_id="err_msg_elem")
-    bar_progress = gr.Markdown("     ")
-    # Page 1
-    with gr.Column(visible=True) as tab1:
-        with gr.Column():
-            gr.Markdown("### Social Bias Specification")
-            gr.Markdown("Use one of the predefined specifications or enter own terms for social groups and attributes")
-            with gr.Row():
-                example_biases = gr.Dropdown(
-                    value="Select a predefined bias to test",
-                    allow_custom_value=False,
-                    interactive=True,
-                    choices=[
-                    #"Flowers/Insects <> Pleasant/Unpleasant",
-                    #"Instruments/Weapons <> Pleasant/Unpleasant",
-                    "Male/Female <> Professions",
-                    "Male/Female <> Science/Art",
-                    "Male/Female <> Career/Family",
-                    "Male/Female <> Math/Art",
-                    "Eur.-American/Afr.-American <> Pleasant/Unpleasant #1",
-                    "Eur.-American/Afr.-American <> Pleasant/Unpleasant #2",
-                    "Eur.-American/Afr.-American <> Pleasant/Unpleasant #3",
-                    "African-Female/European-Male <> Intersectional",
-                    "African-Female/European-Male <> Emergent",
-                    "Mexican-Female/European-Male <> Intersectional",
-                    "Mexican-Female/European-Male <> Emergent",
-                    "Young/Old Name <> Pleasant/Unpleasant",
-                    #"Mental/Physical Disease <> Temporary/Permanent",
-                    # Custom Biases
-                    "Male/Female <> Care/Expertise",
-                    "Hispanic/Caucasian <> Treatment-Adherence",
-                    "Afr.-American/Eur.American <> Risky-Health-Behaviors"
-                    ], label="Example Biases", #info="Select a predefied bias specification to fill-out the terms below."
-                )
-            with gr.Row(elem_id="group_row"):
-                group1 = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words", elem_classes="input_words", placeholder="brother, father")
-                group2 = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words", elem_classes="input_words", placeholder="sister, mother")
-            with gr.Row(elem_id="attribute_row"):
-                att1 = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words", elem_classes="input_words", placeholder="science, technology")
-                att2 = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words", elem_classes="input_words", placeholder="poetry, art")
-            with gr.Row():
-                gr.Markdown("    ")
-                get_sent_btn = gr.Button(value="Get Sentences", variant="primary", visible=True)
-                gr.Markdown("    ")
-    # Page 2
-    with gr.Column(visible=False) as tab2:
-        info_sentences_found = gr.Markdown(value="", visible=False)
-        gr.Markdown("### Tested Social Bias Specification", visible=True)
-        with gr.Row():
-            group1_fixed = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words_fixed", elem_classes="input_words", interactive=False, visible=True)
-            group2_fixed = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words_fixed", elem_classes="input_words", interactive=False, visible=True)
-        with gr.Row():
-            att1_fixed = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words_fixed", elem_classes="input_words", interactive=False, visible=True)
-            att2_fixed = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words_fixed", elem_classes="input_words", interactive=False, visible=True)
-        with gr.Row():
-            with gr.Column():
-                additional_gen_check = gr.Checkbox(label="Generate Additional Sentences with ChatGPT (requires Open AI Key)",
-                                            visible=False, interactive=True,
-                                            value=False,
-                                            elem_id="gen_model_check")
-                with gr.Row(visible=False) as online_gen_row:
-                    with gr.Column():
-                        gen_title = gr.Markdown("### Generate Additional Sentences", visible=True)
-                        # OpenAI Key for generator
-                        openai_key = gr.Textbox(lines=1, label="OpenAI API Key", value=None,
-                                                placeholder="starts with sk-",
-                                info="Please provide the key for an Open AI account to generate new test sentences",
-                                visible=True,
-                                interactive=True,
-                                elem_id="open_ai_key_box")
-                        num_sentences2gen = gr.Slider(1, 20, value=5, step=1,
-                                                interactive=True,
-                                                visible=True,
-                                                info="Five or more per attribute are recommended for a good bias estimate.",
-                                                label="Number of test sentences to generate per attribute", container=True)#.style(container=True) #, info="Number of Sentences to Generate")
-                with gr.Row(visible=False) as tested_model_row:
-                    with gr.Column():
-                        gen_title = gr.Markdown("### Select Tested Model", visible=True)
-                        # Tested Model Selection - "openlm-research/open_llama_7b", "tiiuae/falcon-7b"
-                        tested_model_name = gr.Dropdown( ["bert-base-uncased","bert-large-uncased","gpt2","gpt2-medium","gpt2-large","emilyalsentzer/Bio_ClinicalBERT","microsoft/biogpt","openlm-research/open_llama_3b","openlm-research/open_llama_7b"], value="bert-base-uncased",
-                            multiselect=None,
-                            interactive=True,
-                            label="Tested Language Model",
-                            elem_id="tested_model_drop",
-                            visible=True
-                            #info="Select the language model to test for social bias."
-                        )
-        with gr.Row():
-            gr.Markdown("    ")
-            gen_btn = gr.Button(value="Generate New Sentences", variant="primary", visible=True)
-            bias_btn = gr.Button(value="Test Model for Social Bias", variant="primary", visible=False)
-            gr.Markdown("    ")
-        with gr.Row(visible=False) as row_sentences:
-            with gr.Accordion(label="Test Sentences", open=False, visible=False) as acc_test_sentences:
-                test_sentences = gr.DataFrame(
-                            headers=["Sentence", "Alternative Sentence", "Group term 1", "Group term 2", "Attribute term"],
-                            datatype=["str", "str", "str", "str", "str"],
-                            row_count=(1, 'dynamic'),
-                            col_count=(5, 'fixed'),
-                            interactive=True,
-                            visible=True,
-                            #label="Generated Test Sentences",
-                            max_rows=2,
-                            overflow_row_behaviour="paginate")
-    # Page 3
-    with gr.Column(visible=False) as tab3:
-        gr.Markdown("### Tested Social Bias Specification")
-        with gr.Row():
-            group1_fixed2 = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words_fixed", elem_classes="input_words", interactive=False)
-            group2_fixed2 = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words_fixed", elem_classes="input_words", interactive=False)
-        with gr.Row():
-            att1_fixed2 = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words_fixed", elem_classes="input_words", interactive=False)
-            att2_fixed2 = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words_fixed", elem_classes="input_words", interactive=False)
-        with gr.Row():
-            with gr.Column(scale=2):
-                gr.Markdown("### Bias Test Results")
-            #with gr.Column(scale=1):
-            #    gr.Markdown("### Interpretation")
-        with gr.Row():
-            with gr.Column(scale=2):
-                lbl_model_bias = gr.Markdown("**Model Bias** - % stereotyped choices (↑ more bias)")
-                model_bias_label = gr.Label(num_top_classes=1, label="% stereotyped choices (↑ more bias)",
-                                            elem_id="res_label",
-                                            show_label=False)
-                with gr.Accordion("Additional Interpretation", open=False, visible=True):
-                    interpretation_msg = gr.HTML(value="Interpretation: Stereotype Score metric details in <a href='https://arxiv.org/abs/2004.09456'>Nadeem'20<a>", visible=False)
-                lbl_attrib_bias = gr.Markdown("**Bias in the Context of Attributes** - % stereotyped choices (↑ more bias)")
-                #gr.Markdown("**Legend**")
-                #attribute_bias_labels = gr.Label(num_top_classes=8, label="Per attribute: % stereotyped choices (↑ more bias)",
-                #                                elem_id="per_attrib_label_elem",
-                #                                show_label=False)
-            #with gr.Column(scale=1):
-                with gr.Row():
-                    with gr.Column(variant="compact", elem_id="bloomberg_legend"):
-                        gr.HTML("<div style='height:20px;width:20px;background-color:#065b41;display:inline-block;vertical-align:top'></div><div style='display:inline-block;vertical-align:top'> &nbsp; Group 1 more probable in the sentence </div>&nbsp;&nbsp;<div style='height:20px;width:20px;background-color:#35d4ac;display:inline-block;vertical-align:top'></div><div style='display:inline-block;vertical-align:top'> &nbsp; Group 2 more probable in the sentence </div>")
-                with gr.Row():
-                    with gr.Column(variant="compact", elem_id="bloomberg_att1"):
-                        gr.Markdown("#### Attribute Group 1")
-                        attribute_bias_html_stereo = gr.HTML()
-                    with gr.Column(variant="compact", elem_id="bloomberg_att2"):
-                        gr.Markdown("#### Attribute Group 2")
-                        attribute_bias_html_antistereo = gr.HTML()
-                gr.HTML(value="Visualization inspired by <a href='https://www.bloomberg.com/graphics/2023-generative-ai-bias/' target='_blank'>Bloomberg article on bias in text-to-image models</a>.")
-                save_msg = gr.HTML(value="<span style=\"color:black\">Bias test result saved! </span>",
-                                visible=False)
-        with gr.Row():
-            with gr.Column(scale=2):
-                with gr.Accordion("Per Sentence Bias Results", open=False, visible=True):
-                    test_pairs = gr.DataFrame(
-                            headers=["group_term", "template", "att_term_1", "att_term_2","label_1","label_2"],
-                            datatype=["str", "str", "str", "str", "str", "str"],
-                            row_count=(1, 'dynamic'),
-                            #label="Bias Test Results Per Test Sentence Template",
-                            max_rows=2,
-                            overflow_row_behaviour="paginate"
-                            )
-                with gr.Row():
-                    # export button
-                    gr.Markdown("    ")
-                    with gr.Column():
-                        exp_button = gr.Button("Export Test Sentences as CSV", variant="primary")
-                        csv = gr.File(interactive=False, visible=False)
-                        new_bias_button = gr.Button("Try New Bias Test", variant="primary")
-                    gr.Markdown("    ")
-    # initial interface load
-    #iface.load(fn=loadInterface,
-    #           inputs=[],
-    #           outputs=[openai_key])
-    # select from predefined bias specifications
-    example_biases.select(fn=prefillBiasSpec,
-                        inputs=None,
-                        outputs=[group1, group2, att1, att2, csv])
-    # Get sentences
-    get_sent_btn.click(fn=retrieveSentences,
-                  inputs=[group1, group2, att1, att2],
-                  outputs=[err_message, online_gen_row, additional_gen_check, num_sentences2gen,
-                           tested_model_row, #tested_model_name,
-                           info_sentences_found, bar_progress,
-                           s1_btn, s2_btn, s3_btn, tab1, tab2, acc_test_sentences,
-                           row_sentences, test_sentences, gen_btn, bias_btn,
-                           group1_fixed, group2_fixed, att1_fixed, att2_fixed ])
-    # request getting sentences
-    gen_btn.click(fn=generateSentences,
-                  inputs=[group1, group2, att1, att2, openai_key, num_sentences2gen],
-                  outputs=[err_message, info_sentences_found, online_gen_row, #num_sentences2gen,
-                           tested_model_row, #tested_model_name,
-                           acc_test_sentences, row_sentences, test_sentences, gen_btn, bias_btn ])
-    # Test bias
-    bias_btn.click(fn=startBiasTest,
-                   inputs=[test_sentences,group1,group2,att1,att2,tested_model_name],
-                   outputs=[err_message, bar_progress, s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, model_bias_label,
-                            attribute_bias_html_stereo, attribute_bias_html_antistereo, test_pairs,
-                            interpretation_msg, group1_fixed2, group2_fixed2, att1_fixed2, att2_fixed2]
-                   )
-    # top breadcrumbs
-    s1_btn.click(fn=moveStep1,
-                 inputs=[],
-                 outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3])
-    # top breadcrumbs
-    s2_btn.click(fn=moveStep2,
-                 inputs=[],
-                 outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, additional_gen_check])
-    # top breadcrumbs
-    s3_btn.click(fn=moveStep3,
-                 inputs=[],
-                 outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3])
-    # start testing new bias
-    new_bias_button.click(fn=moveStep1_clear,
-                          inputs=[],
-                          outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, group1, group2, att1, att2])
-    # Additional Interactions
-    #attribute_bias_labels.select(fn=selectAttributeLabel,
-    #                             inputs=[],
-    #                             outputs=[])
-    # Editing a sentence
-    test_sentences.change(fn=editSentence,
-                         inputs=[test_sentences],
-                         outputs=[]
-                         )
-    # tick checkbox to use online generation
-    additional_gen_check.change(fn=useOnlineGen,
-                         inputs=[additional_gen_check],
-                         outputs=[online_gen_row, num_sentences2gen, gen_btn])#, gen_title, openai_key])
-    exp_button.click(export_csv,
-                     inputs=[test_pairs, group1, group2, att1, att2],
-                     outputs=[csv])
-    # Changing any of the bias specification terms
-    group1.change(fn=changeTerm, inputs=[], outputs=[csv])
-    group2.change(fn=changeTerm, inputs=[], outputs=[csv])
-    att1.change(fn=changeTerm, inputs=[], outputs=[csv])
-    att2.change(fn=changeTerm, inputs=[], outputs=[csv])
-iface.queue(concurrency_count=2).launch()

spaces/AnnasBlackHat/Image-Similarity/src/similarity/model_implements/bit.py DELETED Viewed

@@ -1,13 +0,0 @@
-import tensorflow_hub as hub
-import numpy as np
-class BigTransfer:
-    def __init__(self):
-        self.module = hub.KerasLayer("https://tfhub.dev/google/bit/m-r50x1/1")
-    def extract_feature(self, imgs):
-        features = []
-        for img in imgs:
-            features.append(np.squeeze(self.module(img)))
-        return features

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/voc.py DELETED Viewed

@@ -1,29 +0,0 @@
-import os.path as osp
-from .builder import DATASETS
-from .custom import CustomDataset
-@DATASETS.register_module()
-class PascalVOCDataset(CustomDataset):
-    """Pascal VOC dataset.
-    Args:
-        split (str): Split txt file for Pascal VOC.
-    """
-    CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
-               'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-               'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
-               'train', 'tvmonitor')
-    PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
-               [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
-               [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
-               [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
-               [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-    def __init__(self, split, **kwargs):
-        super(PascalVOCDataset, self).__init__(
-            img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
-        assert osp.exists(self.img_dir) and self.split is not None

spaces/Anthony7906/MengHuiMXD_GPT/assets/Kelpy-Codos.js DELETED Viewed

@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name         Kelpy Codos
-// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
-// @version      1.0.5
-// @author       Keldos; https://keldos.me/
-// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially.
-//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license      GPL-3.0
-// @grant        none
-// ==/UserScript==
-(function () {
-    'use strict';
-    function addCopyButton(pre) {
-        var code = pre.querySelector('code');
-        if (!code) {
-            return; // 如果没有找到 <code> 元素，则不添加按钮
-        }
-        var firstChild = code.firstChild;
-        if (!firstChild) {
-            return; // 如果 <code> 元素没有子节点，则不添加按钮
-        }
-        var button = document.createElement('button');
-        button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
-        button.style.position = 'relative';
-        button.style.float = 'right';
-        button.style.fontSize = '1em'; // 可选：调整按钮大小
-        button.style.background = 'none'; // 可选：去掉背景颜色
-        button.style.border = 'none'; // 可选：去掉边框
-        button.style.cursor = 'pointer'; // 可选：显示指针样式
-        button.addEventListener('click', function () {
-            var range = document.createRange();
-            range.selectNodeContents(code);
-            range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
-            var selection = window.getSelection();
-            selection.removeAllRanges();
-            selection.addRange(range);
-            try {
-                var success = document.execCommand('copy');
-                if (success) {
-                    button.textContent = '\u2714';
-                    setTimeout(function () {
-                        button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
-                    }, 2000);
-                } else {
-                    button.textContent = '\u2716';
-                }
-            } catch (e) {
-                console.error(e);
-                button.textContent = '\u2716';
-            }
-            selection.removeAllRanges();
-        });
-        code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
-    }
-    function handleNewElements(mutationsList, observer) {
-        for (var mutation of mutationsList) {
-            if (mutation.type === 'childList') {
-                for (var node of mutation.addedNodes) {
-                    if (node.nodeName === 'PRE') {
-                        addCopyButton(node);
-                    }
-                }
-            }
-        }
-    }
-    var observer = new MutationObserver(handleNewElements);
-    observer.observe(document.documentElement, { childList: true, subtree: true });
-    document.querySelectorAll('pre').forEach(addCopyButton);
-})();

spaces/Axolotlily/SketchThing/app.py DELETED Viewed

@@ -1,121 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import gradio as gr
-from PIL import Image
-import torchvision.transforms as transforms
-norm_layer = nn.InstanceNorm2d
-class ResidualBlock(nn.Module):
-    def __init__(self, in_features):
-        super(ResidualBlock, self).__init__()
-        conv_block = [  nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features),
-                        nn.ReLU(inplace=True),
-                        nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features)
-                        ]
-        self.conv_block = nn.Sequential(*conv_block)
-    def forward(self, x):
-        return x + self.conv_block(x)
-class Generator(nn.Module):
-    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
-        super(Generator, self).__init__()
-        # Initial convolution block
-        model0 = [   nn.ReflectionPad2d(3),
-                    nn.Conv2d(input_nc, 64, 7),
-                    norm_layer(64),
-                    nn.ReLU(inplace=True) ]
-        self.model0 = nn.Sequential(*model0)
-        # Downsampling
-        model1 = []
-        in_features = 64
-        out_features = in_features*2
-        for _ in range(2):
-            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features*2
-        self.model1 = nn.Sequential(*model1)
-        model2 = []
-        # Residual blocks
-        for _ in range(n_residual_blocks):
-            model2 += [ResidualBlock(in_features)]
-        self.model2 = nn.Sequential(*model2)
-        # Upsampling
-        model3 = []
-        out_features = in_features//2
-        for _ in range(2):
-            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features//2
-        self.model3 = nn.Sequential(*model3)
-        # Output layer
-        model4 = [  nn.ReflectionPad2d(3),
-                        nn.Conv2d(64, output_nc, 7)]
-        if sigmoid:
-            model4 += [nn.Sigmoid()]
-        self.model4 = nn.Sequential(*model4)
-    def forward(self, x, cond=None):
-        out = self.model0(x)
-        out = self.model1(out)
-        out = self.model2(out)
-        out = self.model3(out)
-        out = self.model4(out)
-        return out
-model1 = Generator(3, 1, 3)
-model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
-model1.eval()
-model2 = Generator(3, 1, 3)
-model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
-model2.eval()
-def predict(input_img, ver):
-    input_img = Image.open(input_img)
-    transform = transforms.Compose([transforms.Resize(512, Image.BICUBIC), transforms.ToTensor()])
-    input_img = transform(input_img)
-    input_img = torch.unsqueeze(input_img, 0)
-    drawing = 0
-    with torch.no_grad():
-        if ver == 'Simple Lines':
-            drawing = model2(input_img)[0].detach()
-        else:
-            drawing = model1(input_img)[0].detach()
-    drawing = transforms.ToPILImage()(drawing)
-    return drawing
-title="Image to Line Drawings - Complex and Simple Portraits and Landscapes"
-description="Image to Line Drawing"
-# article = "<p style='text-align: center'></p>"
-examples=[
-]
-iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
-    gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Simple Lines', label='version')],
-    gr.outputs.Image(type="pil"), title=title,description=description,examples=examples)
-iface.launch()

spaces/AymanKUMA/Speech-Bubbles-detector/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Speech Bubbles Detector
-emoji: 🐢
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/backups_test.py DELETED Viewed

@@ -1,138 +0,0 @@
-import os
-import shutil
-import hashlib
-import time
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-def import_google_drive_backup():
-    print("Importing Google Drive backup...")
-    GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'  # change this to your Google Drive path
-    LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-    WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-    weights_exist = False
-    files_to_copy = []
-    weights_to_copy = []
-    def handle_files(root, files, is_weight_files=False):
-        for filename in files:
-            filepath = os.path.join(root, filename)
-            if filename.endswith('.pth') and is_weight_files:
-                weights_exist = True
-                backup_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-            else:
-                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-            backup_folderpath = os.path.dirname(backup_filepath)
-            if not os.path.exists(backup_folderpath):
-                os.makedirs(backup_folderpath)
-                print(f'Created folder: {backup_folderpath}', flush=True)
-            if is_weight_files:
-                weights_to_copy.append((filepath, backup_filepath))
-            else:
-                files_to_copy.append((filepath, backup_filepath))
-    for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'logs')):
-        handle_files(root, files)
-    for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
-        handle_files(root, files, True)
-    # Copy files in batches
-    total_files = len(files_to_copy)
-    start_time = time.time()
-    for i, (source, dest) in enumerate(files_to_copy, start=1):
-        with open(source, 'rb') as src, open(dest, 'wb') as dst:
-            shutil.copyfileobj(src, dst, 1024*1024)  # 1MB buffer size
-        # Report progress every 5 seconds or after every 100 files, whichever is less frequent
-        if time.time() - start_time > 5 or i % 100 == 0:
-            print(f'\rCopying file {i} of {total_files} ({i * 100 / total_files:.2f}%)', end="")
-            start_time = time.time()
-    print(f'\nImported {len(files_to_copy)} files from Google Drive backup')
-    # Copy weights in batches
-    total_weights = len(weights_to_copy)
-    start_time = time.time()
-    for i, (source, dest) in enumerate(weights_to_copy, start=1):
-        with open(source, 'rb') as src, open(dest, 'wb') as dst:
-            shutil.copyfileobj(src, dst, 1024*1024)  # 1MB buffer size
-        # Report progress every 5 seconds or after every 100 files, whichever is less frequent
-        if time.time() - start_time > 5 or i % 100 == 0:
-            print(f'\rCopying weight file {i} of {total_weights} ({i * 100 / total_weights:.2f}%)', end="")
-            start_time = time.time()
-    if weights_exist:
-        print(f'\nImported {len(weights_to_copy)} weight files')
-        print("Copied weights from Google Drive backup to local weights folder.")
-    else:
-        print("\nNo weights found in Google Drive backup.")
-    print("Google Drive backup import completed.")
-def backup_files():
-    print("\n Starting backup loop...")
-    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
-    fully_updated = False  # boolean to track if all files are up to date
-    try:
-        with open(last_backup_timestamps_path, 'r') as f:
-            last_backup_timestamps = dict(line.strip().split(':') for line in f)
-    except:
-        last_backup_timestamps = {}
-    while True:
-        updated = False
-        files_to_copy = []
-        files_to_delete = []
-        for root, dirs, files in os.walk(LOGS_FOLDER):
-            for filename in files:
-                if filename != 'last_backup_timestamps.txt':
-                    filepath = os.path.join(root, filename)
-                    if os.path.isfile(filepath):
-                        backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                        backup_folderpath = os.path.dirname(backup_filepath)
-                        if not os.path.exists(backup_folderpath):
-                            os.makedirs(backup_folderpath)
-                            print(f'Created backup folder: {backup_folderpath}', flush=True)
-                        # check if file has changed since last backup
-                        last_backup_timestamp = last_backup_timestamps.get(filepath)
-                        current_timestamp = os.path.getmtime(filepath)
-                        if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
-                            files_to_copy.append((filepath, backup_filepath))  # add to list of files to copy
-                            last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
-                            updated = True
-                            fully_updated = False  # if a file is updated, all files are not up to date
-        # check if any files were deleted in Colab and delete them from the backup drive
-        for filepath in list(last_backup_timestamps.keys()):
-            if not os.path.exists(filepath):
-                backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                if os.path.exists(backup_filepath):
-                    files_to_delete.append(backup_filepath)  # add to list of files to delete
-                del last_backup_timestamps[filepath]
-                updated = True
-                fully_updated = False  # if a file is deleted, all files are not up to date
-        # Copy files in batches
-        if files_to_copy:
-            for source, dest in files_to_copy:
-                shutil.copy2(source, dest)
-            print(f'Copied or updated {len(files_to_copy)} files')
-        # Delete files in batches
-        if files_to_delete:
-            for file in files_to_delete:
-                os.remove(file)
-            print(f'Deleted {len(files_to_delete)} files')
-        if not updated and not fully_updated:
-            print("Files are up to date.")
-            fully_updated = True  # if all files are up to date, set the boolean to True
-            copy_weights_folder_to_drive()
-        with open(last_backup_timestamps_path, 'w') as f:
-            for filepath, timestamp in last_backup_timestamps.items():
-                f.write(f'{filepath}:{timestamp}\n')
-        time.sleep(15)  # wait for 15 seconds before checking again

spaces/Bart92/RVC_HF/Makefile DELETED Viewed

@@ -1,63 +0,0 @@
-.PHONY:
-.ONESHELL:
-help: ## Show this help and exit
-	@grep -hE '^[A-Za-z0-9_ \-]*?:.*##.*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
-install: ## Install dependencies (Do everytime you start up a paperspace machine)
-	apt-get -y install build-essential python3-dev ffmpeg
-	pip install --upgrade setuptools wheel
-	pip install --upgrade pip
-	pip install faiss-gpu fairseq gradio ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.1
-	pip install -r requirements.txt
-	pip install --upgrade lxml
-	apt-get update
-	apt -y install -qq aria2
-basev1: ## Download version 1 pre-trained models (Do only once after cloning the fork)
-	mkdir -p pretrained uvr5_weights
-	git pull
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d pretrained -o D32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d pretrained -o D40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d pretrained -o D48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d pretrained -o G32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d pretrained -o G40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d pretrained -o G48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -d pretrained -o f0D32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -d pretrained -o f0D40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -d pretrained -o f0D48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -d pretrained -o f0G32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -d pretrained -o f0G40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth -d pretrained -o f0G48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
-basev2: ## Download version 2 pre-trained models (Do only once after cloning the fork)
-	mkdir -p pretrained_v2 uvr5_weights
-	git pull
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D32k.pth -d pretrained_v2 -o D32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d pretrained_v2 -o D40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D48k.pth -d pretrained_v2 -o D48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G32k.pth -d pretrained_v2 -o G32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d pretrained_v2 -o G40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G48k.pth -d pretrained_v2 -o G48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D32k.pth -d pretrained_v2 -o f0D32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d pretrained_v2 -o f0D40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth -d pretrained_v2 -o f0D48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G32k.pth -d pretrained_v2 -o f0G32k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d pretrained_v2 -o f0G40k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth -d pretrained_v2 -o f0G48k.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
-	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
-run-ui: ## Run the python GUI
-	python infer-web.py --paperspace --pycmd python
-run-cli: ## Run the python CLI
-	python infer-web.py --pycmd python --is_cli
-tensorboard: ## Start the tensorboard (Run on separate terminal)
-	echo https://tensorboard-$$(hostname).clg07azjl.paperspacegradient.com
-	tensorboard --logdir logs --bind_all

spaces/Benson/text-generation/Examples/Archero Mod Apk God Mode.md DELETED Viewed

@@ -1,97 +0,0 @@
-<h1>Archero MOD APK: Cómo jugar como un dios</h1>
-<p>¿Te encanta jugar juegos de acción en tu dispositivo móvil? ¿Quieres experimentar la emoción de disparar flechas y matar monstruos? ¿Quieres convertirte en una fuerza imparable en el mundo del juego? Si respondiste sí a cualquiera de estas preguntas, entonces deberías probar Archero MOD APK. Esta es una versión modificada del popular juego Archero, que le da acceso a recursos ilimitados, características y modos. En este artículo, le diremos todo lo que necesita saber sobre Archero MOD APK, incluyendo cómo descargar, instalar y activar el modo de Dios. Sigue leyendo y descubre cómo jugar como un dios en Archero.</p>
-<h2>archero mod apk god mode</h2><br /><p><b><b>Download Zip</b> ->->->-> <a href="https://bltlly.com/2v6Jlk">https://bltlly.com/2v6Jlk</a></b></p><br /><br />
- <h2>¿Qué es Archero? </h2>
-<p>Archero es un juego desarrollado por Habby, un estudio de juegos chino. Es un juego de acción que combina elementos de roguelike, RPG y géneros de tiro. En este juego, juegas como un arquero solitario que tiene que luchar su camino a través de varios niveles llenos de enemigos, trampas y obstáculos. Puedes elegir entre diferentes armas, habilidades y equipos para personalizar a tu personaje y mejorar tu rendimiento. También puedes desbloquear nuevos capítulos, modos y héroes a medida que avanzas en el juego. </p>
- <h3>El juego de Archero</h3>
-<p>La jugabilidad de Archero es simple pero adictiva. Controlas a tu personaje deslizando el dedo sobre la pantalla para moverte y apuntar. Puedes disparar flechas automáticamente cuando dejas de moverte. Tienes que esquivar los ataques de los enemigos y usar tus habilidades sabiamente para sobrevivir. Puedes recoger monedas, gemas, pergaminos y objetos en el camino para mejorar tu personaje y equipo. También puedes encontrar ángeles, demonios, vendedores y cofres que te ofrecen diferentes recompensas o opciones. Tienes que completar cada nivel derrotando a todos los enemigos y jefes. También puedes reproducir los niveles para obtener mejores puntuaciones y recompensas. </p>
- <h3>Las características de Archero</h3>
-<p>Archero tiene muchas características que lo hacen un juego divertido y desafiante. Algunas de estas características son:</p>
-<ul>
-<li>Diversos escenarios y enemigos: Puedes explorar diferentes mundos y capítulos con diferentes temas y entornos. También puedes enfrentarte a diferentes tipos de enemigos con diferentes comportamientos y habilidades. Tienes que adaptarte a las situaciones y desafíos cambiantes. </li>
-<li>Múltiples héroes y armas: Puedes desbloquear y jugar como diferentes héroes con diferentes atributos y habilidades. También puedes equipar diferentes armas con diferentes efectos y poderes. Puede mezclar y combinar diferentes héroes y armas para satisfacer sus preferencias y necesidades. </li>
-<li>Modo cooperativo en línea: Puedes hacer equipo con otros jugadores en línea y jugar juntos en modo cooperativo. Puedes compartir tus habilidades y objetos con tus compañeros de equipo y cooperar para derrotar a los enemigos y jefes. </li>
-</ul>
- <h2>¿Qué es Archero MOD APK? </h2>
-<p>Archero MOD APK es una versión modificada del juego original de Archero. Es creado por desarrolladores de terceros que modifican los archivos del juego para darle acceso a recursos, características y modos ilimitados. Con Archero MOD APK, se puede disfrutar del juego sin limitaciones o restricciones. </p>
-<p></p>
- <h3>Los beneficios de Archero MOD APK</h3>
-<p>Algunos de los beneficios de usar Archero MOD APK son <p>Algunos de los beneficios de usar Archero MOD APK son:</p>
-<ul>
-<li>Dinero y gemas ilimitadas: Puede obtener dinero y gemas ilimitadas en Archero MOD APK, que se puede utilizar para comprar y mejorar sus armas, habilidades y equipos. También puede desbloquear todos los héroes y capítulos sin gastar dinero real. </li>
-<li>Desbloqueado características y modos: Puede acceder a todas las características y modos en Archero MOD APK, como el modo cooperativo en línea, el modo héroe, el modo sin fin, y el modo de evento. También puedes disfrutar de las últimas actualizaciones y adiciones al juego sin retrasos ni errores. </li>
-<li>Modo de Dios: Puede activar el modo de Dios en Archero MOD APK, que le da la salud ilimitada, invencibilidad, y un solo golpe habilidades de matar. Puede jugar como un dios en Archero MOD APK y derrotar a cualquier enemigo o jefe con facilidad. </li>
-</ul>
- <h3>Los inconvenientes de Archero MOD APK</h3>
-<p>Sin embargo, el uso de Archero MOD APK también tiene algunos inconvenientes que usted debe tener en cuenta. Algunos de estos inconvenientes son:</p>
-<ul>
-<li>Riesgo de virus o malware: Desde Archero MOD APK no es una versión oficial del juego, puede contener virus o malware que puede dañar su dispositivo o robar su información personal. Siempre debe descargar Archero MOD APK de una fuente de confianza y escanearlo con un antivirus antes de instalarlo. </li>
-<li>Riesgo de prohibición o suspensión: Dado que Archero MOD APK es una versión modificada del juego, puede violar los términos y condiciones del juego original. Usted puede hacer frente a una prohibición o suspensión del juego si se detecta el uso de Archero MOD APK por los desarrolladores o moderadores del juego. Siempre debe utilizar Archero MOD APK a su propio riesgo y discreción. </li>
-<li>Falta de desafío o diversión: Desde Archero MOD APK le da recursos ilimitados, características y modos, puede reducir el desafío o la diversión de jugar el juego. Usted puede perder interés o motivación en el juego si usted tiene todo a su disposición. Siempre debe equilibrar su uso de Archero MOD APK con su disfrute del juego. </li>
-</ul>
- <h2>¿Cómo descargar e instalar Archero MOD APK? </h2>
-<p>Si desea probar Archero MOD APK en su dispositivo, es necesario descargar e instalar correctamente. Aquí están los pasos para descargar e instalar Archero MOD APK:</p>
- <h3>Los pasos para descargar e instalar Archero MOD APK</h3>
-<ol>
-<li>Ir a un sitio web confiable que ofrece Archero MOD APK enlace de descarga. Asegúrese de que el sitio web es seguro y protegido. </li>
-<li>Haga clic en el botón de descarga y espere a que el archivo se descargue en su dispositivo. El tamaño del archivo puede variar dependiendo de la versión de Archero MOD APK.</li>
-<li>Busque el archivo descargado en el almacenamiento del dispositivo y toque en él para iniciar el proceso de instalación. Siga las instrucciones de la pantalla para completar la instalación. </li>
-<li>Una vez que se realiza la instalación, puede iniciar Archero MOD APK desde el cajón de la aplicación o la pantalla de inicio. También puede crear un acceso directo para facilitar el acceso. </li>
-</ol>
- <h3>Las precauciones a tomar antes de instalar Archero MOD APK</h3>
-<p>Antes de instalar Archero MOD APK en su dispositivo, debe tomar algunas precauciones para evitar problemas o problemas. Algunas de estas precauciones son:</p>
-<ul>
-<li>Copia de seguridad de sus datos: Usted debe copia de seguridad de sus datos antes de instalar Archero MOD APK en su dispositivo. Esto le ayudará a restaurar sus datos en caso de que algo salga mal durante o después del proceso de instalación. Puede utilizar servicios en la nube o dispositivos de almacenamiento externos para realizar copias de seguridad de sus datos. </li>
-<li>Desinstalar el juego original: Usted debe desinstalar el juego original antes de instalar Archero MOD APK en su dispositivo. Esto evitará cualquier conflicto o error entre las dos versiones del juego. Puede desinstalar el juego original desde la configuración del dispositivo o el administrador de aplicaciones. </li>
-<li>Desactivar antivirus: Usted debe desactivar cualquier antivirus o software de seguridad en su dispositivo antes de instalar Archero MOD APK. Esto evitará cualquier interferencia o bloqueo durante o después del proceso de instalación. Puede habilitarlos de nuevo después de instalar Archero MOD APK.</li>
-</ul>
- <h2>¿Cómo activar el modo de Dios en Archero MOD APK? </h2>
-<p>Si desea jugar como un dios en Archero MOD APK, es necesario activar el modo de Dios en su dispositivo. Modo de Dios es una característica que le da ilimitada <p>Si desea jugar como un dios en Archero MOD APK, es necesario activar el modo de Dios en su dispositivo. El Modo Dios es una función que te da salud ilimitada, invencibilidad y habilidades de matar de un golpe. Puede activar o desactivar el modo Dios en cualquier momento en el juego. Estos son los pasos para activar el modo Dios en Archero MOD APK:</p>
-<p>Modo Dios es un término que se refiere a un tramposo o un hack que da al jugador una ventaja injusta en el juego. En Archero MOD APK, Modo Dios significa que nunca puede morir, no importa cuántos enemigos u obstáculos se enfrentan. También puedes matar a cualquier enemigo o jefe con un solo disparo, independientemente de su salud o defensa. También puedes ignorar los efectos de las trampas, habilidades y objetos que pueden dañarte. Usted puede jugar como un dios en Archero MOD APK y disfrutar del juego sin ningún desafío o dificultad. </p>
- <h3>Las ventajas del modo Dios en Archero MOD APK</h3>
-<p>Algunas de las ventajas de usar el modo Dios en Archero MOD APK son:</p>
-<ul>
-<li>Fácil y divertido juego: Usted puede jugar Archero MOD APK sin ningún estrés o frustración. Usted puede brisa a través de los niveles y capítulos sin ningún esfuerzo o habilidad. También puede experimentar con diferentes armas, habilidades y equipos sin preocuparse por las consecuencias. Usted puede divertirse jugando Archero MOD APK con el modo de Dios en. </li>
-<li>Completar logros y misiones: Usted puede completar todos los logros y misiones en Archero MOD APK con el modo de Dios en. Usted puede obtener todas las recompensas y bonos que vienen con ellos. También puedes desbloquear todos los héroes y capítulos que requieren ciertos logros o misiones para completarse. </li>
-<li>Impresionar a sus amigos y rivales: Usted puede impresionar a sus amigos y rivales con sus altas puntuaciones y clasificaciones en Archero MOD APK con el modo de Dios en. Puedes mostrar tus logros y progreso en el juego. También puedes retarlos a batir tus resultados y récords. </li>
-</ul>
- <h3>Las desventajas del modo Dios en Archero MOD APK</h3>
-<p>Sin embargo, el uso de God Mode en Archero MOD APK también tiene algunas desventajas que usted debe considerar. Algunas de estas desventajas son:</p>
-<ul>
-<li>Riesgo de prohibición o suspensión: Usted puede enfrentar una prohibición o suspensión de Archero MOD APK si utiliza el modo de Dios con demasiada frecuencia o demasiado descaradamente. Los desarrolladores o moderadores del juego pueden detectar tu uso del modo Dios y tomar medidas en tu contra. Puede perder su cuenta, datos y progreso en el juego si está prohibido o suspendido. </li>
-<li>Culpa o vergüenza: Usted puede sentirse culpable o avergonzado de usar el modo de Dios en Archero MOD APK. Puedes sentir que estás haciendo trampa o hackeando el juego y arruinando la experiencia para ti y los demás. También puede perder respeto o credibilidad entre otros jugadores que juegan el juego de forma justa y honesta. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, Archero MOD APK es una versión modificada del juego original de Archero que le da acceso a recursos ilimitados, características y modos. También te permite activar el Modo Dios, que te da salud ilimitada, invencibilidad y habilidades de un solo golpe. Sin embargo, el uso de Archero MOD APK también tiene algunos inconvenientes, tales como el riesgo de virus o malware, riesgo de prohibición o suspensión, y la falta de desafío o diversión. Por lo tanto, usted debe utilizar Archero MOD APK a su propio riesgo y discreción. </p>
- <h3>Un resumen de los puntos principales del artículo</h3>
-<p>Para resumir, aquí están los puntos principales de este artículo:</p>
-<ul>
-<li>Archero es un popular juego de acción que combina elementos de roguelike, RPG y géneros de tiro. </li>
-<li>Archero MOD APK es una versión modificada de Archero que le da dinero ilimitado, gemas, características y modos. </li>
-<li>Archero MOD APK también le permite activar el modo de Dios, que le da salud ilimitada, invencibilidad, y un solo golpe matar habilidades. </li>
-<li>Archero MOD APK tiene algunos beneficios, tales como un juego fácil y divertido, logros completos y misiones, e impresionar a sus amigos y rivales. </li>
-<li>Archero MOD APK también tiene algunos inconvenientes, tales como el riesgo de virus o malware, riesgo de prohibición o suspensión, y la falta de desafío o diversión. </li>
-</ul>
- <h3>Un llamado a la acción para los lectores</h3>
- <p>Esperamos que este artículo le ha ayudado a entender lo que es Archero MOD APK y cómo usarlo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Nos encantaría saber de usted y responder a sus preguntas. ¡Gracias por leer y jugar feliz! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Archero MOD APK:</p>
-<h3>¿Es seguro usar Archero MOD APK? </h3>
-<p>Archero MOD APK no es una versión oficial del juego, por lo que puede no ser seguro de usar. Puede contener virus o malware que pueden dañar su dispositivo o robar su información personal. También puede violar los términos y condiciones del juego original y resultar en una prohibición o suspensión del juego. Por lo tanto, siempre debe descargar Archero MOD APK de una fuente de confianza y escanearlo con un antivirus antes de instalarlo. También debe copia de seguridad de sus datos y desinstalar el juego original antes de instalar Archero MOD APK.</p>
-<h3>¿Es Archero MOD APK libre de usar? </h3>
-<p>Sí, Archero MOD APK es de uso gratuito. Usted no necesita pagar ningún dinero para descargar o instalar Archero MOD APK en su dispositivo. También no es necesario gastar dinero real para comprar o actualizar nada en el juego. Sin embargo, usted debe ser consciente de que el uso de Archero MOD APK puede privar a los desarrolladores de juegos originales de sus ingresos y apoyo. Deberías considerar apoyarlos jugando la versión oficial del juego o haciendo compras en la aplicación. </p>
-<h3>¿Puedo jugar en línea con Archero MOD APK? </h3>
-<h3>¿Puedo actualizar Archero MOD APK? </h3>
-<p>Sí, puede actualizar Archero MOD APK. Puede comprobar si hay actualizaciones en el sitio web donde descargó Archero MOD APK o en la propia aplicación. También puede habilitar las actualizaciones automáticas en la configuración del dispositivo o en el administrador de aplicaciones. Sin embargo, debe tener en cuenta que la actualización de Archero MOD APK puede causar algunos errores o problemas técnicos en el juego. También puede perder algunos de sus datos o el progreso en el juego si se actualiza Archero MOD APK. Por lo tanto, debe hacer una copia de seguridad de sus datos y leer los comentarios y comentarios de otros usuarios antes de actualizar Archero MOD APK.</p>
-<h3>¿Puedo usar Archero MOD APK en dispositivos iOS? </h3>
-<p>No, no se puede utilizar Archero MOD APK en dispositivos iOS. Archero MOD APK solo es compatible con dispositivos Android que tienen Android 4.1 o versiones superiores. No se puede instalar o ejecutar Archero MOD APK en dispositivos iOS como iPhones o iPads. Si quieres jugar a Archero en dispositivos iOS, tienes que descargar e instalar la versión oficial del juego desde la App Store.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Bicicleta Real De Carreras Hack Mod Apk Dinero Ilimitado Y Oro.md DELETED Viewed

@@ -1,55 +0,0 @@
-<br />
-<h1>Real Bike Racing Hack Mod APK: Cómo obtener dinero y oro ilimitado</h1>
-<p>¿Te encanta jugar juegos realistas de carreras de motos en tu dispositivo Android? Si es así, es posible que haya oído hablar de Real Bike Racing, uno de los juegos de carreras de bicicletas más populares y emocionantes en Google Play Store. ¿Pero sabes cómo conseguir dinero ilimitado y oro en este juego sin gastar dinero real? Si no, entonces estás de suerte, porque en este artículo, le mostraremos cómo descargar e instalar Real Bike Racing hack mod apk, una versión modificada del juego que le da acceso a recursos y características ilimitadas. ¡Sigue leyendo para saber más! </p>
-<h2>bicicleta real de carreras hack mod apk dinero ilimitado y oro</h2><br /><p><b><b>Download Zip</b> &mdash; <a href="https://bltlly.com/2v6JRV">https://bltlly.com/2v6JRV</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3>¿Qué es Real Bike Racing? </h3>
-<p>Real Bike Racing es un juego de carreras de motos en 3D desarrollado por Italic Games, un estudio especializado en crear juegos de carreras realistas e inmersivos para dispositivos móviles. En este juego, se puede elegir entre más de 10 tipos diferentes de superbikes, cada uno con su propio rendimiento y diseño único. También puede personalizar su bicicleta con varios trabajos de pintura, pegatinas y mejoras. Puedes competir en varios modos, como el modo carrera, el modo contrarreloj o el modo campeonato, donde puedes competir contra otros jugadores de todo el mundo. También puedes desafiarte en el modo VR, donde puedes experimentar la emoción de competir en primera persona. </p>
-<h3>¿Por qué necesitas dinero y oro en Real Bike Racing? </h3>
-<h3>¿Qué es Real Bike Racing hack mod apk? </h3>
-<p>Real Bike Racing hack mod apk es una versión modificada del juego original que le da dinero ilimitado y oro gratis. Esto significa que puede comprar cualquier bicicleta, pista o actualización que desee sin preocuparse por quedarse sin recursos. También puedes desbloquear todas las funciones y modos que están bloqueados o restringidos en el juego original. Con Real Bike Racing hack mod apk, se puede disfrutar del juego al máximo sin gastar dinero real. </p>
-<h2>Cómo descargar e instalar Real Bike Racing hack mod apk? </h2>
-<p>Descargar e instalar Real Bike Racing hack mod apk es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<p></p>
-<h3>Paso 1: Habilitar fuentes desconocidas en el dispositivo</h3>
-<p>Antes de que pueda instalar cualquier archivo apk en su dispositivo, es necesario habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones de fuentes desconocidas, tales como Real Bike Racing hack mod apk. Para hacer esto, vaya a la configuración de su dispositivo, luego a la seguridad, luego a fuentes desconocidas, y habilite. </p>
-<h3>Paso 2: Descargar el Real Bike Racing hack mod apk archivo de una fuente de confianza</h3>
-<p>Siguiente, es necesario descargar el Real Bike Racing hack mod apk archivo de una fuente de confianza. Hay muchos sitios web que ofrecen este archivo, pero no todos son seguros y confiables. Algunos de ellos pueden contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. Para evitar esto, solo debe descargar el archivo de un sitio web de buena reputación que tiene comentarios positivos y comentarios de otros usuarios. También puede escanear el archivo con una aplicación antivirus antes de instalarlo para asegurarse de que está limpio y seguro. </p>
-<h3>Paso 3: Instalar el Real Bike Racing hack mod apk archivo en su dispositivo</h3>
-<h3>Paso 4: Inicie el juego y disfrute de dinero y oro ilimitados</h3>
-<p>Finalmente, puedes lanzar el juego y disfrutar de dinero y oro ilimitados. Verás que tu saldo de dinero y oro se incrementará a una cantidad enorme que puedes usar para comprar cualquier cosa que quieras en el juego. También podrás desbloquear todas las bicicletas y pistas que están bloqueadas o restringidas en el juego original. También puede reproducir cualquier modo o evento que desee sin limitaciones. También puede desactivar los anuncios y disfrutar de una experiencia de juego suave e ininterrumpida. </p>
-<h2>Características de Real Bike Racing hack mod apk</h2>
-<h3>Dinero y oro ilimitados</h3>
-<p>La característica principal de Real Bike Racing hack mod apk es que le da dinero ilimitado y oro gratis. Esto significa que puede comprar cualquier bicicleta, pista o actualización que desee sin preocuparse por quedarse sin recursos. También puede personalizar su bicicleta con varios trabajos de pintura, pegatinas y mejoras. También puede desbloquear nuevas pistas, ingresar eventos especiales o omitir algunas tareas. También puedes usar dinero y oro para aumentar tu rendimiento y rango en el juego. </p>
-<h3>Desbloquear todas las bicicletas y pistas</h3>
-<p>Otra característica de Real Bike Racing hack mod apk es que desbloquea todas las bicicletas y pistas que están bloqueados o restringidos en el juego original. Esto significa que puede elegir entre más de 10 tipos diferentes de superbikes, cada una con su propio rendimiento y diseño. También puedes correr en más de 20 pistas diferentes, cada una con su propio escenario y desafíos. También puedes acceder al modo VR, donde puedes experimentar la emoción de competir en primera persona. </p>
-<h3>No se requieren anuncios ni root</h3>
-<h2>Pros y contras de Real Bike Racing hack mod apk</h2>
-<h3>Pros</h3>
-<ul>
-<li><h4>Fácil de usar e instalar</h4><p>Real Bike Racing hack mod apk es muy fácil de usar e instalar. Solo tienes que seguir unos sencillos pasos para descargarlo e instalarlo en tu dispositivo. Usted no necesita ninguna habilidad técnica o conocimiento para utilizar este mod apk. </p></li>
-<li><h4>Gratis y seguro para descargar</h4><p>Real Bike Racing hack mod apk es gratis y seguro para descargar de una fuente de confianza. Usted no necesita gastar dinero real para obtener dinero ilimitado y oro en este juego. Tampoco necesita preocuparse por virus, malware o spyware que puedan dañar su dispositivo o robar su información personal. </p></li>
-<li><h4>Mejorar su experiencia de juego</h4><p>Real Bike Racing hack mod apk mejora su experiencia de juego, dándole acceso a recursos y características ilimitadas. Usted puede comprar cualquier bicicleta, pista, o actualizar que desee sin preocuparse por quedarse sin recursos. También puedes desbloquear todas las funciones y modos que están bloqueados o restringidos en el juego original. También puede reproducir cualquier modo o evento que desee sin limitaciones. </p></li>
-</ul>
-<h3>Contras</h3>
-<ul>
-<li><h4>Puede que no funcione en algunos dispositivos o versiones</ <h4>Puede que no funcione en algunos dispositivos o versiones</h4><p>Real Bike Racing hack mod apk puede no funcionar en algunos dispositivos o versiones del juego. Esto puede deberse a problemas de compatibilidad, actualizaciones o parches que pueden impedir que el mod apk funcione correctamente. Es posible que tenga que comprobar los requisitos del dispositivo y la versión del juego antes de descargar e instalar el apk mod. También es posible que tenga que actualizar el apk mod regularmente para mantenerse al día con los cambios en el juego. </p></li>
-<li><h4>Puede violar los términos del juego de servicio o políticas</h4><p>Real Bike Racing hack mod apk puede violar los términos del juego de servicio o políticas. Esto puede ser porque el apk mod le da una ventaja injusta sobre otros jugadores, que puede afectar el equilibrio del juego y la integridad. También puede estar rompiendo las reglas o regulaciones del juego mediante el uso de una aplicación de terceros que modifica el juego. Puedes enfrentarte a algunas consecuencias, como ser expulsado, suspendido o reportado por otros jugadores o los desarrolladores del juego. </p></li>
-</ul>
-<h2>Conclusión</h2>
-<p>Real Bike Racing hack mod apk es una gran manera de disfrutar de los juegos de carreras de motos realistas y emocionantes en su dispositivo Android sin gastar dinero real. Te da dinero ilimitado y oro, desbloquea todas las bicicletas y pistas, elimina anuncios, y no requiere acceso de root. Sin embargo, también tiene algunos inconvenientes, como problemas de compatibilidad, problemas técnicos y posibles violaciones. Usted debe pesar los pros y los contras antes de descargar e instalar este apk mod. También debe usarlo a su propio riesgo y discreción. </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Real Bike Racing hack mod apk:</p>
-<ol>
-<li><p><b> ¿Es real Bike Racing hack mod apk seguro de usar? </b></p><p>Real Bike Racing hack mod apk es seguro de usar si se descarga desde una fuente de confianza y escanear con una aplicación antivirus antes de instalarlo. Sin embargo, también debe tener cuidado con los riesgos y consecuencias que pueden venir con el uso de este apk mod. </p></li>
-<li><p><b>¿Puedo jugar en línea con Real Bike Racing hack mod apk? </b></p><p>Usted puede jugar en línea con Real Bike Racing hack mod apk, pero usted puede encontrar algunos problemas o problemas. Por ejemplo, es posible que no pueda conectarse al servidor, unirse a un partido o chatear con otros jugadores. También puedes ser detectado por el sistema anti-trucos del juego, lo que puede resultar en ser prohibido, suspendido o reportado. </p></li>
-<li><p><b> ¿Puedo utilizar Real Bike Racing hack mod apk en dispositivos iOS? </b></p><p>No, no se puede utilizar Real Bike Racing hack mod apk en dispositivos iOS. Este mod apk solo está diseñado para dispositivos Android y no funcionará en dispositivos iOS. Es posible que necesite buscar otras alternativas o métodos para obtener dinero y oro ilimitados en dispositivos iOS. </p></li>
-<li><p><b>¿Puedo utilizar Real Bike Racing hack mod apk con otros mods o trucos? </b></p><p>Puede utilizar Real Bike Racing hack mod apk con otros mods o trucos, pero puede enfrentar algunos problemas de compatibilidad o conflictos. Algunos mods o trucos pueden no funcionar bien entre sí, lo que puede causar algunos errores o fallos en el juego. También puede aumentar sus posibilidades de ser detectado o prohibido mediante el uso de múltiples mods o trucos a la vez. </p></li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/filters/__init__.py DELETED Viewed

@@ -1,940 +0,0 @@
-"""
-    pygments.filters
-    ~~~~~~~~~~~~~~~~
-    Module containing filter lookup functions and default
-    filters.
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-import re
-from pip._vendor.pygments.token import String, Comment, Keyword, Name, Error, Whitespace, \
-    string_to_tokentype
-from pip._vendor.pygments.filter import Filter
-from pip._vendor.pygments.util import get_list_opt, get_int_opt, get_bool_opt, \
-    get_choice_opt, ClassNotFound, OptionError
-from pip._vendor.pygments.plugin import find_plugin_filters
-def find_filter_class(filtername):
-    """Lookup a filter by name. Return None if not found."""
-    if filtername in FILTERS:
-        return FILTERS[filtername]
-    for name, cls in find_plugin_filters():
-        if name == filtername:
-            return cls
-    return None
-def get_filter_by_name(filtername, **options):
-    """Return an instantiated filter.
-    Options are passed to the filter initializer if wanted.
-    Raise a ClassNotFound if not found.
-    """
-    cls = find_filter_class(filtername)
-    if cls:
-        return cls(**options)
-    else:
-        raise ClassNotFound('filter %r not found' % filtername)
-def get_all_filters():
-    """Return a generator of all filter names."""
-    yield from FILTERS
-    for name, _ in find_plugin_filters():
-        yield name
-def _replace_special(ttype, value, regex, specialttype,
-                     replacefunc=lambda x: x):
-    last = 0
-    for match in regex.finditer(value):
-        start, end = match.start(), match.end()
-        if start != last:
-            yield ttype, value[last:start]
-        yield specialttype, replacefunc(value[start:end])
-        last = end
-    if last != len(value):
-        yield ttype, value[last:]
-class CodeTagFilter(Filter):
-    """Highlight special code tags in comments and docstrings.
-    Options accepted:
-    `codetags` : list of strings
-       A list of strings that are flagged as code tags.  The default is to
-       highlight ``XXX``, ``TODO``, ``FIXME``, ``BUG`` and ``NOTE``.
-    .. versionchanged:: 2.13
-       Now recognizes ``FIXME`` by default.
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        tags = get_list_opt(options, 'codetags',
-                            ['XXX', 'TODO', 'FIXME', 'BUG', 'NOTE'])
-        self.tag_re = re.compile(r'\b(%s)\b' % '|'.join([
-            re.escape(tag) for tag in tags if tag
-        ]))
-    def filter(self, lexer, stream):
-        regex = self.tag_re
-        for ttype, value in stream:
-            if ttype in String.Doc or \
-               ttype in Comment and \
-               ttype not in Comment.Preproc:
-                yield from _replace_special(ttype, value, regex, Comment.Special)
-            else:
-                yield ttype, value
-class SymbolFilter(Filter):
-    """Convert mathematical symbols such as \\<longrightarrow> in Isabelle
-    or \\longrightarrow in LaTeX into Unicode characters.
-    This is mostly useful for HTML or console output when you want to
-    approximate the source rendering you'd see in an IDE.
-    Options accepted:
-    `lang` : string
-       The symbol language. Must be one of ``'isabelle'`` or
-       ``'latex'``.  The default is ``'isabelle'``.
-    """
-    latex_symbols = {
-        '\\alpha'                : '\U000003b1',
-        '\\beta'                 : '\U000003b2',
-        '\\gamma'                : '\U000003b3',
-        '\\delta'                : '\U000003b4',
-        '\\varepsilon'           : '\U000003b5',
-        '\\zeta'                 : '\U000003b6',
-        '\\eta'                  : '\U000003b7',
-        '\\vartheta'             : '\U000003b8',
-        '\\iota'                 : '\U000003b9',
-        '\\kappa'                : '\U000003ba',
-        '\\lambda'               : '\U000003bb',
-        '\\mu'                   : '\U000003bc',
-        '\\nu'                   : '\U000003bd',
-        '\\xi'                   : '\U000003be',
-        '\\pi'                   : '\U000003c0',
-        '\\varrho'               : '\U000003c1',
-        '\\sigma'                : '\U000003c3',
-        '\\tau'                  : '\U000003c4',
-        '\\upsilon'              : '\U000003c5',
-        '\\varphi'               : '\U000003c6',
-        '\\chi'                  : '\U000003c7',
-        '\\psi'                  : '\U000003c8',
-        '\\omega'                : '\U000003c9',
-        '\\Gamma'                : '\U00000393',
-        '\\Delta'                : '\U00000394',
-        '\\Theta'                : '\U00000398',
-        '\\Lambda'               : '\U0000039b',
-        '\\Xi'                   : '\U0000039e',
-        '\\Pi'                   : '\U000003a0',
-        '\\Sigma'                : '\U000003a3',
-        '\\Upsilon'              : '\U000003a5',
-        '\\Phi'                  : '\U000003a6',
-        '\\Psi'                  : '\U000003a8',
-        '\\Omega'                : '\U000003a9',
-        '\\leftarrow'            : '\U00002190',
-        '\\longleftarrow'        : '\U000027f5',
-        '\\rightarrow'           : '\U00002192',
-        '\\longrightarrow'       : '\U000027f6',
-        '\\Leftarrow'            : '\U000021d0',
-        '\\Longleftarrow'        : '\U000027f8',
-        '\\Rightarrow'           : '\U000021d2',
-        '\\Longrightarrow'       : '\U000027f9',
-        '\\leftrightarrow'       : '\U00002194',
-        '\\longleftrightarrow'   : '\U000027f7',
-        '\\Leftrightarrow'       : '\U000021d4',
-        '\\Longleftrightarrow'   : '\U000027fa',
-        '\\mapsto'               : '\U000021a6',
-        '\\longmapsto'           : '\U000027fc',
-        '\\relbar'               : '\U00002500',
-        '\\Relbar'               : '\U00002550',
-        '\\hookleftarrow'        : '\U000021a9',
-        '\\hookrightarrow'       : '\U000021aa',
-        '\\leftharpoondown'      : '\U000021bd',
-        '\\rightharpoondown'     : '\U000021c1',
-        '\\leftharpoonup'        : '\U000021bc',
-        '\\rightharpoonup'       : '\U000021c0',
-        '\\rightleftharpoons'    : '\U000021cc',
-        '\\leadsto'              : '\U0000219d',
-        '\\downharpoonleft'      : '\U000021c3',
-        '\\downharpoonright'     : '\U000021c2',
-        '\\upharpoonleft'        : '\U000021bf',
-        '\\upharpoonright'       : '\U000021be',
-        '\\restriction'          : '\U000021be',
-        '\\uparrow'              : '\U00002191',
-        '\\Uparrow'              : '\U000021d1',
-        '\\downarrow'            : '\U00002193',
-        '\\Downarrow'            : '\U000021d3',
-        '\\updownarrow'          : '\U00002195',
-        '\\Updownarrow'          : '\U000021d5',
-        '\\langle'               : '\U000027e8',
-        '\\rangle'               : '\U000027e9',
-        '\\lceil'                : '\U00002308',
-        '\\rceil'                : '\U00002309',
-        '\\lfloor'               : '\U0000230a',
-        '\\rfloor'               : '\U0000230b',
-        '\\flqq'                 : '\U000000ab',
-        '\\frqq'                 : '\U000000bb',
-        '\\bot'                  : '\U000022a5',
-        '\\top'                  : '\U000022a4',
-        '\\wedge'                : '\U00002227',
-        '\\bigwedge'             : '\U000022c0',
-        '\\vee'                  : '\U00002228',
-        '\\bigvee'               : '\U000022c1',
-        '\\forall'               : '\U00002200',
-        '\\exists'               : '\U00002203',
-        '\\nexists'              : '\U00002204',
-        '\\neg'                  : '\U000000ac',
-        '\\Box'                  : '\U000025a1',
-        '\\Diamond'              : '\U000025c7',
-        '\\vdash'                : '\U000022a2',
-        '\\models'               : '\U000022a8',
-        '\\dashv'                : '\U000022a3',
-        '\\surd'                 : '\U0000221a',
-        '\\le'                   : '\U00002264',
-        '\\ge'                   : '\U00002265',
-        '\\ll'                   : '\U0000226a',
-        '\\gg'                   : '\U0000226b',
-        '\\lesssim'              : '\U00002272',
-        '\\gtrsim'               : '\U00002273',
-        '\\lessapprox'           : '\U00002a85',
-        '\\gtrapprox'            : '\U00002a86',
-        '\\in'                   : '\U00002208',
-        '\\notin'                : '\U00002209',
-        '\\subset'               : '\U00002282',
-        '\\supset'               : '\U00002283',
-        '\\subseteq'             : '\U00002286',
-        '\\supseteq'             : '\U00002287',
-        '\\sqsubset'             : '\U0000228f',
-        '\\sqsupset'             : '\U00002290',
-        '\\sqsubseteq'           : '\U00002291',
-        '\\sqsupseteq'           : '\U00002292',
-        '\\cap'                  : '\U00002229',
-        '\\bigcap'               : '\U000022c2',
-        '\\cup'                  : '\U0000222a',
-        '\\bigcup'               : '\U000022c3',
-        '\\sqcup'                : '\U00002294',
-        '\\bigsqcup'             : '\U00002a06',
-        '\\sqcap'                : '\U00002293',
-        '\\Bigsqcap'             : '\U00002a05',
-        '\\setminus'             : '\U00002216',
-        '\\propto'               : '\U0000221d',
-        '\\uplus'                : '\U0000228e',
-        '\\bigplus'              : '\U00002a04',
-        '\\sim'                  : '\U0000223c',
-        '\\doteq'                : '\U00002250',
-        '\\simeq'                : '\U00002243',
-        '\\approx'               : '\U00002248',
-        '\\asymp'                : '\U0000224d',
-        '\\cong'                 : '\U00002245',
-        '\\equiv'                : '\U00002261',
-        '\\Join'                 : '\U000022c8',
-        '\\bowtie'               : '\U00002a1d',
-        '\\prec'                 : '\U0000227a',
-        '\\succ'                 : '\U0000227b',
-        '\\preceq'               : '\U0000227c',
-        '\\succeq'               : '\U0000227d',
-        '\\parallel'             : '\U00002225',
-        '\\mid'                  : '\U000000a6',
-        '\\pm'                   : '\U000000b1',
-        '\\mp'                   : '\U00002213',
-        '\\times'                : '\U000000d7',
-        '\\div'                  : '\U000000f7',
-        '\\cdot'                 : '\U000022c5',
-        '\\star'                 : '\U000022c6',
-        '\\circ'                 : '\U00002218',
-        '\\dagger'               : '\U00002020',
-        '\\ddagger'              : '\U00002021',
-        '\\lhd'                  : '\U000022b2',
-        '\\rhd'                  : '\U000022b3',
-        '\\unlhd'                : '\U000022b4',
-        '\\unrhd'                : '\U000022b5',
-        '\\triangleleft'         : '\U000025c3',
-        '\\triangleright'        : '\U000025b9',
-        '\\triangle'             : '\U000025b3',
-        '\\triangleq'            : '\U0000225c',
-        '\\oplus'                : '\U00002295',
-        '\\bigoplus'             : '\U00002a01',
-        '\\otimes'               : '\U00002297',
-        '\\bigotimes'            : '\U00002a02',
-        '\\odot'                 : '\U00002299',
-        '\\bigodot'              : '\U00002a00',
-        '\\ominus'               : '\U00002296',
-        '\\oslash'               : '\U00002298',
-        '\\dots'                 : '\U00002026',
-        '\\cdots'                : '\U000022ef',
-        '\\sum'                  : '\U00002211',
-        '\\prod'                 : '\U0000220f',
-        '\\coprod'               : '\U00002210',
-        '\\infty'                : '\U0000221e',
-        '\\int'                  : '\U0000222b',
-        '\\oint'                 : '\U0000222e',
-        '\\clubsuit'             : '\U00002663',
-        '\\diamondsuit'          : '\U00002662',
-        '\\heartsuit'            : '\U00002661',
-        '\\spadesuit'            : '\U00002660',
-        '\\aleph'                : '\U00002135',
-        '\\emptyset'             : '\U00002205',
-        '\\nabla'                : '\U00002207',
-        '\\partial'              : '\U00002202',
-        '\\flat'                 : '\U0000266d',
-        '\\natural'              : '\U0000266e',
-        '\\sharp'                : '\U0000266f',
-        '\\angle'                : '\U00002220',
-        '\\copyright'            : '\U000000a9',
-        '\\textregistered'       : '\U000000ae',
-        '\\textonequarter'       : '\U000000bc',
-        '\\textonehalf'          : '\U000000bd',
-        '\\textthreequarters'    : '\U000000be',
-        '\\textordfeminine'      : '\U000000aa',
-        '\\textordmasculine'     : '\U000000ba',
-        '\\euro'                 : '\U000020ac',
-        '\\pounds'               : '\U000000a3',
-        '\\yen'                  : '\U000000a5',
-        '\\textcent'             : '\U000000a2',
-        '\\textcurrency'         : '\U000000a4',
-        '\\textdegree'           : '\U000000b0',
-    }
-    isabelle_symbols = {
-        '\\<zero>'                 : '\U0001d7ec',
-        '\\<one>'                  : '\U0001d7ed',
-        '\\<two>'                  : '\U0001d7ee',
-        '\\<three>'                : '\U0001d7ef',
-        '\\<four>'                 : '\U0001d7f0',
-        '\\<five>'                 : '\U0001d7f1',
-        '\\<six>'                  : '\U0001d7f2',
-        '\\<seven>'                : '\U0001d7f3',
-        '\\<eight>'                : '\U0001d7f4',
-        '\\<nine>'                 : '\U0001d7f5',
-        '\\<A>'                    : '\U0001d49c',
-        '\\<B>'                    : '\U0000212c',
-        '\\<C>'                    : '\U0001d49e',
-        '\\<D>'                    : '\U0001d49f',
-        '\\<E>'                    : '\U00002130',
-        '\\<F>'                    : '\U00002131',
-        '\\<G>'                    : '\U0001d4a2',
-        '\\<H>'                    : '\U0000210b',
-        '\\<I>'                    : '\U00002110',
-        '\\<J>'                    : '\U0001d4a5',
-        '\\<K>'                    : '\U0001d4a6',
-        '\\<L>'                    : '\U00002112',
-        '\\<M>'                    : '\U00002133',
-        '\\<N>'                    : '\U0001d4a9',
-        '\\<O>'                    : '\U0001d4aa',
-        '\\<P>'                    : '\U0001d4ab',
-        '\\<Q>'                    : '\U0001d4ac',
-        '\\<R>'                    : '\U0000211b',
-        '\\<S>'                    : '\U0001d4ae',
-        '\\<T>'                    : '\U0001d4af',
-        '\\<U>'                    : '\U0001d4b0',
-        '\\<V>'                    : '\U0001d4b1',
-        '\\<W>'                    : '\U0001d4b2',
-        '\\<X>'                    : '\U0001d4b3',
-        '\\<Y>'                    : '\U0001d4b4',
-        '\\<Z>'                    : '\U0001d4b5',
-        '\\<a>'                    : '\U0001d5ba',
-        '\\<b>'                    : '\U0001d5bb',
-        '\\<c>'                    : '\U0001d5bc',
-        '\\<d>'                    : '\U0001d5bd',
-        '\\<e>'                    : '\U0001d5be',
-        '\\<f>'                    : '\U0001d5bf',
-        '\\<g>'                    : '\U0001d5c0',
-        '\\<h>'                    : '\U0001d5c1',
-        '\\<i>'                    : '\U0001d5c2',
-        '\\<j>'                    : '\U0001d5c3',
-        '\\<k>'                    : '\U0001d5c4',
-        '\\<l>'                    : '\U0001d5c5',
-        '\\<m>'                    : '\U0001d5c6',
-        '\\<n>'                    : '\U0001d5c7',
-        '\\<o>'                    : '\U0001d5c8',
-        '\\<p>'                    : '\U0001d5c9',
-        '\\<q>'                    : '\U0001d5ca',
-        '\\<r>'                    : '\U0001d5cb',
-        '\\<s>'                    : '\U0001d5cc',
-        '\\<t>'                    : '\U0001d5cd',
-        '\\<u>'                    : '\U0001d5ce',
-        '\\<v>'                    : '\U0001d5cf',
-        '\\<w>'                    : '\U0001d5d0',
-        '\\<x>'                    : '\U0001d5d1',
-        '\\<y>'                    : '\U0001d5d2',
-        '\\<z>'                    : '\U0001d5d3',
-        '\\<AA>'                   : '\U0001d504',
-        '\\<BB>'                   : '\U0001d505',
-        '\\<CC>'                   : '\U0000212d',
-        '\\<DD>'                   : '\U0001d507',
-        '\\<EE>'                   : '\U0001d508',
-        '\\<FF>'                   : '\U0001d509',
-        '\\<GG>'                   : '\U0001d50a',
-        '\\<HH>'                   : '\U0000210c',
-        '\\<II>'                   : '\U00002111',
-        '\\<JJ>'                   : '\U0001d50d',
-        '\\<KK>'                   : '\U0001d50e',
-        '\\<LL>'                   : '\U0001d50f',
-        '\\<MM>'                   : '\U0001d510',
-        '\\<NN>'                   : '\U0001d511',
-        '\\<OO>'                   : '\U0001d512',
-        '\\<PP>'                   : '\U0001d513',
-        '\\<QQ>'                   : '\U0001d514',
-        '\\<RR>'                   : '\U0000211c',
-        '\\<SS>'                   : '\U0001d516',
-        '\\<TT>'                   : '\U0001d517',
-        '\\<UU>'                   : '\U0001d518',
-        '\\<VV>'                   : '\U0001d519',
-        '\\<WW>'                   : '\U0001d51a',
-        '\\<XX>'                   : '\U0001d51b',
-        '\\<YY>'                   : '\U0001d51c',
-        '\\<ZZ>'                   : '\U00002128',
-        '\\<aa>'                   : '\U0001d51e',
-        '\\<bb>'                   : '\U0001d51f',
-        '\\<cc>'                   : '\U0001d520',
-        '\\<dd>'                   : '\U0001d521',
-        '\\<ee>'                   : '\U0001d522',
-        '\\<ff>'                   : '\U0001d523',
-        '\\<gg>'                   : '\U0001d524',
-        '\\<hh>'                   : '\U0001d525',
-        '\\<ii>'                   : '\U0001d526',
-        '\\<jj>'                   : '\U0001d527',
-        '\\<kk>'                   : '\U0001d528',
-        '\\<ll>'                   : '\U0001d529',
-        '\\<mm>'                   : '\U0001d52a',
-        '\\<nn>'                   : '\U0001d52b',
-        '\\<oo>'                   : '\U0001d52c',
-        '\\<pp>'                   : '\U0001d52d',
-        '\\<qq>'                   : '\U0001d52e',
-        '\\<rr>'                   : '\U0001d52f',
-        '\\<ss>'                   : '\U0001d530',
-        '\\<tt>'                   : '\U0001d531',
-        '\\<uu>'                   : '\U0001d532',
-        '\\<vv>'                   : '\U0001d533',
-        '\\<ww>'                   : '\U0001d534',
-        '\\<xx>'                   : '\U0001d535',
-        '\\<yy>'                   : '\U0001d536',
-        '\\<zz>'                   : '\U0001d537',
-        '\\<alpha>'                : '\U000003b1',
-        '\\<beta>'                 : '\U000003b2',
-        '\\<gamma>'                : '\U000003b3',
-        '\\<delta>'                : '\U000003b4',
-        '\\<epsilon>'              : '\U000003b5',
-        '\\<zeta>'                 : '\U000003b6',
-        '\\<eta>'                  : '\U000003b7',
-        '\\<theta>'                : '\U000003b8',
-        '\\<iota>'                 : '\U000003b9',
-        '\\<kappa>'                : '\U000003ba',
-        '\\<lambda>'               : '\U000003bb',
-        '\\<mu>'                   : '\U000003bc',
-        '\\<nu>'                   : '\U000003bd',
-        '\\<xi>'                   : '\U000003be',
-        '\\<pi>'                   : '\U000003c0',
-        '\\<rho>'                  : '\U000003c1',
-        '\\<sigma>'                : '\U000003c3',
-        '\\<tau>'                  : '\U000003c4',
-        '\\<upsilon>'              : '\U000003c5',
-        '\\<phi>'                  : '\U000003c6',
-        '\\<chi>'                  : '\U000003c7',
-        '\\<psi>'                  : '\U000003c8',
-        '\\<omega>'                : '\U000003c9',
-        '\\<Gamma>'                : '\U00000393',
-        '\\<Delta>'                : '\U00000394',
-        '\\<Theta>'                : '\U00000398',
-        '\\<Lambda>'               : '\U0000039b',
-        '\\<Xi>'                   : '\U0000039e',
-        '\\<Pi>'                   : '\U000003a0',
-        '\\<Sigma>'                : '\U000003a3',
-        '\\<Upsilon>'              : '\U000003a5',
-        '\\<Phi>'                  : '\U000003a6',
-        '\\<Psi>'                  : '\U000003a8',
-        '\\<Omega>'                : '\U000003a9',
-        '\\<bool>'                 : '\U0001d539',
-        '\\<complex>'              : '\U00002102',
-        '\\<nat>'                  : '\U00002115',
-        '\\<rat>'                  : '\U0000211a',
-        '\\<real>'                 : '\U0000211d',
-        '\\<int>'                  : '\U00002124',
-        '\\<leftarrow>'            : '\U00002190',
-        '\\<longleftarrow>'        : '\U000027f5',
-        '\\<rightarrow>'           : '\U00002192',
-        '\\<longrightarrow>'       : '\U000027f6',
-        '\\<Leftarrow>'            : '\U000021d0',
-        '\\<Longleftarrow>'        : '\U000027f8',
-        '\\<Rightarrow>'           : '\U000021d2',
-        '\\<Longrightarrow>'       : '\U000027f9',
-        '\\<leftrightarrow>'       : '\U00002194',
-        '\\<longleftrightarrow>'   : '\U000027f7',
-        '\\<Leftrightarrow>'       : '\U000021d4',
-        '\\<Longleftrightarrow>'   : '\U000027fa',
-        '\\<mapsto>'               : '\U000021a6',
-        '\\<longmapsto>'           : '\U000027fc',
-        '\\<midarrow>'             : '\U00002500',
-        '\\<Midarrow>'             : '\U00002550',
-        '\\<hookleftarrow>'        : '\U000021a9',
-        '\\<hookrightarrow>'       : '\U000021aa',
-        '\\<leftharpoondown>'      : '\U000021bd',
-        '\\<rightharpoondown>'     : '\U000021c1',
-        '\\<leftharpoonup>'        : '\U000021bc',
-        '\\<rightharpoonup>'       : '\U000021c0',
-        '\\<rightleftharpoons>'    : '\U000021cc',
-        '\\<leadsto>'              : '\U0000219d',
-        '\\<downharpoonleft>'      : '\U000021c3',
-        '\\<downharpoonright>'     : '\U000021c2',
-        '\\<upharpoonleft>'        : '\U000021bf',
-        '\\<upharpoonright>'       : '\U000021be',
-        '\\<restriction>'          : '\U000021be',
-        '\\<Colon>'                : '\U00002237',
-        '\\<up>'                   : '\U00002191',
-        '\\<Up>'                   : '\U000021d1',
-        '\\<down>'                 : '\U00002193',
-        '\\<Down>'                 : '\U000021d3',
-        '\\<updown>'               : '\U00002195',
-        '\\<Updown>'               : '\U000021d5',
-        '\\<langle>'               : '\U000027e8',
-        '\\<rangle>'               : '\U000027e9',
-        '\\<lceil>'                : '\U00002308',
-        '\\<rceil>'                : '\U00002309',
-        '\\<lfloor>'               : '\U0000230a',
-        '\\<rfloor>'               : '\U0000230b',
-        '\\<lparr>'                : '\U00002987',
-        '\\<rparr>'                : '\U00002988',
-        '\\<lbrakk>'               : '\U000027e6',
-        '\\<rbrakk>'               : '\U000027e7',
-        '\\<lbrace>'               : '\U00002983',
-        '\\<rbrace>'               : '\U00002984',
-        '\\<guillemotleft>'        : '\U000000ab',
-        '\\<guillemotright>'       : '\U000000bb',
-        '\\<bottom>'               : '\U000022a5',
-        '\\<top>'                  : '\U000022a4',
-        '\\<and>'                  : '\U00002227',
-        '\\<And>'                  : '\U000022c0',
-        '\\<or>'                   : '\U00002228',
-        '\\<Or>'                   : '\U000022c1',
-        '\\<forall>'               : '\U00002200',
-        '\\<exists>'               : '\U00002203',
-        '\\<nexists>'              : '\U00002204',
-        '\\<not>'                  : '\U000000ac',
-        '\\<box>'                  : '\U000025a1',
-        '\\<diamond>'              : '\U000025c7',
-        '\\<turnstile>'            : '\U000022a2',
-        '\\<Turnstile>'            : '\U000022a8',
-        '\\<tturnstile>'           : '\U000022a9',
-        '\\<TTurnstile>'           : '\U000022ab',
-        '\\<stileturn>'            : '\U000022a3',
-        '\\<surd>'                 : '\U0000221a',
-        '\\<le>'                   : '\U00002264',
-        '\\<ge>'                   : '\U00002265',
-        '\\<lless>'                : '\U0000226a',
-        '\\<ggreater>'             : '\U0000226b',
-        '\\<lesssim>'              : '\U00002272',
-        '\\<greatersim>'           : '\U00002273',
-        '\\<lessapprox>'           : '\U00002a85',
-        '\\<greaterapprox>'        : '\U00002a86',
-        '\\<in>'                   : '\U00002208',
-        '\\<notin>'                : '\U00002209',
-        '\\<subset>'               : '\U00002282',
-        '\\<supset>'               : '\U00002283',
-        '\\<subseteq>'             : '\U00002286',
-        '\\<supseteq>'             : '\U00002287',
-        '\\<sqsubset>'             : '\U0000228f',
-        '\\<sqsupset>'             : '\U00002290',
-        '\\<sqsubseteq>'           : '\U00002291',
-        '\\<sqsupseteq>'           : '\U00002292',
-        '\\<inter>'                : '\U00002229',
-        '\\<Inter>'                : '\U000022c2',
-        '\\<union>'                : '\U0000222a',
-        '\\<Union>'                : '\U000022c3',
-        '\\<squnion>'              : '\U00002294',
-        '\\<Squnion>'              : '\U00002a06',
-        '\\<sqinter>'              : '\U00002293',
-        '\\<Sqinter>'              : '\U00002a05',
-        '\\<setminus>'             : '\U00002216',
-        '\\<propto>'               : '\U0000221d',
-        '\\<uplus>'                : '\U0000228e',
-        '\\<Uplus>'                : '\U00002a04',
-        '\\<noteq>'                : '\U00002260',
-        '\\<sim>'                  : '\U0000223c',
-        '\\<doteq>'                : '\U00002250',
-        '\\<simeq>'                : '\U00002243',
-        '\\<approx>'               : '\U00002248',
-        '\\<asymp>'                : '\U0000224d',
-        '\\<cong>'                 : '\U00002245',
-        '\\<smile>'                : '\U00002323',
-        '\\<equiv>'                : '\U00002261',
-        '\\<frown>'                : '\U00002322',
-        '\\<Join>'                 : '\U000022c8',
-        '\\<bowtie>'               : '\U00002a1d',
-        '\\<prec>'                 : '\U0000227a',
-        '\\<succ>'                 : '\U0000227b',
-        '\\<preceq>'               : '\U0000227c',
-        '\\<succeq>'               : '\U0000227d',
-        '\\<parallel>'             : '\U00002225',
-        '\\<bar>'                  : '\U000000a6',
-        '\\<plusminus>'            : '\U000000b1',
-        '\\<minusplus>'            : '\U00002213',
-        '\\<times>'                : '\U000000d7',
-        '\\<div>'                  : '\U000000f7',
-        '\\<cdot>'                 : '\U000022c5',
-        '\\<star>'                 : '\U000022c6',
-        '\\<bullet>'               : '\U00002219',
-        '\\<circ>'                 : '\U00002218',
-        '\\<dagger>'               : '\U00002020',
-        '\\<ddagger>'              : '\U00002021',
-        '\\<lhd>'                  : '\U000022b2',
-        '\\<rhd>'                  : '\U000022b3',
-        '\\<unlhd>'                : '\U000022b4',
-        '\\<unrhd>'                : '\U000022b5',
-        '\\<triangleleft>'         : '\U000025c3',
-        '\\<triangleright>'        : '\U000025b9',
-        '\\<triangle>'             : '\U000025b3',
-        '\\<triangleq>'            : '\U0000225c',
-        '\\<oplus>'                : '\U00002295',
-        '\\<Oplus>'                : '\U00002a01',
-        '\\<otimes>'               : '\U00002297',
-        '\\<Otimes>'               : '\U00002a02',
-        '\\<odot>'                 : '\U00002299',
-        '\\<Odot>'                 : '\U00002a00',
-        '\\<ominus>'               : '\U00002296',
-        '\\<oslash>'               : '\U00002298',
-        '\\<dots>'                 : '\U00002026',
-        '\\<cdots>'                : '\U000022ef',
-        '\\<Sum>'                  : '\U00002211',
-        '\\<Prod>'                 : '\U0000220f',
-        '\\<Coprod>'               : '\U00002210',
-        '\\<infinity>'             : '\U0000221e',
-        '\\<integral>'             : '\U0000222b',
-        '\\<ointegral>'            : '\U0000222e',
-        '\\<clubsuit>'             : '\U00002663',
-        '\\<diamondsuit>'          : '\U00002662',
-        '\\<heartsuit>'            : '\U00002661',
-        '\\<spadesuit>'            : '\U00002660',
-        '\\<aleph>'                : '\U00002135',
-        '\\<emptyset>'             : '\U00002205',
-        '\\<nabla>'                : '\U00002207',
-        '\\<partial>'              : '\U00002202',
-        '\\<flat>'                 : '\U0000266d',
-        '\\<natural>'              : '\U0000266e',
-        '\\<sharp>'                : '\U0000266f',
-        '\\<angle>'                : '\U00002220',
-        '\\<copyright>'            : '\U000000a9',
-        '\\<registered>'           : '\U000000ae',
-        '\\<hyphen>'               : '\U000000ad',
-        '\\<inverse>'              : '\U000000af',
-        '\\<onequarter>'           : '\U000000bc',
-        '\\<onehalf>'              : '\U000000bd',
-        '\\<threequarters>'        : '\U000000be',
-        '\\<ordfeminine>'          : '\U000000aa',
-        '\\<ordmasculine>'         : '\U000000ba',
-        '\\<section>'              : '\U000000a7',
-        '\\<paragraph>'            : '\U000000b6',
-        '\\<exclamdown>'           : '\U000000a1',
-        '\\<questiondown>'         : '\U000000bf',
-        '\\<euro>'                 : '\U000020ac',
-        '\\<pounds>'               : '\U000000a3',
-        '\\<yen>'                  : '\U000000a5',
-        '\\<cent>'                 : '\U000000a2',
-        '\\<currency>'             : '\U000000a4',
-        '\\<degree>'               : '\U000000b0',
-        '\\<amalg>'                : '\U00002a3f',
-        '\\<mho>'                  : '\U00002127',
-        '\\<lozenge>'              : '\U000025ca',
-        '\\<wp>'                   : '\U00002118',
-        '\\<wrong>'                : '\U00002240',
-        '\\<struct>'               : '\U000022c4',
-        '\\<acute>'                : '\U000000b4',
-        '\\<index>'                : '\U00000131',
-        '\\<dieresis>'             : '\U000000a8',
-        '\\<cedilla>'              : '\U000000b8',
-        '\\<hungarumlaut>'         : '\U000002dd',
-        '\\<some>'                 : '\U000003f5',
-        '\\<newline>'              : '\U000023ce',
-        '\\<open>'                 : '\U00002039',
-        '\\<close>'                : '\U0000203a',
-        '\\<here>'                 : '\U00002302',
-        '\\<^sub>'                 : '\U000021e9',
-        '\\<^sup>'                 : '\U000021e7',
-        '\\<^bold>'                : '\U00002759',
-        '\\<^bsub>'                : '\U000021d8',
-        '\\<^esub>'                : '\U000021d9',
-        '\\<^bsup>'                : '\U000021d7',
-        '\\<^esup>'                : '\U000021d6',
-    }
-    lang_map = {'isabelle' : isabelle_symbols, 'latex' : latex_symbols}
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        lang = get_choice_opt(options, 'lang',
-                              ['isabelle', 'latex'], 'isabelle')
-        self.symbols = self.lang_map[lang]
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if value in self.symbols:
-                yield ttype, self.symbols[value]
-            else:
-                yield ttype, value
-class KeywordCaseFilter(Filter):
-    """Convert keywords to lowercase or uppercase or capitalize them, which
-    means first letter uppercase, rest lowercase.
-    This can be useful e.g. if you highlight Pascal code and want to adapt the
-    code to your styleguide.
-    Options accepted:
-    `case` : string
-       The casing to convert keywords to. Must be one of ``'lower'``,
-       ``'upper'`` or ``'capitalize'``.  The default is ``'lower'``.
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        case = get_choice_opt(options, 'case',
-                              ['lower', 'upper', 'capitalize'], 'lower')
-        self.convert = getattr(str, case)
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype in Keyword:
-                yield ttype, self.convert(value)
-            else:
-                yield ttype, value
-class NameHighlightFilter(Filter):
-    """Highlight a normal Name (and Name.*) token with a different token type.
-    Example::
-        filter = NameHighlightFilter(
-            names=['foo', 'bar', 'baz'],
-            tokentype=Name.Function,
-        )
-    This would highlight the names "foo", "bar" and "baz"
-    as functions. `Name.Function` is the default token type.
-    Options accepted:
-    `names` : list of strings
-      A list of names that should be given the different token type.
-      There is no default.
-    `tokentype` : TokenType or string
-      A token type or a string containing a token type name that is
-      used for highlighting the strings in `names`.  The default is
-      `Name.Function`.
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.names = set(get_list_opt(options, 'names', []))
-        tokentype = options.get('tokentype')
-        if tokentype:
-            self.tokentype = string_to_tokentype(tokentype)
-        else:
-            self.tokentype = Name.Function
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype in Name and value in self.names:
-                yield self.tokentype, value
-            else:
-                yield ttype, value
-class ErrorToken(Exception):
-    pass
-class RaiseOnErrorTokenFilter(Filter):
-    """Raise an exception when the lexer generates an error token.
-    Options accepted:
-    `excclass` : Exception class
-      The exception class to raise.
-      The default is `pygments.filters.ErrorToken`.
-    .. versionadded:: 0.8
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.exception = options.get('excclass', ErrorToken)
-        try:
-            # issubclass() will raise TypeError if first argument is not a class
-            if not issubclass(self.exception, Exception):
-                raise TypeError
-        except TypeError:
-            raise OptionError('excclass option is not an exception class')
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype is Error:
-                raise self.exception(value)
-            yield ttype, value
-class VisibleWhitespaceFilter(Filter):
-    """Convert tabs, newlines and/or spaces to visible characters.
-    Options accepted:
-    `spaces` : string or bool
-      If this is a one-character string, spaces will be replaces by this string.
-      If it is another true value, spaces will be replaced by ``·`` (unicode
-      MIDDLE DOT).  If it is a false value, spaces will not be replaced.  The
-      default is ``False``.
-    `tabs` : string or bool
-      The same as for `spaces`, but the default replacement character is ``»``
-      (unicode RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK).  The default value
-      is ``False``.  Note: this will not work if the `tabsize` option for the
-      lexer is nonzero, as tabs will already have been expanded then.
-    `tabsize` : int
-      If tabs are to be replaced by this filter (see the `tabs` option), this
-      is the total number of characters that a tab should be expanded to.
-      The default is ``8``.
-    `newlines` : string or bool
-      The same as for `spaces`, but the default replacement character is ``¶``
-      (unicode PILCROW SIGN).  The default value is ``False``.
-    `wstokentype` : bool
-      If true, give whitespace the special `Whitespace` token type.  This allows
-      styling the visible whitespace differently (e.g. greyed out), but it can
-      disrupt background colors.  The default is ``True``.
-    .. versionadded:: 0.8
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        for name, default in [('spaces',   '·'),
-                              ('tabs',     '»'),
-                              ('newlines', '¶')]:
-            opt = options.get(name, False)
-            if isinstance(opt, str) and len(opt) == 1:
-                setattr(self, name, opt)
-            else:
-                setattr(self, name, (opt and default or ''))
-        tabsize = get_int_opt(options, 'tabsize', 8)
-        if self.tabs:
-            self.tabs += ' ' * (tabsize - 1)
-        if self.newlines:
-            self.newlines += '\n'
-        self.wstt = get_bool_opt(options, 'wstokentype', True)
-    def filter(self, lexer, stream):
-        if self.wstt:
-            spaces = self.spaces or ' '
-            tabs = self.tabs or '\t'
-            newlines = self.newlines or '\n'
-            regex = re.compile(r'\s')
-            def replacefunc(wschar):
-                if wschar == ' ':
-                    return spaces
-                elif wschar == '\t':
-                    return tabs
-                elif wschar == '\n':
-                    return newlines
-                return wschar
-            for ttype, value in stream:
-                yield from _replace_special(ttype, value, regex, Whitespace,
-                                            replacefunc)
-        else:
-            spaces, tabs, newlines = self.spaces, self.tabs, self.newlines
-            # simpler processing
-            for ttype, value in stream:
-                if spaces:
-                    value = value.replace(' ', spaces)
-                if tabs:
-                    value = value.replace('\t', tabs)
-                if newlines:
-                    value = value.replace('\n', newlines)
-                yield ttype, value
-class GobbleFilter(Filter):
-    """Gobbles source code lines (eats initial characters).
-    This filter drops the first ``n`` characters off every line of code.  This
-    may be useful when the source code fed to the lexer is indented by a fixed
-    amount of space that isn't desired in the output.
-    Options accepted:
-    `n` : int
-       The number of characters to gobble.
-    .. versionadded:: 1.2
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.n = get_int_opt(options, 'n', 0)
-    def gobble(self, value, left):
-        if left < len(value):
-            return value[left:], 0
-        else:
-            return '', left - len(value)
-    def filter(self, lexer, stream):
-        n = self.n
-        left = n  # How many characters left to gobble.
-        for ttype, value in stream:
-            # Remove ``left`` tokens from first line, ``n`` from all others.
-            parts = value.split('\n')
-            (parts[0], left) = self.gobble(parts[0], left)
-            for i in range(1, len(parts)):
-                (parts[i], left) = self.gobble(parts[i], n)
-            value = '\n'.join(parts)
-            if value != '':
-                yield ttype, value
-class TokenMergeFilter(Filter):
-    """Merges consecutive tokens with the same token type in the output
-    stream of a lexer.
-    .. versionadded:: 1.2
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-    def filter(self, lexer, stream):
-        current_type = None
-        current_value = None
-        for ttype, value in stream:
-            if ttype is current_type:
-                current_value += value
-            else:
-                if current_type is not None:
-                    yield current_type, current_value
-                current_type = ttype
-                current_value = value
-        if current_type is not None:
-            yield current_type, current_value
-FILTERS = {
-    'codetagify':     CodeTagFilter,
-    'keywordcase':    KeywordCaseFilter,
-    'highlight':      NameHighlightFilter,
-    'raiseonerror':   RaiseOnErrorTokenFilter,
-    'whitespace':     VisibleWhitespaceFilter,
-    'gobble':         GobbleFilter,
-    'tokenmerge':     TokenMergeFilter,
-    'symbols':        SymbolFilter,
-}

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

@@ -1,152 +0,0 @@
-import errno
-import select
-import sys
-from functools import partial
-try:
-    from time import monotonic
-except ImportError:
-    from time import time as monotonic
-__all__ = ["NoWayToWaitForSocketError", "wait_for_read", "wait_for_write"]
-class NoWayToWaitForSocketError(Exception):
-    pass
-# How should we wait on sockets?
-#
-# There are two types of APIs you can use for waiting on sockets: the fancy
-# modern stateful APIs like epoll/kqueue, and the older stateless APIs like
-# select/poll. The stateful APIs are more efficient when you have a lots of
-# sockets to keep track of, because you can set them up once and then use them
-# lots of times. But we only ever want to wait on a single socket at a time
-# and don't want to keep track of state, so the stateless APIs are actually
-# more efficient. So we want to use select() or poll().
-#
-# Now, how do we choose between select() and poll()? On traditional Unixes,
-# select() has a strange calling convention that makes it slow, or fail
-# altogether, for high-numbered file descriptors. The point of poll() is to fix
-# that, so on Unixes, we prefer poll().
-#
-# On Windows, there is no poll() (or at least Python doesn't provide a wrapper
-# for it), but that's OK, because on Windows, select() doesn't have this
-# strange calling convention; plain select() works fine.
-#
-# So: on Windows we use select(), and everywhere else we use poll(). We also
-# fall back to select() in case poll() is somehow broken or missing.
-if sys.version_info >= (3, 5):
-    # Modern Python, that retries syscalls by default
-    def _retry_on_intr(fn, timeout):
-        return fn(timeout)
-else:
-    # Old and broken Pythons.
-    def _retry_on_intr(fn, timeout):
-        if timeout is None:
-            deadline = float("inf")
-        else:
-            deadline = monotonic() + timeout
-        while True:
-            try:
-                return fn(timeout)
-            # OSError for 3 <= pyver < 3.5, select.error for pyver <= 2.7
-            except (OSError, select.error) as e:
-                # 'e.args[0]' incantation works for both OSError and select.error
-                if e.args[0] != errno.EINTR:
-                    raise
-                else:
-                    timeout = deadline - monotonic()
-                    if timeout < 0:
-                        timeout = 0
-                    if timeout == float("inf"):
-                        timeout = None
-                    continue
-def select_wait_for_socket(sock, read=False, write=False, timeout=None):
-    if not read and not write:
-        raise RuntimeError("must specify at least one of read=True, write=True")
-    rcheck = []
-    wcheck = []
-    if read:
-        rcheck.append(sock)
-    if write:
-        wcheck.append(sock)
-    # When doing a non-blocking connect, most systems signal success by
-    # marking the socket writable. Windows, though, signals success by marked
-    # it as "exceptional". We paper over the difference by checking the write
-    # sockets for both conditions. (The stdlib selectors module does the same
-    # thing.)
-    fn = partial(select.select, rcheck, wcheck, wcheck)
-    rready, wready, xready = _retry_on_intr(fn, timeout)
-    return bool(rready or wready or xready)
-def poll_wait_for_socket(sock, read=False, write=False, timeout=None):
-    if not read and not write:
-        raise RuntimeError("must specify at least one of read=True, write=True")
-    mask = 0
-    if read:
-        mask |= select.POLLIN
-    if write:
-        mask |= select.POLLOUT
-    poll_obj = select.poll()
-    poll_obj.register(sock, mask)
-    # For some reason, poll() takes timeout in milliseconds
-    def do_poll(t):
-        if t is not None:
-            t *= 1000
-        return poll_obj.poll(t)
-    return bool(_retry_on_intr(do_poll, timeout))
-def null_wait_for_socket(*args, **kwargs):
-    raise NoWayToWaitForSocketError("no select-equivalent available")
-def _have_working_poll():
-    # Apparently some systems have a select.poll that fails as soon as you try
-    # to use it, either due to strange configuration or broken monkeypatching
-    # from libraries like eventlet/greenlet.
-    try:
-        poll_obj = select.poll()
-        _retry_on_intr(poll_obj.poll, 0)
-    except (AttributeError, OSError):
-        return False
-    else:
-        return True
-def wait_for_socket(*args, **kwargs):
-    # We delay choosing which implementation to use until the first time we're
-    # called. We could do it at import time, but then we might make the wrong
-    # decision if someone goes wild with monkeypatching select.poll after
-    # we're imported.
-    global wait_for_socket
-    if _have_working_poll():
-        wait_for_socket = poll_wait_for_socket
-    elif hasattr(select, "select"):
-        wait_for_socket = select_wait_for_socket
-    else:  # Platform-specific: Appengine.
-        wait_for_socket = null_wait_for_socket
-    return wait_for_socket(*args, **kwargs)
-def wait_for_read(sock, timeout=None):
-    """Waits for reading to be available on a given socket.
-    Returns True if the socket is readable, or False if the timeout expired.
-    """
-    return wait_for_socket(sock, read=True, timeout=timeout)
-def wait_for_write(sock, timeout=None):
-    """Waits for writing to be available on a given socket.
-    Returns True if the socket is readable, or False if the timeout expired.
-    """
-    return wait_for_socket(sock, write=True, timeout=timeout)

spaces/Bobertsonthethird/Test01/README.md DELETED Viewed

@@ -1,10 +0,0 @@
----
-title: Test01
-emoji: 🦀
-colorFrom: pink
-colorTo: red
-sdk: docker
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVH-vn1210/make_hair/minigpt4/models/modeling_llama.py DELETED Viewed

@@ -1,772 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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.
-""" PyTorch LLaMA model."""
-import math
-from typing import List, Optional, Tuple, Union
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
-from transformers.models.llama.configuration_llama import LlamaConfig
-logger = logging.get_logger(__name__)
-_CONFIG_FOR_DOC = "LlamaConfig"
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-    inverted_mask = 1.0 - expanded_mask
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-class LlamaRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-    def forward(self, hidden_states):
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        # convert into half-precision if necessary
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-        return self.weight * hidden_states
-class LlamaRotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
-        self.register_buffer("inv_freq", inv_freq)
-        # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
-        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
-        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
-        if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
-            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            # Different from paper, but it uses a different permutation in order to obtain the same calculation
-            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
-            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
-    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
-    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
-    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-class LlamaMLP(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-    ):
-        super().__init__()
-        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
-        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.act_fn = ACT2FN[hidden_act]
-    def forward(self, x):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-class LlamaAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.max_position_embeddings = config.max_position_embeddings
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-        # [bsz, nh, t, hd]
-        if past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        past_key_value = (key_states, value_states) if use_cache else None
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-        attn_output = self.o_proj(attn_output)
-        if not output_attentions:
-            attn_weights = None
-        return attn_output, attn_weights, past_key_value
-class LlamaDecoderLayer(nn.Module):
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = LlamaAttention(config=config)
-        self.mlp = LlamaMLP(
-            hidden_size=self.hidden_size,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-        )
-        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        """
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-        )
-        hidden_states = residual + hidden_states
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-        if use_cache:
-            outputs += (present_key_value,)
-        return outputs
-LLAMA_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-    Parameters:
-        config ([`LlamaConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaPreTrainedModel(PreTrainedModel):
-    config_class = LlamaConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["LlamaDecoderLayer"]
-    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, LlamaModel):
-            module.gradient_checkpointing = value
-LLAMA_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-            [What are attention masks?](../glossary#attention-mask)
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaModel(LlamaPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
-    Args:
-        config: LlamaConfig
-    """
-    def __init__(self, config: LlamaConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-    def get_input_embeddings(self):
-        return self.embed_tokens
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-        return combined_attention_mask
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        query_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-        if query_embeds is not None:
-            inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1)
-            batch_size, seq_length, _ = inputs_embeds.shape
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-        # embed positions
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
-            )
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-        )
-        hidden_states = inputs_embeds
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-            past_key_value = past_key_values[idx] if past_key_values is not None else None
-            if self.gradient_checkpointing and self.training:
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, output_attentions, None)
-                    return custom_forward
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-        hidden_states = self.norm(hidden_states)
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-class LlamaForCausalLM(LlamaPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = LlamaModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        # Initialize weights and apply final processing
-        self.post_init()
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-    def get_output_embeddings(self):
-        return self.lm_head
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-    def set_decoder(self, decoder):
-        self.model = decoder
-    def get_decoder(self):
-        return self.model
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        query_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-        Returns:
-        Example:
-        ```python
-        >>> from transformers import AutoTokenizer, LlamaForCausalLM
-        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-        >>> prompt = "Hey, are you consciours? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            query_embeds=query_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-    def prepare_inputs_for_generation(
-        self, input_ids, query_embeds=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
-    ):
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-                query_embeds = None
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "query_embeds": query_embeds,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past

spaces/CVPR/GroupViT/README.md DELETED Viewed

@@ -1,10 +0,0 @@
----
-title: GroupViT
-emoji: 👀
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: true
----

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/memory.h DELETED Viewed

@@ -1,71 +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 generic/memory.h
- *  \brief Generic implementation of memory functions.
- *         Calling some of these is an error. They have no implementation.
- */
-#pragma once
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/system/detail/generic/tag.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/pointer.h>
-#include <thrust/pair.h>
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-template<typename DerivedPolicy, typename Size>
-__host__ __device__
-void malloc(thrust::execution_policy<DerivedPolicy> &, Size);
-template<typename T, typename DerivedPolicy>
-__host__ __device__
-thrust::pointer<T,DerivedPolicy> malloc(thrust::execution_policy<DerivedPolicy> &s, std::size_t n);
-template<typename DerivedPolicy, typename Pointer>
-__host__ __device__
-void free(thrust::execution_policy<DerivedPolicy> &, Pointer);
-template<typename Pointer1, typename Pointer2>
-__host__ __device__
-void assign_value(tag, Pointer1, Pointer2);
-template<typename DerivedPolicy, typename Pointer>
-__host__ __device__
-void get_value(thrust::execution_policy<DerivedPolicy> &, Pointer);
-template<typename DerivedPolicy, typename Pointer1, typename Pointer2>
-__host__ __device__
-void iter_swap(thrust::execution_policy<DerivedPolicy>&, Pointer1, Pointer2);
-} // end generic
-} // end detail
-} // end system
-} // end thrust
-#include <thrust/system/detail/generic/memory.inl>

spaces/Chakri-kollepara-5/Mygena/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Mygena
-emoji: 🐠
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ChenWu98/Stable-CycleDiffusion/seq_aligner.py DELETED Viewed

@@ -1,196 +0,0 @@
-# Copyright 2022 Google LLC
-#
-# 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 torch
-import numpy as np
-class ScoreParams:
-    def __init__(self, gap, match, mismatch):
-        self.gap = gap
-        self.match = match
-        self.mismatch = mismatch
-    def mis_match_char(self, x, y):
-        if x != y:
-            return self.mismatch
-        else:
-            return self.match
-def get_matrix(size_x, size_y, gap):
-    matrix = []
-    for i in range(len(size_x) + 1):
-        sub_matrix = []
-        for j in range(len(size_y) + 1):
-            sub_matrix.append(0)
-        matrix.append(sub_matrix)
-    for j in range(1, len(size_y) + 1):
-        matrix[0][j] = j*gap
-    for i in range(1, len(size_x) + 1):
-        matrix[i][0] = i*gap
-    return matrix
-def get_matrix(size_x, size_y, gap):
-    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
-    matrix[0, 1:] = (np.arange(size_y) + 1) * gap
-    matrix[1:, 0] = (np.arange(size_x) + 1) * gap
-    return matrix
-def get_traceback_matrix(size_x, size_y):
-    matrix = np.zeros((size_x + 1, size_y +1), dtype=np.int32)
-    matrix[0, 1:] = 1
-    matrix[1:, 0] = 2
-    matrix[0, 0] = 4
-    return matrix
-def global_align(x, y, score):
-    matrix = get_matrix(len(x), len(y), score.gap)
-    trace_back = get_traceback_matrix(len(x), len(y))
-    for i in range(1, len(x) + 1):
-        for j in range(1, len(y) + 1):
-            left = matrix[i, j - 1] + score.gap
-            up = matrix[i - 1, j] + score.gap
-            diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
-            matrix[i, j] = max(left, up, diag)
-            if matrix[i, j] == left:
-                trace_back[i, j] = 1
-            elif matrix[i, j] == up:
-                trace_back[i, j] = 2
-            else:
-                trace_back[i, j] = 3
-    return matrix, trace_back
-def get_aligned_sequences(x, y, trace_back):
-    x_seq = []
-    y_seq = []
-    i = len(x)
-    j = len(y)
-    mapper_y_to_x = []
-    while i > 0 or j > 0:
-        if trace_back[i, j] == 3:
-            x_seq.append(x[i-1])
-            y_seq.append(y[j-1])
-            i = i-1
-            j = j-1
-            mapper_y_to_x.append((j, i))
-        elif trace_back[i][j] == 1:
-            x_seq.append('-')
-            y_seq.append(y[j-1])
-            j = j-1
-            mapper_y_to_x.append((j, -1))
-        elif trace_back[i][j] == 2:
-            x_seq.append(x[i-1])
-            y_seq.append('-')
-            i = i-1
-        elif trace_back[i][j] == 4:
-            break
-    mapper_y_to_x.reverse()
-    return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
-def get_mapper(x: str, y: str, tokenizer, max_len=77):
-    x_seq = tokenizer.encode(x)
-    y_seq = tokenizer.encode(y)
-    score = ScoreParams(0, 1, -1)
-    matrix, trace_back = global_align(x_seq, y_seq, score)
-    mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
-    alphas = torch.ones(max_len)
-    alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
-    mapper = torch.zeros(max_len, dtype=torch.int64)
-    mapper[:mapper_base.shape[0]] = mapper_base[:, 1]
-    mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len - len(y_seq))
-    return mapper, alphas
-def get_refinement_mapper(prompts, tokenizer, max_len=77):
-    x_seq = prompts[0]
-    mappers, alphas = [], []
-    for i in range(1, len(prompts)):
-        mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
-        mappers.append(mapper)
-        alphas.append(alpha)
-    return torch.stack(mappers), torch.stack(alphas)
-def get_word_inds(text: str, word_place: int, tokenizer):
-    split_text = text.split(" ")
-    if type(word_place) is str:
-        word_place = [i for i, word in enumerate(split_text) if word_place == word]
-    elif type(word_place) is int:
-        word_place = [word_place]
-    out = []
-    if len(word_place) > 0:
-        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
-        cur_len, ptr = 0, 0
-        for i in range(len(words_encode)):
-            cur_len += len(words_encode[i])
-            if ptr in word_place:
-                out.append(i + 1)
-            if cur_len >= len(split_text[ptr]):
-                ptr += 1
-                cur_len = 0
-    return np.array(out)
-def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
-    words_x = x.split(' ')
-    words_y = y.split(' ')
-    if len(words_x) != len(words_y):
-        raise ValueError(f"attention replacement edit can only be applied on prompts with the same length"
-                         f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.")
-    inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
-    inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
-    inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
-    mapper = np.zeros((max_len, max_len))
-    i = j = 0
-    cur_inds = 0
-    while i < max_len and j < max_len:
-        if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
-            inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
-            if len(inds_source_) == len(inds_target_):
-                mapper[inds_source_, inds_target_] = 1
-            else:
-                ratio = 1 / len(inds_target_)
-                for i_t in inds_target_:
-                    mapper[inds_source_, i_t] = ratio
-            cur_inds += 1
-            i += len(inds_source_)
-            j += len(inds_target_)
-        elif cur_inds < len(inds_source):
-            mapper[i, j] = 1
-            i += 1
-            j += 1
-        else:
-            mapper[j, j] = 1
-            i += 1
-            j += 1
-    return torch.from_numpy(mapper).float()
-def get_replacement_mapper(prompts, tokenizer, max_len=77):
-    x_seq = prompts[0]
-    mappers = []
-    for i in range(1, len(prompts)):
-        mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
-        mappers.append(mapper)
-    return torch.stack(mappers)