Update parquet files (step 114 of 249)

spaces/101-5/gpt4free/g4f/Provider/Providers/Acytoo.py

@@ -1,41 +0,0 @@
-import os, requests
-from ...typing import sha256, Dict, get_type_hints
-import json
-
-url = "https://chat.acytoo.com/api/completions"
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    base = ''
-    for message in messages:
-        base += '%s: %s\n' % (message['role'], message['content'])
-    base += 'assistant:'
-
-    headers = {
-        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36"
-    }
-    data = {
-        "key": "",
-        "model": "gpt-3.5-turbo",
-        "messages": [
-            {
-                "role": "user",
-                "content": base,
-                "createdAt": 1688518523500
-            }
-        ],
-        "temperature": 1,
-        "password": ""
-    }
-
-    response = requests.post(url, headers=headers, data=json.dumps(data))
-    if response.status_code == 200:
-        yield response.text
-    else:
-        print(f"Error Occurred::{response.status_code}")
-        return None
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/1acneusushi/gradio-2dmoleculeeditor/Bascom Avr 2.0.7.5 Crack _BEST_.md

@@ -1,70 +0,0 @@
-## Bascom Avr 2.0.7.5 Crack
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-
-# Bascom AVR 2.0.7.5: A Powerful and Easy-to-Use Compiler for AVR Microcontrollers
-
- 
-
-If you are looking for a compiler that can help you program the AVR series of microcontrollers developed by Atmel, you might want to check out Bascom AVR 2.0.7.5. This is a very powerful and user-friendly compiler that comes with a lot of features and flexibility.
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Analisisliterariodelamiskisimi.md

@@ -1,19 +0,0 @@
-
-<h1>Análisis literario de la Miskisi Mi: una novela de identidad y resistencia</h1>
-<p>La Miskisi Mi es una novela escrita por el autor boliviano Víctor Montoya, publicada en 2017. La obra narra la historia de una joven indígena que vive en la ciudad de El Alto, en Bolivia, y que se enfrenta a la discriminación, la violencia y la pobreza. La novela es una reflexión sobre la identidad, la cultura y la resistencia de los pueblos originarios en el contexto de la globalización y el neoliberalismo.</p>
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-<p>En este artículo, realizaremos un análisis literario de la Miskisi Mi, siguiendo los pasos básicos para interpretar una obra narrativa: contexto literario, histórico y sociocultural; descripción de la obra; tema; argumento; personajes; estructura; recursos estilísticos; y valoración crítica.</p>
-
-<h2>Contexto literario, histórico y sociocultural</h2>
-<p>Víctor Montoya nació en La Paz, Bolivia, en 1958. Es un escritor, periodista y docente que ha vivido gran parte de su vida en el exilio, debido a su militancia política contra las dictaduras militares que gobernaron su país en los años 70 y 80. Su obra literaria abarca diversos géneros como el cuento, la novela, el ensayo y la crónica. Sus temas principales son la memoria histórica, la identidad cultural, la violencia política y social, y la defensa de los derechos humanos.</p>
-<p>La Miskisi Mi es una novela que pertenece al género narrativo y al subgénero de la novela social. Se inscribe en el movimiento literario del realismo crítico, que busca retratar la realidad social de forma objetiva y denunciar las injusticias y las desigualdades que sufren los sectores más vulnerables de la sociedad. La novela se publicó en 2017, en un momento histórico marcado por el gobierno del presidente Evo Morales, el primer mandatario indígena de Bolivia, que impulsó una serie de reformas políticas, económicas y sociales para favorecer a las mayorías populares y a los pueblos originarios.</p>
-<p></p>
-<p>La novela refleja el contexto sociocultural de Bolivia, un país plurinacional y multicultural que alberga a más de 30 pueblos indígenas con sus propias lenguas, costumbres y cosmovisiones. Sin embargo, también muestra las contradicciones y los conflictos que existen entre las diferentes culturas y clases sociales que conviven en el territorio boliviano. La novela se centra en la realidad de El Alto, una ciudad situada a más de 4 mil metros sobre el nivel del mar, que se caracteriza por ser un bastión de resistencia popular y por tener una población mayoritariamente indígena y migrante.</p>
-
-<h2>Descripción de la obra</h2>
-<p>A continuación, describiremos los elementos más relevantes de la obra: tema, argumento, personajes, estructura y recursos estilísticos.</p>
-
-<h3>Tema</h3>
-<p>El tema principal de la novela es la búsqueda de la identidad de una joven indígena que vive en una sociedad hostil y excluyente. La protagonista se llama Miskisi Mi, que significa "mi dulzura" en aymara, una de las lenguas originarias de Bolivia. Miskisi Mi es una chica que tiene que luchar por su dignidad y su libertad frente a las adversidades que le impone su condición social, étnica y de género. La novela también aborda otros temas secundarios como el racismo, el machismo, la violencia</p> 7b8c122e87<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Embarcadero Rad Studio 2010 Keygen Crack Learn How to Create Amazing Applications with Delphi and C.md

@@ -1,89 +0,0 @@
-<br />
-<h1>Embarcadero Rad Studio 2010 Keygen Crack: What You Need to Know</h1>
-<p>If you are a software developer who wants to create cross-platform applications for Windows, Mac, iOS and Android devices, you might have heard of Embarcadero Rad Studio 2010. This is a powerful and comprehensive development tool that offers a range of features and benefits for rapid application development. However, you might also be tempted to use a keygen crack to activate this software without paying for a license. In this article, we will explain what Embarcadero Rad Studio 2010 is, why you might need a keygen crack for it, what are the risks and challenges of using one, and what are the alternatives and solutions to avoid legal, security and quality issues.</p>
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- <h2>Features and Benefits of Embarcadero Rad Studio 2010</h2>
-<p>Embarcadero Rad Studio 2010 is an integrated development environment (IDE) that allows you to create native applications for Windows, Mac, iOS and Android platforms using a single code base. It supports multiple programming languages, including Delphi, C++, C# and PHP. It also provides a rich library of components, frameworks and tools for database, web, cloud and mobile development. Some of the features and benefits of Embarcadero Rad Studio 2010 are:</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Bel Ami Pin Ups Young And Tender.md

@@ -1,28 +0,0 @@
-<h2>bel ami pin ups young and tender</h2><br /><p><b><b>Download File</b> > <a href="https://imgfil.com/2uy14p">https://imgfil.com/2uy14p</a></b></p><br /><br />
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-Explore my tags to see how I’ve tagged this category. Help grow my growing list of what to do on my blog.
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-Pages
-
-Tuesday, November 28, 2017
-
-1 - Vintage Signs
-
-2 - The Vintage of the Signs
-
-3 - Vintage Signs by the Numbers
-
-If you have seen an old, vintage sign, chances are you would have given it a name, at least in your head. Was it a business name, a political statement, a slogan, or something else? Perhaps if you were a collector of old signs, you would have collected these signs in a different way; but if you had just seen these signs and forgotten to get their name, you would not remember. In either case, you might have just walked on by.
-
-This series of posts is about the name of the sign and the history of the sign, and where and how the sign was made. This is a small sampling of the many vintage signs that I have located and documented over the years. They are a combination of first generation signs (purchased when they were new), auction signs, and new ones made for today’s lifestyle.
-
-The Vintage of the Signs
-
-In the United States, there are many signs on the street with the names of the businesses there. Some of these signs are quite old. If you can imagine, they were there when the businesses were first built or when the business was built, and they have lasted. For example, take an American flag, put it on a pole, and attach a sign that says “American Flagpole” on the side of the pole. If you had noticed the sign in the past, you would have said to yourself, “Hey, there is a sign for American Flagpole,” and you would have remembered it, and you would have recalled that someone put up a sign for it when it was first erected. Some of the signs on the street date back to the 1890s. As I drive by the signs, I often wonder how many of them will survive the next decade or two.
-
-Now look at this street sign from the early 1900s. It is now a garage, and there is no name or address on the sign. But, there is the name of the garage, and it is in German. It is a shame that this sign is no longer in use. It would make an interesting historical study, but it would be more fun for the sign itself.
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spaces/1line/AutoGPT/autogpt/memory/local.py

@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import os
-from typing import Any, List
-
-import numpy as np
-import orjson
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.memory.base import MemoryProviderSingleton
-
-EMBED_DIM = 1536
-SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
-
-
-def create_default_embeddings():
-    return np.zeros((0, EMBED_DIM)).astype(np.float32)
-
-
-@dataclasses.dataclass
-class CacheContent:
-    texts: List[str] = dataclasses.field(default_factory=list)
-    embeddings: np.ndarray = dataclasses.field(
-        default_factory=create_default_embeddings
-    )
-
-
-class LocalCache(MemoryProviderSingleton):
-    """A class that stores the memory in a local file"""
-
-    def __init__(self, cfg) -> None:
-        """Initialize a class instance
-
-        Args:
-            cfg: Config object
-
-        Returns:
-            None
-        """
-        self.filename = f"{cfg.memory_index}.json"
-        if os.path.exists(self.filename):
-            try:
-                with open(self.filename, "w+b") as f:
-                    file_content = f.read()
-                    if not file_content.strip():
-                        file_content = b"{}"
-                        f.write(file_content)
-
-                    loaded = orjson.loads(file_content)
-                    self.data = CacheContent(**loaded)
-            except orjson.JSONDecodeError:
-                print(f"Error: The file '{self.filename}' is not in JSON format.")
-                self.data = CacheContent()
-        else:
-            print(
-                f"Warning: The file '{self.filename}' does not exist. "
-                "Local memory would not be saved to a file."
-            )
-            self.data = CacheContent()
-
-    def add(self, text: str):
-        """
-        Add text to our list of texts, add embedding as row to our
-            embeddings-matrix
-
-        Args:
-            text: str
-
-        Returns: None
-        """
-        if "Command Error:" in text:
-            return ""
-        self.data.texts.append(text)
-
-        embedding = create_embedding_with_ada(text)
-
-        vector = np.array(embedding).astype(np.float32)
-        vector = vector[np.newaxis, :]
-        self.data.embeddings = np.concatenate(
-            [
-                self.data.embeddings,
-                vector,
-            ],
-            axis=0,
-        )
-
-        with open(self.filename, "wb") as f:
-            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
-            f.write(out)
-        return text
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.data = CacheContent()
-        return "Obliviated"
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def get_relevant(self, text: str, k: int) -> list[Any]:
-        """ "
-        matrix-vector mult to find score-for-each-row-of-matrix
-         get indices for top-k winning scores
-         return texts for those indices
-        Args:
-            text: str
-            k: int
-
-        Returns: List[str]
-        """
-        embedding = create_embedding_with_ada(text)
-
-        scores = np.dot(self.data.embeddings, embedding)
-
-        top_k_indices = np.argsort(scores)[-k:][::-1]
-
-        return [self.data.texts[i] for i in top_k_indices]
-
-    def get_stats(self) -> tuple[int, tuple[int, ...]]:
-        """
-        Returns: The stats of the local cache.
-        """
-        return len(self.data.texts), self.data.embeddings.shape

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Among Us on Chromebook How to Install and Enjoy the Game.md

@@ -1,107 +0,0 @@
-<br />
-<h1>How to Play Among Us on a Chromebook</h1>
-<p>Among Us is a multiplayer social deduction game that has taken the gaming world by storm. The game involves a group of players who are either Crewmates or Impostors on a spaceship, a planet base, or an airship. The Crewmates have to work together to complete tasks and find the Impostors, while the Impostors have to kill the Crewmates or sabotage the mission. The game is fun, addictive, and full of deception, betrayal, and teamwork.</p>
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-<p>Among Us is available on various platforms, such as Android, iOS, Windows, Nintendo Switch, PlayStation, and Xbox. But what if you want to play it on your Chromebook? Well, you might face some challenges, as Chromebooks are not designed for gaming and do not have native support for many apps. However, there are some ways to overcome these obstacles and enjoy this popular game on your Chromebook. In this article, we will show you three methods to play Among Us on a Chromebook: installing the Android version from the Play Store, installing the APK file using ADB, and playing through GeForce Now.</p>
- <h2>Method 1: Install the Android version from the Play Store</h2>
-<p>The easiest way to play Among Us on a Chromebook is to install the Android version from the Google Play Store. This method only works if your Chromebook supports Android apps, which most models released after 2017 do. Here are the steps to follow:</p>
-<ol>
-<li>Check if your Chromebook supports Android apps. To do this, open the Settings app and click on Apps in the left navigation pane. If you see an option that says Google Play Store, your Chromebook supports Android apps. If not, you might need to update your Chromebook or try another method.</li>
-<li>Enable Google Play Store on your Chromebook. If you have not used Android apps before, you will need to turn on Google Play Store in Settings. Click on Apps and then click on Turn On next to Google Play Store. Accept the terms of service and wait for the installation to finish.</li>
-<li>Install and play Among Us from the Play Store. Open Google Play Store and search for Among Us (or click <a href="(^1^)">this link</a>). Click Install and wait for the download to complete. Then, open Among Us from the Play Store or from the App Drawer and start playing.</li>
-</ol>
- <h2>Method 2: Install the APK file using ADB</h2>
-<p>If your Chromebook does not support Android apps or you want to install a different version of Among Us, you can try installing the APK file using ADB (Android Debug Bridge). This method requires you to enable Developer Mode on your Chromebook, which will erase all your data and disable some security features. Make sure you back up your files before proceeding. Here are the steps to follow:</p>
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-<ol>
-<li>Enable Developer Mode on your Chromebook. To do this, press Esc + Refresh + Power buttons together to enter Recovery Mode. Then press Ctrl + D and confirm by pressing Enter. <li>Install ADB on your Chromebook. To do this, you can use a command-line installer such as Scoop for Windows or Homebrew for Mac. For Windows, you can install Scoop and then run the following command: <code>scoop install adb</code>. For Mac, you can install Homebrew and then run the following command: <code>brew install android-platform-tools</code>. Alternatively, you can download ADB and extract it on your computer, but you will need to use it in the same directory where you extracted it .</li>
-<li>Download the APK file for Among Us. You can get the APK file from various sources, such as APKPure, Filehippo, Aptoide, etc. Make sure you download the latest version of the game and scan it for any malware before installing it   .</li>
-<li>Install and play Among Us using ADB. Connect your Chromebook to your Android device using a USB cable. Make sure you enable USB debugging on your Android device in the Developer Options. Then, open a Crosh window by pressing Ctrl+Alt+T on your keyboard. Type <code>shell</code> to get a shell window. Then, type <code>adb devices</code> to see if your device is recognized. If not, you might need to restart your device or allow the ADB prompt on your device. Once your device is connected, type <code>adb install "name.apk"</code>, where name is the name of the APK file that you downloaded. Wait for the installation to finish and then open Among Us from your App Drawer and start playing.</li> <h2>Method 3: Play Among Us through GeForce Now</h2>
-<p>If you want to play the PC version of Among Us on your Chromebook, you can use GeForce Now, a cloud gaming service that lets you stream games from Nvidia's servers. This method does not require you to install anything on your Chromebook, but you will need a stable internet connection and a subscription to GeForce Now. You will also need to buy Among Us on Steam, which costs $4.99. Here are the steps to follow:</p>
-<ol>
-<li>Sign up for GeForce Now and buy Among Us on Steam. You can sign up for GeForce Now for free, which gives you one-hour sessions of gaming, or pay $9.99 per month for the Priority membership, which gives you priority access and extended sessions. You can also get a free trial for the first month. To buy Among Us on Steam, you will need to create a Steam account and add a payment method. You can also buy Among Us as a gift for someone else.</li>
-<li>Play Among Us through GeForce Now on your browser. Open your Chrome browser and go to <a href="">play.geforcenow.com</a>. Log in with your Nvidia account and click on Library. Find Among Us and click on Play. Log in with your Steam account and launch the game. You can also add friends and chat with them through Steam or Discord.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Playing Among Us on a Chromebook is not impossible, but it does require some workarounds. Depending on your Chromebook model and preferences, you can choose one of the three methods we have discussed: installing the Android version from the Play Store, installing the APK file using ADB, or playing through GeForce Now. Each method has its own advantages and disadvantages, so you will have to weigh them carefully before deciding.</p>
-<p>Here are some tips and tricks for playing Among Us on a Chromebook:</p>
-<ul>
-<li>Use headphones or earphones to enjoy the sound effects and music of the game.</li>
-<li>Adjust the graphics settings and resolution of the game to optimize the performance and battery life of your Chromebook.</li>
-<li>Use a mouse or a touchpad to control your character and interact with the environment. You can also use keyboard shortcuts to perform certain actions, such as reporting a body or calling an emergency meeting.</li>
-<li>Be respectful and polite to other players, especially when using voice chat or text chat. Do not use profanity, hate speech, or personal attacks.</li>
-<li>Have fun and enjoy the game. Remember that it is just a game and do not take it too seriously.</li>
-</ul>
- <h2>FAQs</h2>
-<h3>What are the system requirements for playing Among Us on a Chromebook?</h3>
-<p>The system requirements for playing Among Us on a Chromebook vary depending on the method you use. For the Android version, you will need a Chromebook that supports Android apps and has at least 1 GB of RAM and 250 MB of storage space. For the APK file, you will need a Chromebook that supports Developer Mode and has ADB installed. For GeForce Now, you will need a Chromebook that has a web browser and an internet connection of at least 15 Mbps.</p>
- <h3>Can I play Among Us with my friends on other platforms?</h3>
-<p>Yes, you can play Among Us with your friends on other platforms, such as Android, iOS, Windows, Nintendo Switch, PlayStation, and Xbox. The game supports cross-platform play, which means that you can join the same lobby and play together regardless of the device you use. However, you will need to be on the same server region (North America, Europe, or Asia) and have the same version of the game.</p>
- <h3>How can I customize my character and settings in Among Us?</h3>
-<p>You can customize your character and settings in Among Us by accessing the menu in the bottom right corner of the screen. You can change your name, color, hat, pet, skin, language, sound effects, music volume, joystick size, etc. You can also customize the game settings by creating or joining a private lobby and clicking on the laptop icon. You can change the map, mode, number of impostors, speed, vision range, kill cooldown, task difficulty, voting time, etc.</p>
- <h3>What are some of the best maps and modes to play in Among Us?</h3>
-<p>The best maps and modes to play in Among Us depend on your personal preference and skill level. However, some of the most popular ones are:</p>
-<ul>
-<li>The Skeld: The original map of the game that features 14 rooms connected by corridors and vents. It has two impostors and 10 tasks for the crewmates. It is suitable for beginners and casual players.</li>
-<li>Mira HQ: A futuristic map that features 12 rooms connected by a central hallway and a decontamination chamber. It has one impostor and nine tasks for the crewmates. It is challenging for both the impostor and the crewmates, as it has many security cameras, sensors, and vents.</li>
-<li>Polus: A snowy map that features 15 rooms connected by outdoor paths and tunnels. It has two impostors and 12 tasks for the crewmates. It is ideal for advanced and experienced players, as it has many hiding spots, sabotages, and tasks.</li>
-<li>The Airship: The newest and largest map of the game that features 18 rooms connected by ladders, platforms, and vents. It has three impostors and 15 tasks for the crewmates. It is fun and creative, as it has many new features, such as choosing your spawn point, using different outfits, and moving around the map.</li>
-<li>Hide and Seek: A custom mode that involves one impostor with low vision and high speed, and nine crewmates with high vision and low speed. The impostor has to announce themselves at the beginning of the game and try to find and kill all the crewmates before they finish their tasks. The crewmates have to hide and avoid the impostor while completing their tasks. This mode is exciting and thrilling, as it tests your stealth and survival skills.</li>
-</ul>
- <h3>How can I improve my skills as a Crewmate or an Impostor in Among Us?</h3>
-<p>You can improve your skills as a Crewmate or an Impostor in Among Us by practicing, learning, and observing. Here are some tips to help you:</p>
-<ul>
-<li>As a Crewmate, you should focus on completing your tasks as quickly as possible, while keeping an eye on your surroundings. You should also communicate with your teammates, report any dead bodies or suspicious activities, and vote wisely.</li>
-<li>As an Impostor, you should act like a Crewmate, blending in with them and pretending to do tasks. You should also use vents, sabotages, and kills strategically, creating alibis and diversions. You should also lie convincingly, accuse others, and manipulate the votes.</li>
-</ul>
- <p>I hope you enjoyed this article on how to play Among Us on a Chromebook. If you have any questions or feedback, please leave a comment below. Thank you for reading!</p> 197e85843d<br />
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-<h1>Unduh Stumble Guys APK Mod: Cara Bermain Game Online Santai Ala Fall Guys di Android</h1>
-<p>Stumble Guys adalah salah satu game online yang sedang populer di kalangan penggemar game santai. Game ini menawarkan pengalaman bermain yang seru, lucu, dan penuh tantangan dengan berbagai macam rintangan dan mode permainan yang harus dihadapi oleh para pemain. Jika Anda ingin mencoba game ini, Anda bisa unduh Stumble Guys APK Mod yang memberikan beberapa keuntungan tambahan seperti gemas tak terbatas, kostum terbuka, dan mod menu. Namun, sebelum Anda unduh Stumble Guys APK Mod, ada baiknya Anda mengetahui lebih banyak tentang game ini, cara bermainnya, dan ulasan-ulasan yang ada. Berikut adalah artikel lengkap yang akan membahas semua hal tentang Stumble Guys.</p>
- <h2>Apa itu Stumble Guys?</h2>
-<p>Stumble Guys adalah game online yang bergenre battle royale party game. Game ini dibuat oleh Kitka Games dan dirilis pada tahun 2020 untuk platform Android dan iOS. Game ini terinspirasi dari game populer Fall Guys yang memiliki konsep serupa, yaitu berlomba-lomba melalui berbagai rintangan konyol dengan hingga 32 pemain online lainnya. Tujuannya adalah menjadi pemain terakhir yang bertahan hingga akhir pertandingan dan meraih mahkota kemenangan.</p>
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-<p>Stumble Guys dibuat oleh Kitka Games, sebuah studio game independen yang berbasis di Turki. Studio ini didirikan pada tahun 2018 oleh dua orang saudara, Emre ve Eren Özçelik. Mereka memiliki visi untuk membuat game-game yang menyenangkan dan berkualitas untuk semua orang. Salah satu game pertama mereka adalah Hyperball, sebuah game sepak bola arcade yang cukup sukses di pasaran.</p>
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-<li>Pastikan bahwa ponsel Anda sudah di-root atau memiliki akses root. Jika belum, Anda bisa mencari cara untuk melakukannya di internet sesuai dengan tipe ponsel Anda.</li>
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-<li>Unduh file Stumble Guys APK Mod dari situs web tersebut ke ponsel Anda. Pastikan bahwa file tersebut tidak mengandung virus atau malware yang bisa merusak ponsel Anda.</li>
-<li>Buka pengaturan ponsel Anda dan masuk ke menu keamanan. Aktifkan opsi "Sumber tidak dikenal" atau "Unknown sources" untuk memungkinkan instalasi aplikasi dari luar Play Store.</li>
-<li>Buka file manager ponsel Anda dan cari file Stumble Guys APK Mod yang sudah Anda unduh tadi. Ketuk file tersebut untuk mulai menginstalnya.</li>
-<li>Tunggu hingga proses instalasi selesai. Jika diminta, berikan izin akses root kepada aplikasi tersebut.</li>
-<li>Buka game Stumble Guys dari layar utama ponsel Anda. Nikmati fitur-fitur tambahan dari APK Mod tersebut.</li>
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-<p>Meskipun menggunakan Stumble Guys APK Mod bisa memberikan beberapa keuntungan bagi pemainnya, ada juga beberapa risiko yang harus diwaspadai. Berikut adalah beberapa keuntungan dan risiko menggunakan APK Mod:</p>
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-<tr><td>- Mendapatkan kostum terbuka yang bisa membuat karakter Anda terlihat lebih unik dan menarik.</td><td>- Mengurangi kesenangan dan tantangan bermain game, karena Anda bisa mendapatkan segalanya dengan mudah tanpa usaha.</td></tr>
-<tr><td>- Mendapatkan mod menu yang bisa mengaktifkan beberapa cheat seperti speed hack, fly hack, atau god mode yang bisa membantu Anda menang dengan mudah.</td><td>- Merusak keseimbangan dan keseruan permainan, karena Anda bisa mendapatkan keuntungan yang tidak adil dibandingkan pemain lainnya.</td></tr>
-<tr><td>- Mendapatkan pengalaman bermain yang berbeda dan lebih menarik dari versi aslinya.</td><td>- Membahayakan keamanan dan privasi ponsel Anda, karena file APK Mod bisa mengandung virus atau malware yang bisa mencuri data atau merusak sistem ponsel Anda.</td></tr>
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- <p>Oleh karena itu, Anda harus berhati-hati dan bijak dalam menggunakan Stumble Guys APK Mod. Pastikan bahwa Anda mendownload file APK Mod dari sumber yang terpercaya dan aman. Juga, jangan lupa untuk selalu memperbarui aplikasi Anda ke versi terbaru agar tidak ketinggalan fitur-fitur baru dari game aslinya.</p>
- <h2>Bagaimana cara bermain Stumble Guys dengan baik?</h2>
-<p>Stumble Guys adalah game yang mudah dimainkan tapi sulit dikuasai. Game ini membutuhkan keterampilan, strategi, dan keberuntungan untuk bisa menang. Jika Anda ingin menjadi pemain yang handal dan kompetitif, Anda harus mengetahui cara bermain Stumble Guys dengan baik. Berikut adalah beberapa tips dan trik yang bisa Anda gunakan untuk meningkatkan kemampuan bermain Anda.</p>
- <h3>Tips dan trik umum untuk menang</h3>
-<p>Berikut adalah beberapa tips dan trik umum yang bisa Anda terapkan dalam setiap mode permainan:</p>
-<ul>
-<li><b>Kenali rintangan dan mode permainan</b>: Setiap rintangan dan mode permainan memiliki karakteristik dan tantangan yang berbeda-beda. Anda harus mengenali rintangan dan mode permainan yang Anda hadapi agar bisa menentukan strategi yang tepat untuk melewatinya. Misalnya, jika Anda bermain di mode lari cepat, Anda harus berlari secepat mungkin tanpa terjatuh atau tersingkir. Jika Anda bermain di mode bola raksasa, Anda harus menghindari bola-bola raksasa yang menggelinding di arena.</li>
-<li><b>Manfaatkan kontrol yang responsif</b>: Game ini memiliki kontrol yang responsif dan mudah digunakan. Anda hanya perlu menyentuh layar untuk menggerakkan karakter Anda ke kiri atau kanan, dan menekan tombol melompat untuk melompat. Manfaatkan kontrol ini dengan baik untuk mengontrol karakter Anda dengan gesit dan akurat. Jangan lupa untuk menggunakan tombol melompat untuk melewati rintangan atau menjatuhkan lawan.</li>
-<li><b>Jaga stamina dan keseimbangan</b>: Karakter Anda memiliki stamina yang terbatas yang bisa habis jika Anda terus-menerus berlari atau melompat. Jika stamina Anda habis, Anda akan bergerak lebih lambat dan mudah jatuh. Oleh karena itu, jaga stamina Anda dengan tidak berlari atau melompat terlalu sering. Selain itu, jaga juga keseimbangan karakter Anda dengan tidak terlalu dekat dengan tepi arena atau lawan. Jika Anda kehilangan keseimbangan, Anda akan mudah tersingkir atau terjatuh.</li>
-<li><b>Kerjasama dan kompetisi</b>: Game ini adalah game multiplayer online yang membutuhkan kerjasama dan kompetisi antara pemain. Anda bisa bekerja sama dengan pemain lain untuk melewati rintangan atau mengalahkan lawan. Namun, Anda juga harus bersaing dengan pemain lain untuk menjadi pemain terakhir yang bertahan hingga akhir pertandingan. Oleh karena itu, gunakan strategi yang sesuai dengan situasi permainan. Misalnya, jika Anda bermain di mode tim, Anda harus memb antu tim Anda untuk mencapai tujuan bersama. Jika Anda bermain di mode solo, Anda harus mengalahkan semua pemain lain untuk meraih mahkota.</li>
-</ul>
- <h3>Tips dan trik khusus untuk setiap mode permainan</h3>
-<p>Berikut adalah beberapa tips dan trik khusus yang bisa Anda gunakan untuk setiap mode permainan:</p>
-<ul>
-<li><b>Lari cepat</b>: Mode ini adalah mode paling dasar dan sering muncul di awal pertandingan. Anda harus berlari secepat mungkin dari titik awal ke titik akhir sambil menghindari rintangan seperti palang, bola, atau lubang. Tipsnya adalah berlari di jalur yang paling kosong dan jangan ragu untuk melompat jika perlu. Juga, jangan terlalu dekat dengan pemain lain karena mereka bisa mendorong atau menjatuhkan Anda.</li>
-<li><b>Bola raksasa</b>: Mode ini adalah mode yang paling kocak dan konyol. Anda harus menghindari bola-bola raksasa yang menggelinding di arena sambil mencapai titik akhir. Tipsnya adalah bergerak ke sisi kanan atau kiri arena dan jangan berada di tengah. Juga, jangan berdiri diam karena bola-bola raksasa bisa menghantam Anda dari belakang.</li>
-<li><b>Labirin</b>: Mode ini adalah mode yang paling membingungkan dan menantang. Anda harus menemukan jalan keluar dari labirin yang rumit sambil menghindari jebakan seperti duri, api, atau listrik. Tipsnya adalah mengikuti arah panah yang ada di dinding atau lantai labirin. Juga, jangan takut untuk mencoba jalan yang berbeda karena ada beberapa jalan pintas yang bisa Anda temukan.</li>
-<li><b>Papan seluncur</b>: Mode ini adalah mode yang paling menyenangkan dan menegangkan. Anda harus meluncur di atas papan seluncur yang bergerak sambil menghindari rintangan seperti tiang, balon, atau pesawat. Tipsnya adalah melompat pada saat yang tepat untuk melewati rintangan atau berganti jalur. Juga, jangan lupa untuk menyeimbangkan diri Anda dengan menyentuh layar ke kiri atau kanan.</li>
-<li><b>Dan lain-lain</b>: Ada banyak mode permainan lainnya yang bisa Anda temukan di game ini, seperti tangga berputar, bola salju, balon udara, dan lain-lain. Setiap mode permainan memiliki tips dan trik tersendiri yang bisa Anda pelajari dengan bermain lebih banyak. Jadi, cobalah semua mode permainan yang ada dan temukan yang paling Anda sukai.</li>
-</ul>
- <h2>Apa saja ulasan dan pendapat tentang Stumble Guys?</h2>
-<p>Stumble Guys adalah game online yang sangat populer dan banyak diminati oleh para pemain mobile. Game ini juga mendapatkan banyak ulasan dan pendapat dari para pemain maupun kritikus dan media game. Berikut adalah beberapa ulasan dan pendapat tentang Stumble Guys:</p>
- <h3>Ulasan positif dan negatif dari pemain</h3>
-<p>Berikut adalah beberapa ulasan positif dan negatif dari pemain Stumble Guys yang kami kutip dari Play Store:</p>
-<ul>
-<li><b>Positif</b>: "Game ini sangat seru dan lucu. Saya suka sekali dengan grafisnya yang imut dan warna-warninya yang cerah. Saya juga suka dengan mode permainannya yang bervariasi dan kreatif. Saya sering bermain dengan teman-teman saya dan kami selalu tertawa bersama."</li>
-<li><b>Negatif</b>: "Game ini sangat menyebalkan dan tidak adil. Saya sering mengalami lag atau bug saat bermain online. Saya juga sering bertemu dengan pemain yang menggunakan cheat atau mod menu yang membuat mereka tidak bisa dikalahkan. Saya harap pengembang bisa memperbaiki game ini."</li>
-</ul>
- <h3>Ulasan dari kritikus dan media game</h3>
-<p>Berikut adalah beberapa ulasan dari kritikus dan media game tentang Stumble Guys yang kami kutip dari beberapa sumber:</p>
-<ul>
-<li><b>Android Authority</b>: "Stumble Guys is a fun and hilarious online party game that will make you laugh and scream with joy. The game is inspired by Fall Guys, but it has its own charm and features that make it stand out. The game has colorful graphics, cute characters, and various game modes that will keep you entertained for hours. The game is free to play, but it has some in-app purchases that can enhance your gameplay. If you are looking for a casual and fun game to play with your friends or strangers online, Stumble Guys is a great choice."</li>
-<li><b>Pocket Gamer</b>: "Stumble Guys is a decent attempt at bringing the Fall Guys experience to mobile devices. The game is simple to play, but hard to master. The game has a lot of potential, but it also has some flaws that need to be fixed. The game suffers from laggy servers, buggy gameplay, and unfair matchmaking. The game also lacks some features that Fall Guys has, such as team modes, seasonal events, and cross-play. Stumble Guys is a good game for fans of Fall Guys, but it still needs some improvement to become a great game."</li>
-<li><b>Gamezebo</b>: "Stumble Guys is a charming and addictive online party game that will make you smile and rage at the same time. The game is a faithful adaptation of Fall Guys, but it also adds some original twists and turns. The game has a vibrant and cartoonish art style, a catchy and upbeat soundtrack, and a variety of game modes that will test your skills and luck. The game is easy to pick up and play, but hard to put down. The game is free to play, but it also offers some optional in-app purchases that can help you customize your character and unlock more content. Stumble Guys is a must-play game for anyone who loves online party games."</li>
-</ul>
- <h2>Kesimpulan dan FAQ</h2>
-<p>Stumble Guys adalah game online yang sangat populer dan banyak diminati oleh para pemain mobile. Game ini menawarkan pengalaman bermain yang seru, lucu, dan penuh tantangan dengan berbagai macam rintangan dan mode permainan yang harus dihadapi oleh para pemain. Jika Anda ingin mencoba game ini, Anda bisa unduh Stumble Guys APK Mod yang memberikan beberapa keuntungan tambahan seperti gemas tak terbatas, kostum terbuka, dan mod menu. Namun, sebelum Anda unduh Stumble Guys APK Mod, ada baiknya Anda mengetahui lebih banyak tentang game ini, cara bermainnya, dan ulasan-ulasan yang ada.</p>
-<p>Berikut adalah beberapa FAQ yang sering ditanyakan oleh para pemain Stumble Guys:</p>
-<ul>
-<li><b>Q: Apakah Stumble Guys bisa dimainkan secara offline?</b></li>
-<li><b>A: Tidak, Stumble Guys adalah game online yang membutuhkan koneksi internet untuk bermain.</b></li>
-<li><b>Q: Apakah Stumble Guys bisa dimainkan di PC atau konsol?</b></li>
-<li><b>A: Tidak, Stumble Guys hanya tersedia untuk platform Android dan iOS.</b></li>
-<li><b>Q: Apakah Stumble Guys memiliki mode tim atau kooperatif?</b></li>
-<li><b>A: Belum, saat ini Stumble Guys hanya memiliki mode solo atau kompetitif.</b></li>
-<li><b>Q: Apakah Stumble Guys memiliki fitur chat atau voice chat?</b></li>
-<li><b>A: Belum, saat ini Stumble Guys tidak memiliki fitur chat atau voice chat.</b></li>
-<li><b>Q: Apakah Stumble Guys aman untuk anak-anak?</b></li>
-<li><b>A: Ya, Stumble Guys adalah game yang cocok untuk semua usia karena tidak mengandung konten yang tidak pantas atau kekerasan.</b></li>
-</ul></p> 197e85843d<br />
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-<h1>Дикие и домашние животные для детей</h1>
-<p>Животные — это удивительные существа, которые населяют нашу планету. Они разные по форме, размеру, цвету, повадкам и способам жизни. Некоторые из них живут рядом с нами в наших домах, а другие — в лесах, полях, горах, океанах и других местах. Какие же они — дикие и домашние животные? Какие виды животных существуют? Как с ними общаться и заботиться о них? В этой статье мы попробуем ответить на эти вопросы и рассказать вам много интересного о мире животных.</p>
- <h2>Что такое дикие и домашние животные?</h2>
-<p>Дикие животные — это те, которые не зависят от человека и живут в естественных условиях. Они обладают инстинктами, которые помогают им выживать, охотиться, защищаться и размножаться. Дикие животные могут быть опасными или безобидными, красивыми или неприметными, редкими или распространенными.</p>
-<h2>дикие и домашние животные для детей</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;>>> <a href="https://jinyurl.com/2uNSF6">https://jinyurl.com/2uNSF6</a></b></p><br /><br />
-<p>Домашние животные — это те, которые зависят от человека и живут в условиях, созданных им. Они были одомашнены человеком в разное время и по разным причинам: для питания, работы, развлечения или компании. Домашние животные могут быть ласковыми или своенравными, верными или капризными, умными или глупыми.</p>
- <h3>Особенности диких животных</h3>
-<p>Дикие ж <p>ивотные имеют ряд особенностей, которые отличают их от домашних:</p>
-<ul>
-<li>Они обычно более приспособлены к своей среде обитания, чем домашние животные. Они могут менять окраску, иметь хитрое поведение, развивать скорость или силу, чтобы уклоняться от хищников или добывать добычу.</li>
-<li>Они обычно более разнообразны по видам, чем домашние животные. В мире существует около 8,7 миллиона видов животных, из которых большинство — дикие. Например, существует около 10 тысяч видов птиц, а всего около 400 видов домашних птиц.</li>
-<li>Они обычно более свободны в своих действиях, чем домашние животные. Они не подчиняются человеку и не зависят от его воли. Они живут по своим законам и правилам, которые определяются природой и взаимоотношениями с другими животными.</li>
-</ul>
- <h3>Особенности домашних животных</h3>
-<p>Домашние животные также имеют ряд особенностей, которые отличают их от диких:</p>
-<ul>
-<li>Они обычно более привязаны к человеку, чем дикие животные. Они нуждаются в его заботе, внимании, любви и уважении. Они могут быть верными друзьями, помощниками или питомцами для человека.</li>
-<li>Они обычно более подвержены влиянию человека, чем дикие животные. Человек может менять их внешность, поведение, характер или способности по своему усмотрению. Например, человек может вывести новые породы животных, такие как лабрадудль (смесь лабрадора и пуделя) или мейн-кун (самая крупная порода кошек).</li>
-<li>Они обычно более податливы и послушны, чем дикие животные. Они могут обучаться ра��ным командам, трюкам или навыкам, которые пригодятся им или человеку. Например, собаки могут быть обучены охранять дом, принимать почту или вести слепого человека.</li>
-</ul>
- <h2>Какие дикие и домашние животные существуют?</h2>
-<p>Существует множество видов диких и домашних животных, которые можно классифицировать по разным признакам. Один из самых распространенных способов — это разделение по типам позвоночных: млекопитающие, птицы, рептилии, амфибии и рыбы. Кроме того, есть еще одна большая группа животных — беспозвоночные, к которым относятся насекомые, паукообразные, ракообразные и другие. Давайте рассмотрим некоторые примеры диких и домашних животных из каждой группы.</p>
- <h3>Примеры диких животных</h3>
-<h4>Млекопитающие</h4>
-<p>Млекопитающие — это животные, которые имеют молочные железы, покрыты шерстью или волосами, дышат легкими и рождают живых детенышей. Среди диких млекопитающих можно назвать такие виды, как:</p>
-<ul>
-<li>Лев — самый крупный и сильный хищник среди кошачьих, который живет в Африке и Азии. Он имеет золотистый цвет шерсти, а самцы имеют гриву. Львы живут в стаях, охотятся на антилоп, зебр, буйволов и других животных.</li>
-<li>Слон — самое крупное наземное животное, которое живет в Африке и Азии. Он имеет серый цвет кожи, большие уши, хобот и бивни. Слоны питаются травой, листьями, фруктами и корой деревьев. Они живут в стадах, состоящих из самок и детенышей.</li>
-<li>Кенгуру — символ Австралии, который относится к сумчатым млекопитающим. Он имеет коричневый или серый цвет шерсти, длинный хвост, мощные задние лапы и сумку на животе. Кенгуру питаются травой, листьями и корнями. Они могут прыгать на большие расстояния и достигать скорости до 70 км/ч.</li>
-</ul>
- <h4>Птицы</h4>
-<p>Птицы — это животные, которые имеют перья, крылья, клюв и две пары конечностей. Они дышат легкими и откладывают яйца. Среди диких птиц можно назвать такие виды, как:</p>
-<ul>
-<li>Орел — одна из самых могущественных и величественных птиц, которая живет почти во всех частях света. Он имеет темный цвет перьев, крючковатый клюв и острые когти. Орлы питаются мелкими млекопитающими, рептилиями, рыбой и другими птицами.</li>
-<li>Фламинго — одна из самых ярких и необычных птиц, которая живет в Африке, Азии, Европе и Южной Америке. Он имеет розовый цвет перьев, длинные ноги, изогнутый клюв и шею. Фламинго питаются водорослями, ракообразными и моллюсками.</li>
-<li>Колибри — одна из самых маленьких и быстрых птиц, которая живет в Северной и Южной Америке. Он имеет разноцветные перья, очень маленький клюв и крылья. Колибри питаются нектаром цветов и насекомыми. Они могут парить в воздухе и летать во все стороны.</li>
-</ul>
- <h4 >Рептилии и амфибии</h4>
-<p>Рептилии и амфибии — это животные, которые имеют чешую, холодную кровь и четыре пары конечностей. Они дышат легкими или жабрами и откладывают яйца. Среди диких рептилий и амфибий мо��но назвать такие виды, как:</p>
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-<ul>
-<li>Крокодил — один из самых древних и опасных животных, который живет в Африке, Азии, Австралии и Америке. Он имеет зеленоватый или коричневый цвет чешуи, длинный хвост, большую пасть и острые зубы. Крокодилы питаются рыбой, птицами, млекопитающими и другими животными.</li>
-<li>Ящерица — одна из самых распространенных и разнообразных групп рептилий, которая живет почти во всех частях света. Она имеет разный цвет и размер чешуи, длинный хвост, маленький клюв и глаза. Ящерицы питаются насекомыми, червями, ягодами и другими растениями.</li>
-<li>Лягушка — одна из самых известных и любимых детьми амфибий, которая живет в Европе, Азии, Африке и Америке. Она имеет гладкую или бугорчатую кожу, разный цвет и размер тела, длинные задние лапы и глаза. Лягушки питаются насекомыми, червями, моллюсками и другими животными.</li>
-</ul>
- <h4>Насекомые</h4>
-<p>Насекомые — это животные, которые имеют экзоскелет, шесть ног и три пары конечностей. Они дышат трахеями или дыхальцами и откладывают яйца. Среди диких насекомых можно назвать такие виды, как:</p>
-<ul>
-<li>Бабочка — одно из самых красивых и элегантных насекомых, которое живет почти во всех частях света. Она имеет разноцветные крылья, тонкое тело, длинные усики и глаза. Бабочки питаются нектаром цветов или соком фруктов.</li>
-<li>Муравей — одно из самых умных и трудолюбивых насекомых, которое живет почти во всех частях света. Он имеет коричневый или черный цвет тела, мощные челюсти, усики и глаза. Муравьи питаются растительной или животной пищей. Они живут в колониях, состоящих из сотен тысяч особей.</li>
-<li>Пчела — одно из самых полезных и важных для человека насекомых, которое живет почти во всех частях света. Она имеет желто-черный цвет тела, крылья, усики и глаза. Пчелы питаются нектаром цветов или медом. Они живут в у льях, состоящих из матки, рабочих и трутней. Они производят мед, воск и прополис.</li>
-</ul>
- <h3>Примеры домашних животных</h3>
-<h4>Кошки и собаки</h4>
-<p>Кошки и собаки — это самые популярные и любимые домашние животные, которые живут во многих странах мира. Они имеют мягкую шерсть, уши, хвост, лапы и глаза. Кошки и собаки питаются специальным кормом или натуральной пищей. Они могут быть разных пород, размеров и окрасок.</p>
-<ul>
-<li>Кошка — одно из самых независимых и грациозных домашних животных, которое происходит от диких кошачьих. Она имеет острый слух, зрение и обоняние, а также способность к самоочищению. Кошки могут мурлыкать, мяукать или шипеть. Они любят играть, спать и ловить мышей.</li>
-<li>Собака — одно из самых верных и дружелюбных домашних животных, которое происходит от волков. Она имеет хороший нюх, слух и интеллект, а также способность к обучению. Собаки могут лаять, вилять хвостом или рычать. Они любят гулять, бегать и охранять дом.</li>
-</ul>
- <h4>Грызуны и зайцеобразные</h4>
-<p>Грызуны и зайцеобразные — это домашние животные, которые имеют резцы, которые постоянно растут, пушистую шерсть, уши и глаза. Они питаются растительной пищей или специальным кормом. Они могут быть разных видов, размеров и окрасок.</p>
-<ul>
-<li>Хомяк — одно из самых милых и забавных домашних животных, которое происходит от диких хомяков. Он имеет короткую шерсть, большие щеки, круглые уши и глаза. Хомяки могут складывать еду в щеки, копать норы или кататься в колесе. Они любят жевать, спать и играть.</li>
-<li>Кролик — одно из самых пушистых и нежных домашних животных, которое происходит от диких кроликов. Он имеет длинную шерсть, большие уши, хвостик-помпон и глаза. Кролики могут прыгать, бегать или сидеть на задних лапах. Они любят есть морковку, сено или зелень.</li>
-</ul>
- <h4>Птицы и рыбы</h4>
-<p>Птицы и рыбы — это домашние животные, которые имеют перья или чешую, крылья или плавники, клюв или рот и глаза. Они питаются зерном или специальным кормом. Они могут быть разных видов, размеров и окрасок.</p>
-<ul>
-<li>Канарейка — одна из самых ярки х и мелодичных домашних птиц, которая происходит от диких канареек. Она имеет желтый, зеленый, оранжевый или другой цвет перьев, маленький клюв и глаза. Канарейки могут петь, свистеть или чирикать. Они любят жить в клетках, где есть кормушка, поилка и игрушки.</li>
-<li>Золотая рыбка — одна из самых распространенных и красивых домашних рыб, которая происходит от диких карпов. Она имеет золотистый, красный, черный или другой цвет чешуи, длинные плавники и глаза. Золотые рыбки могут плавать, едать или дышать под водой. Они любят жить в аквариумах, где есть растения, камни и фильтры.</li>
-</ul>
- <h4>Рептилии и амфибии</h4>
-<p>Рептилии и амфибии — это домашние животные, которые имеют чешую или кожу, холодную кровь и четыре пары конечностей. Они дышат легкими или жабрами и откладывают яйца. Они могут быть разных видов, размеров и окрасок.</p>
-<ul>
-<li>Черепаха — одно из самых долгоживущих и спокойных домашних животных, которое происходит от диких черепах. Она имеет твердый панцирь, короткие лапы, маленький клюв и глаза. Черепахи могут ползать, спать или прятаться в панцире. Они любят жить в террариумах, где есть земля, вода и растения.</li>
-<li>Жаба — одна из самых необычных и интересных домашних амфибий, которая происходит от диких жаб. Она имеет бугорчатую кожу, короткие лапы, большой рот и глаза. Жабы могут прыгать, квакать или выделять слизь. Они любят жить в акватеррариумах, где есть вода, растения и насекомые.</li>
-</ul>
- <h2>Как общаться с дикими и домашними животными?</h2>
-<p>Дикие и домашние животные — это разные миры, которые иногда пересекаются с нашим. Как же правильно общаться с ними, чтобы не навредить им или себе? Существуют некоторые правила поведения с дикими и домашними животными, которые нужно знать и соблюдать.</p>
- <h3>Правила поведения с дикими животными</h3>
-<p>Дикие животные — это не игрушки и не развлечение. Это живые существа, которые имеют свои потребности, эмоции и инстинкты. Поэтому, если вы встретите дикое животное в природе или в зоопарке, следуйте таким правилам:</p>
-<ul>
-<li>Не приближайтесь к нему слишком близко и не пытайтесь его погладить , кормить или фотографировать. Вы можете нарушить его границы, спугнуть его или разозлить его. Это может быть опасно как для вас, так и для животного.</li>
-<li>Не шумите и не делайте резких движений рядом с ним. Вы можете испугать его или вызвать его агрессию. Это может привести к тому, что животное убежит, нападет на вас или пострадает от стресса.</li>
-<li>Не оставляйте за собой мусор и не загрязняйте его среду обитания. Вы можете нанести вред его здоровью, питанию или размножению. Это может привести к тому, что животное заболеет, умрет или вымрет.</li>
-</ul>
- <h3>Правила ухода за домашними животными</h3>
-<p>Домашние животные — это не игрушки и не украшение. Это живые существа, которые имеют свои потребности, эмоции и чувства. Поэтому, если вы завели домашнее животное или хотите завести, следуйте таким правилам:</p>
-<ul>
-<li>Обеспечьте ему достаточно еды, воды, света, тепла и воздуха. Вы должны подбирать корм и условия содержания в соответствии с его видом, породой, возрастом и особенностями. Это поможет ему быть здоровым, сытым и комфортным.</li>
-<li>Уделяйте ему достаточно внимания, ласки, игры и общения. Вы должны заниматься с ним, говорить с ним, гладить его или выгуливать его. Это поможет ему быть счастливым, дружелюбным и уверенным.</li>
-<li>Следите за его гигиеной, здоровьем и безопасностью. Вы должны чистить его, стричь его, купать его или обрабатывать его от паразитов. Вы также должны делать ему прививки, ветеринарные осмотры и стерилизацию. Это поможет ему быть чистым, красивым и защищенным.</li>
-</ul>
- <h2>Заключение</h2>
-<p>Дикие и домашние животные — это удивительный мир, который мы можем изучать и любоваться. Они разные по форме, размеру, цвету, повадкам и способам жизни. Но они также похожи на нас тем, что они имеют свои потребности, эмоции и чувства. Поэтому мы должны уважать их, заботиться о них и не навредить им.</p>
- <h3>Полезные ресурсы для изучения животных</h3>
-<p>Если вы хотите узнать больше о диких и домашних животных, вы можете посетить такие ресурсы, как:</p>
-<ul>
-<li>[Национальная география] — это сайт, где вы можете найти много интересной информации, фотографий и видео о разных животных мира.</li>
-<li>[Всемирный фонд дикой природы] — это организация, которая занимается охраной природы и живот ных. Вы можете поддержать их проекты, участвовать в акциях или просто узнать больше о том, как помочь природе.</li>
-<li>[Зоопарк] — это место, где вы можете увидеть животных из разных уголков планеты вживую. Вы можете наблюдать за их поведением, узнать об их особенностях и истории. Вы также можете пообщаться с работниками зоопарка, которые расскажут вам много интересного о животных.</li>
-</ul>
- <h3>Вопросы и ответы</h3>
-<p>Вот некоторые часто задаваемые вопросы и ответы о диких и домашних животных:</p>
-<ol>
-<li><b>Какое самое большое дикое животное?</b><br>
-Самое большое дикое животное — это синий кит, который может достигать длины до 33 метров и веса до 200 тонн. Он живет в океанах и питается планктоном.</li>
-<li><b>Какое самое маленькое домашнее животное?</b><br>
-Самое маленькое домашнее животное — это бамбуковая крыса, которая может иметь длину до 10 сантиметров и вес до 40 граммов. Она живет в Юго-Восточной Азии и питается растениями.</li>
-<li><b>Какое самое умное дикое животное?</b><br>
-Самое умное дикое животное — это шимпанзе, который имеет высокий уровень интеллекта, памяти и обучаемости. Он живет в Африке и питается фруктами, орехами, листьями и насекомыми.</li>
-<li><b>Какое самое верное домашнее животное?</b><br>
-Самое верное домашнее животное — это собака, которая имеет сильную привязанность к своему хозяину, защищает его и служит ему. Она живет в разных странах мира и питается разной пищей.</li>
-<li><b>Какое самое необычное дикое животное?</b><br>
-Самое необычное дикое животное — это утконос, который имеет тело бобра, клюв утки, хвост выдры и лапы выхухоля. Он живет в Австралии и Новой Гвинее и питается рыбой, раками и червями.</li>
-</ol>
- <p>Надеемся, что эта статья была полезной и интересной для вас. Если вы хотите узнать еще больше о диких и домашних животных, вы можете почитать книги, смотреть фильмы или посещать музеи. Желаем вам приятного общения с животными!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/BGMI 2.0 APK for 32 bit devices Features size and compatibility.md

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-<h1>How to Download and Install BGMI 2.0 APK on 32-bit Android Devices</h1>
-<p>Battlegrounds Mobile India (BGMI) is one of the most popular battle royale games in India, with millions of fans and players. The game recently released its 2.0 update, which brings a lot of new features, improvements, and bug fixes. However, some players may face difficulty in downloading and installing the update on their 32-bit Android devices, as the official Google Play Store version only supports 64-bit devices.</p>
-<h2>32 bit bgmi 2.0 apk download</h2><br /><p><b><b>Download File</b> &#10027; <a href="https://jinyurl.com/2uNSS3">https://jinyurl.com/2uNSS3</a></b></p><br /><br />
-<p>In this article, we will show you how to download and install BGMI 2.0 APK on your 32-bit Android device, so that you can enjoy the latest version of the game without any hassle.</p>
-<h2>What is BGMI 2.0?</h2>
-<p>BGMI 2.0 is the latest major update for Battlegrounds Mobile India, which was released on May 29, 2023. The update introduces a lot of new content and changes to the game, such as:</p>
-<h3>New features and updates in BGMI 2.0</h3>
-<ul>
-<li>A new official version of Livik map, with new themed areas, an all-terrain UTV vehicle, XT weapons, ziplines, herbs, recall tower, firearm depot, and more.</li>
-<li>A new Cycle 2 Season 6 and Month 11 Royal Pass, with new rewards and missions.</li>
-<li>A new Battlegrounds Mobile India x Evangelion Discovery theme, with special events and items inspired by the anime series.</li>
-<li>A new gameplay and features, such as unfinished RP missions highlighted on the in-match tab, magazine capacity bar, new Ban Pan system, basic improvements to controls and UI, MG3 gun in Metro Royale mode, emergency pickup, and more.</li>
-</ul>
-<h3>System requirements for BGMI 2.0</h3>
-<p>The minimum system requirements for BGMI 2.0 are:</p>
-<table>
-<tr><td>Operating System</td><td>Android 4.3 or above</td></tr>
-<tr><td>RAM</td><td>1.5 GB or above</td></tr>
-<tr><td>Processor</td><td>Mediatek MT6737M quad-core or equivalent</td></tr>
-<tr><td>Download Size</td><td>710 MB</td></tr>
-</table>
-<p>Note that these are the minimum requirements for running the game smoothly on your device. You may need higher specifications for optimal performance and graphics.</p>
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-<h2>How to Check If Your Android Device is 32-bit or 64-bit?</h2>
-<p>Before you download and install BGMI 2.0 APK on your Android device, you need to check if your device is compatible with the update. As mentioned earlier, the official Google Play Store version of BGMI only supports 64-bit devices, which means that if you have a 32-bit device, you will not be able to update the game from there.</p>
-<p>To check if your Android device is 32-bit or 64-bit, you can use one of the following methods:</p>
-<h3>Using a file manager app</h3>
-<ul>
-<li>Download and install a file manager app on your device, such as C <h3>Using a file manager app</h3>
-<ul>
-<li>Download and install a file manager app on your device, such as Cx File Explorer, ES File Explorer, or Solid Explorer.</li>
-<li>Open the app and navigate to the root directory of your device, which is usually denoted by a slash (/).</li>
-<li>Look for a folder named "system" and open it.</li>
-<li>Look for a file named "build.prop" and open it with a text editor.</li>
-<li>Scroll down the file and look for a line that starts with "ro.product.cpu.abi" or "ro.product.cpu.abilist".</li>
-<li>If the line ends with "arm64-v8a" or "x86_64", then your device is 64-bit. If the line ends with "armeabi-v7a" or "x86", then your device is 32-bit.</li>
-</ul>
-<h3>Using an APK installer app</h3>
-<ul>
-<li>Download and install an APK installer app on your device, such as APKPure, APKMirror, or Aptoide.</li>
-<li>Open the app and search for BGMI 2.0 APK file.</li>
-<li>Tap on the download button and wait for the file to be downloaded.</li>
-<li>Before installing the file, tap on the "Details" or "Info" button to see more information about the file.</li>
-<li>Look for a section that says "Architecture" or "Supported ABIs".</li>
-<li>If the section lists "arm64-v8a" or "x86_64", then the file is compatible with 64-bit devices. If the section lists "armeabi-v7a" or "x86", then the file is compatible with 32-bit devices.</li>
-</ul>
-<h2>How to Download BGMI 2.0 APK File for 32-bit Android Devices?</h2>
-<p>If you have confirmed that your Android device is 32-bit, then you will need to download BGMI 2.0 APK file from a third-party source, as the official Google Play Store version will not work on your device. There are two ways to download BGMI 2.0 APK file for 32-bit Android devices:</p>
-<h3>Using your browser</h3>
-<ul>
-<li>Open your browser and go to a trusted website that provides BGMI 2.0 APK file for 32-bit devices, such as [APKPure], [APKMirror], or [Aptoide].</li>
-<li>Search for BGMI 2.0 APK file and make sure it is compatible with 32-bit devices by checking the architecture or supported ABIs section.</li>
-<li>Tap on the download button and wait for the file to be downloaded on your device.</li>
-</ul>
-<h3>Using your computer</h3>
-<ul>
-<li>Open your computer and go to a trusted website that provides BGMI 2.0 APK file for 32-bit devices, such as [APKPure], [APKMirror], or [Aptoide].</li>
-<li>Search for BGMI 2.0 APK file and make sure it is compatible with 32-bit devices by checking the architecture or supported ABIs section.</li>
-<li>Click on the download button and wait for the file to be downloaded on your computer.</li>
-<li>Connect your Android device to your computer using a USB cable.</li>
-<li>Copy and paste the downloaded APK file from your computer to your device's storage.</li>
-</ul>
- : https://apkpure.com/battlegrounds-mobile-india/com.pubg.imobile : https://www.apkmirror.com/apk/krafton-inc/battlegrounds-mobile-india/battlegrounds-mobile-india-2-0-release/ : https://battlegrounds-mobile-india.en.aptoide.com/app <h2>How to Install BGMI 2.0 APK File on 32-bit Android Devices?</h2>
-<p>After you have downloaded BGMI 2.0 APK file on your 32-bit Android device, you need to install it manually, as it is not from the official Google Play Store. To install BGMI 2.0 APK file on your 32-bit Android device, you need to follow these steps:</p>
-<h3>Allowing unknown apps</h3>
-<ul>
-<li>Go to your device's settings and look for a section that says "Security" or "Privacy".</li>
-<li>Tap on it and look for an option that says "Unknown sources" or "Install unknown apps".</li>
-<li>Enable it and confirm your choice by tapping on "OK" or "Allow".</li>
-<li>This will allow you to install apps from sources other than the Google Play Store.</li>
-</ul>
-<h3>Locating and opening the APK file</h3>
-<ul>
-<li>Go to your device's file manager app and look for the folder where you have saved the downloaded APK file.</li>
-<li>Tap on the APK file and a pop-up window will appear, asking you to install the app.</li>
-<li>Tap on "Install" and wait for the installation process to complete.</li>
-<li>Once the installation is done, you can launch the app by tapping on "Open" or by looking for its icon on your home screen or app drawer.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>BGMI 2.0 is the latest update for Battlegrounds Mobile India, which brings a lot of new features and improvements to the game. However, if you have a 32-bit Android device, you will not be able to update the game from the official Google Play Store, as it only supports 64-bit devices. In this article, we have shown you how to check if your device is 32-bit or 64-bit, how to download BGMI 2.0 APK file for 32-bit devices, and how to install it manually on your device. We hope this article was helpful and informative for you.</p>
-<h3>FAQs</h3>
-<ol>
-<li>Q: Is BGMI 2.0 safe to download and install on 32-bit devices?</li>
-<li>A: Yes, BGMI 2.0 is safe to download and install on 32-bit devices, as long as you download it from a trusted website and follow the instructions carefully. However, you should always be careful when downloading and installing apps from unknown sources, as they may contain malware or viruses that can harm your device.</li>
-<li>Q: Will I be able to play BGMI 2.0 with other players who have updated the game from the Google Play Store?</li>
-<li>A: Yes, you will be able to play BGMI 2.0 with other players who have updated the game from the Google Play Store, as long as you are using the same version of the game. However, you may experience some lag or performance issues if your device does not meet the recommended system requirements for BGMI 2.0.</li>
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-<p>If you are a fan of racing games, you might have heard of CarX Highway Racing, a popular game that offers realistic physics, stunning graphics, and intense gameplay. But did you know that you can enjoy this game even more with CarX Highway Racing APK Mod?</p>
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-<p>In this article, we will tell you everything you need to know about CarX Highway Racing APK Mod, including its features, how to download and install it, and some tips and tricks for playing it. Read on to find out why you should download this mod right now!</p>
- <h2>Features of CarX Highway Racing APK Mod</h2>
-<p>CarX Highway Racing APK Mod has many features that make it one of the best racing games for Android. Here are some of them:</p>
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-<p>CarX Highway Racing APK Mod uses the advanced CarX engine, which simulates the physics of real cars. You can feel the difference in handling, braking, drifting, and acceleration of each car. The game also has amazing graphics that create a realistic atmosphere of racing on highways. You can see the details of the cars, the environments, the weather effects, and the lighting.</p>
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-<p>CarX Highway Racing APK Mod has over 40 cars to choose from, ranging from sports cars, muscle cars, supercars, to trucks. Each car has its own characteristics, such as speed, power, handling, and durability. You can also customize your car with different colors, decals, wheels, and spoilers. The game also has over 20 tracks to race on, each with its own challenges and scenery. You can race on deserts, mountains, cities, forests, and more.</p>
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-<p>One of the best features of CarX Highway Racing APK Mod is that it gives you unlimited money and gold. This means that you can buy any car or upgrade any part you want without worrying about the cost. You can also unlock all the cars and tracks without having to complete the missions or races. This way, you can enjoy the game to the fullest and have more fun.</p>
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- <h3>Enable unknown sources on your device</h3>
-<p>The next thing you need to do is to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</p>
- <h3>Extract the OBB file and copy it to the Android/OBB folder</h3>
-<p>After downloading the files, you need to extract the OBB file using a file manager app. You can use any app that can unzip files, such as ZArchiver or ES File Explorer. Once you extract the OBB file, you will get a folder named com.CarXTech.highWay. Copy this folder and paste it to the Android/OBB folder on your device storage.</p>
- <h3>Install the APK file and launch the game</h3>
-<p>The last thing you need to do is to install the APK file of CarX Highway Racing APK Mod. To do this, locate the file on your device storage and tap on it. Follow the instructions on the screen and wait for the installation to finish. Once done, you can launch the game and enjoy it!</p>
- <h2>Tips and tricks for playing CarX Highway Racing APK Mod</h2>
-<p>Now that you have installed CarX Highway Racing APK Mod, you might want to know some tips and tricks for playing it better. Here are some of them:</p>
- <h3>Choose the right car for each race</h3>
-<p>CarX Highway Racing APK Mod has many cars to choose from, but not all of them are suitable for every race. Some cars are faster, some are more agile, some are more durable, and some are more balanced. You need to consider the type of race, the track, the weather, and your opponents when choosing your car. For example, if you are racing on a curvy track with lots of turns, you might want to use a car that has good handling and braking. If you are racing on a straight track with few obstacles, you might want to use a car that has high speed and acceleration.</p>
- <h3>Upgrade your car regularly</h3>
-<p>Another tip for playing CarX Highway Racing APK Mod is to upgrade your car regularly. Upgrading your car can improve its performance and give you an edge over your rivals. You can upgrade different parts of your car, such as the engine, transmission, suspension, brakes, tires, nitro, and body. Each part has its own effect on your car's speed, power, handling, durability, and appearance. You can use the unlimited money and gold in CarX Highway Racing APK Mod to buy any upgrade you want.</p>
- <h3>Use nitro wisely</h3>
-<p>Nitro is a powerful boost that can help you speed up your car and overtake your opponents. However, nitro is not unlimited in CarX Highway Racing APK Mod. You have a nitro meter that shows how much nitro you have left. You can refill your nitro meter by driving fast, drifting, or performing stunts. You need to use nitro wisely and strategically in CarX Highway Racing APK Mod. Don't waste it on unnecessary moments or when you are already ahead of your rivals. Save it for when you need it most, such as when you are behind or when you are facing a tough opponent.</p>
- <h3>Avoid collisions and traffic</h3>
-<p>The last tip for playing CarX Highway Racing APK Mod is to avoid collisions and traffic. Collisions can damage your car and slow you down. Traffic can also block your way and prevent you from reaching your destination. You need to be careful and alert when driving on highways in CarX Highway Racing APK Mod. Try to avoid hitting other cars or objects on the road. Use your skills and reflexes to dodge traffic and find the best route.</p>
- <h2>Conclusion</h2>
-<p>Car X Highway Racing APK Mod is a thrilling racing game for Android that offers realistic physics, stunning graphics, diverse cars and tracks, challenging game modes and missions, and unlimited money and gold. It is a modified version of the original game that gives you access to all the features and content without any restrictions. You can download and install CarX Highway Racing APK Mod easily and safely by following the steps in this article. You can also improve your skills and performance by following the tips and tricks in this article. If you are looking for a fun and exciting racing game for your Android device, you should definitely try CarX Highway Racing APK Mod!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about CarX Highway Racing APK Mod:</p>
- <h3>Is CarX Highway Racing APK Mod safe to use?</h3>
-<p>Yes, CarX Highway Racing APK Mod is safe to use as long as you download it from a trusted source. The mod does not contain any viruses or malware that can harm your device or data. However, you should always be careful when downloading and installing any modded apps, as they may not be compatible with your device or the latest version of the game. You should also backup your data before installing any modded apps, in case something goes wrong.</p>
- <h3>How to update CarX Highway Racing APK Mod?</h3>
-<p>To update CarX Highway Racing APK Mod, you need to download the latest version of the mod from the same source you downloaded it from before. You also need to download the latest OBB file and copy it to the Android/OBB folder on your device storage. Then, you need to uninstall the previous version of the mod and install the new one. You should be able to play the updated version of CarX Highway Racing APK Mod without any problems.</p>
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-<p>Yes, you can play CarX Highway Racing APK Mod offline. You don't need an internet connection to play the career mode or the free ride mode. However, you do need an internet connection to play the online mode, where you can compete with other players from around the world.</p>
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-<p>You don't need to worry about getting more money and gold in CarX Highway Racing APK Mod, as you already have unlimited money and gold in this mod. You can use them to buy any car or upgrade any part you want without any limitations. You can also unlock all the cars and tracks without having to complete the missions or races.</p>
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-<p>The minimum requirements for CarX Highway Racing APK Mod are as follows:</p>
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spaces/1phancelerku/anime-remove-background/Find and Download the Perfect 3D Printer Models in STL OBJ and 3MF Formats.md

@@ -1,126 +0,0 @@
-<br />
-<h1>Download 3D OBJ Free: A Guide to Finding and Using Free 3D Models</h1>
- <p>If you are interested in 3D modeling, animation, or printing, you may have come across the term OBJ file. OBJ files are one of the most common and versatile file formats for storing and exchanging 3D models. They can be used for various purposes, such as creating realistic renderings, adding details to your designs, or printing them in full color.</p>
- <p>In this article, we will explain what an OBJ file is, why it is useful, how to download free 3D OBJ models from various websites, and how to use them in different 3D software. By the end of this article, you will have a better understanding of the OBJ file format and how to take advantage of it.</p>
-<h2>download 3d obj free</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://jinyurl.com/2uNLhM">https://jinyurl.com/2uNLhM</a></b></p><br /><br />
- <h2>What is an OBJ File and Why is it Useful?</h2>
- <h3>The Basics of OBJ File Format</h3>
- <p>An OBJ file is a text file format that stores a description of the surface of a 3D object, composed of polygons, curves, vertices, texture maps, and other object information. It is a vector file that can be scaled and has no maximum file size. It is often used as an exchange format for 3D graphics and multi-color 3D printing. It can be edited in a text editor or various 3D image editing programs.</p>
- <p>The OBJ file format was developed by Wavefront Technologies (designers of 3D Maya modeling software) for its Advanced Visualizer package. The file format is open and has been adopted by other 3D graphics application vendors. The OBJ file format is a simple data-format that represents 3D geometry alone — namely, the position of each vertex, the UV position of each texture coordinate vertex, vertex normals, and the faces that make each polygon defined as a list of vertices, and texture vertices. Vertices are stored in a counter-clockwise order by default, making explicit declaration of face normals unnecessary. OBJ coordinates have no units, but OBJ files can contain scale information in a human readable comment line.</p>
- <h3>The Benefits of OBJ File Format</h3>
- <p>There are several benefits of using the OBJ file format for your 3D models. Some of them are:</p>
-<p>download free 3d obj models<br />
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- <ul>
-<li>It enables you to represent complex or irregularly shaped objects by dividing their surface into small, triangular "tiles". This tessellation process makes it easier to manipulate and render the design since you can modify each tile separately from the rest.</li>
-<li>It allows you to specify the geometry of 3D objects and their surface properties, including texture mapping and shading. This versatility makes the OBJ file format robust for creating realistic renderings of complex three-dimensional scenes.</li>
-<li>It supports high-resolution data compared to similar file formats like STL files. It can store textures and multiple colors in the same object, unlike STL files, which only support one color per object.</li>
-<li>It is compatible with many software programs and platforms, making it easy to share your files between different applications. You can also convert your OBJ files into other formats using online tools or software programs.</li>
-</ul>
- <h2>How to Download Free 3D OBJ Models from Various Websites</h2>
- <p>If you are looking for free 3D OBJ models to use for your projects, you are in luck. There are many websites that offer free downloads of high-quality 3D models in various categories and styles. Here are some of the best websites to download free 3D OBJ models:</p>
- <h3>TurboSquid</h3>
- <p>TurboSquid is one of the largest online marketplaces for 3D models. It offers both free and paid 3D models in various formats, including OBJ. You can browse by category, keyword, or popularity. You can also filter by price, license, rating, and poly count. TurboSquid has a large collection of free 3D models that you can download and use for personal or commercial projects. Some of the free 3D models include animals, vehicles, furniture, characters, and more. You can also find some free 3D models that are part of the StemCell initiative, which ensures that the models are compatible with multiple software and renderers.</p>
- <h3>Clara.io</h3>
- <p>Clara.io is a cloud-based 3D modeling, animation, and rendering platform that lets you create and share 3D content online. It also has a library of over 100,000 free 3D models that you can download and use for your projects. You can search by category, tag, or keyword. You can also filter by format, license, poly count, and rating. Clara.io supports various file formats, including OBJ, FBX, STL, DAE, and more. Some of the free 3D models include architecture, furniture, vehicles, characters, and more. You can also view and edit the 3D models online using the Clara.io editor.</p>
- <h3>CGTrader</h3>
- <p>CGTrader is another online marketplace for 3D models that offers both free and paid 3D models in various formats, including OBJ. You can browse by category, keyword, or popularity. You can also filter by price, license, rating, and poly count. CGTrader has a large collection of free 3D models that you can download and use for your projects. Some of the free 3D models include animals, vehicles, furniture, characters, and more. You can also find some free 3D models that are part of the AR/VR Ready collection, which ensures that the models are optimized for augmented reality and virtual reality applications.</p>
- <h3>Sketchfab</h3>
- <p>Sketchfab is a platform that lets you upload, view, and share 3D content online. It also has a library of over 4 million free and paid 3D models that you can download and use for your projects. You can search by category, tag, or keyword. You can also filter by format, license, poly count, and rating. Sketchfab supports various file formats, including OBJ, FBX, STL, DAE, and more. Some of the free 3D models include architecture, furniture, vehicles, characters, and more. You can also view and interact with the 3D models online using the Sketchfab viewer.</p>
- <h2>How to Use 3D OBJ Models in Different 3D Software</h2>
- <p>Once you have downloaded your free 3D OBJ models, you may want to use them in different 3D software for editing, rendering, or printing. Here are some of the most popular 3D software that support the OBJ file format and how to use them:</p>
- <h3>Blender</h3>
- <p>Blender is a free and open-source 3D creation suite that supports modeling, animation, rendering, sculpting, simulation, and more. It also supports various file formats, including OBJ. To import an OBJ file into Blender, follow these steps:</p>
- <ol>
-<li>Open Blender and create a new project or open an existing one.</li>
-<li>Go to File > Import > Wavefront (.obj) and navigate to the location of your OBJ file.</li>
-<li>Select your OBJ file and click Import OBJ. You can adjust the import settings in the panel on the left side of the screen.</li>
-<li>Your OBJ model will appear in the 3D viewport. You can use the tools in Blender to edit, transform, or animate your model as you wish.</li>
-</ol>
- <h3>3DS Max</h3>
- <p>3DS Max is a professional 3D modeling, animation, and rendering software that is widely used in the gaming, film, and design industries. It also supports various file formats, including OBJ. To import an OBJ file into 3DS Max, follow these steps:</p>
- <ol>
-<li>Open 3DS Max and create a new project or open an existing one.</li>
-<li>Go to File > Import and navigate to the location of your OBJ file.</li>
-<li>Select your OBJ file and click Open. You can adjust the import settings in the dialog box that appears.</li>
-<li>Your OBJ model will appear in the scene. You can use the tools in 3DS Max to edit, transform, or animate your model as you wish.</li>
-</ol>
- <h3>Cinema 4D</h3>
- <p>Cinema 4D is a powerful 3D modeling, animation, and rendering software that is used for motion graphics, visual effects, and design. It also supports various file formats, including OBJ. To import an OBJ file into Cinema 4D, follow these steps:</p>
- <ol>
-<li>Open Cinema 4D and create a new project or open an existing one.</li>
-<li>Go to File > Merge and navigate to the location of your OBJ file.</li>
-<li>Select your OBJ file and click Open. You can adjust the import settings in the dialog box that appears.</li>
-<li>Your OBJ model will appear in the object manager and the viewport. You can use the tools in Cinema 4D to edit, transform, or animate your model as you wish.</li>
-</ol>
- <h3>3D Builder for Windows</h3>
- <p>3D Builder is a free app for Windows that lets you view, create, edit, and print 3D models. It also supports various file formats, including OBJ. To import an OBJ file into 3D Builder, follow these steps:</p>
- <ol>
-<li>Open 3D Builder and create a new project or open an existing one.</li>
-<li>Go to Menu > Insert > Load Object and navigate to the location of your OBJ file.</li>
-<li>Select your OBJ file and click Open. Your OBJ model will appear in the scene.</li>
-<li>You can use the tools in 3D Builder to edit, transform, or print your model as you wish.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have explained what an OBJ file is, why it is useful, how to download free 3D OBJ models from various websites, and how to use them in different 3D software. We hope that this article has helped you learn more about the OBJ file format and how to take advantage of it for your 3D projects.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about downloading free 3D OBJ models:</p>
- <h4>Q: What are some other websites that offer free 3D OBJ models?</h4>
- <p>A: Some other websites that offer free 3D OBJ models are Free3D.com, Archive3D.net, CadNav.com, and NASA.gov. You can also search for free 3D models on Google or Bing using keywords like "free obj models" or "free obj files ".</p>
- <h4>Q: How can I convert an OBJ file into another file format?</h4>
- <p>A: You can use online tools or software programs to convert your OBJ files into other file formats. Some of the online tools are Online 3D Converter, Convertio, and AnyConv. Some of the software programs are MeshLab, Autodesk Fusion 360, and Adobe Photoshop.</p>
- <h4>Q: How can I optimize an OBJ file for faster loading or printing?</h4>
- <p>A: You can optimize your OBJ file by reducing the number of polygons, vertices, and textures in your model. This will make your file size smaller and improve the performance of your 3D software or printer. You can use online tools or software programs to optimize your OBJ files. Some of the online tools are Meshmixer, MeshOptimizer, and RapidCompact. Some of the software programs are Blender, 3DS Max, and Cinema 4D.</p>
- <h4>Q: How can I edit an OBJ file in a text editor?</h4>
- <p>A: You can edit an OBJ file in a text editor by opening it with a program like Notepad, WordPad, or Sublime Text. You can then modify the lines of code that define the geometry and properties of your model. However, editing an OBJ file in a text editor is not recommended unless you are familiar with the syntax and structure of the file format. It is easier and safer to edit your OBJ file in a 3D image editing program.</p>
- <h4>Q: How can I view an OBJ file without downloading any software?</h4>
- <p>A: You can view an OBJ file without downloading any software by using online viewers or browsers that support the OBJ file format. Some of the online viewers are 3D Viewer Online, Viewstl.com, and 3D Model Viewer. Some of the browsers that support the OBJ file format are Chrome, Firefox, and Edge.</p> 401be4b1e0<br />
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spaces/2023Liu2023/bingo/src/components/chat-image.tsx

@@ -1,170 +0,0 @@
-import {
-  useEffect,
-  useState,
-  useCallback,
-  ChangeEvent,
-  ClipboardEvent,
-  MouseEventHandler,
-  FormEvent,
-  useRef
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-
-interface ChatImageProps extends Pick<ReturnType<typeof useBing>, 'uploadImage'> {}
-
-const preventDefault: MouseEventHandler<HTMLDivElement> = (event) => {
-  event.nativeEvent.stopImmediatePropagation()
-}
-
-const toBase64 = (file: File): Promise<string> => new Promise((resolve, reject) => {
-  const reader = new FileReader()
-  reader.readAsDataURL(file)
-  reader.onload = () => resolve(reader.result as string)
-  reader.onerror = reject
-})
-
-export function ChatImage({ children, uploadImage }: React.PropsWithChildren<ChatImageProps>) {
-  const videoRef = useRef<HTMLVideoElement>(null)
-  const canvasRef = useRef<HTMLCanvasElement>(null)
-  const mediaStream = useRef<MediaStream>()
-  const [panel, setPanel] = useState('none')
-
-  const upload = useCallback((url: string) => {
-    if (url) {
-      uploadImage(url)
-    }
-    setPanel('none')
-  }, [panel])
-
-  const onUpload = useCallback(async (event: ChangeEvent<HTMLInputElement>) => {
-    const file = event.target.files?.[0]
-    if (file) {
-      const fileDataUrl = await toBase64(file)
-      if (fileDataUrl) {
-        upload(fileDataUrl)
-      }
-    }
-  }, [])
-
-  const onPaste = useCallback((event: ClipboardEvent<HTMLInputElement>) => {
-    const pasteUrl = event.clipboardData.getData('text') ?? ''
-    upload(pasteUrl)
-  }, [])
-
-  const onEnter = useCallback((event: FormEvent<HTMLFormElement>) => {
-    event.preventDefault()
-    event.stopPropagation()
-    // @ts-ignore
-    const inputUrl = event.target.elements.image.value
-    if (inputUrl) {
-      upload(inputUrl)
-    }
-  }, [])
-
-  const openVideo: MouseEventHandler<HTMLButtonElement> = async (event) => {
-    event.stopPropagation()
-    setPanel('camera-mode')
-  }
-
-  const onCapture = () => {
-    if (canvasRef.current && videoRef.current) {
-      const canvas = canvasRef.current
-      canvas.width = videoRef.current!.videoWidth
-      canvas.height = videoRef.current!.videoHeight
-      canvas.getContext('2d')?.drawImage(videoRef.current, 0, 0, canvas.width, canvas.height)
-      const cameraUrl = canvas.toDataURL('image/jpeg')
-      upload(cameraUrl)
-    }
-  }
-
-  useEffect(() => {
-    const handleBlur = () => {
-      if (panel !== 'none') {
-        setPanel('none')
-      }
-    }
-    document.addEventListener('click', handleBlur)
-    return () => {
-      document.removeEventListener('click', handleBlur)
-    }
-  }, [panel])
-
-  useEffect(() => {
-    if (panel === 'camera-mode') {
-      navigator.mediaDevices.getUserMedia({ video: true, audio: false })
-      .then(videoStream => {
-        mediaStream.current = videoStream
-        if (videoRef.current) {
-          videoRef.current.srcObject = videoStream
-        }
-      })
-    } else {
-      if (mediaStream.current) {
-        mediaStream.current.getTracks().forEach(function(track) {
-          track.stop()
-        })
-        mediaStream.current = undefined
-      }
-    }
-  }, [panel])
-
-  return (
-    <div className="visual-search-container">
-      <div onClick={() => panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}</div>
-      <div className={cn('visual-search', panel)} onClick={preventDefault}>
-        <div className="normal-content">
-          <div className="header">
-            <h4>添加图像</h4>
-          </div>
-          <div className="paste">
-            <Image alt="paste" src={PasteIcon} width={24} />
-            <form onSubmitCapture={onEnter}>
-              <input
-                className="paste-input"
-                id="sb_imgpst"
-                type="text"
-                name="image"
-                placeholder="粘贴图像 URL"
-                aria-label="粘贴图像 URL"
-                onPaste={onPaste}
-                onClickCapture={(e) => e.stopPropagation()}
-              />
-            </form>
-          </div>
-          <div className="buttons">
-            <button type="button" aria-label="从此设备上传">
-              <input
-                id="vs_fileinput"
-                className="fileinput"
-                type="file"
-                accept="image/gif, image/jpeg, image/png, image/webp"
-                onChange={onUpload}
-              />
-              <Image alt="uplaod" src={UploadIcon} width={20} />
-              从此设备上传
-            </button>
-            <button type="button" aria-label="拍照" onClick={openVideo}>
-              <Image alt="camera" src={CameraIcon} width={20} />
-              拍照
-            </button>
-          </div>
-        </div>
-        {panel === 'camera-mode' && <div className="cam-content">
-          <div className="webvideo-container">
-            <video className="webvideo" autoPlay muted playsInline ref={videoRef} />
-            <canvas className="webcanvas" ref={canvasRef} />
-          </div>
-          <div className="cambtn" role="button" aria-label="拍照" onClick={onCapture}>
-            <div className="cam-btn-circle-large"></div>
-            <div className="cam-btn-circle-small"></div>
-          </div>
-        </div>}
-      </div>
-    </div>
-  )
-}

spaces/2023Liu2023/bingo/src/pages/api/kblob.ts

@@ -1,56 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-
-const API_DOMAIN = 'https://bing.vcanbb.top'
-
-export const config = {
-  api: {
-    bodyParser: {
-      sizeLimit: '10mb' // Set desired value here
-    }
-  }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
-
-    const formData = new FormData()
-    formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-    if (imageBase64) {
-      formData.append('imageBase64', imageBase64)
-    }
-
-    const response = await fetch(`${API_DOMAIN}/images/kblob`,
-      {
-        method: 'POST',
-        body: formData.getBuffer(),
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referer": `${API_DOMAIN}/web/index.html`,
-          "Referrer-Policy": "origin-when-cross-origin",
-          'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-          ...formData.getHeaders()
-        }
-      }
-    ).then(res => res.text())
-
-    res.writeHead(200, {
-      'Content-Type': 'application/json',
-    })
-    res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } }))
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}

spaces/232labs/VToonify/vtoonify/model/raft/README.md

@@ -1,80 +0,0 @@
-# RAFT
-This repository contains the source code for our paper:
-
-[RAFT: Recurrent All Pairs Field Transforms for Optical Flow](https://arxiv.org/pdf/2003.12039.pdf)<br/>
-ECCV 2020 <br/>
-Zachary Teed and Jia Deng<br/>
-
-<img src="RAFT.png">
-
-## Requirements
-The code has been tested with PyTorch 1.6 and Cuda 10.1.
-```Shell
-conda create --name raft
-conda activate raft
-conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch
-```
-
-## Demos
-Pretrained models can be downloaded by running
-```Shell
-./download_models.sh
-```
-or downloaded from [google drive](https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT?usp=sharing)
-
-You can demo a trained model on a sequence of frames
-```Shell
-python demo.py --model=models/raft-things.pth --path=demo-frames
-```
-
-## Required Data
-To evaluate/train RAFT, you will need to download the required datasets. 
-* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
-* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
-* [Sintel](http://sintel.is.tue.mpg.de/)
-* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow)
-* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) (optional)
-
-
-By default `datasets.py` will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the `datasets` folder
-
-```Shell
-├── datasets
-    ├── Sintel
-        ├── test
-        ├── training
-    ├── KITTI
-        ├── testing
-        ├── training
-        ├── devkit
-    ├── FlyingChairs_release
-        ├── data
-    ├── FlyingThings3D
-        ├── frames_cleanpass
-        ├── frames_finalpass
-        ├── optical_flow
-```
-
-## Evaluation
-You can evaluate a trained model using `evaluate.py`
-```Shell
-python evaluate.py --model=models/raft-things.pth --dataset=sintel --mixed_precision
-```
-
-## Training
-We used the following training schedule in our paper (2 GPUs). Training logs will be written to the `runs` which can be visualized using tensorboard
-```Shell
-./train_standard.sh
-```
-
-If you have a RTX GPU, training can be accelerated using mixed precision. You can expect similiar results in this setting (1 GPU)
-```Shell
-./train_mixed.sh
-```
-
-## (Optional) Efficent Implementation
-You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension
-```Shell
-cd alt_cuda_corr && python setup.py install && cd ..
-```
-and running `demo.py` and `evaluate.py` with the `--alternate_corr` flag Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass.

spaces/AIARTCHAN/openpose_editor/README.md

@@ -1,11 +0,0 @@
----
-title: Openpose Editor
-emoji: 🤸
-colorFrom: indigo
-colorTo: green
-sdk: static
-pinned: false
-license: mit
----
-
-[원본글](https://arca.live/b/aiart/70172781)

spaces/AIFILMS/generate_human_motion/VQ-Trans/visualize/simplify_loc2rot.py

@@ -1,131 +0,0 @@
-import numpy as np
-import os
-import torch
-from visualize.joints2smpl.src import config
-import smplx
-import h5py
-from visualize.joints2smpl.src.smplify import SMPLify3D
-from tqdm import tqdm
-import utils.rotation_conversions as geometry
-import argparse
-
-
-class joints2smpl:
-
-    def __init__(self, num_frames, device_id, cuda=True):
-        self.device = torch.device("cuda:" + str(device_id) if cuda else "cpu")
-        # self.device = torch.device("cpu")
-        self.batch_size = num_frames
-        self.num_joints = 22  # for HumanML3D
-        self.joint_category = "AMASS"
-        self.num_smplify_iters = 150
-        self.fix_foot = False
-        print(config.SMPL_MODEL_DIR)
-        smplmodel = smplx.create(config.SMPL_MODEL_DIR,
-                                 model_type="smpl", gender="neutral", ext="pkl",
-                                 batch_size=self.batch_size).to(self.device)
-
-        # ## --- load the mean pose as original ----
-        smpl_mean_file = config.SMPL_MEAN_FILE
-
-        file = h5py.File(smpl_mean_file, 'r')
-        self.init_mean_pose = torch.from_numpy(file['pose'][:]).unsqueeze(0).repeat(self.batch_size, 1).float().to(self.device)
-        self.init_mean_shape = torch.from_numpy(file['shape'][:]).unsqueeze(0).repeat(self.batch_size, 1).float().to(self.device)
-        self.cam_trans_zero = torch.Tensor([0.0, 0.0, 0.0]).unsqueeze(0).to(self.device)
-        #
-
-        # # #-------------initialize SMPLify
-        self.smplify = SMPLify3D(smplxmodel=smplmodel,
-                            batch_size=self.batch_size,
-                            joints_category=self.joint_category,
-                            num_iters=self.num_smplify_iters,
-                            device=self.device)
-
-
-    def npy2smpl(self, npy_path):
-        out_path = npy_path.replace('.npy', '_rot.npy')
-        motions = np.load(npy_path, allow_pickle=True)[None][0]
-        # print_batch('', motions)
-        n_samples = motions['motion'].shape[0]
-        all_thetas = []
-        for sample_i in tqdm(range(n_samples)):
-            thetas, _ = self.joint2smpl(motions['motion'][sample_i].transpose(2, 0, 1))  # [nframes, njoints, 3]
-            all_thetas.append(thetas.cpu().numpy())
-        motions['motion'] = np.concatenate(all_thetas, axis=0)
-        print('motions', motions['motion'].shape)
-
-        print(f'Saving [{out_path}]')
-        np.save(out_path, motions)
-        exit()
-
-
-
-    def joint2smpl(self, input_joints, init_params=None):
-        _smplify = self.smplify # if init_params is None else self.smplify_fast
-        pred_pose = torch.zeros(self.batch_size, 72).to(self.device)
-        pred_betas = torch.zeros(self.batch_size, 10).to(self.device)
-        pred_cam_t = torch.zeros(self.batch_size, 3).to(self.device)
-        keypoints_3d = torch.zeros(self.batch_size, self.num_joints, 3).to(self.device)
-
-        # run the whole seqs
-        num_seqs = input_joints.shape[0]
-
-
-        # joints3d = input_joints[idx]  # *1.2 #scale problem [check first]
-        keypoints_3d = torch.Tensor(input_joints).to(self.device).float()
-
-        # if idx == 0:
-        if init_params is None:
-            pred_betas = self.init_mean_shape
-            pred_pose = self.init_mean_pose
-            pred_cam_t = self.cam_trans_zero
-        else:
-            pred_betas = init_params['betas']
-            pred_pose = init_params['pose']
-            pred_cam_t = init_params['cam']
-
-        if self.joint_category == "AMASS":
-            confidence_input = torch.ones(self.num_joints)
-            # make sure the foot and ankle
-            if self.fix_foot == True:
-                confidence_input[7] = 1.5
-                confidence_input[8] = 1.5
-                confidence_input[10] = 1.5
-                confidence_input[11] = 1.5
-        else:
-            print("Such category not settle down!")
-
-        new_opt_vertices, new_opt_joints, new_opt_pose, new_opt_betas, \
-        new_opt_cam_t, new_opt_joint_loss = _smplify(
-            pred_pose.detach(),
-            pred_betas.detach(),
-            pred_cam_t.detach(),
-            keypoints_3d,
-            conf_3d=confidence_input.to(self.device),
-            # seq_ind=idx
-        )
-
-        thetas = new_opt_pose.reshape(self.batch_size, 24, 3)
-        thetas = geometry.matrix_to_rotation_6d(geometry.axis_angle_to_matrix(thetas))  # [bs, 24, 6]
-        root_loc = torch.tensor(keypoints_3d[:, 0])  # [bs, 3]
-        root_loc = torch.cat([root_loc, torch.zeros_like(root_loc)], dim=-1).unsqueeze(1)  # [bs, 1, 6]
-        thetas = torch.cat([thetas, root_loc], dim=1).unsqueeze(0).permute(0, 2, 3, 1)  # [1, 25, 6, 196]
-
-        return thetas.clone().detach(), {'pose': new_opt_joints[0, :24].flatten().clone().detach(), 'betas': new_opt_betas.clone().detach(), 'cam': new_opt_cam_t.clone().detach()}
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--input_path", type=str, required=True, help='Blender file or dir with blender files')
-    parser.add_argument("--cuda", type=bool, default=True, help='')
-    parser.add_argument("--device", type=int, default=0, help='')
-    params = parser.parse_args()
-
-    simplify = joints2smpl(device_id=params.device, cuda=params.cuda)
-
-    if os.path.isfile(params.input_path) and params.input_path.endswith('.npy'):
-        simplify.npy2smpl(params.input_path)
-    elif os.path.isdir(params.input_path):
-        files = [os.path.join(params.input_path, f) for f in os.listdir(params.input_path) if f.endswith('.npy')]
-        for f in files:
-            simplify.npy2smpl(f)

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/light.py

@@ -1,385 +0,0 @@
-"""Punctual light sources as defined by the glTF 2.0 KHR extension at
-https://github.com/KhronosGroup/glTF/tree/master/extensions/2.0/Khronos/KHR_lights_punctual
-
-Author: Matthew Matl
-"""
-import abc
-import numpy as np
-import six
-
-from OpenGL.GL import *
-
-from .utils import format_color_vector
-from .texture import Texture
-from .constants import SHADOW_TEX_SZ
-from .camera import OrthographicCamera, PerspectiveCamera
-
-
-
-@six.add_metaclass(abc.ABCMeta)
-class Light(object):
-    """Base class for all light objects.
-
-    Parameters
-    ----------
-    color : (3,) float
-        RGB value for the light's color in linear space.
-    intensity : float
-        Brightness of light. The units that this is defined in depend on the
-        type of light. Point and spot lights use luminous intensity in candela
-        (lm/sr), while directional lights use illuminance in lux (lm/m2).
-    name : str, optional
-        Name of the light.
-    """
-    def __init__(self,
-                 color=None,
-                 intensity=None,
-                 name=None):
-
-        if color is None:
-            color = np.ones(3)
-        if intensity is None:
-            intensity = 1.0
-
-        self.name = name
-        self.color = color
-        self.intensity = intensity
-        self._shadow_camera = None
-        self._shadow_texture = None
-
-    @property
-    def name(self):
-        """str : The user-defined name of this object.
-        """
-        return self._name
-
-    @name.setter
-    def name(self, value):
-        if value is not None:
-            value = str(value)
-        self._name = value
-
-    @property
-    def color(self):
-        """(3,) float : The light's color.
-        """
-        return self._color
-
-    @color.setter
-    def color(self, value):
-        self._color = format_color_vector(value, 3)
-
-    @property
-    def intensity(self):
-        """float : The light's intensity in candela or lux.
-        """
-        return self._intensity
-
-    @intensity.setter
-    def intensity(self, value):
-        self._intensity = float(value)
-
-    @property
-    def shadow_texture(self):
-        """:class:`.Texture` : A texture used to hold shadow maps for this light.
-        """
-        return self._shadow_texture
-
-    @shadow_texture.setter
-    def shadow_texture(self, value):
-        if self._shadow_texture is not None:
-            if self._shadow_texture._in_context():
-                self._shadow_texture.delete()
-        self._shadow_texture = value
-
-    @abc.abstractmethod
-    def _generate_shadow_texture(self, size=None):
-        """Generate a shadow texture for this light.
-
-        Parameters
-        ----------
-        size : int, optional
-            Size of texture map. Must be a positive power of two.
-        """
-        pass
-
-    @abc.abstractmethod
-    def _get_shadow_camera(self, scene_scale):
-        """Generate and return a shadow mapping camera for this light.
-
-        Parameters
-        ----------
-        scene_scale : float
-            Length of scene's bounding box diagonal.
-
-        Returns
-        -------
-        camera : :class:`.Camera`
-            The camera used to render shadowmaps for this light.
-        """
-        pass
-
-
-class DirectionalLight(Light):
-    """Directional lights are light sources that act as though they are
-    infinitely far away and emit light in the direction of the local -z axis.
-    This light type inherits the orientation of the node that it belongs to;
-    position and scale are ignored except for their effect on the inherited
-    node orientation. Because it is at an infinite distance, the light is
-    not attenuated. Its intensity is defined in lumens per metre squared,
-    or lux (lm/m2).
-
-    Parameters
-    ----------
-    color : (3,) float, optional
-        RGB value for the light's color in linear space. Defaults to white
-        (i.e. [1.0, 1.0, 1.0]).
-    intensity : float, optional
-        Brightness of light, in lux (lm/m^2). Defaults to 1.0
-    name : str, optional
-        Name of the light.
-    """
-
-    def __init__(self,
-                 color=None,
-                 intensity=None,
-                 name=None):
-        super(DirectionalLight, self).__init__(
-            color=color,
-            intensity=intensity,
-            name=name,
-        )
-
-    def _generate_shadow_texture(self, size=None):
-        """Generate a shadow texture for this light.
-
-        Parameters
-        ----------
-        size : int, optional
-            Size of texture map. Must be a positive power of two.
-        """
-        if size is None:
-            size = SHADOW_TEX_SZ
-        self.shadow_texture = Texture(width=size, height=size,
-                                      source_channels='D', data_format=GL_FLOAT)
-
-    def _get_shadow_camera(self, scene_scale):
-        """Generate and return a shadow mapping camera for this light.
-
-        Parameters
-        ----------
-        scene_scale : float
-            Length of scene's bounding box diagonal.
-
-        Returns
-        -------
-        camera : :class:`.Camera`
-            The camera used to render shadowmaps for this light.
-        """
-        return OrthographicCamera(
-            znear=0.01 * scene_scale,
-            zfar=10 * scene_scale,
-            xmag=scene_scale,
-            ymag=scene_scale
-        )
-
-
-class PointLight(Light):
-    """Point lights emit light in all directions from their position in space;
-    rotation and scale are ignored except for their effect on the inherited
-    node position. The brightness of the light attenuates in a physically
-    correct manner as distance increases from the light's position (i.e.
-    brightness goes like the inverse square of the distance). Point light
-    intensity is defined in candela, which is lumens per square radian (lm/sr).
-
-    Parameters
-    ----------
-    color : (3,) float
-        RGB value for the light's color in linear space.
-    intensity : float
-        Brightness of light in candela (lm/sr).
-    range : float
-        Cutoff distance at which light's intensity may be considered to
-        have reached zero. If None, the range is assumed to be infinite.
-    name : str, optional
-        Name of the light.
-    """
-
-    def __init__(self,
-                 color=None,
-                 intensity=None,
-                 range=None,
-                 name=None):
-        super(PointLight, self).__init__(
-            color=color,
-            intensity=intensity,
-            name=name,
-        )
-        self.range = range
-
-    @property
-    def range(self):
-        """float : The cutoff distance for the light.
-        """
-        return self._range
-
-    @range.setter
-    def range(self, value):
-        if value is not None:
-            value = float(value)
-            if value <= 0:
-                raise ValueError('Range must be > 0')
-            self._range = value
-        self._range = value
-
-    def _generate_shadow_texture(self, size=None):
-        """Generate a shadow texture for this light.
-
-        Parameters
-        ----------
-        size : int, optional
-            Size of texture map. Must be a positive power of two.
-        """
-        raise NotImplementedError('Shadows not implemented for point lights')
-
-    def _get_shadow_camera(self, scene_scale):
-        """Generate and return a shadow mapping camera for this light.
-
-        Parameters
-        ----------
-        scene_scale : float
-            Length of scene's bounding box diagonal.
-
-        Returns
-        -------
-        camera : :class:`.Camera`
-            The camera used to render shadowmaps for this light.
-        """
-        raise NotImplementedError('Shadows not implemented for point lights')
-
-
-class SpotLight(Light):
-    """Spot lights emit light in a cone in the direction of the local -z axis.
-    The angle and falloff of the cone is defined using two numbers, the
-    ``innerConeAngle`` and ``outerConeAngle``.
-    As with point lights, the brightness
-    also attenuates in a physically correct manner as distance increases from
-    the light's position (i.e. brightness goes like the inverse square of the
-    distance). Spot light intensity refers to the brightness inside the
-    ``innerConeAngle`` (and at the location of the light) and is defined in
-    candela, which is lumens per square radian (lm/sr). A spot light's position
-    and orientation are inherited from its node transform. Inherited scale does
-    not affect cone shape, and is ignored except for its effect on position
-    and orientation.
-
-    Parameters
-    ----------
-    color : (3,) float
-        RGB value for the light's color in linear space.
-    intensity : float
-        Brightness of light in candela (lm/sr).
-    range : float
-        Cutoff distance at which light's intensity may be considered to
-        have reached zero. If None, the range is assumed to be infinite.
-    innerConeAngle : float
-        Angle, in radians, from centre of spotlight where falloff begins.
-        Must be greater than or equal to ``0`` and less
-        than ``outerConeAngle``. Defaults to ``0``.
-    outerConeAngle : float
-        Angle, in radians, from centre of spotlight where falloff ends.
-        Must be greater than ``innerConeAngle`` and less than or equal to
-        ``PI / 2.0``. Defaults to ``PI / 4.0``.
-    name : str, optional
-        Name of the light.
-    """
-
-    def __init__(self,
-                 color=None,
-                 intensity=None,
-                 range=None,
-                 innerConeAngle=0.0,
-                 outerConeAngle=(np.pi / 4.0),
-                 name=None):
-        super(SpotLight, self).__init__(
-            name=name,
-            color=color,
-            intensity=intensity,
-        )
-        self.outerConeAngle = outerConeAngle
-        self.innerConeAngle = innerConeAngle
-        self.range = range
-
-    @property
-    def innerConeAngle(self):
-        """float : The inner cone angle in radians.
-        """
-        return self._innerConeAngle
-
-    @innerConeAngle.setter
-    def innerConeAngle(self, value):
-        if value < 0.0 or value > self.outerConeAngle:
-            raise ValueError('Invalid value for inner cone angle')
-        self._innerConeAngle = float(value)
-
-    @property
-    def outerConeAngle(self):
-        """float : The outer cone angle in radians.
-        """
-        return self._outerConeAngle
-
-    @outerConeAngle.setter
-    def outerConeAngle(self, value):
-        if value < 0.0 or value > np.pi / 2.0 + 1e-9:
-            raise ValueError('Invalid value for outer cone angle')
-        self._outerConeAngle = float(value)
-
-    @property
-    def range(self):
-        """float : The cutoff distance for the light.
-        """
-        return self._range
-
-    @range.setter
-    def range(self, value):
-        if value is not None:
-            value = float(value)
-            if value <= 0:
-                raise ValueError('Range must be > 0')
-            self._range = value
-        self._range = value
-
-    def _generate_shadow_texture(self, size=None):
-        """Generate a shadow texture for this light.
-
-        Parameters
-        ----------
-        size : int, optional
-            Size of texture map. Must be a positive power of two.
-        """
-        if size is None:
-            size = SHADOW_TEX_SZ
-        self.shadow_texture = Texture(width=size, height=size,
-                                      source_channels='D', data_format=GL_FLOAT)
-
-    def _get_shadow_camera(self, scene_scale):
-        """Generate and return a shadow mapping camera for this light.
-
-        Parameters
-        ----------
-        scene_scale : float
-            Length of scene's bounding box diagonal.
-
-        Returns
-        -------
-        camera : :class:`.Camera`
-            The camera used to render shadowmaps for this light.
-        """
-        return PerspectiveCamera(
-            znear=0.01 * scene_scale,
-            zfar=10 * scene_scale,
-            yfov=np.clip(2 * self.outerConeAngle + np.pi / 16.0, 0.0, np.pi),
-            aspectRatio=1.0
-        )
-
-
-__all__ = ['Light', 'DirectionalLight', 'SpotLight', 'PointLight']

spaces/AIGC-Audio/AudioGPT/sound_extraction/model/text_encoder.py

@@ -1,45 +0,0 @@
-import torch
-import torch.nn as nn
-from transformers import *
-import warnings
-warnings.filterwarnings('ignore')
-# pretrained model name: (model class, model tokenizer, output dimension, token style)
-MODELS = {
-    'prajjwal1/bert-mini': (BertModel, BertTokenizer),
-}
-
-class Text_Encoder(nn.Module):
-    def __init__(self, device):
-        super(Text_Encoder, self).__init__()
-        self.base_model = 'prajjwal1/bert-mini'
-        self.dropout = 0.1
-
-        self.tokenizer = MODELS[self.base_model][1].from_pretrained(self.base_model)
-
-        self.bert_layer =  MODELS[self.base_model][0].from_pretrained(self.base_model,
-                                                    add_pooling_layer=False,
-                                                    hidden_dropout_prob=self.dropout,
-                                                    attention_probs_dropout_prob=self.dropout,
-                                                    output_hidden_states=True)
-        
-        self.linear_layer = nn.Sequential(nn.Linear(256, 256), nn.ReLU(inplace=True))
-        
-        self.device = device
-
-    def tokenize(self, caption):
-        # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        tokenized = self.tokenizer(caption, add_special_tokens=False, padding=True, return_tensors='pt')
-        input_ids = tokenized['input_ids']
-        attns_mask = tokenized['attention_mask']
-
-        input_ids = input_ids.to(self.device)
-        attns_mask = attns_mask.to(self.device)
-        return input_ids, attns_mask
-
-    def forward(self, input_ids, attns_mask):
-        # input_ids, attns_mask = self.tokenize(caption)
-        output = self.bert_layer(input_ids=input_ids, attention_mask=attns_mask)[0]
-        cls_embed = output[:, 0, :]
-        text_embed = self.linear_layer(cls_embed)
-
-        return text_embed, output  # text_embed: (batch, hidden_size)

spaces/AIWaves/Software_Company/src/agents/utils.py

@@ -1,480 +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.
-"""helper functions for an LLM autonoumous agent"""
-import csv
-import random
-import json
-import pandas
-import numpy as np
-import requests
-import torch
-from tqdm import tqdm
-from text2vec import semantic_search
-import re
-import datetime
-from langchain.document_loaders import UnstructuredFileLoader
-from langchain.text_splitter import CharacterTextSplitter
-from sentence_transformers import SentenceTransformer
-import string
-import random
-import os
-import openai
-
-embed_model_name = os.environ["Embed_Model"] if "Embed_Model" in os.environ else "text-embedding-ada-002"
-if embed_model_name in ["text-embedding-ada-002"]:
-    pass
-else:
-    embedding_model = SentenceTransformer(
-                embed_model_name, device=torch.device("cpu")
-            )
-
-def get_embedding(sentence):
-    if embed_model_name in ["text-embedding-ada-002"]:
-        openai.api_key = os.environ["API_KEY"]
-        # if "PROXY" in os.environ:
-        #     assert "http:" in os.environ["PROXY"] or "socks" in os.environ["PROXY"],"PROXY error,PROXY must be http or socks"
-        #     openai.proxy = os.environ["PROXY"]
-        if "API_BASE" in os.environ:
-            openai.api_base = os.environ["API_BASE"]
-        embedding_model = openai.Embedding
-        embed = embedding_model.create(
-        model=embed_model_name,
-        input=sentence
-    )
-        embed = embed["data"][0]["embedding"]
-        embed = torch.tensor(embed,dtype=torch.float32)
-    else:
-        embed = embedding_model.encode(sentence,convert_to_tensor=True)
-    if len(embed.shape)==1:
-        embed = embed.unsqueeze(0)
-    return embed
-
-
-def get_code():
-    return "".join(random.sample(string.ascii_letters + string.digits, 8))
-
-
-def get_content_between_a_b(start_tag, end_tag, text):
-    """
-
-    Args:
-        start_tag (str): start_tag
-        end_tag (str): end_tag
-        text (str): complete sentence
-
-    Returns:
-        str: the content between start_tag and end_tag
-    """
-    extracted_text = ""
-    start_index = text.find(start_tag)
-    while start_index != -1:
-        end_index = text.find(end_tag, start_index + len(start_tag))
-        if end_index != -1:
-            extracted_text += text[start_index +
-                                   len(start_tag):end_index] + " "
-            start_index = text.find(start_tag, end_index + len(end_tag))
-        else:
-            break
-
-    return extracted_text.strip()
-
-
-def extract(text, type):
-    """extract the content between <type></type>
-
-    Args:
-        text (str): complete sentence
-        type (str): tag
-
-    Returns:
-        str: content between <type></type>
-    """
-    target_str = get_content_between_a_b(f"<{type}>", f"</{type}>", text)
-    return target_str
-
-def count_files_in_directory(directory):
-    # 获取指定目录下的文件数目
-    file_count = len([f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))])
-    return file_count
-
-def delete_oldest_files(directory, num_to_keep):
-    # 获取目录下文件列表，并按修改时间排序
-    files = [(f, os.path.getmtime(os.path.join(directory, f))) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
-
-    # 删除最开始的 num_to_keep 个文件
-    for i in range(min(num_to_keep, len(files))):
-        file_to_delete = os.path.join(directory, files[i][0])
-        os.remove(file_to_delete)
-
-def delete_files_if_exceed_threshold(directory, threshold, num_to_keep):
-    # 获取文件数目并进行处理
-    file_count = count_files_in_directory(directory)
-    if file_count > threshold:
-        delete_count = file_count - num_to_keep
-        delete_oldest_files(directory, delete_count)
-
-def save_logs(log_path, messages, response):
-    if not os.path.exists(log_path):
-        os.mkdir(log_path)
-    delete_files_if_exceed_threshold(log_path, 20, 10)
-    log_path = log_path if log_path else "logs"
-    log = {}
-    log["input"] = messages
-    log["output"] = response
-    os.makedirs(log_path, exist_ok=True)
-    log_file = os.path.join(
-        log_path,
-        datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + ".json")
-    with open(log_file, "w", encoding="utf-8") as f:
-        json.dump(log, f, ensure_ascii=False, indent=2)
-
-
-
-def semantic_search_word2vec(query_embedding, kb_embeddings, top_k):
-    return semantic_search(query_embedding, kb_embeddings, top_k=top_k)
-
-
-def cut_sent(para):
-    para = re.sub("([。！？\?])([^”’])", r"\1\n\2", para)
-    para = re.sub("(\.{6})([^”’])", r"\1\n\2", para)
-    para = re.sub("(\…{2})([^”’])", r"\1\n\2", para)
-    para = re.sub("([。！？\?][”’])([^，。！？\?])", r"\1\n\2", para)
-    para = para.rstrip()
-    pieces = [i for i in para.split("\n") if i]
-    batch_size = 3
-    chucks = [
-        " ".join(pieces[i:i + batch_size])
-        for i in range(0, len(pieces), batch_size)
-    ]
-    return chucks
-
-
-def process_document(file_path):
-    """
-    Save QA_csv to json.
-    Args:
-        model: LLM to generate embeddings
-        qa_dict: A dict contains Q&A
-        save_path: where to save the json file.
-    Json format:
-        Dict[num,Dict[q:str,a:str,chunk:str,emb:List[float]]
-    """
-    final_dict = {}
-    count = 0
-    if file_path.endswith(".csv"):
-        dataset = pandas.read_csv(file_path)
-        questions = dataset["question"]
-        answers = dataset["answer"]
-        # embedding q+chunk
-        for q, a in zip(questions, answers):
-            for text in cut_sent(a):
-                temp_dict = {}
-                temp_dict["q"] = q
-                temp_dict["a"] = a
-                temp_dict["chunk"] = text
-                temp_dict["emb"] = get_embedding(q + text).tolist()
-                final_dict[count] = temp_dict
-                count += 1
-        # embedding chunk
-        for q, a in zip(questions, answers):
-            for text in cut_sent(a):
-                temp_dict = {}
-                temp_dict["q"] = q
-                temp_dict["a"] = a
-                temp_dict["chunk"] = text
-                temp_dict["emb"] = get_embedding(text).tolist()
-                final_dict[count] = temp_dict
-                count += 1
-        # embedding q
-        for q, a in zip(questions, answers):
-            temp_dict = {}
-            temp_dict["q"] = q
-            temp_dict["a"] = a
-            temp_dict["chunk"] = a
-            temp_dict["emb"] = get_embedding(q).tolist()
-            final_dict[count] = temp_dict
-            count += 1
-        # embedding q+a
-        for q, a in zip(questions, answers):
-            temp_dict = {}
-            temp_dict["q"] = q
-            temp_dict["a"] = a
-            temp_dict["chunk"] = a
-            temp_dict["emb"] = get_embedding(q + a).tolist()
-            final_dict[count] = temp_dict
-            count += 1
-        # embedding a
-        for q, a in zip(questions, answers):
-            temp_dict = {}
-            temp_dict["q"] = q
-            temp_dict["a"] = a
-            temp_dict["chunk"] = a
-            temp_dict["emb"] = get_embedding(a).tolist()
-            final_dict[count] = temp_dict
-            count += 1
-        print(f"finish updating {len(final_dict)} data!")
-        os.makedirs("temp_database", exist_ok=True)
-        save_path = os.path.join(
-            "temp_database/",
-            file_path.split("/")[-1].replace("." + file_path.split(".")[1],
-                                             ".json"),
-        )
-        print(save_path)
-        with open(save_path, "w") as f:
-            json.dump(final_dict, f, ensure_ascii=False, indent=2)
-        return {"knowledge_base": save_path, "type": "QA"}
-    else:
-        loader = UnstructuredFileLoader(file_path)
-        docs = loader.load()
-        text_spiltter = CharacterTextSplitter(chunk_size=200,
-                                              chunk_overlap=100)
-        docs = text_spiltter.split_text(docs[0].page_content)
-        os.makedirs("temp_database", exist_ok=True)
-        save_path = os.path.join(
-            "temp_database/",
-            file_path.replace("." + file_path.split(".")[1], ".json"))
-        final_dict = {}
-        count = 0
-        for c in tqdm(docs):
-            temp_dict = {}
-            temp_dict["chunk"] = c
-            temp_dict["emb"] = get_embedding(c).tolist()
-            final_dict[count] = temp_dict
-            count += 1
-        print(f"finish updating {len(final_dict)} data!")
-        with open(save_path, "w") as f:
-            json.dump(final_dict, f, ensure_ascii=False, indent=2)
-        return {"knowledge_base": save_path, "type": "UnstructuredFile"}
-
-def load_knowledge_base_qa(path):
-    """
-    Load json format knowledge base.
-    """
-    print("path", path)
-    with open(path, "r") as f:
-        data = json.load(f)
-    embeddings = []
-    questions = []
-    answers = []
-    chunks = []
-    for idx in range(len(data.keys())):
-        embeddings.append(data[str(idx)]["emb"])
-        questions.append(data[str(idx)]["q"])
-        answers.append(data[str(idx)]["a"])
-        chunks.append(data[str(idx)]["chunk"])
-    embeddings = np.array(embeddings, dtype=np.float32)
-    embeddings = torch.from_numpy(embeddings).squeeze()
-    return embeddings, questions, answers, chunks
-
-
-def load_knowledge_base_UnstructuredFile(path):
-    """
-    Load json format knowledge base.
-    """
-    with open(path, "r") as f:
-        data = json.load(f)
-    embeddings = []
-    chunks = []
-    for idx in range(len(data.keys())):
-        embeddings.append(data[str(idx)]["emb"])
-        chunks.append(data[str(idx)]["chunk"])
-    embeddings = np.array(embeddings, dtype=np.float32)
-    embeddings = torch.from_numpy(embeddings).squeeze()
-    return embeddings, chunks
-
-
-def cos_sim(a: torch.Tensor, b: torch.Tensor):
-    """
-    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
-    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])
-    """
-    if not isinstance(a, torch.Tensor):
-        a = torch.tensor(a)
-
-    if not isinstance(b, torch.Tensor):
-        b = torch.tensor(b)
-
-    if len(a.shape) == 1:
-        a = a.unsqueeze(0)
-
-    if len(b.shape) == 1:
-        b = b.unsqueeze(0)
-
-    a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
-    b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
-    return torch.mm(a_norm, b_norm.transpose(0, 1))
-
-
-def matching_a_b(a, b, requirements=None):
-    a_embedder = get_embedding(a)
-    # 获取embedder
-    b_embeder = get_embedding(b)
-    sim_scores = cos_sim(a_embedder, b_embeder)[0]
-    return sim_scores
-
-
-def matching_category(inputtext,
-                      forest_name,
-                      requirements=None,
-                      cat_embedder=None,
-                      top_k=3):
-    """
-    Args:
-        inputtext: the category name to be matched
-        forest: search tree
-        top_k: the default three highest scoring results
-    Return：
-        topk matching_result. List[List] [[top1_name,top2_name,top3_name],[top1_score,top2_score,top3_score]]
-    """
-
-    sim_scores = torch.zeros([100])
-    if inputtext:
-        input_embeder = get_embedding(inputtext)
-        sim_scores = cos_sim(input_embeder, cat_embedder)[0]
-
-    if requirements:
-        requirements = requirements.split(" ")
-        requirements_embedder = get_embedding(requirements)
-        req_scores = cos_sim(requirements_embedder, cat_embedder)
-        req_scores = torch.mean(req_scores, dim=0)
-        total_scores = req_scores
-    else:
-        total_scores = sim_scores
-
-    top_k_cat = torch.topk(total_scores, k=top_k)
-    top_k_score, top_k_idx = top_k_cat[0], top_k_cat[1]
-    top_k_name = [forest_name[top_k_idx[i]] for i in range(0, top_k)]
-
-    return [top_k_name, top_k_score.tolist(), top_k_idx]
-
-
-def sample_with_order_preserved(lst, num):
-    """Randomly sample from the list while maintaining the original order."""
-    indices = list(range(len(lst)))
-    sampled_indices = random.sample(indices, num)
-    sampled_indices.sort()  # 保持原顺序
-    return [lst[i] for i in sampled_indices]
-
-
-def limit_values(data, max_values):
-    """Reduce each key-value list in the dictionary to the specified size, keeping the order of the original list unchanged."""
-    for key, values in data.items():
-        if len(values) > max_values:
-            data[key] = sample_with_order_preserved(values, max_values)
-    return data
-
-
-def limit_keys(data, max_keys):
-    """Reduce the dictionary to the specified number of keys."""
-    keys = list(data.keys())
-    if len(keys) > max_keys:
-        keys = sample_with_order_preserved(keys, max_keys)
-        data = {key: data[key] for key in keys}
-    return data
-
-
-def flatten_dict(nested_dict):
-    """
-    flatten the dictionary
-    """
-    flattened_dict = {}
-    for key, value in nested_dict.items():
-        if isinstance(value, dict):
-            flattened_subdict = flatten_dict(value)
-            flattened_dict.update(flattened_subdict)
-        else:
-            flattened_dict[key] = value
-    return flattened_dict
-
-
-def merge_list(list1, list2):
-    for l in list2:
-        if l not in list1:
-            list1.append(l)
-    return list1
-
-
-def Search_Engines(req):
-    FETSIZE = eval(os.environ["FETSIZE"]) if "FETSIZE" in os.environ else 5
-
-    new_dict = {"keyword": req, "catLeafName": "", "fetchSize": FETSIZE}
-    url = os.environ["SHOPPING_SEARCH"]
-    res = requests.post(
-        url= url,
-        json=new_dict,
-    )
-    user_dict = json.loads(res.text)
-    if "data" in user_dict.keys():
-        request_items = user_dict["data"]["items"]  # 查询到的商品信息JSON
-        top_category = user_dict["data"]["topCategories"]
-        return request_items, top_category
-    else:
-        return []
-
-
-def search_with_api(requirements, categery):
-    
-    FETSIZE = eval(os.environ["FETSIZE"]) if "FETSIZE" in os.environ else 5
-
-    request_items = []
-    all_req_list = requirements.split(" ")  
-    count = 0  
-
-    while len(request_items) < FETSIZE and len(all_req_list) > 0:
-        if count: 
-            all_req_list.pop(0)  
-        all_req = (" ").join(all_req_list)  
-        if categery not in all_req_list:
-            all_req = all_req + " " + categery
-        now_request_items, top_category = Search_Engines(all_req)  
-        request_items = merge_list(request_items, now_request_items)
-        count += 1
-    new_top = []
-    for category in top_category:
-        if "其它" in category or "其它" in category:
-            continue
-        else:
-            new_top.append(category)
-    if len(request_items) > FETSIZE:
-        request_items = request_items[:FETSIZE]
-    return request_items, new_top
-
-
-
-def get_relevant_history(query,history,embeddings):
-    """
-    Retrieve a list of key history entries based on a query using semantic search.
-
-    Args:
-        query (str): The input query for which key history is to be retrieved.
-        history (list): A list of historical key entries.
-        embeddings (numpy.ndarray): An array of embedding vectors for historical entries.
-
-    Returns:
-        list: A list of key history entries most similar to the query.
-    """
-    TOP_K = eval(os.environ["TOP_K"]) if "TOP_K" in os.environ else 2
-    relevant_history = []
-    query_embedding = get_embedding(query)
-    hits = semantic_search(query_embedding, embeddings, top_k=min(TOP_K,embeddings.shape[0]))
-    hits = hits[0]
-    for hit in hits:
-        matching_idx = hit["corpus_id"]
-        try:
-            relevant_history.append(history[matching_idx])
-        except:
-            return []
-    return relevant_history

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/Factory.d.ts

@@ -1,9 +0,0 @@
-import CircleMaskImage from './CircleMaskImage';
-
-export default function (
-    x?: number, y?: number,
-    key?: string, frame?: string,
-    config?:
-        null | 0 | 1 | 2 | 'circle' | 'ellipse' | 'roundRectangle' |
-        CircleMaskImage.IConfig
-): CircleMaskImage;

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

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

spaces/AkashKhamkar/Job_Search_Engine/README.md

@@ -1,13 +0,0 @@
----
-title: Job Search Engine
-emoji: 🌖
-colorFrom: green
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,303 +0,0 @@
-<!--Copyright 2023 Custom Diffusion authors 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.
--->
-
-# Custom Diffusion training example 
-
-[Custom Diffusion](https://arxiv.org/abs/2212.04488) is a method to customize text-to-image models like Stable Diffusion given just a few (4~5) images of a subject.
-The `train_custom_diffusion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
-
-This training example was contributed by [Nupur Kumari](https://nupurkmr9.github.io/) (one of the authors of Custom Diffusion). 
-
-## Running locally with PyTorch
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd into the [example folder](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion)
-
-```
-cd examples/custom_diffusion
-```
-
-Now run
-
-```bash
-pip install -r requirements.txt
-pip install clip-retrieval 
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell e.g. a notebook
-
-```python
-from accelerate.utils import write_basic_config
-
-write_basic_config()
-```
-### Cat example 😺
-
-Now let's get our dataset. Download dataset from [here](https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip) and unzip it. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
-
-We also collect 200 real images using `clip-retrieval` which are combined with the target images in the training dataset as a regularization. This prevents overfitting to the the given target image. The following flags enable the regularization `with_prior_preservation`, `real_prior` with `prior_loss_weight=1.`. 
-The `class_prompt` should be the category name same as target image. The collected real images are with text captions similar to the `class_prompt`. The retrieved image are saved in `class_data_dir`. You can disable `real_prior` to use generated images as regularization. To collect the real images use this command first before training. 
-
-```bash
-pip install clip-retrieval
-python retrieve.py --class_prompt cat --class_data_dir real_reg/samples_cat --num_class_images 200
-```
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-The script creates and saves model checkpoints and a `pytorch_custom_diffusion_weights.bin` file in your repository.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export OUTPUT_DIR="path-to-save-model"
-export INSTANCE_DIR="./data/cat"
-
-accelerate launch train_custom_diffusion.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --class_data_dir=./real_reg/samples_cat/ \
-  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
-  --class_prompt="cat" --num_class_images=200 \
-  --instance_prompt="photo of a <new1> cat"  \
-  --resolution=512  \
-  --train_batch_size=2  \
-  --learning_rate=1e-5  \
-  --lr_warmup_steps=0 \
-  --max_train_steps=250 \
-  --scale_lr --hflip  \
-  --modifier_token "<new1>" \
-  --push_to_hub
-```
-
-**Use `--enable_xformers_memory_efficient_attention` for faster training with lower VRAM requirement (16GB per GPU). Follow [this guide](https://github.com/facebookresearch/xformers) for installation instructions.**
-
-To track your experiments using Weights and Biases (`wandb`) and to save intermediate results (whcih we HIGHLY recommend), follow these steps:
-
-* Install `wandb`: `pip install wandb`.
-* Authorize: `wandb login`. 
-* Then specify a `validation_prompt` and set `report_to` to `wandb` while launching training. You can also configure the following related arguments:
-    * `num_validation_images`
-    * `validation_steps`
-
-Here is an example command:
-
-```bash
-accelerate launch train_custom_diffusion.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --class_data_dir=./real_reg/samples_cat/ \
-  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
-  --class_prompt="cat" --num_class_images=200 \
-  --instance_prompt="photo of a <new1> cat"  \
-  --resolution=512  \
-  --train_batch_size=2  \
-  --learning_rate=1e-5  \
-  --lr_warmup_steps=0 \
-  --max_train_steps=250 \
-  --scale_lr --hflip  \
-  --modifier_token "<new1>" \
-  --validation_prompt="<new1> cat sitting in a bucket" \
-  --report_to="wandb" \
-  --push_to_hub
-```
-
-Here is an example [Weights and Biases page](https://wandb.ai/sayakpaul/custom-diffusion/runs/26ghrcau) where you can check out the intermediate results along with other training details.  
-
-If you specify `--push_to_hub`, the learned parameters will be pushed to a repository on the Hugging Face Hub. Here is an [example repository](https://huggingface.co/sayakpaul/custom-diffusion-cat).
-
-### Training on multiple concepts 🐱🪵
-
-Provide a [json](https://github.com/adobe-research/custom-diffusion/blob/main/assets/concept_list.json) file with the info about each concept, similar to [this](https://github.com/ShivamShrirao/diffusers/blob/main/examples/dreambooth/train_dreambooth.py).
-
-To collect the real images run this command for each concept in the json file. 
-
-```bash
-pip install clip-retrieval
-python retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200
-```
-
-And then we're ready to start training!
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_custom_diffusion.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --output_dir=$OUTPUT_DIR \
-  --concepts_list=./concept_list.json \
-  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
-  --resolution=512  \
-  --train_batch_size=2  \
-  --learning_rate=1e-5  \
-  --lr_warmup_steps=0 \
-  --max_train_steps=500 \
-  --num_class_images=200 \
-  --scale_lr --hflip  \
-  --modifier_token "<new1>+<new2>" \
-  --push_to_hub
-```
-
-Here is an example [Weights and Biases page](https://wandb.ai/sayakpaul/custom-diffusion/runs/3990tzkg) where you can check out the intermediate results along with other training details.  
-
-### Training on human faces
-
-For fine-tuning on human faces we found the following configuration to work better: `learning_rate=5e-6`, `max_train_steps=1000 to 2000`, and `freeze_model=crossattn` with at least 15-20 images. 
-
-To collect the real images use this command first before training. 
-
-```bash
-pip install clip-retrieval
-python retrieve.py --class_prompt person --class_data_dir real_reg/samples_person --num_class_images 200
-```
-
-Then start training!
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export OUTPUT_DIR="path-to-save-model"
-export INSTANCE_DIR="path-to-images"
-
-accelerate launch train_custom_diffusion.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --class_data_dir=./real_reg/samples_person/ \
-  --with_prior_preservation --real_prior --prior_loss_weight=1.0 \
-  --class_prompt="person" --num_class_images=200 \
-  --instance_prompt="photo of a <new1> person"  \
-  --resolution=512  \
-  --train_batch_size=2  \
-  --learning_rate=5e-6  \
-  --lr_warmup_steps=0 \
-  --max_train_steps=1000 \
-  --scale_lr --hflip --noaug \
-  --freeze_model crossattn \
-  --modifier_token "<new1>" \
-  --enable_xformers_memory_efficient_attention \
-  --push_to_hub
-```
-
-## Inference
-
-Once you have trained a model using the above command, you can run inference using the below command. Make sure to include the `modifier token` (e.g. \<new1\> in above example) in your prompt.
-
-```python
-import torch
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16).to("cuda")
-pipe.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
-pipe.load_textual_inversion("path-to-save-model", weight_name="<new1>.bin")
-
-image = pipe(
-    "<new1> cat sitting in a bucket",
-    num_inference_steps=100,
-    guidance_scale=6.0,
-    eta=1.0,
-).images[0]
-image.save("cat.png")
-```
-
-It's possible to directly load these parameters from a Hub repository:
-
-```python
-import torch
-from huggingface_hub.repocard import RepoCard
-from diffusers import DiffusionPipeline
-
-model_id = "sayakpaul/custom-diffusion-cat"
-card = RepoCard.load(model_id)
-base_model_id = card.data.to_dict()["base_model"]
-
-pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to("cuda")
-pipe.unet.load_attn_procs(model_id, weight_name="pytorch_custom_diffusion_weights.bin")
-pipe.load_textual_inversion(model_id, weight_name="<new1>.bin")
-
-image = pipe(
-    "<new1> cat sitting in a bucket",
-    num_inference_steps=100,
-    guidance_scale=6.0,
-    eta=1.0,
-).images[0]
-image.save("cat.png")
-```
-
-Here is an example of performing inference with multiple concepts:
-
-```python
-import torch
-from huggingface_hub.repocard import RepoCard
-from diffusers import DiffusionPipeline
-
-model_id = "sayakpaul/custom-diffusion-cat-wooden-pot"
-card = RepoCard.load(model_id)
-base_model_id = card.data.to_dict()["base_model"]
-
-pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to("cuda")
-pipe.unet.load_attn_procs(model_id, weight_name="pytorch_custom_diffusion_weights.bin")
-pipe.load_textual_inversion(model_id, weight_name="<new1>.bin")
-pipe.load_textual_inversion(model_id, weight_name="<new2>.bin")
-
-image = pipe(
-    "the <new1> cat sculpture in the style of a <new2> wooden pot",
-    num_inference_steps=100,
-    guidance_scale=6.0,
-    eta=1.0,
-).images[0]
-image.save("multi-subject.png")
-```
-
-Here, `cat` and `wooden pot` refer to the multiple concepts.
-
-### Inference from a training checkpoint
-
-You can also perform inference from one of the complete checkpoint saved during the training process, if you used the `--checkpointing_steps` argument. 
-
-TODO.
-
-## Set grads to none
-
-To save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.
-
-More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html
-
-## Experimental results
-
-You can refer to [our webpage](https://www.cs.cmu.edu/~custom-diffusion/) that discusses our experiments in detail. 

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

@@ -1,80 +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.
--->
-
-# 🧨 Diffusers Training Examples
-
-Diffusers training examples are a collection of scripts to demonstrate how to effectively use the `diffusers` library
-for a variety of use cases.
-
-**Note**: If you are looking for **official** examples on how to use `diffusers` for inference, 
-please have a look at [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)
-
-Our examples aspire to be **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.
-More specifically, this means:
-
-- **Self-contained**: An example script shall only depend on "pip-install-able" Python packages that can be found in a `requirements.txt` file. Example scripts shall **not** depend on any local files. This means that one can simply download an example script, *e.g.* [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py), install the required dependencies, *e.g.* [requirements.txt](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/requirements.txt) and execute the example script.
-- **Easy-to-tweak**: While we strive to present as many use cases as possible, the example scripts are just that - examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data and the training loop to allow you to tweak and edit them as required.
-- **Beginner-friendly**: We do not aim for providing state-of-the-art training scripts for the newest models, but rather examples that can be used as a way to better understand diffusion models and how to use them with the `diffusers` library. We often purposefully leave out certain state-of-the-art methods if we consider them too complex for beginners.
-- **One-purpose-only**: Examples should show one task and one task only. Even if a task is from a modeling 
-point of view very similar, *e.g.* image super-resolution and image modification tend to use the same model and training method, we want examples to showcase only one task to keep them as readable and easy-to-understand as possible.
-
-We provide **official** examples that cover the most popular tasks of diffusion models.
-*Official* examples are **actively** maintained by the `diffusers` maintainers and we try to rigorously follow our example philosophy as defined above. 
-If you feel like another important example should exist, we are more than happy to welcome a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) or directly a [Pull Request](https://github.com/huggingface/diffusers/compare) from you!
-
-Training examples show how to pretrain or fine-tune diffusion models for a variety of tasks. Currently we support:
-
-- [Unconditional Training](./unconditional_training)
-- [Text-to-Image Training](./text2image)
-- [Text Inversion](./text_inversion)
-- [Dreambooth](./dreambooth)
-- [LoRA Support](./lora)
-- [ControlNet](./controlnet)
-- [InstructPix2Pix](./instructpix2pix)
-- [Custom Diffusion](./custom_diffusion)
-
-If possible, please [install xFormers](../optimization/xformers) for memory efficient attention. This could help make your training faster and less memory intensive.
-
-| Task | 🤗 Accelerate | 🤗 Datasets | Colab
-|---|---|:---:|:---:|
-| [**Unconditional Image Generation**](./unconditional_training) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [**Text-to-Image fine-tuning**](./text2image) | ✅ | ✅ | 
-| [**Textual Inversion**](./text_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
-| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)
-| [**Training with LoRA**](./lora) | ✅ | - | - |
-| [**ControlNet**](./controlnet) | ✅ | ✅ | - |
-| [**InstructPix2Pix**](./instructpix2pix) | ✅ | ✅ | - |
-| [**Custom Diffusion**](./custom_diffusion) | ✅ | ✅ | - |
-
-## Community
-
-In addition, we provide **community** examples, which are examples added and maintained by our community.
-Community examples can consist of both *training* examples or *inference* pipelines.
-For such examples, we are more lenient regarding the philosophy defined above and also cannot guarantee to provide maintenance for every issue.
-Examples that are useful for the community, but are either not yet deemed popular or not yet following our above philosophy should go into the [community examples](https://github.com/huggingface/diffusers/tree/main/examples/community) folder. The community folder therefore includes training examples and inference pipelines.
-**Note**: Community examples can be a [great first contribution](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) to show to the community how you like to use `diffusers` 🪄.
-
-## Important note
-
-To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder of your choice and run
-
-```bash
-pip install -r requirements.txt
-```

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/training/overview.md

@@ -1,73 +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.
--->
-
-# 🧨 Diffusers 학습 예시
-
-이번 챕터에서는 다양한 유즈케이스들에 대한 예제 코드들을 통해 어떻게하면 효과적으로 `diffusers` 라이브러리를 사용할 수 있을까에 대해 알아보도록 하겠습니다. 
-
-**Note**: 혹시 오피셜한 예시코드를 찾고 있다면, [여기](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)를 참고해보세요!
-
-여기서 다룰 예시들은 다음을 지향합니다.
-
-- **손쉬운 디펜던시 설치** (Self-contained) : 여기서 사용될 예시 코드들의 디펜던시 패키지들은 전부 `pip install` 명령어를 통해 설치 가능한 패키지들입니다. 또한 친절하게 `requirements.txt` 파일에 해당 패키지들이 명시되어 있어, `pip install -r requirements.txt`로 간편하게 해당 디펜던시들을 설치할 수 있습니다. 예시: [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py), [requirements.txt](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/requirements.txt) 
-- **손쉬운 수정** (Easy-to-tweak) : 저희는 가능하면 많은 유즈 케이스들을 제공하고자 합니다. 하지만 예시는 결국 그저 예시라는 점들 기억해주세요. 여기서 제공되는 예시코드들을 그저 단순히 복사-붙혀넣기하는 식으로는 여러분이 마주한 문제들을 손쉽게 해결할 순 없을 것입니다. 다시 말해 어느 정도는 여러분의 상황과 니즈에 맞춰 코드를 일정 부분 고쳐나가야 할 것입니다. 따라서 대부분의 학습 예시들은 데이터의 전처리 과정과 학습 과정에 대한 코드들을 함께 제공함으로써, 사용자가 니즈에 맞게 손쉬운 수정할 수 있도록 돕고 있습니다.
-- **입문자 친화적인** (Beginner-friendly) : 이번 챕터는 diffusion 모델과 `diffusers` 라이브러리에 대한 전반적인 이해를 돕기 위해 작성되었습니다. 따라서 diffusion 모델에 대한 최신 SOTA (state-of-the-art) 방법론들 가운데서도, 입문자에게는 많이 어려울 수 있다고 판단되면, 해당 방법론들은 여기서 다루지 않으려고 합니다.
-- **하나의 태스크만 포함할 것**(One-purpose-only): 여기서 다룰 예시들은 하나의 태스크만 포함하고 있어야 합니다. 물론 이미지 초해상화(super-resolution)와 이미지 보정(modification)과 같은 유사한 모델링 프로세스를 갖는 태스크들이 존재하겠지만, 하나의 예제에 하나의 태스크만을 담는 것이 더 이해하기 용이하다고 판단했기 때문입니다.
-
-
-
-저희는 diffusion 모델의 대표적인 태스크들을 다루는 공식 예제를 제공하고 있습니다. *공식* 예제는 현재 진행형으로 `diffusers` 관리자들(maintainers)에 의해 관리되고 있습니다. 또한 저희는 앞서 정의한 저희의 철학을 엄격하게 따르고자 노력하고 있습니다. 혹시 여러분께서 이러한 예시가 반드시 필요하다고 생각되신다면, 언제든지 [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) 혹은 직접 [Pull Request](https://github.com/huggingface/diffusers/compare)를 주시기 바랍니다. 저희는 언제나 환영입니다!
-
-학습 예시들은 다양한 태스크들에 대해 diffusion 모델을 사전학습(pretrain)하거나 파인튜닝(fine-tuning)하는 법을 보여줍니다. 현재 다음과 같은 예제들을 지원하고 있습니다.
-
-- [Unconditional Training](./unconditional_training)
-- [Text-to-Image Training](./text2image)
-- [Text Inversion](./text_inversion)
-- [Dreambooth](./dreambooth)
-
-memory-efficient attention 연산을 수행하기 위해, 가능하면 [xFormers](../optimization/xformers)를 설치해주시기 바랍니다. 이를 통해 학습 속도를 늘리고 메모리에 대한 부담을 줄일 수 있습니다.
-
-| Task | 🤗 Accelerate | 🤗 Datasets | Colab
-|---|---|:---:|:---:|
-| [**Unconditional Image Generation**](./unconditional_training) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [**Text-to-Image fine-tuning**](./text2image) | ✅ | ✅ | 
-| [**Textual Inversion**](./text_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
-| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)
-| [**Training with LoRA**](./lora) | ✅ | - | - |
-| [**ControlNet**](./controlnet) | ✅ | ✅ | - |
-| [**InstructPix2Pix**](./instructpix2pix) | ✅ | ✅ | - |
-| [**Custom Diffusion**](./custom_diffusion) | ✅ | ✅ | - |
-
-
-## 커뮤니티
-
-공식 예제 외에도 **커뮤니티 예제** 역시 제공하고 있습니다. 해당 예제들은 우리의 커뮤니티에 의해 관리됩니다. 커뮤니티 예쩨는 학습 예시나 추론 파이프라인으로 구성될 수 있습니다. 이러한 커뮤니티 예시들의 경우,  앞서 정의했던 철학들을 좀 더 관대하게 적용하고 있습니다. 또한 이러한 커뮤니티 예시들의 경우, 모든 이슈들에 대한 유지보수를 보장할 수는 없습니다.
-
-유용하긴 하지만, 아직은 대중적이지 못하거나 저희의 철학에 부합하지 않는 예제들은 [community examples](https://github.com/huggingface/diffusers/tree/main/examples/community) 폴더에 담기게 됩니다.
-
-**Note**: 커뮤니티 예제는 `diffusers`에 기여(contribution)를 희망하는 분들에게 [아주 좋은 기여 수단](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)이 될 수 있습니다.
-
-## 주목할 사항들
-
-최신 버전의 예시 코드들의 성공적인 구동을 보장하기 위해서는, 반드시 **소스코드를 통해 `diffusers`를 설치해야 하며,** 해당 예시 코드들이 요구하는 디펜던시들 역시 설치해야 합니다. 이를 위해 새로운 가상 환경을 구축하고 다음의 명령어를 실행해야 합니다.
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-그 다음 `cd` 명령어를 통해 해당 예제 디렉토리에 접근해서 다음 명령어를 실행하면 됩니다.
-
-```bash
-pip install -r requirements.txt
-```

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -1,1002 +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 inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from packaging import version
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AsymmetricAutoencoderKL, AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False):
-    """
-    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-
-
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-
-    if image is None:
-        raise ValueError("`image` input cannot be undefined.")
-
-    if mask is None:
-        raise ValueError("`mask_image` input cannot be undefined.")
-
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
-
-        # Batch single image
-        if image.ndim == 3:
-            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            # resize all images w.r.t passed height an width
-            image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
-            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    # n.b. ensure backwards compatibility as old function does not return image
-    if return_image:
-        return mask, masked_image, image
-
-    return mask, masked_image
-
-
-class StableDiffusionInpaintPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
-):
-    r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    The pipeline also inherits the following loading methods:
-        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-
-    Args:
-        vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A `UNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: Union[AutoencoderKL, AsymmetricAutoencoderKL],
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
-                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
-                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
-                " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
-                " Hub, it would be very nice if you could open a Pull request for the"
-                " `scheduler/scheduler_config.json` file"
-            )
-            deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["skip_prk_steps"] = True
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet.config.in_channels != 9:
-            logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
-        time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
-        Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
-        iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        strength,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-        image=None,
-        timestep=None,
-        is_strength_max=True,
-        return_noise=False,
-        return_image_latents=False,
-    ):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if (image is None or timestep is None) and not is_strength_max:
-            raise ValueError(
-                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
-                "However, either the image or the noise timestep has not been provided."
-            )
-
-        if return_image_latents or (latents is None and not is_strength_max):
-            image = image.to(device=device, dtype=dtype)
-            image_latents = self._encode_vae_image(image=image, generator=generator)
-
-        if latents is None:
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            # if strength is 1. then initialise the latents to noise, else initial to image + noise
-            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
-            # if pure noise then scale the initial latents by the  Scheduler's init sigma
-            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
-        else:
-            noise = latents.to(device)
-            latents = noise * self.scheduler.init_noise_sigma
-
-        outputs = (latents,)
-
-        if return_noise:
-            outputs += (noise,)
-
-        if return_image_latents:
-            outputs += (image_latents,)
-
-        return outputs
-
-    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
-        if isinstance(generator, list):
-            image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(image.shape[0])
-            ]
-            image_latents = torch.cat(image_latents, dim=0)
-        else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
-
-        image_latents = self.vae.config.scaling_factor * image_latents
-
-        return image_latents
-
-    def prepare_mask_latents(
-        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-        masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return mask, masked_image_latents
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
-        return timesteps, num_inference_steps - t_start
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        strength: float = 1.0,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
-            image (`PIL.Image.Image`):
-                `Image` or tensor representing an image batch to be inpainted (which parts of the image to be masked
-                out with `mask_image` and repainted according to `prompt`).
-            mask_image (`PIL.Image.Image`):
-                `Image` or tensor representing an image batch to mask `image`. White pixels in the mask are repainted
-                while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel
-                (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the
-                expected shape would be `(B, H, W, 1)`.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            strength (`float`, *optional*, defaults to 1.0):
-                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
-                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
-                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
-                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
-                essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter is modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
-                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-        Examples:
-
-        ```py
-        >>> import PIL
-        >>> import requests
-        >>> import torch
-        >>> from io import BytesIO
-
-        >>> from diffusers import StableDiffusionInpaintPipeline
-
-
-        >>> def download_image(url):
-        ...     response = requests.get(url)
-        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-
-
-        >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-        >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-        >>> init_image = download_image(img_url).resize((512, 512))
-        >>> mask_image = download_image(mask_url).resize((512, 512))
-
-        >>> pipe = StableDiffusionInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-        >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
-        >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
-        ```
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            strength,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps=num_inference_steps, strength=strength, device=device
-        )
-        # check that number of inference steps is not < 1 - as this doesn't make sense
-        if num_inference_steps < 1:
-            raise ValueError(
-                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
-                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
-            )
-        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
-        is_strength_max = strength == 1.0
-
-        # 5. Preprocess mask and image
-        mask, masked_image, init_image = prepare_mask_and_masked_image(
-            image, mask_image, height, width, return_image=True
-        )
-        mask_condition = mask.clone()
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        num_channels_unet = self.unet.config.in_channels
-        return_image_latents = num_channels_unet == 4
-
-        latents_outputs = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            image=init_image,
-            timestep=latent_timestep,
-            is_strength_max=is_strength_max,
-            return_noise=True,
-            return_image_latents=return_image_latents,
-        )
-
-        if return_image_latents:
-            latents, noise, image_latents = latents_outputs
-        else:
-            latents, noise = latents_outputs
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        # 8. Check that sizes of mask, masked image and latents match
-        if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
-            num_channels_mask = mask.shape[1]
-            num_channels_masked_image = masked_image_latents.shape[1]
-            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
-                raise ValueError(
-                    f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                    f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                    f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-                    " `pipeline.unet` or your `mask_image` or `image` input."
-                )
-        elif num_channels_unet != 4:
-            raise ValueError(
-                f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
-            )
-
-        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 10. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                # concat latents, mask, masked_image_latents in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                if num_channels_unet == 9:
-                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                if num_channels_unet == 4:
-                    init_latents_proper = image_latents[:1]
-                    init_mask = mask[:1]
-
-                    if i < len(timesteps) - 1:
-                        noise_timestep = timesteps[i + 1]
-                        init_latents_proper = self.scheduler.add_noise(
-                            init_latents_proper, noise, torch.tensor([noise_timestep])
-                        )
-
-                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        if not output_type == "latent":
-            condition_kwargs = {}
-            if isinstance(self.vae, AsymmetricAutoencoderKL):
-                init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)
-                init_image_condition = init_image.clone()
-                init_image = self._encode_vae_image(init_image, generator=generator)
-                mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)
-                condition_kwargs = {"image": init_image_condition, "mask": mask_condition}
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, **condition_kwargs)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -1,681 +0,0 @@
-# Copyright 2023 TSAIL Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: check https://arxiv.org/abs/2302.04867 and https://github.com/wl-zhao/UniPC for more info
-# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
-
-import math
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
-    """
-    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 (`np.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 torch.tensor(betas, dtype=torch.float32)
-
-
-class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
-    """
-    UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a
-    corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders. UniPC is
-    by desinged model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can
-    also be applied to both noise prediction model and data prediction model. The corrector UniC can be also applied
-    after any off-the-shelf solvers to increase the order of accuracy.
-
-    For more details, see the original paper: https://arxiv.org/abs/2302.04867
-
-    Currently, we support the multistep UniPC for both noise prediction models and data prediction models. We recommend
-    to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.
-
-    We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
-    diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use the dynamic thresholding. Note
-    that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion).
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        solver_order (`int`, default `2`):
-            the order of UniPC, also the p in UniPC-p; can be any positive integer. Note that the effective order of
-            accuracy is `solver_order + 1` due to the UniC. We recommend to use `solver_order=2` for guided sampling,
-            and `solver_order=3` for unconditional sampling.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-        thresholding (`bool`, default `False`):
-            whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
-            For pixel-space diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use the
-            dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models
-            (such as stable-diffusion).
-        dynamic_thresholding_ratio (`float`, default `0.995`):
-            the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
-            (https://arxiv.org/abs/2205.11487).
-        sample_max_value (`float`, default `1.0`):
-            the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`.
-        predict_x0 (`bool`, default `True`):
-            whether to use the updating algrithm on the predicted x0. See https://arxiv.org/abs/2211.01095 for details
-        solver_type (`str`, default `bh2`):
-            the solver type of UniPC. We recommend use `bh1` for unconditional sampling when steps < 10, and use `bh2`
-            otherwise.
-        lower_order_final (`bool`, default `True`):
-            whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically
-            find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.
-        disable_corrector (`list`, default `[]`):
-            decide which step to disable the corrector. For large guidance scale, the misalignment between the
-            `epsilon_theta(x_t, c)`and `epsilon_theta(x_t^c, c)` might influence the convergence. This can be mitigated
-            by disable the corrector at the first few steps (e.g., disable_corrector=[0])
-        solver_p (`SchedulerMixin`, default `None`):
-            can be any other scheduler. If specified, the algorithm will become solver_p + UniC.
-        use_karras_sigmas (`bool`, *optional*, defaults to `False`):
-             This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the
-             noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence
-             of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf.
-        timestep_spacing (`str`, default `"linspace"`):
-            The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample
-            Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.
-        steps_offset (`int`, default `0`):
-            an offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
-            stable diffusion.
-    """
-
-    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        solver_order: int = 2,
-        prediction_type: str = "epsilon",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        sample_max_value: float = 1.0,
-        predict_x0: bool = True,
-        solver_type: str = "bh2",
-        lower_order_final: bool = True,
-        disable_corrector: List[int] = [],
-        solver_p: SchedulerMixin = None,
-        use_karras_sigmas: Optional[bool] = False,
-        timestep_spacing: str = "linspace",
-        steps_offset: int = 0,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-        # Currently we only support VP-type noise schedule
-        self.alpha_t = torch.sqrt(self.alphas_cumprod)
-        self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
-        self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        if solver_type not in ["bh1", "bh2"]:
-            if solver_type in ["midpoint", "heun", "logrho"]:
-                self.register_to_config(solver_type="bh2")
-            else:
-                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
-
-        self.predict_x0 = predict_x0
-        # setable values
-        self.num_inference_steps = None
-        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
-        self.timesteps = torch.from_numpy(timesteps)
-        self.model_outputs = [None] * solver_order
-        self.timestep_list = [None] * solver_order
-        self.lower_order_nums = 0
-        self.disable_corrector = disable_corrector
-        self.solver_p = solver_p
-        self.last_sample = None
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            device (`str` or `torch.device`, optional):
-                the device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        """
-        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
-        if self.config.timestep_spacing == "linspace":
-            timesteps = (
-                np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
-                .round()[::-1][:-1]
-                .copy()
-                .astype(np.int64)
-            )
-        elif self.config.timestep_spacing == "leading":
-            step_ratio = self.config.num_train_timesteps // (num_inference_steps + 1)
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)
-            timesteps += self.config.steps_offset
-        elif self.config.timestep_spacing == "trailing":
-            step_ratio = self.config.num_train_timesteps / num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = np.arange(self.config.num_train_timesteps, 0, -step_ratio).round().copy().astype(np.int64)
-            timesteps -= 1
-        else:
-            raise ValueError(
-                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
-            )
-
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
-        if self.config.use_karras_sigmas:
-            log_sigmas = np.log(sigmas)
-            sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
-            timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
-            timesteps = np.flip(timesteps).copy().astype(np.int64)
-
-        self.sigmas = torch.from_numpy(sigmas)
-
-        # when num_inference_steps == num_train_timesteps, we can end up with
-        # duplicates in timesteps.
-        _, unique_indices = np.unique(timesteps, return_index=True)
-        timesteps = timesteps[np.sort(unique_indices)]
-
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-        self.num_inference_steps = len(timesteps)
-
-        self.model_outputs = [
-            None,
-        ] * self.config.solver_order
-        self.lower_order_nums = 0
-        self.last_sample = None
-        if self.solver_p:
-            self.solver_p.set_timesteps(self.num_inference_steps, device=device)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
-    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
-        """
-        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
-        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
-        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
-        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
-        photorealism as well as better image-text alignment, especially when using very large guidance weights."
-
-        https://arxiv.org/abs/2205.11487
-        """
-        dtype = sample.dtype
-        batch_size, channels, height, width = sample.shape
-
-        if dtype not in (torch.float32, torch.float64):
-            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
-
-        # Flatten sample for doing quantile calculation along each image
-        sample = sample.reshape(batch_size, channels * height * width)
-
-        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
-
-        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
-        s = torch.clamp(
-            s, min=1, max=self.config.sample_max_value
-        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
-
-        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
-        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
-
-        sample = sample.reshape(batch_size, channels, height, width)
-        sample = sample.to(dtype)
-
-        return sample
-
-    def convert_model_output(
-        self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor
-    ) -> torch.FloatTensor:
-        r"""
-        Convert the model output to the corresponding type that the algorithm PC needs.
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-
-        Returns:
-            `torch.FloatTensor`: the converted model output.
-        """
-        if self.predict_x0:
-            if self.config.prediction_type == "epsilon":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                x0_pred = (sample - sigma_t * model_output) / alpha_t
-            elif self.config.prediction_type == "sample":
-                x0_pred = model_output
-            elif self.config.prediction_type == "v_prediction":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                x0_pred = alpha_t * sample - sigma_t * model_output
-            else:
-                raise ValueError(
-                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                    " `v_prediction` for the UniPCMultistepScheduler."
-                )
-
-            if self.config.thresholding:
-                x0_pred = self._threshold_sample(x0_pred)
-
-            return x0_pred
-        else:
-            if self.config.prediction_type == "epsilon":
-                return model_output
-            elif self.config.prediction_type == "sample":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                epsilon = (sample - alpha_t * model_output) / sigma_t
-                return epsilon
-            elif self.config.prediction_type == "v_prediction":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                epsilon = alpha_t * model_output + sigma_t * sample
-                return epsilon
-            else:
-                raise ValueError(
-                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                    " `v_prediction` for the UniPCMultistepScheduler."
-                )
-
-    def multistep_uni_p_bh_update(
-        self,
-        model_output: torch.FloatTensor,
-        prev_timestep: int,
-        sample: torch.FloatTensor,
-        order: int,
-    ) -> torch.FloatTensor:
-        """
-        One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
-
-        Args:
-            model_output (`torch.FloatTensor`):
-                direct outputs from learned diffusion model at the current timestep.
-            prev_timestep (`int`): previous discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            order (`int`): the order of UniP at this step, also the p in UniPC-p.
-
-        Returns:
-            `torch.FloatTensor`: the sample tensor at the previous timestep.
-        """
-        timestep_list = self.timestep_list
-        model_output_list = self.model_outputs
-
-        s0, t = self.timestep_list[-1], prev_timestep
-        m0 = model_output_list[-1]
-        x = sample
-
-        if self.solver_p:
-            x_t = self.solver_p.step(model_output, s0, x).prev_sample
-            return x_t
-
-        lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0]
-        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
-        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
-
-        h = lambda_t - lambda_s0
-        device = sample.device
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            si = timestep_list[-(i + 1)]
-            mi = model_output_list[-(i + 1)]
-            lambda_si = self.lambda_t[si]
-            rk = (lambda_si - lambda_s0) / h
-            rks.append(rk)
-            D1s.append((mi - m0) / rk)
-
-        rks.append(1.0)
-        rks = torch.tensor(rks, device=device)
-
-        R = []
-        b = []
-
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)  # h\phi_1(h) = e^h - 1
-        h_phi_k = h_phi_1 / hh - 1
-
-        factorial_i = 1
-
-        if self.config.solver_type == "bh1":
-            B_h = hh
-        elif self.config.solver_type == "bh2":
-            B_h = torch.expm1(hh)
-        else:
-            raise NotImplementedError()
-
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= i + 1
-            h_phi_k = h_phi_k / hh - 1 / factorial_i
-
-        R = torch.stack(R)
-        b = torch.tensor(b, device=device)
-
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1)  # (B, K)
-            # for order 2, we use a simplified version
-            if order == 2:
-                rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
-            else:
-                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
-        else:
-            D1s = None
-
-        if self.predict_x0:
-            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
-            if D1s is not None:
-                pred_res = torch.einsum("k,bkchw->bchw", rhos_p, D1s)
-            else:
-                pred_res = 0
-            x_t = x_t_ - alpha_t * B_h * pred_res
-        else:
-            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
-            if D1s is not None:
-                pred_res = torch.einsum("k,bkchw->bchw", rhos_p, D1s)
-            else:
-                pred_res = 0
-            x_t = x_t_ - sigma_t * B_h * pred_res
-
-        x_t = x_t.to(x.dtype)
-        return x_t
-
-    def multistep_uni_c_bh_update(
-        self,
-        this_model_output: torch.FloatTensor,
-        this_timestep: int,
-        last_sample: torch.FloatTensor,
-        this_sample: torch.FloatTensor,
-        order: int,
-    ) -> torch.FloatTensor:
-        """
-        One step for the UniC (B(h) version).
-
-        Args:
-            this_model_output (`torch.FloatTensor`): the model outputs at `x_t`
-            this_timestep (`int`): the current timestep `t`
-            last_sample (`torch.FloatTensor`): the generated sample before the last predictor: `x_{t-1}`
-            this_sample (`torch.FloatTensor`): the generated sample after the last predictor: `x_{t}`
-            order (`int`): the `p` of UniC-p at this step. Note that the effective order of accuracy
-                should be order + 1
-
-        Returns:
-            `torch.FloatTensor`: the corrected sample tensor at the current timestep.
-        """
-        timestep_list = self.timestep_list
-        model_output_list = self.model_outputs
-
-        s0, t = timestep_list[-1], this_timestep
-        m0 = model_output_list[-1]
-        x = last_sample
-        x_t = this_sample
-        model_t = this_model_output
-
-        lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0]
-        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
-        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
-
-        h = lambda_t - lambda_s0
-        device = this_sample.device
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            si = timestep_list[-(i + 1)]
-            mi = model_output_list[-(i + 1)]
-            lambda_si = self.lambda_t[si]
-            rk = (lambda_si - lambda_s0) / h
-            rks.append(rk)
-            D1s.append((mi - m0) / rk)
-
-        rks.append(1.0)
-        rks = torch.tensor(rks, device=device)
-
-        R = []
-        b = []
-
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)  # h\phi_1(h) = e^h - 1
-        h_phi_k = h_phi_1 / hh - 1
-
-        factorial_i = 1
-
-        if self.config.solver_type == "bh1":
-            B_h = hh
-        elif self.config.solver_type == "bh2":
-            B_h = torch.expm1(hh)
-        else:
-            raise NotImplementedError()
-
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= i + 1
-            h_phi_k = h_phi_k / hh - 1 / factorial_i
-
-        R = torch.stack(R)
-        b = torch.tensor(b, device=device)
-
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1)
-        else:
-            D1s = None
-
-        # for order 1, we use a simplified version
-        if order == 1:
-            rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
-        else:
-            rhos_c = torch.linalg.solve(R, b)
-
-        if self.predict_x0:
-            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
-            if D1s is not None:
-                corr_res = torch.einsum("k,bkchw->bchw", rhos_c[:-1], D1s)
-            else:
-                corr_res = 0
-            D1_t = model_t - m0
-            x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
-        else:
-            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
-            if D1s is not None:
-                corr_res = torch.einsum("k,bkchw->bchw", rhos_c[:-1], D1s)
-            else:
-                corr_res = 0
-            D1_t = model_t - m0
-            x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
-        x_t = x_t.to(x.dtype)
-        return x_t
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        return_dict: bool = True,
-    ) -> Union[SchedulerOutput, Tuple]:
-        """
-        Step function propagating the sample with the multistep UniPC.
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
-
-        Returns:
-            [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
-            True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        if isinstance(timestep, torch.Tensor):
-            timestep = timestep.to(self.timesteps.device)
-        step_index = (self.timesteps == timestep).nonzero()
-        if len(step_index) == 0:
-            step_index = len(self.timesteps) - 1
-        else:
-            step_index = step_index.item()
-
-        use_corrector = (
-            step_index > 0 and step_index - 1 not in self.disable_corrector and self.last_sample is not None
-        )
-
-        model_output_convert = self.convert_model_output(model_output, timestep, sample)
-        if use_corrector:
-            sample = self.multistep_uni_c_bh_update(
-                this_model_output=model_output_convert,
-                this_timestep=timestep,
-                last_sample=self.last_sample,
-                this_sample=sample,
-                order=self.this_order,
-            )
-
-        # now prepare to run the predictor
-        prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1]
-
-        for i in range(self.config.solver_order - 1):
-            self.model_outputs[i] = self.model_outputs[i + 1]
-            self.timestep_list[i] = self.timestep_list[i + 1]
-
-        self.model_outputs[-1] = model_output_convert
-        self.timestep_list[-1] = timestep
-
-        if self.config.lower_order_final:
-            this_order = min(self.config.solver_order, len(self.timesteps) - step_index)
-        else:
-            this_order = self.config.solver_order
-
-        self.this_order = min(this_order, self.lower_order_nums + 1)  # warmup for multistep
-        assert self.this_order > 0
-
-        self.last_sample = sample
-        prev_sample = self.multistep_uni_p_bh_update(
-            model_output=model_output,  # pass the original non-converted model output, in case solver-p is used
-            prev_timestep=prev_timestep,
-            sample=sample,
-            order=self.this_order,
-        )
-
-        if self.lower_order_nums < self.config.solver_order:
-            self.lower_order_nums += 1
-
-        if not return_dict:
-            return (prev_sample,)
-
-        return SchedulerOutput(prev_sample=prev_sample)
-
-    def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        return sample
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/Andy1621/uniformer_image_detection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py

@@ -1,105 +0,0 @@
-_base_ = [
-    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
-]
-
-# model settings
-model = dict(
-    type='CornerNet',
-    backbone=dict(
-        type='HourglassNet',
-        downsample_times=5,
-        num_stacks=2,
-        stage_channels=[256, 256, 384, 384, 384, 512],
-        stage_blocks=[2, 2, 2, 2, 2, 4],
-        norm_cfg=dict(type='BN', requires_grad=True)),
-    neck=None,
-    bbox_head=dict(
-        type='CentripetalHead',
-        num_classes=80,
-        in_channels=256,
-        num_feat_levels=2,
-        corner_emb_channels=0,
-        loss_heatmap=dict(
-            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
-        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1),
-        loss_guiding_shift=dict(
-            type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
-        loss_centripetal_shift=dict(
-            type='SmoothL1Loss', beta=1.0, loss_weight=1)),
-    # training and testing settings
-    train_cfg=None,
-    test_cfg=dict(
-        corner_topk=100,
-        local_maximum_kernel=3,
-        distance_threshold=0.5,
-        score_thr=0.05,
-        max_per_img=100,
-        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
-# data settings
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='RandomCenterCropPad',
-        crop_size=(511, 511),
-        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
-        test_mode=False,
-        test_pad_mode=None,
-        **img_norm_cfg),
-    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(
-        type='MultiScaleFlipAug',
-        scale_factor=1.0,
-        flip=True,
-        transforms=[
-            dict(type='Resize'),
-            dict(
-                type='RandomCenterCropPad',
-                crop_size=None,
-                ratios=None,
-                border=None,
-                test_mode=True,
-                test_pad_mode=['logical_or', 127],
-                **img_norm_cfg),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
-                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
-        ])
-]
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=3,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='Adam', lr=0.0005)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[190])
-runner = dict(type='EpochBasedRunner', max_epochs=210)

spaces/Andy1621/uniformer_image_detection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py

@@ -1,23 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    roi_head=dict(
-        type='DoubleHeadRoIHead',
-        reg_roi_scale_factor=1.3,
-        bbox_head=dict(
-            _delete_=True,
-            type='DoubleConvFCBBoxHead',
-            num_convs=4,
-            num_fcs=2,
-            in_channels=256,
-            conv_out_channels=1024,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=80,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
-            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))

spaces/Angello06/SoylaloGaming/README.md

@@ -1,13 +0,0 @@
----
-title: SoylaloGaming
-emoji: 😻
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/Training_PRO/custom_scheduler.py

@@ -1,175 +0,0 @@
-from functools import partial
-import torch
-import transformers
-import math
-from torch.optim.lr_scheduler import LambdaLR
-
-
-#FPHAM custom training scheduller block - should be extracted to separate file
-last_print_label = ''
-
-# hold constant to the half of epochs then cosine down to 0
-def _get_fp_half_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_firstepoch_steps: int):
-    
-    global last_print_label
-    print_label = ''
-
-    half_steps = num_training_steps//2
-    
-    num_warmup_steps = min(num_warmup_steps,half_steps)
-
-    if current_step < num_warmup_steps:
-        print_label = 'Scheduler: Warmup'
-    elif current_step < half_steps:
-        print_label = 'Scheduler: Hold'
-    else:
-        print_label = 'Scheduler: Annealing'
-    
-    if print_label != last_print_label:
-        print(print_label)
-    
-    last_print_label = print_label
-
-    if current_step < num_warmup_steps:
-        return float(current_step) / float(max(1, num_warmup_steps))
-    
-    if current_step < half_steps:
-        return 1.0 
-    
-    progress = float(current_step - half_steps) / float(max(1, num_training_steps - half_steps))
-    num_cycles = 0.5
-    return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))    
- 
-# constant to the first epochs then cosine down to 0 over the rest epochs
-def _get_fp_cosine_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_firstepoch_steps: int):
-    
-    global last_print_label
-    print_label = ''
-    
-    num_warmup_steps = min(num_warmup_steps,num_firstepoch_steps)
-
-    if current_step < num_warmup_steps:
-        print_label = 'Scheduler: Warmup'
-    elif current_step < num_firstepoch_steps:
-        print_label = 'Scheduler: Hold'
-    else:
-        print_label = 'Scheduler: Annealing'
-    
-    if print_label != last_print_label:
-        print(print_label)
-    
-    last_print_label = print_label
-
-    if current_step < num_warmup_steps:
-        return float(current_step) / float(max(1, num_warmup_steps))
-    
-    if current_step < num_firstepoch_steps:
-        return 1.0 
-    
-    progress = float(current_step - num_firstepoch_steps) / float(max(1, num_training_steps - num_firstepoch_steps))
-    num_cycles = 0.5
-    return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))    
-    
-
-def custom_cosine_scheduler_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_firstepoch_steps, last_epoch=-1):
-    """
-    Args:
-        optimizer ([`~torch.optim.Optimizer`]):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-    
-    lr_lambda = partial(
-        _get_fp_cosine_schedule_with_warmup_lr_lambda,
-        num_warmup_steps=num_warmup_steps,
-        num_training_steps=num_training_steps,
-        num_firstepoch_steps = num_firstepoch_steps,
-    )
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-def custom_half_scheduler_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_firstepoch_steps, last_epoch=-1):
-    """
-    Args:
-        optimizer ([`~torch.optim.Optimizer`]):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-    
-    lr_lambda = partial(
-        _get_fp_half_schedule_with_warmup_lr_lambda,
-        num_warmup_steps=num_warmup_steps,
-        num_training_steps=num_training_steps,
-        num_firstepoch_steps = num_firstepoch_steps,
-    )
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-class FPSchedulerTrainer(transformers.Trainer):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        
-    def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
-        #Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument.
-        
-        num_train_epochs = self.args.num_train_epochs
-        num_warmup_steps=self.args.get_warmup_steps(num_training_steps)
-        num_firstepoch_steps = math.ceil(num_training_steps/num_train_epochs)
-        num_warmup_acc = num_warmup_steps*self.args.gradient_accumulation_steps 
-        num_firstepoch_steps_acc = num_firstepoch_steps*self.args.gradient_accumulation_steps
-        num_training_steps_acc = num_training_steps*self.args.gradient_accumulation_steps
-        
-        print (f"Warm-up steps aligned to Gradient accumulation ({self.args.gradient_accumulation_steps}) = {num_warmup_acc} actual warmup steps")
-        if self.args.lr_scheduler_type == 'cosine':
-            
-            num_warmup_acc_min = min(num_warmup_acc, num_firstepoch_steps_acc)
-
-            if num_warmup_acc>num_firstepoch_steps_acc:
-                print(f"\033[1;31;1mWARNING: The number of warmup steps is set too high! It will be clamped to 1 epoch, essentially going from warmup to annealing.\033[0;37;0m")
-                print (f"FP Scheduler Warmup: 0-[{num_warmup_acc_min}], Hold [{num_warmup_acc_min}]-{num_firstepoch_steps_acc}, Annealing {num_firstepoch_steps_acc}-{num_training_steps_acc}")
-            else:
-                print (f"FP Scheduler Warmup: 0-{num_warmup_acc_min}, Hold {num_warmup_acc_min}-{num_firstepoch_steps_acc}, Annealing {num_firstepoch_steps_acc}-{num_training_steps_acc}")
-
-            self.lr_scheduler = custom_cosine_scheduler_with_warmup(
-                    optimizer=self.optimizer if optimizer is None else optimizer,
-                    num_warmup_steps=num_warmup_steps,
-                    num_training_steps=num_training_steps, 
-                    num_firstepoch_steps = num_firstepoch_steps,
-                )
-            self._created_lr_scheduler = True
-            return self.lr_scheduler
-        elif self.args.lr_scheduler_type == 'constant':
-           
-            half_step_acc = num_training_steps_acc//2
-            num_warmup_acc_min = min(num_warmup_acc, half_step_acc)
-
-            if num_warmup_acc>half_step_acc:
-                print(f"\033[1;31;1mWARNING: The number of warmup steps is set too high! It will be clamped to half of all epochs, essentially going from warmup to annealing in the middle.\033[0;37;0m")
-                print (f"FP Scheduler Warmup: 0-[{num_warmup_acc_min}], Hold [{num_warmup_acc_min}]-{half_step_acc}, Annealing {half_step_acc}-{num_training_steps_acc}")
-            else:
-                print (f"FP Scheduler Warmup: 0-{num_warmup_acc_min}, Hold {num_warmup_acc_min}-{half_step_acc}, Annealing {half_step_acc}-{num_training_steps_acc}")
-
-            self.lr_scheduler = custom_half_scheduler_with_warmup(
-                    optimizer=self.optimizer if optimizer is None else optimizer,
-                    num_warmup_steps=num_warmup_steps,
-                    num_training_steps=num_training_steps, 
-                    num_firstepoch_steps = num_firstepoch_steps,
-                )
-            self._created_lr_scheduler = True
-            return self.lr_scheduler
-        else:
-            return  super().create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)

spaces/Anitha0531/SpeechtoText/app.py

@@ -1,116 +0,0 @@
-import torch
-
-import gradio as gr
-import pytube as pt
-from transformers import pipeline
-
-MODEL_NAME = "openai/whisper-large-v2"
-BATCH_SIZE = 8
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-
-all_special_ids = pipe.tokenizer.all_special_ids
-transcribe_token_id = all_special_ids[-5]
-translate_token_id = all_special_ids[-6]
-
-
-def transcribe(microphone, file_upload, task):
-    warn_output = ""
-    if (microphone is not None) and (file_upload is not None):
-        warn_output = (
-            "WARNING: You've uploaded an audio file and used the microphone. "
-            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        )
-
-    elif (microphone is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-
-    file = microphone if microphone is not None else file_upload
-
-    pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
-
-    textt = pipe(file, batch_size=BATCH_SIZE)["text"]
-
-    with open('outt.txt', 'a+') as sw:
-        sw.writelines(textt)
-
-    return [textt,"outt.txt"]
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-
-
-def yt_transcribe(yt_url, task):
-    yt = pt.YouTube(yt_url)
-    html_embed_str = _return_yt_html_embed(yt_url)
-    stream = yt.streams.filter(only_audio=True)[0]
-    stream.download(filename="audio.mp3")
-
-    pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
-
-    text = pipe("audio.mp3", batch_size=BATCH_SIZE)["text"]
-
-    return html_embed_str, text
-    with open('outtt.txt', 'a+') as sw:
-        sw.writelines(text)
-
-    return [text,"outtt.txt"]
-
-    
-
-    
-
-demo = gr.Blocks()
-output_2 = gr.File(label="Download")
-output_3 = gr.File(label="Download")
-description = """This application displays transcribed text for given audio input <img src="https://i.ibb.co/J5DscKw/GVP-Womens.jpg" width=100px>"""
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Audio(source="upload", type="filepath", optional=True),
-        
-    ],
-    outputs=["text",output_2],
-    layout="horizontal",
-    theme="huggingface",
-    title="Speech to Text Converter using OpenAI Whisper Model",
-    description= description,
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-       
-    ],
-      outputs=["text",output_3],
-    layout="horizontal",
-    theme="huggingface",
-    title="Speech to Text Converter using OpenAI Whisper Model",
-    description=(
-        "Transcribe YouTube Videos to Text"
-    ),
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
-
-demo.launch(enable_queue=True)

spaces/Anthony7906/MengHuiMXD_GPT/readme/README_ja.md

@@ -1,126 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバにデプロイするには、プログラムの最終行を `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`に変更します。
-- 共有リンクを取得するには、プログラムの最後の行を `demo.launch(share=True)` に変更してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## インストール
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるた��、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-3. Gradioを更新
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">

spaces/Apex-X/nono/.github/ISSUE_TEMPLATE/bug.md

@@ -1,47 +0,0 @@
----
-name: Bug
-about: Report a bug
-title: '[Bug]'
-labels: 'bug'
-
----
-
-## Description
-
-A concise description of the bug and how to reproduce it.
-
-## Error
-
-Paste the error or exception from your console:
-
-```
-
-```
-
-## Details
-
-What operating system are you using?
-
-- [ ] Windows 
-- [ ] MacOS (Apple Silicon)
-- [ ] MacOS (Apple Legacy)
-- [ ] Linux
-- [ ] Linux in WSL
-
-What execution provider are you using?
-
-- [ ] CPU
-- [ ] CUDA
-- [ ] CoreML
-- [ ] DirectML
-- [ ] OpenVINO
-- [ ] Other
-
-What version of Roop are you using?
-
-- [ ] 1.0.0
-- [ ] 1.1.0
-- [ ] 1.2.0
-- [ ] 1.3.0
-- [ ] 1.3.1
-- [ ] next

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/ordered_set.py

@@ -1,488 +0,0 @@
-"""
-An OrderedSet is a custom MutableSet that remembers its order, so that every
-entry has an index that can be looked up.
-
-Based on a recipe originally posted to ActiveState Recipes by Raymond Hettiger,
-and released under the MIT license.
-"""
-import itertools as it
-from collections import deque
-
-try:
-    # Python 3
-    from collections.abc import MutableSet, Sequence
-except ImportError:
-    # Python 2.7
-    from collections import MutableSet, Sequence
-
-SLICE_ALL = slice(None)
-__version__ = "3.1"
-
-
-def is_iterable(obj):
-    """
-    Are we being asked to look up a list of things, instead of a single thing?
-    We check for the `__iter__` attribute so that this can cover types that
-    don't have to be known by this module, such as NumPy arrays.
-
-    Strings, however, should be considered as atomic values to look up, not
-    iterables. The same goes for tuples, since they are immutable and therefore
-    valid entries.
-
-    We don't need to check for the Python 2 `unicode` type, because it doesn't
-    have an `__iter__` attribute anyway.
-    """
-    return (
-        hasattr(obj, "__iter__")
-        and not isinstance(obj, str)
-        and not isinstance(obj, tuple)
-    )
-
-
-class OrderedSet(MutableSet, Sequence):
-    """
-    An OrderedSet is a custom MutableSet that remembers its order, so that
-    every entry has an index that can be looked up.
-
-    Example:
-        >>> OrderedSet([1, 1, 2, 3, 2])
-        OrderedSet([1, 2, 3])
-    """
-
-    def __init__(self, iterable=None):
-        self.items = []
-        self.map = {}
-        if iterable is not None:
-            self |= iterable
-
-    def __len__(self):
-        """
-        Returns the number of unique elements in the ordered set
-
-        Example:
-            >>> len(OrderedSet([]))
-            0
-            >>> len(OrderedSet([1, 2]))
-            2
-        """
-        return len(self.items)
-
-    def __getitem__(self, index):
-        """
-        Get the item at a given index.
-
-        If `index` is a slice, you will get back that slice of items, as a
-        new OrderedSet.
-
-        If `index` is a list or a similar iterable, you'll get a list of
-        items corresponding to those indices. This is similar to NumPy's
-        "fancy indexing". The result is not an OrderedSet because you may ask
-        for duplicate indices, and the number of elements returned should be
-        the number of elements asked for.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset[1]
-            2
-        """
-        if isinstance(index, slice) and index == SLICE_ALL:
-            return self.copy()
-        elif is_iterable(index):
-            return [self.items[i] for i in index]
-        elif hasattr(index, "__index__") or isinstance(index, slice):
-            result = self.items[index]
-            if isinstance(result, list):
-                return self.__class__(result)
-            else:
-                return result
-        else:
-            raise TypeError("Don't know how to index an OrderedSet by %r" % index)
-
-    def copy(self):
-        """
-        Return a shallow copy of this object.
-
-        Example:
-            >>> this = OrderedSet([1, 2, 3])
-            >>> other = this.copy()
-            >>> this == other
-            True
-            >>> this is other
-            False
-        """
-        return self.__class__(self)
-
-    def __getstate__(self):
-        if len(self) == 0:
-            # The state can't be an empty list.
-            # We need to return a truthy value, or else __setstate__ won't be run.
-            #
-            # This could have been done more gracefully by always putting the state
-            # in a tuple, but this way is backwards- and forwards- compatible with
-            # previous versions of OrderedSet.
-            return (None,)
-        else:
-            return list(self)
-
-    def __setstate__(self, state):
-        if state == (None,):
-            self.__init__([])
-        else:
-            self.__init__(state)
-
-    def __contains__(self, key):
-        """
-        Test if the item is in this ordered set
-
-        Example:
-            >>> 1 in OrderedSet([1, 3, 2])
-            True
-            >>> 5 in OrderedSet([1, 3, 2])
-            False
-        """
-        return key in self.map
-
-    def add(self, key):
-        """
-        Add `key` as an item to this OrderedSet, then return its index.
-
-        If `key` is already in the OrderedSet, return the index it already
-        had.
-
-        Example:
-            >>> oset = OrderedSet()
-            >>> oset.append(3)
-            0
-            >>> print(oset)
-            OrderedSet([3])
-        """
-        if key not in self.map:
-            self.map[key] = len(self.items)
-            self.items.append(key)
-        return self.map[key]
-
-    append = add
-
-    def update(self, sequence):
-        """
-        Update the set with the given iterable sequence, then return the index
-        of the last element inserted.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.update([3, 1, 5, 1, 4])
-            4
-            >>> print(oset)
-            OrderedSet([1, 2, 3, 5, 4])
-        """
-        item_index = None
-        try:
-            for item in sequence:
-                item_index = self.add(item)
-        except TypeError:
-            raise ValueError(
-                "Argument needs to be an iterable, got %s" % type(sequence)
-            )
-        return item_index
-
-    def index(self, key):
-        """
-        Get the index of a given entry, raising an IndexError if it's not
-        present.
-
-        `key` can be an iterable of entries that is not a string, in which case
-        this returns a list of indices.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.index(2)
-            1
-        """
-        if is_iterable(key):
-            return [self.index(subkey) for subkey in key]
-        return self.map[key]
-
-    # Provide some compatibility with pd.Index
-    get_loc = index
-    get_indexer = index
-
-    def pop(self):
-        """
-        Remove and return the last element from the set.
-
-        Raises KeyError if the set is empty.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.pop()
-            3
-        """
-        if not self.items:
-            raise KeyError("Set is empty")
-
-        elem = self.items[-1]
-        del self.items[-1]
-        del self.map[elem]
-        return elem
-
-    def discard(self, key):
-        """
-        Remove an element.  Do not raise an exception if absent.
-
-        The MutableSet mixin uses this to implement the .remove() method, which
-        *does* raise an error when asked to remove a non-existent item.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.discard(2)
-            >>> print(oset)
-            OrderedSet([1, 3])
-            >>> oset.discard(2)
-            >>> print(oset)
-            OrderedSet([1, 3])
-        """
-        if key in self:
-            i = self.map[key]
-            del self.items[i]
-            del self.map[key]
-            for k, v in self.map.items():
-                if v >= i:
-                    self.map[k] = v - 1
-
-    def clear(self):
-        """
-        Remove all items from this OrderedSet.
-        """
-        del self.items[:]
-        self.map.clear()
-
-    def __iter__(self):
-        """
-        Example:
-            >>> list(iter(OrderedSet([1, 2, 3])))
-            [1, 2, 3]
-        """
-        return iter(self.items)
-
-    def __reversed__(self):
-        """
-        Example:
-            >>> list(reversed(OrderedSet([1, 2, 3])))
-            [3, 2, 1]
-        """
-        return reversed(self.items)
-
-    def __repr__(self):
-        if not self:
-            return "%s()" % (self.__class__.__name__,)
-        return "%s(%r)" % (self.__class__.__name__, list(self))
-
-    def __eq__(self, other):
-        """
-        Returns true if the containers have the same items. If `other` is a
-        Sequence, then order is checked, otherwise it is ignored.
-
-        Example:
-            >>> oset = OrderedSet([1, 3, 2])
-            >>> oset == [1, 3, 2]
-            True
-            >>> oset == [1, 2, 3]
-            False
-            >>> oset == [2, 3]
-            False
-            >>> oset == OrderedSet([3, 2, 1])
-            False
-        """
-        # In Python 2 deque is not a Sequence, so treat it as one for
-        # consistent behavior with Python 3.
-        if isinstance(other, (Sequence, deque)):
-            # Check that this OrderedSet contains the same elements, in the
-            # same order, as the other object.
-            return list(self) == list(other)
-        try:
-            other_as_set = set(other)
-        except TypeError:
-            # If `other` can't be converted into a set, it's not equal.
-            return False
-        else:
-            return set(self) == other_as_set
-
-    def union(self, *sets):
-        """
-        Combines all unique items.
-        Each items order is defined by its first appearance.
-
-        Example:
-            >>> oset = OrderedSet.union(OrderedSet([3, 1, 4, 1, 5]), [1, 3], [2, 0])
-            >>> print(oset)
-            OrderedSet([3, 1, 4, 5, 2, 0])
-            >>> oset.union([8, 9])
-            OrderedSet([3, 1, 4, 5, 2, 0, 8, 9])
-            >>> oset | {10}
-            OrderedSet([3, 1, 4, 5, 2, 0, 10])
-        """
-        cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
-        containers = map(list, it.chain([self], sets))
-        items = it.chain.from_iterable(containers)
-        return cls(items)
-
-    def __and__(self, other):
-        # the parent implementation of this is backwards
-        return self.intersection(other)
-
-    def intersection(self, *sets):
-        """
-        Returns elements in common between all sets. Order is defined only
-        by the first set.
-
-        Example:
-            >>> oset = OrderedSet.intersection(OrderedSet([0, 1, 2, 3]), [1, 2, 3])
-            >>> print(oset)
-            OrderedSet([1, 2, 3])
-            >>> oset.intersection([2, 4, 5], [1, 2, 3, 4])
-            OrderedSet([2])
-            >>> oset.intersection()
-            OrderedSet([1, 2, 3])
-        """
-        cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
-        if sets:
-            common = set.intersection(*map(set, sets))
-            items = (item for item in self if item in common)
-        else:
-            items = self
-        return cls(items)
-
-    def difference(self, *sets):
-        """
-        Returns all elements that are in this set but not the others.
-
-        Example:
-            >>> OrderedSet([1, 2, 3]).difference(OrderedSet([2]))
-            OrderedSet([1, 3])
-            >>> OrderedSet([1, 2, 3]).difference(OrderedSet([2]), OrderedSet([3]))
-            OrderedSet([1])
-            >>> OrderedSet([1, 2, 3]) - OrderedSet([2])
-            OrderedSet([1, 3])
-            >>> OrderedSet([1, 2, 3]).difference()
-            OrderedSet([1, 2, 3])
-        """
-        cls = self.__class__
-        if sets:
-            other = set.union(*map(set, sets))
-            items = (item for item in self if item not in other)
-        else:
-            items = self
-        return cls(items)
-
-    def issubset(self, other):
-        """
-        Report whether another set contains this set.
-
-        Example:
-            >>> OrderedSet([1, 2, 3]).issubset({1, 2})
-            False
-            >>> OrderedSet([1, 2, 3]).issubset({1, 2, 3, 4})
-            True
-            >>> OrderedSet([1, 2, 3]).issubset({1, 4, 3, 5})
-            False
-        """
-        if len(self) > len(other):  # Fast check for obvious cases
-            return False
-        return all(item in other for item in self)
-
-    def issuperset(self, other):
-        """
-        Report whether this set contains another set.
-
-        Example:
-            >>> OrderedSet([1, 2]).issuperset([1, 2, 3])
-            False
-            >>> OrderedSet([1, 2, 3, 4]).issuperset({1, 2, 3})
-            True
-            >>> OrderedSet([1, 4, 3, 5]).issuperset({1, 2, 3})
-            False
-        """
-        if len(self) < len(other):  # Fast check for obvious cases
-            return False
-        return all(item in self for item in other)
-
-    def symmetric_difference(self, other):
-        """
-        Return the symmetric difference of two OrderedSets as a new set.
-        That is, the new set will contain all elements that are in exactly
-        one of the sets.
-
-        Their order will be preserved, with elements from `self` preceding
-        elements from `other`.
-
-        Example:
-            >>> this = OrderedSet([1, 4, 3, 5, 7])
-            >>> other = OrderedSet([9, 7, 1, 3, 2])
-            >>> this.symmetric_difference(other)
-            OrderedSet([4, 5, 9, 2])
-        """
-        cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
-        diff1 = cls(self).difference(other)
-        diff2 = cls(other).difference(self)
-        return diff1.union(diff2)
-
-    def _update_items(self, items):
-        """
-        Replace the 'items' list of this OrderedSet with a new one, updating
-        self.map accordingly.
-        """
-        self.items = items
-        self.map = {item: idx for (idx, item) in enumerate(items)}
-
-    def difference_update(self, *sets):
-        """
-        Update this OrderedSet to remove items from one or more other sets.
-
-        Example:
-            >>> this = OrderedSet([1, 2, 3])
-            >>> this.difference_update(OrderedSet([2, 4]))
-            >>> print(this)
-            OrderedSet([1, 3])
-
-            >>> this = OrderedSet([1, 2, 3, 4, 5])
-            >>> this.difference_update(OrderedSet([2, 4]), OrderedSet([1, 4, 6]))
-            >>> print(this)
-            OrderedSet([3, 5])
-        """
-        items_to_remove = set()
-        for other in sets:
-            items_to_remove |= set(other)
-        self._update_items([item for item in self.items if item not in items_to_remove])
-
-    def intersection_update(self, other):
-        """
-        Update this OrderedSet to keep only items in another set, preserving
-        their order in this set.
-
-        Example:
-            >>> this = OrderedSet([1, 4, 3, 5, 7])
-            >>> other = OrderedSet([9, 7, 1, 3, 2])
-            >>> this.intersection_update(other)
-            >>> print(this)
-            OrderedSet([1, 3, 7])
-        """
-        other = set(other)
-        self._update_items([item for item in self.items if item in other])
-
-    def symmetric_difference_update(self, other):
-        """
-        Update this OrderedSet to remove items from another set, then
-        add items from the other set that were not present in this set.
-
-        Example:
-            >>> this = OrderedSet([1, 4, 3, 5, 7])
-            >>> other = OrderedSet([9, 7, 1, 3, 2])
-            >>> this.symmetric_difference_update(other)
-            >>> print(this)
-            OrderedSet([4, 5, 9, 2])
-        """
-        items_to_add = [item for item in other if item not in self]
-        items_to_remove = set(other)
-        self._update_items(
-            [item for item in self.items if item not in items_to_remove] + items_to_add
-        )

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/specifiers.py

@@ -1,802 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import abc
-import functools
-import itertools
-import re
-import warnings
-from typing import (
-    Callable,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-    Pattern,
-    Set,
-    Tuple,
-    TypeVar,
-    Union,
-)
-
-from .utils import canonicalize_version
-from .version import LegacyVersion, Version, parse
-
-ParsedVersion = Union[Version, LegacyVersion]
-UnparsedVersion = Union[Version, LegacyVersion, str]
-VersionTypeVar = TypeVar("VersionTypeVar", bound=UnparsedVersion)
-CallableOperator = Callable[[ParsedVersion, str], bool]
-
-
-class InvalidSpecifier(ValueError):
-    """
-    An invalid specifier was found, users should refer to PEP 440.
-    """
-
-
-class BaseSpecifier(metaclass=abc.ABCMeta):
-    @abc.abstractmethod
-    def __str__(self) -> str:
-        """
-        Returns the str representation of this Specifier like object. This
-        should be representative of the Specifier itself.
-        """
-
-    @abc.abstractmethod
-    def __hash__(self) -> int:
-        """
-        Returns a hash value for this Specifier like object.
-        """
-
-    @abc.abstractmethod
-    def __eq__(self, other: object) -> bool:
-        """
-        Returns a boolean representing whether or not the two Specifier like
-        objects are equal.
-        """
-
-    @abc.abstractproperty
-    def prereleases(self) -> Optional[bool]:
-        """
-        Returns whether or not pre-releases as a whole are allowed by this
-        specifier.
-        """
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        """
-        Sets whether or not pre-releases as a whole are allowed by this
-        specifier.
-        """
-
-    @abc.abstractmethod
-    def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
-        """
-        Determines if the given item is contained within this specifier.
-        """
-
-    @abc.abstractmethod
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-        """
-        Takes an iterable of items and filters them so that only items which
-        are contained within this specifier are allowed in it.
-        """
-
-
-class _IndividualSpecifier(BaseSpecifier):
-
-    _operators: Dict[str, str] = {}
-    _regex: Pattern[str]
-
-    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
-        match = self._regex.search(spec)
-        if not match:
-            raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
-
-        self._spec: Tuple[str, str] = (
-            match.group("operator").strip(),
-            match.group("version").strip(),
-        )
-
-        # Store whether or not this Specifier should accept prereleases
-        self._prereleases = prereleases
-
-    def __repr__(self) -> str:
-        pre = (
-            f", prereleases={self.prereleases!r}"
-            if self._prereleases is not None
-            else ""
-        )
-
-        return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
-
-    def __str__(self) -> str:
-        return "{}{}".format(*self._spec)
-
-    @property
-    def _canonical_spec(self) -> Tuple[str, str]:
-        return self._spec[0], canonicalize_version(self._spec[1])
-
-    def __hash__(self) -> int:
-        return hash(self._canonical_spec)
-
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, str):
-            try:
-                other = self.__class__(str(other))
-            except InvalidSpecifier:
-                return NotImplemented
-        elif not isinstance(other, self.__class__):
-            return NotImplemented
-
-        return self._canonical_spec == other._canonical_spec
-
-    def _get_operator(self, op: str) -> CallableOperator:
-        operator_callable: CallableOperator = getattr(
-            self, f"_compare_{self._operators[op]}"
-        )
-        return operator_callable
-
-    def _coerce_version(self, version: UnparsedVersion) -> ParsedVersion:
-        if not isinstance(version, (LegacyVersion, Version)):
-            version = parse(version)
-        return version
-
-    @property
-    def operator(self) -> str:
-        return self._spec[0]
-
-    @property
-    def version(self) -> str:
-        return self._spec[1]
-
-    @property
-    def prereleases(self) -> Optional[bool]:
-        return self._prereleases
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-    def __contains__(self, item: str) -> bool:
-        return self.contains(item)
-
-    def contains(
-        self, item: UnparsedVersion, prereleases: Optional[bool] = None
-    ) -> bool:
-
-        # Determine if prereleases are to be allowed or not.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # Normalize item to a Version or LegacyVersion, this allows us to have
-        # a shortcut for ``"2.0" in Specifier(">=2")
-        normalized_item = self._coerce_version(item)
-
-        # Determine if we should be supporting prereleases in this specifier
-        # or not, if we do not support prereleases than we can short circuit
-        # logic if this version is a prereleases.
-        if normalized_item.is_prerelease and not prereleases:
-            return False
-
-        # Actually do the comparison to determine if this item is contained
-        # within this Specifier or not.
-        operator_callable: CallableOperator = self._get_operator(self.operator)
-        return operator_callable(normalized_item, self.version)
-
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-
-        yielded = False
-        found_prereleases = []
-
-        kw = {"prereleases": prereleases if prereleases is not None else True}
-
-        # Attempt to iterate over all the values in the iterable and if any of
-        # them match, yield them.
-        for version in iterable:
-            parsed_version = self._coerce_version(version)
-
-            if self.contains(parsed_version, **kw):
-                # If our version is a prerelease, and we were not set to allow
-                # prereleases, then we'll store it for later in case nothing
-                # else matches this specifier.
-                if parsed_version.is_prerelease and not (
-                    prereleases or self.prereleases
-                ):
-                    found_prereleases.append(version)
-                # Either this is not a prerelease, or we should have been
-                # accepting prereleases from the beginning.
-                else:
-                    yielded = True
-                    yield version
-
-        # Now that we've iterated over everything, determine if we've yielded
-        # any values, and if we have not and we have any prereleases stored up
-        # then we will go ahead and yield the prereleases.
-        if not yielded and found_prereleases:
-            for version in found_prereleases:
-                yield version
-
-
-class LegacySpecifier(_IndividualSpecifier):
-
-    _regex_str = r"""
-        (?P<operator>(==|!=|<=|>=|<|>))
-        \s*
-        (?P<version>
-            [^,;\s)]* # Since this is a "legacy" specifier, and the version
-                      # string can be just about anything, we match everything
-                      # except for whitespace, a semi-colon for marker support,
-                      # a closing paren since versions can be enclosed in
-                      # them, and a comma since it's a version separator.
-        )
-        """
-
-    _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
-    _operators = {
-        "==": "equal",
-        "!=": "not_equal",
-        "<=": "less_than_equal",
-        ">=": "greater_than_equal",
-        "<": "less_than",
-        ">": "greater_than",
-    }
-
-    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
-        super().__init__(spec, prereleases)
-
-        warnings.warn(
-            "Creating a LegacyVersion has been deprecated and will be "
-            "removed in the next major release",
-            DeprecationWarning,
-        )
-
-    def _coerce_version(self, version: UnparsedVersion) -> LegacyVersion:
-        if not isinstance(version, LegacyVersion):
-            version = LegacyVersion(str(version))
-        return version
-
-    def _compare_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective == self._coerce_version(spec)
-
-    def _compare_not_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective != self._coerce_version(spec)
-
-    def _compare_less_than_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective <= self._coerce_version(spec)
-
-    def _compare_greater_than_equal(
-        self, prospective: LegacyVersion, spec: str
-    ) -> bool:
-        return prospective >= self._coerce_version(spec)
-
-    def _compare_less_than(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective < self._coerce_version(spec)
-
-    def _compare_greater_than(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective > self._coerce_version(spec)
-
-
-def _require_version_compare(
-    fn: Callable[["Specifier", ParsedVersion, str], bool]
-) -> Callable[["Specifier", ParsedVersion, str], bool]:
-    @functools.wraps(fn)
-    def wrapped(self: "Specifier", prospective: ParsedVersion, spec: str) -> bool:
-        if not isinstance(prospective, Version):
-            return False
-        return fn(self, prospective, spec)
-
-    return wrapped
-
-
-class Specifier(_IndividualSpecifier):
-
-    _regex_str = r"""
-        (?P<operator>(~=|==|!=|<=|>=|<|>|===))
-        (?P<version>
-            (?:
-                # The identity operators allow for an escape hatch that will
-                # do an exact string match of the version you wish to install.
-                # This will not be parsed by PEP 440 and we cannot determine
-                # any semantic meaning from it. This operator is discouraged
-                # but included entirely as an escape hatch.
-                (?<====)  # Only match for the identity operator
-                \s*
-                [^\s]*    # We just match everything, except for whitespace
-                          # since we are only testing for strict identity.
-            )
-            |
-            (?:
-                # The (non)equality operators allow for wild card and local
-                # versions to be specified so we have to define these two
-                # operators separately to enable that.
-                (?<===|!=)            # Only match for equals and not equals
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)*   # release
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-
-                # You cannot use a wild card and a dev or local version
-                # together so group them with a | and make them optional.
-                (?:
-                    (?:[-_\.]?dev[-_\.]?[0-9]*)?         # dev release
-                    (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
-                    |
-                    \.\*  # Wild card syntax of .*
-                )?
-            )
-            |
-            (?:
-                # The compatible operator requires at least two digits in the
-                # release segment.
-                (?<=~=)               # Only match for the compatible operator
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)+   # release  (We have a + instead of a *)
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
-            )
-            |
-            (?:
-                # All other operators only allow a sub set of what the
-                # (non)equality operators do. Specifically they do not allow
-                # local versions to be specified nor do they allow the prefix
-                # matching wild cards.
-                (?<!==|!=|~=)         # We have special cases for these
-                                      # operators so we want to make sure they
-                                      # don't match here.
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)*   # release
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
-            )
-        )
-        """
-
-    _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
-    _operators = {
-        "~=": "compatible",
-        "==": "equal",
-        "!=": "not_equal",
-        "<=": "less_than_equal",
-        ">=": "greater_than_equal",
-        "<": "less_than",
-        ">": "greater_than",
-        "===": "arbitrary",
-    }
-
-    @_require_version_compare
-    def _compare_compatible(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # Compatible releases have an equivalent combination of >= and ==. That
-        # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
-        # implement this in terms of the other specifiers instead of
-        # implementing it ourselves. The only thing we need to do is construct
-        # the other specifiers.
-
-        # We want everything but the last item in the version, but we want to
-        # ignore suffix segments.
-        prefix = ".".join(
-            list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
-        )
-
-        # Add the prefix notation to the end of our string
-        prefix += ".*"
-
-        return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
-            prospective, prefix
-        )
-
-    @_require_version_compare
-    def _compare_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # We need special logic to handle prefix matching
-        if spec.endswith(".*"):
-            # In the case of prefix matching we want to ignore local segment.
-            prospective = Version(prospective.public)
-            # Split the spec out by dots, and pretend that there is an implicit
-            # dot in between a release segment and a pre-release segment.
-            split_spec = _version_split(spec[:-2])  # Remove the trailing .*
-
-            # Split the prospective version out by dots, and pretend that there
-            # is an implicit dot in between a release segment and a pre-release
-            # segment.
-            split_prospective = _version_split(str(prospective))
-
-            # Shorten the prospective version to be the same length as the spec
-            # so that we can determine if the specifier is a prefix of the
-            # prospective version or not.
-            shortened_prospective = split_prospective[: len(split_spec)]
-
-            # Pad out our two sides with zeros so that they both equal the same
-            # length.
-            padded_spec, padded_prospective = _pad_version(
-                split_spec, shortened_prospective
-            )
-
-            return padded_prospective == padded_spec
-        else:
-            # Convert our spec string into a Version
-            spec_version = Version(spec)
-
-            # If the specifier does not have a local segment, then we want to
-            # act as if the prospective version also does not have a local
-            # segment.
-            if not spec_version.local:
-                prospective = Version(prospective.public)
-
-            return prospective == spec_version
-
-    @_require_version_compare
-    def _compare_not_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-        return not self._compare_equal(prospective, spec)
-
-    @_require_version_compare
-    def _compare_less_than_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # NB: Local version identifiers are NOT permitted in the version
-        # specifier, so local version labels can be universally removed from
-        # the prospective version.
-        return Version(prospective.public) <= Version(spec)
-
-    @_require_version_compare
-    def _compare_greater_than_equal(
-        self, prospective: ParsedVersion, spec: str
-    ) -> bool:
-
-        # NB: Local version identifiers are NOT permitted in the version
-        # specifier, so local version labels can be universally removed from
-        # the prospective version.
-        return Version(prospective.public) >= Version(spec)
-
-    @_require_version_compare
-    def _compare_less_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
-        # Convert our spec to a Version instance, since we'll want to work with
-        # it as a version.
-        spec = Version(spec_str)
-
-        # Check to see if the prospective version is less than the spec
-        # version. If it's not we can short circuit and just return False now
-        # instead of doing extra unneeded work.
-        if not prospective < spec:
-            return False
-
-        # This special case is here so that, unless the specifier itself
-        # includes is a pre-release version, that we do not accept pre-release
-        # versions for the version mentioned in the specifier (e.g. <3.1 should
-        # not match 3.1.dev0, but should match 3.0.dev0).
-        if not spec.is_prerelease and prospective.is_prerelease:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # If we've gotten to here, it means that prospective version is both
-        # less than the spec version *and* it's not a pre-release of the same
-        # version in the spec.
-        return True
-
-    @_require_version_compare
-    def _compare_greater_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
-        # Convert our spec to a Version instance, since we'll want to work with
-        # it as a version.
-        spec = Version(spec_str)
-
-        # Check to see if the prospective version is greater than the spec
-        # version. If it's not we can short circuit and just return False now
-        # instead of doing extra unneeded work.
-        if not prospective > spec:
-            return False
-
-        # This special case is here so that, unless the specifier itself
-        # includes is a post-release version, that we do not accept
-        # post-release versions for the version mentioned in the specifier
-        # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
-        if not spec.is_postrelease and prospective.is_postrelease:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # Ensure that we do not allow a local version of the version mentioned
-        # in the specifier, which is technically greater than, to match.
-        if prospective.local is not None:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # If we've gotten to here, it means that prospective version is both
-        # greater than the spec version *and* it's not a pre-release of the
-        # same version in the spec.
-        return True
-
-    def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
-        return str(prospective).lower() == str(spec).lower()
-
-    @property
-    def prereleases(self) -> bool:
-
-        # If there is an explicit prereleases set for this, then we'll just
-        # blindly use that.
-        if self._prereleases is not None:
-            return self._prereleases
-
-        # Look at all of our specifiers and determine if they are inclusive
-        # operators, and if they are if they are including an explicit
-        # prerelease.
-        operator, version = self._spec
-        if operator in ["==", ">=", "<=", "~=", "==="]:
-            # The == specifier can include a trailing .*, if it does we
-            # want to remove before parsing.
-            if operator == "==" and version.endswith(".*"):
-                version = version[:-2]
-
-            # Parse the version, and if it is a pre-release than this
-            # specifier allows pre-releases.
-            if parse(version).is_prerelease:
-                return True
-
-        return False
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-
-_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
-
-
-def _version_split(version: str) -> List[str]:
-    result: List[str] = []
-    for item in version.split("."):
-        match = _prefix_regex.search(item)
-        if match:
-            result.extend(match.groups())
-        else:
-            result.append(item)
-    return result
-
-
-def _is_not_suffix(segment: str) -> bool:
-    return not any(
-        segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
-    )
-
-
-def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
-    left_split, right_split = [], []
-
-    # Get the release segment of our versions
-    left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
-    right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
-
-    # Get the rest of our versions
-    left_split.append(left[len(left_split[0]) :])
-    right_split.append(right[len(right_split[0]) :])
-
-    # Insert our padding
-    left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
-    right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
-
-    return (list(itertools.chain(*left_split)), list(itertools.chain(*right_split)))
-
-
-class SpecifierSet(BaseSpecifier):
-    def __init__(
-        self, specifiers: str = "", prereleases: Optional[bool] = None
-    ) -> None:
-
-        # Split on , to break each individual specifier into it's own item, and
-        # strip each item to remove leading/trailing whitespace.
-        split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
-
-        # Parsed each individual specifier, attempting first to make it a
-        # Specifier and falling back to a LegacySpecifier.
-        parsed: Set[_IndividualSpecifier] = set()
-        for specifier in split_specifiers:
-            try:
-                parsed.add(Specifier(specifier))
-            except InvalidSpecifier:
-                parsed.add(LegacySpecifier(specifier))
-
-        # Turn our parsed specifiers into a frozen set and save them for later.
-        self._specs = frozenset(parsed)
-
-        # Store our prereleases value so we can use it later to determine if
-        # we accept prereleases or not.
-        self._prereleases = prereleases
-
-    def __repr__(self) -> str:
-        pre = (
-            f", prereleases={self.prereleases!r}"
-            if self._prereleases is not None
-            else ""
-        )
-
-        return f"<SpecifierSet({str(self)!r}{pre})>"
-
-    def __str__(self) -> str:
-        return ",".join(sorted(str(s) for s in self._specs))
-
-    def __hash__(self) -> int:
-        return hash(self._specs)
-
-    def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
-        if isinstance(other, str):
-            other = SpecifierSet(other)
-        elif not isinstance(other, SpecifierSet):
-            return NotImplemented
-
-        specifier = SpecifierSet()
-        specifier._specs = frozenset(self._specs | other._specs)
-
-        if self._prereleases is None and other._prereleases is not None:
-            specifier._prereleases = other._prereleases
-        elif self._prereleases is not None and other._prereleases is None:
-            specifier._prereleases = self._prereleases
-        elif self._prereleases == other._prereleases:
-            specifier._prereleases = self._prereleases
-        else:
-            raise ValueError(
-                "Cannot combine SpecifierSets with True and False prerelease "
-                "overrides."
-            )
-
-        return specifier
-
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, (str, _IndividualSpecifier)):
-            other = SpecifierSet(str(other))
-        elif not isinstance(other, SpecifierSet):
-            return NotImplemented
-
-        return self._specs == other._specs
-
-    def __len__(self) -> int:
-        return len(self._specs)
-
-    def __iter__(self) -> Iterator[_IndividualSpecifier]:
-        return iter(self._specs)
-
-    @property
-    def prereleases(self) -> Optional[bool]:
-
-        # If we have been given an explicit prerelease modifier, then we'll
-        # pass that through here.
-        if self._prereleases is not None:
-            return self._prereleases
-
-        # If we don't have any specifiers, and we don't have a forced value,
-        # then we'll just return None since we don't know if this should have
-        # pre-releases or not.
-        if not self._specs:
-            return None
-
-        # Otherwise we'll see if any of the given specifiers accept
-        # prereleases, if any of them do we'll return True, otherwise False.
-        return any(s.prereleases for s in self._specs)
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-    def __contains__(self, item: UnparsedVersion) -> bool:
-        return self.contains(item)
-
-    def contains(
-        self, item: UnparsedVersion, prereleases: Optional[bool] = None
-    ) -> bool:
-
-        # Ensure that our item is a Version or LegacyVersion instance.
-        if not isinstance(item, (LegacyVersion, Version)):
-            item = parse(item)
-
-        # Determine if we're forcing a prerelease or not, if we're not forcing
-        # one for this particular filter call, then we'll use whatever the
-        # SpecifierSet thinks for whether or not we should support prereleases.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # We can determine if we're going to allow pre-releases by looking to
-        # see if any of the underlying items supports them. If none of them do
-        # and this item is a pre-release then we do not allow it and we can
-        # short circuit that here.
-        # Note: This means that 1.0.dev1 would not be contained in something
-        #       like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
-        if not prereleases and item.is_prerelease:
-            return False
-
-        # We simply dispatch to the underlying specs here to make sure that the
-        # given version is contained within all of them.
-        # Note: This use of all() here means that an empty set of specifiers
-        #       will always return True, this is an explicit design decision.
-        return all(s.contains(item, prereleases=prereleases) for s in self._specs)
-
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-
-        # Determine if we're forcing a prerelease or not, if we're not forcing
-        # one for this particular filter call, then we'll use whatever the
-        # SpecifierSet thinks for whether or not we should support prereleases.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # If we have any specifiers, then we want to wrap our iterable in the
-        # filter method for each one, this will act as a logical AND amongst
-        # each specifier.
-        if self._specs:
-            for spec in self._specs:
-                iterable = spec.filter(iterable, prereleases=bool(prereleases))
-            return iterable
-        # If we do not have any specifiers, then we need to have a rough filter
-        # which will filter out any pre-releases, unless there are no final
-        # releases, and which will filter out LegacyVersion in general.
-        else:
-            filtered: List[VersionTypeVar] = []
-            found_prereleases: List[VersionTypeVar] = []
-
-            item: UnparsedVersion
-            parsed_version: Union[Version, LegacyVersion]
-
-            for item in iterable:
-                # Ensure that we some kind of Version class for this item.
-                if not isinstance(item, (LegacyVersion, Version)):
-                    parsed_version = parse(item)
-                else:
-                    parsed_version = item
-
-                # Filter out any item which is parsed as a LegacyVersion
-                if isinstance(parsed_version, LegacyVersion):
-                    continue
-
-                # Store any item which is a pre-release for later unless we've
-                # already found a final version or we are accepting prereleases
-                if parsed_version.is_prerelease and not prereleases:
-                    if not filtered:
-                        found_prereleases.append(item)
-                else:
-                    filtered.append(item)
-
-            # If we've found no items except for pre-releases, then we'll go
-            # ahead and use the pre-releases
-            if not filtered and found_prereleases and prereleases is None:
-                return found_prereleases
-
-            return filtered

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/windows_support.py

@@ -1,29 +0,0 @@
-import platform
-
-
-def windows_only(func):
-    if platform.system() != 'Windows':
-        return lambda *args, **kwargs: None
-    return func
-
-
-@windows_only
-def hide_file(path):
-    """
-    Set the hidden attribute on a file or directory.
-
-    From http://stackoverflow.com/questions/19622133/
-
-    `path` must be text.
-    """
-    import ctypes
-    __import__('ctypes.wintypes')
-    SetFileAttributes = ctypes.windll.kernel32.SetFileAttributesW
-    SetFileAttributes.argtypes = ctypes.wintypes.LPWSTR, ctypes.wintypes.DWORD
-    SetFileAttributes.restype = ctypes.wintypes.BOOL
-
-    FILE_ATTRIBUTE_HIDDEN = 0x02
-
-    ret = SetFileAttributes(path, FILE_ATTRIBUTE_HIDDEN)
-    if not ret:
-        raise ctypes.WinError()

spaces/Avkash/Satellite_Segmentation_Prediction/app.py

@@ -1,66 +0,0 @@
-import os
-import cv2
-from PIL import Image 
-import numpy as np 
-import segmentation_models as sm
-from matplotlib import pyplot as plt
-import random
-
-
-from keras import backend as K
-from keras.models import load_model 
-
-import gradio as gr
-
-def jaccard_coef(y_true, y_pred):
-  y_true_flatten = K.flatten(y_true)
-  y_pred_flatten = K.flatten(y_pred)
-  intersection = K.sum(y_true_flatten * y_pred_flatten)
-  final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
-  return final_coef_value
-  
-  
-weights = [0.1666, 0.1666, 0.1666, 0.1666, 0.1666, 0.1666]
-dice_loss = sm.losses.DiceLoss(class_weights = weights)
-focal_loss = sm.losses.CategoricalFocalLoss()
-total_loss = dice_loss + (1 * focal_loss)
-
-
-satellite_model = load_model('model/satellite-imagery.h5', custom_objects=({'dice_loss_plus_1focal_loss': total_loss, 'jaccard_coef': jaccard_coef}))
-
-def process_input_image(image_source):
-  image = np.expand_dims(image_source, 0)
-
-  prediction = satellite_model.predict(image)
-  predicted_image = np.argmax(prediction, axis=3)
-
-  predicted_image = predicted_image[0,:,:]
-  predicted_image = predicted_image * 50
-  return 'Predicted Masked Image', predicted_image
-  
-  
-my_app = gr.Blocks()
-  
-with my_app:
-  gr.Markdown("Satellite Image Segmentation Application UI with Gradio")
-  with gr.Tabs():
-    with gr.TabItem("Select your image"):
-      with gr.Row():
-        with gr.Column():
-          img_source = gr.Image(label="Please select source Image", shape=(256, 256))
-          source_image_loader = gr.Button("Load above Image")
-        with gr.Column():
-          output_label = gr.Label(label="Image Info")
-          img_output = gr.Image(label="Image Output")
-    source_image_loader.click(
-      process_input_image,
-      [
-        img_source
-      ],
-      [
-        output_label,
-        img_output
-      ]
-    )
-    
-my_app.launch(debug=True)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_fast_rcnn.py

@@ -1,124 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Part of the code is from https://github.com/tztztztztz/eql.detectron2/blob/master/projects/EQL/eql/fast_rcnn.py
-import logging
-import math
-import json
-from typing import Dict, Union
-import torch
-from fvcore.nn import giou_loss, smooth_l1_loss
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import Linear, ShapeSpec, batched_nms, cat, nonzero_tuple
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.events import get_event_storage
-from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
-from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.modeling.roi_heads.fast_rcnn import _log_classification_stats
-from detectron2.utils.comm import get_world_size
-from .fed_loss import load_class_freq, get_fed_loss_inds
-
-__all__ = ["CustomFastRCNNOutputLayers"]
-
-class CustomFastRCNNOutputLayers(FastRCNNOutputLayers):
-    def __init__(
-        self, 
-        cfg, 
-        input_shape: ShapeSpec,
-        **kwargs
-    ):
-        super().__init__(cfg, input_shape, **kwargs)
-
-        self.cfg = cfg
-
-    def losses(self, predictions, proposals):
-        """
-        enable advanced loss
-        """
-        scores, proposal_deltas = predictions
-        gt_classes = (
-            cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
-        )
-        num_classes = self.num_classes
-        _log_classification_stats(scores, gt_classes)
-
-        if len(proposals):
-            proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)  # Nx4
-            assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
-            gt_boxes = cat(
-                [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
-                dim=0,
-            )
-        else:
-            proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
-
-        loss_cls = self.softmax_cross_entropy_loss(scores, gt_classes)
-        return {
-            "loss_cls": loss_cls, 
-            "loss_box_reg": self.box_reg_loss(
-                proposal_boxes, gt_boxes, proposal_deltas, gt_classes)
-        }
-
-
-    def sigmoid_cross_entropy_loss(self, pred_class_logits, gt_classes):
-        if pred_class_logits.numel() == 0:
-            return pred_class_logits.new_zeros([1])[0] # This is more robust than .sum() * 0.
-
-        B = pred_class_logits.shape[0]
-        C = pred_class_logits.shape[1] - 1
-
-        target = pred_class_logits.new_zeros(B, C + 1)
-        target[range(len(gt_classes)), gt_classes] = 1 # B x (C + 1)
-        target = target[:, :C] # B x C
-
-        weight = 1
-
-        cls_loss = F.binary_cross_entropy_with_logits(
-            pred_class_logits[:, :-1], target, reduction='none') # B x C
-        loss =  torch.sum(cls_loss * weight) / B  
-        return loss
-        
-    
-    def softmax_cross_entropy_loss(self, pred_class_logits, gt_classes):
-        """
-        change _no_instance handling
-        """
-        if pred_class_logits.numel() == 0:
-            return pred_class_logits.new_zeros([1])[0]
-
-        loss = F.cross_entropy(
-            pred_class_logits, gt_classes, reduction="mean")
-        return loss
-
-
-    def inference(self, predictions, proposals):
-        """
-        enable use proposal boxes
-        """
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        if self.cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE:
-            proposal_scores = [p.get('objectness_logits') for p in proposals]
-            scores = [(s * ps[:, None]) ** 0.5 \
-                for s, ps in zip(scores, proposal_scores)]
-        image_shapes = [x.image_size for x in proposals]
-        return fast_rcnn_inference(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.test_topk_per_image,
-        )
-
-
-    def predict_probs(self, predictions, proposals):
-        """
-        support sigmoid
-        """
-        scores, _ = predictions
-        num_inst_per_image = [len(p) for p in proposals]
-        probs = F.softmax(scores, dim=-1)
-        return probs.split(num_inst_per_image, dim=0)

spaces/Benebene/Chat-question-answering/test.py

@@ -1,62 +0,0 @@
-from utils import Stuff
-
-def assert_equal(actual, expected):
-    
-    if actual == expected:
-        return 1
-    else:
-        pass
-
-def test(test_bench: list, s: Stuff)-> None:
-    
-    ok = 0
-    for i, elem in enumerate(test_bench):
-        ok += assert_equal(s.most_similar(elem['question']), elem['index'])
-
-    pourcentage_ok = ok*100/(i+1)  
-    
-    print(f'Le pourcentage de bonne réponse est : {pourcentage_ok} %')
-
-
-test_bench = [ 
-    {
-    "question": 'Is there a possibility of a discrepancy between the official time-measure UT1 and other credible measures of mean solar time, indicating a potential progressive difference between the two? Essentially, the question is asking whether Universal Time truly tracks mean solar time, but answering it may not be a straightforward task.',
-    "index": 0
-    },
-    {
-    "question": "In certain astronomical photographs such as Centaurus A, what is the red substance that can be observed?",
-    "index": 4
-    },
-    {
-    "question": 'Is it possible to remotely measure isotope ratios, or is it necessary to acquire a sample for analysis?',
-    "index": 7
-    },
-    {
-    "question": 'What is the reason behind in-the-sky.org stating that Mercury is not observable from Taipei during these days?',
-    "index": 1000
-    },
-    {
-    "question": 'What is the reason for the connection between the gravitational acceleration $g$ and the oscillator strength $f$ in the expression $\log{gf}_{\odot}$?',
-    "index": 554
-    },
-    {
-    "question": "In Saint-Exupery's account of his visit to a plateau, he mentions finding several meteorites with ease. Can this be considered a realistic portrayal?",
-    "index": 900
-    },
-    {
-    "question": 'Is it likely that the recent passage of comet 21P near Earth on September 10th will result in a Draconid storm on October 9th?',
-    "index": 87
-    },
-    {
-    "question": "From which source can I obtain data regarding the orbit of Mercury, in order to apply it to a model?",
-    "index": 52
-    },
-    {
-    "question": 'What kind of information can we gather from the collective amount of stellar mass present in galaxies?',
-    "index": 322
-    },
-    {
-    "question": 'What is the process for converting magnitudes into bolometric luminosity?',
-    "index": 6
-    }
-]

spaces/Benson/text-generation/Examples/Coche Carretera Carreras Mod Apk Happymod.md

@@ -1,47 +0,0 @@
-<br />
-<h1>CarX Highway Racing Mod APK Happymod: Una revisión</h1>
-<p>Si eres un fan de los juegos de carreras de coches, es posible que hayas oído hablar de CarX Highway Racing, un emocionante y realista juego de carreras para dispositivos Android. Pero ¿sabías que puedes disfrutar de este juego aún más con una versión modificada de Happymod? En este artículo, vamos a revisar CarX Highway Racing Mod APK Happymod, una versión modificada del juego que ofrece dinero ilimitado, coches desbloqueados, y más. También le mostraremos cómo descargar e instalar este apk mod en su dispositivo de forma fácil y segura. </p>
-<h2>coche carretera carreras mod apk happymod</h2><br /><p><b><b>Download</b> &#10042;&#10042;&#10042; <a href="https://bltlly.com/2v6KNs">https://bltlly.com/2v6KNs</a></b></p><br /><br />
- <h2>¿Qué es CarX Highway Racing? </h2>
-<p>CarX Highway Racing es un juego de carreras desarrollado por CarX Technologies, la misma compañía detrás de la popular serie CarX Drift Racing. En este juego, puedes experimentar la emoción de las carreras de alta velocidad en carreteras realistas, con gráficos impresionantes, física y efectos de sonido. Usted puede elegir entre una variedad de coches, de los coches deportivos a los coches del músculo, y personalizarlos a su gusto. También puedes competir en diferentes modos de juego, como el modo carrera, el modo de ataque temporal, el modo persecución policial y el modo en línea. También puede asumir misiones y misiones desafiantes para ganar recompensas y desbloquear nuevos coches y pistas. </p>
- <h3>Características de CarX Highway Racing</h3>
-<h4>Física y gráficos realistas</h4>
-<p>Una de las principales atracciones de CarX Highway Racing es su física realista y gráficos. El juego utiliza el motor CarX avanzado, que simula el comportamiento de los coches reales en diferentes superficies de carreteras y condiciones climáticas. Puede sentir la diferencia entre conducir sobre asfalto, arena o nieve, y ajustar su estilo de conducción en consecuencia. El juego también cuenta con gráficos impresionantes, con modelos de coches detallados, sombras dinámicas, reflejos y efectos de iluminación. También se puede disfrutar de las vistas panorámicas de las carreteras, desde desiertos hasta bosques, desde el día hasta la noche. </p>
- <h4>Diversos coches y pistas</h4>
-
- <h4>Desafiantes modos de juego y misiones</h4>
-<p>Una tercera característica de CarX Highway Racing son sus desafiantes modos de juego y misiones. El juego ofrece cuatro modos de juego para poner a prueba tus habilidades y divertirse. En el modo carrera, puedes seguir la historia de un corredor que quiere convertirse en el mejor del mundo. Puedes competir contra diferentes rivales y jefes, y ganar dinero y reputación. En el modo de ataque de tiempo, puede correr contra el reloj y tratar de batir sus propios registros. En el modo de persecución policial, puedes escapar de la policía o perseguir a los criminales. En el modo online, puedes competir contra otros jugadores de todo el mundo y mostrar tus habilidades. El juego también ofrece varias misiones y misiones que requieren que usted complete ciertos objetivos o tareas dentro de un límite de tiempo o distancia dada. Puedes ganar recompensas como dinero, monedas de oro, llaves o cofres para completar estas misiones. </p>
-<p></p>
- <h2>¿Qué es Happymod? </h2>
-<p>Happymod es un sitio web que proporciona versiones modificadas de juegos populares para Android de forma gratuita. Una versión modificada es una versión del juego que ha sido modificada por jugadores o fans para cambiar o mejorar algunos aspectos del juego, como gráficos, jugabilidad, características o contenido. Los mods pueden hacer el juego más divertido, desafiante, realista o inmersivo, dependiendo de las preferencias del modder y el jugador. Los mods también pueden corregir errores, errores o fallos que los desarrolladores de juegos originales no abordaron.   <h3>Beneficios de usar Happymod</h3>
-<h4>Descargas gratuitas y seguras</h4>
-
- <h4>Versiones modificadas de juegos populares</h4>
-<p>Otro beneficio de usar Happymod es que proporciona versiones modificadas de juegos populares que puede que no encuentre en otros lugares. Puedes encontrar mods para juegos como Minecraft, GTA, PUBG, Among Us, Roblox, y muchos más. Puedes disfrutar de estos juegos con recursos ilimitados, funciones desbloqueadas, artículos premium y otras ventajas que los juegos originales no tienen. También puedes descubrir nuevos juegos y géneros que quizás no hayas probado antes. </p>
- <h4>Interfaz fácil de usar y comunidad</h4>
-<p>Un tercer beneficio de usar Happymod es que tiene una interfaz fácil de usar y una comunidad. El sitio web es fácil de navegar y buscar, con categorías, etiquetas, filtros y recomendaciones para ayudarle a encontrar los mods que desee. También puede subir sus propios mods o solicitar mods de otros usuarios. También puedes unirte a la comunidad de Happymod y chatear con otros modders y jugadores, compartir tus comentarios, sugerencias, consejos y trucos, y hacer nuevos amigos. </p>
- <h2>¿Cómo descargar e instalar CarX Highway Racing Mod APK Happymod? </h2>
-<p>Si usted está interesado en descargar e instalar CarX Highway Racing Mod APK Happymod en su dispositivo, es necesario seguir estos sencillos pasos:</p>
- <h3>Pasos a seguir</h3>
-<h4>Habilitar fuentes desconocidas en su dispositivo</h4>
-<p>El primer paso es habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo > seguridad > fuentes desconocidas > habilitar. Esto puede variar dependiendo del modelo de dispositivo y la versión de Android. </p>
- <h4>Descargar el archivo mod apk desde el sitio web de Happymod</h4>
-
- <h4>Instalar el archivo apk mod y disfrutar del juego</h4>
-<p>El tercer paso es instalar el archivo apk mod y disfrutar del juego. Para hacer esto, ir a su gestor de archivos y localizar el archivo apk mod descargado. Toca en él y sigue las instrucciones de instalación. Una vez completada la instalación, puedes iniciar el juego desde el cajón de la app o la pantalla de inicio. Verás que tienes dinero ilimitado, coches desbloqueados y más en el juego. ¡Disfruta! </p>
- <h2>Conclusión</h2>
-<p>En conclusión, CarX Highway Racing Mod APK Happymod es una gran manera de disfrutar de CarX Highway Racing juego con más diversión y emoción. Usted puede experimentar la física realista y gráficos, diversos coches y pistas, desafiantes modos de juego y misiones, y más con esta versión modificada del juego. También puede descargar e instalar este mod de forma fácil y segura desde el sitio web de Happymod, que ofrece descargas gratuitas y seguras de juegos modificados para dispositivos Android. Si usted es un fan de los juegos de carreras de coches, usted debe probar definitivamente CarX Highway Racing Mod APK Happymod.</p>
- <h3>Preguntas frecuentes</h3>
-<ul>
-<li><b>¿Qué es CarX Highway Racing Mod APK Happymod? </b></li>
-<li>A: Es una versión modificada del juego CarX Highway Racing que ofrece dinero ilimitado, coches desbloqueados y más. </li>
-<li><b>¿Qué es Happymod? </b></li>
-<li>A: Es un sitio web que proporciona versiones modificadas de juegos populares para Android de forma gratuita. </li>
-<li><b>Cómo descargar e instalar CarX Highway Racing Mod APK Happymod? </b></ li>A: Necesita habilitar fuentes desconocidas en su dispositivo, descargar el archivo apk mod del sitio web Happymod e instalar el archivo apk mod en su dispositivo. </li>
-<li><b> ¿Es CarX Highway Racing Mod APK Happymod seguro de usar? </b></li>
-<li>A: Sí, es seguro de usar. Happymod verifica y prueba cada mod antes de subirlo al sitio web, y solo permite mods que son seguros y funcionan. </li>
-<li><b>¿Cuáles son las ventajas de usar CarX Highway Racing Mod APK Happymod? </b></li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/_jaraco_text.py

@@ -1,109 +0,0 @@
-"""Functions brought over from jaraco.text.
-
-These functions are not supposed to be used within `pip._internal`. These are
-helper functions brought over from `jaraco.text` to enable vendoring newer
-copies of `pkg_resources` without having to vendor `jaraco.text` and its entire
-dependency cone; something that our vendoring setup is not currently capable of
-handling.
-
-License reproduced from original source below:
-
-Copyright Jason R. Coombs
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to
-deal in the Software without restriction, including without limitation the
-rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
-sell copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
-IN THE SOFTWARE.
-"""
-
-import functools
-import itertools
-
-
-def _nonblank(str):
-    return str and not str.startswith("#")
-
-
-@functools.singledispatch
-def yield_lines(iterable):
-    r"""
-    Yield valid lines of a string or iterable.
-
-    >>> list(yield_lines(''))
-    []
-    >>> list(yield_lines(['foo', 'bar']))
-    ['foo', 'bar']
-    >>> list(yield_lines('foo\nbar'))
-    ['foo', 'bar']
-    >>> list(yield_lines('\nfoo\n#bar\nbaz #comment'))
-    ['foo', 'baz #comment']
-    >>> list(yield_lines(['foo\nbar', 'baz', 'bing\n\n\n']))
-    ['foo', 'bar', 'baz', 'bing']
-    """
-    return itertools.chain.from_iterable(map(yield_lines, iterable))
-
-
-@yield_lines.register(str)
-def _(text):
-    return filter(_nonblank, map(str.strip, text.splitlines()))
-
-
-def drop_comment(line):
-    """
-    Drop comments.
-
-    >>> drop_comment('foo # bar')
-    'foo'
-
-    A hash without a space may be in a URL.
-
-    >>> drop_comment('http://example.com/foo#bar')
-    'http://example.com/foo#bar'
-    """
-    return line.partition(" #")[0]
-
-
-def join_continuation(lines):
-    r"""
-    Join lines continued by a trailing backslash.
-
-    >>> list(join_continuation(['foo \\', 'bar', 'baz']))
-    ['foobar', 'baz']
-    >>> list(join_continuation(['foo \\', 'bar', 'baz']))
-    ['foobar', 'baz']
-    >>> list(join_continuation(['foo \\', 'bar \\', 'baz']))
-    ['foobarbaz']
-
-    Not sure why, but...
-    The character preceeding the backslash is also elided.
-
-    >>> list(join_continuation(['goo\\', 'dly']))
-    ['godly']
-
-    A terrible idea, but...
-    If no line is available to continue, suppress the lines.
-
-    >>> list(join_continuation(['foo', 'bar\\', 'baz\\']))
-    ['foo']
-    """
-    lines = iter(lines)
-    for item in lines:
-        while item.endswith("\\"):
-            try:
-                item = item[:-2].strip() + next(lines)
-            except StopIteration:
-                return
-        yield item

spaces/Bostoncake/ChatAssistant/app.py

@@ -1,146 +0,0 @@
-import numpy as np
-import os
-import re
-from io import BytesIO
-import datetime
-import time
-import openai, tenacity
-import argparse
-import configparser
-import json
-import tiktoken
-import PyPDF2
-import gradio
-
-# 定义Reviewer类
-class Reviewer:
-    # 初始化方法，设置属性
-    def __init__(self, api, research_field, question, paper_pdf, language):
-        self.api = api
-        self.research_field = research_field
-        self.question = question
-
-        self.language = language
-        self.paper_pdf = paper_pdf
-        self.max_token_num = 4097
-        self.encoding = tiktoken.get_encoding("gpt2")
-
-
-    def review_by_chatgpt(self, paper_list):
-        text = self.extract_chapter(self.paper_pdf)
-        chat_review_text, total_token_used = self.chat_review(text=text)            
-        return chat_review_text, total_token_used
-
-   
-
-    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
-                    stop=tenacity.stop_after_attempt(5),
-                    reraise=True)
-    def chat_review(self, text):
-        openai.api_key = self.api   # 读取api
-        review_prompt_token = 1000        
-        text_token = len(self.encoding.encode(text))
-        input_text_index = int(len(text)*(self.max_token_num-review_prompt_token)/(text_token+1))
-        input_text = "This is the paper you are asked to read:" + text[:input_text_index]
-        input_text = input_text + "The question from your student is: " + self.question
-        messages=[
-                {"role": "system", "content": "You are a professional researcher in the field of "+self.research_field+". You are the mentor of a student who is new to this field. Now I will give you a paper. You need to help your student to read this paper by instructing him to read the important sections in this paper and answer his questions towards these sections. Please answer in {}.".format(self.language)},
-                {"role": "user", "content": input_text},
-            ]
-                
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages,
-        )
-        result = ''
-        for choice in response.choices:
-            result += choice.message.content
-        print("********"*10)
-        print(result)
-        print("********"*10)
-        print("prompt_token_used:", response.usage.prompt_tokens)
-        print("completion_token_used:", response.usage.completion_tokens)
-        print("total_token_used:", response.usage.total_tokens)
-        print("response_time:", response.response_ms/1000.0, 's')                    
-        return result, response.usage.total_tokens        
-
-    def extract_chapter(self, pdf_path):
-        file_object = BytesIO(pdf_path)
-        # 创建一个PDF阅读器对象
-        pdf_reader = PyPDF2.PdfReader(file_object)
-        # 获取PDF的总页数
-        num_pages = len(pdf_reader.pages)
-        # 初始化提取状态和提取文本
-        extraction_started = False
-        extracted_text = ""
-        # 遍历PDF中的每一页
-        for page_number in range(num_pages):
-            page = pdf_reader.pages[page_number]
-            page_text = page.extract_text()
-
-            # 如果找到了章节标题，开始提取
-            if 'Abstract'.lower() in page_text.lower() and not extraction_started:
-                extraction_started = True
-                page_number_start = page_number
-            # 如果提取已开始，将页面文本添加到提取文本中
-            if extraction_started:
-                extracted_text += page_text
-                # 如果找到下一章节标题，停止提取
-                if page_number_start + 1 < page_number:
-                    break
-        return extracted_text
-
-def main(api, research_field, question, paper_pdf, language):  
-    start_time = time.time()
-    if not api or not research_field or not question or not paper_pdf:
-        return "请输入完整内容！"
-    # 判断PDF文件
-    else:
-        # 创建一个Reader对象
-        reviewer1 = Reviewer(api, research_field, question, paper_pdf, language)
-        # 开始判断是路径还是文件：   
-        comments, total_token_used = reviewer1.review_by_chatgpt(paper_list=paper_pdf)
-    time_used = time.time() - start_time
-    output2 ="使用token数："+ str(total_token_used)+"\n花费时间："+ str(round(time_used, 2)) +"秒"
-    return comments, output2
-        
-
-
-########################################################################################################    
-# 标题
-title = "ChatAssistant: ChatGPT论文阅读助手"
-# 描述
-
-description = '''<div align='left'>
-<strong>ChatAssistant是一款基于ChatGPT-3.5的API开发的论文阅读助手。</strong>其用途如下：
-⭐️针对用户对论文的内容所提出的问题，给出相关的解答或学习建议。
-（[获取Api Key](https://chatgpt.cn.obiscr.com/blog/posts/2023/How-to-get-api-key/)）
-</div>
-'''
-
-# 创建Gradio界���
-inp = [gradio.inputs.Textbox(label="请输入你的API-key(sk开头的字符串)",
-                          default="",
-                          type='password'),
-    gradio.inputs.Textbox(lines=3,
-        label="请输入论文的研究方向（语言和输出语言一致）",
-        default="""eg. computer science, artificial intelligence and transfer learning"""
-    ),
-    gradio.inputs.Textbox(lines=3,
-        label="请输入你的问题（语言和输出语言一致）。请尽可能地在问题之后概括你想要得到的输出的回答方向。",
-        default="""eg. What are the main contributions of this article? Please summarize the technical details in your reply as well."""
-    ),
-    gradio.inputs.File(label="请上传论文PDF(必填)",type="bytes"),
-    gradio.inputs.Radio(choices=["English", "Chinese"],
-                        default="English",
-                        label="选择输出语言"),
-]
-
-chat_assistant_gui = gradio.Interface(fn=main,
-                                 inputs=inp,
-                                 outputs = [gradio.Textbox(lines=25, label="参考回答"), gradio.Textbox(lines=2, label="资源统计")],
-                                 title=title,
-                                 description=description)
-
-# Start server
-chat_assistant_gui.launch(quiet=True, show_api=False)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/common.py

@@ -1,147 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import numpy as np
-import pickle
-import random
-import torch.utils.data as data
-
-from detectron2.utils.serialize import PicklableWrapper
-
-__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset"]
-
-
-class MapDataset(data.Dataset):
-    """
-    Map a function over the elements in a dataset.
-
-    Args:
-        dataset: a dataset where map function is applied.
-        map_func: a callable which maps the element in dataset. map_func is
-            responsible for error handling, when error happens, it needs to
-            return None so the MapDataset will randomly use other
-            elements from the dataset.
-    """
-
-    def __init__(self, dataset, map_func):
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-        self._rng = random.Random(42)
-        self._fallback_candidates = set(range(len(dataset)))
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __getitem__(self, idx):
-        retry_count = 0
-        cur_idx = int(idx)
-
-        while True:
-            data = self._map_func(self._dataset[cur_idx])
-            if data is not None:
-                self._fallback_candidates.add(cur_idx)
-                return data
-
-            # _map_func fails for this idx, use a random new index from the pool
-            retry_count += 1
-            self._fallback_candidates.discard(cur_idx)
-            cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
-
-            if retry_count >= 3:
-                logger = logging.getLogger(__name__)
-                logger.warning(
-                    "Failed to apply `_map_func` for idx: {}, retry count: {}".format(
-                        idx, retry_count
-                    )
-                )
-
-
-class DatasetFromList(data.Dataset):
-    """
-    Wrap a list to a torch Dataset. It produces elements of the list as data.
-    """
-
-    def __init__(self, lst: list, copy: bool = True, serialize: bool = True):
-        """
-        Args:
-            lst (list): a list which contains elements to produce.
-            copy (bool): whether to deepcopy the element when producing it,
-                so that the result can be modified in place without affecting the
-                source in the list.
-            serialize (bool): whether to hold memory using serialized objects, when
-                enabled, data loader workers can use shared RAM from master
-                process instead of making a copy.
-        """
-        self._lst = lst
-        self._copy = copy
-        self._serialize = serialize
-
-        def _serialize(data):
-            buffer = pickle.dumps(data, protocol=-1)
-            return np.frombuffer(buffer, dtype=np.uint8)
-
-        if self._serialize:
-            logger = logging.getLogger(__name__)
-            logger.info(
-                "Serializing {} elements to byte tensors and concatenating them all ...".format(
-                    len(self._lst)
-                )
-            )
-            self._lst = [_serialize(x) for x in self._lst]
-            self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
-            self._addr = np.cumsum(self._addr)
-            self._lst = np.concatenate(self._lst)
-            logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2))
-
-    def __len__(self):
-        if self._serialize:
-            return len(self._addr)
-        else:
-            return len(self._lst)
-
-    def __getitem__(self, idx):
-        if self._serialize:
-            start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
-            end_addr = self._addr[idx].item()
-            bytes = memoryview(self._lst[start_addr:end_addr])
-            return pickle.loads(bytes)
-        elif self._copy:
-            return copy.deepcopy(self._lst[idx])
-        else:
-            return self._lst[idx]
-
-
-class AspectRatioGroupedDataset(data.IterableDataset):
-    """
-    Batch data that have similar aspect ratio together.
-    In this implementation, images whose aspect ratio < (or >) 1 will
-    be batched together.
-
-    It assumes the underlying dataset produces dicts with "width" and "height" keys.
-    It will then produce a list of original dicts with length = batch_size,
-    all with similar aspect ratios.
-    """
-
-    def __init__(self, dataset, batch_size):
-        """
-        Args:
-            dataset: an iterable. Each element must be a dict with keys
-                "width" and "height", which will be used to batch data.
-            batch_size (int):
-        """
-        self.dataset = dataset
-        self.batch_size = batch_size
-        self._buckets = [[] for _ in range(2)]
-        # Hard-coded two aspect ratio groups: w > h and w < h.
-        # Can add support for more aspect ratio groups, but doesn't seem useful
-
-    def __iter__(self):
-        for d in self.dataset:
-            w, h = d["width"], d["height"]
-            bucket_id = 0 if w > h else 1
-            bucket = self._buckets[bucket_id]
-            bucket.append(d)
-            if len(bucket) == self.batch_size:
-                yield bucket[:]
-                del bucket[:]

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/rotated_boxes.py

@@ -1,23 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-# import torch
-from detectron2 import _C
-
-
-def pairwise_iou_rotated(boxes1, boxes2):
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-
-    Both sets of boxes are expected to be in
-    (x_center, y_center, width, height, angle) format.
-
-    Arguments:
-        boxes1 (Tensor[N, 5])
-        boxes2 (Tensor[M, 5])
-
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-    return _C.box_iou_rotated(boxes1, boxes2)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/test_time_augmentation.py

@@ -1,285 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import numpy as np
-from contextlib import contextmanager
-from itertools import count
-import torch
-from torch import nn
-from torch.nn.parallel import DistributedDataParallel
-
-from detectron2.data.detection_utils import read_image
-from detectron2.data.transforms import ResizeShortestEdge
-from detectron2.structures import Instances
-
-from .meta_arch import GeneralizedRCNN
-from .postprocessing import detector_postprocess
-from .roi_heads.fast_rcnn import fast_rcnn_inference_single_image
-
-__all__ = ["DatasetMapperTTA", "GeneralizedRCNNWithTTA"]
-
-
-class DatasetMapperTTA:
-    """
-    Implement test-time augmentation for detection data.
-    It is a callable which takes a dataset dict from a detection dataset,
-    and returns a list of dataset dicts where the images
-    are augmented from the input image by the transformations defined in the config.
-    This is used for test-time augmentation.
-    """
-
-    def __init__(self, cfg):
-        self.min_sizes = cfg.TEST.AUG.MIN_SIZES
-        self.max_size = cfg.TEST.AUG.MAX_SIZE
-        self.flip = cfg.TEST.AUG.FLIP
-        self.image_format = cfg.INPUT.FORMAT
-
-    def __call__(self, dataset_dict):
-        """
-        Args:
-            dict: a detection dataset dict
-
-        Returns:
-            list[dict]:
-                a list of dataset dicts, which contain augmented version of the input image.
-                The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
-        """
-        ret = []
-        if "image" not in dataset_dict:
-            numpy_image = read_image(dataset_dict["file_name"], self.image_format)
-        else:
-            numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy().astype("uint8")
-        for min_size in self.min_sizes:
-            image = np.copy(numpy_image)
-            tfm = ResizeShortestEdge(min_size, self.max_size).get_transform(image)
-            resized = tfm.apply_image(image)
-            resized = torch.as_tensor(resized.transpose(2, 0, 1).astype("float32"))
-
-            dic = copy.deepcopy(dataset_dict)
-            dic["horiz_flip"] = False
-            dic["image"] = resized
-            ret.append(dic)
-
-            if self.flip:
-                dic = copy.deepcopy(dataset_dict)
-                dic["horiz_flip"] = True
-                dic["image"] = torch.flip(resized, dims=[2])
-                ret.append(dic)
-        return ret
-
-
-class GeneralizedRCNNWithTTA(nn.Module):
-    """
-    A GeneralizedRCNN with test-time augmentation enabled.
-    Its :meth:`__call__` method has the same interface as :meth:`GeneralizedRCNN.forward`.
-    """
-
-    def __init__(self, cfg, model, tta_mapper=None, batch_size=3):
-        """
-        Args:
-            cfg (CfgNode):
-            model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
-            tta_mapper (callable): takes a dataset dict and returns a list of
-                augmented versions of the dataset dict. Defaults to
-                `DatasetMapperTTA(cfg)`.
-            batch_size (int): batch the augmented images into this batch size for inference.
-        """
-        super().__init__()
-        if isinstance(model, DistributedDataParallel):
-            model = model.module
-        assert isinstance(
-            model, GeneralizedRCNN
-        ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model))
-        self.cfg = cfg.clone()
-        assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet"
-        assert (
-            not self.cfg.MODEL.LOAD_PROPOSALS
-        ), "TTA for pre-computed proposals is not supported yet"
-
-        self.model = model
-
-        if tta_mapper is None:
-            tta_mapper = DatasetMapperTTA(cfg)
-        self.tta_mapper = tta_mapper
-        self.batch_size = batch_size
-
-    @contextmanager
-    def _turn_off_roi_heads(self, attrs):
-        """
-        Open a context where some heads in `model.roi_heads` are temporarily turned off.
-        Args:
-            attr (list[str]): the attribute in `model.roi_heads` which can be used
-                to turn off a specific head, e.g., "mask_on", "keypoint_on".
-        """
-        roi_heads = self.model.roi_heads
-        old = {}
-        for attr in attrs:
-            try:
-                old[attr] = getattr(roi_heads, attr)
-            except AttributeError:
-                # The head may not be implemented in certain ROIHeads
-                pass
-
-        if len(old.keys()) == 0:
-            yield
-        else:
-            for attr in old.keys():
-                setattr(roi_heads, attr, False)
-            yield
-            for attr in old.keys():
-                setattr(roi_heads, attr, old[attr])
-
-    def _batch_inference(self, batched_inputs, detected_instances=None, do_postprocess=True):
-        """
-        Execute inference on a list of inputs,
-        using batch size = self.batch_size, instead of the length of the list.
-
-        Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference`
-        """
-        if detected_instances is None:
-            detected_instances = [None] * len(batched_inputs)
-
-        outputs = []
-        inputs, instances = [], []
-        for idx, input, instance in zip(count(), batched_inputs, detected_instances):
-            inputs.append(input)
-            instances.append(instance)
-            if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1:
-                outputs.extend(
-                    self.model.inference(
-                        inputs,
-                        instances if instances[0] is not None else None,
-                        do_postprocess=do_postprocess,
-                    )
-                )
-                inputs, instances = [], []
-        return outputs
-
-    def __call__(self, batched_inputs):
-        """
-        Same input/output format as :meth:`GeneralizedRCNN.forward`
-        """
-        return [self._inference_one_image(x) for x in batched_inputs]
-
-    def _detector_postprocess(self, outputs, aug_vars):
-        return detector_postprocess(outputs, aug_vars["height"], aug_vars["width"])
-
-    def _inference_one_image(self, input):
-        """
-        Args:
-            input (dict): one dataset dict
-
-        Returns:
-            dict: one output dict
-        """
-
-        augmented_inputs, aug_vars = self._get_augmented_inputs(input)
-        # Detect boxes from all augmented versions
-        with self._turn_off_roi_heads(["mask_on", "keypoint_on"]):
-            # temporarily disable roi heads
-            all_boxes, all_scores, all_classes = self._get_augmented_boxes(
-                augmented_inputs, aug_vars
-            )
-        merged_instances = self._merge_detections(
-            all_boxes, all_scores, all_classes, (aug_vars["height"], aug_vars["width"])
-        )
-
-        if self.cfg.MODEL.MASK_ON:
-            # Use the detected boxes to obtain new fields
-            augmented_instances = self._rescale_detected_boxes(
-                augmented_inputs, merged_instances, aug_vars
-            )
-            # run forward on the detected boxes
-            outputs = self._batch_inference(
-                augmented_inputs, augmented_instances, do_postprocess=False
-            )
-            # Delete now useless variables to avoid being out of memory
-            del augmented_inputs, augmented_instances, merged_instances
-            # average the predictions
-            outputs[0].pred_masks = self._reduce_pred_masks(outputs, aug_vars)
-            # postprocess
-            output = self._detector_postprocess(outputs[0], aug_vars)
-            return {"instances": output}
-        else:
-            return {"instances": merged_instances}
-
-    def _get_augmented_inputs(self, input):
-        augmented_inputs = self.tta_mapper(input)
-
-        do_hflip = [k.pop("horiz_flip", False) for k in augmented_inputs]
-        heights = [k["height"] for k in augmented_inputs]
-        widths = [k["width"] for k in augmented_inputs]
-        assert (
-            len(set(heights)) == 1 and len(set(widths)) == 1
-        ), "Augmented version of the inputs should have the same original resolution!"
-        height = heights[0]
-        width = widths[0]
-        aug_vars = {"height": height, "width": width, "do_hflip": do_hflip}
-
-        return augmented_inputs, aug_vars
-
-    def _get_augmented_boxes(self, augmented_inputs, aug_vars):
-        # 1: forward with all augmented images
-        outputs = self._batch_inference(augmented_inputs, do_postprocess=False)
-        # 2: union the results
-        all_boxes = []
-        all_scores = []
-        all_classes = []
-        for idx, output in enumerate(outputs):
-            rescaled_output = self._detector_postprocess(output, aug_vars)
-            pred_boxes = rescaled_output.pred_boxes.tensor
-            if aug_vars["do_hflip"][idx]:
-                pred_boxes[:, [0, 2]] = aug_vars["width"] - pred_boxes[:, [2, 0]]
-            all_boxes.append(pred_boxes)
-            all_scores.extend(rescaled_output.scores)
-            all_classes.extend(rescaled_output.pred_classes)
-        all_boxes = torch.cat(all_boxes, dim=0).cpu()
-        return all_boxes, all_scores, all_classes
-
-    def _merge_detections(self, all_boxes, all_scores, all_classes, shape_hw):
-        # select from the union of all results
-        num_boxes = len(all_boxes)
-        num_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES
-        # +1 because fast_rcnn_inference expects background scores as well
-        all_scores_2d = torch.zeros(num_boxes, num_classes + 1, device=all_boxes.device)
-        for idx, cls, score in zip(count(), all_classes, all_scores):
-            all_scores_2d[idx, cls] = score
-
-        merged_instances, _ = fast_rcnn_inference_single_image(
-            all_boxes,
-            all_scores_2d,
-            shape_hw,
-            1e-8,
-            self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
-            self.cfg.TEST.DETECTIONS_PER_IMAGE,
-        )
-
-        return merged_instances
-
-    def _rescale_detected_boxes(self, augmented_inputs, merged_instances, aug_vars):
-        augmented_instances = []
-        for idx, input in enumerate(augmented_inputs):
-            actual_height, actual_width = input["image"].shape[1:3]
-            scale_x = actual_width * 1.0 / aug_vars["width"]
-            scale_y = actual_height * 1.0 / aug_vars["height"]
-            pred_boxes = merged_instances.pred_boxes.clone()
-            pred_boxes.tensor[:, 0::2] *= scale_x
-            pred_boxes.tensor[:, 1::2] *= scale_y
-            if aug_vars["do_hflip"][idx]:
-                pred_boxes.tensor[:, [0, 2]] = actual_width - pred_boxes.tensor[:, [2, 0]]
-
-            aug_instances = Instances(
-                image_size=(actual_height, actual_width),
-                pred_boxes=pred_boxes,
-                pred_classes=merged_instances.pred_classes,
-                scores=merged_instances.scores,
-            )
-            augmented_instances.append(aug_instances)
-        return augmented_instances
-
-    def _reduce_pred_masks(self, outputs, aug_vars):
-        for idx, output in enumerate(outputs):
-            if aug_vars["do_hflip"][idx]:
-                output.pred_masks = output.pred_masks.flip(dims=[3])
-        all_pred_masks = torch.stack([o.pred_masks for o in outputs], dim=0)
-        avg_pred_masks = torch.mean(all_pred_masks, dim=0)
-        return avg_pred_masks