diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/graphql/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/graphql/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/__init__.py
deleted file mode 100644
index 00f7f75d0e33d106a514a98fdb20234fbf80d6c2..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/__init__.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import string
-import time
-import re
-import json
-import requests
-import fake_useragent
-import random
-from password_generator import PasswordGenerator
-
-from .utils import create_email, check_email
-
-
-class Account:
-    @staticmethod
-    def create(logging: bool = False):
-        is_custom_domain = input(
-            "Do you want to use your custom domain name for temporary email? [Y/n]: "
-        ).upper()
-
-        if is_custom_domain == "Y":
-            mail_address = create_email(custom_domain=True, logging=logging)
-        elif is_custom_domain == "N":
-            mail_address = create_email(custom_domain=False, logging=logging)
-        else:
-            print("Please, enter either Y or N")
-            return
-
-        name = string.ascii_lowercase + string.digits
-        username = "".join(random.choice(name) for i in range(20))
-
-        pwo = PasswordGenerator()
-        pwo.minlen = 8
-        password = pwo.generate()
-
-        session = requests.Session()
-
-        register_url = "https://ai.usesless.com/api/cms/auth/local/register"
-        register_json = {
-            "username": username,
-            "password": password,
-            "email": mail_address,
-        }
-        headers = {
-            "authority": "ai.usesless.com",
-            "accept": "application/json, text/plain, */*",
-            "accept-language": "en-US,en;q=0.5",
-            "cache-control": "no-cache",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": fake_useragent.UserAgent().random,
-        }
-        register = session.post(register_url, json=register_json, headers=headers)
-        if logging:
-            if register.status_code == 200:
-                print("Registered successfully")
-            else:
-                print(register.status_code)
-                print(register.json())
-                print("There was a problem with account registration, try again")
-
-        if register.status_code != 200:
-            quit()
-
-        while True:
-            time.sleep(5)
-            messages = check_email(mail=mail_address, logging=logging)
-
-            # Check if method `message_list()` didn't return None or empty list.
-            if not messages or len(messages) == 0:
-                # If it returned None or empty list sleep for 5 seconds to wait for new message.
-                continue
-
-            message_text = messages[0]["content"]
-            verification_url = re.findall(
-                r"http:\/\/ai\.usesless\.com\/api\/cms\/auth\/email-confirmation\?confirmation=\w.+\w\w",
-                message_text,
-            )[0]
-            if verification_url:
-                break
-
-        session.get(verification_url)
-        login_json = {"identifier": mail_address, "password": password}
-        login_request = session.post(
-            url="https://ai.usesless.com/api/cms/auth/local", json=login_json
-        )
-
-        token = login_request.json()["jwt"]
-        if logging and token:
-            print(f"Token: {token}")
-
-        with open("account.json", "w") as file:
-            json.dump({"email": mail_address, "token": token}, file)
-            if logging:
-                print(
-                    "\nNew account credentials has been successfully saved in 'account.json' file"
-                )
-
-        return token
-
-
-class Completion:
-    @staticmethod
-    def create(
-        token: str,
-        systemMessage: str = "You are a helpful assistant",
-        prompt: str = "",
-        parentMessageId: str = "",
-        presence_penalty: float = 1,
-        temperature: float = 1,
-        model: str = "gpt-3.5-turbo",
-    ):
-        headers = {
-            "authority": "ai.usesless.com",
-            "accept": "application/json, text/plain, */*",
-            "accept-language": "en-US,en;q=0.5",
-            "cache-control": "no-cache",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": fake_useragent.UserAgent().random,
-            "Authorization": f"Bearer {token}",
-        }
-
-        json_data = {
-            "openaiKey": "",
-            "prompt": prompt,
-            "options": {
-                "parentMessageId": parentMessageId,
-                "systemMessage": systemMessage,
-                "completionParams": {
-                    "presence_penalty": presence_penalty,
-                    "temperature": temperature,
-                    "model": model,
-                },
-            },
-        }
-
-        url = "https://ai.usesless.com/api/chat-process"
-        request = requests.post(url, headers=headers, json=json_data)
-        request.encoding = request.apparent_encoding
-        content = request.content
-
-        response = Completion.__response_to_json(content)
-        return response
-
-
-    @classmethod
-    def __response_to_json(cls, text) -> str:
-        text = str(text.decode("utf-8"))
-
-        split_text = text.rsplit("\n", 1)
-        if len(split_text) > 1:
-            to_json = json.loads(split_text[1])
-            return to_json
-        else:
-            return None
-
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2009 Xforce Keygen 32 Bit Free.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2009 Xforce Keygen 32 Bit Free.md
deleted file mode 100644
index e2c465cdcc37cdd6fbf70493ff42974563fbd1dd..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2009 Xforce Keygen 32 Bit Free.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>How to Activate AutoCAD 2009 with Xforce Keygen 32 Bit</h1>
-<p>AutoCAD 2009 is a popular software for designing and drafting in various fields, such as architecture, engineering, construction and more. However, to use AutoCAD 2009, you need to activate it with a valid license key. Otherwise, you will not be able to access all the features and functions of the software.</p>
-<p>One way to activate AutoCAD 2009 is to use Xforce keygen 32 bit. Xforce keygen is a tool that can generate license keys for various software products, including AutoCAD 2009. Xforce keygen 32 bit is compatible with Windows 32 bit operating systems.</p>
-<h2>autocad 2009 xforce keygen 32 bit</h2><br /><p><b><b>Download</b> &#127379; <a href="https://byltly.com/2uKzSY">https://byltly.com/2uKzSY</a></b></p><br /><br />
-<p>In this article, we will show you how to activate AutoCAD 2009 with Xforce keygen 32 bit in a few simple steps.</p>
-<h2>Step 1: Download and install AutoCAD 2009</h2>
-<p>The first step is to download and install AutoCAD 2009 on your computer. You can download AutoCAD 2009 from the official website of Autodesk or from other sources. Make sure you download the correct version for your operating system (32 bit or 64 bit).</p>
-<p>To install AutoCAD 2009, follow the installation wizard and accept the terms and conditions. When prompted for a serial number and a product key, enter any numbers you want. You will activate them later with Xforce keygen.</p>
-<h2>Step 2: Download and run Xforce keygen 32 bit</h2>
-<p>The next step is to download and run Xforce keygen 32 bit. You can download Xforce keygen 32 bit from various websites or torrent sites. However, be careful of viruses and malware that may harm your computer. Always scan the files before opening them.</p>
-<p>To run Xforce keygen 32 bit, right-click on the file and select "Run as administrator". A window will open with a list of software products. Select "AutoCAD 2009" from the list and click on "Generate". A license key will be generated for you.</p>
-<h2>Step 3: Activate AutoCAD 2009 with the license key</h2>
-<p>The final step is to activate AutoCAD 2009 with the license key generated by Xforce keygen. To do this, open AutoCAD 2009 and click on "Activate" in the upper right corner. A window will pop up asking you to enter the serial number and the product key.</p>
-<p></p>
-<p>Enter the serial number and the product key that you entered during the installation process. Then click on "Next". Another window will appear asking you to select an activation method. Choose "I have an activation code from Autodesk" and click on "Next".</p>
-<p>A request code will be displayed on the screen. Copy this code and paste it into Xforce keygen in the "Request" field. Then click on "Calculate". An activation code will be generated for you. Copy this code and paste it into AutoCAD in the "Activation" field. Then click on "Next".</p>
-<p>Congratulations! You have successfully activated AutoCAD 2009 with Xforce keygen 32 bit. You can now enjoy all the features and functions of AutoCAD 2009 without any limitations.</p>
-<h2>Conclusion</h2>
-<p>Activating AutoCAD 2009 with Xforce keygen 32 bit is a simple and easy process that can save you time and money. However, it is not a legal or ethical way to use AutoCAD 2009. Xforce keygen is a crack tool that violates the terms and conditions of Autodesk. Using it may expose you to legal risks and security threats.</p>
-<p>If you want to use AutoCAD 2009 legally and safely, we recommend you to buy a genuine license from Autodesk or its authorized dealers. This way, you can support the developers of AutoCAD and get access to technical support and updates.</p> ddb901b051<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Biztree Business in a Box Product Key Crack The Ultimate Solution for Your Business Needs.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Biztree Business in a Box Product Key Crack The Ultimate Solution for Your Business Needs.md
deleted file mode 100644
index 183e9bc96381facc4c210335749f2f0011cb08f5..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Biztree Business in a Box Product Key Crack The Ultimate Solution for Your Business Needs.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-<br />
-<h1>Warcraft III: Reign of Chaos No CD Crack Download</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of real-time strategy games, you probably have played or heard of Warcraft III: Reign of Chaos, one of the most popular and influential titles in the genre. Released in 2002 by Blizzard Entertainment, Warcraft III: Reign of Chaos is the third installment in the Warcraft series, which also includes the massively multiplayer online role-playing game World of Warcraft. In this game, you can choose from four different races: Humans, Orcs, Night Elves, and Undead, and lead them in epic battles across the fantasy world of Azeroth.</p>
-<h2>warcraft iii reign of chaos no cd crack download</h2><br /><p><b><b>Download File</b> ->>->>->> <a href="https://byltly.com/2uKyiL">https://byltly.com/2uKyiL</a></b></p><br /><br />
- <p>However, if you want to play Warcraft III: Reign of Chaos on your PC, you will need to have the original CD inserted in your drive every time you launch the game. This can be inconvenient and annoying for many reasons, such as having to swap CDs, wasting disk space, risking damage to your CD, or losing your CD altogether. That's why many players look for a way to play Warcraft III: Reign of Chaos without the CD, by using a no CD crack.</p>
- <p>A no CD crack is a modified version of the game's executable file that bypasses the CD check and allows you to run the game without inserting the CD. In this article, we will show you how to download and install a no CD crack for Warcraft III: Reign of Chaos, as well as the benefits and risks of using it.</p>
- <h2>How to download and install a no CD crack?</h2>
- <p>Before you download and install a no CD crack for Warcraft III: Reign of Chaos, you will need to have the game installed on your PC. You can either use the original CD or download the game from Blizzard's official website. You will also need to update the game to the latest version (1.27b) to ensure compatibility with the no CD crack.</p>
- <p>Once you have the game installed and updated, you can follow these steps to download and install a no CD crack:</p>
- <ol>
-<li>Go to a reputable website that offers no CD cracks for various games, such as GameCopyWorld or MegaGames.</li>
-<li>Search for Warcraft III: Reign of Chaos and find the no CD crack that matches your game version and language.</li>
-<li>Download the no CD crack file and extract it using a program like WinRAR or 7-Zip.</li>
-<li>Copy the extracted file (usually named war3.exe) and paste it into your game folder, where you installed Warcraft III: Reign of Chaos. You may need to overwrite the existing file.</li>
-<li>Run the game from the no CD crack file (war3.exe) and enjoy playing without the CD.</li>
-</ol>
- <h2>Benefits of using a no CD crack</h2>
- <p>Using a no CD crack for Warcraft III: Reign of Chaos can have several benefits for you as a player. Here are some of them:</p>
- <h3>Play without the CD</h3>
- the CD every time you launch the game. This can save you time and hassle, especially if you have multiple games that require CDs. You can also play the game on any PC that has the game installed, without carrying the CD with you.</p>
- <h3>Save disk space</h3>
- <p>Another benefit of using a no CD crack is that you can save disk space on your PC. When you install Warcraft III: Reign of Chaos from the CD, the game will copy some files from the CD to your hard drive. These files can take up several hundred megabytes of space, depending on your game version and language. By using a no CD crack, you can delete these files and free up some space on your hard drive.</p>
- <h3>Avoid scratches and damages</h3>
- <p>A no CD crack can also help you avoid scratches and damages to your CD. CDs are fragile and can be easily scratched or broken by accidents or mishandling. If your CD gets damaged, you may not be able to play the game or install it on another PC. By using a no CD crack, you can protect your CD from wear and tear and keep it in good condition.</p>
- <h3>Backup your game files</h3>
- <p>Finally, a no CD crack can allow you to backup your game files and restore them in case of any problems. Sometimes, your game files may get corrupted or deleted by viruses, malware, or human errors. If this happens, you may lose your progress, settings, or custom maps. By using a no CD crack, you can copy your game folder to another location or device and restore it if needed.</p>
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- <h2>Risks of using a no CD crack</h2>
- <p>While using a no CD crack for Warcraft III: Reign of Chaos can have some benefits, it also comes with some risks that you should be aware of. Here are some of them:</p>
- <h3>Legal issues</h3>
- <p>The first and most important risk of using a no CD crack is that it may be illegal in your country or region. According to Blizzard's End User License Agreement (EULA), you are not allowed to modify, copy, distribute, or reverse engineer the game or any part of it. A no CD crack is considered a modification of the game's executable file and may violate Blizzard's intellectual property rights. If you use a no CD crack, you may face legal consequences such as fines or lawsuits from Blizzard or other authorities.</p>
- <h3>Virus and malware infections</h3>
- <p>Another risk of using a no CD crack is that it may contain viruses or malware that can harm your PC or steal your personal information. Some websites that offer no CD cracks may be malicious or fraudulent and may infect your PC with spyware, ransomware, keyloggers, or other types of malware. These malware can slow down your PC, damage your files, monitor your online activities, or steal your passwords, credit card numbers, or other sensitive data. To avoid this risk, you should only download no CD cracks from reputable and trusted websites and scan them with an antivirus program before installing them.</p>
- <h3>Compatibility and performance problems</h3>
- game's executable file that bypasses the CD check and allows you to run the game without inserting the CD. It can have some benefits, such as playing without the CD, saving disk space, avoiding scratches and damages, and backing up your game files. However, it can also have some risks, such as legal issues, virus and malware infections, compatibility and performance problems, and online multiplayer restrictions.</p>
- <h3>How to download and install a no CD crack for Warcraft III: Reign of Chaos?</h3>
- <p>To download and install a no CD crack for Warcraft III: Reign of Chaos, you will need to have the game installed and updated on your PC. Then, you can follow these steps:</p>
- <ol>
-<li>Go to a reputable website that offers no CD cracks for various games, such as GameCopyWorld or MegaGames.</li>
-<li>Search for Warcraft III: Reign of Chaos and find the no CD crack that matches your game version and language.</li>
-<li>Download the no CD crack file and extract it using a program like WinRAR or 7-Zip.</li>
-<li>Copy the extracted file (usually named war3.exe) and paste it into your game folder, where you installed Warcraft III: Reign of Chaos. You may need to overwrite the existing file.</li>
-<li>Run the game from the no CD crack file (war3.exe) and enjoy playing without the CD.</li>
-</ol>
- <h3>Is using a no CD crack legal?</h3>
- <p>Using a no CD crack may be illegal in your country or region. According to Blizzard's End User License Agreement (EULA), you are not allowed to modify, copy, distribute, or reverse engineer the game or any part of it. A no CD crack is considered a modification of the game's executable file and may violate Blizzard's intellectual property rights. If you use a no CD crack, you may face legal consequences such as fines or lawsuits from Blizzard or other authorities.</p>
- <h3>Can I play online multiplayer mode with a no CD crack?</h3>
- <p>You may not be able to play online multiplayer mode with a no CD crack. Warcraft III: Reign of Chaos has a built-in online multiplayer mode that allows you to play with or against other players on Blizzard's Battle.net service. However, to access this mode, you will need to have a valid CD key that is registered on your Battle.net account. A no CD crack may not work with Battle.net or may be detected as a cheat or hack by Blizzard's anti-cheat system. If this happens, you may not be able to join or host online games or you may get banned from Battle.net permanently. To avoid this risk, you should only use a no CD crack for offline single-player mode and use your original CD and CD key for online multiplayer mode.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargar gratis imagenes movibles para easyworship consejos y recursos para mejorar tus proyecciones.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargar gratis imagenes movibles para easyworship consejos y recursos para mejorar tus proyecciones.md
deleted file mode 100644
index 7c4624627946d8fc17835efbb8554b9012db0c10..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargar gratis imagenes movibles para easyworship consejos y recursos para mejorar tus proyecciones.md	
+++ /dev/null
@@ -1,116 +0,0 @@
-
-<h1>Descargar gratis imagenes movibles para easyworship</h1>
- <p>Si eres una persona que se encarga de preparar y proyectar las presentaciones multimedia en tu iglesia, seguramente te interesa saber cómo puedes descargar gratis imagenes movibles para easyworship. En este artículo te vamos a explicar qué es easyworship, por qué usarlo, dónde encontrar y cómo descargar imagenes movibles para easyworship, cómo usarlas en las presentaciones y qué beneficios tiene usarlas. Así que sigue leyendo y descubre cómo puedes mejorar tus presentaciones con estas imágenes tan especiales.</p>
-<h2>descargar gratis imagenes movibles para easyworship</h2><br /><p><b><b>Download Zip</b> &#10084; <a href="https://byltly.com/2uKvTx">https://byltly.com/2uKvTx</a></b></p><br /><br />
- <h2>¿Qué es easyworship y por qué usarlo?</h2>
- <h3>Easyworship es un programa de presentación multimedia para iglesias</h3>
- <p>Easyworship es un software diseñado especialmente para crear y proyectar presentaciones multimedia en las iglesias. Con este programa puedes combinar imágenes, videos, textos, canciones y otros elementos para crear presentaciones dinámicas y profesionales que acompañen tus cultos, sermones, eventos o actividades. Easyworship es fácil de usar, compatible con varios formatos de archivos y adaptable a diferentes pantallas y proyectores.</p>
- <h3>Easyworship permite crear presentaciones dinámicas y atractivas con imágenes, videos, textos y canciones</h3>
- <p>Con easyworship puedes crear presentaciones que se adapten al tema, al estilo y al público de cada ocasión. Puedes elegir entre una gran variedad de plantillas prediseñadas o crear tus propias plantillas personalizadas. Puedes agregar imágenes, videos, textos y canciones desde tu computadora o desde internet. Puedes editar el contenido, el orden, el tiempo y la transición de cada elemento. Puedes añadir efectos especiales como sombras, reflejos, bordes o animaciones. Puedes sincronizar las letras de las canciones con la música o con los videos. Puedes mostrar versículos bíblicos o citas inspiradoras con diferentes fuentes y colores.</p>
- <h3>Easyworship ofrece una gran variedad de opciones para personalizar y adaptar las presentaciones a cada ocasión</h3>
- <p>Con easyworship puedes personalizar y adaptar tus presentaciones según tus necesidades y preferencias. Puedes configurar el tamaño, la resolución, el brillo y el contraste de la pantalla o del proyector. Puedes ajustar el volumen, el balance y el ecualizador del sonido. Puedes crear listas de reproducción con diferentes presentaciones o elementos. Puedes programar las presentaciones para que se inicien automáticamente o manualmente. Puedes controlar las presentaciones desde tu computadora o desde un dispositivo móvil.</p>
- <h2>¿Dónde encontrar y cómo descargar imagenes movibles para easyworship?</h2>
- <h3>Hay muchas fuentes en internet que ofrecen imagenes movibles para easyworship de forma gratuita</h3>
- <p>Si quieres descargar gratis imagenes movibles para easyworship, puedes encontrar muchas opciones en internet. Hay sitios web que ofrecen cientos o miles de imágenes animadas con diferentes temáticas, estilos y calidades. Algunas imágenes son libres de derechos de autor y otras requieren dar crédito al autor o a la fuente. Algunas imágenes son gratuitas y otras requieren una suscripción o un pago.</p>
- <h3>Algunas de las fuentes más populares son Pinterest, Recursos Bíblicos y EasyWorship Media</h3>
- <p>A continuación te mencionamos algunas de las fuentes más populares donde puedes descargar gratis imagenes movibles para easyworship:</p>
- <ul>
-<li>Pinterest: Es una red social donde puedes encontrar miles de imágenes animadas sobre diversos temas como naturaleza, arte, religión, humor, etc. Solo tienes que buscar "fondos animados para easyworship" o "imagenes movibles para easyworship" en el buscador y verás los resultados. Para descargar una imagen solo tienes que hacer clic derecho sobre ella y elegir la opción "guardar imagen como".</li>
-<li>Recursos Bíblicos: Es un sitio web que ofrece más de 200 imágenes animadas para easyworship con temática cristiana. Estas imágenes están hechas a una resolución apropiada para el programa y pueden ser utilizadas de diversas formas en las presentaciones de la iglesia. Para descargar un paquete de imágenes solo tienes que hacer clic en el enlace "descargar" que aparece debajo de cada imagen.</li>
-<li>EasyWorship Media: Es la tienda oficial de medios de easyworship donde puedes encontrar una gran selección de fondos animados para tus presentaciones. Estas imágenes son creadas por talentosos artistas cristianos que ofrecen su trabajo de forma gratuita o a un precio accesible. Para descargar una imagen solo tienes que hacer clic en el botón "download" que aparece debajo de cada imagen.</li>
-</ul>
- <h3>Para descargar las imagenes movibles para easyworship se debe seguir los pasos que indica cada sitio web</h3>
- <p>Para descargar gratis imagenes movibles para easyworship se debe seguir los pasos que indica cada sitio web donde se encuentran las imágenes. En general estos pasos son:</p>
- <ol>
-<li>Ingresar al sitio web donde se encuentran las imágenes.</li>
-<li>Buscar las imágenes que se desean descargar usando el buscador o navegando por las categorías.</li>
-<li>Seleccionar la imagen que se desea descargar haciendo clic sobre ella.</li>
-<li>Verificar si la imagen tiene algún requisito o condición como dar crédito al autor o pagar una cuota.</li>
-<li>Hacer clic derecho sobre la imagen y elegir la opción "guardar imagen como" o hacer clic en el botón "descargar" si lo tiene.</li>
-<li>Elegir la carpeta donde se desea guardar la imagen en la computadora.</li>
-<li>Repetir el proceso con todas las imágenes que se deseen descargar.</li>
-</ol>
- <h2>¿Cómo usar las imagenes movibles para easyworship en las presentaciones?</h2>
- <h3>Las imagenes movibles para easyworship se pueden usar como fondos, transiciones, ilustraciones o adornos</h3>
- <p>Las imagenes movibles para easyworship se pueden usar de varias formas en las presentaciones dependiendo del efecto que se quiera lograr. Algunas formas son:</p>
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-<p>Para usar las imagenes movibles para easyworship se debe importarlas al programa desde la carpeta donde se guardaron en la computadora. Para importar una imagen se debe hacer clic en el botón "import" que aparece en la parte superior del panel de medios. Luego se debe buscar la carpeta donde se encuentra la imagen y seleccionarla. La imagen aparecerá en el panel de medios y se podrá arrastrar a los paneles de fondos, transiciones o diapositivas según el uso que se le quiera dar.</p>
- <h3>Se puede ajustar el tamaño, la posición, la velocidad y el efecto de las imagenes movibles para easyworship según el gusto y el propósito</h3>
- <p>Se puede ajustar el tamaño, la posición, la velocidad y el efecto de las imagenes movibles para easyworship según el gusto y el propósito que se tenga. Para ajustar el tamaño y la posición de una imagen se debe hacer clic sobre ella y arrastrar los bordes o las esquinas hasta obtener el tamaño y la posición deseada. Para ajustar la velocidad y el efecto de una imagen se debe hacer clic derecho sobre ella y elegir la opción "propiedades". Se abrirá una ventana donde se podrá modificar la duración, el ciclo, el retraso y el efecto de la imagen.</p>
- <h2>¿Qué beneficios tiene usar imagenes movibles para easyworship en las presentaciones?</h2>
- <h3>Las imagenes movibles para easyworship le dan vida y movimiento a las presentaciones</h3>
- <p>Las imagenes movibles para easyworship le dan vida y movimiento a las presentaciones al crear un ambiente dinámico y variado. Las imágenes animadas pueden expresar diferentes sensaciones, emociones, mensajes o conceptos de forma visual y creativa. Las imágenes animadas pueden complementar o contrastar con el contenido verbal o musical de la presentación. Las imágenes animadas pueden generar interés, curiosidad, sorpresa o admiración en los espectadores.</p>
- <h3>Las imagenes movibles para easyworship captan la atención y el interés de los espectadores</h3>
- <p>Las imagenes movibles para easyworship captan la atención y el interés de los espectadores al estimular sus sentidos y su memoria. Las imágenes animadas son más llamativas y memorables que las imágenes estáticas o los textos. Las imágenes animadas pueden ayudar a los espectadores a concentrarse, a comprender, a recordar o a aplicar lo que ven o escuchan en la presentación. Las imágenes animadas pueden motivar a los espectadores a participar, a interactuar o a responder a la presentación.</p>
- <h3>Las imagenes movibles para easyworship transmiten mensajes y emociones de forma visual y creativa</h3>
- <p>Las imagenes movibles para easyworship transmiten mensajes y emociones de forma visual y creativa al usar diferentes elementos como colores, formas, texturas, sonidos o movimientos. Las imágenes animadas pueden representar diferentes temas como naturaleza, arte, religión, humor, etc. Las imágenes animadas pueden reflejar diferentes estados de ánimo como alegría, paz, amor, fe, etc. Las imágenes animadas pueden inspirar diferentes acciones como alabar, orar, servir, etc.</p>
- <h2>Conclusión</h2>
- <p>En conclusión, descargar gratis imagenes movibles para easyworship es una forma sencilla y efectiva de mejorar tus presentaciones multimedia en tu iglesia. Con estas imágenes puedes crear presentaciones dinámicas, atractivas, personalizadas y adaptadas a cada ocasión. Con estas imágenes puedes usar easyworship como una herramienta poderosa para comunicar el mensaje de Dios de forma visual y creativa.</p>
- <h2>Preguntas frecuentes</h2>
- <ul>
-<li><b>¿Qué es easyworship?</b><br>
-Easyworship es un programa de presentación multimedia para iglesias que permite combinar imágenes, videos, textos, canciones y otros elementos para crear presentaciones dinámicas y profesionales.</li>
-<li><b>¿Dónde puedo descargar gratis imagenes movibles para easyworship?</b><br>
-Puedes descargar gratis imagenes movibles para easyworship en sitios web como Pinterest, Recursos Bíblicos o EasyWorship Media.</li>
-<li><b>¿Cómo puedo usar las imagenes movibles para easyworship en las presentaciones?</b><br>
-<ul>
-<li><b>¿Cómo puedo usar las imagenes movibles para easyworship en las presentaciones?</b><br>
-Puedes usar las imagenes movibles para easyworship como fondos, transiciones, ilustraciones o adornos. Para usarlas debes importarlas al programa y seleccionarlas en el panel de medios. Luego puedes ajustar su tamaño, posición, velocidad y efecto según tu gusto y propósito.</li>
-<li><b>¿Qué beneficios tiene usar imagenes movibles para easyworship en las presentaciones?</b><br>
-Usar imagenes movibles para easyworship tiene muchos beneficios como dar vida y movimiento a las presentaciones, captar la atención y el interés de los espectadores y transmitir mensajes y emociones de forma visual y creativa.</li>
-<li><b>¿Qué requisitos o condiciones tienen las imagenes movibles para easyworship?</b><br>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Agrar Simulator 2011 Bga Crack [UPD].md b/spaces/1gistliPinn/ChatGPT4/Examples/Agrar Simulator 2011 Bga Crack [UPD].md
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Call of Duty Mobile Mod Apk - How to Unlock All Skins and Weapons with Unlimited CP.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Call of Duty Mobile Mod Apk - How to Unlock All Skins and Weapons with Unlimited CP.md
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-<p>Call of Duty Mobile is a mobile version of the famous FPS franchise that was released in 2019 by Activision and Tencent. It features various game modes, maps, characters, weapons, and items from different Call of Duty titles such as Modern Warfare, Black Ops, and Warzone. It also has its own original content and storylines that expand the Call of Duty universe.</p>
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-</ul>
- <h2>What is CP and why do you need it?</h2>
-<p>CP stands for Call of Duty Points, which is the premium currency of Call of Duty Mobile. You can use CP to buy various items and benefits that can enhance your gameplay and appearance.</p>
- <h3>CP is the premium currency of Call of Duty Mobile</h3>
-<p>CP is different from Credits, which is the free currency that you can earn by playing the game, completing tasks, and opening crates. CP can only be obtained by spending real money or using special offers and promotions. You can buy CP in different amounts and packages, depending on your region and platform.</p>
- <h3>CP can be used to buy various items and benefits</h3>
-<p>Some of the things that you can buy with CP are:</p>
-<ul>
-<li>Battle Pass: This is a seasonal pass that gives you access to exclusive rewards such as weapons, operators, skins, camos, emotes, calling cards, etc. You can buy the regular Battle Pass for 220 CP or the Battle Pass Bundle for 520 CP, which also unlocks 12 tiers instantly.</li>
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-</ul>
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-<p>Call of Duty Mobile Unlimited CP Mod APK is a modified version of the game that gives you unlimited CP and other features that are not available in the official version. It is also known as Call of Duty Mobile Hack or Call of Duty Mobile Cheat.</p>
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-<li>ESP: This feature lets you see the enemy's location, health, name, distance, weapon, etc. through walls and obstacles. This helps you to spot and target them easily.</li>
-<li>Aimbot: This feature lets you automatically aim and shoot at the enemy's head or body with perfect accuracy and speed. This helps you to kill them instantly and win every gunfight.</li>
-<li>Wall Hack: This feature lets you shoot through walls and obstacles without any damage reduction or bullet drop. This helps you to hit the enemy even if they are hiding or cover.</li>
-<li>No Recoil: This feature lets you fire your weapon without any recoil or kickback. This helps you to control your aim and spray better.</li>
-<li>No Spread: This feature lets you fire your weapon without any bullet spread or deviation. This helps you to hit the enemy with every shot.</li>
-<li>No Reload: This feature lets you fire your weapon without any need to reload or change magazines. This helps you to keep shooting without any interruption.</li>
-<li>God Mode: This feature lets you become invincible and immune to any damage from the enemy or the environment. This helps you to survive any situation and explore the map freely.</li>
-</ul>
- <h2>How to download and install Call of Duty Mobile Unlimited CP Mod APK?</h2>
-<p>If you want to try Call of Duty Mobile Unlimited CP Mod APK, you need to follow these steps to download and install it on your device:</p>
- <h3>Follow these steps to download and install the mod apk</h3>
-<ol>
-<li>Find a reliable and safe source that provides the latest version of Call of Duty Mobile Unlimited CP Mod APK. You can search online or use the link below.</li>
-<li>Download the mod apk file and the obb data file from the source. Make sure you have enough storage space on your device.</li>
-<li>Enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and turning it on.</li>
-<li>Locate the downloaded files on your device and tap on them to install them. You may need to grant some permissions for the installation process.</li>
-<li>Copy the obb data file to the Android/obb folder on your device. If the folder does not exist, create it manually.</li>
-<li>Launch the game and enjoy unlimited CP and other features.</li>
-</ol>
- <h3>Beware of the risks and consequences of using the mod apk</h3>
-<p>While Call of Duty Mobile Unlimited CP Mod APK may sound tempting, it is not recommended to use it for several reasons:</p>
-<ul>
-<li>It violates the game's terms of service and can result in bans or legal actions. Activision and Tencent have a strict anti-cheat system that detects and punishes players who use hacks or mods. You may lose your account, progress, items, etc. or face legal consequences if you use the mod apk.</li>
-<li>It ruins the game's balance and fairness and can affect other players' enjoyment. Using hacks or mods gives you an unfair advantage over other players who play legitimately. This can make the game boring, frustrating, or unfair for them.</li>
-<li>It exposes your device and data to malware and viruses that can harm your device or steal your information. The mod apk may contain malicious code or hidden programs that can infect your device or access your data without your consent. You may risk losing your files, contacts, photos, etc. or compromising your privacy or security if you use the mod apk.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Call of Duty Mobile Unlimited CP Mod APK is a modified version of the game that gives you unlimited CP and other features that are not available in the official version. It may seem like a good option for some players who want to access more content without spending real money or who want to dominate their opponents with ease. However, it is not recommended to use it as it violates the game's terms of service and can result in bans or legal actions. It also ruins the game's balance and fairness and can affect other players' enjoyment. It also exposes your device and data to malware and viruses that can harm your device or steal your information.</p>
-<p>If you want to enjoy Call of Duty Mobile in a safe and legitimate way, you should avoid using hacks or mods and play the game as intended. You can still get CP by spending real money or using special offers and promotions. You can also improve your skills and performance by practicing, learning, and following tips and tricks from other players. You can also have fun with your friends by playing together in different game modes.</p>
- <h2>FAQs</h2>
-<h3>Q1. Is Call of Duty Mobile free to play?</h3>
-<p>A1. Yes, Call of Duty Mobile is free to play on both Android and iOS devices. You can download it from Google Play Store or Apple App Store respectively. However, it also has optional in-game purchases that you can make with real money or in-game currency.</p>
- <h3>Q2. How can I get CP legitimately in Call of Duty Mobile?</h3>
-<p>A2. There are several ways to get CP legitimately in Call of Duty Mobile, such as:</p>
-<ul>
-<li>Buying CP with real money from the in-game store or the official website.</li>
-<li>Using special offers and promotions that give you CP as a bonus or a reward.</li>
-<li>Participating in the CODM Championship and winning CP as a prize.</li>
-<li>Using third-party apps or websites that offer CP as a gift or a reward for completing tasks or surveys. However, you should be careful and check the credibility and security of these sources before using them.</li>
-</ul>
- <h3>Q3. What are the best weapons and loadouts in Call of Duty Mobile?</h3>
-<p>A3. The best weapons and loadouts in Call of Duty Mobile depend on your personal preference, playstyle, game mode, map, and situation. However, some of the most popular and effective weapons and loadouts are:</p>
-<ul>
-<li>Assault Rifles: These are versatile weapons that can perform well in most ranges and situations. Some of the best assault rifles are ASM10, AK-47, DR-H, BK57, and M4.</li>
-<li>Submachine Guns: These are fast-firing weapons that excel in close-range combat and mobility. Some of the best submachine guns are QQ9, QXR, RUS-79U, MSMC, and PDW-57.</li>
-<li>Sniper Rifles: These are powerful weapons that can kill enemies with one shot from long distances. Some of the best sniper rifles are DL Q33, Locus, Arctic .50, Outlaw, and M21 EBR.</li>
-<li>Shotguns: These are devastating weapons that can deal massive damage in close-range combat. Some of the best shotguns are KRM-262, Echo, BY15, HS0405, and Striker.</li>
-<li>Pistols: These are secondary weapons that can be used as a backup or a finisher. Some of the best pistols are MW11, J358, .50 GS, Renetti, and AGR 556.</li>
-<li>Loadouts: These are combinations of weapons, attachments, perks, grenades, and operator skills that suit your playstyle and strategy. You can create up to 10 custom loadouts and switch between them during the game. You can also use the default loadouts or the recommended loadouts provided by the game.</li>
-</ul>
- <h3>Q4. How can I improve my skills and performance in Call of Duty Mobile?</h3>
-<p>A4. There are several tips and tricks that can help you improve your skills and performance in Call of Duty Mobile, such as:</p>
-<ul>
-<li>Practice: The best way to improve your skills is to practice regularly and learn from your mistakes. You can play different game modes and maps, try different weapons and loadouts, watch replays and tutorials, and challenge yourself with different goals and objectives.</li>
-<li>Learn: The more you know about the game, the better you can play it. You can learn about the game's mechanics, features, modes, maps, weapons, items, operators, skills, perks, etc. You can also learn from other players, streamers, youtubers, guides, forums, etc.</li>
-<li>Adjust: The more you adapt to the game, the better you can play it. You can adjust your settings, controls, sensitivity, layout, graphics, sound, etc. to suit your device and preference. You can also adjust your strategy, tactics, loadouts, etc. to suit the game mode, map, situation, etc.</li>
-<li>Communicate: The more you cooperate with your team, the better you can play it. You can communicate with your teammates using voice or text chat, ping system, gestures, etc. You can also coordinate your actions, roles, objectives, etc. with your teammates.</li>
-<li>Enjoy: The more you have fun with the game, the better you can play it. You can play the game casually or competitively, solo or with friends, online or offline, etc. You can also try new things, experiment with different options, and challenge yourself with different modes.</li>
-</ul>
- <h3>Q5. Where can I find more tips and tricks for Call of Duty Mobile?</h3>
-<p>A5. There are many sources where you can find more tips and tricks for Call of Duty Mobile, such as:</p>
-<ul>
-<li>The official website and social media accounts of Call of Duty Mobile that provide news, updates, events, announcements, etc.</li>
-<li>The in-game community section that provides forums, clans, leaderboards, feedbacks, support, etc.</li>
-<li>The online community platforms such as Reddit, Discord, Facebook, Twitter, Instagram, YouTube, Twitch, etc. that provide discussions, reviews, guides, videos, streams, etc.</li>
-<li>The online gaming websites and magazines that provide articles, blogs, podcasts, etc.</li>
-<li>The online gaming experts and influencers that provide tips, tricks, advice, recommendations, etc.</li>
-</ul>
- <p>I hope this article has helped you to understand what Call of Duty Mobile Unlimited CP Mod APK is and what you need to know about it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/APK Evozi How to Save Time and Data with the Fastest and Easiest APK Downloader.md b/spaces/1phancelerku/anime-remove-background/APK Evozi How to Save Time and Data with the Fastest and Easiest APK Downloader.md
deleted file mode 100644
index 6b4c5e507e63a057db89c0a7457a0a76b0bf61ab..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/APK Evozi How to Save Time and Data with the Fastest and Easiest APK Downloader.md	
+++ /dev/null
@@ -1,92 +0,0 @@
-<br />
-<h1>Apk Evozi: A Guide to Downloading Android Apps and Games</h1>
- <p>Do you want to download Android apps and games that are not available on Google Play? Do you want to save bandwidth and storage space by downloading only the APK file? Do you want to update your apps and games faster than waiting for Google Play? Do you want to avoid ads and in-app purchases by downloading modded or hacked versions of apps and games?</p>
- <p>If you answered yes to any of these questions, then you need to know about Apk Evozi, a website that lets you download any Android app or game from Google Play as an APK file. In this article, we will explain what Apk Evozi is, how it works, why you should use it, how to use it, and what are the benefits of using it. Let's get started!</p>
-<h2>apk evozi</h2><br /><p><b><b>Download</b> ===> <a href="https://jinyurl.com/2uNUv6">https://jinyurl.com/2uNUv6</a></b></p><br /><br />
- <h2>What is Apk Evozi?</h2>
- <p>Apk Evozi is a website that allows you to download any Android app or game from Google Play as an APK file. An APK file is the installation package of an Android app or game, which contains all the files and data needed to run it on your device. By downloading the APK file, you can install the app or game without using Google Play.</p>
- <p>Apk Evozi is developed by Evozi, a team of developers who create innovative apps for Android devices. Some of their popular apps include Network Speed, Internet Speed Meter, Device ID, and App Downloader. You can find more about them on their [Google Play page](^1^).</p>
- <h3>How does Apk Evozi work?</h3>
- <p>Apk Evozi works by using a script that fetches the APK file from Google Play servers and generates a download link for you. The script is updated regularly to ensure that it works with the latest versions of apps and games. The website also has a database of over 40 million APK files that you can browse and download.</p>
- <h3>Why use Apk Evozi?</h3>
- <p>There are many reasons why you might want to use Apk Evozi to download Android apps and games. Here are some of them:</p>
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- <ul>
-<li>You want to access apps and games that are not available in your region or device. For example, some apps and games are restricted to certain countries or devices due to licensing issues or compatibility issues. By using Apk Evozi, you can bypass these restrictions and download any app or game you want.</li>
-<li>You want to save bandwidth and storage space by downloading only the APK file. For example, some apps and games are very large in size and take a lot of time and data to download from Google Play. By using Apk Evozi, you can download only the APK file, which is usually much smaller than the full app or game. You can then transfer the APK file to your device using a USB cable or a cloud service and install it offline.</li>
-<li>You want to update your apps and games faster than waiting for Google Play. For example, some apps and games take a long time to receive updates from Google Play due to various reasons. By using Apk Evozi, you can download the latest version of any app or game as soon as it is released on Google Play.</li>
-<li>You want to avoid ads and in-app purchases by downloading modded or hacked versions of apps and games <h2>What are the benefits of using Apk Evozi?</h2>
- <p>By using Apk Evozi, you can enjoy many benefits that Google Play does not offer. Here are some of them:</p>
- <h3>Access to apps and games that are not available in your region or device</h3>
- <p>As we mentioned before, some apps and games are restricted to certain countries or devices due to licensing issues or compatibility issues. This can be frustrating if you want to try out a new app or game that is popular or useful in another region or device. By using Apk Evozi, you can download any app or game from any region or device and install it on your own device. For example, you can download the app Spotify, which is not available in some countries, or the game PUBG Mobile, which is not compatible with some devices.</p>
- <h3>Save bandwidth and storage space by downloading only the APK file</h3>
- <p>Another benefit of using Apk Evozi is that you can save bandwidth and storage space by downloading only the APK file. Some apps and games are very large in size and take a lot of time and data to download from Google Play. For example, the game Asphalt 9: Legends has a size of over 2 GB and requires an additional 1.5 GB of data to download after installation. By using Apk Evozi, you can download only the APK file, which is usually much smaller than the full app or game. For example, the APK file of Asphalt 9: Legends has a size of only 99 MB. You can then transfer the APK file to your device using a USB cable or a cloud service and install it offline. This way, you can save bandwidth and storage space on your device.</p>
- <h3>Update your apps and games faster than waiting for Google Play</h3>
- <p>A third benefit of using Apk Evozi is that you can update your apps and games faster than waiting for Google Play. Some apps and games take a long time to receive updates from Google Play due to various reasons. For example, some developers release updates in batches or stages to test their stability and performance before rolling them out to all users. Some users may also experience delays or errors in receiving updates from Google Play due to network issues or device settings. By using Apk Evozi, you can download the latest version of any app or game as soon as it is released on Google Play. You can also check the version history of any app or game on the Apk Evozi website to see if there are any new updates available.</p>
- <h3>Avoid ads and in-app purchases by downloading modded or hacked versions of apps and games</h3>
- <p>A fourth benefit of using Apk Evozi is that you can avoid ads and in-app purchases by downloading modded or hacked versions of apps and games. Some apps and games have annoying ads or require you to pay for extra features or content. For example, some apps have banner ads or pop-up ads that interrupt your experience or consume your data. Some games have in-app purchases that make you spend real money to unlock items, levels, characters, etc. By using Apk Evozi, you can download modded or hacked versions of apps and games that remove the ads or unlock the premium features or content for free. For example, you can download the modded version of Spotify that gives you unlimited skips, offline mode, no ads, etc. You can also download the hacked version of PUBG Mobile that gives you unlimited health, ammo, coins, etc. However, you should be careful when downloading modded or hacked versions of apps and games as they may contain malware or viruses that can harm your device or account.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Apk Evozi is a website that lets you download any Android app or game from Google Play as an APK file. By using Apk Evozi, you can access apps and games that are not available in your region or device, save bandwidth and storage space by downloading only the APK file, update your apps and games faster than waiting for Google Play, and avoid ads and in-app purchases by downloading modded or hacked versions of apps and games. To use Apk Evozi, you just need to find the app or game you want to download, copy its Google Play URL, paste it into the Apk Evozi downloader, click on the "Generate Download Link" button, and download the APK file and install it on your device.</p>
- <p>If you are looking for a way to download Android apps and games without using Google Play, then Apk Evozi is a great option for you. Try it out today and enjoy the benefits of downloading APK files!</p>
- <h2>FAQs</h2>
- <ul>
-<li><b>Q: Is Apk Evozi safe to use?</b></li>
-<li>A A: Apk Evozi is generally safe to use as it fetches the APK files from Google Play servers and does not modify them. However, you should always scan the APK files with a reliable antivirus software before installing them on your device. You should also be careful when downloading modded or hacked versions of apps and games as they may contain malware or viruses that can harm your device or account.</li>
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-<li>A: Apk Evozi is legal to use as long as you do not violate the terms and conditions of Google Play or the app or game developers. You should only download apps and games that you have purchased or are free to use. You should also respect the intellectual property rights of the app or game developers and not distribute or share the APK files without their permission.</li>
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diff --git a/spaces/AIConsultant/MusicGen/audiocraft/quantization/vq.py b/spaces/AIConsultant/MusicGen/audiocraft/quantization/vq.py
deleted file mode 100644
index aa57bea59db95ddae35e0657f723ca3a29ee943b..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/quantization/vq.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regularization.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation."""
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n
diff --git a/spaces/AIConsultant/MusicGen/tests/models/test_audiogen.py b/spaces/AIConsultant/MusicGen/tests/models/test_audiogen.py
deleted file mode 100644
index 3850af066cedd5ea38bd9aead9634d6aaf938218..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/tests/models/test_audiogen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pytest
-import torch
-
-from audiocraft.models import AudioGen
-
-
-class TestAudioGenModel:
-    def get_audiogen(self):
-        ag = AudioGen.get_pretrained(name='debug', device='cpu')
-        ag.set_generation_params(duration=2.0, extend_stride=2.)
-        return ag
-
-    def test_base(self):
-        ag = self.get_audiogen()
-        assert ag.frame_rate == 25
-        assert ag.sample_rate == 16000
-        assert ag.audio_channels == 1
-
-    def test_generate_continuation(self):
-        ag = self.get_audiogen()
-        prompt = torch.randn(3, 1, 16000)
-        wav = ag.generate_continuation(prompt, 16000)
-        assert list(wav.shape) == [3, 1, 32000]
-
-        prompt = torch.randn(2, 1, 16000)
-        wav = ag.generate_continuation(
-            prompt, 16000, ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 32000]
-
-        prompt = torch.randn(2, 1, 16000)
-        with pytest.raises(AssertionError):
-            wav = ag.generate_continuation(
-                prompt, 16000, ['youpi', 'lapin dort', 'one too many'])
-
-    def test_generate(self):
-        ag = self.get_audiogen()
-        wav = ag.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 32000]
-
-    def test_generate_long(self):
-        ag = self.get_audiogen()
-        ag.max_duration = 3.
-        ag.set_generation_params(duration=4., extend_stride=2.)
-        wav = ag.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 16000 * 4]
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py
deleted file mode 100644
index 4b470973c46073748803bac2f736eca615e3cb00..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py
+++ /dev/null
@@ -1,25 +0,0 @@
-_base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
-
-deepen_factor = 1.0
-widen_factor = 1.0
-train_batch_size_per_gpu = 32
-train_num_workers = 8
-
-load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth'  # noqa
-
-model = dict(
-    backbone=dict(
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-    ),
-    neck=dict(
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-    ),
-    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu, num_workers=train_num_workers)
-
-optim_wrapper = dict(
-    optimizer=dict(batch_size_per_gpu=train_batch_size_per_gpu))
diff --git a/spaces/Ababababababbababa/AraPoet/app.py b/spaces/Ababababababbababa/AraPoet/app.py
deleted file mode 100644
index af769dff8abd1dbf74587cd2d33de416baf01ade..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/AraPoet/app.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# coding=utf8
-
-import json
-import torch
-import gradio as gr
-import pyarabic.araby as araby
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
-
-feature_names = [ 
-    "Title",
-    "Meter",
-    "Theme",
-    "Name",
-    "Era",
-    "Country",
-    "Type"
-]
-
-with open("./poet_names.json", 'r', encoding="utf-8") as fin:
-    poet_names = json.load(fin)
-
-def normalize_text(text):
-    text = araby.strip_tatweel(text)
-    return text
-
-def generate_poem(country, era, meter, theme, lang_type, poet, num_lines, num_poems, title):
-
-    num_poems = int(num_poems)
-    prompt = title
-    prompt = normalize_text(prompt)
-
-    features = [prompt, meter, theme, poet, era, country, lang_type]
-
-    prompt = ""
-    for name, feat in zip(feature_names, features):
-        prompt += f"{name}: {feat}; "
-    prompt += f"Length: {num_lines}; Poem:"
-
-    num_beams = 5
-    top_k = 50
-    top_p = 0.9
-    r_penalty = 5.
-
-    input_ids = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
-    print(f"> Running: {prompt} | {num_poems} Poems")
-    outputs = model.generate(input_ids=input_ids, 
-        min_length=32, 
-        max_length=256, 
-        do_sample=True,
-        top_k=top_k,
-        top_p=top_p,
-        repetition_penalty=r_penalty,
-        num_beams=num_beams, 
-        num_return_sequences=num_poems,
-        early_stopping=True
-    )
-
-    poems = []
-    print(f"> # of Outputs: {len(outputs)}")
-    for output in outputs:
-        raw = tokenizer.decode(output)
-        raw = raw.replace("<pad>", "").replace("</s>", "")
-        print("="*100)
-        print(raw)
-        print("="*100)
-        poems += ['\n'.join(raw.split("<s>"))]
-
-    return "\n\n".join(poems)   
-    
-meters = ['البسيط', 'التفعيله', 'الحداء', 'الخفيف', 'الدوبيت', 'الرجز', 'الرمل', 'السريع', 'السلسلة', 'الصخري', 'الطويل', 'الكامل', 'الكان كان', 'اللويحاني', 'المتدارك', 'المتقارب', 'المجتث', 'المديد', 'المسحوب', 'المضارع', 'المقتضب', 'المنسرح', 'المواليا', 'الموشح', 'الهجيني', 'الهزج', 'الوافر', 'بحر أحذ الكامل', 'بحر أحذ المديد', 'بحر أحذ الوافر', 'بحر البسيط', 'بحر التفعيله', 'بحر الخبب', 'بحر الخفيف', 'بحر الدوبيت', 'بحر الرجز', 'بحر الرمل', 'بحر السريع', 'بحر السلسلة', 'بحر الطويل', 'بحر القوما', 'بحر الكامل', 'بحر الكامل المقطوع', 'بحر المتدارك', 'بحر المتدارك المنهوك', 'بحر المتقارب', 'بحر المجتث', 'بحر المديد', 'بحر المضارع', 'بحر المقتضب', 'بحر المنسرح', 'بحر المواليا', 'بحر الهزج', 'بحر الوافر', 'بحر تفعيلة الرجز', 'بحر تفعيلة الرمل', 'بحر تفعيلة الكامل', 'بحر تفعيلة المتقارب', 'بحر مجزوء البسيط', 'بحر مجزوء الخفيف', 'بحر مجزوء الدوبيت', 'بحر مجزوء الرجز', 'بحر مجزوء الرمل', 'بحر مجزوء الرمل ', 'بحر مجزوء السريع', 'بحر مجزوء الطويل', 'بحر مجزوء الكامل', 'بحر مجزوء المتدارك', 'بحر مجزوء المتقارب', 'بحر مجزوء المجتث', 'بحر مجزوء المديد', 'بحر مجزوء المنسرح', 'بحر مجزوء المواليا', 'بحر مجزوء الهزج', 'بحر مجزوء الوافر', 'بحر مجزوء موشح', 'بحر مخلع البسيط', 'بحر مخلع الرجز', 'بحر مخلع الرمل', 'بحر مخلع السريع', 'بحر مخلع الكامل', 'بحر مخلع موشح', 'بحر مربع البسيط', 'بحر مربع الرجز', 'بحر مشطور الرجز', 'بحر مشطور السريع', 'بحر مشطور الطويل', 'بحر منهوك البسيط', 'بحر منهوك الرجز', 'بحر منهوك الكامل', 'بحر منهوك المنسرح', 'بحر موشح', 'بسيط', 'زجل', 'شعر التفعيلة', 'شعر حر', 'عامي', 'عدة أبحر', 'عموديه', 'مجزوء الخفيف', 'نثريه', 'None']
-themes = ['قصيدة اعتذار', 'قصيدة الاناشيد', 'قصيدة المعلقات', 'قصيدة حزينه', 'قصيدة دينية', 'قصيدة ذم', 'قصيدة رثاء', 'قصيدة رومنسيه', 'قصيدة سياسية', 'قصيدة شوق', 'قصيدة عامه', 'قصيدة عتاب', 'قصيدة غزل', 'قصيدة فراق', 'قصيدة قصيره', 'قصيدة مدح', 'قصيدة هجاء', 'قصيدة وطنيه', 'None']
-language_types = ['شعبي', 'عامي', 'فصحى', 'فصيح', '-', 'None']
-poet_era = ['العصر الأموي', 'العصر الأندلسي', 'العصر الأيوبي', 'العصر الإسلامي', 'العصر الجاهلي', 'العصر الحديث', 'العصر العباسي', 'العصر العثماني', 'العصر الفاطمي', 'العصر المملوكي', 'المخضرمين', 'المغرب والأندلس', 'عصر بين الدولتين', 'قبل الإسلام', 'None']
-countries = ['الأردن', 'الإمارات', 'البحرين', 'الجزائر', 'السعودية', 'السنغال', 'السودان', 'الصومال', 'العراق', 'الكويت', 'المغرب', 'اليمن', 'تونس', 'سوريا', 'سورية', 'عمان', 'فلسطين', 'قطر', 'لبنان', 'ليبيا', 'مصر', 'موريتانيا', 'None']
-
-tokenizer: AutoTokenizer = AutoTokenizer.from_pretrained("bkhmsi/arapoet-mt5", use_auth_token="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")
-model: AutoModelForSeq2SeqLM = AutoModelForSeq2SeqLM.from_pretrained("bkhmsi/arapoet-mt5", use_auth_token="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")
-model.eval()
-
-title = ""
-with gr.Blocks(title=title) as demo:
-    inputs = []
-
-    gr.Markdown(
-        """
-        # AraPoet: Controlled Arabic Poetry Generation
-        
-        The model hosted here is a finetuned version of [mT5-large](https://huggingface.co/google/mt5-large) (∼ 1.2B parameters) on the largest repository of Arabic poems, the [ashaar](https://huggingface.co/datasets/arbml/ashaar) dataset. 
-        The model can be conditioned on a set of attributes to control the style of the generated poem. 
-        Namely: the poet name, country, era, meter, theme, language type, title and the length of the poem.
-        You can start by clicking on one of the examples below or try your own input.
-        """
-    )
-
-    with gr.Row():
-        inputs += [gr.Dropdown(countries, label="Country", value="مصر")]
-        inputs += [gr.Dropdown(poet_era, label="Era", value="العصر الحديث")]
-    with gr.Row():
-        inputs += [gr.Dropdown(meters, label="Meter", value="بحر السريع")]
-        inputs += [gr.Dropdown(themes, label="Theme", value="قصيدة رومنسيه")]
-    with gr.Row():
-        inputs += [gr.Dropdown(language_types, label="Language Type", value="فصحى")]
-        inputs += [gr.Dropdown(poet_names, label="Poet", value="أحمد شوقي")]
-    with gr.Row():
-        inputs += [gr.Slider(2, 20, value=6, step=1, label="Number of Lines")]
-        inputs += [gr.Slider(1, 4, value=1, step=1, label="Number of Samples")]
-    with gr.Row():
-        inputs += [gr.Textbox(label="Title", value="إثن عنان القلب واسلم به")]
-
-    btn = gr.Button("Generate")
-    examples = gr.Examples(examples="./examples", inputs=inputs)
-    btn.click(generate_poem, inputs, gr.TextArea(label="Generation"))
-
-
-    gr.Markdown(
-        """
-        Checkout our [AraPoet Preprint](https://github.com/BKHMSI/BKHMSI.github.io/blob/master/archive/resources/AraPoet.pdf) for more details about the model.
-        """
-    )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/Dockerfile b/spaces/AbandonedMuse/UnlimitedMusicGen/Dockerfile
deleted file mode 100644
index efc2431ec0fe674c22fe2fdb9d7045cdf6cd2748..0000000000000000000000000000000000000000
--- a/spaces/AbandonedMuse/UnlimitedMusicGen/Dockerfile
+++ /dev/null
@@ -1,26 +0,0 @@
-FROM nvidia/cuda:11.8.0-base-ubuntu22.04
-
-ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONUNBUFFERED=1 \
-    PYTHONIOENCODING=UTF-8
-RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt apt update &&\
-    apt install -y \
-    wget \
-    git \
-    pkg-config \
-    python3 \
-    python3-pip \
-    python-is-python3 \
-    ffmpeg \
-    libnvrtc11.2 \
-    libtcmalloc-minimal4
-
-RUN useradd -m -u 1000 ac
-RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip wheel
-ENV TORCH_COMMAND="pip install torch==2.0.1+cu118 torchaudio --extra-index-url https://download.pytorch.org/whl/cu118"
-RUN --mount=type=cache,target=/root/.cache python -m $TORCH_COMMAND
-RUN ln -s /usr/lib/x86_64-linux-gnu/libnvrtc.so.11.2 /usr/lib/x86_64-linux-gnu/libnvrtc.so
-USER 1000
-RUN mkdir ~/.cache
-RUN --mount=type=cache,target=/home/ac/.cache --mount=source=.,target=/home/ac/audiocraft python -m pip install -r /home/ac/audiocraft/requirements.txt
-WORKDIR /home/ac/audiocraft
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/__init__.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/__init__.py
deleted file mode 100644
index 5c66c87fa30e77def4d61737299ce32be3b6de9f..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .AiService     import AiService
-from .CodeLinkAva   import CodeLinkAva
-from .DfeHub        import DfeHub
-from .EasyChat      import EasyChat
-from .Forefront     import Forefront
-from .GetGpt        import GetGpt
-from .Opchatgpts    import Opchatgpts
-from .Lockchat      import Lockchat
-from .Wewordle      import Wewordle
-from .Equing        import Equing
-from .Wuguokai      import Wuguokai
-from .V50           import V50
-from .FastGpt       import FastGpt
-from .ChatgptLogin  import ChatgptLogin
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/role_assigner/__init__.py b/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/role_assigner/__init__.py
deleted file mode 100644
index 2504dda5b6e3ca052d91b83ae3dd8b2c0e7f4b41..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/role_assigner/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from agentverse.registry import Registry
-
-role_assigner_registry = Registry(name="RoleAssignerRegistry")
-
-from .base import BaseRoleAssigner
-from .role_description import DescriptionAssigner
diff --git a/spaces/AgentVerse/agentVerse/agentverse/utils.py b/spaces/AgentVerse/agentVerse/agentverse/utils.py
deleted file mode 100644
index 196d0ba2e26e5a707574d5bb37306513fd63a202..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/utils.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from typing import NamedTuple, Union
-from enum import Enum
-
-import abc
-
-
-class AgentAction(NamedTuple):
-    """Agent's action to take."""
-
-    tool: str
-    tool_input: Union[str, dict]
-    log: str
-
-
-class AgentFinish(NamedTuple):
-    """Agent's return value."""
-
-    return_values: dict
-    log: str
-
-
-class AgentCriticism(NamedTuple):
-    """Agent's criticism."""
-
-    is_agree: bool
-    criticism: str
-    sender_agent: object = None
-
-
-class AGENT_TYPES(Enum):
-    ROLE_ASSIGNMENT = 0
-    SOLVER = 1
-    CRITIC = 2
-    EXECUTION = 3
-    EVALUATION = 4
-    MANAGER = 5
-
-
-class Singleton(abc.ABCMeta, type):
-    """
-    Singleton metaclass for ensuring only one instance of a class.
-    """
-
-    _instances = {}
-
-    def __call__(cls, *args, **kwargs):
-        """Call method for the singleton metaclass."""
-        if cls not in cls._instances:
-            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
-        return cls._instances[cls]
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/puff/Puff.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/puff/Puff.d.ts
deleted file mode 100644
index dfe78f8463022a04291f0776ab05f5d20f6a20ba..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/puff/Puff.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import Base from '../base/Base';
-export default class Puff extends Base { }
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetEndPoint.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetEndPoint.js
deleted file mode 100644
index b4a303cc4077fe4124f08744a5317ff6537feaa3..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetEndPoint.js
+++ /dev/null
@@ -1,27 +0,0 @@
-import GetThumbAlignPoint from './GetThumbAlignPoint.js';
-
-const AlignRight = Phaser.Display.Align.RIGHT_CENTER;
-const AlignBottom = Phaser.Display.Align.BOTTOM_CENTER;
-
-var GetEndoint = function (out) {
-    if (out === undefined) {
-        out = tmpPoint;
-    }
-    if (this.childrenMap.thumb) {
-        var align = (this.orientation === 0) ? AlignRight : AlignBottom;
-        GetThumbAlignPoint.call(this, align, out);
-    } else {
-        if (this.orientation === 0) {
-            out.x = this.innerRight - 1; // Add 1 pixel margin
-            out.y = this.centerY;
-        } else {
-            out.x = this.centerX;
-            out.y = this.innerBottom - 1; // Add 1 pixel margin
-        }
-    }
-    return out;
-}
-
-var tmpPoint = {};
-
-export default GetEndoint;
\ No newline at end of file
diff --git a/spaces/Aki004/herta-so-vits/demo.py b/spaces/Aki004/herta-so-vits/demo.py
deleted file mode 100644
index 617eadac96c56c4517ddd69bb1b71d44c9629148..0000000000000000000000000000000000000000
--- a/spaces/Aki004/herta-so-vits/demo.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import edge_tts
-import asyncio
-import librosa
-import soundfile
-import io
-
-from inference.infer_tool import Svc
-
-TEXT = "私はヘルタ。今は忙しいから、リモート人形のオート返答機能に任せる。こんにちは、こんにちは、ごきげんよう、良い日になりますように。それじゃ"
-VOICE = "ja-JP-NanamiNeural"
-OUTPUT_FILE = "test.mp3"
-
-asyncio.run(edge_tts.Communicate(TEXT, VOICE).save(OUTPUT_FILE))
-audio, sr = librosa.load(OUTPUT_FILE, sr=16000, mono=True)
-raw_path = io.BytesIO()
-soundfile.write(raw_path, audio, 16000, format="wav")
-raw_path.seek(0)
-print('checkpoint 1')
-
-model = Svc(fr"Herta-Svc/G_10000.pth", f"Herta-Svc/config.json", device = 'cpu')
-print('checkpoint 2')
-
-out_audio, out_sr = model.infer('speaker0', 0, raw_path,
-                                       auto_predict_f0 = True,
-                                       )
-print('checkpoint 3')
-
-soundfile.write('out_audio.wav', out_audio.cpu().numpy(), 44100)
-
-print("done")
\ No newline at end of file
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/rearrange_speaker.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/rearrange_speaker.py
deleted file mode 100644
index de0f7545904cc088377c552cc6d9b058c5e9d342..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/rearrange_speaker.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-import argparse
-import json
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", type=str, default="./OUTPUT_MODEL/G_latest.pth")
-    parser.add_argument("--config_dir", type=str, default="./configs/modified_finetune_speaker.json")
-    args = parser.parse_args()
-
-    model_sd = torch.load(args.model_dir, map_location='cpu')
-    with open(args.config_dir, 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-
-    valid_speakers = list(hps['speakers'].keys())
-    if hps['data']['n_speakers'] > len(valid_speakers):
-        new_emb_g = torch.zeros([len(valid_speakers), 256])
-        old_emb_g = model_sd['model']['emb_g.weight']
-        for i, speaker in enumerate(valid_speakers):
-            new_emb_g[i, :] = old_emb_g[hps['speakers'][speaker], :]
-            hps['speakers'][speaker] = i
-        hps['data']['n_speakers'] = len(valid_speakers)
-        model_sd['model']['emb_g.weight'] = new_emb_g
-        with open("./finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-        torch.save(model_sd, "./G_latest.pth")
-    else:
-        with open("./finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-        torch.save(model_sd, "./G_latest.pth")
-    # save another config file copy in MoeGoe format
-    hps['speakers'] = valid_speakers
-    with open("./moegoe_config.json", 'w', encoding='utf-8') as f:
-        json.dump(hps, f, indent=2)
-
-
-
diff --git a/spaces/Alex123aaa/1234/README.md b/spaces/Alex123aaa/1234/README.md
deleted file mode 100644
index 77da4c0cb3ee265910403789f4f169e9ea3f165f..0000000000000000000000000000000000000000
--- a/spaces/Alex123aaa/1234/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 1234
-emoji: 🌍
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.45.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\346\200\273\347\273\223word\346\226\207\346\241\243.py" "b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\346\200\273\347\273\223word\346\226\207\346\241\243.py"
deleted file mode 100644
index f1fe20171cc54aec0c79f4961e71b57845f252d5..0000000000000000000000000000000000000000
--- "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\346\200\273\347\273\223word\346\226\207\346\241\243.py"
+++ /dev/null
@@ -1,127 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, os
-    # pip install python-docx 用于docx格式，跨平台
-    # pip install pywin32 用于doc格式，仅支持Win平台
-    for index, fp in enumerate(file_manifest):
-        if fp.split(".")[-1] == "docx":
-            from docx import Document
-            doc = Document(fp)
-            file_content = "\n".join([para.text for para in doc.paragraphs])
-        else:
-            import win32com.client
-            word = win32com.client.Dispatch("Word.Application")
-            word.visible = False
-            # 打开文件
-            print('fp', os.getcwd())
-            doc = word.Documents.Open(os.getcwd() + '/' + fp)
-            # file_content = doc.Content.Text
-            doc = word.ActiveDocument
-            file_content = doc.Range().Text
-            doc.Close()
-            word.Quit()
-
-        print(file_content)
-        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        max_token = model_info[llm_kwargs['llm_model']]['max_token']
-        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  
-            get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'], 
-            limit=TOKEN_LIMIT_PER_FRAGMENT
-        )
-        this_paper_history = []
-        for i, paper_frag in enumerate(paper_fragments):
-            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
-            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user,gpt_say])
-            this_paper_history.extend([i_say_show_user,gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了，
-        if len(paper_fragments) > 1:
-            i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=this_paper_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say,gpt_say])
-            this_paper_history.extend([i_say,gpt_say])
-
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    res = write_results_to_file(history)
-    chatbot.append(("所有文件都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-@CatchException
-def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结Word文档。函数插件贡献者: JasonGuo1"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        from docx import Document
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    if txt.endswith('.docx') or txt.endswith('.doc'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
-                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py
deleted file mode 100644
index a4b54b9724fb7630f841253aee2fa44743fc6367..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py
+++ /dev/null
@@ -1,540 +0,0 @@
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import deprecate, logging
-from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def preprocess(image):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return 2.0 * image - 1.0
-
-
-def preprocess_mask(mask, scale_factor=8):
-    mask = mask.convert("L")
-    w, h = mask.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST)
-    mask = np.array(mask).astype(np.float32) / 255.0
-    mask = np.tile(mask, (4, 1, 1))
-    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
-    mask = 1 - mask  # repaint white, keep black
-    return mask
-
-
-class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to
-    provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], 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 details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-    _is_onnx = True
-
-    vae_encoder: OnnxRuntimeModel
-    vae_decoder: OnnxRuntimeModel
-    text_encoder: OnnxRuntimeModel
-    tokenizer: CLIPTokenizer
-    unet: OnnxRuntimeModel
-    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
-    safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPImageProcessor
-
-    def __init__(
-        self,
-        vae_encoder: OnnxRuntimeModel,
-        vae_decoder: OnnxRuntimeModel,
-        text_encoder: OnnxRuntimeModel,
-        tokenizer: CLIPTokenizer,
-        unet: OnnxRuntimeModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: OnnxRuntimeModel,
-        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, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. 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."
-            )
-
-        self.register_modules(
-            vae_encoder=vae_encoder,
-            vae_decoder=vae_decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        num_images_per_prompt: Optional[int],
-        do_classifier_free_guidance: bool,
-        negative_prompt: Optional[str],
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                prompt to be encoded
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            prompt_embeds (`np.ndarray`, *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 (`np.ndarray`, *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.
-        """
-        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:
-            # get prompt text embeddings
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
-
-            if not np.array_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}"
-                )
-
-            prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
-
-        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
-
-        # 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 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] * batch_size
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
-
-            # 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 = np.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    def check_inputs(
-        self,
-        prompt,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        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 __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[np.ndarray, PIL.Image.Image] = None,
-        mask_image: Union[np.ndarray, PIL.Image.Image] = None,
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: int = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`nd.ndarray` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process. This is the image whose masked region will be inpainted.
-            mask_image (`nd.ndarray` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
-                PIL image, it will be 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)`.uu
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (?) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`np.random.RandomState`, *optional*):
-                A np.random.RandomState to make generation deterministic.
-            prompt_embeds (`np.ndarray`, *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 (`np.ndarray`, *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.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        # check inputs. Raise error if not correct
-        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
-
-        # 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]
-
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
-        if generator is None:
-            generator = np.random
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
-
-        # 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
-
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        latents_dtype = prompt_embeds.dtype
-        image = image.astype(latents_dtype)
-
-        # encode the init image into latents and scale the latents
-        init_latents = self.vae_encoder(sample=image)[0]
-        init_latents = 0.18215 * init_latents
-
-        # Expand init_latents for batch_size and num_images_per_prompt
-        init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
-        init_latents_orig = init_latents
-
-        # preprocess mask
-        if not isinstance(mask_image, np.ndarray):
-            mask_image = preprocess_mask(mask_image, 8)
-        mask_image = mask_image.astype(latents_dtype)
-        mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0)
-
-        # check sizes
-        if not mask.shape == init_latents.shape:
-            raise ValueError("The mask and image should be the same size!")
-
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-
-        timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
-        timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
-
-        # add noise to latents using the timesteps
-        noise = generator.randn(*init_latents.shape).astype(latents_dtype)
-        init_latents = self.scheduler.add_noise(
-            torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
-        )
-        init_latents = init_latents.numpy()
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (?) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to ? 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
-
-        latents = init_latents
-
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:].numpy()
-        timestep_dtype = next(
-            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
-        )
-        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            timestep = np.array([t], dtype=timestep_dtype)
-            noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
-                0
-            ]
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 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(
-                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
-            ).prev_sample
-
-            latents = latents.numpy()
-
-            init_latents_proper = self.scheduler.add_noise(
-                torch.from_numpy(init_latents_orig), torch.from_numpy(noise), torch.from_numpy(np.array([t]))
-            )
-
-            init_latents_proper = init_latents_proper.numpy()
-
-            latents = (init_latents_proper * mask) + (latents * (1 - mask))
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        latents = 1 / 0.18215 * latents
-        # image = self.vae_decoder(latent_sample=latents)[0]
-        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
-        image = np.concatenate(
-            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
-        )
-
-        image = np.clip(image / 2 + 0.5, 0, 1)
-        image = image.transpose((0, 2, 3, 1))
-
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(
-                self.numpy_to_pil(image), return_tensors="np"
-            ).pixel_values.astype(image.dtype)
-            # There will throw an error if use safety_checker batchsize>1
-            images, has_nsfw_concept = [], []
-            for i in range(image.shape[0]):
-                image_i, has_nsfw_concept_i = self.safety_checker(
-                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
-                )
-                images.append(image_i)
-                has_nsfw_concept.append(has_nsfw_concept_i[0])
-            image = np.concatenate(images)
-        else:
-            has_nsfw_concept = None
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/utils.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/utils.py
deleted file mode 100644
index ab9b53f37f7be1f52fe63c5e53df64ac1303b9e0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/utils.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-
-
-def images_to_levels(target, num_levels):
-    """Convert targets by image to targets by feature level.
-
-    [target_img0, target_img1] -> [target_level0, target_level1, ...]
-    """
-    target = torch.stack(target, 0)
-    level_targets = []
-    start = 0
-    for n in num_levels:
-        end = start + n
-        # level_targets.append(target[:, start:end].squeeze(0))
-        level_targets.append(target[:, start:end])
-        start = end
-    return level_targets
-
-
-def anchor_inside_flags(flat_anchors,
-                        valid_flags,
-                        img_shape,
-                        allowed_border=0):
-    """Check whether the anchors are inside the border.
-
-    Args:
-        flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4).
-        valid_flags (torch.Tensor): An existing valid flags of anchors.
-        img_shape (tuple(int)): Shape of current image.
-        allowed_border (int, optional): The border to allow the valid anchor.
-            Defaults to 0.
-
-    Returns:
-        torch.Tensor: Flags indicating whether the anchors are inside a \
-            valid range.
-    """
-    img_h, img_w = img_shape[:2]
-    if allowed_border >= 0:
-        inside_flags = valid_flags & \
-            (flat_anchors[:, 0] >= -allowed_border) & \
-            (flat_anchors[:, 1] >= -allowed_border) & \
-            (flat_anchors[:, 2] < img_w + allowed_border) & \
-            (flat_anchors[:, 3] < img_h + allowed_border)
-    else:
-        inside_flags = valid_flags
-    return inside_flags
-
-
-def calc_region(bbox, ratio, featmap_size=None):
-    """Calculate a proportional bbox region.
-
-    The bbox center are fixed and the new h' and w' is h * ratio and w * ratio.
-
-    Args:
-        bbox (Tensor): Bboxes to calculate regions, shape (n, 4).
-        ratio (float): Ratio of the output region.
-        featmap_size (tuple): Feature map size used for clipping the boundary.
-
-    Returns:
-        tuple: x1, y1, x2, y2
-    """
-    x1 = torch.round((1 - ratio) * bbox[0] + ratio * bbox[2]).long()
-    y1 = torch.round((1 - ratio) * bbox[1] + ratio * bbox[3]).long()
-    x2 = torch.round(ratio * bbox[0] + (1 - ratio) * bbox[2]).long()
-    y2 = torch.round(ratio * bbox[1] + (1 - ratio) * bbox[3]).long()
-    if featmap_size is not None:
-        x1 = x1.clamp(min=0, max=featmap_size[1])
-        y1 = y1.clamp(min=0, max=featmap_size[0])
-        x2 = x2.clamp(min=0, max=featmap_size[1])
-        y2 = y2.clamp(min=0, max=featmap_size[0])
-    return (x1, y1, x2, y2)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/pisa_loss.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/pisa_loss.py
deleted file mode 100644
index 4a48adfcd400bb07b719a6fbd5a8af0508820629..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/pisa_loss.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import mmcv
-import torch
-
-from mmdet.core import bbox_overlaps
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def isr_p(cls_score,
-          bbox_pred,
-          bbox_targets,
-          rois,
-          sampling_results,
-          loss_cls,
-          bbox_coder,
-          k=2,
-          bias=0,
-          num_class=80):
-    """Importance-based Sample Reweighting (ISR_P), positive part.
-
-    Args:
-        cls_score (Tensor): Predicted classification scores.
-        bbox_pred (Tensor): Predicted bbox deltas.
-        bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are
-            labels, label_weights, bbox_targets, bbox_weights, respectively.
-        rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs
-            (two_stage) in shape (n, 5).
-        sampling_results (obj): Sampling results.
-        loss_cls (func): Classification loss func of the head.
-        bbox_coder (obj): BBox coder of the head.
-        k (float): Power of the non-linear mapping.
-        bias (float): Shift of the non-linear mapping.
-        num_class (int): Number of classes, default: 80.
-
-    Return:
-        tuple([Tensor]): labels, imp_based_label_weights, bbox_targets,
-            bbox_target_weights
-    """
-
-    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
-    pos_label_inds = ((labels >= 0) &
-                      (labels < num_class)).nonzero().reshape(-1)
-    pos_labels = labels[pos_label_inds]
-
-    # if no positive samples, return the original targets
-    num_pos = float(pos_label_inds.size(0))
-    if num_pos == 0:
-        return labels, label_weights, bbox_targets, bbox_weights
-
-    # merge pos_assigned_gt_inds of per image to a single tensor
-    gts = list()
-    last_max_gt = 0
-    for i in range(len(sampling_results)):
-        gt_i = sampling_results[i].pos_assigned_gt_inds
-        gts.append(gt_i + last_max_gt)
-        if len(gt_i) != 0:
-            last_max_gt = gt_i.max() + 1
-    gts = torch.cat(gts)
-    assert len(gts) == num_pos
-
-    cls_score = cls_score.detach()
-    bbox_pred = bbox_pred.detach()
-
-    # For single stage detectors, rois here indicate anchors, in shape (N, 4)
-    # For two stage detectors, rois are in shape (N, 5)
-    if rois.size(-1) == 5:
-        pos_rois = rois[pos_label_inds][:, 1:]
-    else:
-        pos_rois = rois[pos_label_inds]
-
-    if bbox_pred.size(-1) > 4:
-        bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
-        pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4)
-    else:
-        pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4)
-
-    # compute iou of the predicted bbox and the corresponding GT
-    pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4)
-    pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred)
-    target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target)
-    ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True)
-
-    pos_imp_weights = label_weights[pos_label_inds]
-    # Two steps to compute IoU-HLR. Samples are first sorted by IoU locally,
-    # then sorted again within the same-rank group
-    max_l_num = pos_labels.bincount().max()
-    for label in pos_labels.unique():
-        l_inds = (pos_labels == label).nonzero().view(-1)
-        l_gts = gts[l_inds]
-        for t in l_gts.unique():
-            t_inds = l_inds[l_gts == t]
-            t_ious = ious[t_inds]
-            _, t_iou_rank_idx = t_ious.sort(descending=True)
-            _, t_iou_rank = t_iou_rank_idx.sort()
-            ious[t_inds] += max_l_num - t_iou_rank.float()
-        l_ious = ious[l_inds]
-        _, l_iou_rank_idx = l_ious.sort(descending=True)
-        _, l_iou_rank = l_iou_rank_idx.sort()  # IoU-HLR
-        # linearly map HLR to label weights
-        pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num
-
-    pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k)
-
-    # normalize to make the new weighted loss value equal to the original loss
-    pos_loss_cls = loss_cls(
-        cls_score[pos_label_inds], pos_labels, reduction_override='none')
-    if pos_loss_cls.dim() > 1:
-        ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:,
-                                                                        None]
-        new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None]
-    else:
-        ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds]
-        new_pos_loss_cls = pos_loss_cls * pos_imp_weights
-    pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum()
-    pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio
-    label_weights[pos_label_inds] = pos_imp_weights
-
-    bbox_targets = labels, label_weights, bbox_targets, bbox_weights
-    return bbox_targets
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def carl_loss(cls_score,
-              labels,
-              bbox_pred,
-              bbox_targets,
-              loss_bbox,
-              k=1,
-              bias=0.2,
-              avg_factor=None,
-              sigmoid=False,
-              num_class=80):
-    """Classification-Aware Regression Loss (CARL).
-
-    Args:
-        cls_score (Tensor): Predicted classification scores.
-        labels (Tensor): Targets of classification.
-        bbox_pred (Tensor): Predicted bbox deltas.
-        bbox_targets (Tensor): Target of bbox regression.
-        loss_bbox (func): Regression loss func of the head.
-        bbox_coder (obj): BBox coder of the head.
-        k (float): Power of the non-linear mapping.
-        bias (float): Shift of the non-linear mapping.
-        avg_factor (int): Average factor used in regression loss.
-        sigmoid (bool): Activation of the classification score.
-        num_class (int): Number of classes, default: 80.
-
-    Return:
-        dict: CARL loss dict.
-    """
-    pos_label_inds = ((labels >= 0) &
-                      (labels < num_class)).nonzero().reshape(-1)
-    if pos_label_inds.numel() == 0:
-        return dict(loss_carl=cls_score.sum()[None] * 0.)
-    pos_labels = labels[pos_label_inds]
-
-    # multiply pos_cls_score with the corresponding bbox weight
-    # and remain gradient
-    if sigmoid:
-        pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]
-    else:
-        pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]
-    carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)
-
-    # normalize carl_loss_weight to make its sum equal to num positive
-    num_pos = float(pos_cls_score.size(0))
-    weight_ratio = num_pos / carl_loss_weights.sum()
-    carl_loss_weights *= weight_ratio
-
-    if avg_factor is None:
-        avg_factor = bbox_targets.size(0)
-    # if is class agnostic, bbox pred is in shape (N, 4)
-    # otherwise, bbox pred is in shape (N, #classes, 4)
-    if bbox_pred.size(-1) > 4:
-        bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
-        pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]
-    else:
-        pos_bbox_preds = bbox_pred[pos_label_inds]
-    ori_loss_reg = loss_bbox(
-        pos_bbox_preds,
-        bbox_targets[pos_label_inds],
-        reduction_override='none') / avg_factor
-    loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()
-    return dict(loss_carl=loss_carl[None])
diff --git a/spaces/Andy1621/uniformer_video_demo/uniformer.py b/spaces/Andy1621/uniformer_video_demo/uniformer.py
deleted file mode 100644
index 3c239b96656c9ebdb5c66048c9c6cdfc27d44c34..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_video_demo/uniformer.py
+++ /dev/null
@@ -1,379 +0,0 @@
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-from functools import partial
-from timm.models.layers import trunc_normal_, DropPath, to_2tuple
-
-
-def conv_3xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
-    return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (2, stride, stride), (1, 0, 0), groups=groups)
-    
-def conv_1xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
-    return nn.Conv3d(inp, oup, (1, kernel_size, kernel_size), (1, stride, stride), (0, 0, 0), groups=groups)
-
-def conv_3xnxn_std(inp, oup, kernel_size=3, stride=3, groups=1):
-    return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (1, stride, stride), (1, 0, 0), groups=groups)
-
-def conv_1x1x1(inp, oup, groups=1):
-    return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
-
-def conv_3x3x3(inp, oup, groups=1):
-    return nn.Conv3d(inp, oup, (3, 3, 3), (1, 1, 1), (1, 1, 1), groups=groups)
-
-def conv_5x5x5(inp, oup, groups=1):
-    return nn.Conv3d(inp, oup, (5, 5, 5), (1, 1, 1), (2, 2, 2), groups=groups)
-
-def bn_3d(dim):
-    return nn.BatchNorm3d(dim)
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class CMlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = conv_1x1x1(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = conv_1x1x1(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-    
-    
-class CBlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
-        self.norm1 = bn_3d(dim)
-        self.conv1 = conv_1x1x1(dim, dim, 1)
-        self.conv2 = conv_1x1x1(dim, dim, 1)
-        self.attn = conv_5x5x5(dim, dim, groups=dim)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = bn_3d(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x   
-
-
-class SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, C, T, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.transpose(1, 2).reshape(B, C, T, H, W)
-        return x    
-
-
-class SplitSABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
-        self.t_norm = norm_layer(dim)
-        self.t_attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, C, T, H, W = x.shape
-        attn = x.view(B, C, T, H * W).permute(0, 3, 2, 1).contiguous()
-        attn = attn.view(B * H * W, T, C)
-        attn = attn + self.drop_path(self.t_attn(self.t_norm(attn)))
-        attn = attn.view(B, H * W, T, C).permute(0, 2, 1, 3).contiguous()
-        attn = attn.view(B * T, H * W, C)
-        residual = x.view(B, C, T, H * W).permute(0, 2, 3, 1).contiguous()
-        residual = residual.view(B * T, H * W, C)
-        attn = residual + self.drop_path(self.attn(self.norm1(attn)))
-        attn = attn.view(B, T * H * W, C)
-        out = attn + self.drop_path(self.mlp(self.norm2(attn)))
-        out = out.transpose(1, 2).reshape(B, C, T, H, W)
-        return out
-
-
-class SpeicalPatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        self.proj = conv_3xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-
-    def forward(self, x):
-        B, C, T, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1], \
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x)
-        B, C, T, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
-        return x
-    
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, std=False):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        if std:
-            self.proj = conv_3xnxn_std(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-        else:
-            self.proj = conv_1xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-
-    def forward(self, x):
-        B, C, T, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1], \
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x)
-        B, C, T, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
-        return x
-
-
-class Uniformer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, depth=[5, 8, 20, 7], num_classes=400, img_size=224, in_chans=3, embed_dim=[64, 128, 320, 512],
-                 head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0.3, attn_drop_rate=0., drop_path_rate=0., norm_layer=None, split=False, std=False):
-        super().__init__()
-
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = partial(nn.LayerNorm, eps=1e-6) 
-        
-        self.patch_embed1 = SpeicalPatchEmbed(
-            img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
-        self.patch_embed2 = PatchEmbed(
-            img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1], std=std)
-        self.patch_embed3 = PatchEmbed(
-            img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2], std=std)
-        self.patch_embed4 = PatchEmbed(
-            img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3], std=std)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))]  # stochastic depth decay rule
-        num_heads = [dim // head_dim for dim in embed_dim]
-        self.blocks1 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
-            for i in range(depth[0])])
-        self.blocks2 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]], norm_layer=norm_layer)
-            for i in range(depth[1])])
-        if split:
-            self.blocks3 = nn.ModuleList([
-                SplitSABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
-                for i in range(depth[2])])
-            self.blocks4 = nn.ModuleList([
-                SplitSABlock(
-                    dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
-            for i in range(depth[3])])
-        else:
-            self.blocks3 = nn.ModuleList([
-                SABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
-                for i in range(depth[2])])
-            self.blocks4 = nn.ModuleList([
-                SABlock(
-                    dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
-            for i in range(depth[3])])
-        self.norm = bn_3d(embed_dim[-1])
-        
-        # Representation layer
-        if representation_size:
-            self.num_features = representation_size
-            self.pre_logits = nn.Sequential(OrderedDict([
-                ('fc', nn.Linear(embed_dim, representation_size)),
-                ('act', nn.Tanh())
-            ]))
-        else:
-            self.pre_logits = nn.Identity()
-        
-        # Classifier head
-        self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
-        
-        self.apply(self._init_weights)
-
-        for name, p in self.named_parameters():
-            # fill proj weight with 1 here to improve training dynamics. Otherwise temporal attention inputs
-            # are multiplied by 0*0, which is hard for the model to move out of.
-            if 't_attn.qkv.weight' in name:
-                nn.init.constant_(p, 0)
-            if 't_attn.qkv.bias' in name:
-                nn.init.constant_(p, 0)
-            if 't_attn.proj.weight' in name:
-                nn.init.constant_(p, 1)
-            if 't_attn.proj.bias' in name:
-                nn.init.constant_(p, 0)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        x = self.patch_embed1(x)
-        x = self.pos_drop(x)
-        for blk in self.blocks1:
-            x = blk(x)
-        x = self.patch_embed2(x)
-        for blk in self.blocks2:
-            x = blk(x)
-        x = self.patch_embed3(x)
-        for blk in self.blocks3:
-            x = blk(x)
-        x = self.patch_embed4(x)
-        for blk in self.blocks4:
-            x = blk(x)
-        x = self.norm(x)
-        x = self.pre_logits(x)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = x.flatten(2).mean(-1)
-        x = self.head(x)
-        return x
-        
-
-def uniformer_small():
-    return Uniformer(
-        depth=[3, 4, 8, 3], embed_dim=[64, 128, 320, 512], 
-        head_dim=64, drop_rate=0.1)
-
-def uniformer_base():
-    return Uniformer(
-        depth=[5, 8, 20, 7], embed_dim=[64, 128, 320, 512], 
-        head_dim=64, drop_rate=0.3)
\ No newline at end of file
diff --git a/spaces/ArpitM/chat-llm-streaming/README.md b/spaces/ArpitM/chat-llm-streaming/README.md
deleted file mode 100644
index 349f818153093c6374a473a4ca5d5cb6cc8d1b52..0000000000000000000000000000000000000000
--- a/spaces/ArpitM/chat-llm-streaming/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chat Llm Streaming
-emoji: 📊
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/syntax.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/syntax.py
deleted file mode 100644
index 25b226a3a986c507747c8b40dc17f7a8017e73e1..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/syntax.py
+++ /dev/null
@@ -1,950 +0,0 @@
-import os.path
-import platform
-import re
-import sys
-import textwrap
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import (
-    Any,
-    Dict,
-    Iterable,
-    List,
-    NamedTuple,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pip._vendor.pygments.lexer import Lexer
-from pip._vendor.pygments.lexers import get_lexer_by_name, guess_lexer_for_filename
-from pip._vendor.pygments.style import Style as PygmentsStyle
-from pip._vendor.pygments.styles import get_style_by_name
-from pip._vendor.pygments.token import (
-    Comment,
-    Error,
-    Generic,
-    Keyword,
-    Name,
-    Number,
-    Operator,
-    String,
-    Token,
-    Whitespace,
-)
-from pip._vendor.pygments.util import ClassNotFound
-
-from pip._vendor.rich.containers import Lines
-from pip._vendor.rich.padding import Padding, PaddingDimensions
-
-from ._loop import loop_first
-from .cells import cell_len
-from .color import Color, blend_rgb
-from .console import Console, ConsoleOptions, JustifyMethod, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment, Segments
-from .style import Style, StyleType
-from .text import Text
-
-TokenType = Tuple[str, ...]
-
-WINDOWS = platform.system() == "Windows"
-DEFAULT_THEME = "monokai"
-
-# The following styles are based on https://github.com/pygments/pygments/blob/master/pygments/formatters/terminal.py
-# A few modifications were made
-
-ANSI_LIGHT: Dict[TokenType, Style] = {
-    Token: Style(),
-    Whitespace: Style(color="white"),
-    Comment: Style(dim=True),
-    Comment.Preproc: Style(color="cyan"),
-    Keyword: Style(color="blue"),
-    Keyword.Type: Style(color="cyan"),
-    Operator.Word: Style(color="magenta"),
-    Name.Builtin: Style(color="cyan"),
-    Name.Function: Style(color="green"),
-    Name.Namespace: Style(color="cyan", underline=True),
-    Name.Class: Style(color="green", underline=True),
-    Name.Exception: Style(color="cyan"),
-    Name.Decorator: Style(color="magenta", bold=True),
-    Name.Variable: Style(color="red"),
-    Name.Constant: Style(color="red"),
-    Name.Attribute: Style(color="cyan"),
-    Name.Tag: Style(color="bright_blue"),
-    String: Style(color="yellow"),
-    Number: Style(color="blue"),
-    Generic.Deleted: Style(color="bright_red"),
-    Generic.Inserted: Style(color="green"),
-    Generic.Heading: Style(bold=True),
-    Generic.Subheading: Style(color="magenta", bold=True),
-    Generic.Prompt: Style(bold=True),
-    Generic.Error: Style(color="bright_red"),
-    Error: Style(color="red", underline=True),
-}
-
-ANSI_DARK: Dict[TokenType, Style] = {
-    Token: Style(),
-    Whitespace: Style(color="bright_black"),
-    Comment: Style(dim=True),
-    Comment.Preproc: Style(color="bright_cyan"),
-    Keyword: Style(color="bright_blue"),
-    Keyword.Type: Style(color="bright_cyan"),
-    Operator.Word: Style(color="bright_magenta"),
-    Name.Builtin: Style(color="bright_cyan"),
-    Name.Function: Style(color="bright_green"),
-    Name.Namespace: Style(color="bright_cyan", underline=True),
-    Name.Class: Style(color="bright_green", underline=True),
-    Name.Exception: Style(color="bright_cyan"),
-    Name.Decorator: Style(color="bright_magenta", bold=True),
-    Name.Variable: Style(color="bright_red"),
-    Name.Constant: Style(color="bright_red"),
-    Name.Attribute: Style(color="bright_cyan"),
-    Name.Tag: Style(color="bright_blue"),
-    String: Style(color="yellow"),
-    Number: Style(color="bright_blue"),
-    Generic.Deleted: Style(color="bright_red"),
-    Generic.Inserted: Style(color="bright_green"),
-    Generic.Heading: Style(bold=True),
-    Generic.Subheading: Style(color="bright_magenta", bold=True),
-    Generic.Prompt: Style(bold=True),
-    Generic.Error: Style(color="bright_red"),
-    Error: Style(color="red", underline=True),
-}
-
-RICH_SYNTAX_THEMES = {"ansi_light": ANSI_LIGHT, "ansi_dark": ANSI_DARK}
-NUMBERS_COLUMN_DEFAULT_PADDING = 2
-
-
-class SyntaxTheme(ABC):
-    """Base class for a syntax theme."""
-
-    @abstractmethod
-    def get_style_for_token(self, token_type: TokenType) -> Style:
-        """Get a style for a given Pygments token."""
-        raise NotImplementedError  # pragma: no cover
-
-    @abstractmethod
-    def get_background_style(self) -> Style:
-        """Get the background color."""
-        raise NotImplementedError  # pragma: no cover
-
-
-class PygmentsSyntaxTheme(SyntaxTheme):
-    """Syntax theme that delegates to Pygments theme."""
-
-    def __init__(self, theme: Union[str, Type[PygmentsStyle]]) -> None:
-        self._style_cache: Dict[TokenType, Style] = {}
-        if isinstance(theme, str):
-            try:
-                self._pygments_style_class = get_style_by_name(theme)
-            except ClassNotFound:
-                self._pygments_style_class = get_style_by_name("default")
-        else:
-            self._pygments_style_class = theme
-
-        self._background_color = self._pygments_style_class.background_color
-        self._background_style = Style(bgcolor=self._background_color)
-
-    def get_style_for_token(self, token_type: TokenType) -> Style:
-        """Get a style from a Pygments class."""
-        try:
-            return self._style_cache[token_type]
-        except KeyError:
-            try:
-                pygments_style = self._pygments_style_class.style_for_token(token_type)
-            except KeyError:
-                style = Style.null()
-            else:
-                color = pygments_style["color"]
-                bgcolor = pygments_style["bgcolor"]
-                style = Style(
-                    color="#" + color if color else "#000000",
-                    bgcolor="#" + bgcolor if bgcolor else self._background_color,
-                    bold=pygments_style["bold"],
-                    italic=pygments_style["italic"],
-                    underline=pygments_style["underline"],
-                )
-            self._style_cache[token_type] = style
-        return style
-
-    def get_background_style(self) -> Style:
-        return self._background_style
-
-
-class ANSISyntaxTheme(SyntaxTheme):
-    """Syntax theme to use standard colors."""
-
-    def __init__(self, style_map: Dict[TokenType, Style]) -> None:
-        self.style_map = style_map
-        self._missing_style = Style.null()
-        self._background_style = Style.null()
-        self._style_cache: Dict[TokenType, Style] = {}
-
-    def get_style_for_token(self, token_type: TokenType) -> Style:
-        """Look up style in the style map."""
-        try:
-            return self._style_cache[token_type]
-        except KeyError:
-            # Styles form a hierarchy
-            # We need to go from most to least specific
-            # e.g. ("foo", "bar", "baz") to ("foo", "bar")  to ("foo",)
-            get_style = self.style_map.get
-            token = tuple(token_type)
-            style = self._missing_style
-            while token:
-                _style = get_style(token)
-                if _style is not None:
-                    style = _style
-                    break
-                token = token[:-1]
-            self._style_cache[token_type] = style
-            return style
-
-    def get_background_style(self) -> Style:
-        return self._background_style
-
-
-SyntaxPosition = Tuple[int, int]
-
-
-class _SyntaxHighlightRange(NamedTuple):
-    """
-    A range to highlight in a Syntax object.
-    `start` and `end` are 2-integers tuples, where the first integer is the line number
-    (starting from 1) and the second integer is the column index (starting from 0).
-    """
-
-    style: StyleType
-    start: SyntaxPosition
-    end: SyntaxPosition
-
-
-class Syntax(JupyterMixin):
-    """Construct a Syntax object to render syntax highlighted code.
-
-    Args:
-        code (str): Code to highlight.
-        lexer (Lexer | str): Lexer to use (see https://pygments.org/docs/lexers/)
-        theme (str, optional): Color theme, aka Pygments style (see https://pygments.org/docs/styles/#getting-a-list-of-available-styles). Defaults to "monokai".
-        dedent (bool, optional): Enable stripping of initial whitespace. Defaults to False.
-        line_numbers (bool, optional): Enable rendering of line numbers. Defaults to False.
-        start_line (int, optional): Starting number for line numbers. Defaults to 1.
-        line_range (Tuple[int | None, int | None], optional): If given should be a tuple of the start and end line to render.
-            A value of None in the tuple indicates the range is open in that direction.
-        highlight_lines (Set[int]): A set of line numbers to highlight.
-        code_width: Width of code to render (not including line numbers), or ``None`` to use all available width.
-        tab_size (int, optional): Size of tabs. Defaults to 4.
-        word_wrap (bool, optional): Enable word wrapping.
-        background_color (str, optional): Optional background color, or None to use theme color. Defaults to None.
-        indent_guides (bool, optional): Show indent guides. Defaults to False.
-        padding (PaddingDimensions): Padding to apply around the syntax. Defaults to 0 (no padding).
-    """
-
-    _pygments_style_class: Type[PygmentsStyle]
-    _theme: SyntaxTheme
-
-    @classmethod
-    def get_theme(cls, name: Union[str, SyntaxTheme]) -> SyntaxTheme:
-        """Get a syntax theme instance."""
-        if isinstance(name, SyntaxTheme):
-            return name
-        theme: SyntaxTheme
-        if name in RICH_SYNTAX_THEMES:
-            theme = ANSISyntaxTheme(RICH_SYNTAX_THEMES[name])
-        else:
-            theme = PygmentsSyntaxTheme(name)
-        return theme
-
-    def __init__(
-        self,
-        code: str,
-        lexer: Union[Lexer, str],
-        *,
-        theme: Union[str, SyntaxTheme] = DEFAULT_THEME,
-        dedent: bool = False,
-        line_numbers: bool = False,
-        start_line: int = 1,
-        line_range: Optional[Tuple[Optional[int], Optional[int]]] = None,
-        highlight_lines: Optional[Set[int]] = None,
-        code_width: Optional[int] = None,
-        tab_size: int = 4,
-        word_wrap: bool = False,
-        background_color: Optional[str] = None,
-        indent_guides: bool = False,
-        padding: PaddingDimensions = 0,
-    ) -> None:
-        self.code = code
-        self._lexer = lexer
-        self.dedent = dedent
-        self.line_numbers = line_numbers
-        self.start_line = start_line
-        self.line_range = line_range
-        self.highlight_lines = highlight_lines or set()
-        self.code_width = code_width
-        self.tab_size = tab_size
-        self.word_wrap = word_wrap
-        self.background_color = background_color
-        self.background_style = (
-            Style(bgcolor=background_color) if background_color else Style()
-        )
-        self.indent_guides = indent_guides
-        self.padding = padding
-
-        self._theme = self.get_theme(theme)
-        self._stylized_ranges: List[_SyntaxHighlightRange] = []
-
-    @classmethod
-    def from_path(
-        cls,
-        path: str,
-        encoding: str = "utf-8",
-        lexer: Optional[Union[Lexer, str]] = None,
-        theme: Union[str, SyntaxTheme] = DEFAULT_THEME,
-        dedent: bool = False,
-        line_numbers: bool = False,
-        line_range: Optional[Tuple[int, int]] = None,
-        start_line: int = 1,
-        highlight_lines: Optional[Set[int]] = None,
-        code_width: Optional[int] = None,
-        tab_size: int = 4,
-        word_wrap: bool = False,
-        background_color: Optional[str] = None,
-        indent_guides: bool = False,
-        padding: PaddingDimensions = 0,
-    ) -> "Syntax":
-        """Construct a Syntax object from a file.
-
-        Args:
-            path (str): Path to file to highlight.
-            encoding (str): Encoding of file.
-            lexer (str | Lexer, optional): Lexer to use. If None, lexer will be auto-detected from path/file content.
-            theme (str, optional): Color theme, aka Pygments style (see https://pygments.org/docs/styles/#getting-a-list-of-available-styles). Defaults to "emacs".
-            dedent (bool, optional): Enable stripping of initial whitespace. Defaults to True.
-            line_numbers (bool, optional): Enable rendering of line numbers. Defaults to False.
-            start_line (int, optional): Starting number for line numbers. Defaults to 1.
-            line_range (Tuple[int, int], optional): If given should be a tuple of the start and end line to render.
-            highlight_lines (Set[int]): A set of line numbers to highlight.
-            code_width: Width of code to render (not including line numbers), or ``None`` to use all available width.
-            tab_size (int, optional): Size of tabs. Defaults to 4.
-            word_wrap (bool, optional): Enable word wrapping of code.
-            background_color (str, optional): Optional background color, or None to use theme color. Defaults to None.
-            indent_guides (bool, optional): Show indent guides. Defaults to False.
-            padding (PaddingDimensions): Padding to apply around the syntax. Defaults to 0 (no padding).
-
-        Returns:
-            [Syntax]: A Syntax object that may be printed to the console
-        """
-        code = Path(path).read_text(encoding=encoding)
-
-        if not lexer:
-            lexer = cls.guess_lexer(path, code=code)
-
-        return cls(
-            code,
-            lexer,
-            theme=theme,
-            dedent=dedent,
-            line_numbers=line_numbers,
-            line_range=line_range,
-            start_line=start_line,
-            highlight_lines=highlight_lines,
-            code_width=code_width,
-            tab_size=tab_size,
-            word_wrap=word_wrap,
-            background_color=background_color,
-            indent_guides=indent_guides,
-            padding=padding,
-        )
-
-    @classmethod
-    def guess_lexer(cls, path: str, code: Optional[str] = None) -> str:
-        """Guess the alias of the Pygments lexer to use based on a path and an optional string of code.
-        If code is supplied, it will use a combination of the code and the filename to determine the
-        best lexer to use. For example, if the file is ``index.html`` and the file contains Django
-        templating syntax, then "html+django" will be returned. If the file is ``index.html``, and no
-        templating language is used, the "html" lexer will be used. If no string of code
-        is supplied, the lexer will be chosen based on the file extension..
-
-        Args:
-             path (AnyStr): The path to the file containing the code you wish to know the lexer for.
-             code (str, optional): Optional string of code that will be used as a fallback if no lexer
-                is found for the supplied path.
-
-        Returns:
-            str: The name of the Pygments lexer that best matches the supplied path/code.
-        """
-        lexer: Optional[Lexer] = None
-        lexer_name = "default"
-        if code:
-            try:
-                lexer = guess_lexer_for_filename(path, code)
-            except ClassNotFound:
-                pass
-
-        if not lexer:
-            try:
-                _, ext = os.path.splitext(path)
-                if ext:
-                    extension = ext.lstrip(".").lower()
-                    lexer = get_lexer_by_name(extension)
-            except ClassNotFound:
-                pass
-
-        if lexer:
-            if lexer.aliases:
-                lexer_name = lexer.aliases[0]
-            else:
-                lexer_name = lexer.name
-
-        return lexer_name
-
-    def _get_base_style(self) -> Style:
-        """Get the base style."""
-        default_style = self._theme.get_background_style() + self.background_style
-        return default_style
-
-    def _get_token_color(self, token_type: TokenType) -> Optional[Color]:
-        """Get a color (if any) for the given token.
-
-        Args:
-            token_type (TokenType): A token type tuple from Pygments.
-
-        Returns:
-            Optional[Color]: Color from theme, or None for no color.
-        """
-        style = self._theme.get_style_for_token(token_type)
-        return style.color
-
-    @property
-    def lexer(self) -> Optional[Lexer]:
-        """The lexer for this syntax, or None if no lexer was found.
-
-        Tries to find the lexer by name if a string was passed to the constructor.
-        """
-
-        if isinstance(self._lexer, Lexer):
-            return self._lexer
-        try:
-            return get_lexer_by_name(
-                self._lexer,
-                stripnl=False,
-                ensurenl=True,
-                tabsize=self.tab_size,
-            )
-        except ClassNotFound:
-            return None
-
-    def highlight(
-        self,
-        code: str,
-        line_range: Optional[Tuple[Optional[int], Optional[int]]] = None,
-    ) -> Text:
-        """Highlight code and return a Text instance.
-
-        Args:
-            code (str): Code to highlight.
-            line_range(Tuple[int, int], optional): Optional line range to highlight.
-
-        Returns:
-            Text: A text instance containing highlighted syntax.
-        """
-
-        base_style = self._get_base_style()
-        justify: JustifyMethod = (
-            "default" if base_style.transparent_background else "left"
-        )
-
-        text = Text(
-            justify=justify,
-            style=base_style,
-            tab_size=self.tab_size,
-            no_wrap=not self.word_wrap,
-        )
-        _get_theme_style = self._theme.get_style_for_token
-
-        lexer = self.lexer
-
-        if lexer is None:
-            text.append(code)
-        else:
-            if line_range:
-                # More complicated path to only stylize a portion of the code
-                # This speeds up further operations as there are less spans to process
-                line_start, line_end = line_range
-
-                def line_tokenize() -> Iterable[Tuple[Any, str]]:
-                    """Split tokens to one per line."""
-                    assert lexer  # required to make MyPy happy - we know lexer is not None at this point
-
-                    for token_type, token in lexer.get_tokens(code):
-                        while token:
-                            line_token, new_line, token = token.partition("\n")
-                            yield token_type, line_token + new_line
-
-                def tokens_to_spans() -> Iterable[Tuple[str, Optional[Style]]]:
-                    """Convert tokens to spans."""
-                    tokens = iter(line_tokenize())
-                    line_no = 0
-                    _line_start = line_start - 1 if line_start else 0
-
-                    # Skip over tokens until line start
-                    while line_no < _line_start:
-                        try:
-                            _token_type, token = next(tokens)
-                        except StopIteration:
-                            break
-                        yield (token, None)
-                        if token.endswith("\n"):
-                            line_no += 1
-                    # Generate spans until line end
-                    for token_type, token in tokens:
-                        yield (token, _get_theme_style(token_type))
-                        if token.endswith("\n"):
-                            line_no += 1
-                            if line_end and line_no >= line_end:
-                                break
-
-                text.append_tokens(tokens_to_spans())
-
-            else:
-                text.append_tokens(
-                    (token, _get_theme_style(token_type))
-                    for token_type, token in lexer.get_tokens(code)
-                )
-            if self.background_color is not None:
-                text.stylize(f"on {self.background_color}")
-
-        if self._stylized_ranges:
-            self._apply_stylized_ranges(text)
-
-        return text
-
-    def stylize_range(
-        self, style: StyleType, start: SyntaxPosition, end: SyntaxPosition
-    ) -> None:
-        """
-        Adds a custom style on a part of the code, that will be applied to the syntax display when it's rendered.
-        Line numbers are 1-based, while column indexes are 0-based.
-
-        Args:
-            style (StyleType): The style to apply.
-            start (Tuple[int, int]): The start of the range, in the form `[line number, column index]`.
-            end (Tuple[int, int]): The end of the range, in the form `[line number, column index]`.
-        """
-        self._stylized_ranges.append(_SyntaxHighlightRange(style, start, end))
-
-    def _get_line_numbers_color(self, blend: float = 0.3) -> Color:
-        background_style = self._theme.get_background_style() + self.background_style
-        background_color = background_style.bgcolor
-        if background_color is None or background_color.is_system_defined:
-            return Color.default()
-        foreground_color = self._get_token_color(Token.Text)
-        if foreground_color is None or foreground_color.is_system_defined:
-            return foreground_color or Color.default()
-        new_color = blend_rgb(
-            background_color.get_truecolor(),
-            foreground_color.get_truecolor(),
-            cross_fade=blend,
-        )
-        return Color.from_triplet(new_color)
-
-    @property
-    def _numbers_column_width(self) -> int:
-        """Get the number of characters used to render the numbers column."""
-        column_width = 0
-        if self.line_numbers:
-            column_width = (
-                len(str(self.start_line + self.code.count("\n")))
-                + NUMBERS_COLUMN_DEFAULT_PADDING
-            )
-        return column_width
-
-    def _get_number_styles(self, console: Console) -> Tuple[Style, Style, Style]:
-        """Get background, number, and highlight styles for line numbers."""
-        background_style = self._get_base_style()
-        if background_style.transparent_background:
-            return Style.null(), Style(dim=True), Style.null()
-        if console.color_system in ("256", "truecolor"):
-            number_style = Style.chain(
-                background_style,
-                self._theme.get_style_for_token(Token.Text),
-                Style(color=self._get_line_numbers_color()),
-                self.background_style,
-            )
-            highlight_number_style = Style.chain(
-                background_style,
-                self._theme.get_style_for_token(Token.Text),
-                Style(bold=True, color=self._get_line_numbers_color(0.9)),
-                self.background_style,
-            )
-        else:
-            number_style = background_style + Style(dim=True)
-            highlight_number_style = background_style + Style(dim=False)
-        return background_style, number_style, highlight_number_style
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-
-        _, right, _, left = Padding.unpack(self.padding)
-        padding = left + right
-        if self.code_width is not None:
-            width = self.code_width + self._numbers_column_width + padding + 1
-            return Measurement(self._numbers_column_width, width)
-        lines = self.code.splitlines()
-        width = (
-            self._numbers_column_width
-            + padding
-            + (max(cell_len(line) for line in lines) if lines else 0)
-        )
-        if self.line_numbers:
-            width += 1
-        return Measurement(self._numbers_column_width, width)
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        segments = Segments(self._get_syntax(console, options))
-        if self.padding:
-            yield Padding(
-                segments, style=self._theme.get_background_style(), pad=self.padding
-            )
-        else:
-            yield segments
-
-    def _get_syntax(
-        self,
-        console: Console,
-        options: ConsoleOptions,
-    ) -> Iterable[Segment]:
-        """
-        Get the Segments for the Syntax object, excluding any vertical/horizontal padding
-        """
-        transparent_background = self._get_base_style().transparent_background
-        code_width = (
-            (
-                (options.max_width - self._numbers_column_width - 1)
-                if self.line_numbers
-                else options.max_width
-            )
-            if self.code_width is None
-            else self.code_width
-        )
-
-        ends_on_nl, processed_code = self._process_code(self.code)
-        text = self.highlight(processed_code, self.line_range)
-
-        if not self.line_numbers and not self.word_wrap and not self.line_range:
-            if not ends_on_nl:
-                text.remove_suffix("\n")
-            # Simple case of just rendering text
-            style = (
-                self._get_base_style()
-                + self._theme.get_style_for_token(Comment)
-                + Style(dim=True)
-                + self.background_style
-            )
-            if self.indent_guides and not options.ascii_only:
-                text = text.with_indent_guides(self.tab_size, style=style)
-                text.overflow = "crop"
-            if style.transparent_background:
-                yield from console.render(
-                    text, options=options.update(width=code_width)
-                )
-            else:
-                syntax_lines = console.render_lines(
-                    text,
-                    options.update(width=code_width, height=None, justify="left"),
-                    style=self.background_style,
-                    pad=True,
-                    new_lines=True,
-                )
-                for syntax_line in syntax_lines:
-                    yield from syntax_line
-            return
-
-        start_line, end_line = self.line_range or (None, None)
-        line_offset = 0
-        if start_line:
-            line_offset = max(0, start_line - 1)
-        lines: Union[List[Text], Lines] = text.split("\n", allow_blank=ends_on_nl)
-        if self.line_range:
-            if line_offset > len(lines):
-                return
-            lines = lines[line_offset:end_line]
-
-        if self.indent_guides and not options.ascii_only:
-            style = (
-                self._get_base_style()
-                + self._theme.get_style_for_token(Comment)
-                + Style(dim=True)
-                + self.background_style
-            )
-            lines = (
-                Text("\n")
-                .join(lines)
-                .with_indent_guides(self.tab_size, style=style)
-                .split("\n", allow_blank=True)
-            )
-
-        numbers_column_width = self._numbers_column_width
-        render_options = options.update(width=code_width)
-
-        highlight_line = self.highlight_lines.__contains__
-        _Segment = Segment
-        new_line = _Segment("\n")
-
-        line_pointer = "> " if options.legacy_windows else "❱ "
-
-        (
-            background_style,
-            number_style,
-            highlight_number_style,
-        ) = self._get_number_styles(console)
-
-        for line_no, line in enumerate(lines, self.start_line + line_offset):
-            if self.word_wrap:
-                wrapped_lines = console.render_lines(
-                    line,
-                    render_options.update(height=None, justify="left"),
-                    style=background_style,
-                    pad=not transparent_background,
-                )
-            else:
-                segments = list(line.render(console, end=""))
-                if options.no_wrap:
-                    wrapped_lines = [segments]
-                else:
-                    wrapped_lines = [
-                        _Segment.adjust_line_length(
-                            segments,
-                            render_options.max_width,
-                            style=background_style,
-                            pad=not transparent_background,
-                        )
-                    ]
-
-            if self.line_numbers:
-                wrapped_line_left_pad = _Segment(
-                    " " * numbers_column_width + " ", background_style
-                )
-                for first, wrapped_line in loop_first(wrapped_lines):
-                    if first:
-                        line_column = str(line_no).rjust(numbers_column_width - 2) + " "
-                        if highlight_line(line_no):
-                            yield _Segment(line_pointer, Style(color="red"))
-                            yield _Segment(line_column, highlight_number_style)
-                        else:
-                            yield _Segment("  ", highlight_number_style)
-                            yield _Segment(line_column, number_style)
-                    else:
-                        yield wrapped_line_left_pad
-                    yield from wrapped_line
-                    yield new_line
-            else:
-                for wrapped_line in wrapped_lines:
-                    yield from wrapped_line
-                    yield new_line
-
-    def _apply_stylized_ranges(self, text: Text) -> None:
-        """
-        Apply stylized ranges to a text instance,
-        using the given code to determine the right portion to apply the style to.
-
-        Args:
-            text (Text): Text instance to apply the style to.
-        """
-        code = text.plain
-        newlines_offsets = [
-            # Let's add outer boundaries at each side of the list:
-            0,
-            # N.B. using "\n" here is much faster than using metacharacters such as "^" or "\Z":
-            *[
-                match.start() + 1
-                for match in re.finditer("\n", code, flags=re.MULTILINE)
-            ],
-            len(code) + 1,
-        ]
-
-        for stylized_range in self._stylized_ranges:
-            start = _get_code_index_for_syntax_position(
-                newlines_offsets, stylized_range.start
-            )
-            end = _get_code_index_for_syntax_position(
-                newlines_offsets, stylized_range.end
-            )
-            if start is not None and end is not None:
-                text.stylize(stylized_range.style, start, end)
-
-    def _process_code(self, code: str) -> Tuple[bool, str]:
-        """
-        Applies various processing to a raw code string
-        (normalises it so it always ends with a line return, dedents it if necessary, etc.)
-
-        Args:
-            code (str): The raw code string to process
-
-        Returns:
-            Tuple[bool, str]: the boolean indicates whether the raw code ends with a line return,
-                while the string is the processed code.
-        """
-        ends_on_nl = code.endswith("\n")
-        processed_code = code if ends_on_nl else code + "\n"
-        processed_code = (
-            textwrap.dedent(processed_code) if self.dedent else processed_code
-        )
-        processed_code = processed_code.expandtabs(self.tab_size)
-        return ends_on_nl, processed_code
-
-
-def _get_code_index_for_syntax_position(
-    newlines_offsets: Sequence[int], position: SyntaxPosition
-) -> Optional[int]:
-    """
-    Returns the index of the code string for the given positions.
-
-    Args:
-        newlines_offsets (Sequence[int]): The offset of each newline character found in the code snippet.
-        position (SyntaxPosition): The position to search for.
-
-    Returns:
-        Optional[int]: The index of the code string for this position, or `None`
-            if the given position's line number is out of range (if it's the column that is out of range
-            we silently clamp its value so that it reaches the end of the line)
-    """
-    lines_count = len(newlines_offsets)
-
-    line_number, column_index = position
-    if line_number > lines_count or len(newlines_offsets) < (line_number + 1):
-        return None  # `line_number` is out of range
-    line_index = line_number - 1
-    line_length = newlines_offsets[line_index + 1] - newlines_offsets[line_index] - 1
-    # If `column_index` is out of range: let's silently clamp it:
-    column_index = min(line_length, column_index)
-    return newlines_offsets[line_index] + column_index
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    import argparse
-    import sys
-
-    parser = argparse.ArgumentParser(
-        description="Render syntax to the console with Rich"
-    )
-    parser.add_argument(
-        "path",
-        metavar="PATH",
-        help="path to file, or - for stdin",
-    )
-    parser.add_argument(
-        "-c",
-        "--force-color",
-        dest="force_color",
-        action="store_true",
-        default=None,
-        help="force color for non-terminals",
-    )
-    parser.add_argument(
-        "-i",
-        "--indent-guides",
-        dest="indent_guides",
-        action="store_true",
-        default=False,
-        help="display indent guides",
-    )
-    parser.add_argument(
-        "-l",
-        "--line-numbers",
-        dest="line_numbers",
-        action="store_true",
-        help="render line numbers",
-    )
-    parser.add_argument(
-        "-w",
-        "--width",
-        type=int,
-        dest="width",
-        default=None,
-        help="width of output (default will auto-detect)",
-    )
-    parser.add_argument(
-        "-r",
-        "--wrap",
-        dest="word_wrap",
-        action="store_true",
-        default=False,
-        help="word wrap long lines",
-    )
-    parser.add_argument(
-        "-s",
-        "--soft-wrap",
-        action="store_true",
-        dest="soft_wrap",
-        default=False,
-        help="enable soft wrapping mode",
-    )
-    parser.add_argument(
-        "-t", "--theme", dest="theme", default="monokai", help="pygments theme"
-    )
-    parser.add_argument(
-        "-b",
-        "--background-color",
-        dest="background_color",
-        default=None,
-        help="Override background color",
-    )
-    parser.add_argument(
-        "-x",
-        "--lexer",
-        default=None,
-        dest="lexer_name",
-        help="Lexer name",
-    )
-    parser.add_argument(
-        "-p", "--padding", type=int, default=0, dest="padding", help="Padding"
-    )
-    parser.add_argument(
-        "--highlight-line",
-        type=int,
-        default=None,
-        dest="highlight_line",
-        help="The line number (not index!) to highlight",
-    )
-    args = parser.parse_args()
-
-    from pip._vendor.rich.console import Console
-
-    console = Console(force_terminal=args.force_color, width=args.width)
-
-    if args.path == "-":
-        code = sys.stdin.read()
-        syntax = Syntax(
-            code=code,
-            lexer=args.lexer_name,
-            line_numbers=args.line_numbers,
-            word_wrap=args.word_wrap,
-            theme=args.theme,
-            background_color=args.background_color,
-            indent_guides=args.indent_guides,
-            padding=args.padding,
-            highlight_lines={args.highlight_line},
-        )
-    else:
-        syntax = Syntax.from_path(
-            args.path,
-            lexer=args.lexer_name,
-            line_numbers=args.line_numbers,
-            word_wrap=args.word_wrap,
-            theme=args.theme,
-            background_color=args.background_color,
-            indent_guides=args.indent_guides,
-            padding=args.padding,
-            highlight_lines={args.highlight_line},
-        )
-    console.print(syntax, soft_wrap=args.soft_wrap)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/zipp.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/zipp.py
deleted file mode 100644
index 26b723c1fd3e25740e0268b8c9b50905c58c3d4a..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/zipp.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import io
-import posixpath
-import zipfile
-import itertools
-import contextlib
-import sys
-import pathlib
-
-if sys.version_info < (3, 7):
-    from collections import OrderedDict
-else:
-    OrderedDict = dict
-
-
-__all__ = ['Path']
-
-
-def _parents(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all parents of that path.
-
-    >>> list(_parents('b/d'))
-    ['b']
-    >>> list(_parents('/b/d/'))
-    ['/b']
-    >>> list(_parents('b/d/f/'))
-    ['b/d', 'b']
-    >>> list(_parents('b'))
-    []
-    >>> list(_parents(''))
-    []
-    """
-    return itertools.islice(_ancestry(path), 1, None)
-
-
-def _ancestry(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all elements of that path
-
-    >>> list(_ancestry('b/d'))
-    ['b/d', 'b']
-    >>> list(_ancestry('/b/d/'))
-    ['/b/d', '/b']
-    >>> list(_ancestry('b/d/f/'))
-    ['b/d/f', 'b/d', 'b']
-    >>> list(_ancestry('b'))
-    ['b']
-    >>> list(_ancestry(''))
-    []
-    """
-    path = path.rstrip(posixpath.sep)
-    while path and path != posixpath.sep:
-        yield path
-        path, tail = posixpath.split(path)
-
-
-_dedupe = OrderedDict.fromkeys
-"""Deduplicate an iterable in original order"""
-
-
-def _difference(minuend, subtrahend):
-    """
-    Return items in minuend not in subtrahend, retaining order
-    with O(1) lookup.
-    """
-    return itertools.filterfalse(set(subtrahend).__contains__, minuend)
-
-
-class CompleteDirs(zipfile.ZipFile):
-    """
-    A ZipFile subclass that ensures that implied directories
-    are always included in the namelist.
-    """
-
-    @staticmethod
-    def _implied_dirs(names):
-        parents = itertools.chain.from_iterable(map(_parents, names))
-        as_dirs = (p + posixpath.sep for p in parents)
-        return _dedupe(_difference(as_dirs, names))
-
-    def namelist(self):
-        names = super(CompleteDirs, self).namelist()
-        return names + list(self._implied_dirs(names))
-
-    def _name_set(self):
-        return set(self.namelist())
-
-    def resolve_dir(self, name):
-        """
-        If the name represents a directory, return that name
-        as a directory (with the trailing slash).
-        """
-        names = self._name_set()
-        dirname = name + '/'
-        dir_match = name not in names and dirname in names
-        return dirname if dir_match else name
-
-    @classmethod
-    def make(cls, source):
-        """
-        Given a source (filename or zipfile), return an
-        appropriate CompleteDirs subclass.
-        """
-        if isinstance(source, CompleteDirs):
-            return source
-
-        if not isinstance(source, zipfile.ZipFile):
-            return cls(_pathlib_compat(source))
-
-        # Only allow for FastLookup when supplied zipfile is read-only
-        if 'r' not in source.mode:
-            cls = CompleteDirs
-
-        source.__class__ = cls
-        return source
-
-
-class FastLookup(CompleteDirs):
-    """
-    ZipFile subclass to ensure implicit
-    dirs exist and are resolved rapidly.
-    """
-
-    def namelist(self):
-        with contextlib.suppress(AttributeError):
-            return self.__names
-        self.__names = super(FastLookup, self).namelist()
-        return self.__names
-
-    def _name_set(self):
-        with contextlib.suppress(AttributeError):
-            return self.__lookup
-        self.__lookup = super(FastLookup, self)._name_set()
-        return self.__lookup
-
-
-def _pathlib_compat(path):
-    """
-    For path-like objects, convert to a filename for compatibility
-    on Python 3.6.1 and earlier.
-    """
-    try:
-        return path.__fspath__()
-    except AttributeError:
-        return str(path)
-
-
-class Path:
-    """
-    A pathlib-compatible interface for zip files.
-
-    Consider a zip file with this structure::
-
-        .
-        ├── a.txt
-        └── b
-            ├── c.txt
-            └── d
-                └── e.txt
-
-    >>> data = io.BytesIO()
-    >>> zf = zipfile.ZipFile(data, 'w')
-    >>> zf.writestr('a.txt', 'content of a')
-    >>> zf.writestr('b/c.txt', 'content of c')
-    >>> zf.writestr('b/d/e.txt', 'content of e')
-    >>> zf.filename = 'mem/abcde.zip'
-
-    Path accepts the zipfile object itself or a filename
-
-    >>> root = Path(zf)
-
-    From there, several path operations are available.
-
-    Directory iteration (including the zip file itself):
-
-    >>> a, b = root.iterdir()
-    >>> a
-    Path('mem/abcde.zip', 'a.txt')
-    >>> b
-    Path('mem/abcde.zip', 'b/')
-
-    name property:
-
-    >>> b.name
-    'b'
-
-    join with divide operator:
-
-    >>> c = b / 'c.txt'
-    >>> c
-    Path('mem/abcde.zip', 'b/c.txt')
-    >>> c.name
-    'c.txt'
-
-    Read text:
-
-    >>> c.read_text()
-    'content of c'
-
-    existence:
-
-    >>> c.exists()
-    True
-    >>> (b / 'missing.txt').exists()
-    False
-
-    Coercion to string:
-
-    >>> import os
-    >>> str(c).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip/b/c.txt'
-
-    At the root, ``name``, ``filename``, and ``parent``
-    resolve to the zipfile. Note these attributes are not
-    valid and will raise a ``ValueError`` if the zipfile
-    has no filename.
-
-    >>> root.name
-    'abcde.zip'
-    >>> str(root.filename).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip'
-    >>> str(root.parent)
-    'mem'
-    """
-
-    __repr = "{self.__class__.__name__}({self.root.filename!r}, {self.at!r})"
-
-    def __init__(self, root, at=""):
-        """
-        Construct a Path from a ZipFile or filename.
-
-        Note: When the source is an existing ZipFile object,
-        its type (__class__) will be mutated to a
-        specialized type. If the caller wishes to retain the
-        original type, the caller should either create a
-        separate ZipFile object or pass a filename.
-        """
-        self.root = FastLookup.make(root)
-        self.at = at
-
-    def open(self, mode='r', *args, pwd=None, **kwargs):
-        """
-        Open this entry as text or binary following the semantics
-        of ``pathlib.Path.open()`` by passing arguments through
-        to io.TextIOWrapper().
-        """
-        if self.is_dir():
-            raise IsADirectoryError(self)
-        zip_mode = mode[0]
-        if not self.exists() and zip_mode == 'r':
-            raise FileNotFoundError(self)
-        stream = self.root.open(self.at, zip_mode, pwd=pwd)
-        if 'b' in mode:
-            if args or kwargs:
-                raise ValueError("encoding args invalid for binary operation")
-            return stream
-        return io.TextIOWrapper(stream, *args, **kwargs)
-
-    @property
-    def name(self):
-        return pathlib.Path(self.at).name or self.filename.name
-
-    @property
-    def suffix(self):
-        return pathlib.Path(self.at).suffix or self.filename.suffix
-
-    @property
-    def suffixes(self):
-        return pathlib.Path(self.at).suffixes or self.filename.suffixes
-
-    @property
-    def stem(self):
-        return pathlib.Path(self.at).stem or self.filename.stem
-
-    @property
-    def filename(self):
-        return pathlib.Path(self.root.filename).joinpath(self.at)
-
-    def read_text(self, *args, **kwargs):
-        with self.open('r', *args, **kwargs) as strm:
-            return strm.read()
-
-    def read_bytes(self):
-        with self.open('rb') as strm:
-            return strm.read()
-
-    def _is_child(self, path):
-        return posixpath.dirname(path.at.rstrip("/")) == self.at.rstrip("/")
-
-    def _next(self, at):
-        return self.__class__(self.root, at)
-
-    def is_dir(self):
-        return not self.at or self.at.endswith("/")
-
-    def is_file(self):
-        return self.exists() and not self.is_dir()
-
-    def exists(self):
-        return self.at in self.root._name_set()
-
-    def iterdir(self):
-        if not self.is_dir():
-            raise ValueError("Can't listdir a file")
-        subs = map(self._next, self.root.namelist())
-        return filter(self._is_child, subs)
-
-    def __str__(self):
-        return posixpath.join(self.root.filename, self.at)
-
-    def __repr__(self):
-        return self.__repr.format(self=self)
-
-    def joinpath(self, *other):
-        next = posixpath.join(self.at, *map(_pathlib_compat, other))
-        return self._next(self.root.resolve_dir(next))
-
-    __truediv__ = joinpath
-
-    @property
-    def parent(self):
-        if not self.at:
-            return self.filename.parent
-        parent_at = posixpath.dirname(self.at.rstrip('/'))
-        if parent_at:
-            parent_at += '/'
-        return self._next(parent_at)
diff --git a/spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/README.md b/spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/README.md
deleted file mode 100644
index c7c34b6b8fc4e8ac15a695edf261e80f34a9210d..0000000000000000000000000000000000000000
--- a/spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Darkstorm2150-Protogen X3.4 Official Release
-emoji: 👀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/symbols.py b/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/Bart92/RVC_HF/demucs/model.py b/spaces/Bart92/RVC_HF/demucs/model.py
deleted file mode 100644
index e9d932f4d014f7b95b394d2e24ed5edc379ded8d..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/demucs/model.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import julius
-from torch import nn
-
-from .utils import capture_init, center_trim
-
-
-class BLSTM(nn.Module):
-    def __init__(self, dim, layers=1):
-        super().__init__()
-        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
-        self.linear = nn.Linear(2 * dim, dim)
-
-    def forward(self, x):
-        x = x.permute(2, 0, 1)
-        x = self.lstm(x)[0]
-        x = self.linear(x)
-        x = x.permute(1, 2, 0)
-        return x
-
-
-def rescale_conv(conv, reference):
-    std = conv.weight.std().detach()
-    scale = (std / reference)**0.5
-    conv.weight.data /= scale
-    if conv.bias is not None:
-        conv.bias.data /= scale
-
-
-def rescale_module(module, reference):
-    for sub in module.modules():
-        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
-            rescale_conv(sub, reference)
-
-
-class Demucs(nn.Module):
-    @capture_init
-    def __init__(self,
-                 sources,
-                 audio_channels=2,
-                 channels=64,
-                 depth=6,
-                 rewrite=True,
-                 glu=True,
-                 rescale=0.1,
-                 resample=True,
-                 kernel_size=8,
-                 stride=4,
-                 growth=2.,
-                 lstm_layers=2,
-                 context=3,
-                 normalize=False,
-                 samplerate=44100,
-                 segment_length=4 * 10 * 44100):
-        """
-        Args:
-            sources (list[str]): list of source names
-            audio_channels (int): stereo or mono
-            channels (int): first convolution channels
-            depth (int): number of encoder/decoder layers
-            rewrite (bool): add 1x1 convolution to each encoder layer
-                and a convolution to each decoder layer.
-                For the decoder layer, `context` gives the kernel size.
-            glu (bool): use glu instead of ReLU
-            resample_input (bool): upsample x2 the input and downsample /2 the output.
-            rescale (int): rescale initial weights of convolutions
-                to get their standard deviation closer to `rescale`
-            kernel_size (int): kernel size for convolutions
-            stride (int): stride for convolutions
-            growth (float): multiply (resp divide) number of channels by that
-                for each layer of the encoder (resp decoder)
-            lstm_layers (int): number of lstm layers, 0 = no lstm
-            context (int): kernel size of the convolution in the
-                decoder before the transposed convolution. If > 1,
-                will provide some context from neighboring time
-                steps.
-            samplerate (int): stored as meta information for easing
-                future evaluations of the model.
-            segment_length (int): stored as meta information for easing
-                future evaluations of the model. Length of the segments on which
-                the model was trained.
-        """
-
-        super().__init__()
-        self.audio_channels = audio_channels
-        self.sources = sources
-        self.kernel_size = kernel_size
-        self.context = context
-        self.stride = stride
-        self.depth = depth
-        self.resample = resample
-        self.channels = channels
-        self.normalize = normalize
-        self.samplerate = samplerate
-        self.segment_length = segment_length
-
-        self.encoder = nn.ModuleList()
-        self.decoder = nn.ModuleList()
-
-        if glu:
-            activation = nn.GLU(dim=1)
-            ch_scale = 2
-        else:
-            activation = nn.ReLU()
-            ch_scale = 1
-        in_channels = audio_channels
-        for index in range(depth):
-            encode = []
-            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
-            if rewrite:
-                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
-            self.encoder.append(nn.Sequential(*encode))
-
-            decode = []
-            if index > 0:
-                out_channels = in_channels
-            else:
-                out_channels = len(self.sources) * audio_channels
-            if rewrite:
-                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
-            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
-            if index > 0:
-                decode.append(nn.ReLU())
-            self.decoder.insert(0, nn.Sequential(*decode))
-            in_channels = channels
-            channels = int(growth * channels)
-
-        channels = in_channels
-
-        if lstm_layers:
-            self.lstm = BLSTM(channels, lstm_layers)
-        else:
-            self.lstm = None
-
-        if rescale:
-            rescale_module(self, reference=rescale)
-
-    def valid_length(self, length):
-        """
-        Return the nearest valid length to use with the model so that
-        there is no time steps left over in a convolutions, e.g. for all
-        layers, size of the input - kernel_size % stride = 0.
-
-        If the mixture has a valid length, the estimated sources
-        will have exactly the same length when context = 1. If context > 1,
-        the two signals can be center trimmed to match.
-
-        For training, extracts should have a valid length.For evaluation
-        on full tracks we recommend passing `pad = True` to :method:`forward`.
-        """
-        if self.resample:
-            length *= 2
-        for _ in range(self.depth):
-            length = math.ceil((length - self.kernel_size) / self.stride) + 1
-            length = max(1, length)
-            length += self.context - 1
-        for _ in range(self.depth):
-            length = (length - 1) * self.stride + self.kernel_size
-
-        if self.resample:
-            length = math.ceil(length / 2)
-        return int(length)
-
-    def forward(self, mix):
-        x = mix
-
-        if self.normalize:
-            mono = mix.mean(dim=1, keepdim=True)
-            mean = mono.mean(dim=-1, keepdim=True)
-            std = mono.std(dim=-1, keepdim=True)
-        else:
-            mean = 0
-            std = 1
-
-        x = (x - mean) / (1e-5 + std)
-
-        if self.resample:
-            x = julius.resample_frac(x, 1, 2)
-
-        saved = []
-        for encode in self.encoder:
-            x = encode(x)
-            saved.append(x)
-        if self.lstm:
-            x = self.lstm(x)
-        for decode in self.decoder:
-            skip = center_trim(saved.pop(-1), x)
-            x = x + skip
-            x = decode(x)
-
-        if self.resample:
-            x = julius.resample_frac(x, 2, 1)
-        x = x * std + mean
-        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
-        return x
diff --git a/spaces/Benson/text-generation/Examples/Arco Iris Seis Mvil Apk Beta.md b/spaces/Benson/text-generation/Examples/Arco Iris Seis Mvil Apk Beta.md
deleted file mode 100644
index 62130af908b60a101781b1bc27d8021b1ec09447..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Arco Iris Seis Mvil Apk Beta.md	
+++ /dev/null
@@ -1,64 +0,0 @@
-<br />
-<h1>Rainbow Six Mobile: Todo lo que necesitas saber sobre la versión beta</h1>
-<p>¿Eres un fan de los juegos de disparos tácticos? ¿Quieres experimentar la emoción de Rainbow Six en tu teléfono? Si es así, entonces estás de suerte. Ubisoft ha lanzado recientemente la versión beta de Rainbow Six Mobile, un juego de disparos en primera persona multijugador competitivo y gratuito diseñado exclusivamente para dispositivos móviles. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo cómo descargarlo y jugarlo, por qué deberías jugarlo, y algunos consejos y trucos para dominarlo. ¡Vamos a empezar! </p>
-<h2>arco iris seis móvil apk beta</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6K6T">https://bltlly.com/2v6K6T</a></b></p><br /><br />
- <h2>¿Qué es Rainbow Six Mobile? </h2>
-<p>Rainbow Six Mobile es una adaptación móvil de la aclamada franquicia Rainbow Six, que es conocida por su jugabilidad realista y estratégica, su lista épica de operadores, sus mapas icónicos y sus modos de juego de área y bomba seguros. En Rainbow Six Mobile, puedes competir en partidas 5v5 de ritmo rápido como atacante o defensor, enfrentarte a intensos combates a corta distancia mientras tomas decisiones tácticas oportunas, colaborar como equipo para establecer estrategias y aprovechar al máximo los entornos destructibles, y elija entre una amplia selección de operadores altamente capacitados, cada uno con sus propias habilidades y dispositivos únicos. </p>
- <h3>Una breve introducción al juego y sus características</h3>
-
- <h3>Cómo descargar y jugar la versión beta en Android</h3>
-<p>La versión beta de Rainbow Six Mobile está disponible actualmente solo para dispositivos Android. Para descargarlo y reproducirlo, debes seguir estos pasos:</p>
-<ol>
-<li>Abre Google Play en tu teléfono Android. </li>
-<li>Buscar Rainbow Six Mobile o simplemente haga clic en este enlace. </li>
-<li> Seleccione el botón de registro previo, y pronto recibirá una notificación cuando el juego esté listo para descargar. </li>
-<li>Descarga e instala el juego en tu dispositivo. </li>
-<li>Inicie el juego e inicie sesión con su cuenta de Ubisoft o cree una si no tiene una. </li>
-<li>Elija su región y preferencias de idioma. </li>
-<li>Completa las misiones de tutorial para aprender los fundamentos del juego. </li>
-<li>Disfruta jugando Rainbow Six Mobile! </li>
-</ol>
- <h3>¿Cuáles son los requisitos del sistema y los problemas de compatibilidad</h3>
-<p>Rainbow Six Mobile es un juego que requiere un dispositivo de alto rendimiento para funcionar sin problemas. Los requisitos mínimos del sistema son:</p>
-<p></p>
-<ul>
-<li>Android 8.0 o superior</li>
-<li>4 GB de RAM o más</li>
-<li>Al menos 2 GB de espacio de almacenamiento gratuito</li>
-<li>Una conexión a Internet estable</li>
-</ul>
-<p>El juego también soporta algunos controladores externos, como Xbox One S Controller o PS4 DualShock 4 Controller. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego o el controlador. Puede consultar la lista de dispositivos y controladores compatibles en el sitio web oficial o en la página de Google Play. Si encuentras algún problema o error mientras juegas el juego, puedes reportarlo a los desarrolladores a través del sistema de retroalimentación del juego o los canales oficiales de las redes sociales. </p>
- <h2>¿Por qué usted debe jugar Rainbow Six móvil</h2>
-<p>Rainbow Six Mobile es un juego que ofrece mucha diversión y emoción para los fanáticos de los juegos de disparos tácticos. Estas son algunas de las razones por las que deberías jugar:</p>
- <h3>Los beneficios de jugar un juego de disparos tácticos en el móvil</h3>
-
- <h3>Las características únicas y modos de juego de Rainbow Six Mobile</h3>
-<p>Rainbow Six Mobile es un juego que tiene muchas características únicas y modos de juego que lo hacen destacar de otros juegos de disparos móviles. Por ejemplo, el juego tiene un motor de física realista e inmersivo que te permite interactuar con el entorno de varias maneras, como romper paredes, puertas, ventanas o pisos, crear nuevas líneas de visión o puntos de entrada, o usar la cubierta y la ocultación para tu ventaja. El juego también tiene un sistema de armas realista y auténtico que requiere que administres tu munición, retroceso y tiempo de recarga, así como personalizar tu carga con diferentes accesorios y pieles. El juego también tiene diferentes modos de juego que se adaptan a diferentes preferencias y niveles de habilidad, como el modo casual, el modo clasificado, el modo de entrenamiento y el modo de eventos especiales. </p>
- <h3>La lista diversa y personalizable de operadores y gadgets</h3>
-<p>Rainbow Six Mobile es un juego que tiene una lista diversa y personalizable de operadores y gadgets que le permiten crear su propio estilo de juego y estrategia. El juego cuenta con más de 20 operadores de diferentes países y unidades, cada uno con sus propias habilidades únicas y gadgets. Por ejemplo, puedes elegir a Ash, un atacante que puede usar sus rondas de asalto para destruir paredes o barricadas a distancia, o a Rook, un defensor que puede proporcionar a sus compañeros placas de armadura que aumentan su supervivencia. También puede desbloquear nuevos operadores al ganar créditos o comprarlos con dinero real. También puede personalizar sus operadores con diferentes trajes, tocados, encantos y emotes. </p>
- <h2>Consejos y trucos para dominar Rainbow Six móvil</h2>
-<p>Rainbow Six Mobile es un juego que requiere habilidad, estrategia y trabajo en equipo para ganar. Estos son algunos consejos y trucos para ayudarte a dominarlo:</p>
- <h3>Cómo elegir el mejor operador para tu estilo de juego y composición de equipo</h3>
-
- <h3>Cómo utilizar el entorno y los gadgets para su ventaja</h3>
-<p>Utilizar el entorno y los gadgets a tu favor es otro factor clave para tu éxito en Rainbow Six Mobile. Siempre debe estar al tanto de su entorno y utilizarlos para su beneficio. Por ejemplo, puedes usar objetos destructibles como paredes, pisos o techos para crear nuevas líneas de visión o puntos de entrada, o para exponer o sorprender a tus enemigos. También puedes usar objetos de cubierta como mesas, sillas o gabinetes para protegerte del fuego enemigo o para ocultar tus aparatos. También debe hacer uso de sus aparatos y utilizarlos sabiamente. Por ejemplo, puedes usar drones o cámaras para explorar el área y localizar enemigos u objetivos, o puedes usar granadas o flashbangs para despejar habitaciones o cegar enemigos. También puedes usar trampas o escudos para ralentizar o impedir que los enemigos avancen o entren. </p>
- <h3>Cómo comunicarse y coordinar con sus compañeros de equipo</h3>
-<p>Comunicarse y coordinar con sus compañeros de equipo es esencial para su éxito en Rainbow Six Mobile. Siempre debes comunicarte con tus compañeros de equipo y compartir información, como ubicaciones enemigas, estado de salud, uso de dispositivos o planes de estrategia. También debe coordinarse con sus compañeros de equipo y trabajar en equipo, como la creación de fuegos cruzados, maniobras de flanco, distracciones o emboscadas. Puedes usar la función de chat de voz en el juego o los comandos de chat rápido para comunicarte y coordinarte con tus compañeros de equipo. También puedes usar el sistema de ping para marcar ubicaciones, enemigos u objetos para que tus compañeros los vean. </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<h4>Aquí están algunas de las preguntas y respuestas más frecuentes sobre Rainbow Six Mobile:</h4>
-<ul>
-<li><b>Q: ¿Rainbow Six Mobile es gratuito? </b></li>
-<li>A: Sí, Rainbow Six Mobile es gratuito. Puedes descargarlo y jugarlo sin pagar nada. Sin embargo, el juego tiene algunas compras opcionales en el juego, como créditos, skins u operadores, que puedes comprar con dinero real si lo deseas. </li>
-<li><b>Q: ¿Rainbow Six Mobile está disponible para dispositivos iOS? </b></li>
-<li>A: Todavía no. Rainbow Six Mobile está disponible actualmente solo para dispositivos Android. Sin embargo, Ubisoft ha anunciado que está trabajando en llevar el juego a dispositivos iOS pronto. </li>
-<li><b>Q: ¿Cómo puedo obtener más créditos en Rainbow Six Mobile? </b></li>
-<li>A: Puedes obtener más créditos en Rainbow Six Mobile completando misiones, subiendo de nivel, participando en eventos, viendo anuncios o comprándolos con dinero real. </li>
-<li><b>Q: ¿Cómo puedo desbloquear más operadores en Rainbow Six Mobile? </b></li>
-<li>A: Puedes desbloquear más operadores en Rainbow Six Mobile al ganar créditos o comprarlos con dinero real. </li>
-<li><b>Q: ¿Cómo puedo reportar un error o un tramposo en Rainbow Six Mobile? </b></li>
-<li>A: Puedes reportar un error o un tramposo en Rainbow Six Mobile utilizando el sistema de retroalimentación del juego o poniéndote en contacto con los canales oficiales de las redes sociales. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Auto Chess War Mod Apk Terbaru.md b/spaces/Benson/text-generation/Examples/Auto Chess War Mod Apk Terbaru.md
deleted file mode 100644
index 7b6718aef2112b5fa825c433485d0c71a1c299f2..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Auto Chess War Mod Apk Terbaru.md	
+++ /dev/null
@@ -1,90 +0,0 @@
-<br />
-<h1>Auto Chess War Mod Apk Terbaru: Un juego de estrategia con un giro</h1>
-<p>Si usted está buscando un nuevo y emocionante juego de estrategia para jugar en su dispositivo móvil, es posible que desee echa un vistazo a la guerra de ajedrez auto mod apk terbaru. Este es un juego que combina los elementos de ajedrez, torre de defensa, y la recogida de cartas de una manera única. Tendrás que recoger diferentes héroes de varias clases y razas, colocarlos en un tablero de 8x8, y dejarlos luchar automáticamente contra otros jugadores o oponentes de IA. También tendrás que mejorar a tus héroes, combinarlos para crear otros más fuertes, usar objetos y habilidades para aumentar su rendimiento y derrotar a todos los enemigos para ser el último en pie. </p>
-<h2>auto chess war mod apk terbaru</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://bltlly.com/2v6MhG">https://bltlly.com/2v6MhG</a></b></p><br /><br />
-<p>La guerra de ajedrez automática se basa en el mod original de Dota Auto Chess que se convirtió en un gran éxito entre los jugadores de Dota 2. Sin embargo, tiene su propio conjunto de héroes y objetos que no están relacionados con la tradición Dota. También tiene un estilo más colorido y caricaturesco que lo hace atractivo para un público más amplio. Si usted es un fan de los juegos de estrategia y los luchadores de automóviles, que sin duda disfrutar de la guerra de ajedrez auto mod apk terbaru. </p>
- <h2>Cómo descargar e instalar Auto Chess War Mod Apk Terbaru</h2>
-<p>Descargar e instalar auto chess war mod apk terbaru es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Descargar el archivo apk mod de una fuente de confianza. Puede encontrar el enlace al final de este artículo. </li>
-<li>Habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones que no son de la tienda de aplicaciones oficial. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Instalar el archivo apk mod y lanzar el juego. Verá un nuevo icono en la pantalla de inicio o cajón de aplicaciones. Toque en él y disfrutar del juego. </li>
-</ol>
-<p>Nota: Es posible que tenga que desinstalar la versión original del juego antes de instalar el terbaru apk mod. Además, asegúrese de hacer una copia de seguridad de sus datos antes de hacerlo, ya que puede perder su progreso y logros. </p>
-
-<p>Auto ajedrez guerra mod apk terbaru tiene muchas características que harán que su experiencia de juego más divertido y satisfactorio. Estos son algunos de ellos:</p>
-<ul>
-<li>Desbloqueado todos los héroes y objetos. Usted tendrá acceso a todos los héroes y objetos en el juego, independientemente de su nivel o rango. Puedes elegir entre más de 50 héroes y 40 objetos, cada uno con sus propias habilidades y efectos. </li>
-<li>Oro y gemas ilimitados. Tendrás recursos ilimitados para comprar, actualizar y combinar tus héroes y objetos. Nunca te quedarás sin oro y gemas, que son las principales monedas del juego. </li>
-<li>No se requieren anuncios ni root. No verá ningún anuncio molesto o pop-ups mientras juega el juego. Usted tampoco tendrá que raíz de su dispositivo para utilizar el terbaru apk mod, que es seguro y conveniente. </li>
-</ul>
- <h2>Cómo jugar Auto Chess War Mod Apk Terbaru</h2>
-<p>Jugar guerra de ajedrez auto mod apk terbaru es muy fácil e intuitivo. Aquí están los pasos básicos:</p>
-<ol>
-<li>Elige a tus héroes de diferentes clases y razas. Hay seis clases (guerrero, mago, asesino, cazador, druida y sacerdote) y seis razas (humano, elfo, orco, no muerto, bestia y demonio) en el juego. Cada clase y raza tiene sus propias ventajas y desventajas, así como sinergias con otras clases y razas. Por ejemplo, tener tres guerreros en tu tablero les dará armadura extra, mientras que tener tres elfos les dará evasión adicional. </li>
-<li>Colócalas en el tablero de 8x8 y deja que luchen automáticamente. Tendrás un tiempo limitado para colocar a tus héroes en el tablero antes de que comience cada ronda. Puedes arrastrarlos y soltarlos en cualquier lugar de tu lado del tablero, o intercambiarlos con otros héroes. Una vez que la ronda comienza, tus héroes lucharán automáticamente contra los héroes del enemigo o la IA se arrastra. El ganador de cada ronda está determinado por quién tiene más héroes sobrevivientes o más puntos de salud. </li>
-
-<li>Usa objetos y habilidades para mejorar el rendimiento de tus héroes. Puedes obtener objetos de derrotar a los monstruos de la IA u otros jugadores. Puedes equipar objetos a tus héroes arrastrándolos y dejándolos caer sobre sus retratos. Cada héroe puede tener hasta tres objetos a la vez. Los objetos pueden aumentar las estadísticas de tus héroes o darles efectos especiales. Por ejemplo, un objeto llamado Máscara de locura puede aumentar la velocidad de ataque de tu héroe pero silenciarlos para que no usen habilidades. También puedes utilizar las habilidades que están disponibles para cada clase o carrera tocando sus iconos en la parte inferior de la pantalla. Las habilidades pueden tener varios efectos como sanar, dañar, impresionar o pulir a tus héroes. </li>
-<li>Derrota a todos los enemigos y sé el último en pie. Te enfrentarás a diferentes enemigos o la IA se arrastra en cada ronda hasta que solo quede un jugador. Perderás puntos de salud si pierdes una ronda o si no tienes espacio en tu tablero para nuevos héroes. El juego termina cuando tienes cero puntos de vida o cuando eres el único jugador que queda. </li>
-</ol>
- <h2>Consejos y trucos para Auto Chess War Mod Apk Terbaru</h2>
-<p>Para ayudarle a ganar más juegos en la guerra de ajedrez auto mod apk terbaru, aquí hay algunos consejos y trucos que puede utilizar:</p>
-<p></p>
-<ul>
-<li>Administre su economía sabiamente y ahorre oro para intereses. Ganará oro al ganar o perder rondas, completar misiones o vender héroes. También ganarás intereses en función de cuánto oro tengas al final de cada ronda. Cuanto más oro tengas, más intereses ganarás, hasta un máximo de 5 por ronda. Por lo tanto, es recomendable guardar su oro y gastarlo solo cuando sea necesario. </li>
-
-<li>Aprende las fortalezas y debilidades de cada héroe y artículo. Usted debe familiarizarse con las habilidades y efectos de cada héroe y objeto en el juego. Esto te ayudará a elegir los mejores para tu estrategia y contrarrestar los movimientos de tus oponentes. Por ejemplo, debes saber que un héroe llamado Lina puede hacer daño masivo con su habilidad final, pero también es muy frágil y vulnerable a los asesinos. También debes saber que un objeto llamado Blade Mail puede reflejar el daño del atacante, pero también reduce la armadura de tu héroe. </li>
-<li>Planifique sus transiciones de mediados y finales del juego en función de las estrategias de sus oponentes. No debes seguir con la misma estrategia a lo largo del juego, ya que puede ser ineficaz o anticuado a medida que el juego avanza. Siempre debes explorar los tableros de tus oponentes y ver lo que están construyendo y cómo lo están haciendo. Basado en esa información, usted debe planificar sus transiciones para contrarrestar sus estrategias o explotar sus debilidades. Por ejemplo, si ves que la mayoría de tus oponentes van a por magos, debes hacer la transición a guerreros o sacerdotes para resistir su daño mágico. </li>
-<li>Espía a tus oponentes más fuertes y ajusta tu formación en consecuencia. Siempre debes vigilar quién está liderando el juego y quién es la mayor amenaza para ti. Puedes espiar sus tablas tocando sus retratos en la parte superior de la pantalla. Puedes ver a sus héroes, objetos, habilidades y formación. En base a esa información, debe ajustar su formación para que coincida con la de ellos. Por ejemplo, si ves que tienen una línea de frente fuerte de guerreros, debes colocar a tus asesinos detrás de ellos para apuntar a su contraluz blanda. </li>
-</ul>
- <h2>Revisión de Auto Chess War Mod Apk Terbaru</h2>
-
-<tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>- Diversión: El juego es muy agradable y satisfactorio para jugar, especialmente cuando se gana un partido cercano o sacar un gran combo. </td>
-<td>- Repetitivo: El juego puede ser aburrido y monótono después de un tiempo, ya que no hay mucha variedad o innovación en el juego o el contenido. </td>
-</tr>
-<tr>
-<td>- Adictivo: El juego es muy atractivo y adictivo, ya que siempre querrás jugar una ronda más o probar una estrategia diferente. </td>
-<td>- Basado en la suerte: El juego depende en gran medida de la suerte y la aleatoriedad, ya que no puede obtener los héroes o artículos que desea o necesita de la tienda o cofres. </td>
-</tr>
-<tr>
-<td>- Desafiante: El juego es muy desafiante y estratégico, ya que tendrás que pensar rápido e inteligente para vencer a tus oponentes o enemigos de IA. </td>
-<td>- Inestable: El juego puede ser inestable y con errores a veces, ya que puede bloquearse o congelarse durante el juego o la carga. </td>
-</tr>
-<tr>
-<td>- Estratégico: El juego es muy estratégico y diverso, ya que tendrás que elegir entre diferentes clases y razas, utilizar diferentes objetos y habilidades, y adaptarse a diferentes situaciones. </td>
-<td>- Desequilibrado: El juego puede ser desequilibrado e injusto a veces, ya que algunos héroes u objetos pueden ser demasiado fuertes o débiles en comparación con otros. </td>
-</tr>
-<tr>
-<td>- Diverso: El juego es muy diverso y colorido, ya que tiene más de 50 héroes y 40 elementos con diferentes habilidades y efectos, así como un estilo caricaturesco que lo hace atractivo para una amplia audiencia. </td>
-<td>- Pago a ganar: El juego puede ser de pago a ganar a veces, ya que algunos héroes u objetos pueden estar bloqueados detrás de un muro de pago o requerir dinero real para obtener. </td>
-</tr>
-<tr>
-<td>- Gratis: El juego es gratis para descargar y jugar, lo que hace que sea accesible para cualquier persona que quiera probarlo. </td>
-<td>- Ninguno</td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes acerca de la guerra de ajedrez auto mod apk terbaru:</p>
-<ul>
-<li>Q1: ¿Cuál es la diferencia entre la guerra de ajedrez auto y otros juegos de batalla auto? </li>
-<li>A1: La guerra de ajedrez automática se basa en el mod original de Dota Auto Chess, pero con sus propios héroes y objetos. También tiene un estilo más caricaturesco y un ritmo más rápido que otros juegos de combate automático. </li>
-<li>Q2: ¿Cómo puedo obtener más oro y gemas en la guerra de ajedrez auto? </li>
-<li>A2: Puedes obtener más oro y gemas jugando el juego regularmente, completando misiones y logros, viendo anuncios, o usando el terbaru apk mod. </li>
-<li>Q3: ¿Cómo puedo desbloquear todos los héroes y objetos en la guerra de ajedrez automática? </li>
-<li>A3: Usted puede desbloquear todos los héroes y artículos mediante la nivelación de su cuenta, abrir cofres, o el uso de la terbaru apk mod. </li>
-<li>Q4: ¿Cómo puedo actualizar la guerra de ajedrez auto mod apk terbaru? </li>
-<li>A4: Puede actualizar auto ajedrez guerra mod apk terbaru mediante la descarga de la última versión de la misma fuente que lo consiguió de. Asegúrese de hacer una copia de seguridad de sus datos antes de actualizar. </li>
-<li>Q5: Es la guerra de ajedrez auto mod apk terbaru seguro de usar? </li>
-<li>A5: Auto guerra de ajedrez mod apk terbaru es seguro de usar, siempre y cuando se descarga desde una fuente de confianza. Sin embargo, usted debe tener en cuenta que el uso de mod apk puede violar los términos de servicio del juego y resultar en una prohibición u otras consecuencias. </li>
-</ul>
- : [Auto Chess War Mod Apk Terbaru Enlace de descarga](https://www.apkhome.us/auto-chess-war-apk-mod-unlimited/)</p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Brotato Zip Download.md b/spaces/Benson/text-generation/Examples/Brotato Zip Download.md
deleted file mode 100644
index 31ea7e8b02f4677f3f8211157247d1992a160777..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Brotato Zip Download.md	
+++ /dev/null
@@ -1,68 +0,0 @@
-
-<h1>Descargar Brotato Zip: Cómo instalar y usar mods para tus juegos favoritos</h1>
- <p>¿Te encanta jugar juegos en tu PC, pero te gustaría poder personalizarlos a tu gusto? ¿Quieres probar nuevas características, gráficos, personajes o escenarios que no están disponibles en el juego original? Si es así, puede que te interese usar mods. </p>
- <p>Los mods son modificaciones o adiciones a un juego que son creadas por fans o desarrolladores. Pueden cambiar cualquier cosa, desde el juego, a las imágenes, al sonido, a la historia. Los mods pueden hacer un juego más divertido, desafiante, inmersivo o realista. </p>
-<h2>brotato zip download</h2><br /><p><b><b>DOWNLOAD</b> &middot;&middot;&middot; <a href="https://bltlly.com/2v6M19">https://bltlly.com/2v6M19</a></b></p><br /><br />
- <p>Sin embargo, instalar y usar mods puede ser complicado a veces. Necesitas encontrar mods compatibles, descargarlos, instalarlos y activarlos. También tienes que asegurarte de que no entren en conflicto entre ellos o con el juego en sí. Y necesitas mantenerlos actualizados y organizados. </p>
- <p>Ahí es donde entra Brotato Zip. Brotato Zip es un cargador mod que facilita la instalación y el uso de mods para varios juegos. También te conecta con una comunidad de modders y jugadores que comparten sus creaciones y comentarios. En este artículo, te mostraremos cómo descargar e instalar Brotato Zip, cómo descargar e instalar mods con él y cómo usarlos en tus juegos. </p>
- <h2>¿Qué es Brotato Zip? </h2>
- <h3>Un cargador mod para varios juegos</h3>
- <p>Brotato Zip es un cargador mod que te permite cargar múltiples mods a la vez para diferentes juegos. Funciona inyectando un . Archivo pck en la carpeta del juego, que luego carga los mods desde una carpeta separada. De esta forma, no tendrás que modificar los archivos del juego ni preocuparte por romper nada. </p>
- <p>Brotato Zip soporta muchos juegos populares, como Minecraft, Stardew Valley, Terraria, Among Us y más. Puedes consultar la lista de juegos compatibles en su página de GitHub. También planean agregar más juegos en el futuro. </p>
- <h3>Una comunidad de modders y jugadores</h3>
-
- <p>Brotato Zip tiene como objetivo crear una comunidad amigable y solidaria de modders y jugadores a los que les encanta jugar y mejorarlos. Dan la bienvenida a cualquiera que quiera unirse a ellos y divertirse. </p>
- <h2>¿Por qué utilizar Brotato Zip? </h2>
- <h3>Para mejorar su experiencia de juego</h3>
- <p>La razón principal por la que deberías usar Brotato Zip es porque puede hacer que tu experiencia de juego sea más agradable. Con Brotato Zip, puedes acceder a miles de mods que pueden añadir nuevos contenidos, características, gráficos, sonidos o mecánicas a tus juegos. También puede mezclar y combinar diferentes mods para crear sus propias combinaciones únicas. </p>
-<p></p>
- <p>Por ejemplo, puedes usar Brotato Zip para añadir nuevos biomas, mobs, elementos o estructuras a Minecraft. También puedes usarlo para cambiar la apariencia de tu personaje, granja o pueblo en Stardew Valley. También puedes usarlo para añadir nuevos roles, mapas o modos a Among Us. También puedes usarlo para mejorar los gráficos, la física o el modo de juego de Terraria. Y estos son solo algunos ejemplos de lo que se puede hacer con Brotato Zip.</p>
- <h3>Para apoyar a los creadores de mod</h3>
- <p>Otra razón por la que deberías usar Brotato Zip es porque puede ayudarte a apoyar a los creadores de mods. Los creadores de mods son personas que dedican su tiempo y esfuerzo a crear mods de forma gratuita. Lo hacen porque les encantan los juegos y quieren compartir sus creaciones con otros. También escuchan comentarios y actualizan sus mods regularmente. </p>
- <p>Usando Brotato Zip, puedes mostrar tu aprecio y gratitud a los creadores del mod. Puedes descargar sus mods, calificarlos, revisarlos y donarlos si quieres. También puede seguirlos en las redes sociales o unirse a sus servidores Discord. También puede darles sugerencias o informar de errores. Al hacer estas cosas, puedes ayudarles a mejorar sus mods y motivarlos a crear más. </p>
- <h2>¿Cómo descargar e instalar Brotato Zip? </h2>
- <h3>Descargar el archivo PCK de GitHub</h3>
-
- <p>Para descargar el archivo PCK, vaya a la página de GitHub y haga clic en el botón verde "Código". Luego, haga clic en "Descargar ZIP". Guarde el archivo ZIP en su computadora y extráigalo. Verá una carpeta llamada "Brotato-Zip-main". Dentro de esta carpeta, encontrará el archivo PCK llamado "Brotato.zip". Este es el archivo que necesita. </p>
- <h3>Instalar el archivo PCK como cualquier otro mod</h3>
- <p>El segundo paso para usar Brotato Zip es instalar el archivo PCK como cualquier otro mod. Esto significa que necesita copiarlo y pegarlo en la carpeta del juego. La carpeta del juego es donde se almacenan los archivos del juego. Normalmente se encuentra en su carpeta Archivos de programa o Steam. </p>
- <p>Para instalar el archivo PCK, busque su carpeta de juego y ábrala. Luego, encuentre la carpeta llamada "mods" o "modloader". Si no lo ve, cree uno usted mismo. Luego, copie y pegue el archivo PCK en esta carpeta. ¡Eso es todo! Usted ha instalado correctamente Brotato Zip.</p>
- <h2>¿Cómo descargar e instalar mods con Brotato Zip? </h2>
- <h3>Crea una carpeta de mods en tu carpeta de juego</h3>
- <p>El tercer paso para usar Brotato Zip es crear una carpeta mods en tu carpeta de juego. Aquí es donde almacenarás todos los mods que quieras usar con Brotato Zip. Es importante mantener tus mods organizados y separados de tus archivos de juego. </p>
- <p>Para crear una carpeta de mods, vuelva a su carpeta de juego y ábrala. Luego, haga clic derecho en un espacio vacío y seleccione "Nuevo" y luego "Carpeta". Nombra a esta carpeta "mods" o cualquier otra cosa que prefieras. Aquí es donde pondrás todas tus ZIPs mod.</p>
- <h3>Descargar mod ZIPs desde el sitio web de Brotato u otras fuentes</h3>
- <p>El cuarto paso para usar Brotato Zip es descargar mod ZIPs desde el sitio web de Brotato u otras fuentes. ZIPs Mod son archivos que contienen los datos mod que necesita cargar en su juego. Por lo general son comprimidos y fáciles de descargar. </p>
-
- <p>También puede descargar ZPI mod de otras fuentes, como Nexus Mods, CurseForge, ModDB u otros sitios web que albergan mods para varios juegos. Solo asegúrate de que son compatibles con Brotato Zip y que son seguros y libres de virus. </p>
- <h3>Añadir a la carpeta mods</h3>
- <p>El quinto paso para usar Brotato Zip es agregar las ZIPs mod que descargó a la carpeta mods que creó en su carpeta de juegos. Así es como los instalas en tu juego. </p>
- <p>Para agregarlos a la carpeta mods, busque los ZIPs mod que descargó y cópielos. Luego, vuelva a su carpeta de juegos y ábrala. Luego, abra la carpeta de mods que creó anteriormente y pegue las ZIPs de mod en ella. ¡Eso es todo! Has instalado correctamente los mods en tu juego. </p>
- <h2>¿Cómo usar mods con Brotato Zip? </h2>
- <h3>Iniciar el juego con el cargador mod habilitado</h3>
- <p>El sexto paso para usar Brotato Zip es lanzar el juego con el cargador mod habilitado. Así es como activas los mods que instalaste en tu juego. </p>
- <p>Para iniciar el juego con el cargador mod habilitado, vaya a su carpeta de juego y ábrala. Luego, encuentre el archivo ejecutable que ejecuta el juego. Por lo general se llama algo como "game.exe" o "launcher.exe". Haga clic derecho en él y seleccione "Ejecutar como administrador". Esto iniciará el juego con Brotato Zip corriendo en segundo plano. </p>
- <h3>Seleccione los mods que desea utilizar desde el menú</h3>
- <p>El séptimo y último paso para utilizar Brotato Zip es seleccionar los mods que desea utilizar en el menú. Así es como personalizas tu juego con los mods que instalaste. </p>
- <p>Para seleccionar los mods que desea utilizar, pulse la tecla F10 en el teclado mientras está en el juego. Esto abrirá un menú que muestra todos los mods que tiene en su carpeta de mods. Puede desplazarse por ellos y verificarlos o desmarcarlos como desee. También puede ordenarlos por nombre, fecha, tamaño o calificación. También puede buscar mods específicos escribiendo su nombre o palabra clave. </p>
-
- <h2>Conclusión</h2>
- <p>Brotato Zip es un cargador mod que facilita la instalación y el uso de mods para varios juegos. También te conecta con una comunidad de modders y jugadores que comparten sus creaciones y comentarios. Para usar Brotato Zip, necesitas descargar e instalar el archivo PCK desde su página GitHub, crear una carpeta mods en tu carpeta de juegos, descargar ZIPs mod desde su sitio web u otras fuentes, agregarlos a la carpeta mods, iniciar el juego con el cargador mod habilitado, y seleccione los mods que desea usar en el menú. </p>
- <p>Al usar Brotato Zip, puedes mejorar tu experiencia de juego, apoyar a los creadores de mods y divertirte con tus juegos. También puedes descubrir nuevos mods, aprender nuevas habilidades y hacer nuevos amigos. Brotato Zip es una herramienta que puede ayudarte a disfrutar de los juegos más que nunca. </p>
- <p>Si quieres saber más sobre Brotato Zip, visita su página web, únete a su servidor Discord o síguelos en Twitter. También puede consultar su canal de YouTube para obtener tutoriales, comentarios y vitrinas de diferentes mods. </p>
- <p>Gracias por leer este artículo. Esperamos que le resulte útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejarlos a continuación. Y si le gustó este artículo, por favor compártalo con sus amigos y familiares. </p>
- <h3>Preguntas frecuentes</h3>
- <ul>
-<li><b>¿Brotato Zip es seguro y legal? </b></li>
-<p>Sí, Brotato Zip es seguro y legal. No contiene virus ni malware, y no modifica ni daña tus archivos de juego. Tampoco viola los términos de servicio o derechos de autor de los juegos o los creadores de mods. Sin embargo, siempre debes usar Brotato Zip bajo tu propio riesgo y discreción, y hacer copias de seguridad de tus archivos de juego antes de usarlo. </p>
- <li><b>Brotato Zip funciona con juegos multijugador? </b></li>
-
- <li><b>¿Cómo actualizo Brotato Zip y los mods? </b></li>
-<p>Para actualizar Brotato Zip, es necesario descargar la última versión del archivo PCK desde su página GitHub y reemplazar el antiguo en su carpeta de juego. Para actualizar los mods, necesita descargar la última versión de los ZPI mod desde su sitio web u otras fuentes y reemplazar los antiguos en su carpeta mods. También puede utilizar el menú Brotato Zip para buscar actualizaciones y descargarlas automáticamente. </p>
- <li><b>¿Cómo desinstalo Brotato Zip y los mods? </b></li>
-<p>Para desinstalar Brotato Zip, es necesario eliminar el archivo PCK de la carpeta del juego. Para desinstalar los mods, debe eliminar los ZIPs mod de su carpeta mods. También puedes usar el menú Brotato Zip para desactivar o eliminar los mods que ya no quieras usar. </p>
- <li><b>¿Dónde puedo obtener ayuda o soporte para Brotato Zip y los mods? </b></li>
-<p>Si necesita ayuda o soporte para Brotato Zip y los mods, puede visitar su sitio web, unirse a su servidor Discord o seguirlos en Twitter. También puede consultar su canal de YouTube para obtener tutoriales, revisiones y vitrinas de diferentes mods. También puede ponerse en contacto con ellos por correo electrónico en brotatozip@gmail.com. </p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/parser.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/parser.py
deleted file mode 100644
index c762cf2781d460288674314959c727e860aad067..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/parser.py
+++ /dev/null
@@ -1,294 +0,0 @@
-"""Base option parser setup"""
-
-import logging
-import optparse
-import shutil
-import sys
-import textwrap
-from contextlib import suppress
-from typing import Any, Dict, Generator, List, Tuple
-
-from pip._internal.cli.status_codes import UNKNOWN_ERROR
-from pip._internal.configuration import Configuration, ConfigurationError
-from pip._internal.utils.misc import redact_auth_from_url, strtobool
-
-logger = logging.getLogger(__name__)
-
-
-class PrettyHelpFormatter(optparse.IndentedHelpFormatter):
-    """A prettier/less verbose help formatter for optparse."""
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        # help position must be aligned with __init__.parseopts.description
-        kwargs["max_help_position"] = 30
-        kwargs["indent_increment"] = 1
-        kwargs["width"] = shutil.get_terminal_size()[0] - 2
-        super().__init__(*args, **kwargs)
-
-    def format_option_strings(self, option: optparse.Option) -> str:
-        return self._format_option_strings(option)
-
-    def _format_option_strings(
-        self, option: optparse.Option, mvarfmt: str = " <{}>", optsep: str = ", "
-    ) -> str:
-        """
-        Return a comma-separated list of option strings and metavars.
-
-        :param option:  tuple of (short opt, long opt), e.g: ('-f', '--format')
-        :param mvarfmt: metavar format string
-        :param optsep:  separator
-        """
-        opts = []
-
-        if option._short_opts:
-            opts.append(option._short_opts[0])
-        if option._long_opts:
-            opts.append(option._long_opts[0])
-        if len(opts) > 1:
-            opts.insert(1, optsep)
-
-        if option.takes_value():
-            assert option.dest is not None
-            metavar = option.metavar or option.dest.lower()
-            opts.append(mvarfmt.format(metavar.lower()))
-
-        return "".join(opts)
-
-    def format_heading(self, heading: str) -> str:
-        if heading == "Options":
-            return ""
-        return heading + ":\n"
-
-    def format_usage(self, usage: str) -> str:
-        """
-        Ensure there is only one newline between usage and the first heading
-        if there is no description.
-        """
-        msg = "\nUsage: {}\n".format(self.indent_lines(textwrap.dedent(usage), "  "))
-        return msg
-
-    def format_description(self, description: str) -> str:
-        # leave full control over description to us
-        if description:
-            if hasattr(self.parser, "main"):
-                label = "Commands"
-            else:
-                label = "Description"
-            # some doc strings have initial newlines, some don't
-            description = description.lstrip("\n")
-            # some doc strings have final newlines and spaces, some don't
-            description = description.rstrip()
-            # dedent, then reindent
-            description = self.indent_lines(textwrap.dedent(description), "  ")
-            description = f"{label}:\n{description}\n"
-            return description
-        else:
-            return ""
-
-    def format_epilog(self, epilog: str) -> str:
-        # leave full control over epilog to us
-        if epilog:
-            return epilog
-        else:
-            return ""
-
-    def indent_lines(self, text: str, indent: str) -> str:
-        new_lines = [indent + line for line in text.split("\n")]
-        return "\n".join(new_lines)
-
-
-class UpdatingDefaultsHelpFormatter(PrettyHelpFormatter):
-    """Custom help formatter for use in ConfigOptionParser.
-
-    This is updates the defaults before expanding them, allowing
-    them to show up correctly in the help listing.
-
-    Also redact auth from url type options
-    """
-
-    def expand_default(self, option: optparse.Option) -> str:
-        default_values = None
-        if self.parser is not None:
-            assert isinstance(self.parser, ConfigOptionParser)
-            self.parser._update_defaults(self.parser.defaults)
-            assert option.dest is not None
-            default_values = self.parser.defaults.get(option.dest)
-        help_text = super().expand_default(option)
-
-        if default_values and option.metavar == "URL":
-            if isinstance(default_values, str):
-                default_values = [default_values]
-
-            # If its not a list, we should abort and just return the help text
-            if not isinstance(default_values, list):
-                default_values = []
-
-            for val in default_values:
-                help_text = help_text.replace(val, redact_auth_from_url(val))
-
-        return help_text
-
-
-class CustomOptionParser(optparse.OptionParser):
-    def insert_option_group(
-        self, idx: int, *args: Any, **kwargs: Any
-    ) -> optparse.OptionGroup:
-        """Insert an OptionGroup at a given position."""
-        group = self.add_option_group(*args, **kwargs)
-
-        self.option_groups.pop()
-        self.option_groups.insert(idx, group)
-
-        return group
-
-    @property
-    def option_list_all(self) -> List[optparse.Option]:
-        """Get a list of all options, including those in option groups."""
-        res = self.option_list[:]
-        for i in self.option_groups:
-            res.extend(i.option_list)
-
-        return res
-
-
-class ConfigOptionParser(CustomOptionParser):
-    """Custom option parser which updates its defaults by checking the
-    configuration files and environmental variables"""
-
-    def __init__(
-        self,
-        *args: Any,
-        name: str,
-        isolated: bool = False,
-        **kwargs: Any,
-    ) -> None:
-        self.name = name
-        self.config = Configuration(isolated)
-
-        assert self.name
-        super().__init__(*args, **kwargs)
-
-    def check_default(self, option: optparse.Option, key: str, val: Any) -> Any:
-        try:
-            return option.check_value(key, val)
-        except optparse.OptionValueError as exc:
-            print(f"An error occurred during configuration: {exc}")
-            sys.exit(3)
-
-    def _get_ordered_configuration_items(
-        self,
-    ) -> Generator[Tuple[str, Any], None, None]:
-        # Configuration gives keys in an unordered manner. Order them.
-        override_order = ["global", self.name, ":env:"]
-
-        # Pool the options into different groups
-        section_items: Dict[str, List[Tuple[str, Any]]] = {
-            name: [] for name in override_order
-        }
-        for section_key, val in self.config.items():
-            # ignore empty values
-            if not val:
-                logger.debug(
-                    "Ignoring configuration key '%s' as it's value is empty.",
-                    section_key,
-                )
-                continue
-
-            section, key = section_key.split(".", 1)
-            if section in override_order:
-                section_items[section].append((key, val))
-
-        # Yield each group in their override order
-        for section in override_order:
-            for key, val in section_items[section]:
-                yield key, val
-
-    def _update_defaults(self, defaults: Dict[str, Any]) -> Dict[str, Any]:
-        """Updates the given defaults with values from the config files and
-        the environ. Does a little special handling for certain types of
-        options (lists)."""
-
-        # Accumulate complex default state.
-        self.values = optparse.Values(self.defaults)
-        late_eval = set()
-        # Then set the options with those values
-        for key, val in self._get_ordered_configuration_items():
-            # '--' because configuration supports only long names
-            option = self.get_option("--" + key)
-
-            # Ignore options not present in this parser. E.g. non-globals put
-            # in [global] by users that want them to apply to all applicable
-            # commands.
-            if option is None:
-                continue
-
-            assert option.dest is not None
-
-            if option.action in ("store_true", "store_false"):
-                try:
-                    val = strtobool(val)
-                except ValueError:
-                    self.error(
-                        "{} is not a valid value for {} option, "  # noqa
-                        "please specify a boolean value like yes/no, "
-                        "true/false or 1/0 instead.".format(val, key)
-                    )
-            elif option.action == "count":
-                with suppress(ValueError):
-                    val = strtobool(val)
-                with suppress(ValueError):
-                    val = int(val)
-                if not isinstance(val, int) or val < 0:
-                    self.error(
-                        "{} is not a valid value for {} option, "  # noqa
-                        "please instead specify either a non-negative integer "
-                        "or a boolean value like yes/no or false/true "
-                        "which is equivalent to 1/0.".format(val, key)
-                    )
-            elif option.action == "append":
-                val = val.split()
-                val = [self.check_default(option, key, v) for v in val]
-            elif option.action == "callback":
-                assert option.callback is not None
-                late_eval.add(option.dest)
-                opt_str = option.get_opt_string()
-                val = option.convert_value(opt_str, val)
-                # From take_action
-                args = option.callback_args or ()
-                kwargs = option.callback_kwargs or {}
-                option.callback(option, opt_str, val, self, *args, **kwargs)
-            else:
-                val = self.check_default(option, key, val)
-
-            defaults[option.dest] = val
-
-        for key in late_eval:
-            defaults[key] = getattr(self.values, key)
-        self.values = None
-        return defaults
-
-    def get_default_values(self) -> optparse.Values:
-        """Overriding to make updating the defaults after instantiation of
-        the option parser possible, _update_defaults() does the dirty work."""
-        if not self.process_default_values:
-            # Old, pre-Optik 1.5 behaviour.
-            return optparse.Values(self.defaults)
-
-        # Load the configuration, or error out in case of an error
-        try:
-            self.config.load()
-        except ConfigurationError as err:
-            self.exit(UNKNOWN_ERROR, str(err))
-
-        defaults = self._update_defaults(self.defaults.copy())  # ours
-        for option in self._get_all_options():
-            assert option.dest is not None
-            default = defaults.get(option.dest)
-            if isinstance(default, str):
-                opt_str = option.get_opt_string()
-                defaults[option.dest] = option.check_value(opt_str, default)
-        return optparse.Values(defaults)
-
-    def error(self, msg: str) -> None:
-        self.print_usage(sys.stderr)
-        self.exit(UNKNOWN_ERROR, f"{msg}\n")
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/webencodings/tests.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/webencodings/tests.py
deleted file mode 100644
index e12c10d033026f09cf97b81d29555e12aae8c762..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/webencodings/tests.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# coding: utf-8
-"""
-
-    webencodings.tests
-    ~~~~~~~~~~~~~~~~~~
-
-    A basic test suite for Encoding.
-
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-
-"""
-
-from __future__ import unicode_literals
-
-from . import (lookup, LABELS, decode, encode, iter_decode, iter_encode,
-               IncrementalDecoder, IncrementalEncoder, UTF8)
-
-
-def assert_raises(exception, function, *args, **kwargs):
-    try:
-        function(*args, **kwargs)
-    except exception:
-        return
-    else:  # pragma: no cover
-        raise AssertionError('Did not raise %s.' % exception)
-
-
-def test_labels():
-    assert lookup('utf-8').name == 'utf-8'
-    assert lookup('Utf-8').name == 'utf-8'
-    assert lookup('UTF-8').name == 'utf-8'
-    assert lookup('utf8').name == 'utf-8'
-    assert lookup('utf8').name == 'utf-8'
-    assert lookup('utf8 ').name == 'utf-8'
-    assert lookup(' \r\nutf8\t').name == 'utf-8'
-    assert lookup('u8') is None  # Python label.
-    assert lookup('utf-8 ') is None  # Non-ASCII white space.
-
-    assert lookup('US-ASCII').name == 'windows-1252'
-    assert lookup('iso-8859-1').name == 'windows-1252'
-    assert lookup('latin1').name == 'windows-1252'
-    assert lookup('LATIN1').name == 'windows-1252'
-    assert lookup('latin-1') is None
-    assert lookup('LATİN1') is None  # ASCII-only case insensitivity.
-
-
-def test_all_labels():
-    for label in LABELS:
-        assert decode(b'', label) == ('', lookup(label))
-        assert encode('', label) == b''
-        for repeat in [0, 1, 12]:
-            output, _ = iter_decode([b''] * repeat, label)
-            assert list(output) == []
-            assert list(iter_encode([''] * repeat, label)) == []
-        decoder = IncrementalDecoder(label)
-        assert decoder.decode(b'') == ''
-        assert decoder.decode(b'', final=True) == ''
-        encoder = IncrementalEncoder(label)
-        assert encoder.encode('') == b''
-        assert encoder.encode('', final=True) == b''
-    # All encoding names are valid labels too:
-    for name in set(LABELS.values()):
-        assert lookup(name).name == name
-
-
-def test_invalid_label():
-    assert_raises(LookupError, decode, b'\xEF\xBB\xBF\xc3\xa9', 'invalid')
-    assert_raises(LookupError, encode, 'é', 'invalid')
-    assert_raises(LookupError, iter_decode, [], 'invalid')
-    assert_raises(LookupError, iter_encode, [], 'invalid')
-    assert_raises(LookupError, IncrementalDecoder, 'invalid')
-    assert_raises(LookupError, IncrementalEncoder, 'invalid')
-
-
-def test_decode():
-    assert decode(b'\x80', 'latin1') == ('€', lookup('latin1'))
-    assert decode(b'\x80', lookup('latin1')) == ('€', lookup('latin1'))
-    assert decode(b'\xc3\xa9', 'utf8') == ('é', lookup('utf8'))
-    assert decode(b'\xc3\xa9', UTF8) == ('é', lookup('utf8'))
-    assert decode(b'\xc3\xa9', 'ascii') == ('é', lookup('ascii'))
-    assert decode(b'\xEF\xBB\xBF\xc3\xa9', 'ascii') == ('é', lookup('utf8'))  # UTF-8 with BOM
-
-    assert decode(b'\xFE\xFF\x00\xe9', 'ascii') == ('é', lookup('utf-16be'))  # UTF-16-BE with BOM
-    assert decode(b'\xFF\xFE\xe9\x00', 'ascii') == ('é', lookup('utf-16le'))  # UTF-16-LE with BOM
-    assert decode(b'\xFE\xFF\xe9\x00', 'ascii') == ('\ue900', lookup('utf-16be'))
-    assert decode(b'\xFF\xFE\x00\xe9', 'ascii') == ('\ue900', lookup('utf-16le'))
-
-    assert decode(b'\x00\xe9', 'UTF-16BE') == ('é', lookup('utf-16be'))
-    assert decode(b'\xe9\x00', 'UTF-16LE') == ('é', lookup('utf-16le'))
-    assert decode(b'\xe9\x00', 'UTF-16') == ('é', lookup('utf-16le'))
-
-    assert decode(b'\xe9\x00', 'UTF-16BE') == ('\ue900', lookup('utf-16be'))
-    assert decode(b'\x00\xe9', 'UTF-16LE') == ('\ue900', lookup('utf-16le'))
-    assert decode(b'\x00\xe9', 'UTF-16') == ('\ue900', lookup('utf-16le'))
-
-
-def test_encode():
-    assert encode('é', 'latin1') == b'\xe9'
-    assert encode('é', 'utf8') == b'\xc3\xa9'
-    assert encode('é', 'utf8') == b'\xc3\xa9'
-    assert encode('é', 'utf-16') == b'\xe9\x00'
-    assert encode('é', 'utf-16le') == b'\xe9\x00'
-    assert encode('é', 'utf-16be') == b'\x00\xe9'
-
-
-def test_iter_decode():
-    def iter_decode_to_string(input, fallback_encoding):
-        output, _encoding = iter_decode(input, fallback_encoding)
-        return ''.join(output)
-    assert iter_decode_to_string([], 'latin1') == ''
-    assert iter_decode_to_string([b''], 'latin1') == ''
-    assert iter_decode_to_string([b'\xe9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'hello'], 'latin1') == 'hello'
-    assert iter_decode_to_string([b'he', b'llo'], 'latin1') == 'hello'
-    assert iter_decode_to_string([b'hell', b'o'], 'latin1') == 'hello'
-    assert iter_decode_to_string([b'\xc3\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'\xEF\xBB\xBF\xc3\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'\xEF\xBB\xBF', b'\xc3', b'\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'\xEF\xBB\xBF', b'a', b'\xc3'], 'latin1') == 'a\uFFFD'
-    assert iter_decode_to_string([
-        b'', b'\xEF', b'', b'', b'\xBB\xBF\xc3', b'\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'\xEF\xBB\xBF'], 'latin1') == ''
-    assert iter_decode_to_string([b'\xEF\xBB'], 'latin1') == 'ï»'
-    assert iter_decode_to_string([b'\xFE\xFF\x00\xe9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'\xFF\xFE\xe9\x00'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'', b'\xFF', b'', b'', b'\xFE\xe9', b'\x00'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'', b'h\xe9', b'llo'], 'x-user-defined') == 'h\uF7E9llo'
-
-
-def test_iter_encode():
-    assert b''.join(iter_encode([], 'latin1')) == b''
-    assert b''.join(iter_encode([''], 'latin1')) == b''
-    assert b''.join(iter_encode(['é'], 'latin1')) == b'\xe9'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'latin1')) == b'\xe9'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16')) == b'\xe9\x00'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16le')) == b'\xe9\x00'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16be')) == b'\x00\xe9'
-    assert b''.join(iter_encode([
-        '', 'h\uF7E9', '', 'llo'], 'x-user-defined')) == b'h\xe9llo'
-
-
-def test_x_user_defined():
-    encoded = b'2,\x0c\x0b\x1aO\xd9#\xcb\x0f\xc9\xbbt\xcf\xa8\xca'
-    decoded = '2,\x0c\x0b\x1aO\uf7d9#\uf7cb\x0f\uf7c9\uf7bbt\uf7cf\uf7a8\uf7ca'
-    encoded = b'aa'
-    decoded = 'aa'
-    assert decode(encoded, 'x-user-defined') == (decoded, lookup('x-user-defined'))
-    assert encode(decoded, 'x-user-defined') == encoded
diff --git a/spaces/BramVanroy/llama-2-13b-chat-dutch-space/model.py b/spaces/BramVanroy/llama-2-13b-chat-dutch-space/model.py
deleted file mode 100644
index de6f9507c9cf25de4c1e411339d472dc0c051220..0000000000000000000000000000000000000000
--- a/spaces/BramVanroy/llama-2-13b-chat-dutch-space/model.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from threading import Thread
-from typing import Iterator
-
-import torch
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-USE_QUANTIZATION = True
-if USE_QUANTIZATION:
-    model_id = "TheBloke/Llama-2-13B-Chat-Dutch-GPTQ"
-else:
-    model_id = "BramVanroy/Llama-2-13b-chat-dutch"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-config = AutoConfig.from_pretrained(model_id)
-config.pretraining_tp = 1
-
-model = AutoModelForCausalLM.from_pretrained(model_id, config=config, torch_dtype=torch.float16, device_map="auto")
-
-# Torch.compile not supported on Windows
-try:
-    model = torch.compile(model)
-except Exception:
-    pass
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-
-def get_prompt(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> str:
-    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]
-    # The first user input is _not_ stripped
-    do_strip = False
-    for user_input, response in chat_history:
-        user_input = user_input.strip() if do_strip else user_input
-        do_strip = True
-        texts.append(f"{user_input} [/INST] {response.strip()} </s><s>[INST] ")
-    message = message.strip() if do_strip else message
-    texts.append(f"{message} [/INST]")
-    return "".join(texts)
-
-
-def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
-    prompt = get_prompt(message, chat_history, system_prompt)
-    input_ids = tokenizer([prompt], return_tensors="np", add_special_tokens=False)["input_ids"]
-    return input_ids.shape[-1]
-
-
-def run(
-    message: str,
-    chat_history: list[tuple[str, str]],
-    system_prompt: str,
-    max_new_tokens: int = 1024,
-    temperature: float = 0.8,
-    top_p: float = 0.95,
-    top_k: int = 50,
-    no_repeat_ngram_size: int = 3,
-) -> Iterator[str]:
-    prompt = get_prompt(message, chat_history, system_prompt)
-    inputs = tokenizer([prompt], return_tensors="pt", add_special_tokens=False).to(device)
-
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
-    generate_kwargs = dict(
-        inputs,
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature,
-        no_repeat_ngram_size=no_repeat_ngram_size,
-        num_beams=1,
-    )
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
-
-    outputs = []
-    for text in streamer:
-        outputs.append(text)
-        yield "".join(outputs)
diff --git a/spaces/BraydenMoore/a-random-unsecured-camera/templates/index.html b/spaces/BraydenMoore/a-random-unsecured-camera/templates/index.html
deleted file mode 100644
index 387809043167caf50c1e6338fe373ed39f936644..0000000000000000000000000000000000000000
--- a/spaces/BraydenMoore/a-random-unsecured-camera/templates/index.html
+++ /dev/null
@@ -1,483 +0,0 @@
-<!DOCTYPE html>
-<html>
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<head>
-  
-  <meta property="og:title" content="a random unsecured camera" />
-  <meta property="og:image" content="{{ url_for('static', filename='searching.png') }}" />
-  <meta property="og:url" content="https://unsecuredcamera.com" />
-
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css">
-  <link rel="preload" href="{{ url_for('static', filename='eye.gif') }}" as="image">
-  <link rel="icon" type="image/png" href="https://i.ibb.co/gvKPXJD/eye.gif">
-    <title>{{ page_title|safe }}</title>
-    <style>
-        body {
-            justify-content: center;
-            background-color: black;
-            display: flex;
-            align-items:center;
-            height: 100vh;
-            margin: auto;
-        }
-      /* Webkit browsers like Chrome, Safari */
-        ::-webkit-scrollbar {
-            width: 10px;
-            border-radius: 5px;
-        }
-
-        ::-webkit-scrollbar-thumb {
-            background:  black;
-            border-radius: 5px;
-        }
-
-        ::-webkit-scrollbar-thumb:hover {
-            background:  black;
-        }
-
-        /* Firefox */
-        * {
-            scrollbar-width: thin;
-            scrollbar-color: black yellow;
-        }
-        h3 {
-            margin-bottom: 0px;
-            margin-top: 0px !important;
-            border-bottom: 2px solid yellow; 
-            color:rgb(53, 53, 53); 
-            font-family: 'Helvetica'; font-weight: 50;
-        }
-        .pulse {
-            width: 40px;
-            height: 40px;
-            border-radius: 50%;
-            position: absolute;
-            background-color: yellow;
-            opacity: 0.5;
-            animation: pulse-animation 5s infinite;
-            margin-left: -20px;
-            margin-top: -20px; 
-        }
-        .dot {
-            border-style: solid;
-            border-width: 2px;
-            border-color: black;
-            width: 5px;
-            height: 5px;
-            border-radius: 50%;
-            position: absolute;
-            background-color: yellow;
-            margin-left: -4.5px;
-            margin-top: -4.5px;
-        }
-        @keyframes pulse-animation {
-            0% { transform: scale(0.1); opacity: 0.4; }
-            100% { transform: scale(3); opacity: 0; }
-        }
-        .flex-container {
-            display: flex;
-            justify-content: left;
-            align-items: top;
-        }
-        .outer-container {
-            display: inline-block;
-            justify-content: left;
-            max-width: 70vw;
-        }
-        #feed-div {
-            display: flex;
-            justify-content: right; 
-            width: 90%;
-            height: 90%;
-            position: relative;
-            margin-right: 20px;
-        }
-        h1 {
-          margin: 0px;
-          margin-top: 20px;
-        }
-        .feed {
-          transition: opacity 0.3s ease;
-          width: 100%;
-          height: 100%;
-          max-height: 80vh;
-          border-radius: 5px;
-        }
-        .map-div {
-            position: relative;
-            width: 280px;
-            height: 190px;
-            margin-top: 3%;
-            margin-bottom: 3%;
-            box-sizing: border-box;
-        }
-        .info {
-            cursor: default;
-            display: flex;
-            flex-direction: column;
-            align-items: flex-start;
-            width: 300px;
-            transition 0.3s ease;
-        }
-        #info {
-            transition 0.5s ease;
-        }
-        #info-text {
-            margin-top: 20px;
-            margin-bottom: 20px;
-        }
-        a:hover {
-            background-color: yellow;
-            color: black;
-            transition: 0.5s ease;
-        }
-        a {
-            background-color: transparent;
-            border-radius: 5px;
-            text-decoration: none;
-            color:rgb(53, 53, 53);
-            transition: 0.5s ease;
-        }
-        .hoverButton {
-              color: rgb(53,53,53);
-        }
-        .tag {
-            font-family: 'Helvetica'; 
-            font-weight: 50;
-            margin: auto;
-            color:rgb(53,53,53);
-        }
-
-        #share {
-          margin: auto;
-          margin-left: 10px;
-          color: yellow;
-          transition: 0.3s ease;
-        }
-        #share:hover {
-          opacity: 0.5;
-        }
-        #show-more {
-          cursor: pointer;
-        }
-        #additional-info {
-          display: block;
-          height: 0px;
-          overflow: hidden;
-          transition: height 0.3s ease;
-        }
-        #additional-info.expanded {
-          height: auto; 
-        }
-
-
-        @media only screen and (orientation: portrait) {
-            .body {
-              height: 85vh;
-            }  
-            h1 {
-              margin-top: 20px !important;
-            }
-            .flex-container {
-                flex-direction: column;
-                align-items: left;
-            }
-
-            .map-div {
-                width: 100%;
-                height: 100%;
-            }
-            .outer-container {
-                max-width: 85vw;
-            }
-            #feed-div {
-                justify-content: left;
-                width: 100%;
-                max-height: 45%;
-                margin-right: 0;
-            }
-            .feed {
-              height: 100%;
-              width: 100%;
-              max-height: 100%;
-            }
-        }
-    </style>
-</head>
-
-<body style="background-color: black;">
-    <div class="outer-container">
-            <div class="flex-container">
-
-                    <div id="feed-div">
-                      <img id="feed" class="feed" src="static/eye.gif" />
-                    </div>
-
-              
-                    <div class="info" id="info">
-                      <h1 id="country" style="color:rgb(53, 53, 53); margin-top: 10px; font-family: 'Helvetica'; font-weight: 50; margin-bottom: 10px;"> searching...</h1>        
-                      <a href="{{ ip_link }}" target="_blank"> <h3 style="border-bottom: 2px solid yellow;" id="location-name">{{ page_title|safe }}</h3></a>
-
-                      <div style="display: flex; margin-top: 22px; margin-bottom: 0%;">
-  
-                        <a href="?new=true" style="margin-right: 10px; display: inline-block;">
-                          <button class="hoverButton" style="border-radius: 5px; border: 2px solid yellow; background-color: transparent; padding: 10px;">
-                            another
-                          </button>
-                        </a>
-                        <!--<a  class="abortButton" href="?new=false&id={{ id }}" id="refreshSameFeedButton" style="display: inline-block;">-->
-                        <a  class="abortButton" href="?new=false&id={{ id }}" id="refreshSameFeedButton" style="display: inline-block;">
-                          <button class="hoverButton" style="border-radius: 5px; border: 2px solid rgb(53, 53, 53); background-color: transparent; padding: 10px;">
-                            refresh
-                          </button>
-                        </a>
-                        
-                        <i id="share" class="fa-solid fa-link" data-id="{{ id }}"></i>
-                        <p id="copied" class="tag" style="visibility: hidden;">copied</p>
-                      </div>
-
-                        <p id="info-text" style="color:rgb(53, 53, 53); font-family: 'Helvetica'; font-weight: 50;">
-
-                          time: <span id="time"></span><br>
-
-                          <span id="additional-info">
-                            owner: {{ owner }}<br>
-                            ip: {{ ip }}<br>  
-                            lat, lon: {{ loc }}
-                          </span>
-                          
-                          <span id="show-more">more <i style="margin-bottom:20px; color:rgb(53, 53, 53);" id="more-button" class="fa-solid fa-caret-down"></i></span>
-
-                          <span class="tag"><br>a brayden moore website<br></span>
-                          thanks for visiting<br>
-                          <a href="https://huggingface.co/spaces/BraydenMoore/a-random-unsecured-camera/tree/main" target="_blank">see the code</a>
-                        
-                        </p>
-                    
-
-                      <!--
-                        <div class="map-div">
-                            <img id="map" src="{{ url_for('static', filename='map_populated.png') }}" style="width: 100%; height: 100%;" />
-                            <div class="dot" style="left: {{ X }}%; top: {{ Y }}%;"></div>
-                            <div class="pulse" style="left: {{ X }}%; top: {{ Y }}%;"></div>
-                        </div>
-                      -->
-                    </div>
-
-                </div>
-    </div>
-
-
-    <script>
-      
-     // Handle feed load and refresh
-    let loadingGif = "{{ url_for('static', filename='eye.gif') }}";
-    let currentFeed;
-    let firstLoad = true;
-    document.addEventListener("DOMContentLoaded", function() {
-        const feed = document.getElementById("feed");
-        feed.style.width = "80px";
-        feed.style.height = "50px";
-        feed.src = loadingGif;
-        feed.style.opacity = "0.2";
-        const infoDiv = document.getElementById("info");
-        const infoText = document.getElementById("info-text");
-        infoText.style.opacity = "0";
-
-        const countryElement = document.getElementById("country");
-      
-        const locationName = document.getElementById('location-name');
-        locationName.textContent = "{{ page_title|safe }}";
-      
-        const newUrl = "{{ url|safe }}";
-
-        let contentTypeChecked = false;
-        let isJpeg = false;
-        function refreshImage() {
-            if (!isJpeg && !contentTypeChecked) {
-                let xhr = new XMLHttpRequest();
-                xhr.open('HEAD', newUrl, true);
-                xhr.onreadystatechange = function() {
-                    if (xhr.readyState === 4 && xhr.status === 200) {
-                        if (xhr.getResponseHeader('Content-Type') === 'image/jpeg') {
-                            img.src = newUrl + "?r=" + new Date().getTime();
-                            if (firstLoad){
-                                countryElement.textContent = "connecting..."; 
-                            }
-                            isJpeg = true;
-                            contentTypeChecked = true;
-                        }
-                    }
-                };
-                xhr.send();
-            } else if (isJpeg && contentTypeChecked) {
-                img.src = newUrl + "?r=" + new Date().getTime();
-            }
-        }
-        
-        const img = new Image();
-        img.onload = function() {
-            if (firstLoad) {
-              infoDiv.style.opacity = "0";
-            }
-            firstLoad = false;
-            old = newUrl;
-            setTimeout(() => {
-              feed.src = this.src;
-              locationName.textContent = "{{ name|safe }}";
-              feed.style.width = "100%";
-              feed.style.height = "70%";
-              feed.style.opacity = "1";
-              infoDiv.style.opacity = "1";
-            }, 100);
-            const infoText = document.getElementById("info-text");
-            infoText.style.opacity = "1";
-          
-            setTimeout(refreshImage, 500);
-            country.textContent = "{{ country }}"
-        };
-        
-        img.onerror = function(event) {
-            feed.style.width = "80px";
-            feed.style.height = "50px";
-            feed.src = loadingGif;
-            feed.style.opacity = "0.2";
-            infoText.style.opacity = "0";
-            locationName.textContent = "{{ page_title|safe }}";
-            console.log("Image loading failed:", event);
-            
-            const urlParams = new URLSearchParams(window.location.search);
-            if (urlParams.get('new') === 'false') {
-                countryElement.textContent = "couldn't connect.";
-            }
-            else {
-                window.location.href = "?new=true";
-            }
-        };
-        
-        img.src = newUrl;
-    });
-
-
-    // Time count
-    document.addEventListener("DOMContentLoaded", function() {
-      const timezone = "{{ timezone }}"; 
-      setInterval(() => {
-        const now = new Date();
-        const options = { 
-          timeZone: timezone, 
-          hour: '2-digit', 
-          minute: '2-digit', 
-          second: '2-digit',
-          hour12: true
-        };
-        const timeString = now.toLocaleTimeString('en-US', options);
-        document.getElementById("time").textContent = timeString;
-      }, 200);
-    });
-
-    // Copy link
-    document.addEventListener("DOMContentLoaded", function() {
-      const shareIcon = document.getElementById("share");
-      const copiedText = document.getElementById("copied");
-
-      shareIcon.addEventListener("click", function() {
-          const id = this.getAttribute("data-id");
-          const urlToCopy = `${window.location.origin}/?new=false&id=${id}`;
-          
-          navigator.clipboard.writeText(urlToCopy).then(() => {
-              console.log("URL copied to clipboard");
-              shareIcon.className = "fa-solid fa-check";
-              //copiedText.style.visibility = "visible";
-          }).catch(err => {
-              console.log("Could not copy text: ", err);
-          });
-      });
-  });
-
-  // Connect vs search
-  document.addEventListener("DOMContentLoaded", function() {
-      const urlParams = new URLSearchParams(window.location.search);
-      const countryElement = document.getElementById("country");
-
-      if (urlParams.get('new') === 'false') {
-          countryElement.textContent = "connecting...";
-      }
-  });
-
-
-  // Fullscreen
-    document.addEventListener("DOMContentLoaded", function() {
-    const feed = document.getElementById("feed");
-    let isFullscreen = false; 
-
-    function toggleFullScreen() {
-        if (!isFullscreen) {
-            if (this.requestFullscreen) {
-                this.requestFullscreen();
-            } else if (this.mozRequestFullScreen) {
-                this.mozRequestFullScreen();
-            } else if (this.webkitRequestFullscreen) {
-                this.webkitRequestFullscreen();
-            } else if (this.msRequestFullscreen) {
-                this.msRequestFullscreen();
-            }
-        } else {
-            if (document.exitFullscreen) {
-                document.exitFullscreen();
-            } else if (document.mozCancelFullScreen) {
-                document.mozCancelFullScreen();
-            } else if (document.webkitExitFullscreen) {
-                document.webkitExitFullscreen();
-            } else if (document.msExitFullscreen) {
-                document.msExitFullscreen();
-            }
-        }
-    }
-
-    feed.addEventListener("click", toggleFullScreen);
-    feed.addEventListener("touchstart", toggleFullScreen);
-
-    document.addEventListener("fullscreenchange", function() {
-        isFullscreen = !isFullscreen;
-    });
-});
-
-  // Change feed on space bar press
-    document.addEventListener('keydown', function(event) {
-      if (event.keyCode === 32) { 
-        window.location.href = '/?new=true';
-      }
-    });
-
-  // Show more info
-  document.addEventListener('DOMContentLoaded', function() {
-    const infoText = document.getElementById('info-text');
-    const additionalInfo = document.getElementById('additional-info');
-    const showMore = document.getElementById('show-more');
-    const moreButton = document.getElementById('more-button');
-  
-    // Initial setup
-    additionalInfo.style.height = '0';
-    additionalInfo.style.overflow = 'hidden';
-    additionalInfo.style.transition = 'height 0.3s ease-in-out';
-  
-    showMore.addEventListener('click', function() {
-      if (additionalInfo.style.height === '0px') {
-        const scrollHeight = additionalInfo.scrollHeight;
-        additionalInfo.style.height = `${scrollHeight}px`;
-        showMore.innerHTML = 'less <i style="margin-bottom:20px; color:rgb(53, 53, 53);" id="more-button" class="fa-solid fa-caret-up"></i>';
-      } else {
-        additionalInfo.style.height = '0';
-        showMore.innerHTML = 'more <i style="margin-bottom:20px; color:rgb(53, 53, 53);" id="more-button" class="fa-solid fa-caret-down"></i>';
-      }
-    });
-  });
-
-
-
-      
-    </script>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/CAMP-ViL/Xplainer/description.md b/spaces/CAMP-ViL/Xplainer/description.md
deleted file mode 100644
index 6d0f97b1fb7ebdb7d6cbded61b3ca716698cfb9e..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/description.md
+++ /dev/null
@@ -1,3 +0,0 @@
-This demo provides a playground for testing the model of our paper "Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis", which was accepted for publication at MICCAI 2023. You can test our pre-defined prompts and define your own prompts and diseases.
-
-**Paper**: [arxiv](https://arxiv.org/pdf/2303.13391.pdf), **Code**: [Github](https://github.com/ChantalMP/Xplainer)
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/thrust/testing/unittest/meta.h b/spaces/CVPR/LIVE/thrust/testing/unittest/meta.h
deleted file mode 100644
index 39c62edb645361dcb9064b439b9dfc4d86b741e0..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/testing/unittest/meta.h
+++ /dev/null
@@ -1,260 +0,0 @@
-/*! \file meta.h
- *  \brief Defines template classes
- *         for metaprogramming in the
- *         unit tests.
- */
-
-#pragma once
-
-namespace unittest
-{
-
-// mark the absence of a type
-struct null_type {}; 
-
-// this type encapsulates a list of
-// up to 10 types
-template<typename T0 = null_type,
-         typename T1 = null_type,
-         typename T2 = null_type,
-         typename T3 = null_type,
-         typename T4 = null_type,
-         typename T5 = null_type,
-         typename T6 = null_type,
-         typename T7 = null_type,
-         typename T8 = null_type,
-         typename T9 = null_type,
-         typename T10 = null_type,
-         typename T11 = null_type,
-         typename T12 = null_type,
-         typename T13 = null_type,
-         typename T14 = null_type,
-         typename T15 = null_type,
-         typename T16 = null_type,
-         typename T17 = null_type,
-         typename T18 = null_type,
-         typename T19 = null_type>
-  struct type_list
-{
-  typedef T0 type_0;
-  typedef T1 type_1;
-  typedef T2 type_2;
-  typedef T3 type_3;
-  typedef T4 type_4;
-  typedef T5 type_5;
-  typedef T6 type_6;
-  typedef T7 type_7;
-  typedef T8 type_8;
-  typedef T9 type_9;
-  typedef T10 type_10;
-  typedef T11 type_11;
-  typedef T12 type_12;
-  typedef T13 type_13;
-  typedef T14 type_14;
-  typedef T15 type_15;
-  typedef T16 type_16;
-  typedef T17 type_17;
-  typedef T18 type_18;
-  typedef T19 type_19;
-};
-
-// this type provides a way of indexing
-// into a type_list
-template<typename List, unsigned int i>
-  struct get_type
-{
-  typedef null_type type;
-};
-
-template<typename List>  struct get_type<List,0> { typedef typename List::type_0 type; };
-template<typename List>  struct get_type<List,1> { typedef typename List::type_1 type; };
-template<typename List>  struct get_type<List,2> { typedef typename List::type_2 type; };
-template<typename List>  struct get_type<List,3> { typedef typename List::type_3 type; };
-template<typename List>  struct get_type<List,4> { typedef typename List::type_4 type; };
-template<typename List>  struct get_type<List,5> { typedef typename List::type_5 type; };
-template<typename List>  struct get_type<List,6> { typedef typename List::type_6 type; };
-template<typename List>  struct get_type<List,7> { typedef typename List::type_7 type; };
-template<typename List>  struct get_type<List,8> { typedef typename List::type_8 type; };
-template<typename List>  struct get_type<List,9> { typedef typename List::type_9 type; };
-template<typename List>  struct get_type<List,10> { typedef typename List::type_10 type; };
-template<typename List>  struct get_type<List,11> { typedef typename List::type_11 type; };
-template<typename List>  struct get_type<List,12> { typedef typename List::type_12 type; };
-template<typename List>  struct get_type<List,13> { typedef typename List::type_13 type; };
-template<typename List>  struct get_type<List,14> { typedef typename List::type_14 type; };
-template<typename List>  struct get_type<List,15> { typedef typename List::type_15 type; };
-template<typename List>  struct get_type<List,16> { typedef typename List::type_16 type; };
-template<typename List>  struct get_type<List,17> { typedef typename List::type_17 type; };
-template<typename List>  struct get_type<List,18> { typedef typename List::type_18 type; };
-template<typename List>  struct get_type<List,19> { typedef typename List::type_19 type; };
-
-// this type and its specialization provides a way to
-// iterate over a type_list, and
-// applying a unary function to each type
-template<typename TypeList,
-         template <typename> class Function,
-         typename T,
-         unsigned int i = 0>
-  struct for_each_type
-{
-  template<typename U>
-    void operator()(U n)
-  {
-    // run the function on type T
-    Function<T> f;
-    f(n);
-
-    // get the next type
-    typedef typename get_type<TypeList,i+1>::type next_type;
-
-    // recurse to i + 1
-    for_each_type<TypeList, Function, next_type, i + 1> loop;
-    loop(n);
-  }
-
-  void operator()(void)
-  {
-    // run the function on type T
-    Function<T> f;
-    f();
-
-    // get the next type
-    typedef typename get_type<TypeList,i+1>::type next_type;
-
-    // recurse to i + 1
-    for_each_type<TypeList, Function, next_type, i + 1> loop;
-    loop();
-  }
-};
-
-// terminal case: do nothing when encountering null_type
-template<typename TypeList,
-         template <typename> class Function,
-         unsigned int i>
-  struct for_each_type<TypeList, Function, null_type, i>
-{
-  template<typename U>
-    void operator()(U)
-  {
-    // no-op
-  }
-
-  void operator()(void)
-  {
-    // no-op
-  }
-};
-
-// this type and its specialization instantiates
-// a template by applying T to Template.
-// if T == null_type, then its result is also null_type
-template<template <typename> class Template,
-         typename T>
-  struct ApplyTemplate1
-{
-  typedef Template<T> type;
-};
-
-template<template <typename> class Template>
-  struct ApplyTemplate1<Template, null_type>
-{
-  typedef null_type type;
-};
-
-// this type and its specializations instantiates
-// a template by applying T1 & T2 to Template.
-// if either T1 or T2 == null_type, then its result
-// is also null_type
-template<template <typename,typename> class Template,
-         typename T1,
-         typename T2>
-  struct ApplyTemplate2
-{
-  typedef Template<T1,T2> type;
-};
-
-template<template <typename,typename> class Template,
-         typename T>
-  struct ApplyTemplate2<Template, T, null_type>
-{
-  typedef null_type type;
-};
-
-template<template <typename,typename> class Template,
-         typename T>
-  struct ApplyTemplate2<Template, null_type, T>
-{
-  typedef null_type type;
-};
-
-template<template <typename,typename> class Template>
-  struct ApplyTemplate2<Template, null_type, null_type>
-{
-  typedef null_type type;
-};
-
-// this type creates a new type_list by applying a Template to each of
-// the Type_list's types
-template<typename TypeList,
-         template <typename> class Template>
-  struct transform1
-{
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,0>::type>::type type_0;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,1>::type>::type type_1;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,2>::type>::type type_2;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,3>::type>::type type_3;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,4>::type>::type type_4;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,5>::type>::type type_5;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,6>::type>::type type_6;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,7>::type>::type type_7;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,8>::type>::type type_8;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,9>::type>::type type_9;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,10>::type>::type type_10;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,11>::type>::type type_11;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,12>::type>::type type_12;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,13>::type>::type type_13;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,14>::type>::type type_14;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,15>::type>::type type_15;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,16>::type>::type type_16;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,17>::type>::type type_17;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,18>::type>::type type_18;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,19>::type>::type type_19;
-
-  typedef type_list<type_0, type_1, type_2, type_3, type_4, type_5, type_6, type_7, type_8, type_9,
-                    type_10, type_11, type_12, type_13, type_14, type_15, type_16, type_17, type_18, type_19> type;
-};
-
-// this type creates a new type_list by applying a Template to each of
-// two type_list's types
-template<typename TypeList1,
-         typename TypeList2,
-         template <typename,typename> class Template>
-  struct transform2
-{
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,0>::type, typename get_type<TypeList2,0>::type>::type type_0;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,1>::type, typename get_type<TypeList2,1>::type>::type type_1;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,2>::type, typename get_type<TypeList2,2>::type>::type type_2;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,3>::type, typename get_type<TypeList2,3>::type>::type type_3;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,4>::type, typename get_type<TypeList2,4>::type>::type type_4;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,5>::type, typename get_type<TypeList2,5>::type>::type type_5;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,6>::type, typename get_type<TypeList2,6>::type>::type type_6;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,7>::type, typename get_type<TypeList2,7>::type>::type type_7;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,8>::type, typename get_type<TypeList2,8>::type>::type type_8;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,9>::type, typename get_type<TypeList2,9>::type>::type type_9;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,10>::type, typename get_type<TypeList2,10>::type>::type type_10;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,11>::type, typename get_type<TypeList2,11>::type>::type type_11;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,12>::type, typename get_type<TypeList2,12>::type>::type type_12;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,13>::type, typename get_type<TypeList2,13>::type>::type type_13;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,14>::type, typename get_type<TypeList2,14>::type>::type type_14;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,15>::type, typename get_type<TypeList2,15>::type>::type type_15;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,16>::type, typename get_type<TypeList2,16>::type>::type type_16;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,17>::type, typename get_type<TypeList2,17>::type>::type type_17;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,18>::type, typename get_type<TypeList2,18>::type>::type type_18;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,19>::type, typename get_type<TypeList2,19>::type>::type type_19;
-  
-
-  typedef type_list<type_0, type_1, type_2, type_3, type_4, type_5, type_6, type_7, type_8, type_9,
-                    type_10, type_11, type_12, type_13, type_14, type_15, type_16, type_17, type_18, type_19> type;
-};
-
-} // end unittest
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/pointer.h b/spaces/CVPR/LIVE/thrust/thrust/detail/pointer.h
deleted file mode 100644
index e9204978f5d5990476698917842a1d77b779b5ba..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/pointer.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_adaptor.h>
-#include <thrust/iterator/detail/iterator_traversal_tags.h>
-#include <thrust/detail/type_traits/pointer_traits.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/reference_forward_declaration.h>
-#include <ostream>
-
-
-namespace thrust
-{
-
-// declare pointer with default values of template parameters
-template<typename Element, typename Tag, typename Reference = use_default, typename Derived = use_default> class pointer;
-
-} // end thrust
-
-
-// specialize thrust::iterator_traits to avoid problems with the name of
-// pointer's constructor shadowing its nested pointer type
-// do this before pointer is defined so the specialization is correctly
-// used inside the definition
-namespace thrust
-{
-
-template<typename Element, typename Tag, typename Reference, typename Derived>
-  struct iterator_traits<thrust::pointer<Element,Tag,Reference,Derived> >
-{
-  private:
-    typedef thrust::pointer<Element,Tag,Reference,Derived> ptr;
-
-  public:
-    typedef typename ptr::iterator_category iterator_category;
-    typedef typename ptr::value_type        value_type;
-    typedef typename ptr::difference_type   difference_type;
-    // XXX implement this type (the result of operator->) later
-    typedef void                             pointer;
-    typedef typename ptr::reference         reference;
-}; // end iterator_traits
-
-} // end thrust
-
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// this metafunction computes the type of iterator_adaptor thrust::pointer should inherit from
-template<typename Element, typename Tag, typename Reference, typename Derived>
-  struct pointer_base
-{
-  // void pointers should have no element type
-  // note that we remove_cv from the Element type to get the value_type
-  typedef typename thrust::detail::eval_if<
-    thrust::detail::is_void<typename thrust::detail::remove_const<Element>::type>::value,
-    thrust::detail::identity_<void>,
-    thrust::detail::remove_cv<Element>
-  >::type value_type;
-
-  // if no Derived type is given, just use pointer
-  typedef typename thrust::detail::eval_if<
-    thrust::detail::is_same<Derived,use_default>::value,
-    thrust::detail::identity_<pointer<Element,Tag,Reference,Derived> >,
-    thrust::detail::identity_<Derived>
-  >::type derived_type;
-
-  // void pointers should have no reference type
-  // if no Reference type is given, just use reference
-  typedef typename thrust::detail::eval_if<
-    thrust::detail::is_void<typename thrust::detail::remove_const<Element>::type>::value,
-    thrust::detail::identity_<void>,
-    thrust::detail::eval_if<
-      thrust::detail::is_same<Reference,use_default>::value,
-      thrust::detail::identity_<reference<Element,derived_type> >,
-      thrust::detail::identity_<Reference>
-    >
-  >::type reference_arg;
-
-  typedef thrust::iterator_adaptor<
-    derived_type,                        // pass along the type of our Derived class to iterator_adaptor
-    Element *,                           // we adapt a raw pointer
-    value_type,                          // the value type
-    Tag,                                 // system tag
-    thrust::random_access_traversal_tag, // pointers have random access traversal
-    reference_arg,                       // pass along our Reference type
-    std::ptrdiff_t
-  > type;
-}; // end pointer_base
-
-
-} // end detail
-
-
-// the base type for all of thrust's tagged pointers.
-// for reasonable pointer-like semantics, derived types should reimplement the following:
-// 1. no-argument constructor
-// 2. constructor from OtherElement *
-// 3. constructor from OtherPointer related by convertibility
-// 4. constructor from OtherPointer to void
-// 5. assignment from OtherPointer related by convertibility
-// These should just call the corresponding members of pointer.
-template<typename Element, typename Tag, typename Reference, typename Derived>
-  class pointer
-    : public thrust::detail::pointer_base<Element,Tag,Reference,Derived>::type
-{
-  private:
-    typedef typename thrust::detail::pointer_base<Element,Tag,Reference,Derived>::type         super_t;
-
-    typedef typename thrust::detail::pointer_base<Element,Tag,Reference,Derived>::derived_type derived_type;
-
-    // friend iterator_core_access to give it access to dereference
-    friend class thrust::iterator_core_access;
-
-    __host__ __device__
-    typename super_t::reference dereference() const;
-
-    // don't provide access to this part of super_t's interface
-    using super_t::base;
-    using typename super_t::base_type;
-
-  public:
-    typedef typename super_t::base_type raw_pointer;
-
-    // constructors
-
-    __host__ __device__
-    pointer();
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    pointer(decltype(nullptr));
-    #endif
-
-    // OtherValue shall be convertible to Value
-    // XXX consider making the pointer implementation a template parameter which defaults to Element *
-    template<typename OtherElement>
-    __host__ __device__
-    explicit pointer(OtherElement *ptr);
-
-    // OtherPointer's element_type shall be convertible to Element
-    // OtherPointer's system shall be convertible to Tag
-    template<typename OtherPointer>
-    __host__ __device__
-    pointer(const OtherPointer &other,
-            typename thrust::detail::enable_if_pointer_is_convertible<
-              OtherPointer,
-              pointer<Element,Tag,Reference,Derived>
-            >::type * = 0);
-
-    // OtherPointer's element_type shall be void
-    // OtherPointer's system shall be convertible to Tag
-    template<typename OtherPointer>
-    __host__ __device__
-    explicit
-    pointer(const OtherPointer &other,
-            typename thrust::detail::enable_if_void_pointer_is_system_convertible<
-              OtherPointer,
-              pointer<Element,Tag,Reference,Derived>
-            >::type * = 0);
-
-    // assignment
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    derived_type& operator=(decltype(nullptr));
-    #endif
-
-    // OtherPointer's element_type shall be convertible to Element
-    // OtherPointer's system shall be convertible to Tag
-    template<typename OtherPointer>
-    __host__ __device__
-    typename thrust::detail::enable_if_pointer_is_convertible<
-      OtherPointer,
-      pointer,
-      derived_type &
-    >::type
-    operator=(const OtherPointer &other);
-
-    // observers
-
-    __host__ __device__
-    Element *get() const;
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    explicit operator bool() const;
-    #endif
-
-    __host__ __device__
-    static derived_type pointer_to(typename thrust::detail::pointer_traits_detail::pointer_to_param<Element>::type r)
-    {
-      return thrust::detail::pointer_traits<derived_type>::pointer_to(r);
-    }
-}; // end pointer
-
-// Output stream operator
-template<typename Element, typename Tag, typename Reference, typename Derived,
-         typename charT, typename traits>
-__host__
-std::basic_ostream<charT, traits> &
-operator<<(std::basic_ostream<charT, traits> &os,
-           const pointer<Element, Tag, Reference, Derived> &p);
-
-#if THRUST_CPP_DIALECT >= 2011
-// NOTE: This is needed so that Thrust smart pointers can be used in
-// `std::unique_ptr`.
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator==(decltype(nullptr), pointer<Element, Tag, Reference, Derived> p);
-
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator==(pointer<Element, Tag, Reference, Derived> p, decltype(nullptr));
-
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator!=(decltype(nullptr), pointer<Element, Tag, Reference, Derived> p);
-
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator!=(pointer<Element, Tag, Reference, Derived> p, decltype(nullptr));
-#endif
-
-} // end thrust
-
-#include <thrust/detail/pointer.inl>
-
diff --git a/spaces/CVPR/drawings-to-human/README.md b/spaces/CVPR/drawings-to-human/README.md
deleted file mode 100644
index 5f6247d3dc5c8a9e89b103f641f97aa1543d6967..0000000000000000000000000000000000000000
--- a/spaces/CVPR/drawings-to-human/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Drawings to Human
-emoji: ✍️🧍🏽‍♀️🧍🏻
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.24
-pinned: false
-app_file: main.py
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/lama-example/saicinpainting/training/visualizers/noop.py b/spaces/CVPR/lama-example/saicinpainting/training/visualizers/noop.py
deleted file mode 100644
index 4175089a54a8484d51e6c879c1a99c4e4d961d15..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/training/visualizers/noop.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from saicinpainting.training.visualizers.base import BaseVisualizer
-
-
-class NoopVisualizer(BaseVisualizer):
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        pass
diff --git a/spaces/CarperAI/StableVicuna/README.md b/spaces/CarperAI/StableVicuna/README.md
deleted file mode 100644
index 3592aac607f78a1bf79e804fc4adeb1630ff3d29..0000000000000000000000000000000000000000
--- a/spaces/CarperAI/StableVicuna/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: StableVicuna
-emoji: 🦙
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Chris4K/llms_compare/Wondershare-Dvd-Slideshow-Builder-Deluxe-3d-Style-Pack-Registration-Code-Keygen.md b/spaces/Chris4K/llms_compare/Wondershare-Dvd-Slideshow-Builder-Deluxe-3d-Style-Pack-Registration-Code-Keygen.md
deleted file mode 100644
index cfea030fb120ed7958a75b506402c124d00d7743..0000000000000000000000000000000000000000
--- a/spaces/Chris4K/llms_compare/Wondershare-Dvd-Slideshow-Builder-Deluxe-3d-Style-Pack-Registration-Code-Keygen.md
+++ /dev/null
@@ -1,74 +0,0 @@
-## Wondershare Dvd Slideshow Builder Deluxe 3d Style Pack Registration Code Keygen
-
-
-
- 
-
- ![Wondershare Dvd Slideshow Builder Deluxe 3d Style Pack Registration Code Keygen](https://3.bp.blogspot.com/-D0F19vRb9Xw/Uwv0JAKVuCI/AAAAAAAACOE/WsptycKZY_0/s1600/3D+Style+Pack+Box.jpg)
-
- 
-
- 
-
-**CLICK HERE ····· [https://urluso.com/2tBNxk](https://urluso.com/2tBNxk)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Make Amazing 3D Slideshows with Wondershare DVD Slideshow Builder Deluxe and 3D Style Pack
-
-  
-
-If you want to create stunning 3D slideshows from your photos and videos, you need a powerful and easy-to-use software that can handle the task. Wondershare DVD Slideshow Builder Deluxe is a professional slideshow maker that lets you turn your photos and videos into amazing DVD slideshows with music, transitions, effects, and more. But what if you want to add some extra flair to your slideshows with 3D movie effects? That's where Wondershare 3D Style Pack comes in.
-
-  
-
-Wondershare 3D Style Pack is an exclusive expansion pack for Wondershare DVD Slideshow Builder Deluxe that gives you access to more than 10 "Styles" of popular 3D video effects, such as 3D Cube, Photo Flow, 3D Wall, 3D Square, 3D Carousel, 3D Box, and so on. With these 3D styles, you can make your slideshows more dynamic and eye-catching. You can also customize the 3D effects with parameters like angle, distance, depth, and color.
-
-  
-
-To use Wondershare 3D Style Pack, you need to purchase both the software and the style pack from the official website[^1^]. Then, you can download and install them on your computer. After that, you can launch Wondershare DVD Slideshow Builder Deluxe and choose Standard mode. Then, you can import your photos and videos to the storyboard and apply the 3D styles to them. You can preview the effects in real time and adjust them as you like. Finally, you can burn your slideshow to DVD or save it as a video file.
-
-  
-
-Wondershare 3D Style Pack is a great way to enhance your slideshows with 3D movie effects. It's compatible with Windows XP/Vista/7/8/10 and supports various formats of photos and videos. You can also get free updates and technical support from Wondershare. If you want to try it before buying it, you can download a free trial version of Wondershare DVD Slideshow Builder Deluxe from the official website[^1^] and use some of the 3D styles for free.
-
-  
-
-So what are you waiting for? Get Wondershare DVD Slideshow Builder Deluxe and 3D Style Pack today and start making amazing 3D slideshows with your photos and videos!
-
-  
-
-## How to Use Wondershare DVD Slideshow Builder Deluxe
-
-  
-
-Wondershare DVD Slideshow Builder Deluxe is a user-friendly and versatile software that lets you create professional-looking slideshows with your photos and videos. You can use it to make slideshows for various occasions, such as weddings, birthdays, anniversaries, vacations, and more. You can also use it to make slideshows for business presentations, education, and marketing.
-
-  
-
-To use Wondershare DVD Slideshow Builder Deluxe, you need to download and install it on your computer. Then, you can launch it and choose between two modes: Advanced Mode and Standard Mode[^1^]. Advanced Mode gives you more control and customization options for your slideshows, while Standard Mode is simpler and faster to use. You can switch between the modes at any time.
-
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-In both modes, you can import your photos and videos to the storyboard by clicking the Add Files button or dragging and dropping them. You can also add background music, voiceovers, text, clipart, and other elements to your slideshows. You can edit your photos and videos with basic tools like crop, rotate, trim, adjust brightness, contrast, saturation, etc. You can also apply transitions, effects, filters, and themes to your slideshows to make them more attractive.
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-Once you are satisfied with your slideshows, you can preview them in full screen and make any changes if needed. Then, you can save your slideshows as a video file in various formats like MP4, AVI, WMV, MOV, FLV, etc. You can also burn your slideshows to DVD discs or ISO files with a built-in DVD menu. You can also share your slideshows online via YouTube, Facebook, Vimeo, etc.
-
- 145887f19f
-
- 
-
- 
-
diff --git a/spaces/Cong723/gpt-academic-public/crazy_functions/crazy_functions_test.py b/spaces/Cong723/gpt-academic-public/crazy_functions/crazy_functions_test.py
deleted file mode 100644
index 6020fa2ffc3cdcb288f03e55ff37313b0be78222..0000000000000000000000000000000000000000
--- a/spaces/Cong723/gpt-academic-public/crazy_functions/crazy_functions_test.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""
-这是什么？
-    这个文件用于函数插件的单元测试
-    运行方法 python crazy_functions/crazy_functions_test.py
-"""
-
-def validate_path():
-    import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-    os.chdir(root_dir_assume)
-    sys.path.append(root_dir_assume)
-    
-validate_path() # validate path so you can run from base directory
-from colorful import *
-from toolbox import get_conf, ChatBotWithCookies
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
-    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
-
-llm_kwargs = {
-    'api_key': API_KEY,
-    'llm_model': LLM_MODEL,
-    'top_p':1.0, 
-    'max_length': None,
-    'temperature':1.0,
-}
-plugin_kwargs = { }
-chatbot = ChatBotWithCookies(llm_kwargs)
-history = []
-system_prompt = "Serve me as a writing and programming assistant."
-web_port = 1024
-
-
-def test_解析一个Python项目():
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    txt = "crazy_functions/test_project/python/dqn"
-    for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_解析一个Cpp项目():
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    txt = "crazy_functions/test_project/cpp/cppipc"
-    for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Latex英文润色():
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    txt = "crazy_functions/test_project/latex/attention"
-    for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Markdown中译英():
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    txt = "README.md"
-    for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_批量翻译PDF文档():
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_谷歌检索小助手():
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG="
-    for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_总结word文档():
-    from crazy_functions.总结word文档 import 总结word文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_下载arxiv论文并翻译摘要():
-    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-    txt = "1812.10695"
-    for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_联网回答问题():
-    from crazy_functions.联网的ChatGPT import 连接网络回答问题
-    # txt = "“我们称之为高效”是什么梗？"
-    # >>        从第0份、第1份、第2份搜索结果可以看出，“我们称之为高效”是指在游戏社区中，用户们用来形容一些游戏策略或行为非常高效且能够带来好的效果的用语。这个用语最初可能是在群星（Stellaris）这个游戏里面流行起来的，后来也传播到了其他游戏中，比如巨像（Titan）等游戏。其中第1份搜索结果中的一篇文章也指出，“我们称之为高效”这 一用语来源于群星（Stellaris）游戏中的一个情节。
-    # txt = "为什么说枪毙P社玩家没有一个冤枉的？"
-    # >>        它们都是关于一个知乎用户所发的帖子，引用了一群游戏玩家对于需要对P社玩家进行枪毙的讨论，这个话题的本质是玩家们对于P 社游戏中的政治与历史元素的不同看法，以及其中不少玩家以极端立场宣扬的想法和言论，因此有人就以枪毙这些玩家来回应此类言论。但是这个话题本身并没有实质内容，只是一个玩笑或者恶搞，并不应该被当做真实的态度或者观点，因此这种说法没有实际意义。
-    # txt = "谁是应急食品？"
-    # >>        '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。'
-    # txt = "道路千万条，安全第一条。后面两句是？"
-    # >>        '行车不规范，亲人两行泪。'
-    # txt = "What is in the canister?"
-    # >>        Rainbow Six Siege 游戏中 Smoke 的 Canister 中装有何种物质相关的官方信息。
-    # txt = "失败的man是什么?"
-    # >>        根据第1份搜索结果，可以得知失败的man是指一位在B站购买了蜘蛛侠COS服后穿上后被网友嘲笑的UP主，而“失败的man”是蜘蛛侠英文名“spiderman”的谐音梗，并且网友们还 给这位UP主起了“苍蝇侠”的外号。因此，失败的man是指这位UP主在穿上蜘蛛侠COS服后被网友嘲笑的情况。
-    # txt = "老六是什么，起源于哪里？"
-    # >>        老六是网络流行语，最初起源于游戏《CSGO》，指游戏中玩家中独来独往、游离于队伍之外的“自由人”或玩得比较菜或者玩得比较阴险的人 ，后来逐渐演变成指玩得比较阴险的玩家。
-    # txt = "罗小黑战记因为什么经常被吐槽？"
-    # >>        3. 更新速度。罗小黑战记的更新时间不定，时而快时而慢，给观众留下了等待的时间过长的印象。
-    # txt = "沙特、伊朗最近的关系如何？"
-    # >>        最近在中国的斡旋下，沙特和伊朗于3月10日达成了恢复两国外交关系的协议，这表明两国关系已经重新回到正常化状态。
-    # txt = "You should have gone for the head. What does that mean?"
-    # >>        The phrase "You should have gone for the head" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame.
-    txt = "AutoGPT是什么？"
-    # >>        AutoGPT是一个基于GPT-4语言模型的开源应用程序。它可以根据用户需求自主执行任务，包括事件分析、营销方案撰写、代码编程、数学运算等等，并完全不需要用户插手。它可以自己思考，给出实现的步骤和实现细节，甚至可以自问自答执 行任务。最近它在GitHub上爆火，成为了业内最热门的项目之一。
-    # txt = "钟离带什么圣遗物？"
-    for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): 
-        print("当前问答：", cb[-1][-1].replace("\n"," "))
-    for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
-
-def test_解析ipynb文件():
-    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    txt = "crazy_functions/test_samples"
-    for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-
-# test_解析一个Python项目()
-# test_Latex英文润色()
-# test_Markdown中译英()
-# test_批量翻译PDF文档()
-# test_谷歌检索小助手()
-# test_总结word文档()
-# test_下载arxiv论文并翻译摘要()
-# test_解析一个Cpp项目()
-# test_联网回答问题()
-test_解析ipynb文件()
-
-input("程序完成，回车退出。")
-print("退出。")
\ No newline at end of file
diff --git a/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/ip_segment.py b/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/ip_segment.py
deleted file mode 100644
index a4c02cb1989e2939bb38eb692c2f4fb021a6ff16..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/ip_segment.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import cv2
-import numpy as np
-import shutil
-import os
-from os.path import join as pjoin
-
-
-def segment_img(org, segment_size, output_path, overlap=100):
-    if not os.path.exists(output_path):
-        os.mkdir(output_path)
-
-    height, width = np.shape(org)[0], np.shape(org)[1]
-    top = 0
-    bottom = segment_size
-    segment_no = 0
-    while top < height and bottom < height:
-        segment = org[top:bottom]
-        cv2.imwrite(os.path.join(output_path, str(segment_no) + '.png'), segment)
-        segment_no += 1
-        top += segment_size - overlap
-        bottom = bottom + segment_size - overlap if bottom + segment_size - overlap <= height else height
-
-
-def clipping(img, components, pad=0, show=False):
-    """
-    :param adjust: shrink(negative) or expand(positive) the bounding box
-    :param img: original image
-    :param corners: ((column_min, row_min),(column_max, row_max))
-    :return: list of clipping images
-    """
-    clips = []
-    for component in components:
-        clip = component.compo_clipping(img, pad=pad)
-        clips.append(clip)
-        if show:
-            cv2.imshow('clipping', clip)
-            cv2.waitKey()
-    return clips
-
-
-def dissemble_clip_img_hollow(clip_root, org, compos):
-    if os.path.exists(clip_root):
-        shutil.rmtree(clip_root)
-    os.mkdir(clip_root)
-    cls_dirs = []
-
-    bkg = org.copy()
-    hollow_out = np.ones(bkg.shape[:2], dtype=np.uint8) * 255
-    for compo in compos:
-        cls = compo.category
-        c_root = pjoin(clip_root, cls)
-        c_path = pjoin(c_root, str(compo.id) + '.jpg')
-        if cls not in cls_dirs:
-            os.mkdir(c_root)
-            cls_dirs.append(cls)
-        clip = compo.compo_clipping(org)
-        cv2.imwrite(c_path, clip)
-
-        col_min, row_min, col_max, row_max = compo.put_bbox()
-        hollow_out[row_min: row_max, col_min: col_max] = 0
-
-    bkg = cv2.merge((bkg, hollow_out))
-    cv2.imwrite(os.path.join(clip_root, 'bkg.png'), bkg)
-
-
-def dissemble_clip_img_fill(clip_root, org, compos, flag='most'):
-
-    def average_pix_around(pad=6, offset=3):
-        up = row_min - pad if row_min - pad >= 0 else 0
-        left = col_min - pad if col_min - pad >= 0 else 0
-        bottom = row_max + pad if row_max + pad < org.shape[0] - 1 else org.shape[0] - 1
-        right = col_max + pad if col_max + pad < org.shape[1] - 1 else org.shape[1] - 1
-
-        average = []
-        for i in range(3):
-            avg_up = np.average(org[up:row_min - offset, left:right, i])
-            avg_bot = np.average(org[row_max + offset:bottom, left:right, i])
-            avg_left = np.average(org[up:bottom, left:col_min - offset, i])
-            avg_right = np.average(org[up:bottom, col_max + offset:right, i])
-            average.append(int((avg_up + avg_bot + avg_left + avg_right)/4))
-        return average
-
-    def most_pix_around(pad=6, offset=2):
-        up = row_min - pad if row_min - pad >= 0 else 0
-        left = col_min - pad if col_min - pad >= 0 else 0
-        bottom = row_max + pad if row_max + pad < org.shape[0] - 1 else org.shape[0] - 1
-        right = col_max + pad if col_max + pad < org.shape[1] - 1 else org.shape[1] - 1
-
-        most = []
-        for i in range(3):
-            val = np.concatenate((org[up:row_min - offset, left:right, i].flatten(),
-                            org[row_max + offset:bottom, left:right, i].flatten(),
-                            org[up:bottom, left:col_min - offset, i].flatten(),
-                            org[up:bottom, col_max + offset:right, i].flatten()))
-            # print(val)
-            # print(np.argmax(np.bincount(val)))
-            most.append(int(np.argmax(np.bincount(val))))
-        return most
-
-    if os.path.exists(clip_root):
-        shutil.rmtree(clip_root)
-    os.mkdir(clip_root)
-    cls_dirs = []
-
-    bkg = org.copy()
-    for compo in compos:
-        cls = compo.category
-        c_root = pjoin(clip_root, cls)
-        c_path = pjoin(c_root, str(compo.id) + '.jpg')
-        if cls not in cls_dirs:
-            os.mkdir(c_root)
-            cls_dirs.append(cls)
-        clip = compo.compo_clipping(org)
-        cv2.imwrite(c_path, clip)
-
-        col_min, row_min, col_max, row_max = compo.put_bbox()
-        if flag == 'average':
-            color = average_pix_around()
-        elif flag == 'most':
-            color = most_pix_around()
-        cv2.rectangle(bkg, (col_min, row_min), (col_max, row_max), color, -1)
-
-    cv2.imwrite(os.path.join(clip_root, 'bkg.png'), bkg)
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/roi_heads/boundary_head/roi_boundary_feature_extractors.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/roi_heads/boundary_head/roi_boundary_feature_extractors.py
deleted file mode 100644
index 96fe5b019a54ae06799065cf39adea7ba452442d..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/roi_heads/boundary_head/roi_boundary_feature_extractors.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from torch import nn
-from torch.nn import functional as F
-
-# from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor
-from maskrcnn_benchmark.modeling.poolers import Pooler
-from maskrcnn_benchmark.modeling.make_layers import make_conv3x3
-
-
-class BOUNDARYRCNNFPNFeatureExtractor(nn.Module):
-    """
-    Heads for FPN for classification
-    """
-
-    def __init__(self, cfg, in_channels):
-        """
-        Arguments:
-            num_classes (int): number of output classes
-            input_size (int): number of channels of the input once it's flattened
-            representation_size (int): size of the intermediate representation
-        """
-        super(BOUNDARYRCNNFPNFeatureExtractor, self).__init__()
-
-        resolution = cfg.MODEL.ROI_BOUNDARY_HEAD.POOLER_RESOLUTION
-        scales = cfg.MODEL.ROI_BOUNDARY_HEAD.POOLER_SCALES
-        sampling_ratio = cfg.MODEL.ROI_BOUNDARY_HEAD.POOLER_SAMPLING_RATIO
-        pooler = Pooler(
-            output_size=(resolution, resolution),
-            scales=scales,
-            sampling_ratio=sampling_ratio,
-            deformable=cfg.MODEL.ROI_BOUNDARY_HEAD.DEFORMABLE_POOLING
-        # deformable = True
-        )
-        input_size = in_channels
-        self.pooler = pooler
-
-        layers = cfg.MODEL.ROI_BOUNDARY_HEAD.CONV_LAYERS
-        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
-        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION
-
-        next_feature = input_size
-        self.blocks = []
-        for layer_idx, layer_features in enumerate(layers, 1):
-            layer_name = "boundary_fcn{}".format(layer_idx)
-            module = make_conv3x3(
-                next_feature, layer_features,
-                dilation=dilation, stride=1, use_gn=use_gn
-            )
-            self.add_module(layer_name, module)
-            next_feature = layer_features
-            self.blocks.append(layer_name)
-
-    def forward(self, x, proposals):
-        x = self.pooler(x, proposals)
-
-        for layer_name in self.blocks:
-            x = F.relu(getattr(self, layer_name)(x))
-
-        return x
-
-
-_ROI_KE_FEATURE_EXTRACTORS = {
-    "BoundaryRCNNFPNFeatureExtractor": BOUNDARYRCNNFPNFeatureExtractor,
-}
-
-
-def make_roi_boundary_feature_extractor(cfg, in_channels):
-    func = _ROI_KE_FEATURE_EXTRACTORS[cfg.MODEL.ROI_BOUNDARY_HEAD.FEATURE_EXTRACTOR]
-    return func(cfg, in_channels)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/_core/_streams.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/_core/_streams.py
deleted file mode 100644
index 54ea2b2bafd321a4f88dfa6fd19993213eec8105..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/_core/_streams.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import math
-from typing import Any, TypeVar, overload
-
-from ..streams.memory import (
-    MemoryObjectReceiveStream,
-    MemoryObjectSendStream,
-    MemoryObjectStreamState,
-)
-
-T_Item = TypeVar("T_Item")
-
-
-@overload
-def create_memory_object_stream(
-    max_buffer_size: float = ...,
-) -> tuple[MemoryObjectSendStream[Any], MemoryObjectReceiveStream[Any]]:
-    ...
-
-
-@overload
-def create_memory_object_stream(
-    max_buffer_size: float = ..., item_type: type[T_Item] = ...
-) -> tuple[MemoryObjectSendStream[T_Item], MemoryObjectReceiveStream[T_Item]]:
-    ...
-
-
-def create_memory_object_stream(
-    max_buffer_size: float = 0, item_type: type[T_Item] | None = None
-) -> tuple[MemoryObjectSendStream[Any], MemoryObjectReceiveStream[Any]]:
-    """
-    Create a memory object stream.
-
-    :param max_buffer_size: number of items held in the buffer until ``send()`` starts blocking
-    :param item_type: type of item, for marking the streams with the right generic type for
-        static typing (not used at run time)
-    :return: a tuple of (send stream, receive stream)
-
-    """
-    if max_buffer_size != math.inf and not isinstance(max_buffer_size, int):
-        raise ValueError("max_buffer_size must be either an integer or math.inf")
-    if max_buffer_size < 0:
-        raise ValueError("max_buffer_size cannot be negative")
-
-    state: MemoryObjectStreamState = MemoryObjectStreamState(max_buffer_size)
-    return MemoryObjectSendStream(state), MemoryObjectReceiveStream(state)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/zoneinfo/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/zoneinfo/__init__.py
deleted file mode 100644
index 34f11ad66c88047f2c049a4cdcc937b4b78ea6d6..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/zoneinfo/__init__.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# -*- coding: utf-8 -*-
-import warnings
-import json
-
-from tarfile import TarFile
-from pkgutil import get_data
-from io import BytesIO
-
-from dateutil.tz import tzfile as _tzfile
-
-__all__ = ["get_zonefile_instance", "gettz", "gettz_db_metadata"]
-
-ZONEFILENAME = "dateutil-zoneinfo.tar.gz"
-METADATA_FN = 'METADATA'
-
-
-class tzfile(_tzfile):
-    def __reduce__(self):
-        return (gettz, (self._filename,))
-
-
-def getzoneinfofile_stream():
-    try:
-        return BytesIO(get_data(__name__, ZONEFILENAME))
-    except IOError as e:  # TODO  switch to FileNotFoundError?
-        warnings.warn("I/O error({0}): {1}".format(e.errno, e.strerror))
-        return None
-
-
-class ZoneInfoFile(object):
-    def __init__(self, zonefile_stream=None):
-        if zonefile_stream is not None:
-            with TarFile.open(fileobj=zonefile_stream) as tf:
-                self.zones = {zf.name: tzfile(tf.extractfile(zf), filename=zf.name)
-                              for zf in tf.getmembers()
-                              if zf.isfile() and zf.name != METADATA_FN}
-                # deal with links: They'll point to their parent object. Less
-                # waste of memory
-                links = {zl.name: self.zones[zl.linkname]
-                         for zl in tf.getmembers() if
-                         zl.islnk() or zl.issym()}
-                self.zones.update(links)
-                try:
-                    metadata_json = tf.extractfile(tf.getmember(METADATA_FN))
-                    metadata_str = metadata_json.read().decode('UTF-8')
-                    self.metadata = json.loads(metadata_str)
-                except KeyError:
-                    # no metadata in tar file
-                    self.metadata = None
-        else:
-            self.zones = {}
-            self.metadata = None
-
-    def get(self, name, default=None):
-        """
-        Wrapper for :func:`ZoneInfoFile.zones.get`. This is a convenience method
-        for retrieving zones from the zone dictionary.
-
-        :param name:
-            The name of the zone to retrieve. (Generally IANA zone names)
-
-        :param default:
-            The value to return in the event of a missing key.
-
-        .. versionadded:: 2.6.0
-
-        """
-        return self.zones.get(name, default)
-
-
-# The current API has gettz as a module function, although in fact it taps into
-# a stateful class. So as a workaround for now, without changing the API, we
-# will create a new "global" class instance the first time a user requests a
-# timezone. Ugly, but adheres to the api.
-#
-# TODO: Remove after deprecation period.
-_CLASS_ZONE_INSTANCE = []
-
-
-def get_zonefile_instance(new_instance=False):
-    """
-    This is a convenience function which provides a :class:`ZoneInfoFile`
-    instance using the data provided by the ``dateutil`` package. By default, it
-    caches a single instance of the ZoneInfoFile object and returns that.
-
-    :param new_instance:
-        If ``True``, a new instance of :class:`ZoneInfoFile` is instantiated and
-        used as the cached instance for the next call. Otherwise, new instances
-        are created only as necessary.
-
-    :return:
-        Returns a :class:`ZoneInfoFile` object.
-
-    .. versionadded:: 2.6
-    """
-    if new_instance:
-        zif = None
-    else:
-        zif = getattr(get_zonefile_instance, '_cached_instance', None)
-
-    if zif is None:
-        zif = ZoneInfoFile(getzoneinfofile_stream())
-
-        get_zonefile_instance._cached_instance = zif
-
-    return zif
-
-
-def gettz(name):
-    """
-    This retrieves a time zone from the local zoneinfo tarball that is packaged
-    with dateutil.
-
-    :param name:
-        An IANA-style time zone name, as found in the zoneinfo file.
-
-    :return:
-        Returns a :class:`dateutil.tz.tzfile` time zone object.
-
-    .. warning::
-        It is generally inadvisable to use this function, and it is only
-        provided for API compatibility with earlier versions. This is *not*
-        equivalent to ``dateutil.tz.gettz()``, which selects an appropriate
-        time zone based on the inputs, favoring system zoneinfo. This is ONLY
-        for accessing the dateutil-specific zoneinfo (which may be out of
-        date compared to the system zoneinfo).
-
-    .. deprecated:: 2.6
-        If you need to use a specific zoneinfofile over the system zoneinfo,
-        instantiate a :class:`dateutil.zoneinfo.ZoneInfoFile` object and call
-        :func:`dateutil.zoneinfo.ZoneInfoFile.get(name)` instead.
-
-        Use :func:`get_zonefile_instance` to retrieve an instance of the
-        dateutil-provided zoneinfo.
-    """
-    warnings.warn("zoneinfo.gettz() will be removed in future versions, "
-                  "to use the dateutil-provided zoneinfo files, instantiate a "
-                  "ZoneInfoFile object and use ZoneInfoFile.zones.get() "
-                  "instead. See the documentation for details.",
-                  DeprecationWarning)
-
-    if len(_CLASS_ZONE_INSTANCE) == 0:
-        _CLASS_ZONE_INSTANCE.append(ZoneInfoFile(getzoneinfofile_stream()))
-    return _CLASS_ZONE_INSTANCE[0].zones.get(name)
-
-
-def gettz_db_metadata():
-    """ Get the zonefile metadata
-
-    See `zonefile_metadata`_
-
-    :returns:
-        A dictionary with the database metadata
-
-    .. deprecated:: 2.6
-        See deprecation warning in :func:`zoneinfo.gettz`. To get metadata,
-        query the attribute ``zoneinfo.ZoneInfoFile.metadata``.
-    """
-    warnings.warn("zoneinfo.gettz_db_metadata() will be removed in future "
-                  "versions, to use the dateutil-provided zoneinfo files, "
-                  "ZoneInfoFile object and query the 'metadata' attribute "
-                  "instead. See the documentation for details.",
-                  DeprecationWarning)
-
-    if len(_CLASS_ZONE_INSTANCE) == 0:
-        _CLASS_ZONE_INSTANCE.append(ZoneInfoFile(getzoneinfofile_stream()))
-    return _CLASS_ZONE_INSTANCE[0].metadata
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/ttProgram.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/ttProgram.py
deleted file mode 100644
index 84aa63f36301ec9a4ae21acff0cbc95010d956b7..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/ttProgram.py
+++ /dev/null
@@ -1,593 +0,0 @@
-"""ttLib.tables.ttProgram.py -- Assembler/disassembler for TrueType bytecode programs."""
-from __future__ import annotations
-
-from fontTools.misc.textTools import num2binary, binary2num, readHex, strjoin
-import array
-from io import StringIO
-from typing import List
-import re
-import logging
-
-
-log = logging.getLogger(__name__)
-
-# fmt: off
-
-# first, the list of instructions that eat bytes or words from the instruction stream
-
-streamInstructions = [
-#
-#   opcode  mnemonic   argBits    descriptive name         pops  pushes         eats from instruction stream          pushes
-#
-    (0x40,  'NPUSHB',        0,   'PushNBytes',              0, -1),    #                      n, b1, b2,...bn      b1,b2...bn
-    (0x41,  'NPUSHW',        0,   'PushNWords',              0, -1),    #                       n, w1, w2,...w      w1,w2...wn
-    (0xb0,  'PUSHB',         3,   'PushBytes',               0, -1),    #                          b0, b1,..bn  b0, b1, ...,bn
-    (0xb8,  'PUSHW',         3,   'PushWords',               0, -1),    #                           w0,w1,..wn   w0 ,w1, ...wn
-]
-
-
-# next,    the list of "normal" instructions
-
-instructions = [
-#
-#   opcode  mnemonic   argBits     descriptive name        pops  pushes         eats from instruction stream          pushes
-#
-    (0x7f,  'AA',            0,    'AdjustAngle',            1,  0),    #                                    p               -
-    (0x64,  'ABS',           0,    'Absolute',               1,  1),    #                                    n             |n|
-    (0x60,  'ADD',           0,    'Add',                    2,  1),    #                               n2, n1       (n1 + n2)
-    (0x27,  'ALIGNPTS',      0,    'AlignPts',               2,  0),    #                               p2, p1               -
-    (0x3c,  'ALIGNRP',       0,    'AlignRelativePt',       -1,  0),    #             p1, p2, ... , ploopvalue               -
-    (0x5a,  'AND',           0,    'LogicalAnd',             2,  1),    #                               e2, e1               b
-    (0x2b,  'CALL',          0,    'CallFunction',           1,  0),    #                                    f               -
-    (0x67,  'CEILING',       0,    'Ceiling',                1,  1),    #                                    n         ceil(n)
-    (0x25,  'CINDEX',        0,    'CopyXToTopStack',        1,  1),    #                                    k              ek
-    (0x22,  'CLEAR',         0,    'ClearStack',            -1,  0),    #               all items on the stack               -
-    (0x4f,  'DEBUG',         0,    'DebugCall',              1,  0),    #                                    n               -
-    (0x73,  'DELTAC1',       0,    'DeltaExceptionC1',      -1,  0),    #    argn, cn, argn-1,cn-1, , arg1, c1               -
-    (0x74,  'DELTAC2',       0,    'DeltaExceptionC2',      -1,  0),    #    argn, cn, argn-1,cn-1, , arg1, c1               -
-    (0x75,  'DELTAC3',       0,    'DeltaExceptionC3',      -1,  0),    #    argn, cn, argn-1,cn-1, , arg1, c1               -
-    (0x5d,  'DELTAP1',       0,    'DeltaExceptionP1',      -1,  0),    #   argn, pn, argn-1, pn-1, , arg1, p1               -
-    (0x71,  'DELTAP2',       0,    'DeltaExceptionP2',      -1,  0),    #   argn, pn, argn-1, pn-1, , arg1, p1               -
-    (0x72,  'DELTAP3',       0,    'DeltaExceptionP3',      -1,  0),    #   argn, pn, argn-1, pn-1, , arg1, p1               -
-    (0x24,  'DEPTH',         0,    'GetDepthStack',          0,  1),    #                                    -               n
-    (0x62,  'DIV',           0,    'Divide',                 2,  1),    #                               n2, n1   (n1 * 64)/ n2
-    (0x20,  'DUP',           0,    'DuplicateTopStack',      1,  2),    #                                    e            e, e
-    (0x59,  'EIF',           0,    'EndIf',                  0,  0),    #                                    -               -
-    (0x1b,  'ELSE',          0,    'Else',                   0,  0),    #                                    -               -
-    (0x2d,  'ENDF',          0,    'EndFunctionDefinition',  0,  0),    #                                    -               -
-    (0x54,  'EQ',            0,    'Equal',                  2,  1),    #                               e2, e1               b
-    (0x57,  'EVEN',          0,    'Even',                   1,  1),    #                                    e               b
-    (0x2c,  'FDEF',          0,    'FunctionDefinition',     1,  0),    #                                    f               -
-    (0x4e,  'FLIPOFF',       0,    'SetAutoFlipOff',         0,  0),    #                                    -               -
-    (0x4d,  'FLIPON',        0,    'SetAutoFlipOn',          0,  0),    #                                    -               -
-    (0x80,  'FLIPPT',        0,    'FlipPoint',             -1,  0),    #              p1, p2, ..., ploopvalue               -
-    (0x82,  'FLIPRGOFF',     0,    'FlipRangeOff',           2,  0),    #                                 h, l               -
-    (0x81,  'FLIPRGON',      0,    'FlipRangeOn',            2,  0),    #                                 h, l               -
-    (0x66,  'FLOOR',         0,    'Floor',                  1,  1),    #                                    n        floor(n)
-    (0x46,  'GC',            1,    'GetCoordOnPVector',      1,  1),    #                                    p               c
-    (0x88,  'GETINFO',       0,    'GetInfo',                1,  1),    #                             selector          result
-    (0x91,  'GETVARIATION',  0,    'GetVariation',           0, -1),    #                                    -        a1,..,an
-    (0x0d,  'GFV',           0,    'GetFVector',             0,  2),    #                                    -          px, py
-    (0x0c,  'GPV',           0,    'GetPVector',             0,  2),    #                                    -          px, py
-    (0x52,  'GT',            0,    'GreaterThan',            2,  1),    #                               e2, e1               b
-    (0x53,  'GTEQ',          0,    'GreaterThanOrEqual',     2,  1),    #                               e2, e1               b
-    (0x89,  'IDEF',          0,    'InstructionDefinition',  1,  0),    #                                    f               -
-    (0x58,  'IF',            0,    'If',                     1,  0),    #                                    e               -
-    (0x8e,  'INSTCTRL',      0,    'SetInstrExecControl',    2,  0),    #                                 s, v               -
-    (0x39,  'IP',            0,    'InterpolatePts',        -1,  0),    #             p1, p2, ... , ploopvalue               -
-    (0x0f,  'ISECT',         0,    'MovePtToIntersect',      5,  0),    #                    a1, a0, b1, b0, p               -
-    (0x30,  'IUP',           1,    'InterpolateUntPts',      0,  0),    #                                    -               -
-    (0x1c,  'JMPR',          0,    'Jump',                   1,  0),    #                               offset               -
-    (0x79,  'JROF',          0,    'JumpRelativeOnFalse',    2,  0),    #                            e, offset               -
-    (0x78,  'JROT',          0,    'JumpRelativeOnTrue',     2,  0),    #                            e, offset               -
-    (0x2a,  'LOOPCALL',      0,    'LoopAndCallFunction',    2,  0),    #                             f, count               -
-    (0x50,  'LT',            0,    'LessThan',               2,  1),    #                               e2, e1               b
-    (0x51,  'LTEQ',          0,    'LessThenOrEqual',        2,  1),    #                               e2, e1               b
-    (0x8b,  'MAX',           0,    'Maximum',                2,  1),    #                               e2, e1     max(e1, e2)
-    (0x49,  'MD',            1,    'MeasureDistance',        2,  1),    #                                p2,p1               d
-    (0x2e,  'MDAP',          1,    'MoveDirectAbsPt',        1,  0),    #                                    p               -
-    (0xc0,  'MDRP',          5,    'MoveDirectRelPt',        1,  0),    #                                    p               -
-    (0x3e,  'MIAP',          1,    'MoveIndirectAbsPt',      2,  0),    #                                 n, p               -
-    (0x8c,  'MIN',           0,    'Minimum',                2,  1),    #                               e2, e1     min(e1, e2)
-    (0x26,  'MINDEX',        0,    'MoveXToTopStack',        1,  1),    #                                    k              ek
-    (0xe0,  'MIRP',          5,    'MoveIndirectRelPt',      2,  0),    #                                 n, p               -
-    (0x4b,  'MPPEM',         0,    'MeasurePixelPerEm',      0,  1),    #                                    -            ppem
-    (0x4c,  'MPS',           0,    'MeasurePointSize',       0,  1),    #                                    -       pointSize
-    (0x3a,  'MSIRP',         1,    'MoveStackIndirRelPt',    2,  0),    #                                 d, p               -
-    (0x63,  'MUL',           0,    'Multiply',               2,  1),    #                               n2, n1    (n1 * n2)/64
-    (0x65,  'NEG',           0,    'Negate',                 1,  1),    #                                    n              -n
-    (0x55,  'NEQ',           0,    'NotEqual',               2,  1),    #                               e2, e1               b
-    (0x5c,  'NOT',           0,    'LogicalNot',             1,  1),    #                                    e       ( not e )
-    (0x6c,  'NROUND',        2,    'NoRound',                1,  1),    #                                   n1              n2
-    (0x56,  'ODD',           0,    'Odd',                    1,  1),    #                                    e               b
-    (0x5b,  'OR',            0,    'LogicalOr',              2,  1),    #                               e2, e1               b
-    (0x21,  'POP',           0,    'PopTopStack',            1,  0),    #                                    e               -
-    (0x45,  'RCVT',          0,    'ReadCVT',                1,  1),    #                             location           value
-    (0x7d,  'RDTG',          0,    'RoundDownToGrid',        0,  0),    #                                    -               -
-    (0x7a,  'ROFF',          0,    'RoundOff',               0,  0),    #                                    -               -
-    (0x8a,  'ROLL',          0,    'RollTopThreeStack',      3,  3),    #                                a,b,c           b,a,c
-    (0x68,  'ROUND',         2,    'Round',                  1,  1),    #                                   n1              n2
-    (0x43,  'RS',            0,    'ReadStore',              1,  1),    #                                    n               v
-    (0x3d,  'RTDG',          0,    'RoundToDoubleGrid',      0,  0),    #                                    -               -
-    (0x18,  'RTG',           0,    'RoundToGrid',            0,  0),    #                                    -               -
-    (0x19,  'RTHG',          0,    'RoundToHalfGrid',        0,  0),    #                                    -               -
-    (0x7c,  'RUTG',          0,    'RoundUpToGrid',          0,  0),    #                                    -               -
-    (0x77,  'S45ROUND',      0,    'SuperRound45Degrees',    1,  0),    #                                    n               -
-    (0x7e,  'SANGW',         0,    'SetAngleWeight',         1,  0),    #                               weight               -
-    (0x85,  'SCANCTRL',      0,    'ScanConversionControl',  1,  0),    #                                    n               -
-    (0x8d,  'SCANTYPE',      0,    'ScanType',               1,  0),    #                                    n               -
-    (0x48,  'SCFS',          0,    'SetCoordFromStackFP',    2,  0),    #                                 c, p               -
-    (0x1d,  'SCVTCI',        0,    'SetCVTCutIn',            1,  0),    #                                    n               -
-    (0x5e,  'SDB',           0,    'SetDeltaBaseInGState',   1,  0),    #                                    n               -
-    (0x86,  'SDPVTL',        1,    'SetDualPVectorToLine',   2,  0),    #                               p2, p1               -
-    (0x5f,  'SDS',           0,    'SetDeltaShiftInGState',  1,  0),    #                                    n               -
-    (0x0b,  'SFVFS',         0,    'SetFVectorFromStack',    2,  0),    #                                 y, x               -
-    (0x04,  'SFVTCA',        1,    'SetFVectorToAxis',       0,  0),    #                                    -               -
-    (0x08,  'SFVTL',         1,    'SetFVectorToLine',       2,  0),    #                               p2, p1               -
-    (0x0e,  'SFVTPV',        0,    'SetFVectorToPVector',    0,  0),    #                                    -               -
-    (0x34,  'SHC',           1,    'ShiftContourByLastPt',   1,  0),    #                                    c               -
-    (0x32,  'SHP',           1,    'ShiftPointByLastPoint', -1,  0),    #              p1, p2, ..., ploopvalue               -
-    (0x38,  'SHPIX',         0,    'ShiftZoneByPixel',      -1,  0),    #           d, p1, p2, ..., ploopvalue               -
-    (0x36,  'SHZ',           1,    'ShiftZoneByLastPoint',   1,  0),    #                                    e               -
-    (0x17,  'SLOOP',         0,    'SetLoopVariable',        1,  0),    #                                    n               -
-    (0x1a,  'SMD',           0,    'SetMinimumDistance',     1,  0),    #                             distance               -
-    (0x0a,  'SPVFS',         0,    'SetPVectorFromStack',    2,  0),    #                                 y, x               -
-    (0x02,  'SPVTCA',        1,    'SetPVectorToAxis',       0,  0),    #                                    -               -
-    (0x06,  'SPVTL',         1,    'SetPVectorToLine',       2,  0),    #                               p2, p1               -
-    (0x76,  'SROUND',        0,    'SuperRound',             1,  0),    #                                    n               -
-    (0x10,  'SRP0',          0,    'SetRefPoint0',           1,  0),    #                                    p               -
-    (0x11,  'SRP1',          0,    'SetRefPoint1',           1,  0),    #                                    p               -
-    (0x12,  'SRP2',          0,    'SetRefPoint2',           1,  0),    #                                    p               -
-    (0x1f,  'SSW',           0,    'SetSingleWidth',         1,  0),    #                                    n               -
-    (0x1e,  'SSWCI',         0,    'SetSingleWidthCutIn',    1,  0),    #                                    n               -
-    (0x61,  'SUB',           0,    'Subtract',               2,  1),    #                               n2, n1       (n1 - n2)
-    (0x00,  'SVTCA',         1,    'SetFPVectorToAxis',      0,  0),    #                                    -               -
-    (0x23,  'SWAP',          0,    'SwapTopStack',           2,  2),    #                               e2, e1          e1, e2
-    (0x13,  'SZP0',          0,    'SetZonePointer0',        1,  0),    #                                    n               -
-    (0x14,  'SZP1',          0,    'SetZonePointer1',        1,  0),    #                                    n               -
-    (0x15,  'SZP2',          0,    'SetZonePointer2',        1,  0),    #                                    n               -
-    (0x16,  'SZPS',          0,    'SetZonePointerS',        1,  0),    #                                    n               -
-    (0x29,  'UTP',           0,    'UnTouchPt',              1,  0),    #                                    p               -
-    (0x70,  'WCVTF',         0,    'WriteCVTInFUnits',       2,  0),    #                                 n, l               -
-    (0x44,  'WCVTP',         0,    'WriteCVTInPixels',       2,  0),    #                                 v, l               -
-    (0x42,  'WS',            0,    'WriteStore',             2,  0),    #                                 v, l               -
-]
-
-# fmt: on
-
-
-def bitRepr(value, bits):
-    s = ""
-    for i in range(bits):
-        s = "01"[value & 0x1] + s
-        value = value >> 1
-    return s
-
-
-_mnemonicPat = re.compile(r"[A-Z][A-Z0-9]*$")
-
-
-def _makeDict(instructionList):
-    opcodeDict = {}
-    mnemonicDict = {}
-    for op, mnemonic, argBits, name, pops, pushes in instructionList:
-        assert _mnemonicPat.match(mnemonic)
-        mnemonicDict[mnemonic] = op, argBits, name
-        if argBits:
-            argoffset = op
-            for i in range(1 << argBits):
-                opcodeDict[op + i] = mnemonic, argBits, argoffset, name
-        else:
-            opcodeDict[op] = mnemonic, 0, 0, name
-    return opcodeDict, mnemonicDict
-
-
-streamOpcodeDict, streamMnemonicDict = _makeDict(streamInstructions)
-opcodeDict, mnemonicDict = _makeDict(instructions)
-
-
-class tt_instructions_error(Exception):
-    def __init__(self, error):
-        self.error = error
-
-    def __str__(self):
-        return "TT instructions error: %s" % repr(self.error)
-
-
-_comment = r"/\*.*?\*/"
-_instruction = r"([A-Z][A-Z0-9]*)\s*\[(.*?)\]"
-_number = r"-?[0-9]+"
-_token = "(%s)|(%s)|(%s)" % (_instruction, _number, _comment)
-
-_tokenRE = re.compile(_token)
-_whiteRE = re.compile(r"\s*")
-
-_pushCountPat = re.compile(r"[A-Z][A-Z0-9]*\s*\[.*?\]\s*/\* ([0-9]+).*?\*/")
-
-_indentRE = re.compile(r"^FDEF|IF|ELSE\[ \]\t.+")
-_unindentRE = re.compile(r"^ELSE|ENDF|EIF\[ \]\t.+")
-
-
-def _skipWhite(data, pos):
-    m = _whiteRE.match(data, pos)
-    newPos = m.regs[0][1]
-    assert newPos >= pos
-    return newPos
-
-
-class Program(object):
-    def __init__(self) -> None:
-        pass
-
-    def fromBytecode(self, bytecode: bytes) -> None:
-        self.bytecode = array.array("B", bytecode)
-        if hasattr(self, "assembly"):
-            del self.assembly
-
-    def fromAssembly(self, assembly: List[str] | str) -> None:
-        if isinstance(assembly, list):
-            self.assembly = assembly
-        elif isinstance(assembly, str):
-            self.assembly = assembly.splitlines()
-        else:
-            raise TypeError(f"expected str or List[str], got {type(assembly).__name__}")
-        if hasattr(self, "bytecode"):
-            del self.bytecode
-
-    def getBytecode(self) -> bytes:
-        if not hasattr(self, "bytecode"):
-            self._assemble()
-        return self.bytecode.tobytes()
-
-    def getAssembly(self, preserve=True) -> List[str]:
-        if not hasattr(self, "assembly"):
-            self._disassemble(preserve=preserve)
-        return self.assembly
-
-    def toXML(self, writer, ttFont) -> None:
-        if (
-            not hasattr(ttFont, "disassembleInstructions")
-            or ttFont.disassembleInstructions
-        ):
-            try:
-                assembly = self.getAssembly()
-            except:
-                import traceback
-
-                tmp = StringIO()
-                traceback.print_exc(file=tmp)
-                msg = "An exception occurred during the decompilation of glyph program:\n\n"
-                msg += tmp.getvalue()
-                log.error(msg)
-                writer.begintag("bytecode")
-                writer.newline()
-                writer.comment(msg.strip())
-                writer.newline()
-                writer.dumphex(self.getBytecode())
-                writer.endtag("bytecode")
-                writer.newline()
-            else:
-                if not assembly:
-                    return
-                writer.begintag("assembly")
-                writer.newline()
-                i = 0
-                indent = 0
-                nInstr = len(assembly)
-                while i < nInstr:
-                    instr = assembly[i]
-                    if _unindentRE.match(instr):
-                        indent -= 1
-                    writer.write(writer.indentwhite * indent)
-                    writer.write(instr)
-                    writer.newline()
-                    m = _pushCountPat.match(instr)
-                    i = i + 1
-                    if m:
-                        nValues = int(m.group(1))
-                        line: List[str] = []
-                        j = 0
-                        for j in range(nValues):
-                            if j and not (j % 25):
-                                writer.write(writer.indentwhite * indent)
-                                writer.write(" ".join(line))
-                                writer.newline()
-                                line = []
-                            line.append(assembly[i + j])
-                        writer.write(writer.indentwhite * indent)
-                        writer.write(" ".join(line))
-                        writer.newline()
-                        i = i + j + 1
-                    if _indentRE.match(instr):
-                        indent += 1
-                writer.endtag("assembly")
-                writer.newline()
-        else:
-            bytecode = self.getBytecode()
-            if not bytecode:
-                return
-            writer.begintag("bytecode")
-            writer.newline()
-            writer.dumphex(bytecode)
-            writer.endtag("bytecode")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont) -> None:
-        if name == "assembly":
-            self.fromAssembly(strjoin(content))
-            self._assemble()
-            del self.assembly
-        else:
-            assert name == "bytecode"
-            self.fromBytecode(readHex(content))
-
-    def _assemble(self) -> None:
-        assembly = " ".join(getattr(self, "assembly", []))
-        bytecode: List[int] = []
-        push = bytecode.append
-        lenAssembly = len(assembly)
-        pos = _skipWhite(assembly, 0)
-        while pos < lenAssembly:
-            m = _tokenRE.match(assembly, pos)
-            if m is None:
-                raise tt_instructions_error(
-                    "Syntax error in TT program (%s)" % assembly[pos - 5 : pos + 15]
-                )
-            dummy, mnemonic, arg, number, comment = m.groups()
-            pos = m.regs[0][1]
-            if comment:
-                pos = _skipWhite(assembly, pos)
-                continue
-
-            arg = arg.strip()
-            if mnemonic.startswith("INSTR"):
-                # Unknown instruction
-                op = int(mnemonic[5:])
-                push(op)
-            elif mnemonic not in ("PUSH", "NPUSHB", "NPUSHW", "PUSHB", "PUSHW"):
-                op, argBits, name = mnemonicDict[mnemonic]
-                if len(arg) != argBits:
-                    raise tt_instructions_error(
-                        "Incorrect number of argument bits (%s[%s])" % (mnemonic, arg)
-                    )
-                if arg:
-                    arg = binary2num(arg)
-                    push(op + arg)
-                else:
-                    push(op)
-            else:
-                args = []
-                pos = _skipWhite(assembly, pos)
-                while pos < lenAssembly:
-                    m = _tokenRE.match(assembly, pos)
-                    if m is None:
-                        raise tt_instructions_error(
-                            "Syntax error in TT program (%s)" % assembly[pos : pos + 15]
-                        )
-                    dummy, _mnemonic, arg, number, comment = m.groups()
-                    if number is None and comment is None:
-                        break
-                    pos = m.regs[0][1]
-                    pos = _skipWhite(assembly, pos)
-                    if comment is not None:
-                        continue
-                    args.append(int(number))
-                nArgs = len(args)
-                if mnemonic == "PUSH":
-                    # Automatically choose the most compact representation
-                    nWords = 0
-                    while nArgs:
-                        while (
-                            nWords < nArgs
-                            and nWords < 255
-                            and not (0 <= args[nWords] <= 255)
-                        ):
-                            nWords += 1
-                        nBytes = 0
-                        while (
-                            nWords + nBytes < nArgs
-                            and nBytes < 255
-                            and 0 <= args[nWords + nBytes] <= 255
-                        ):
-                            nBytes += 1
-                        if (
-                            nBytes < 2
-                            and nWords + nBytes < 255
-                            and nWords + nBytes != nArgs
-                        ):
-                            # Will write bytes as words
-                            nWords += nBytes
-                            continue
-
-                        # Write words
-                        if nWords:
-                            if nWords <= 8:
-                                op, argBits, name = streamMnemonicDict["PUSHW"]
-                                op = op + nWords - 1
-                                push(op)
-                            else:
-                                op, argBits, name = streamMnemonicDict["NPUSHW"]
-                                push(op)
-                                push(nWords)
-                            for value in args[:nWords]:
-                                assert -32768 <= value < 32768, (
-                                    "PUSH value out of range %d" % value
-                                )
-                                push((value >> 8) & 0xFF)
-                                push(value & 0xFF)
-
-                        # Write bytes
-                        if nBytes:
-                            pass
-                            if nBytes <= 8:
-                                op, argBits, name = streamMnemonicDict["PUSHB"]
-                                op = op + nBytes - 1
-                                push(op)
-                            else:
-                                op, argBits, name = streamMnemonicDict["NPUSHB"]
-                                push(op)
-                                push(nBytes)
-                            for value in args[nWords : nWords + nBytes]:
-                                push(value)
-
-                        nTotal = nWords + nBytes
-                        args = args[nTotal:]
-                        nArgs -= nTotal
-                        nWords = 0
-                else:
-                    # Write exactly what we've been asked to
-                    words = mnemonic[-1] == "W"
-                    op, argBits, name = streamMnemonicDict[mnemonic]
-                    if mnemonic[0] != "N":
-                        assert nArgs <= 8, nArgs
-                        op = op + nArgs - 1
-                        push(op)
-                    else:
-                        assert nArgs < 256
-                        push(op)
-                        push(nArgs)
-                    if words:
-                        for value in args:
-                            assert -32768 <= value < 32768, (
-                                "PUSHW value out of range %d" % value
-                            )
-                            push((value >> 8) & 0xFF)
-                            push(value & 0xFF)
-                    else:
-                        for value in args:
-                            assert 0 <= value < 256, (
-                                "PUSHB value out of range %d" % value
-                            )
-                            push(value)
-
-            pos = _skipWhite(assembly, pos)
-
-        if bytecode:
-            assert max(bytecode) < 256 and min(bytecode) >= 0
-        self.bytecode = array.array("B", bytecode)
-
-    def _disassemble(self, preserve=False) -> None:
-        assembly = []
-        i = 0
-        bytecode = getattr(self, "bytecode", [])
-        numBytecode = len(bytecode)
-        while i < numBytecode:
-            op = bytecode[i]
-            try:
-                mnemonic, argBits, argoffset, name = opcodeDict[op]
-            except KeyError:
-                if op in streamOpcodeDict:
-                    values = []
-
-                    # Merge consecutive PUSH operations
-                    while bytecode[i] in streamOpcodeDict:
-                        op = bytecode[i]
-                        mnemonic, argBits, argoffset, name = streamOpcodeDict[op]
-                        words = mnemonic[-1] == "W"
-                        if argBits:
-                            nValues = op - argoffset + 1
-                        else:
-                            i = i + 1
-                            nValues = bytecode[i]
-                        i = i + 1
-                        assert nValues > 0
-                        if not words:
-                            for j in range(nValues):
-                                value = bytecode[i]
-                                values.append(repr(value))
-                                i = i + 1
-                        else:
-                            for j in range(nValues):
-                                # cast to signed int16
-                                value = (bytecode[i] << 8) | bytecode[i + 1]
-                                if value >= 0x8000:
-                                    value = value - 0x10000
-                                values.append(repr(value))
-                                i = i + 2
-                        if preserve:
-                            break
-
-                    if not preserve:
-                        mnemonic = "PUSH"
-                    nValues = len(values)
-                    if nValues == 1:
-                        assembly.append("%s[ ]	/* 1 value pushed */" % mnemonic)
-                    else:
-                        assembly.append(
-                            "%s[ ]	/* %s values pushed */" % (mnemonic, nValues)
-                        )
-                    assembly.extend(values)
-                else:
-                    assembly.append("INSTR%d[ ]" % op)
-                    i = i + 1
-            else:
-                if argBits:
-                    assembly.append(
-                        mnemonic
-                        + "[%s]	/* %s */" % (num2binary(op - argoffset, argBits), name)
-                    )
-                else:
-                    assembly.append(mnemonic + "[ ]	/* %s */" % name)
-                i = i + 1
-        self.assembly = assembly
-
-    def __bool__(self) -> bool:
-        """
-        >>> p = Program()
-        >>> bool(p)
-        False
-        >>> bc = array.array("B", [0])
-        >>> p.fromBytecode(bc)
-        >>> bool(p)
-        True
-        >>> p.bytecode.pop()
-        0
-        >>> bool(p)
-        False
-
-        >>> p = Program()
-        >>> asm = ['SVTCA[0]']
-        >>> p.fromAssembly(asm)
-        >>> bool(p)
-        True
-        >>> p.assembly.pop()
-        'SVTCA[0]'
-        >>> bool(p)
-        False
-        """
-        return (hasattr(self, "assembly") and len(self.assembly) > 0) or (
-            hasattr(self, "bytecode") and len(self.bytecode) > 0
-        )
-
-    __nonzero__ = __bool__
-
-    def __eq__(self, other) -> bool:
-        if type(self) != type(other):
-            return NotImplemented
-        return self.__dict__ == other.__dict__
-
-    def __ne__(self, other) -> bool:
-        result = self.__eq__(other)
-        return result if result is NotImplemented else not result
-
-
-def _test():
-    """
-    >>> _test()
-    True
-    """
-
-    bc = b"""@;:9876543210/.-,+*)(\'&%$#"! \037\036\035\034\033\032\031\030\027\026\025\024\023\022\021\020\017\016\015\014\013\012\011\010\007\006\005\004\003\002\001\000,\001\260\030CXEj\260\031C`\260F#D#\020 \260FN\360M/\260\000\022\033!#\0213Y-,\001\260\030CX\260\005+\260\000\023K\260\024PX\261\000@8Y\260\006+\033!#\0213Y-,\001\260\030CXN\260\003%\020\362!\260\000\022M\033 E\260\004%\260\004%#Jad\260(RX!#\020\326\033\260\003%\020\362!\260\000\022YY-,\260\032CX!!\033\260\002%\260\002%I\260\003%\260\003%Ja d\260\020PX!!!\033\260\003%\260\003%I\260\000PX\260\000PX\270\377\3428!\033\260\0208!Y\033\260\000RX\260\0368!\033\270\377\3608!YYYY-,\001\260\030CX\260\005+\260\000\023K\260\024PX\271\000\000\377\3008Y\260\006+\033!#\0213Y-,N\001\212\020\261F\031CD\260\000\024\261\000F\342\260\000\025\271\000\000\377\3608\000\260\000<\260(+\260\002%\020\260\000<-,\001\030\260\000/\260\001\024\362\260\001\023\260\001\025M\260\000\022-,\001\260\030CX\260\005+\260\000\023\271\000\000\377\3408\260\006+\033!#\0213Y-,\001\260\030CXEdj#Edi\260\031Cd``\260F#D#\020 \260F\360/\260\000\022\033!! \212 \212RX\0213\033!!YY-,\001\261\013\012C#Ce\012-,\000\261\012\013C#C\013-,\000\260F#p\261\001F>\001\260F#p\261\002FE:\261\002\000\010\015-,\260\022+\260\002%E\260\002%Ej\260@\213`\260\002%#D!!!-,\260\023+\260\002%E\260\002%Ej\270\377\300\214`\260\002%#D!!!-,\260\000\260\022+!!!-,\260\000\260\023+!!!-,\001\260\006C\260\007Ce\012-, i\260@a\260\000\213 \261,\300\212\214\270\020\000b`+\014d#da\\X\260\003aY-,\261\000\003%EhT\260\034KPZX\260\003%E\260\003%E`h \260\004%#D\260\004%#D\033\260\003% Eh \212#D\260\003%Eh`\260\003%#DY-,\260\003% Eh \212#D\260\003%Edhe`\260\004%\260\001`#D-,\260\011CX\207!\300\033\260\022CX\207E\260\021+\260G#D\260Gz\344\033\003\212E\030i \260G#D\212\212\207 \260\240QX\260\021+\260G#D\260Gz\344\033!\260Gz\344YYY\030-, \212E#Eh`D-,EjB-,\001\030/-,\001\260\030CX\260\004%\260\004%Id#Edi\260@\213a \260\200bj\260\002%\260\002%a\214\260\031C`\260F#D!\212\020\260F\366!\033!!!!Y-,\001\260\030CX\260\002%E\260\002%Ed`j\260\003%Eja \260\004%Ej \212\213e\260\004%#D\214\260\003%#D!!\033 EjD EjDY-,\001 E\260\000U\260\030CZXEh#Ei\260@\213a \260\200bj \212#a \260\003%\213e\260\004%#D\214\260\003%#D!!\033!!\260\031+Y-,\001\212\212Ed#EdadB-,\260\004%\260\004%\260\031+\260\030CX\260\004%\260\004%\260\003%\260\033+\001\260\002%C\260@T\260\002%C\260\000TZX\260\003% E\260@aDY\260\002%C\260\000T\260\002%C\260@TZX\260\004% E\260@`DYY!!!!-,\001KRXC\260\002%E#aD\033!!Y-,\001KRXC\260\002%E#`D\033!!Y-,KRXED\033!!Y-,\001 \260\003%#I\260@`\260 c \260\000RX#\260\002%8#\260\002%e8\000\212c8\033!!!!!Y\001-,KPXED\033!!Y-,\001\260\005%\020# \212\365\000\260\001`#\355\354-,\001\260\005%\020# \212\365\000\260\001a#\355\354-,\001\260\006%\020\365\000\355\354-,F#F`\212\212F# F\212`\212a\270\377\200b# \020#\212\261KK\212pE` \260\000PX\260\001a\270\377\272\213\033\260F\214Y\260\020`h\001:-, E\260\003%FRX\260\002%F ha\260\003%\260\003%?#!8\033!\021Y-, E\260\003%FPX\260\002%F ha\260\003%\260\003%?#!8\033!\021Y-,\000\260\007C\260\006C\013-,\212\020\354-,\260\014CX!\033 F\260\000RX\270\377\3608\033\260\0208YY-, \260\000UX\270\020\000c\260\003%Ed\260\003%Eda\260\000SX\260\002\033\260@a\260\003Y%EiSXED\033!!Y\033!\260\002%E\260\002%Ead\260(QXED\033!!YY-,!!\014d#d\213\270@\000b-,!\260\200QX\014d#d\213\270 \000b\033\262\000@/+Y\260\002`-,!\260\300QX\014d#d\213\270\025Ub\033\262\000\200/+Y\260\002`-,\014d#d\213\270@\000b`#!-,KSX\260\004%\260\004%Id#Edi\260@\213a \260\200bj\260\002%\260\002%a\214\260F#D!\212\020\260F\366!\033!\212\021#\022 9/Y-,\260\002%\260\002%Id\260\300TX\270\377\3708\260\0108\033!!Y-,\260\023CX\003\033\002Y-,\260\023CX\002\033\003Y-,\260\012+#\020 <\260\027+-,\260\002%\270\377\3608\260(+\212\020# \320#\260\020+\260\005CX\300\033<Y \020\021\260\000\022\001-,KS#KQZX8\033!!Y-,\001\260\002%\020\320#\311\001\260\001\023\260\000\024\020\260\001<\260\001\026-,\001\260\000\023\260\001\260\003%I\260\003\0278\260\001\023-,KS#KQZX E\212`D\033!!Y-, 9/-"""
-
-    p = Program()
-    p.fromBytecode(bc)
-    asm = p.getAssembly(preserve=True)
-    p.fromAssembly(asm)
-    print(bc == p.getBytecode())
-
-
-if __name__ == "__main__":
-    import sys
-    import doctest
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/uploader.py b/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/uploader.py
deleted file mode 100644
index 0ce697f0d47325a4d73f92c13304ae5f51df794a..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/uploader.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from __future__ import annotations
-
-from huggingface_hub import HfApi
-
-
-class Uploader:
-    def __init__(self, hf_token: str | None):
-        self.api = HfApi(token=hf_token)
-
-    def get_username(self) -> str:
-        return self.api.whoami()['name']
-
-    def upload(self,
-               folder_path: str,
-               repo_name: str,
-               organization: str = '',
-               repo_type: str = 'model',
-               private: bool = True,
-               delete_existing_repo: bool = False) -> str:
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            raise ValueError
-        if not organization:
-            organization = self.get_username()
-        repo_id = f'{organization}/{repo_name}'
-        if delete_existing_repo:
-            try:
-                self.api.delete_repo(repo_id, repo_type=repo_type)
-            except Exception:
-                pass
-        try:
-            self.api.create_repo(repo_id, repo_type=repo_type, private=private)
-            self.api.upload_folder(repo_id=repo_id,
-                                   folder_path=folder_path,
-                                   path_in_repo='.',
-                                   repo_type=repo_type)
-            url = f'https://huggingface.co/{repo_id}'
-            message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
-        except Exception as e:
-            message = str(e)
-        return message
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/tflib/network.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/tflib/network.py
deleted file mode 100644
index d888a90dd23c1a941b5fb501afec1efcb763b5ea..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/tflib/network.py
+++ /dev/null
@@ -1,591 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Helper for managing networks."""
-
-import types
-import inspect
-import re
-import uuid
-import sys
-import numpy as np
-import tensorflow as tf
-
-from collections import OrderedDict
-from typing import Any, List, Tuple, Union
-
-from . import tfutil
-from .. import util
-
-from .tfutil import TfExpression, TfExpressionEx
-
-_import_handlers = []  # Custom import handlers for dealing with legacy data in pickle import.
-_import_module_src = dict()  # Source code for temporary modules created during pickle import.
-
-
-def import_handler(handler_func):
-    """Function decorator for declaring custom import handlers."""
-    _import_handlers.append(handler_func)
-    return handler_func
-
-
-class Network:
-    """Generic network abstraction.
-
-    Acts as a convenience wrapper for a parameterized network construction
-    function, providing several utility methods and convenient access to
-    the inputs/outputs/weights.
-
-    Network objects can be safely pickled and unpickled for long-term
-    archival purposes. The pickling works reliably as long as the underlying
-    network construction function is defined in a standalone Python module
-    that has no side effects or application-specific imports.
-
-    Args:
-        name: Network name. Used to select TensorFlow name and variable scopes.
-        func_name: Fully qualified name of the underlying network construction function, or a top-level function object.
-        static_kwargs: Keyword arguments to be passed in to the network construction function.
-
-    Attributes:
-        name: User-specified name, defaults to build func name if None.
-        scope: Unique TensorFlow scope containing template graph and variables, derived from the user-specified name.
-        static_kwargs: Arguments passed to the user-supplied build func.
-        components: Container for sub-networks. Passed to the build func, and retained between calls.
-        num_inputs: Number of input tensors.
-        num_outputs: Number of output tensors.
-        input_shapes: Input tensor shapes (NC or NCHW), including minibatch dimension.
-        output_shapes: Output tensor shapes (NC or NCHW), including minibatch dimension.
-        input_shape: Short-hand for input_shapes[0].
-        output_shape: Short-hand for output_shapes[0].
-        input_templates: Input placeholders in the template graph.
-        output_templates: Output tensors in the template graph.
-        input_names: Name string for each input.
-        output_names: Name string for each output.
-        own_vars: Variables defined by this network (local_name => var), excluding sub-networks.
-        vars: All variables (local_name => var).
-        trainables: All trainable variables (local_name => var).
-        var_global_to_local: Mapping from variable global names to local names.
-    """
-
-    def __init__(self, name: str = None, func_name: Any = None, **static_kwargs):
-        tfutil.assert_tf_initialized()
-        assert isinstance(name, str) or name is None
-        assert func_name is not None
-        assert isinstance(func_name, str) or util.is_top_level_function(func_name)
-        assert util.is_pickleable(static_kwargs)
-
-        self._init_fields()
-        self.name = name
-        self.static_kwargs = util.EasyDict(static_kwargs)
-
-        # Locate the user-specified network build function.
-        if util.is_top_level_function(func_name):
-            func_name = util.get_top_level_function_name(func_name)
-        module, self._build_func_name = util.get_module_from_obj_name(func_name)
-        self._build_func = util.get_obj_from_module(module, self._build_func_name)
-        assert callable(self._build_func)
-
-        # Dig up source code for the module containing the build function.
-        self._build_module_src = _import_module_src.get(module, None)
-        if self._build_module_src is None:
-            self._build_module_src = inspect.getsource(module)
-
-        # Init TensorFlow graph.
-        self._init_graph()
-        self.reset_own_vars()
-
-    def _init_fields(self) -> None:
-        self.name = None
-        self.scope = None
-        self.static_kwargs = util.EasyDict()
-        self.components = util.EasyDict()
-        self.num_inputs = 0
-        self.num_outputs = 0
-        self.input_shapes = [[]]
-        self.output_shapes = [[]]
-        self.input_shape = []
-        self.output_shape = []
-        self.input_templates = []
-        self.output_templates = []
-        self.input_names = []
-        self.output_names = []
-        self.own_vars = OrderedDict()
-        self.vars = OrderedDict()
-        self.trainables = OrderedDict()
-        self.var_global_to_local = OrderedDict()
-
-        self._build_func = None  # User-supplied build function that constructs the network.
-        self._build_func_name = None  # Name of the build function.
-        self._build_module_src = None  # Full source code of the module containing the build function.
-        self._run_cache = dict()  # Cached graph data for Network.run().
-
-    def _init_graph(self) -> None:
-        # Collect inputs.
-        self.input_names = []
-
-        for param in inspect.signature(self._build_func).parameters.values():
-            if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
-                self.input_names.append(param.name)
-
-        self.num_inputs = len(self.input_names)
-        assert self.num_inputs >= 1
-
-        # Choose name and scope.
-        if self.name is None:
-            self.name = self._build_func_name
-        assert re.match("^[A-Za-z0-9_.\\-]*$", self.name)
-        with tf.name_scope(None):
-            self.scope = tf.get_default_graph().unique_name(self.name, mark_as_used=True)
-
-        # Finalize build func kwargs.
-        build_kwargs = dict(self.static_kwargs)
-        build_kwargs["is_template_graph"] = True
-        build_kwargs["components"] = self.components
-
-        # Build template graph.
-        with tfutil.absolute_variable_scope(self.scope, reuse=tf.AUTO_REUSE), tfutil.absolute_name_scope(self.scope):  # ignore surrounding scopes
-            assert tf.get_variable_scope().name == self.scope
-            assert tf.get_default_graph().get_name_scope() == self.scope
-            with tf.control_dependencies(None):  # ignore surrounding control dependencies
-                self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
-                out_expr = self._build_func(*self.input_templates, **build_kwargs)
-
-        # Collect outputs.
-        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
-        self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
-        self.num_outputs = len(self.output_templates)
-        assert self.num_outputs >= 1
-        assert all(tfutil.is_tf_expression(t) for t in self.output_templates)
-
-        # Perform sanity checks.
-        if any(t.shape.ndims is None for t in self.input_templates):
-            raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.")
-        if any(t.shape.ndims is None for t in self.output_templates):
-            raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.")
-        if any(not isinstance(comp, Network) for comp in self.components.values()):
-            raise ValueError("Components of a Network must be Networks themselves.")
-        if len(self.components) != len(set(comp.name for comp in self.components.values())):
-            raise ValueError("Components of a Network must have unique names.")
-
-        # List inputs and outputs.
-        self.input_shapes = [tfutil.shape_to_list(t.shape) for t in self.input_templates]
-        self.output_shapes = [tfutil.shape_to_list(t.shape) for t in self.output_templates]
-        self.input_shape = self.input_shapes[0]
-        self.output_shape = self.output_shapes[0]
-        self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates]
-
-        # List variables.
-        self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/"))
-        self.vars = OrderedDict(self.own_vars)
-        self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items())
-        self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable)
-        self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items())
-
-    def reset_own_vars(self) -> None:
-        """Re-initialize all variables of this network, excluding sub-networks."""
-        tfutil.run([var.initializer for var in self.own_vars.values()])
-
-    def reset_vars(self) -> None:
-        """Re-initialize all variables of this network, including sub-networks."""
-        tfutil.run([var.initializer for var in self.vars.values()])
-
-    def reset_trainables(self) -> None:
-        """Re-initialize all trainable variables of this network, including sub-networks."""
-        tfutil.run([var.initializer for var in self.trainables.values()])
-
-    def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]:
-        """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s)."""
-        assert len(in_expr) == self.num_inputs
-        assert not all(expr is None for expr in in_expr)
-
-        # Finalize build func kwargs.
-        build_kwargs = dict(self.static_kwargs)
-        build_kwargs.update(dynamic_kwargs)
-        build_kwargs["is_template_graph"] = False
-        build_kwargs["components"] = self.components
-
-        # Build TensorFlow graph to evaluate the network.
-        with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name):
-            assert tf.get_variable_scope().name == self.scope
-            valid_inputs = [expr for expr in in_expr if expr is not None]
-            final_inputs = []
-            for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes):
-                if expr is not None:
-                    expr = tf.identity(expr, name=name)
-                else:
-                    expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name)
-                final_inputs.append(expr)
-            out_expr = self._build_func(*final_inputs, **build_kwargs)
-
-        # Propagate input shapes back to the user-specified expressions.
-        for expr, final in zip(in_expr, final_inputs):
-            if isinstance(expr, tf.Tensor):
-                expr.set_shape(final.shape)
-
-        # Express outputs in the desired format.
-        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
-        if return_as_list:
-            out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
-        return out_expr
-
-    def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str:
-        """Get the local name of a given variable, without any surrounding name scopes."""
-        assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str)
-        global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name
-        return self.var_global_to_local[global_name]
-
-    def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression:
-        """Find variable by local or global name."""
-        assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str)
-        return self.vars[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name
-
-    def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray:
-        """Get the value of a given variable as NumPy array.
-        Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible."""
-        return self.find_var(var_or_local_name).eval()
-
-    def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None:
-        """Set the value of a given variable based on the given NumPy array.
-        Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible."""
-        tfutil.set_vars({self.find_var(var_or_local_name): new_value})
-
-    def __getstate__(self) -> dict:
-        """Pickle export."""
-        state = dict()
-        state["version"]            = 3
-        state["name"]               = self.name
-        state["static_kwargs"]      = dict(self.static_kwargs)
-        state["components"]         = dict(self.components)
-        state["build_module_src"]   = self._build_module_src
-        state["build_func_name"]    = self._build_func_name
-        state["variables"]          = list(zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values()))))
-        return state
-
-    def __setstate__(self, state: dict) -> None:
-        """Pickle import."""
-        # pylint: disable=attribute-defined-outside-init
-        tfutil.assert_tf_initialized()
-        self._init_fields()
-
-        # Execute custom import handlers.
-        for handler in _import_handlers:
-            state = handler(state)
-
-        # Set basic fields.
-        assert state["version"] in [2, 3]
-        self.name = state["name"]
-        self.static_kwargs = util.EasyDict(state["static_kwargs"])
-        self.components = util.EasyDict(state.get("components", {}))
-        self._build_module_src = state["build_module_src"]
-        self._build_func_name = state["build_func_name"]
-
-        # Create temporary module from the imported source code.
-        module_name = "_tflib_network_import_" + uuid.uuid4().hex
-        module = types.ModuleType(module_name)
-        sys.modules[module_name] = module
-        _import_module_src[module] = self._build_module_src
-        exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used
-
-        # Locate network build function in the temporary module.
-        self._build_func = util.get_obj_from_module(module, self._build_func_name)
-        assert callable(self._build_func)
-
-        # Init TensorFlow graph.
-        self._init_graph()
-        self.reset_own_vars()
-        tfutil.set_vars({self.find_var(name): value for name, value in state["variables"]})
-
-    def clone(self, name: str = None, **new_static_kwargs) -> "Network":
-        """Create a clone of this network with its own copy of the variables."""
-        # pylint: disable=protected-access
-        net = object.__new__(Network)
-        net._init_fields()
-        net.name = name if name is not None else self.name
-        net.static_kwargs = util.EasyDict(self.static_kwargs)
-        net.static_kwargs.update(new_static_kwargs)
-        net._build_module_src = self._build_module_src
-        net._build_func_name = self._build_func_name
-        net._build_func = self._build_func
-        net._init_graph()
-        net.copy_vars_from(self)
-        return net
-
-    def copy_own_vars_from(self, src_net: "Network") -> None:
-        """Copy the values of all variables from the given network, excluding sub-networks."""
-        names = [name for name in self.own_vars.keys() if name in src_net.own_vars]
-        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
-
-    def copy_vars_from(self, src_net: "Network") -> None:
-        """Copy the values of all variables from the given network, including sub-networks."""
-        names = [name for name in self.vars.keys() if name in src_net.vars]
-        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
-
-    def copy_trainables_from(self, src_net: "Network") -> None:
-        """Copy the values of all trainable variables from the given network, including sub-networks."""
-        names = [name for name in self.trainables.keys() if name in src_net.trainables]
-        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
-
-    def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network":
-        """Create new network with the given parameters, and copy all variables from this network."""
-        if new_name is None:
-            new_name = self.name
-        static_kwargs = dict(self.static_kwargs)
-        static_kwargs.update(new_static_kwargs)
-        net = Network(name=new_name, func_name=new_func_name, **static_kwargs)
-        net.copy_vars_from(self)
-        return net
-
-    def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation:
-        """Construct a TensorFlow op that updates the variables of this network
-        to be slightly closer to those of the given network."""
-        with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"):
-            ops = []
-            for name, var in self.vars.items():
-                if name in src_net.vars:
-                    cur_beta = beta if name in self.trainables else beta_nontrainable
-                    new_value = tfutil.lerp(src_net.vars[name], var, cur_beta)
-                    ops.append(var.assign(new_value))
-            return tf.group(*ops)
-
-    def run(self,
-            *in_arrays: Tuple[Union[np.ndarray, None], ...],
-            input_transform: dict = None,
-            output_transform: dict = None,
-            return_as_list: bool = False,
-            print_progress: bool = False,
-            minibatch_size: int = None,
-            num_gpus: int = 1,
-            assume_frozen: bool = False,
-            **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]:
-        """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
-
-        Args:
-            input_transform:    A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network.
-                                The dict must contain a 'func' field that points to a top-level function. The function is called with the input
-                                TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
-            output_transform:   A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network.
-                                The dict must contain a 'func' field that points to a top-level function. The function is called with the output
-                                TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
-            return_as_list:     True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs.
-            print_progress:     Print progress to the console? Useful for very large input arrays.
-            minibatch_size:     Maximum minibatch size to use, None = disable batching.
-            num_gpus:           Number of GPUs to use.
-            assume_frozen:      Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls.
-            dynamic_kwargs:     Additional keyword arguments to be passed into the network build function.
-        """
-        assert len(in_arrays) == self.num_inputs
-        assert not all(arr is None for arr in in_arrays)
-        assert input_transform is None or util.is_top_level_function(input_transform["func"])
-        assert output_transform is None or util.is_top_level_function(output_transform["func"])
-        output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs)
-        num_items = in_arrays[0].shape[0]
-        if minibatch_size is None:
-            minibatch_size = num_items
-
-        # Construct unique hash key from all arguments that affect the TensorFlow graph.
-        key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs)
-        def unwind_key(obj):
-            if isinstance(obj, dict):
-                return [(key, unwind_key(value)) for key, value in sorted(obj.items())]
-            if callable(obj):
-                return util.get_top_level_function_name(obj)
-            return obj
-        key = repr(unwind_key(key))
-
-        # Build graph.
-        if key not in self._run_cache:
-            with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None):
-                with tf.device("/cpu:0"):
-                    in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
-                    in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr]))
-
-                out_split = []
-                for gpu in range(num_gpus):
-                    with tf.device("/gpu:%d" % gpu):
-                        net_gpu = self.clone() if assume_frozen else self
-                        in_gpu = in_split[gpu]
-
-                        if input_transform is not None:
-                            in_kwargs = dict(input_transform)
-                            in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs)
-                            in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu)
-
-                        assert len(in_gpu) == self.num_inputs
-                        out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs)
-
-                        if output_transform is not None:
-                            out_kwargs = dict(output_transform)
-                            out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs)
-                            out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu)
-
-                        assert len(out_gpu) == self.num_outputs
-                        out_split.append(out_gpu)
-
-                with tf.device("/cpu:0"):
-                    out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)]
-                    self._run_cache[key] = in_expr, out_expr
-
-        # Run minibatches.
-        in_expr, out_expr = self._run_cache[key]
-        out_arrays = [np.empty([num_items] + tfutil.shape_to_list(expr.shape)[1:], expr.dtype.name) for expr in out_expr]
-
-        for mb_begin in range(0, num_items, minibatch_size):
-            if print_progress:
-                print("\r%d / %d" % (mb_begin, num_items), end="")
-
-            mb_end = min(mb_begin + minibatch_size, num_items)
-            mb_num = mb_end - mb_begin
-            mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)]
-            mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in)))
-
-            for dst, src in zip(out_arrays, mb_out):
-                dst[mb_begin: mb_end] = src
-
-        # Done.
-        if print_progress:
-            print("\r%d / %d" % (num_items, num_items))
-
-        if not return_as_list:
-            out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays)
-        return out_arrays
-
-    def list_ops(self) -> List[TfExpression]:
-        include_prefix = self.scope + "/"
-        exclude_prefix = include_prefix + "_"
-        ops = tf.get_default_graph().get_operations()
-        ops = [op for op in ops if op.name.startswith(include_prefix)]
-        ops = [op for op in ops if not op.name.startswith(exclude_prefix)]
-        return ops
-
-    def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]:
-        """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to
-        individual layers of the network. Mainly intended to be used for reporting."""
-        layers = []
-
-        def recurse(scope, parent_ops, parent_vars, level):
-            # Ignore specific patterns.
-            if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]):
-                return
-
-            # Filter ops and vars by scope.
-            global_prefix = scope + "/"
-            local_prefix = global_prefix[len(self.scope) + 1:]
-            cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]]
-            cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]]
-            if not cur_ops and not cur_vars:
-                return
-
-            # Filter out all ops related to variables.
-            for var in [op for op in cur_ops if op.type.startswith("Variable")]:
-                var_prefix = var.name + "/"
-                cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)]
-
-            # Scope does not contain ops as immediate children => recurse deeper.
-            contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type != "Identity" for op in cur_ops)
-            if (level == 0 or not contains_direct_ops) and (len(cur_ops) + len(cur_vars)) > 1:
-                visited = set()
-                for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]:
-                    token = rel_name.split("/")[0]
-                    if token not in visited:
-                        recurse(global_prefix + token, cur_ops, cur_vars, level + 1)
-                        visited.add(token)
-                return
-
-            # Report layer.
-            layer_name = scope[len(self.scope) + 1:]
-            layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1]
-            layer_trainables = [var for _name, var in cur_vars if var.trainable]
-            layers.append((layer_name, layer_output, layer_trainables))
-
-        recurse(self.scope, self.list_ops(), list(self.vars.items()), 0)
-        return layers
-
-    def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None:
-        """Print a summary table of the network structure."""
-        rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]]
-        rows += [["---"] * 4]
-        total_params = 0
-
-        for layer_name, layer_output, layer_trainables in self.list_layers():
-            num_params = sum(np.prod(tfutil.shape_to_list(var.shape)) for var in layer_trainables)
-            weights = [var for var in layer_trainables if var.name.endswith("/weight:0")]
-            weights.sort(key=lambda x: len(x.name))
-            if len(weights) == 0 and len(layer_trainables) == 1:
-                weights = layer_trainables
-            total_params += num_params
-
-            if not hide_layers_with_no_params or num_params != 0:
-                num_params_str = str(num_params) if num_params > 0 else "-"
-                output_shape_str = str(layer_output.shape)
-                weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-"
-                rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]]
-
-        rows += [["---"] * 4]
-        rows += [["Total", str(total_params), "", ""]]
-
-        widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-        print()
-        for row in rows:
-            print("  ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths)))
-        print()
-
-    def setup_weight_histograms(self, title: str = None) -> None:
-        """Construct summary ops to include histograms of all trainable parameters in TensorBoard."""
-        if title is None:
-            title = self.name
-
-        with tf.name_scope(None), tf.device(None), tf.control_dependencies(None):
-            for local_name, var in self.trainables.items():
-                if "/" in local_name:
-                    p = local_name.split("/")
-                    name = title + "_" + p[-1] + "/" + "_".join(p[:-1])
-                else:
-                    name = title + "_toplevel/" + local_name
-
-                tf.summary.histogram(name, var)
-
-#----------------------------------------------------------------------------
-# Backwards-compatible emulation of legacy output transformation in Network.run().
-
-_print_legacy_warning = True
-
-def _handle_legacy_output_transforms(output_transform, dynamic_kwargs):
-    global _print_legacy_warning
-    legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"]
-    if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs):
-        return output_transform, dynamic_kwargs
-
-    if _print_legacy_warning:
-        _print_legacy_warning = False
-        print()
-        print("WARNING: Old-style output transformations in Network.run() are deprecated.")
-        print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'")
-        print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.")
-        print()
-    assert output_transform is None
-
-    new_kwargs = dict(dynamic_kwargs)
-    new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs}
-    new_transform["func"] = _legacy_output_transform_func
-    return new_transform, new_kwargs
-
-def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None):
-    if out_mul != 1.0:
-        expr = [x * out_mul for x in expr]
-
-    if out_add != 0.0:
-        expr = [x + out_add for x in expr]
-
-    if out_shrink > 1:
-        ksize = [1, 1, out_shrink, out_shrink]
-        expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr]
-
-    if out_dtype is not None:
-        if tf.as_dtype(out_dtype).is_integer:
-            expr = [tf.round(x) for x in expr]
-        expr = [tf.saturate_cast(x, out_dtype) for x in expr]
-    return expr
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/training/projectors/w_plus_projector.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/training/projectors/w_plus_projector.py
deleted file mode 100644
index b61fa0159b02a052bc8a52341a53ec4b62ced657..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/training/projectors/w_plus_projector.py
+++ /dev/null
@@ -1,163 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Project given image to the latent space of pretrained network pickle."""
-
-import copy
-import wandb
-import numpy as np
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-from configs import global_config, hyperparameters
-import dnnlib
-from utils.log_utils import log_image_from_w
-
-
-def project(
-        G,
-        # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
-        target: torch.Tensor,
-        *,
-        num_steps=1000,
-        w_avg_samples=10000,
-        initial_learning_rate=0.01,
-        initial_noise_factor=0.05,
-        lr_rampdown_length=0.25,
-        lr_rampup_length=0.05,
-        noise_ramp_length=0.75,
-        regularize_noise_weight=1e5,
-        verbose=False,
-        device: torch.device,
-        use_wandb=False,
-        initial_w=None,
-        image_log_step=global_config.image_rec_result_log_snapshot,
-        w_name: str
-):
-    print('inside training/projectors/w_plus_projector')
-    print(target.shape, G.img_channels, G.img_resolution * 2, G.img_resolution)
-    assert target.shape == (
-        G.img_channels, G.img_resolution * 2, G.img_resolution)
-
-    def logprint(*args):
-        if verbose:
-            print(*args)
-
-    G = copy.deepcopy(G).eval().requires_grad_(
-        False).to(device).float()  # type: ignore
-
-    # Compute w stats.
-    logprint(
-        f'Computing W midpoint and stddev using {w_avg_samples} samples...')
-    z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
-    w_samples = G.mapping(torch.from_numpy(
-        z_samples).to(device), None)  # [N, L, C]
-    w_samples = w_samples[:, :1, :].cpu(
-    ).numpy().astype(np.float32)  # [N, 1, C]
-    w_avg = np.mean(w_samples, axis=0, keepdims=True)  # [1, 1, C]
-    w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
-    w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
-
-    start_w = initial_w if initial_w is not None else w_avg
-
-    # Setup noise inputs.
-    noise_bufs = {name: buf for (
-        name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
-
-    # Load VGG16 feature detector.
-    url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    with dnnlib.util.open_url(url) as f:
-        vgg16 = torch.jit.load(f).eval().to(device)
-
-    # Features for target image.
-    target_images = target.unsqueeze(0).to(device).to(torch.float32)
-    if target_images.shape[2] > 256:
-        target_images = F.interpolate(
-            target_images, size=(256, 256), mode='area')
-    target_features = vgg16(
-        target_images, resize_images=False, return_lpips=True)
-
-    start_w = np.repeat(start_w, G.mapping.num_ws, axis=1)
-    w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
-                         requires_grad=True)  # pylint: disable=not-callable
-
-    optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
-                                 lr=hyperparameters.first_inv_lr)
-
-    # Init noise.
-    for buf in noise_bufs.values():
-        buf[:] = torch.randn_like(buf)
-        buf.requires_grad = True
-
-    for step in tqdm(range(num_steps)):
-
-        # Learning rate schedule.
-        t = step / num_steps
-        w_noise_scale = w_std * initial_noise_factor * \
-            max(0.0, 1.0 - t / noise_ramp_length) ** 2
-        lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
-        lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
-        lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
-        lr = initial_learning_rate * lr_ramp
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = lr
-
-        # Synth images from opt_w.
-        w_noise = torch.randn_like(w_opt) * w_noise_scale
-        ws = (w_opt + w_noise)
-
-        synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
-
-        # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
-        synth_images = (synth_images + 1) * (255 / 2)
-        if synth_images.shape[2] > 256:
-            synth_images = F.interpolate(
-                synth_images, size=(256, 256), mode='area')
-
-        # Features for synth images.
-        synth_features = vgg16(
-            synth_images, resize_images=False, return_lpips=True)
-        dist = (target_features - synth_features).square().sum()
-
-        # Noise regularization.
-        reg_loss = 0.0
-        for v in noise_bufs.values():
-            noise = v[None, None, :, :]  # must be [1,1,H,W] for F.avg_pool2d()
-            while True:
-                reg_loss += (noise * torch.roll(noise,
-                             shifts=1, dims=3)).mean() ** 2
-                reg_loss += (noise * torch.roll(noise,
-                             shifts=1, dims=2)).mean() ** 2
-                if noise.shape[2] <= 8:
-                    break
-                noise = F.avg_pool2d(noise, kernel_size=2)
-        loss = dist + reg_loss * regularize_noise_weight
-
-        if step % image_log_step == 0:
-            with torch.no_grad():
-                if use_wandb:
-                    global_config.training_step += 1
-                    wandb.log({f'first projection _{w_name}': loss.detach(
-                    ).cpu()}, step=global_config.training_step)
-                    log_image_from_w(w_opt, G, w_name)
-
-        # Step
-        optimizer.zero_grad(set_to_none=True)
-        loss.backward()
-        optimizer.step()
-        logprint(
-            f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
-
-        # Normalize noise.
-        with torch.no_grad():
-            for buf in noise_bufs.values():
-                buf -= buf.mean()
-                buf *= buf.square().mean().rsqrt()
-
-    del G
-    return w_opt
diff --git a/spaces/DragGan/DragGan/stylegan_human/utils/log_utils.py b/spaces/DragGan/DragGan/stylegan_human/utils/log_utils.py
deleted file mode 100644
index 12171ab4cb73659e163bafaf4491dd15218c7e06..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/utils/log_utils.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-
-import numpy as np
-from PIL import Image
-import wandb
-from pti.pti_configs import global_config
-import torch
-import matplotlib.pyplot as plt
-
-
-def log_image_from_w(w, G, name):
-    img = get_image_from_w(w, G)
-    pillow_image = Image.fromarray(img)
-    wandb.log(
-        {f"{name}": [
-            wandb.Image(pillow_image, caption=f"current inversion {name}")]},
-        step=global_config.training_step)
-
-
-def log_images_from_w(ws, G, names):
-    for name, w in zip(names, ws):
-        w = w.to(global_config.device)
-        log_image_from_w(w, G, name)
-
-
-def plot_image_from_w(w, G):
-    img = get_image_from_w(w, G)
-    pillow_image = Image.fromarray(img)
-    plt.imshow(pillow_image)
-    plt.show()
-
-
-def plot_image(img):
-    img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()
-    pillow_image = Image.fromarray(img[0])
-    plt.imshow(pillow_image)
-    plt.show()
-
-
-def save_image(name, method_type, results_dir, image, run_id):
-    image.save(f'{results_dir}/{method_type}_{name}_{run_id}.jpg')
-
-
-def save_w(w, G, name, method_type, results_dir):
-    im = get_image_from_w(w, G)
-    im = Image.fromarray(im, mode='RGB')
-    save_image(name, method_type, results_dir, im)
-
-
-def save_concat_image(base_dir, image_latents, new_inv_image_latent, new_G,
-                      old_G,
-                      file_name,
-                      extra_image=None):
-    images_to_save = []
-    if extra_image is not None:
-        images_to_save.append(extra_image)
-    for latent in image_latents:
-        images_to_save.append(get_image_from_w(latent, old_G))
-    images_to_save.append(get_image_from_w(new_inv_image_latent, new_G))
-    result_image = create_alongside_images(images_to_save)
-    result_image.save(f'{base_dir}/{file_name}.jpg')
-
-
-def save_single_image(base_dir, image_latent, G, file_name):
-    image_to_save = get_image_from_w(image_latent, G)
-    image_to_save = Image.fromarray(image_to_save, mode='RGB')
-    image_to_save.save(f'{base_dir}/{file_name}.jpg')
-
-
-def create_alongside_images(images):
-    res = np.concatenate([np.array(image) for image in images], axis=1)
-    return Image.fromarray(res, mode='RGB')
-
-
-def get_image_from_w(w, G):
-    if len(w.size()) <= 2:
-        w = w.unsqueeze(0)
-    with torch.no_grad():
-        img = G.synthesis(w, noise_mode='const')
-        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()
-    return img[0]
diff --git a/spaces/ECCV2022/storydalle/dalle/utils/sampling.py b/spaces/ECCV2022/storydalle/dalle/utils/sampling.py
deleted file mode 100644
index 26d544d960e33d3a7f0de63dd98fc1df1a521b6b..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/storydalle/dalle/utils/sampling.py
+++ /dev/null
@@ -1,369 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Minimal DALL-E
-# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------
-
-import torch
-from typing import Optional
-from tqdm import tqdm
-from torch.nn import functional as F
-
-
-torch.set_printoptions(precision=2, threshold=10)
-def cutoff_topk_logits(logits: torch.FloatTensor, k: int) -> torch.FloatTensor:
-    if k is None:
-        return logits
-    else:
-        v, ix = torch.topk(logits, k)
-        out = logits.clone()
-        out[out < v[:, [-1]]] = -float('Inf')
-        return out
-
-
-def cutoff_topp_probs(probs: torch.FloatTensor, p: float) -> torch.FloatTensor:
-    if p is None:
-        return probs
-    else:
-        sorted_probs, sorted_indices = torch.sort(probs, dim=-1, descending=True)
-        cum_probs = torch.cumsum(sorted_probs, dim=-1)
-
-        sorted_idx_remove_cond = cum_probs >= p
-
-        sorted_idx_remove_cond[..., 1:] = sorted_idx_remove_cond[..., :-1].clone()
-        sorted_idx_remove_cond[..., 0] = 0
-
-        indices_to_remove = sorted_idx_remove_cond.scatter(-1, sorted_indices, sorted_idx_remove_cond)
-        probs = probs.masked_fill(indices_to_remove, 0.0)
-        norm_probs = probs / torch.sum(probs, dim=-1, keepdim=True)
-        return norm_probs
-
-
-def get_positional_encoding(inputs: torch.LongTensor, mode: str = '1d') -> torch.LongTensor:
-    device = inputs.device
-    if mode == '1d':
-        B, N = inputs.shape
-        xs_pos = torch.arange(N, device=device).repeat((B, 1))
-    elif mode == '2d':
-        B, H, W = inputs.shape
-        xs_pos_h = torch.arange(H, device=device).repeat(B, W, 1).transpose(1, 2)
-        xs_pos_w = torch.arange(W, device=device).repeat(B, H, 1)
-        xs_pos = (xs_pos_h, xs_pos_w)
-    else:
-        raise ValueError('%s positional encoding invalid' % mode)
-    return xs_pos
-
-
-@torch.no_grad()
-def sampling(model: torch.nn.Module,
-             tokens: torch.LongTensor,
-             top_k: Optional[float] = None,
-             top_p: Optional[float] = None,
-             softmax_temperature: float = 1.0,
-             is_tqdm: bool = True,
-             use_fp16: bool = True,
-             max_seq_len: int = 256,
-             prompt: Optional[torch.tensor] = None,
-             pos_prompt: Optional[torch.Tensor] = None) -> torch.LongTensor:
-
-    code = None
-    past = None
-
-    pbar = tqdm(range(max_seq_len), total=max_seq_len) if is_tqdm else range(max_seq_len)
-    pos_enc_tokens = get_positional_encoding(tokens, mode='1d')
-
-    for cnt, h in enumerate(pbar):
-        if code is None:
-            code_ = None
-            pos_enc_code_ = None
-        else:
-            code_ = code.clone().detach()
-            pos_enc_code_ = get_positional_encoding(code_, mode='1d')
-            code_ = code_[:, cnt-1].unsqueeze(-1)
-            pos_enc_code_ = pos_enc_code_[:, cnt-1].unsqueeze(-1)
-
-        logits, present = model.sampling(images=code_,
-                                         texts=tokens,
-                                         pos_images=pos_enc_code_,
-                                         pos_texts=pos_enc_tokens,
-                                         use_fp16=use_fp16,
-                                         past=past,
-                                         prompt=prompt,
-                                         pos_prompt=pos_prompt)
-
-        logits = logits.to(dtype=torch.float32)
-        logits = logits / softmax_temperature
-
-        # print(len(present), present[0].shape)
-        present = torch.stack(present).clone().detach()
-        if past is None:
-            past = [present]
-        else:
-            past.append(present)
-
-        logits = cutoff_topk_logits(logits, top_k)
-        probs = F.softmax(logits, dim=-1)
-        probs = cutoff_topp_probs(probs, top_p)
-        # print(probs[0])
-
-        idx = torch.multinomial(probs, num_samples=1).clone().detach()
-        # print(idx)
-        code = idx if code is None else torch.cat([code, idx], axis=1)
-
-    del past
-    return code
-
-
-@torch.no_grad()
-def sampling_prefix(model: torch.nn.Module,
-                    tokens: torch.LongTensor,
-                    past: torch.FloatTensor,
-                    top_k: Optional[float] = None,
-                    top_p: Optional[float] = None,
-                    softmax_temperature: float = 1.0,
-                    is_tqdm: bool = True,
-                    use_fp16: bool = True,
-                    max_seq_len: int = 256,
-                    labels = None) -> torch.LongTensor:
-    code = None
-
-    pbar = tqdm(range(max_seq_len), total=max_seq_len) if is_tqdm else range(max_seq_len)
-    pos_enc_tokens = get_positional_encoding(tokens, mode='1d')
-
-    # print("Entering sampling_prefix; ", past.shape)
-    if past is not None:
-        past = [past]
-
-    for cnt, h in enumerate(pbar):
-        if code is None:
-            code_ = None
-            pos_enc_code_ = None
-        else:
-            code_ = code.clone().detach()
-            pos_enc_code_ = get_positional_encoding(code_, mode='1d')
-            code_ = code_[:, cnt-1].unsqueeze(-1)
-            pos_enc_code_ = pos_enc_code_[:, cnt-1].unsqueeze(-1)
-
-        # print("Looop enter")
-        # print(cnt, past[0].shape)
-        # print("-------------------")
-        logits, present = model.sampling(images=code_,
-                                         texts=tokens,
-                                         pos_images=pos_enc_code_,
-                                         pos_texts=pos_enc_tokens,
-                                         use_fp16=use_fp16,
-                                         past=past)
-        logits = logits.to(dtype=torch.float32)
-        logits = logits / softmax_temperature
-
-        present = torch.stack(present).clone().detach()
-
-        # print('Present', present.shape)
-
-        if past is None:
-            past = [present]
-        else:
-            # print("Loop end")
-            # print(present.shape)
-            # print("-----------------")
-
-            # n_layers, temp, _, seq_len, n_dim = present.shape
-            # _, _, bs, n_heads, pre_seq_len, n_dim = past[0].shape
-            # assert temp == 2
-            # past.append(present.view(n_layers, temp, bs, n_heads, seq_len, n_dim))
-
-            past.append(present)
-
-        logits = cutoff_topk_logits(logits, top_k)
-        probs = F.softmax(logits, dim=-1)
-        probs = cutoff_topp_probs(probs, top_p)
-        print(torch.topk(probs, 5, dim=-1))
-        if labels is not None:
-            print(labels[cnt])
-        idx = torch.multinomial(probs, num_samples=1).clone().detach()
-        # print(idx)
-        code = idx if code is None else torch.cat([code, idx], axis=1)
-
-    del past
-    return code
-
-
-@torch.no_grad()
-def sampling_prefix_new(model: torch.nn.Module,
-                    tokens: torch.LongTensor,
-                    past: torch.FloatTensor,
-                    top_k: Optional[float] = None,
-                    top_p: Optional[float] = None,
-                    softmax_temperature: float = 1.0,
-                    is_tqdm: bool = True,
-                    use_fp16: bool = True,
-                    max_seq_len: int = 256) -> torch.LongTensor:
-    code = None
-
-    pbar = tqdm(range(max_seq_len), total=max_seq_len) if is_tqdm else range(max_seq_len)
-    pos_enc_tokens = get_positional_encoding(tokens, mode='1d')
-
-    # print("Entering sampling_prefix; ", past.shape)
-    if past is not None:
-        past = [past]
-
-    for cnt, h in enumerate(pbar):
-        if code is None:
-            code_ = None
-            pos_enc_code_ = None
-        else:
-            code_ = code.clone().detach()
-            pos_enc_code_ = get_positional_encoding(code_, mode='1d')
-            # code_ = code_[:, cnt-1].unsqueeze(-1)
-            # pos_enc_code_ = pos_enc_code_[:, cnt-1].unsqueeze(-1)
-
-        # print("Looop enter")
-        # print(cnt, past[0].shape)
-        # print("-------------------")
-
-        if cnt == 0:
-            logits, present = model.sampling(images=code_,
-                                             texts=tokens,
-                                             pos_images=pos_enc_code_,
-                                             pos_texts=pos_enc_tokens,
-                                             use_fp16=use_fp16,
-                                             past=past)
-            logits = logits.to(dtype=torch.float32)
-            logits = logits / softmax_temperature
-
-            present = torch.stack(present).clone().detach()
-
-            # print('Present', present.shape)
-
-            if past is None:
-                past = [present]
-            else:
-                pass
-
-            logits = cutoff_topk_logits(logits, top_k)
-            probs = F.softmax(logits, dim=-1)
-            probs = cutoff_topp_probs(probs, top_p)
-            # print(torch.topk(probs[0], 5))
-            idx = torch.multinomial(probs, num_samples=1).clone().detach()
-            # print(idx)
-            code = idx if code is None else torch.cat([code, idx], axis=1)
-
-        else:
-            pass
-
-
-    del past
-    return code
-
-@torch.no_grad()
-def sampling_conditional(model: torch.nn.Module,
-                         cross_attention_idxs,
-                         cross_attention_layers,
-                    tokens: torch.LongTensor,
-                    src_codes: torch.FloatTensor,
-                    top_k: Optional[float] = None,
-                    top_p: Optional[float] = None,
-                    softmax_temperature: float = 1.0,
-                    is_tqdm: bool = True,
-                    use_fp16: bool = True,
-                    max_seq_len: int = 256,
-                    prompt: Optional[torch.tensor] = None,
-                    pos_prompt: Optional[torch.Tensor] = None) -> torch.LongTensor:
-
-    code = None
-    past = None
-
-    pbar = tqdm(range(max_seq_len), total=max_seq_len) if is_tqdm else range(max_seq_len)
-    pos_enc_tokens = get_positional_encoding(tokens, mode='1d')
-
-    src_pos_tokens = get_positional_encoding(src_codes, mode='1d')
-    src_tokens = model.tok_emb_img(src_codes)
-    src_tokens = src_tokens + model.pos_emb_img(src_pos_tokens)
-
-    for cnt, h in enumerate(pbar):
-        if code is None:
-            code_ = None
-            pos_enc_code_ = None
-        else:
-            code_ = code.clone().detach()
-            pos_enc_code_ = get_positional_encoding(code_, mode='1d')
-            code_ = code_[:, cnt-1].unsqueeze(-1)
-            pos_enc_code_ = pos_enc_code_[:, cnt-1].unsqueeze(-1)
-
-        logits, present = model.sampling_with_context(images=code_,
-                                                      cross_attention_idxs=cross_attention_idxs,
-                                                      cross_attention_layers=cross_attention_layers,
-                                                      texts=tokens,
-                                                      pos_images=pos_enc_code_,
-                                                      pos_texts=pos_enc_tokens,
-                                                      source_image=src_tokens,
-                                                      use_fp16=use_fp16,
-                                                      past=past,
-                                                      prompt=prompt,
-                                                      pos_prompt=pos_prompt)
-        logits = logits.to(dtype=torch.float32)
-        logits = logits / softmax_temperature
-
-        present = torch.stack(present).clone().detach()
-        if past is None:
-            past = [present]
-        else:
-            past.append(present)
-
-        logits = cutoff_topk_logits(logits, top_k)
-        probs = F.softmax(logits, dim=-1)
-        probs = cutoff_topp_probs(probs, top_p)
-
-        idx = torch.multinomial(probs, num_samples=1).clone().detach()
-        code = idx if code is None else torch.cat([code, idx], axis=1)
-
-    del past
-    return code
-
-
-@torch.no_grad()
-def sampling_igpt(model: torch.nn.Module,
-                  sos: torch.FloatTensor,
-                  top_k: Optional[float] = None,
-                  top_p: Optional[float] = None,
-                  softmax_temperature: float = 1.0,
-                  is_tqdm: bool = True,
-                  use_fp16: bool = True,
-                  max_seq_len: int = 256) -> torch.LongTensor:
-    code = None
-    past = None
-    pbar = tqdm(range(max_seq_len), total=max_seq_len) if is_tqdm else range(max_seq_len)
-
-    for cnt, h in enumerate(pbar):
-        if code is None:
-            code_ = None
-            pos_enc_code_ = None
-        else:
-            code_ = code.clone().detach()
-            pos_enc_code_ = get_positional_encoding(code_, mode='1d')
-            code_ = code_[:, cnt-1].unsqueeze(-1)
-            pos_enc_code_ = pos_enc_code_[:, cnt-1].unsqueeze(-1)
-
-        logits, present = model.sampling(sos=sos,
-                                         codes=code_,
-                                         pos_codes=pos_enc_code_,
-                                         use_fp16=use_fp16,
-                                         past=past)
-        logits = logits.to(dtype=torch.float32)
-        logits = logits / softmax_temperature
-
-        present = torch.stack(present).clone().detach()
-        if past is None:
-            past = [present]
-        else:
-            past.append(present)
-
-        logits = cutoff_topk_logits(logits, top_k)
-        probs = F.softmax(logits, dim=-1)
-        probs = cutoff_topp_probs(probs, top_p)
-
-        idx = torch.multinomial(probs, num_samples=1).clone().detach()
-        code = idx if code is None else torch.cat([code, idx], axis=1)
-
-    del past
-    return code
diff --git a/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/modules/__init__.py b/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/modules/__init__.py
deleted file mode 100644
index 6fdbf03359958f3d67ab00f879bf6b61a6c8f06a..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/modules/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from .ms_deform_attn import MSDeformAttn
diff --git a/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/setup.py b/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/setup.py
deleted file mode 100644
index 244fdec83bee181e187d88800300395f449b0fbc..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/setup.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-import os
-import glob
-
-import torch
-
-from torch.utils.cpp_extension import CUDA_HOME
-from torch.utils.cpp_extension import CppExtension
-from torch.utils.cpp_extension import CUDAExtension
-
-from setuptools import find_packages
-from setuptools import setup
-
-requirements = ["torch", "torchvision"]
-
-def get_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    extensions_dir = os.path.join(this_dir, "src")
-
-    main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
-    source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
-    source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
-
-    sources = main_file + source_cpu
-    extension = CppExtension
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    # Force cuda since torch ask for a device, not if cuda is in fact available.
-    if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:
-        extension = CUDAExtension
-        sources += source_cuda
-        define_macros += [("WITH_CUDA", None)]
-        extra_compile_args["nvcc"] = [
-            "-DCUDA_HAS_FP16=1",
-            "-D__CUDA_NO_HALF_OPERATORS__",
-            "-D__CUDA_NO_HALF_CONVERSIONS__",
-            "-D__CUDA_NO_HALF2_OPERATORS__",
-        ]
-#     else:
-#         if CUDA_HOME is None:
-#             raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')
-#         else:
-#             raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')
-
-    sources = [os.path.join(extensions_dir, s) for s in sources]
-    include_dirs = [extensions_dir]
-    ext_modules = [
-        extension(
-            "MultiScaleDeformableAttention",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-    return ext_modules
-
-setup(
-    name="MultiScaleDeformableAttention",
-    version="1.0",
-    author="Weijie Su",
-    url="https://github.com/fundamentalvision/Deformable-DETR",
-    description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
-    packages=find_packages(exclude=("configs", "tests",)),
-    ext_modules=get_extensions(),
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)
diff --git a/spaces/Emanuel/porttagger/style.css b/spaces/Emanuel/porttagger/style.css
deleted file mode 100644
index dbf5c514e6e13268d8d39ca8d4f864c15facf975..0000000000000000000000000000000000000000
--- a/spaces/Emanuel/porttagger/style.css
+++ /dev/null
@@ -1,70 +0,0 @@
-a {
-    color: inherit;
-    text-decoration: underline;
-}
-
-.gradio-container {
-    font-family: 'IBM Plex Sans', sans-serif;
-}
-
-.gr-button {
-    color: white;
-    border-color: #9d66e5;
-    background: #9d66e5;
-}
-
-.container {
-    max-width: 900px;
-    margin: auto;
-    padding-top: 1.5rem;
-}
-
-.gr-button {
-    white-space: nowrap;
-}
-
-.gr-button:focus {
-    border-color: rgb(147 197 253 / var(--tw-border-opacity));
-    outline: none;
-    box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-    --tw-border-opacity: 1;
-    --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-    --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-    --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-    --tw-ring-opacity: .5;
-}
-
-#advanced-options {
-    margin-bottom: 20px;
-}
-
-.footer {
-    margin-bottom: 45px;
-    margin-top: 35px;
-    text-align: center;
-    border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
-    font-size: .8rem;
-    display: inline-block;
-    padding: 0 10px;
-    transform: translateY(10px);
-    background: white;
-}
-
-.slogan {
-    font-size: 15px;
-    color: #495057;
-}
-
-.row {
-    display: flex;
-    margin-top: 20px;
-}
-
-.column {
-    flex: 33.33%;
-    padding-left: 50px;
-    padding-right: 50px;
-}
\ No newline at end of file
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py b/spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py
deleted file mode 100644
index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py	
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/FYP-23-S1-21/Refineverse_Plugin/Refineverse.py b/spaces/FYP-23-S1-21/Refineverse_Plugin/Refineverse.py
deleted file mode 100644
index 59ea8c04f06dcb3824efb4340214b0e98877a74e..0000000000000000000000000000000000000000
--- a/spaces/FYP-23-S1-21/Refineverse_Plugin/Refineverse.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# Plugin will run on this file & Strictly only contains codes for routing between files!
-from flask import Flask, render_template, request, flash, g
-from TextSummarizationFeature import summarize, getTextSumContents, insertTextSumRow
-from BreakdownFeature import breakdown, getBreakdownContents, insertBreakdownRow
-from TranslationFeature import translate_text, switch, getTranslatedContents, insertTranslationRow
-from GenerationFeature import generate, getTextGenContents, insertTextGenRow
-
-app = Flask(__name__)
-app.secret_key = 'refineverseAdmin' # Used to encrypt cookies & sessions
-
-# Routing to Main Dashboard/homepage file
-@app.route('/')
-def index():
-    return render_template('RefineverseDashboardUI.html')
-
-# Routing to text summarization file
-@app.route('/text_summarization', methods=["POST", "GET"])
-def text_summarization():
-    if request.method == "POST":
-        try:
-            # Grab the user story text from the textarea in html form
-            Entered_story = request.form["input_text"]
-            
-            # The results are stored into a dictionary variable
-            summarizedStory = summarize(Entered_story)
-            
-            flash("Your user story has been summarized!") # Displays a success message using flash, which is part of the Flask framework
-            
-            # Insert into TextSummarization table in Refineverse.db
-            insertTextSumRow(Entered_story, summarizedStory)
-            
-            # Render and display summarized user story
-            return render_template('TextSummarizationUI.html', summarizedStory=summarizedStory)
-        
-        # Exception handling messages for specific errors
-        except ValueError as e:
-            if str(e) == "Empty input!":
-                flash("The input text cannot be empty! Please enter a user story.", 'error')
-                return render_template('TextSummarizationUI.html')
-            elif str(e) == "Incorrect format!":
-                flash("Incorrect user story format! Please enter in the right format.", 'error')
-                return render_template('TextSummarizationUI.html')
-            elif str(e) == "Invalid length!":
-                flash("Your inputted user story is too short to summarize. Please enter a longer story!", 'error')
-                return render_template('TextSummarizationUI.html')
-            else: # As a final resort, simply print out the error name
-                flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-                return render_template('TextSummarizationUI.html')
-        
-        except KeyError:
-            flash("Please enter a valid user story!")
-            return render_template('TextSummarizationUI.html')
-        
-        # Catch-all exception handling
-        except Exception as e:
-            flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-            return render_template('TextSummarizationUI.html')
-    
-    else:
-        return render_template('TextSummarizationUI.html')
-
-# Routing to summarization table file
-@app.route('/summarization_table')
-def summarization_table():
-    # Get the summarization data from the database
-    summarizations = getTextSumContents()
-    
-    # Render the summarization data as an HTML table
-    return render_template('SummarizationTable.html', summarizations=summarizations)
-
-# Routing to Project Task Breakdown file
-@app.route("/project_breakdown", methods=["POST", "GET"]) # This tells flask the route to get to the page
-def project_breakdown():
-    if request.method == "POST": # POST occurs when submitting a form, as specified in the HTML file
-        try:
-            # Grab the user story contents
-            userStory = request.form["user-story-text"]
-            
-            # The results are stored into a dictionary variable
-            processedLabel = breakdown(userStory)
-            
-            # Display success popup message
-            flash("Your user story has been allocated as a " + processedLabel + " task!")
-            
-            insertBreakdownRow(userStory, processedLabel) # Inserts data into the Breakdown table
-            rows = getBreakdownContents() # Grab all contents inside the Breakdown table
-            
-            return render_template('ProjectBreakdownUI.html', rows=rows)
-        
-        # Exception handling messages for specific errors
-        except KeyError:
-            flash("Please enter a valid user story!", 'error')
-            rows = getBreakdownContents()
-            return render_template('ProjectBreakdownUI.html', row=rows)
-        
-        # Catch-all exception handling
-        except Exception as e:
-            flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-            rows = getBreakdownContents()
-            return render_template('ProjectBreakdownUI.html', rows=rows)
-        
-    else: # For "GET" scenarios (loading the page, etc.)
-        rows = getBreakdownContents()  # To always display the table, we must grab the contents of Breakdown every time the page loads
-        return render_template('ProjectBreakdownUI.html', rows=rows)
-
-# Routing to Translation file
-@app.route('/language_translation', methods=["POST", "GET"])
-def language_translation():
-    if request.method == "POST":
-        try:
-            # Grab all relevant information for processing
-            input_text = request.form['input'] # Grab user text input
-            
-            # Grab source language code
-            source_language = request.form['source_language']
-            
-            # Grab target language code
-            target_language = request.form['target_language']
-            
-            # Generate translated text using custom translation function
-            translatedStory = translate_text(input_text, source_language, target_language)
-            
-            # Display success popup message
-            flash("Your user story has been translated to " + switch(target_language) + "  !")
-            
-            # Insert into Translation table in Refineverse.db
-            insertTranslationRow(input_text, translatedStory)
-            
-            # Display the page
-            return render_template('LanguageTranslationUI.html', input_text=input_text, translatedStory=translatedStory)
-        
-        # Exception handling messages for specific errors
-        except ValueError as e:
-            if str(e) == "Empty input!":
-                flash("The input text cannot be empty! Please enter a user story.", 'error')
-                return render_template('LanguageTranslationUI.html')
-            elif str(e) == "Incorrect format!":
-                flash("Unable to translate your user story. Please enter in the correct format.", 'error')
-                return render_template('LanguageTranslationUI.html')
-            else: # As a final resort, simply print out the error name
-                flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-                return render_template('LanguageTranslationUI.html')
-        
-        # Catch-all exception handling
-        except Exception as e:
-            flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-            return render_template('LanguageTranslationUI.html')
-    
-    else:
-        return render_template('LanguageTranslationUI.html') 
-
-# Routing to translation table file
-@app.route('/translation_table')
-def translation_data():
-    # Get the translation data from the database
-    translations = getTranslatedContents()
-    
-    # Render the translation data as an HTML table
-    return render_template('TranslationTable.html', translations=translations)
-
-# Routing to text summarization file
-@app.route('/text_generation', methods=["POST", "GET"])
-def text_generation():
-    if request.method == "POST":
-        try:
-            # Grab the user story text from the textarea in html form
-            Entered_story = request.form["input_text"]
-            
-            # The results are stored into a dictionary variable
-            generatedStory = generate(Entered_story)
-            
-            # Display a success message for the user
-            flash("Your user story has been generated!")
-            
-            # Insert into TextGeneration table in Refineverse.db
-            insertTextGenRow(Entered_story, generatedStory)
-            
-            # Render and display summarized user story
-            return render_template('TextGenerationUI.html', generatedStory=generatedStory)
-        
-        # Exception handling messages for specific errors
-        except ValueError as e:
-            if str(e) == "Empty input!":
-                flash("The input text cannot be empty! Please enter a user story.", 'error')
-                return render_template('TextGenerationUI.html')
-            elif str(e) == "Incorrect format!":
-                flash("Incorrect user story format! Please enter in the right format.", 'error')
-                return render_template('TextGenerationUI.html')
-            else: # As a final resort, simply print out the error name
-                flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-                return render_template('TextGenerationUI.html')
-        
-        except KeyError:
-            flash("Please enter a valid user story!")
-            return render_template('TextGenerationUI.html')
-        
-        # Catch-all exception handling
-        except Exception as e:
-            flash("An error of type '{}' occurred: {}".format(type(e).__name__, str(e)), 'error')
-            return render_template('TextGenerationUI.html')
-    
-    else:
-        return render_template('TextGenerationUI.html')
-
-# Routing to generation table file
-@app.route('/generation_table')
-def generation_table():
-    # Get the generation data from the database
-    generations = getTextGenContents()
-    
-    # Render the generation data as an HTML table
-    return render_template('GenerationTable.html', generations=generations)
-
-# Used when the application is torn down
-# Its purpose is to close the database connection if it has not been closed
-@app.teardown_appcontext
-def close_connection(exception):
-    db = getattr(g, '_database', None)
-    if db is not None:
-        db.close() # Closes the database connection
-
-# Initialise the app
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860) # For HF hosting
-    #app.run(debug=False) # can set to True/False for local testing
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/Story-to-video/app.py b/spaces/Gradio-Blocks/Story-to-video/app.py
deleted file mode 100644
index 5b4831cb7b6e2d87bf92383aed6e8fb8fb58de65..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/Story-to-video/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from PIL import Image
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
-import requests
-import gradio as gr
-import torch
-import re
-from moviepy.editor import *
-import os
-import sys
-from huggingface_hub import snapshot_download
-import base64
-import io
-import cv2
-
-image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
-
-description = "Just upload an image, and generate a short story for the image.\n PS: GPT-2 is not perfect but it's fun to play with.May take a minute for the output to generate. Enjoyy!!!"
-title = "Story generator from images using ViT and GPT2"
-
-tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
-
-model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6")
-
-def get_output_video(text):
-
-  inputs = tokenizer(text, 
-                    max_length=1024, 
-                    truncation=True,
-                    return_tensors="pt")
-    
-  summary_ids = model.generate(inputs["input_ids"])
-  summary = tokenizer.batch_decode(summary_ids, 
-                                  skip_special_tokens=True, 
-                                  clean_up_tokenization_spaces=False)
-    
-
-  plot = list(summary[0].split('.'))
-
-  generated_images = []
-  for senten in plot[:-1]:
-    steps=50
-    width=256
-    height=256
-    num_images=3
-    diversity=6
-    
-    image_bytes = image_gen(senten, steps, width, height, num_images, diversity)
-
-
-    # Algo from spaces/Gradio-Blocks/latent_gpt2_story/blob/main/app.py
-    
-    for image in image_bytes[1]:
-        image_str = image[0]
-        image_str = image_str.replace("data:image/png;base64,","")
-        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
-        img = Image.open(io.BytesIO(decoded_bytes))
-        generated_images.append(img)
-
-  c = 0
-  file_names = []
-
-  for img in generated_images:
-
-    f_name = 'img_'+str(c)+'.jpg'
-    file_names.append(f_name)
-    img = img.save(f_name)
-    c+=1
-
-  #print(file_names)
-  clips = [ImageClip(m).set_duration(2)
-         for m in file_names]
-
-  concat_clip = concatenate_videoclips(clips, method="compose")
-  concat_clip.write_videofile("test.mp4", fps=24)
-  
-  return 'test.mp4'
- 
-
-text = 'Once, there was a boy who became bored when he watched over the village sheep grazing on the hillside. To entertain himself, he sang out, “Wolf! Wolf! The wolf is chasing the sheep!\”.When the villagers heard the cry, they came running up the hill to drive the wolf away. But, when they arrived, they saw no wolf. The boy was amused when seeing their angry faces.Don’t scream wolf, boy,\” warned the villagers, “when there is no wolf!” They angrily went back down the hill.Later, the shepherd boy cried out once again, “Wolf! Wolf! The wolf is chasing the sheep!” To his amusement, he looked on as the villagers came running up the hill to scare the wolf away.As they saw there was no wolf, they said strictly, “Save your frightened cry for when there really is a wolf! Don’t cry ‘wolf’ when there is no wolf!” But the boy grinned at their words while they walked grumbling down the hill once more.Later, the boy saw a real wolf sneaking around his flock. Alarmed, he jumped on his feet and cried out as loud as he could, “Wolf! Wolf!” But the villagers thought he was fooling them again, and so they didn’t come to help.At sunset, the villagers went looking for the boy who hadn’t returned with their sheep. When they went up the hill, they found him weeping.“There really was a wolf here! The flock is gone! I cried out, ‘Wolf!’ but you didn’t come,” he wailed.An old man went to comfort the boy. As he put his arm around him, he said, “Nobody believes a liar, even when he is telling the truth!\"'
-
-demo = gr.Blocks()
-
-with demo:
-
-    gr.Markdown("# A System pipeline to generate bite-sized video from long stories")
-
-    gr.Markdown("A story can be input by user. The story is summarized using DistillBART model. Then, it's step by step sent to the multimodal Diffusion model, to generate images.These are depicted as a video.")
-    with gr.Row():
-    
-        # Left column (inputs)
-        
-        with gr.Column():
-            
-            input_start_text = gr.Textbox(value=text, label="Type your story here, for now a sample story is added already!")
-            
-            
-            
-            
-            with gr.Row():
-                
-                button_gen_video = gr.Button("Generate Video")
-
-            
-
-                
-                
-            
-                
-        # Right column (outputs)
-        with gr.Column():
-            
-            
-            output_interpolation = gr.Video(label="Generated Video")
-
-          
-
-
-            
-    
-    gr.Markdown("<h3>Future Works and Challenges</h3>")
-    gr.Markdown("Though this pipeline, isn't 100% perfect, but one can use similar system to create bite-sized videos from text resources. Effective for creating videos for educational lessons.")
-    button_gen_video.click(fn=get_output_video, inputs=input_start_text, outputs=output_interpolation)
-
-demo.launch(debug=False)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
deleted file mode 100644
index 081b998f6f54d3d805dbab38b26750a378c0d93f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
deleted file mode 100644
index 40f5f62373e59d1c6c01ca3f57777698461127c9..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index e084e95c70b0b7b0c9dcc3388d6b7d3d51d54b6d..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnet18_v1c',
-    backbone=dict(depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/test_video.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/test_video.py
deleted file mode 100644
index c8d30f32f021fddac030262a5ef51283459d293a..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/test_video.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import streamlit as st
-import cv2
-import tempfile
-from PIL import Image
-import os
-from stqdm import stqdm
-
-with st.spinner("Loading model ..."):
-    from model import base_inference
-
-f = st.file_uploader("Upload file")
-
-if f is not None:
-    with st.spinner():
-        tfile = tempfile.NamedTemporaryFile(delete=False) 
-        tfile.write(f.read())
-
-        vf = cv2.VideoCapture(tfile.name)
-
-        frame_width = int(vf.get(3))
-        frame_height = int(vf.get(4))
-        
-        size = (frame_width, frame_height)
-        
-        # Below VideoWriter object will create
-        # a frame of above defined The output 
-        # is stored in 'filename.avi' file.
-        tmp_file_path = 'tmp/video.avi'
-        out_file_path = 'tmp/video.mp4'
-
-        cap = cv2.VideoWriter(
-            tmp_file_path, 
-            cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
-            10, size
-        )
-        length = int(vf.get(cv2.CAP_PROP_FRAME_COUNT))
-
-        sod_stframe = st.empty()
-
-        for _ in stqdm(range(length)):
-            if not vf.isOpened():
-                break
-
-            ret, image = vf.read()
-            if not ret:
-                break
-
-            pred_depth, pred_sod, _ = base_inference(image, None)
-            cap.write(pred_sod)
-
-        vf.release()
-        cap.release()
-
-        os.system(f'ffmpeg -y -i {tmp_file_path} -vcodec libx264 {out_file_path} -hide_banner -loglevel error')
-        
-        video_file = open(out_file_path, 'rb')
-        video_bytes = video_file.read()
-        st.video(video_bytes, format="video/mp4")
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/workspace/erlangshen-deberta-base/pretrain/README.md b/spaces/HaloMaster/chinesesummary/fengshen/workspace/erlangshen-deberta-base/pretrain/README.md
deleted file mode 100644
index 942a13a2a1a2eca8fe42ecaab03d33b16e4c0700..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/workspace/erlangshen-deberta-base/pretrain/README.md
+++ /dev/null
@@ -1,54 +0,0 @@
----
-language: 
-  - zh
-
-license: apache-2.0
-
-tags:
-  - bert
-
-inference: true
-
-widget:
-- text: "生活的真谛是[MASK]。"
----
-# Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
-The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples.
-
-
-## Task Description
-
-Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta [paper](https://readpaper.com/paper/3033187248)
-
-
-## Usage
-```python
-from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
-import torch
-
-tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese', use_fast=False)
-model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese')
-text = '生活的真谛是[MASK]。'
-fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7)
-print(fillmask_pipe(text, top_k=10))
-```
-
-## Finetune
-
-We present the dev results on some tasks.
-
-| Model                              | OCNLI | CMNLI  |
-| ---------------------------------- | ----- | ------ |
-| RoBERTa-base                       | 0.743 | 0.7973 |
-| **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807  |
-
-## Citation
-If you find the resource is useful, please cite the following website in your paper.
-```
-@misc{Fengshenbang-LM,
-  title={Fengshenbang-LM},
-  author={IDEA-CCNL},
-  year={2022},
-  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
-}
-```
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/preprocess_ML50_v1.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/preprocess_ML50_v1.sh
deleted file mode 100644
index 4655936149cab212b3cfa14f306d71153729f9d7..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/preprocess_ML50_v1.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-if [ -z $WORKDIR_ROOT ] ;
-then
-        echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
-        exit
-fi
-
-if [ -z $SPM_PATH ] ;
-then
-    echo "Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting..."
-    exit
-fi
-
-ML50=${WORKDIR_ROOT}/ML50
-
-mkdir -p $ML50/dedup
-mkdir -p $ML50/cleaned_dedup
-
-python ./dedup_all.py --from-folder $ML50/raw --to-folder $ML50/dedup
-python ./remove_valid_test_in_train.py --from-folder $ML50/dedup --to-folder $ML50/clean
-python ./binarize.py --raw-folder $ML50/clean
\ No newline at end of file
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/hifi/prepare_data.sh b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/hifi/prepare_data.sh
deleted file mode 100644
index d620cfeb93d8de9b2f750ad9bd52a937b0b88c33..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/hifi/prepare_data.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-input_wav_path='/home/harveen/en/iitm_data/english/wav_22k' #give multiple folders separated by comma(,)
-gender='male'
-
-output_data_path='../../data/hifi/'$gender
-
-valid_samples=100
-test_samples=10
-
-mkdir -p $output_data_path
-python ../../utils/hifi/prepare_iitm_data_hifi.py -i $input_wav_path -v $valid_samples -t $test_samples -d $output_data_path
diff --git a/spaces/Harveenchadha/en_to_indic_translation/api.py b/spaces/Harveenchadha/en_to_indic_translation/api.py
deleted file mode 100644
index 601dd5ec161baa5d3041be111a0d83dd6f9073c3..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/api.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import time
-
-from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
-from inference.engine import Model
-from flask import Flask, request
-from flask import jsonify
-from flask_cors import CORS, cross_origin
-import webvtt
-from io import StringIO
-
-
-app = Flask(__name__)
-cors = CORS(app)
-app.config['CORS_HEADERS'] = 'Content-Type'
-
-indic2en_model = Model(expdir='../models/v3/indic-en')
-en2indic_model = Model(expdir='../models/v3/en-indic')
-m2m_model = Model(expdir='../models/m2m')
-
-language_dict = {
-    'Assamese': 'as',
-    'Hindi' : 'hi',
-    'Marathi' : 'mr',
-    'Tamil' : 'ta',
-    'Bengali' : 'bn',
-    'Kannada' : 'kn',
-    'Oriya' : 'or',
-    'Telugu' : 'te',
-    'Gujarati' : 'gu',
-    'Malayalam' : 'ml',
-    'Punjabi' : 'pa',
-}
-
-def get_inference_params():
-    model_type = request.form['model_type']
-    source_language = request.form['source_language']
-    target_language = request.form['target_language']
-
-    if model_type == 'indic-en':
-        model = indic2en_model
-        source_lang = language_dict[source_language]
-        assert target_language == 'English'
-        target_lang = 'en'
-    elif model_type == 'en-indic':
-        model = en2indic_model
-        assert source_language == 'English'
-        source_lang = 'en'
-        target_lang = language_dict[target_language]
-    elif model_type == 'm2m':
-        model = m2m_model
-        source_lang = language_dict[source_language]
-        target_lang = language_dict[target_language]
-    
-    return model, source_lang, target_lang
-
-@app.route('/', methods=['GET'])
-def main():
-    return "IndicTrans API"
-
-@app.route("/translate", methods=['POST'])
-@cross_origin()
-def infer_indic_en():
-    model, source_lang, target_lang = get_inference_params()
-    source_text = request.form['text']
-
-    start_time = time.time()
-    target_text = model.translate_paragraph(source_text, source_lang, target_lang)
-    end_time = time.time()
-    return {'text':target_text, 'duration':round(end_time-start_time, 2)}
-
-@app.route("/translate_vtt", methods=['POST'])
-@cross_origin()
-def infer_vtt_indic_en():
-    model, source_lang, target_lang = get_inference_params()
-    source_text = request.form['text']
-    captions = webvtt.read_buffer(StringIO(source_text))
-    source_sentences = [caption.text.replace('\r', '').replace('\n', ' ') for caption in captions]
-
-    start_time = time.time()
-    target_sentences = model.batch_translate(source_sentences, source_lang, target_lang)
-    end_time = time.time()
-
-    for i in range(len(target_sentences)):
-        captions[i].text = target_sentences[i]
-
-    return {'text': captions.content, 'duration':round(end_time-start_time, 2)}
diff --git a/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_library/docs/make.bat b/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_library/docs/make.bat
deleted file mode 100644
index 922152e96a04a242e6fc40f124261d74890617d8..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_library/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/spaces/HighCWu/starganv2vc-paddle/starganv2vc_paddle/meldataset.py b/spaces/HighCWu/starganv2vc-paddle/starganv2vc_paddle/meldataset.py
deleted file mode 100644
index 2302da1926825afa81320643014f911c3086442b..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/starganv2vc-paddle/starganv2vc_paddle/meldataset.py
+++ /dev/null
@@ -1,155 +0,0 @@
-#coding: utf-8
-
-import os
-import time
-import random
-import random
-import paddle
-import paddleaudio
-
-import numpy as np
-import soundfile as sf
-import paddle.nn.functional as F
-
-from paddle import nn
-from paddle.io import DataLoader
-
-import logging
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-np.random.seed(1)
-random.seed(1)
-
-SPECT_PARAMS = {
-    "n_fft": 2048,
-    "win_length": 1200,
-    "hop_length": 300
-}
-MEL_PARAMS = {
-    "n_mels": 80,
-    "n_fft": 2048,
-    "win_length": 1200,
-    "hop_length": 300
-}
-
-class MelDataset(paddle.io.Dataset):
-    def __init__(self,
-                 data_list,
-                 sr=24000,
-                 validation=False,
-                 ):
-
-        _data_list = [l[:-1].split('|') for l in data_list]
-        self.data_list = [(path, int(label)) for path, label in _data_list]
-        self.data_list_per_class = {
-            target: [(path, label) for path, label in self.data_list if label == target] \
-            for target in list(set([label for _, label in self.data_list]))}
-
-        self.sr = sr
-        self.to_melspec = paddleaudio.features.MelSpectrogram(**MEL_PARAMS)
-        self.to_melspec.fbank_matrix[:] = paddle.load(os.path.dirname(__file__) + '/fbank_matrix.pd')['fbank_matrix']
-
-        self.mean, self.std = -4, 4
-        self.validation = validation
-        self.max_mel_length = 192
-
-    def __len__(self):
-        return len(self.data_list)
-
-    def __getitem__(self, idx):
-        with paddle.fluid.dygraph.guard(paddle.CPUPlace()):
-            data = self.data_list[idx]
-            mel_tensor, label = self._load_data(data)
-            ref_data = random.choice(self.data_list)
-            ref_mel_tensor, ref_label = self._load_data(ref_data)
-            ref2_data = random.choice(self.data_list_per_class[ref_label])
-            ref2_mel_tensor, _ = self._load_data(ref2_data)
-            return mel_tensor, label, ref_mel_tensor, ref2_mel_tensor, ref_label
-    
-    def _load_data(self, path):
-        wave_tensor, label = self._load_tensor(path)
-        
-        if not self.validation: # random scale for robustness
-            random_scale = 0.5 + 0.5 * np.random.random()
-            wave_tensor = random_scale * wave_tensor
-
-        mel_tensor = self.to_melspec(wave_tensor)
-        mel_tensor = (paddle.log(1e-5 + mel_tensor) - self.mean) / self.std
-        mel_length = mel_tensor.shape[1]
-        if mel_length > self.max_mel_length:
-            random_start = np.random.randint(0, mel_length - self.max_mel_length)
-            mel_tensor = mel_tensor[:, random_start:random_start + self.max_mel_length]
-
-        return mel_tensor, label
-
-    def _preprocess(self, wave_tensor, ):
-        mel_tensor = self.to_melspec(wave_tensor)
-        mel_tensor = (paddle.log(1e-5 + mel_tensor) - self.mean) / self.std
-        return mel_tensor
-
-    def _load_tensor(self, data):
-        wave_path, label = data
-        label = int(label)
-        wave, sr = sf.read(wave_path)
-        wave_tensor = paddle.from_numpy(wave).astype(paddle.float32)
-        return wave_tensor, label
-
-class Collater(object):
-    """
-    Args:
-      adaptive_batch_size (bool): if true, decrease batch size when long data comes.
-    """
-
-    def __init__(self, return_wave=False):
-        self.text_pad_index = 0
-        self.return_wave = return_wave
-        self.max_mel_length = 192
-        self.mel_length_step = 16
-        self.latent_dim = 16
-
-    def __call__(self, batch):
-        batch_size = len(batch)
-        nmels = batch[0][0].shape[0]
-        mels = paddle.zeros((batch_size, nmels, self.max_mel_length)).astype(paddle.float32)
-        labels = paddle.zeros((batch_size)).astype(paddle.int64)
-        ref_mels = paddle.zeros((batch_size, nmels, self.max_mel_length)).astype(paddle.float32)
-        ref2_mels = paddle.zeros((batch_size, nmels, self.max_mel_length)).astype(paddle.float32)
-        ref_labels = paddle.zeros((batch_size)).astype(paddle.int64)
-
-        for bid, (mel, label, ref_mel, ref2_mel, ref_label) in enumerate(batch):
-            mel_size = mel.shape[1]
-            mels[bid, :, :mel_size] = mel
-            
-            ref_mel_size = ref_mel.shape[1]
-            ref_mels[bid, :, :ref_mel_size] = ref_mel
-            
-            ref2_mel_size = ref2_mel.shape[1]
-            ref2_mels[bid, :, :ref2_mel_size] = ref2_mel
-            
-            labels[bid] = label
-            ref_labels[bid] = ref_label
-
-        z_trg = paddle.randn((batch_size, self.latent_dim))
-        z_trg2 = paddle.randn((batch_size, self.latent_dim))
-        
-        mels, ref_mels, ref2_mels = mels.unsqueeze(1), ref_mels.unsqueeze(1), ref2_mels.unsqueeze(1)
-        return mels, labels, ref_mels, ref2_mels, ref_labels, z_trg, z_trg2
-
-def build_dataloader(path_list,
-                     validation=False,
-                     batch_size=4,
-                     num_workers=1,
-                     collate_config={},
-                     dataset_config={}):
-
-    dataset = MelDataset(path_list, validation=validation)
-    collate_fn = Collater(**collate_config)
-    data_loader = DataLoader(dataset,
-                             batch_size=batch_size,
-                             shuffle=(not validation),
-                             num_workers=num_workers,
-                             drop_last=(not validation),
-                             collate_fn=collate_fn)
-
-    return data_loader
diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/util/get_tokenlizer.py b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/util/get_tokenlizer.py
deleted file mode 100644
index f7dcf7e95f03f95b20546b26442a94225924618b..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/util/get_tokenlizer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased":
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/gpt2_v2.cpp b/spaces/Illumotion/Koboldcpp/otherarch/gpt2_v2.cpp
deleted file mode 100644
index 33ca85e11547aee09db61e8c6d75a8703a069b2d..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/gpt2_v2.cpp
+++ /dev/null
@@ -1,653 +0,0 @@
-#include "ggml_v2.h"
-#include "otherarch.h"
-
-#include "utils.h"
-
-#include <cassert>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-#include <iostream>
-
-#include "model_adapter.h"
-
-
-// load the model's weights from a file
-ModelLoadResult gpt2_v2_model_load(const std::string & fname, gpt2_v2_model & model, gpt_vocab & vocab, FileFormat file_format, int gpulayers) {
-    printf("%s: loading model from '%s'\n", __func__, fname.c_str());
-
-    auto fin = std::ifstream(fname, std::ios::binary);
-    if (!fin) {
-        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
-        return ModelLoadResult::FAIL;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // load hparams
-    {
-        auto & hparams = model.hparams;
-
-        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        fin.read((char *) &hparams.ftype,   sizeof(hparams.ftype));
-
-        const int32_t qntvr = hparams.ftype / GGML_V2_QNT_VERSION_FACTOR;
-
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
-        printf("%s: qntvr   = %d\n", __func__, qntvr);
-
-        hparams.ftype %= GGML_V2_QNT_VERSION_FACTOR;
-    }
-
-    // load vocab
-    {
-        int32_t n_vocab = 0;
-        fin.read((char *) &n_vocab, sizeof(n_vocab));
-
-        if (n_vocab != model.hparams.n_vocab) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
-                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
-            return ModelLoadResult::FAIL;
-        }
-
-        std::string word;
-        std::vector<char> buf(128);
-
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            fin.read((char *) &len, sizeof(len));
-
-            buf.resize(len);
-            fin.read((char *) buf.data(), len);
-            word.assign(buf.data(), len);
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-        }
-    }
-
-    auto memory_type = GGML_V2_TYPE_F16;
-
-    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
-    // in order to save memory and also to speed up the computation
-    ggml_v2_type wtype = ggml_v2_ftype_to_ggml_v2_type((ggml_v2_ftype) (model.hparams.ftype));
-    if (wtype == GGML_V2_TYPE_COUNT) {
-        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
-                __func__, fname.c_str(), model.hparams.ftype);
-        return ModelLoadResult::FAIL;
-    }
-
-    auto & ctx = model.ctx;
-
-    size_t ctx_size = 0;
-
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        ctx_size += n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // ln_f_b
-
-        ctx_size += n_vocab*n_embd*ggml_v2_type_sizef(wtype);         // wte
-        ctx_size +=   n_ctx*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // wpe
-        ctx_size += n_vocab*n_embd*ggml_v2_type_sizef(wtype);         // lm_head
-
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_1_b
-
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_2_g
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_2_b
-
-        ctx_size += n_layer*(3*n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_attn_attn_w
-        ctx_size += n_layer*(       3*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_attn_attn_b
-
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v2_type_sizef(wtype));           // c_attn_proj_w
-        ctx_size += n_layer*(       n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32));   // c_attn_proj_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_mlp_fc_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_mlp_proj_w
-        ctx_size += n_layer*(         n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_mlp_proj_b
-
-        ctx_size += 1.5*(n_ctx*n_layer*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // memory_k
-        ctx_size += 1.5*(n_ctx*n_layer*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // memory_v
-
-        ctx_size += (6 + 12*n_layer)*512; // object overhead
-
-        printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_v2_tensor));
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
-    }
-
-    // create the ggml context
-    {
-        struct ggml_v2_init_params params;
-        params.mem_size   = ctx_size;
-        params.mem_buffer = NULL;
-        params.no_alloc   = false;
-
-
-        model.ctx = ggml_v2_init(params);
-        if (!model.ctx) {
-            fprintf(stderr, "%s: ggml_v2_init() failed\n", __func__);
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // prepare memory for the weights
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        model.layers.resize(n_layer);
-
-        model.ln_f_g = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, n_embd);
-        model.ln_f_b = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, n_embd);
-
-        model.wte     = ggml_v2_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-        model.wpe     = ggml_v2_new_tensor_2d(ctx, GGML_V2_TYPE_F32, n_embd, n_ctx);
-        model.lm_head = ggml_v2_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-
-        // map by name
-        model.tensors["model/ln_f/g"] = model.ln_f_g;
-        model.tensors["model/ln_f/b"] = model.ln_f_b;
-
-        model.tensors["model/wte"]     = model.wte;
-        model.tensors["model/wpe"]     = model.wpe;
-        model.tensors["model/lm_head"] = model.lm_head;
-
-        for (int i = 0; i < n_layer; ++i) {
-            auto & layer = model.layers[i];
-
-            layer.ln_1_g        = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-            layer.ln_1_b        = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            layer.ln_2_g        = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-            layer.ln_2_b        = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            layer.c_attn_attn_w = ggml_v2_new_tensor_2d(ctx, wtype,           n_embd, 3*n_embd);
-            layer.c_attn_attn_b = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, 3*n_embd);
-
-            layer.c_attn_proj_w = ggml_v2_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
-            layer.c_attn_proj_b = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            layer.c_mlp_fc_w    = ggml_v2_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
-            layer.c_mlp_fc_b    = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, 4*n_embd);
-
-            layer.c_mlp_proj_w  = ggml_v2_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
-            layer.c_mlp_proj_b  = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            // map by name
-            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
-            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
-            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w;
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
-        }
-    }
-
-    // key + value memory
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-
-        const int n_mem      = n_layer*n_ctx;
-        const int n_elements = n_embd*n_mem;
-
-        model.memory_k = ggml_v2_new_tensor_1d(ctx, memory_type, n_elements*1.5);
-        model.memory_v = ggml_v2_new_tensor_1d(ctx, memory_type, n_elements*1.5);
-
-        const size_t memory_size = ggml_v2_nbytes(model.memory_k) + ggml_v2_nbytes(model.memory_v);
-
-        printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
-    }
-
-    // load weights
-    {
-        size_t total_size = 0;
-
-        bool has_lm_head = false;
-
-        while (true) {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ttype;
-
-            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
-            fin.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
-
-            if (fin.eof()) {
-                break;
-            }
-
-            int32_t nelements = 1;
-            int32_t ne[2] = { 1, 1 };
-            for (int i = 0; i < n_dims; ++i) {
-                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            fin.read(&name[0], length);
-
-            if (model.tensors.find(name.data()) == model.tensors.end()) {
-                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            auto tensor = model.tensors[name.data()];
-            if (ggml_v2_nelements(tensor) != nelements) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
-                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%ld, %ld], expected [%d, %d]\n",
-                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
-                return ModelLoadResult::FAIL;
-            }
-
-            // for debugging
-            if (0) {
-                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_v2_type_name(ggml_v2_type(ttype)), ggml_v2_nbytes(tensor)/1024.0/1024.0, ggml_v2_nbytes(tensor));
-            }
-
-            const size_t bpe = ggml_v2_type_size(ggml_v2_type(ttype));
-
-            if ((nelements*bpe)/ggml_v2_blck_size(tensor->type) != ggml_v2_nbytes(tensor)) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
-                        __func__, name.data(), ggml_v2_nbytes(tensor), nelements*bpe);
-                return ModelLoadResult::FAIL;
-            }
-
-            fin.read(reinterpret_cast<char *>(tensor->data), ggml_v2_nbytes(tensor));
-
-            // GPT-2 models share the WTE tensor as the LM head
-            if (name == "model/wte" && has_lm_head == false) {
-                memcpy(model.lm_head->data, tensor->data, ggml_v2_nbytes(tensor));
-            }
-
-            if (name == "model/lm_head") {
-                has_lm_head = true;
-            }
-
-            total_size += ggml_v2_nbytes(tensor);
-        }
-
-        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
-    }
-
-    fin.close();
-
-
-
-    return ModelLoadResult::SUCCESS;
-}
-
-// evaluate the transformer
-//
-//   - model:     the model
-//   - n_threads: number of threads to use
-//   - n_past:    the context size so far
-//   - embd_inp:  the embeddings of the tokens in the context
-//   - embd_w:    the predicted logits for the next token
-//
-bool gpt2_v2_eval(
-        const gpt2_v2_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<gpt_vocab::id> & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token,
-              FileFormat file_format) {
-    const int N = embd_inp.size();
-
-    const auto & hparams = model.hparams;
-
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_head  = hparams.n_head;
-    const int n_vocab = hparams.n_vocab;
-
-    static size_t buf_size = 256u*1024*1024;
-    static void * buf = malloc(buf_size);
-
-    if (mem_per_token > 0 && (mem_per_token*N*2 + 64u*1024*1024) > buf_size) {
-        const size_t buf_size_new = 320u*1024*1024 + 2*(mem_per_token*N); // add 10% to account for ggml object overhead
-        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
-        // reallocate
-        if (buf_size_new > buf_size)
-        {
-            buf_size = buf_size_new;
-            buf = realloc(buf, buf_size);
-            if (buf == nullptr)
-            {
-                fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
-                return false;
-            }
-        }
-    }
-
-    struct ggml_v2_init_params params;
-    params.mem_size   = buf_size;
-    params.mem_buffer = buf;
-    params.no_alloc   = false;
-
-
-    struct ggml_v2_context * ctx0 = ggml_v2_init(params);
-    struct ggml_v2_cgraph gf = {};
-    gf.n_threads = n_threads;
-
-    struct ggml_v2_tensor * embd = ggml_v2_new_tensor_1d(ctx0, GGML_V2_TYPE_I32, N);
-    memcpy(embd->data, embd_inp.data(), N*ggml_v2_element_size(embd));
-
-    struct ggml_v2_tensor * position = ggml_v2_new_tensor_1d(ctx0, GGML_V2_TYPE_I32, N);
-    for (int i = 0; i < N; ++i) {
-        ((int32_t *) position->data)[i] = n_past + i;
-    }
-
-    // wte + wpe
-    struct ggml_v2_tensor * inpL =
-        ggml_v2_add(ctx0,
-                ggml_v2_get_rows(ctx0, model.wte, embd),
-                ggml_v2_get_rows(ctx0, model.wpe, position));
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_v2_tensor * cur;
-
-        // norm
-        {
-            // [ 768, N]
-            cur = ggml_v2_norm(ctx0, inpL);
-
-            // cur = ln_1_g*cur + ln_1_b
-            // [ 768, N]
-            cur = ggml_v2_add(ctx0,
-                    ggml_v2_mul(ctx0,
-                        ggml_v2_repeat(ctx0, model.layers[il].ln_1_g, cur),
-                        cur),
-                    ggml_v2_repeat(ctx0, model.layers[il].ln_1_b, cur));
-        }
-
-        // attn
-        // [2304, 768] - model.layers[il].c_attn_attn_w
-        // [2304,   1] - model.layers[il].c_attn_attn_b
-        // [ 768,   N] - cur (in)
-        // [2304,   N] - cur (out)
-        //
-        // cur = attn_w*cur + attn_b
-        // [2304, N]
-        {
-            cur = ggml_v2_mul_mat(ctx0,
-                    model.layers[il].c_attn_attn_w,
-                    cur);
-
-            cur = ggml_v2_add(ctx0,
-                    ggml_v2_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
-                    cur);
-        }
-
-        // self-attention
-        {
-            struct ggml_v2_tensor * Qcur = ggml_v2_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
-            struct ggml_v2_tensor * Kcur = ggml_v2_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
-            struct ggml_v2_tensor * Vcur = ggml_v2_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
-
-            // store key and value to memory
-            if (N >= 1) {
-                struct ggml_v2_tensor * k = ggml_v2_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_v2_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_v2_tensor * v = ggml_v2_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_v2_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
-
-                ggml_v2_build_forward_expand(&gf, ggml_v2_cpy(ctx0, Kcur, k));
-                ggml_v2_build_forward_expand(&gf, ggml_v2_cpy(ctx0, Vcur, v));
-            }
-
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
-            // [64, N, 12]
-            struct ggml_v2_tensor * Q =
-                ggml_v2_permute(ctx0,
-                        ggml_v2_cpy(ctx0,
-                            Qcur,
-                            ggml_v2_new_tensor_3d(ctx0, GGML_V2_TYPE_F32, n_embd/n_head, n_head, N)),
-                        0, 2, 1, 3);
-
-            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
-            // [64, n_past + N, 12]
-            struct ggml_v2_tensor * K =
-                ggml_v2_permute(ctx0,
-                        ggml_v2_reshape_3d(ctx0,
-                            ggml_v2_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_v2_element_size(model.memory_k)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        0, 2, 1, 3);
-
-            // GG: flash attention
-            //struct ggml_v2_tensor * V =
-            //    ggml_v2_cpy(ctx0,
-            //            ggml_v2_permute(ctx0,
-            //                ggml_v2_reshape_3d(ctx0,
-            //                    ggml_v2_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v2_element_size(model.memory_v)*n_embd),
-            //                    n_embd/n_head, n_head, n_past + N),
-            //                1, 2, 0, 3),
-            //            ggml_v2_new_tensor_3d(ctx0, GGML_V2_TYPE_F32, n_past + N, n_embd/n_head, n_head));
-
-            //struct ggml_v2_tensor * KQV = ggml_v2_flash_attn(ctx0, Q, K, V, true);
-
-            // K * Q
-            // [n_past + N, N, 12]
-            struct ggml_v2_tensor * KQ = ggml_v2_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            // [n_past + N, N, 12]
-            struct ggml_v2_tensor * KQ_scaled =
-                ggml_v2_scale_inplace(ctx0,
-                        KQ,
-                        ggml_v2_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
-
-            // KQ_masked = mask_past(KQ_scaled)
-            // [n_past + N, N, 12]
-            struct ggml_v2_tensor * KQ_masked = ggml_v2_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            // [n_past + N, N, 12]
-            struct ggml_v2_tensor * KQ_soft_max = ggml_v2_soft_max_inplace(ctx0, KQ_masked);
-
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            // [n_past + N, 64, 12]
-            struct ggml_v2_tensor * V_trans =
-                ggml_v2_cpy(ctx0,
-                        ggml_v2_permute(ctx0,
-                            ggml_v2_reshape_3d(ctx0,
-                                ggml_v2_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v2_element_size(model.memory_v)*n_embd),
-                                n_embd/n_head, n_head, n_past + N),
-                            1, 2, 0, 3),
-                        ggml_v2_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
-
-            // KQV = transpose(V) * KQ_soft_max
-            // [64, N, 12]
-            struct ggml_v2_tensor * KQV = ggml_v2_mul_mat(ctx0, V_trans, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            // [64, 12, N]
-            struct ggml_v2_tensor * KQV_merged = ggml_v2_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            // [768, N]
-            cur = ggml_v2_cpy(ctx0,
-                    KQV_merged,
-                    ggml_v2_new_tensor_2d(ctx0, GGML_V2_TYPE_F32, n_embd, N));
-        }
-
-        // projection
-        // [ 768, 768] - model.layers[il].c_attn_proj_w
-        // [ 768,   1] - model.layers[il].c_attn_proj_b
-        // [ 768,   N] - cur (in)
-        // [ 768,   N] - cur (out)
-        //
-        // cur = proj_w*cur + proj_b
-        // [768, N]
-        {
-            cur = ggml_v2_mul_mat(ctx0,
-                    model.layers[il].c_attn_proj_w,
-                    cur);
-
-            cur = ggml_v2_add(ctx0,
-                    ggml_v2_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
-                    cur);
-        }
-
-        // add the input
-        cur = ggml_v2_add(ctx0, cur, inpL);
-
-        struct ggml_v2_tensor * inpFF = cur;
-
-        // feed-forward network
-        {
-            // norm
-            {
-                cur = ggml_v2_norm(ctx0, inpFF);
-
-                // cur = ln_2_g*cur + ln_2_b
-                // [ 768, N]
-                cur = ggml_v2_add(ctx0,
-                        ggml_v2_mul(ctx0,
-                            ggml_v2_repeat(ctx0, model.layers[il].ln_2_g, cur),
-                            cur),
-                        ggml_v2_repeat(ctx0, model.layers[il].ln_2_b, cur));
-            }
-
-            // fully connected
-            // [3072, 768] - model.layers[il].c_mlp_fc_w
-            // [3072,   1] - model.layers[il].c_mlp_fc_b
-            // [ 768,   N] - cur (in)
-            // [3072,   N] - cur (out)
-            //
-            // cur = fc_w*cur + fc_b
-            // [3072, N]
-            cur = ggml_v2_mul_mat(ctx0,
-                    model.layers[il].c_mlp_fc_w,
-                    cur);
-
-            cur = ggml_v2_add(ctx0,
-                    ggml_v2_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
-                    cur);
-
-            // GELU activation
-            // [3072, N]
-            cur = ggml_v2_gelu(ctx0, cur);
-
-            // projection
-            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
-            // [ 768,    1] - model.layers[il].c_mlp_proj_b
-            // [3072,    N] - cur (in)
-            // [ 768,    N] - cur (out)
-            //
-            // cur = proj_w*cur + proj_b
-            // [768, N]
-            cur = ggml_v2_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w,
-                    cur);
-
-            cur = ggml_v2_add(ctx0,
-                    ggml_v2_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
-                    cur);
-        }
-
-        // input for next layer
-        inpL = ggml_v2_add(ctx0, cur, inpFF);
-    }
-
-    // norm
-    {
-        // [ 768, N]
-        inpL = ggml_v2_norm(ctx0, inpL);
-
-        // inpL = ln_f_g*inpL + ln_f_b
-        // [ 768, N]
-        inpL = ggml_v2_add(ctx0,
-                ggml_v2_mul(ctx0,
-                    ggml_v2_repeat(ctx0, model.ln_f_g, inpL),
-                    inpL),
-                ggml_v2_repeat(ctx0, model.ln_f_b, inpL));
-    }
-
-    // inpL = WTE * inpL
-    // [ 768, 50257] - model.lm_head
-    // [ 768, N]     - inpL
-    inpL = ggml_v2_mul_mat(ctx0, model.lm_head, inpL);
-
-    // logits -> probs
-    //inpL = ggml_v2_soft_max_inplace(ctx0, inpL);
-
-    // run the computation
-    ggml_v2_build_forward_expand(&gf, inpL);
-    ggml_v2_graph_compute       (ctx0, &gf);
-
-    //if (n_past%100 == 0) {
-    //    ggml_v2_graph_print   (&gf);
-    //    ggml_v2_graph_dump_dot(&gf, NULL, "gpt-2.dot");
-    //}
-
-    //embd_w.resize(n_vocab*N);
-    //memcpy(embd_w.data(), ggml_v2_get_data(inpL), sizeof(float)*n_vocab*N);
-
-    // return result just for the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_v2_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_v2_used_mem(ctx0)/N;
-    }
-    //printf("used_mem = %zu\n", ggml_v2_used_mem(ctx0));
-
-    ggml_v2_free(ctx0);
-
-    return true;
-}
\ No newline at end of file
diff --git a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op_gpu/fused_act.py b/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op_gpu/fused_act.py
deleted file mode 100644
index 815eca1905b7962a2314f6af3b3ab5daeb74a009..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op_gpu/fused_act.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-
-module_path = os.path.dirname(__file__)
-fused = load(
-    "fused",
-    sources=[
-        os.path.join(module_path, "fused_bias_act.cpp"),
-        os.path.join(module_path, "fused_bias_act_kernel.cu"),
-    ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, bias, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused.fused_bias_act(
-            grad_output.contiguous(), empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        if bias:
-            grad_bias = grad_input.sum(dim).detach()
-
-        else:
-            grad_bias = empty
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused.fused_bias_act(
-            gradgrad_input.contiguous(), gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
-        )
-
-        return gradgrad_out, None, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-
-        ctx.bias = bias is not None
-
-        if bias is None:
-            bias = empty
-
-        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale
-        )
-
-        if not ctx.bias:
-            grad_bias = None
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(channel))
-
-        else:
-            self.bias = None
-
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
-    if input.device.type == "cpu":
-        if bias is not None:
-            rest_dim = [1] * (input.ndim - bias.ndim - 1)
-            return (
-                F.leaky_relu(
-                    input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
-                )
-                * scale
-            )
-
-        else:
-            return F.leaky_relu(input, negative_slope=0.2) * scale
-
-    else:
-        return FusedLeakyReLUFunction.apply(input.contiguous(), bias, negative_slope, scale)
diff --git a/spaces/JairParra/Captioning_and_Stable_Diffusion_Generation/README.md b/spaces/JairParra/Captioning_and_Stable_Diffusion_Generation/README.md
deleted file mode 100644
index 10fef57b54bc9a805cbddb2c9b353676038dff4a..0000000000000000000000000000000000000000
--- a/spaces/JairParra/Captioning_and_Stable_Diffusion_Generation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Captioning_and_Stable_Diffusion_Generation
-emoji: ⚡
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: bsl-1.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/JeffTao/anime-remove-background/app.py b/spaces/JeffTao/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/JeffTao/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/JoeyFoursheds/ClonerHug/app.py b/spaces/JoeyFoursheds/ClonerHug/app.py
deleted file mode 100644
index 5a15ae6daaa4e82d2838eacc5cdfd40a8bb11aa6..0000000000000000000000000000000000000000
--- a/spaces/JoeyFoursheds/ClonerHug/app.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import os
-import argparse
-import logging
-logging.getLogger("numba").setLevel(logging.ERROR)
-import gradio as gr
-import model_prepare
-import voice_cloner
-#import whisper
-import librosa
-
-
-if __name__ == '__main__':
-
-    files=False
-    model_cloners = []
-    
-
-    # ... OR Load ALL model_infos from weights/model_info.json
-    model_infos = model_prepare.get_all_model_infos()
-    for model_info in model_infos:
-        # print ("\nname: ", model_info.name, "\n")
-        # get the info for this model 
-        # name, title, author, image, feature_retrieval_index, npy, target_sr, if_fund_freq, is_half, gen_model = model_prepare.unpack_model_info(model_info)
-        name, title, author, image, feature_retrieval_index, npy, target_sr, if_fund_freq, is_half, gen_model = model_prepare.get_model_info(model_info.name)
-        # build a cloner  for this model
-        model_cloner = voice_cloner.create_voice_cloner(target_sr, gen_model, if_fund_freq, feature_retrieval_index, npy, is_half, files=files)
-       # add the model and cloner function to the list of models
-        model_cloners.append((name, title, author, image, model_cloner))
-        print ("\nname: ", model_info.name)
-
-    num_models = len(model_cloners)
-    print("num_models: ", num_models)
-
-    #-------------------------------------------------------------------------
-    #                          Web Page Layout
-    #       Create the layout in different block sections using Gradio
-    #-------------------------------------------------------------------------
-    with gr.Blocks() as app:
-
-        #--------   Headings  ---------
-        gr.Markdown(
-            "# <center> Voice Cloner\n"
-            "## <center> Upload a solo voice sample.\n"
-            "<div style='text-align: right; font-size: 16px; margin-left: 10%; '>"
-            "<a href='http://ai.muringer.com/'>AI Muringer</a><br/> "
-            "<a href='https://AudioNetAI.com/'>AudioNet AI</a>"
-            "</div>"                                                
-            '<div align="center">'
-            f'<div>{title}</div>\n'+
-            #(f'<div>Model author: &nbsp; {author}</div>' if author else "")+
-            (f'<img style="width:auto;height:300px;" src="file/{"waveform_pic.png"}">' if "waveform_pic.png" else "")+
-            '</div>'
-        )
-        # Make a row of 2 Web Layout Colums:  Input stuff   |   Output stuff
-        with gr.Row():
-            #---------------------------
-            #       Input column
-            #---------------------------
-            with gr.Column(variant="panel"):
-                gr.Markdown(
-                  "\n <center>.   \n"
-                  "  \n\n\n # <center> Input"  
-                  "\n#### <center>[Upload a solo voice sample: max 20 seconds])\n"  
-                  "<br/>"
-                )
-
-                example_audio = gr.Audio(label="Example: calvin_harris_-_how_deep_is_your_love.wav", value="calvin_harris_-_how_deep_is_your_love.wav")
-                uploaded_audio = gr.Audio(label="Input audio (max 20 seconds)")
-
-                vc_input_type_radio = gr.Radio(
-                    label="Choose the example file  or upload your own audio file",
-                    choices=["Example", "Uploaded"],
-                    value="Example",
-                    interactive=True,
-                )
-            #---------------------------
-            #       Outputs column
-            #---------------------------
-            with gr.Column(variant="default"):
-                gr.Markdown(
-                    "\n <center>.   \n"
-                    "  \n\n\n # <center> Output"  
-                    "\n#### <center>[Revoiced as another person]\n"  
-                    )
-                for i in range(num_models): 
-                    with gr.Row(variant="panel"):
-                        name, title, author, image, model_cloner = model_cloners[i]
-                        with gr.Row(variant="panel"):
-                            
-                            #gr.Button.style(self, full_width = True)
-                            #--------   Model Button  ---------
-                            apply_model_button = gr.Button(f"{name} ", variant="primary")
-                            
-                            #--------   Transpose Slider  ---------
-                            vc_transpose = 0
-                            vc_transpose = gr.Slider(
-                                minimum=-12,
-                                maximum=12,
-                                step=1,
-                                label="Transpose",
-                                value=0,
-                                interactive=True,
-                            )
-                            with gr.Box(visible=False):
-                                #--------   Algo  ---------
-                                vc_f0method = "Praat"
-                                vc_f0method = gr.Radio(
-                                    label="Pitch algorithhm ",
-                                    choices=["Praat", "Harvest"],
-                                    value="Praat",
-                                    interactive=True,
-                                )
-                                vc_f0method.update(visible=False)
-                                #--------   Feature Ratio  ---------
-                                vc_feature_ratio = 0.6
-                                vc_feature_ratio = gr.Slider(
-                                    minimum=0,
-                                    maximum=1,
-                                    label="Retrieval feature ratio",
-                                    value=0.6,
-                                    interactive=True,
-                                )
-                                vc_feature_ratio.update(visible=False)
-
-                            # gr.Markdown(
-                            #     (f'<div>Model author: &nbsp; {author}</div>' if author else "")
-                            # )
-
-                            cloner_text_output = gr.Textbox(label=f"[Author:              {author}]")
-                            cloner_audio_ouput = gr.Audio(label="Output Audio")
-                            
-                            function = model_cloner
-
-                            inputs = [uploaded_audio, example_audio, vc_transpose, vc_f0method, vc_feature_ratio, vc_input_type_radio]
-                            outputs = [cloner_text_output, cloner_audio_ouput]
-                            # Finally we have eveything we need to create the app: (function, inputs, outputs)
-                            apply_model_button.click(function, inputs=inputs, outputs=outputs) 
-    #----------------------------------  End of Layout  -------------------------------------------
-
-    api_open=False
-    share = False
-    app.queue(concurrency_count=1, max_size=20, api_open=api_open).launch(share=share)
\ No newline at end of file
diff --git a/spaces/K00B404/langchain-llama2-7b-chat-uncensored-ggml/README.md b/spaces/K00B404/langchain-llama2-7b-chat-uncensored-ggml/README.md
deleted file mode 100644
index 3814de909f3af6e2940f69b49c2afb4ae67f2c29..0000000000000000000000000000000000000000
--- a/spaces/K00B404/langchain-llama2-7b-chat-uncensored-ggml/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: langchain-llama2-7b-chat-uncensored-ggml
-emoji: 🚀
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-duplicated_from: mikeee/langchain-llama2-7b-chat-uncensored-ggml
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/KPCGD/bingo/src/components/chat-panel.tsx b/spaces/KPCGD/bingo/src/components/chat-panel.tsx
deleted file mode 100644
index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/components/chat-panel.tsx
+++ /dev/null
@@ -1,153 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import Image from 'next/image'
-import Textarea from 'react-textarea-autosize'
-import { useAtomValue } from 'jotai'
-import { useEnterSubmit } from '@/lib/hooks/use-enter-submit'
-import { cn } from '@/lib/utils'
-
-import BrushIcon from '@/assets/images/brush.svg'
-import ChatIcon from '@/assets/images/chat.svg'
-import VisualSearchIcon from '@/assets/images/visual-search.svg'
-import SendIcon from '@/assets/images/send.svg'
-import PinIcon from '@/assets/images/pin.svg'
-import PinFillIcon from '@/assets/images/pin-fill.svg'
-
-import { useBing } from '@/lib/hooks/use-bing'
-import { voiceListenAtom } from '@/state'
-import Voice from './voice'
-import { ChatImage } from './chat-image'
-import { ChatAttachments } from './chat-attachments'
-
-export interface ChatPanelProps
-  extends Pick<
-    ReturnType<typeof useBing>,
-    | 'generating'
-    | 'input'
-    | 'setInput'
-    | 'sendMessage'
-    | 'resetConversation'
-    | 'isSpeaking'
-    | 'attachmentList'
-    | 'uploadImage'
-    | 'setAttachmentList'
-  > {
-  id?: string
-  className?: string
-}
-
-export function ChatPanel({
-  isSpeaking,
-  generating,
-  input,
-  setInput,
-  className,
-  sendMessage,
-  resetConversation,
-  attachmentList,
-  uploadImage,
-  setAttachmentList
-}: ChatPanelProps) {
-  const inputRef = React.useRef<HTMLTextAreaElement>(null)
-  const {formRef, onKeyDown} = useEnterSubmit()
-  const [focused, setFocused] = React.useState(false)
-  const [active, setActive] = React.useState(false)
-  const [pin, setPin] = React.useState(false)
-  const [tid, setTid] = React.useState<any>()
-  const voiceListening = useAtomValue(voiceListenAtom)
-
-  const setBlur = React.useCallback(() => {
-    clearTimeout(tid)
-    setActive(false)
-    const _tid = setTimeout(() => setFocused(false), 2000);
-    setTid(_tid)
-  }, [tid])
-
-  const setFocus = React.useCallback(() => {
-    setFocused(true)
-    setActive(true)
-    clearTimeout(tid)
-    inputRef.current?.focus()
-  }, [tid])
-
-  React.useEffect(() => {
-    if (input) {
-      setFocus()
-    }
-  }, [input])
-
-  return (
-    <form
-      className={cn('chat-panel', className)}
-      onSubmit={async e => {
-        e.preventDefault()
-        if (generating) {
-          return;
-        }
-        if (!input?.trim()) {
-          return
-        }
-        setInput('')
-        setPin(false)
-        await sendMessage(input)
-      }}
-      ref={formRef}
-    >
-      <div className="action-bar pb-4">
-        <div className={cn('action-root', { focus: active || pin })} speech-state="hidden" visual-search="" drop-target="">
-          <div className="fade bottom">
-            <div className="background"></div>
-          </div>
-          <div className={cn('outside-left-container', { collapsed: focused })}>
-            <div className="button-compose-wrapper">
-              <button className="body-2 button-compose" type="button" aria-label="新主题" onClick={resetConversation}>
-                <div className="button-compose-content">
-                  <Image className="pl-2" alt="brush" src={BrushIcon} width={40} />
-                  <div className="button-compose-text">新主题</div>
-                </div>
-              </button>
-            </div>
-          </div>
-          <div
-            className={cn('main-container', { active: active || pin })}
-            style={{ minHeight: pin ? '360px' : undefined }}
-            onClick={setFocus}
-            onBlur={setBlur}
-          >
-            <div className="main-bar">
-              <Image alt="chat" src={ChatIcon} width={20} color="blue" />
-              <Textarea
-                ref={inputRef}
-                tabIndex={0}
-                onKeyDown={onKeyDown}
-                rows={1}
-                value={input}
-                onChange={e => setInput(e.target.value.slice(0, 4000))}
-                placeholder={voiceListening ? '持续对话中...对话完成说“发送”即可' : 'Shift + Enter 换行'}
-                spellCheck={false}
-                className="message-input min-h-[24px] -mx-1 w-full text-base resize-none bg-transparent focus-within:outline-none"
-              />
-              <ChatImage uploadImage={uploadImage}>
-                <Image alt="visual-search" src={VisualSearchIcon} width={24} />
-              </ChatImage>
-              <Voice setInput={setInput} sendMessage={sendMessage} isSpeaking={isSpeaking} input={input} />
-              <button type="submit">
-                <Image alt="send" src={SendIcon} width={20} style={{ marginTop: '2px' }} />
-              </button>
-            </div>
-            <ChatAttachments attachmentList={attachmentList} setAttachmentList={setAttachmentList} uploadImage={uploadImage} />
-            <div className="body-1 bottom-bar">
-              <div className="letter-counter"><span>{input.length}</span>/4000</div>
-              <button onClick={() => {
-                setPin(!pin)
-              }} className="pr-2">
-               <Image alt="pin" src={pin ? PinFillIcon : PinIcon} width={20} />
-              </button>
-            </div>
-          </div>
-        </div>
-      </div>
-    </form>
-  )
-}
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/load_yaml.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/load_yaml.py
deleted file mode 100644
index 5792ff471dc63bacc8c27a7bcc2d4bd6f1e35da8..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/load_yaml.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import yaml
-
-
-def load_hparams(filename):
-    stream = open(filename, 'r')
-    docs = yaml.safe_load_all(stream)
-    hparams_dict = dict()
-    for doc in docs:
-        for k, v in doc.items():
-            hparams_dict[k] = v
-    return hparams_dict
-
-def merge_dict(user, default):
-    if isinstance(user, dict) and isinstance(default, dict):
-        for k, v in default.items():
-            if k not in user:
-                user[k] = v
-            else:
-                user[k] = merge_dict(user[k], v)
-    return user
-
-class Dotdict(dict):
-    """
-    a dictionary that supports dot notation 
-    as well as dictionary access notation 
-    usage: d = DotDict() or d = DotDict({'val1':'first'})
-    set attributes: d.val2 = 'second' or d['val2'] = 'second'
-    get attributes: d.val2 or d['val2']
-    """
-    __getattr__ = dict.__getitem__
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-    def __init__(self, dct=None):
-        dct = dict() if not dct else dct
-        for key, value in dct.items():
-            if hasattr(value, 'keys'):
-                value = Dotdict(value)
-            self[key] = value
-
-class HpsYaml(Dotdict):
-    def __init__(self, yaml_file):
-        super(Dotdict, self).__init__()
-        hps = load_hparams(yaml_file)
-        hp_dict = Dotdict(hps)
-        for k, v in hp_dict.items():
-            setattr(self, k, v)
-
-    __getattr__ = Dotdict.__getitem__
-    __setattr__ = Dotdict.__setitem__
-    __delattr__ = Dotdict.__delitem__
-    
-
-
-
-
-
-
diff --git a/spaces/Kevin676/NLLB-Translator/README.md b/spaces/Kevin676/NLLB-Translator/README.md
deleted file mode 100644
index 03fa610a490820c4186cabb87ac13dcd6c3e04ee..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/NLLB-Translator/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: NLLB Translator
-emoji: 🗺️
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: wtfpl
-duplicated_from: Narrativaai/NLLB-Translator
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kevin676/NLLB-Translator/app.py b/spaces/Kevin676/NLLB-Translator/app.py
deleted file mode 100644
index b47dff0d8da86adeb0e4b7ad5946e3d469691e8a..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/NLLB-Translator/app.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-import torch
-from langs import LANGS
-
-import numpy as np
-import modin.pandas as pd
-from PIL import Image
-from diffusers import DiffusionPipeline #EulerDiscreteScheduler
-
-device1 = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", safety_checker=None)
-pipe = pipe.to(device1)
-
-pipe1 = pipeline('text-generation', model='daspartho/prompt-extend')
-
-TASK = "translation"
-CKPT = "facebook/nllb-200-distilled-600M"
-
-model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
-tokenizer = AutoTokenizer.from_pretrained(CKPT)
-
-device = 0 if torch.cuda.is_available() else -1
-
-
-def translate(text):
-    """
-    Translate the text from source lang to target lang
-    """
-    src_lang = "zho-Hans"
-    tgt_lang = "eng_Latn"
-    max_length = 400
-    
-    translation_pipeline = pipeline(TASK,
-                                    model=model,
-                                    tokenizer=tokenizer,
-                                    src_lang=src_lang,
-                                    tgt_lang=tgt_lang,
-                                    max_length=max_length,
-                                    device=device)
-
-    result = translation_pipeline(text)
-    return result[0]['translation_text']
-
-#prompt
-
-def extend_prompt(prompt):
-    return pipe1(prompt+',', num_return_sequences=1)[0]["generated_text"]
-
-#stable diffusion
-
-def genie (prompt, negative_prompt, scale, steps, seed):
-     generator = torch.Generator(device=device1).manual_seed(seed)
-     images = pipe(prompt, negative_prompt=negative_prompt, width=768, height=768, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator).images[0]
-     return images
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-            """ # <center>🥳💬💕 - Stable Diffusion All in One，随时随地，想画就画！</center>
-            
-            ### <center>🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！山水AI - Enable the future！</center>
-            
-        """
-    )
-    
-    with gr.Row().style(equal_height=True):
-        inp1 = gr.Textbox(label = "请输入中文提示词", lines=1)
-        button = gr.Button("翻译成英文吧")
-        out1 = gr.Textbox(label = "翻译后的英文提示词", lines=1)
-    
-    button.click(translate, [inp1], [out1])
-    
-    with gr.Row().style(equal_height=True):
-        inp2 = out1
-        btn1 = gr.Button("让您的提示词更详细一些吧")
-        out2 = gr.Textbox(label = "更详细的英文提示词(可编辑此内容)", lines=1)
-    
-    btn1.click(extend_prompt, [inp2], [out2])
-
-    with gr.Row().style(equal_height=True):
-        inp3 = out2
-        inp4 = gr.Textbox(label="反向关键词：删除不想要的图片特点(选填)", placeholder = "low quality, illustration", lines=1)
-        inp5 = gr.Slider(1, 25, 10, label="提示词的符合度", info="数值越大，图片越符合提示词，但同时画质会下降")
-        inp6 = gr.Slider(30, maximum=100, value=50, step=1, label="生成图片的步数", info="数值越大，图片越清晰，但运行时间会变长")
-        inp7 = gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label="seed", info="生成一个随机数")
-
-    with gr.Row().style(equal_height=True):
-        btn2 = gr.Button("开始生成图片吧")
-        out3 = gr.Image(label="为您生成的专属图片")
-
-    btn2.click(genie, [inp3, inp4, inp5, inp6, inp7], [out3])
-    
-    gr.Markdown(
-            """ ### <center>注意❗：请不要输入或生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及娱乐使用。用户输入或生成的内容与程序开发者无关，请自觉合法合规使用，违反者一切后果自负。</center>
-            
-            ### <center>Powered by [Stable Diffusion](https://github.com/Stability-AI/stablediffusion). Thanks to [Manjushri](https://huggingface.co/Manjushri).</center>
-            
-        """
-    )
-        
-    gr.HTML('''
-        <div class="footer">
-                    <p>🎶🖼️🎡 - It’s the intersection of technology and liberal arts that makes our hearts sing. - Steve Jobs
-                    </p>
-        </div>
-    ''')     
-
-demo.queue().launch(show_error=True)
\ No newline at end of file
diff --git a/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/train.py b/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/train.py
deleted file mode 100644
index 619952e8de6c390912fe341403a39169592e585d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/train.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from encoder.visualizations import Visualizations
-from encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset
-from encoder.params_model import *
-from encoder.model import SpeakerEncoder
-from utils.profiler import Profiler
-from pathlib import Path
-import torch
-
-def sync(device: torch.device):
-    # For correct profiling (cuda operations are async)
-    if device.type == "cuda":
-        torch.cuda.synchronize(device)
-    
-
-def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int,
-          backup_every: int, vis_every: int, force_restart: bool, visdom_server: str,
-          no_visdom: bool):
-    # Create a dataset and a dataloader
-    dataset = SpeakerVerificationDataset(clean_data_root)
-    loader = SpeakerVerificationDataLoader(
-        dataset,
-        speakers_per_batch,
-        utterances_per_speaker,
-        num_workers=8,
-    )
-    
-    # Setup the device on which to run the forward pass and the loss. These can be different, 
-    # because the forward pass is faster on the GPU whereas the loss is often (depending on your
-    # hyperparameters) faster on the CPU.
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    # FIXME: currently, the gradient is None if loss_device is cuda
-    loss_device = torch.device("cpu")
-    
-    # Create the model and the optimizer
-    model = SpeakerEncoder(device, loss_device)
-    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init)
-    init_step = 1
-    
-    # Configure file path for the model
-    state_fpath = models_dir.joinpath(run_id + ".pt")
-    backup_dir = models_dir.joinpath(run_id + "_backups")
-
-    # Load any existing model
-    if not force_restart:
-        if state_fpath.exists():
-            print("Found existing model \"%s\", loading it and resuming training." % run_id)
-            checkpoint = torch.load(state_fpath)
-            init_step = checkpoint["step"]
-            model.load_state_dict(checkpoint["model_state"])
-            optimizer.load_state_dict(checkpoint["optimizer_state"])
-            optimizer.param_groups[0]["lr"] = learning_rate_init
-        else:
-            print("No model \"%s\" found, starting training from scratch." % run_id)
-    else:
-        print("Starting the training from scratch.")
-    model.train()
-    
-    # Initialize the visualization environment
-    vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom)
-    vis.log_dataset(dataset)
-    vis.log_params()
-    device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU")
-    vis.log_implementation({"Device": device_name})
-    
-    # Training loop
-    profiler = Profiler(summarize_every=10, disabled=False)
-    for step, speaker_batch in enumerate(loader, init_step):
-        profiler.tick("Blocking, waiting for batch (threaded)")
-        
-        # Forward pass
-        inputs = torch.from_numpy(speaker_batch.data).to(device)
-        sync(device)
-        profiler.tick("Data to %s" % device)
-        embeds = model(inputs)
-        sync(device)
-        profiler.tick("Forward pass")
-        embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device)
-        loss, eer = model.loss(embeds_loss)
-        sync(loss_device)
-        profiler.tick("Loss")
-
-        # Backward pass
-        model.zero_grad()
-        loss.backward()
-        profiler.tick("Backward pass")
-        model.do_gradient_ops()
-        optimizer.step()
-        profiler.tick("Parameter update")
-        
-        # Update visualizations
-        # learning_rate = optimizer.param_groups[0]["lr"]
-        vis.update(loss.item(), eer, step)
-        
-        # Draw projections and save them to the backup folder
-        if umap_every != 0 and step % umap_every == 0:
-            print("Drawing and saving projections (step %d)" % step)
-            backup_dir.mkdir(exist_ok=True)
-            projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step))
-            embeds = embeds.detach().cpu().numpy()
-            vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath)
-            vis.save()
-
-        # Overwrite the latest version of the model
-        if save_every != 0 and step % save_every == 0:
-            print("Saving the model (step %d)" % step)
-            torch.save({
-                "step": step + 1,
-                "model_state": model.state_dict(),
-                "optimizer_state": optimizer.state_dict(),
-            }, state_fpath)
-            
-        # Make a backup
-        if backup_every != 0 and step % backup_every == 0:
-            print("Making a backup (step %d)" % step)
-            backup_dir.mkdir(exist_ok=True)
-            backup_fpath = backup_dir.joinpath("%s_bak_%06d.pt" % (run_id, step))
-            torch.save({
-                "step": step + 1,
-                "model_state": model.state_dict(),
-                "optimizer_state": optimizer.state_dict(),
-            }, backup_fpath)
-            
-        profiler.tick("Extras (visualizations, saving)")
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/plot/locator.py b/spaces/Lianjd/stock_dashboard/backtrader/plot/locator.py
deleted file mode 100644
index d0381ccdd03d5229f9a7a26aa0469c8a82426cee..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/plot/locator.py
+++ /dev/null
@@ -1,248 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-'''
-Redefine/Override matplotlib locators to make them work with index base x axis
-which can be converted from/to dates
-'''
-
-import datetime
-import warnings
-
-from matplotlib.dates import AutoDateLocator as ADLocator
-from matplotlib.dates import RRuleLocator as RRLocator
-from matplotlib.dates import AutoDateFormatter as ADFormatter
-
-from matplotlib.dates import (HOURS_PER_DAY, MIN_PER_HOUR, SEC_PER_MIN,
-                              MONTHS_PER_YEAR, DAYS_PER_WEEK,
-                              SEC_PER_HOUR, SEC_PER_DAY,
-                              num2date, rrulewrapper, YearLocator,
-                              MicrosecondLocator)
-
-from dateutil.relativedelta import relativedelta
-import numpy as np
-
-
-def _idx2dt(idx, dates, tz):
-    if isinstance(idx, datetime.date):
-        return idx
-
-    ldates = len(dates)
-
-    idx = int(round(idx))
-    if idx >= ldates:
-        idx = ldates - 1
-    if idx < 0:
-        idx = 0
-
-    return num2date(dates[idx], tz)
-
-
-class RRuleLocator(RRLocator):
-
-    def __init__(self, dates, o, tz=None):
-        self._dates = dates
-        super(RRuleLocator, self).__init__(o, tz)
-
-    def datalim_to_dt(self):
-        """
-        Convert axis data interval to datetime objects.
-        """
-        dmin, dmax = self.axis.get_data_interval()
-        if dmin > dmax:
-            dmin, dmax = dmax, dmin
-
-        return (_idx2dt(dmin, self._dates, self.tz),
-                _idx2dt(dmax, self._dates, self.tz))
-
-    def viewlim_to_dt(self):
-        """
-        Converts the view interval to datetime objects.
-        """
-        vmin, vmax = self.axis.get_view_interval()
-        if vmin > vmax:
-            vmin, vmax = vmax, vmin
-
-        return (_idx2dt(vmin, self._dates, self.tz),
-                _idx2dt(vmax, self._dates, self.tz))
-
-    def tick_values(self, vmin, vmax):
-        import bisect
-        dtnums = super(RRuleLocator, self).tick_values(vmin, vmax)
-        return [bisect.bisect_left(self._dates, x) for x in dtnums]
-
-
-class AutoDateLocator(ADLocator):
-
-    def __init__(self, dates, *args, **kwargs):
-        self._dates = dates
-        super(AutoDateLocator, self).__init__(*args, **kwargs)
-
-    def datalim_to_dt(self):
-        """
-        Convert axis data interval to datetime objects.
-        """
-        dmin, dmax = self.axis.get_data_interval()
-        if dmin > dmax:
-            dmin, dmax = dmax, dmin
-
-        return (_idx2dt(dmin, self._dates, self.tz),
-                _idx2dt(dmax, self._dates, self.tz))
-
-    def viewlim_to_dt(self):
-        """
-        Converts the view interval to datetime objects.
-        """
-        vmin, vmax = self.axis.get_view_interval()
-        if vmin > vmax:
-            vmin, vmax = vmax, vmin
-
-        return (_idx2dt(vmin, self._dates, self.tz),
-                _idx2dt(vmax, self._dates, self.tz))
-
-    def tick_values(self, vmin, vmax):
-        import bisect
-        dtnums = super(AutoDateLocator, self).tick_values(vmin, vmax)
-        return [bisect.bisect_left(self._dates, x) for x in dtnums]
-
-    def get_locator(self, dmin, dmax):
-        'Pick the best locator based on a distance.'
-        delta = relativedelta(dmax, dmin)
-        tdelta = dmax - dmin
-
-        # take absolute difference
-        if dmin > dmax:
-            delta = -delta
-            tdelta = -tdelta
-
-        # The following uses a mix of calls to relativedelta and timedelta
-        # methods because there is incomplete overlap in the functionality of
-        # these similar functions, and it's best to avoid doing our own math
-        # whenever possible.
-        numYears = float(delta.years)
-        numMonths = (numYears * MONTHS_PER_YEAR) + delta.months
-        numDays = tdelta.days   # Avoids estimates of days/month, days/year
-        numHours = (numDays * HOURS_PER_DAY) + delta.hours
-        numMinutes = (numHours * MIN_PER_HOUR) + delta.minutes
-        numSeconds = np.floor(tdelta.total_seconds())
-        numMicroseconds = np.floor(tdelta.total_seconds() * 1e6)
-
-        nums = [numYears, numMonths, numDays, numHours, numMinutes,
-                numSeconds, numMicroseconds]
-
-        use_rrule_locator = [True] * 6 + [False]
-
-        # Default setting of bymonth, etc. to pass to rrule
-        # [unused (for year), bymonth, bymonthday, byhour, byminute,
-        #  bysecond, unused (for microseconds)]
-        byranges = [None, 1, 1, 0, 0, 0, None]
-
-        usemicro = False  # use as flag to avoid raising an exception
-
-        # Loop over all the frequencies and try to find one that gives at
-        # least a minticks tick positions.  Once this is found, look for
-        # an interval from an list specific to that frequency that gives no
-        # more than maxticks tick positions. Also, set up some ranges
-        # (bymonth, etc.) as appropriate to be passed to rrulewrapper.
-        for i, (freq, num) in enumerate(zip(self._freqs, nums)):
-            # If this particular frequency doesn't give enough ticks, continue
-            if num < self.minticks:
-                # Since we're not using this particular frequency, set
-                # the corresponding by_ to None so the rrule can act as
-                # appropriate
-                byranges[i] = None
-                continue
-
-            # Find the first available interval that doesn't give too many
-            # ticks
-            for interval in self.intervald[freq]:
-                if num <= interval * (self.maxticks[freq] - 1):
-                    break
-            else:
-                # We went through the whole loop without breaking, default to
-                # the last interval in the list and raise a warning
-                warnings.warn('AutoDateLocator was unable to pick an '
-                              'appropriate interval for this date range. '
-                              'It may be necessary to add an interval value '
-                              "to the AutoDateLocator's intervald dictionary."
-                              ' Defaulting to {0}.'.format(interval))
-
-            # Set some parameters as appropriate
-            self._freq = freq
-
-            if self._byranges[i] and self.interval_multiples:
-                byranges[i] = self._byranges[i][::interval]
-                interval = 1
-            else:
-                byranges[i] = self._byranges[i]
-
-            # We found what frequency to use
-            break
-        else:
-            if False:
-                raise ValueError(
-                    'No sensible date limit could be found in the '
-                    'AutoDateLocator.')
-            else:
-                usemicro = True
-
-        if not usemicro and use_rrule_locator[i]:
-            _, bymonth, bymonthday, byhour, byminute, bysecond, _ = byranges
-
-            rrule = rrulewrapper(self._freq, interval=interval,
-                                 dtstart=dmin, until=dmax,
-                                 bymonth=bymonth, bymonthday=bymonthday,
-                                 byhour=byhour, byminute=byminute,
-                                 bysecond=bysecond)
-
-            locator = RRuleLocator(self._dates, rrule, self.tz)
-        else:
-            if usemicro:
-                interval = 1  # not set because the for else: was met
-            locator = MicrosecondLocator(interval, tz=self.tz)
-
-        locator.set_axis(self.axis)
-
-        locator.set_view_interval(*self.axis.get_view_interval())
-        locator.set_data_interval(*self.axis.get_data_interval())
-        return locator
-
-
-class AutoDateFormatter(ADFormatter):
-    def __init__(self, dates, locator, tz=None, defaultfmt='%Y-%m-%d'):
-        self._dates = dates
-        super(AutoDateFormatter, self).__init__(locator, tz, defaultfmt)
-
-    def __call__(self, x, pos=None):
-        '''Return the label for time x at position pos'''
-        x = int(round(x))
-        ldates = len(self._dates)
-        if x >= ldates:
-            x = ldates - 1
-
-        if x < 0:
-            x = 0
-
-        ix = self._dates[x]
-
-        return super(AutoDateFormatter, self).__call__(ix, pos)
diff --git a/spaces/Liu-LAB/GPT-academic/tests/test_utils.py b/spaces/Liu-LAB/GPT-academic/tests/test_utils.py
deleted file mode 100644
index f3a45aa8f2d106df68c76b207fa94786b292254f..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/tests/test_utils.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from toolbox import get_conf
-from toolbox import set_conf
-from toolbox import set_multi_conf
-from toolbox import get_plugin_handle
-from toolbox import get_plugin_default_kwargs
-from toolbox import get_chat_handle
-from toolbox import get_chat_default_kwargs
-from functools import wraps
-import sys
-import os
-
-def chat_to_markdown_str(chat):
-    result = ""
-    for i, cc in enumerate(chat):
-        result += f'\n\n{cc[0]}\n\n{cc[1]}'
-        if i != len(chat)-1:
-            result += '\n\n---'
-    return result
-
-def silence_stdout(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        _original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-        sys.stdout.reconfigure(encoding='utf-8')
-        for q in func(*args, **kwargs):
-            sys.stdout = _original_stdout
-            yield q
-            sys.stdout = open(os.devnull, 'w')
-            sys.stdout.reconfigure(encoding='utf-8')
-        sys.stdout.close()
-        sys.stdout = _original_stdout
-    return wrapper
-
-def silence_stdout_fn(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        _original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-        sys.stdout.reconfigure(encoding='utf-8')
-        result = func(*args, **kwargs)
-        sys.stdout.close()
-        sys.stdout = _original_stdout
-        return result
-    return wrapper
-
-class VoidTerminal():
-    def __init__(self) -> None:
-        pass
-    
-vt = VoidTerminal()
-vt.get_conf = silence_stdout_fn(get_conf)
-vt.set_conf = silence_stdout_fn(set_conf)
-vt.set_multi_conf = silence_stdout_fn(set_multi_conf)
-vt.get_plugin_handle = silence_stdout_fn(get_plugin_handle)
-vt.get_plugin_default_kwargs = silence_stdout_fn(get_plugin_default_kwargs)
-vt.get_chat_handle = silence_stdout_fn(get_chat_handle)
-vt.get_chat_default_kwargs = silence_stdout_fn(get_chat_default_kwargs)
-vt.chat_to_markdown_str = chat_to_markdown_str
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
-    vt.get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
-
-def plugin_test(main_input, plugin, advanced_arg=None):
-    from rich.live import Live
-    from rich.markdown import Markdown
-
-    vt.set_conf(key="API_KEY", value=API_KEY)
-    vt.set_conf(key="LLM_MODEL", value=LLM_MODEL)
-
-    plugin = vt.get_plugin_handle(plugin)
-    plugin_kwargs = vt.get_plugin_default_kwargs()
-    plugin_kwargs['main_input'] = main_input
-    if advanced_arg is not None:
-        plugin_kwargs['plugin_kwargs'] = advanced_arg
-    my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
-
-    with Live(Markdown(""), auto_refresh=False) as live:
-        for cookies, chat, hist, msg in my_working_plugin:
-            md_str = vt.chat_to_markdown_str(chat)
-            md = Markdown(md_str)
-            live.update(md, refresh=True)
\ No newline at end of file
diff --git a/spaces/LiuZiyi/2-image-img2sketch-opencv/app.py b/spaces/LiuZiyi/2-image-img2sketch-opencv/app.py
deleted file mode 100644
index 3c607b3f48200ffbdf9a4406e5e39fbe2379a2b7..0000000000000000000000000000000000000000
--- a/spaces/LiuZiyi/2-image-img2sketch-opencv/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import streamlit as st
-import numpy as np
-from PIL import Image
-import cv2
-
-def dodgeV2(x,y):
-    return cv2.divide(x, 255 - y, scale=256)
-
-def pencilsketch(inp_img):
-    img_gray = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY)
-    img_invert = cv2.bitwise_not(img_gray)
-    img_smoothing = cv2.GaussianBlur(img_invert, (21,21), sigmaX=0, sigmaY=0)
-    final_img = dodgeV2(img_gray, img_smoothing)
-    return(final_img)
-
-st.title("Pencil Sketcher App")
-st.write("This App is to help convert your photos to realist Pencil Sketches")
-
-file_image = st.sidebar.file_uploader("Upload your Photos", type=['jpeg', 'jpg', 'png'])
-
-if file_image is None:
-    st.write("You haven't uploaded any image file")
-else:
-    input_img = Image.open(file_image)
-    final_sketch = pencilsketch(np.array(input_img))
-    st.write("**Input Photo**")
-    st.image(file_image)
-    st.write("**Output Pencil Sketch**")
-    st.image(final_sketch)
diff --git a/spaces/Longtong/foodvision_mini_video/README.md b/spaces/Longtong/foodvision_mini_video/README.md
deleted file mode 100644
index f80c5b4c29c6382e50596064f6eca83f1e2f522d..0000000000000000000000000000000000000000
--- a/spaces/Longtong/foodvision_mini_video/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Foodvision Mini Video
-emoji: 🔥
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Lucifer741/emoji-predictor/README.md b/spaces/Lucifer741/emoji-predictor/README.md
deleted file mode 100644
index 2fc7f7108bcc196df930c13097a46d5958a46e73..0000000000000000000000000000000000000000
--- a/spaces/Lucifer741/emoji-predictor/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Emoji Predictor
-emoji: 😎
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: vincentclaes/emoji-predictor
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MRroboto/Loacker_app/README.md b/spaces/MRroboto/Loacker_app/README.md
deleted file mode 100644
index b7945b0a768b366f3ebf03f04f5dd74627afb9f2..0000000000000000000000000000000000000000
--- a/spaces/MRroboto/Loacker_app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Loacker App
-emoji: 🌍
-colorFrom: indigo
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Manmay/tortoise-tts/tortoise/get_conditioning_latents.py b/spaces/Manmay/tortoise-tts/tortoise/get_conditioning_latents.py
deleted file mode 100644
index aa7e9b7dde64e4867cfdad025d739ca7fbff425f..0000000000000000000000000000000000000000
--- a/spaces/Manmay/tortoise-tts/tortoise/get_conditioning_latents.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import argparse
-import os
-import torch
-
-from api import TextToSpeech
-from tortoise.utils.audio import load_audio, get_voices
-
-"""
-Dumps the conditioning latents for the specified voice to disk. These are expressive latents which can be used for
-other ML models, or can be augmented manually and fed back into Tortoise to affect vocal qualities.
-"""
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--voice', type=str, help='Selects the voice to convert to conditioning latents', default='pat2')
-    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='../results/conditioning_latents')
-    args = parser.parse_args()
-    os.makedirs(args.output_path, exist_ok=True)
-
-    tts = TextToSpeech()
-    voices = get_voices()
-    selected_voices = args.voice.split(',')
-    for voice in selected_voices:
-        cond_paths = voices[voice]
-        conds = []
-        for cond_path in cond_paths:
-            c = load_audio(cond_path, 22050)
-            conds.append(c)
-        conditioning_latents = tts.get_conditioning_latents(conds)
-        torch.save(conditioning_latents, os.path.join(args.output_path, f'{voice}.pth'))
-
diff --git a/spaces/Matthijs/image2reverb/image2reverb/stft.py b/spaces/Matthijs/image2reverb/image2reverb/stft.py
deleted file mode 100644
index 77f83e3d9ad6a89d043823e875dc187bbf8c1f0a..0000000000000000000000000000000000000000
--- a/spaces/Matthijs/image2reverb/image2reverb/stft.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import numpy
-import torch
-import librosa
-
-
-class STFT(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self._eps = 1e-8
-
-    def transform(self, audio):
-        m = numpy.abs(librosa.stft(audio/numpy.abs(audio).max(), 1024, 256))[:-1,:]
-        m = numpy.log(m + self._eps)
-        m = (((m - m.min())/(m.max() - m.min()) * 2) - 1)
-        return (torch.FloatTensor if torch.cuda.is_available() else torch.Tensor)(m * 0.8).unsqueeze(0)
-
-    def inverse(self, spec):
-        s = spec.cpu().detach().numpy()
-        s = numpy.exp((((s + 1) * 0.5) * 19.5) - 17.5) - self._eps # Empirical (average) min and max over test set
-        rp = numpy.random.uniform(-numpy.pi, numpy.pi, s.shape)
-        f = s * (numpy.cos(rp) + (1.j * numpy.sin(rp)))
-        y = librosa.istft(f) # Reconstruct audio
-        return y/numpy.abs(y).max()
diff --git a/spaces/Matthijs/speecht5-tts-demo/README.md b/spaces/Matthijs/speecht5-tts-demo/README.md
deleted file mode 100644
index ee7b28fb044bbfdd88ae3cc89349103116fe720a..0000000000000000000000000000000000000000
--- a/spaces/Matthijs/speecht5-tts-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SpeechT5 Speech Synthesis Demo
-emoji: 👩‍🎤
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/__init__.py
deleted file mode 100644
index a0b6b345640a895368ac8a647afef6f24333d90e..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base import LoggerHook
-from .dvclive import DvcliveLoggerHook
-from .mlflow import MlflowLoggerHook
-from .neptune import NeptuneLoggerHook
-from .pavi import PaviLoggerHook
-from .tensorboard import TensorboardLoggerHook
-from .text import TextLoggerHook
-from .wandb import WandbLoggerHook
-
-__all__ = [
-    'LoggerHook', 'MlflowLoggerHook', 'PaviLoggerHook',
-    'TensorboardLoggerHook', 'TextLoggerHook', 'WandbLoggerHook',
-    'NeptuneLoggerHook', 'DvcliveLoggerHook'
-]
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/plugins/common.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/plugins/common.py
deleted file mode 100644
index 722b53f568002720f28c1683a2304d335b94b883..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/plugins/common.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from mmocr.registry import MODELS
-
-
-@MODELS.register_module()
-class AvgPool2d(nn.Module):
-    """Applies a 2D average pooling over an input signal composed of several
-    input planes.
-
-    It can also be used as a network plugin.
-
-    Args:
-        kernel_size (int or tuple(int)): the size of the window.
-        stride (int or tuple(int), optional): the stride of the window.
-            Defaults to None.
-        padding (int or tuple(int)): implicit zero padding. Defaults to 0.
-    """
-
-    def __init__(self,
-                 kernel_size: Union[int, Tuple[int]],
-                 stride: Optional[Union[int, Tuple[int]]] = None,
-                 padding: Union[int, Tuple[int]] = 0,
-                 **kwargs) -> None:
-        super().__init__()
-        self.model = nn.AvgPool2d(kernel_size, stride, padding)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Forward function.
-        Args:
-            x (Tensor): Input feature map.
-
-        Returns:
-            Tensor: Output tensor after Avgpooling layer.
-        """
-        return self.model(x)
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/bert/README.md b/spaces/NCTCMumbai/NCTC/models/official/nlp/bert/README.md
deleted file mode 100644
index c26a87df520b9d9bb9cccefd515abc0bf4a399c7..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/bert/README.md
+++ /dev/null
@@ -1,368 +0,0 @@
-# BERT (Bidirectional Encoder Representations from Transformers)
-
-The academic paper which describes BERT in detail and provides full results on a
-number of tasks can be found here: https://arxiv.org/abs/1810.04805.
-
-This repository contains TensorFlow 2.x implementation for BERT.
-
-## Contents
-  * [Contents](#contents)
-  * [Pre-trained Models](#pre-trained-models)
-    * [Restoring from Checkpoints](#restoring-from-checkpoints)
-  * [Set Up](#set-up)
-  * [Process Datasets](#process-datasets)
-  * [Fine-tuning with BERT](#fine-tuning-with-bert)
-    * [Cloud GPUs and TPUs](#cloud-gpus-and-tpus)
-    * [Sentence and Sentence-pair Classification Tasks](#sentence-and-sentence-pair-classification-tasks)
-    * [SQuAD 1.1](#squad-1.1)
-
-
-## Pre-trained Models
-
-We released both checkpoints and tf.hub modules as the pretrained models for
-fine-tuning. They are TF 2.x compatible and are converted from the checkpoints
-released in TF 1.x official BERT repository
-[google-research/bert](https://github.com/google-research/bert)
-in order to keep consistent with BERT paper.
-
-
-### Access to Pretrained Checkpoints
-
-Pretrained checkpoints can be found in the following links:
-
-**Note: We have switched BERT implementation
-to use Keras functional-style networks in [nlp/modeling](../modeling).
-The new checkpoints are:**
-
-*   **[`BERT-Large, Uncased (Whole Word Masking)`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/wwm_uncased_L-24_H-1024_A-16.tar.gz)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Large, Cased (Whole Word Masking)`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/wwm_cased_L-24_H-1024_A-16.tar.gz)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Base, Uncased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/uncased_L-12_H-768_A-12.tar.gz)**:
-    12-layer, 768-hidden, 12-heads, 110M parameters
-*   **[`BERT-Large, Uncased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16.tar.gz)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Base, Cased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/cased_L-12_H-768_A-12.tar.gz)**:
-    12-layer, 768-hidden, 12-heads , 110M parameters
-*   **[`BERT-Large, Cased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/cased_L-24_H-1024_A-16.tar.gz)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-
-We recommend to host checkpoints on Google Cloud storage buckets when you use
-Cloud GPU/TPU.
-
-### Restoring from Checkpoints
-
-`tf.train.Checkpoint` is used to manage model checkpoints in TF 2. To restore
-weights from provided pre-trained checkpoints, you can use the following code:
-
-```python
-init_checkpoint='the pretrained model checkpoint path.'
-model=tf.keras.Model() # Bert pre-trained model as feature extractor.
-checkpoint = tf.train.Checkpoint(model=model)
-checkpoint.restore(init_checkpoint)
-```
-
-Checkpoints featuring native serialized Keras models
-(i.e. model.load()/load_weights()) will be available soon.
-
-### Access to Pretrained hub modules.
-
-Pretrained tf.hub modules in TF 2.x SavedModel format can be found in the
-following links:
-
-*   **[`BERT-Large, Uncased (Whole Word Masking)`](https://tfhub.dev/tensorflow/bert_en_wwm_uncased_L-24_H-1024_A-16/1)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Large, Cased (Whole Word Masking)`](https://tfhub.dev/tensorflow/bert_en_wwm_cased_L-24_H-1024_A-16/1)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Base, Uncased`](https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1)**:
-    12-layer, 768-hidden, 12-heads, 110M parameters
-*   **[`BERT-Large, Uncased`](https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Base, Cased`](https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/1)**:
-    12-layer, 768-hidden, 12-heads , 110M parameters
-*   **[`BERT-Large, Cased`](https://tfhub.dev/tensorflow/bert_en_cased_L-24_H-1024_A-16/1)**:
-    24-layer, 1024-hidden, 16-heads, 340M parameters
-*   **[`BERT-Base, Multilingual Cased`](https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/1)**:
-    104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
-*   **[`BERT-Base, Chinese`](https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/1)**:
-    Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads,
-    110M parameters
-
-## Set Up
-
-```shell
-export PYTHONPATH="$PYTHONPATH:/path/to/models"
-```
-
-Install `tf-nightly` to get latest updates:
-
-```shell
-pip install tf-nightly-gpu
-```
-
-With TPU, GPU support is not necessary. First, you need to create a `tf-nightly`
-TPU with [ctpu tool](https://github.com/tensorflow/tpu/tree/master/tools/ctpu):
-
-```shell
-ctpu up -name <instance name> --tf-version=”nightly”
-```
-
-Second, you need to install TF 2 `tf-nightly` on your VM:
-
-```shell
-pip install tf-nightly
-```
-
-## Process Datasets
-
-### Pre-training
-
-There is no change to generate pre-training data. Please use the script
-[`../data/create_pretraining_data.py`](../data/create_pretraining_data.py)
-which is essentially branched from [BERT research repo](https://github.com/google-research/bert)
-to get processed pre-training data and it adapts to TF2 symbols and python3
-compatibility.
-
-
-### Fine-tuning
-
-To prepare the fine-tuning data for final model training, use the
-[`../data/create_finetuning_data.py`](../data/create_finetuning_data.py) script.
-Resulting datasets in `tf_record` format and training meta data should be later
-passed to training or evaluation scripts. The task-specific arguments are
-described in following sections:
-
-* GLUE
-
-Users can download the
-[GLUE data](https://gluebenchmark.com/tasks) by running
-[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
-and unpack it to some directory `$GLUE_DIR`.
-Also, users can download [Pretrained Checkpoint](#access-to-pretrained-checkpoints) and locate on some directory `$BERT_DIR` instead of using checkpoints on Google Cloud Storage.
-
-```shell
-export GLUE_DIR=~/glue
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-
-export TASK_NAME=MNLI
-export OUTPUT_DIR=gs://some_bucket/datasets
-python ../data/create_finetuning_data.py \
- --input_data_dir=${GLUE_DIR}/${TASK_NAME}/ \
- --vocab_file=${BERT_DIR}/vocab.txt \
- --train_data_output_path=${OUTPUT_DIR}/${TASK_NAME}_train.tf_record \
- --eval_data_output_path=${OUTPUT_DIR}/${TASK_NAME}_eval.tf_record \
- --meta_data_file_path=${OUTPUT_DIR}/${TASK_NAME}_meta_data \
- --fine_tuning_task_type=classification --max_seq_length=128 \
- --classification_task_name=${TASK_NAME}
-```
-
-* SQUAD
-
-The [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/) contains
-detailed information about the SQuAD datasets and evaluation.
-
-The necessary files can be found here:
-
-*   [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
-*   [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
-*   [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
-*   [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
-*   [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
-*   [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)
-
-```shell
-export SQUAD_DIR=~/squad
-export SQUAD_VERSION=v1.1
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-export OUTPUT_DIR=gs://some_bucket/datasets
-
-python ../data/create_finetuning_data.py \
- --squad_data_file=${SQUAD_DIR}/train-${SQUAD_VERSION}.json \
- --vocab_file=${BERT_DIR}/vocab.txt \
- --train_data_output_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
- --meta_data_file_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_meta_data \
- --fine_tuning_task_type=squad --max_seq_length=384
-```
-
-## Fine-tuning with BERT
-
-### Cloud GPUs and TPUs
-
-* Cloud Storage
-
-The unzipped pre-trained model files can also be found in the Google Cloud
-Storage folder `gs://cloud-tpu-checkpoints/bert/keras_bert`. For example:
-
-```shell
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-export MODEL_DIR=gs://some_bucket/my_output_dir
-```
-
-Currently, users are able to access to `tf-nightly` TPUs and the following TPU
-script should run with `tf-nightly`.
-
-* GPU -> TPU
-
-Just add the following flags to `run_classifier.py` or `run_squad.py`:
-
-```shell
-  --distribution_strategy=tpu
-  --tpu=grpc://${TPU_IP_ADDRESS}:8470
-```
-
-### Sentence and Sentence-pair Classification Tasks
-
-This example code fine-tunes `BERT-Large` on the Microsoft Research Paraphrase
-Corpus (MRPC) corpus, which only contains 3,600 examples and can fine-tune in a
-few minutes on most GPUs.
-
-We use the `BERT-Large` (uncased_L-24_H-1024_A-16) as an example throughout the
-workflow.
-For GPU memory of 16GB or smaller, you may try to use `BERT-Base`
-(uncased_L-12_H-768_A-12).
-
-```shell
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-export MODEL_DIR=gs://some_bucket/my_output_dir
-export GLUE_DIR=gs://some_bucket/datasets
-export TASK=MRPC
-
-python run_classifier.py \
-  --mode='train_and_eval' \
-  --input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
-  --train_data_path=${GLUE_DIR}/${TASK}_train.tf_record \
-  --eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
-  --bert_config_file=${BERT_DIR}/bert_config.json \
-  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
-  --train_batch_size=4 \
-  --eval_batch_size=4 \
-  --steps_per_loop=1 \
-  --learning_rate=2e-5 \
-  --num_train_epochs=3 \
-  --model_dir=${MODEL_DIR} \
-  --distribution_strategy=mirrored
-```
-
-Alternatively, instead of specifying `init_checkpoint`, you can specify
-`hub_module_url` to employ a pretraind BERT hub module, e.g.,
-` --hub_module_url=https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1`.
-
-After training a model, to get predictions from the classifier, you can set the
-`--mode=predict` and offer the test set tfrecords to `--eval_data_path`.
-Output will be created in file called test_results.tsv in the output folder.
-Each line will contain output for each sample, columns are the class
-probabilities.
-
-```shell
-python run_classifier.py \
-  --mode='predict' \
-  --input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
-  --eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
-  --bert_config_file=${BERT_DIR}/bert_config.json \
-  --eval_batch_size=4 \
-  --model_dir=${MODEL_DIR} \
-  --distribution_strategy=mirrored
-```
-
-To use TPU, you only need to switch distribution strategy type to `tpu` with TPU
-information and use remote storage for model checkpoints.
-
-```shell
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-export TPU_IP_ADDRESS='???'
-export MODEL_DIR=gs://some_bucket/my_output_dir
-export GLUE_DIR=gs://some_bucket/datasets
-export TASK=MRPC
-
-python run_classifier.py \
-  --mode='train_and_eval' \
-  --input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
-  --train_data_path=${GLUE_DIR}/${TASK}_train.tf_record \
-  --eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
-  --bert_config_file=${BERT_DIR}/bert_config.json \
-  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
-  --train_batch_size=32 \
-  --eval_batch_size=32 \
-  --steps_per_loop=1000 \
-  --learning_rate=2e-5 \
-  --num_train_epochs=3 \
-  --model_dir=${MODEL_DIR} \
-  --distribution_strategy=tpu \
-  --tpu=grpc://${TPU_IP_ADDRESS}:8470
-```
-
-Note that, we specify `steps_per_loop=1000` for TPU, because running a loop of
-training steps inside a `tf.function` can significantly increase TPU utilization
-and callbacks will not be called inside the loop.
-
-### SQuAD 1.1
-
-The Stanford Question Answering Dataset (SQuAD) is a popular question answering
-benchmark dataset. See more in [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/).
-
-We use the `BERT-Large` (uncased_L-24_H-1024_A-16) as an example throughout the
-workflow.
-For GPU memory of 16GB or smaller, you may try to use `BERT-Base`
-(uncased_L-12_H-768_A-12).
-
-```shell
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-export SQUAD_DIR=gs://some_bucket/datasets
-export MODEL_DIR=gs://some_bucket/my_output_dir
-export SQUAD_VERSION=v1.1
-
-python run_squad.py \
-  --input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
-  --train_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
-  --predict_file=${SQUAD_DIR}/dev-v1.1.json \
-  --vocab_file=${BERT_DIR}/vocab.txt \
-  --bert_config_file=${BERT_DIR}/bert_config.json \
-  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
-  --train_batch_size=4 \
-  --predict_batch_size=4 \
-  --learning_rate=8e-5 \
-  --num_train_epochs=2 \
-  --model_dir=${MODEL_DIR} \
-  --distribution_strategy=mirrored
-```
-
-Similarily, you can replace `init_checkpoint` FLAG with `hub_module_url` to
-specify a hub module path.
-
-`run_squad.py` writes the prediction for `--predict_file` by default. If you set
-the `--model=predict` and offer the SQuAD test data, the scripts will generate
-the prediction json file.
-
-To use TPU, you need switch distribution strategy type to `tpu` with TPU
-information.
-
-```shell
-export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
-export TPU_IP_ADDRESS='???'
-export MODEL_DIR=gs://some_bucket/my_output_dir
-export SQUAD_DIR=gs://some_bucket/datasets
-export SQUAD_VERSION=v1.1
-
-python run_squad.py \
-  --input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
-  --train_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
-  --predict_file=${SQUAD_DIR}/dev-v1.1.json \
-  --vocab_file=${BERT_DIR}/vocab.txt \
-  --bert_config_file=${BERT_DIR}/bert_config.json \
-  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
-  --train_batch_size=32 \
-  --learning_rate=8e-5 \
-  --num_train_epochs=2 \
-  --model_dir=${MODEL_DIR} \
-  --distribution_strategy=tpu \
-  --tpu=grpc://${TPU_IP_ADDRESS}:8470
-```
-
-The dev set predictions will be saved into a file called predictions.json in the
-model_dir:
-
-```shell
-python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ./squad/predictions.json
-```
-
-
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/tasks/tagging_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/tasks/tagging_test.py
deleted file mode 100644
index 6707a50a824246062be9387c9c83f49c1a3309f5..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/tasks/tagging_test.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Tests for official.nlp.tasks.tagging."""
-import functools
-import os
-import tensorflow as tf
-
-from official.nlp.bert import configs
-from official.nlp.bert import export_tfhub
-from official.nlp.configs import bert
-from official.nlp.configs import encoders
-from official.nlp.tasks import tagging
-
-
-class TaggingTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(TaggingTest, self).setUp()
-    self._encoder_config = encoders.TransformerEncoderConfig(
-        vocab_size=30522, num_layers=1)
-    self._train_data_config = bert.TaggingDataConfig(
-        input_path="dummy", seq_length=128, global_batch_size=1)
-
-  def _run_task(self, config):
-    task = tagging.TaggingTask(config)
-    model = task.build_model()
-    metrics = task.build_metrics()
-
-    strategy = tf.distribute.get_strategy()
-    dataset = strategy.experimental_distribute_datasets_from_function(
-        functools.partial(task.build_inputs, config.train_data))
-
-    iterator = iter(dataset)
-    optimizer = tf.keras.optimizers.SGD(lr=0.1)
-    task.train_step(next(iterator), model, optimizer, metrics=metrics)
-    task.validation_step(next(iterator), model, metrics=metrics)
-
-  def test_task(self):
-    # Saves a checkpoint.
-    encoder = encoders.instantiate_encoder_from_cfg(self._encoder_config)
-    ckpt = tf.train.Checkpoint(encoder=encoder)
-    saved_path = ckpt.save(self.get_temp_dir())
-
-    config = tagging.TaggingConfig(
-        init_checkpoint=saved_path,
-        network=self._encoder_config,
-        train_data=self._train_data_config,
-        num_classes=3)
-    task = tagging.TaggingTask(config)
-    model = task.build_model()
-    metrics = task.build_metrics()
-    dataset = task.build_inputs(config.train_data)
-
-    iterator = iter(dataset)
-    optimizer = tf.keras.optimizers.SGD(lr=0.1)
-    task.train_step(next(iterator), model, optimizer, metrics=metrics)
-    task.validation_step(next(iterator), model, metrics=metrics)
-    task.initialize(model)
-
-  def test_task_with_fit(self):
-    config = tagging.TaggingConfig(
-        network=self._encoder_config,
-        train_data=self._train_data_config,
-        num_classes=3)
-
-    task = tagging.TaggingTask(config)
-    model = task.build_model()
-    model = task.compile_model(
-        model,
-        optimizer=tf.keras.optimizers.SGD(lr=0.1),
-        train_step=task.train_step,
-        metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")])
-    dataset = task.build_inputs(config.train_data)
-    logs = model.fit(dataset, epochs=1, steps_per_epoch=2)
-    self.assertIn("loss", logs.history)
-    self.assertIn("accuracy", logs.history)
-
-  def _export_bert_tfhub(self):
-    bert_config = configs.BertConfig(
-        vocab_size=30522,
-        hidden_size=16,
-        intermediate_size=32,
-        max_position_embeddings=128,
-        num_attention_heads=2,
-        num_hidden_layers=1)
-    _, encoder = export_tfhub.create_bert_model(bert_config)
-    model_checkpoint_dir = os.path.join(self.get_temp_dir(), "checkpoint")
-    checkpoint = tf.train.Checkpoint(model=encoder)
-    checkpoint.save(os.path.join(model_checkpoint_dir, "test"))
-    model_checkpoint_path = tf.train.latest_checkpoint(model_checkpoint_dir)
-
-    vocab_file = os.path.join(self.get_temp_dir(), "uncased_vocab.txt")
-    with tf.io.gfile.GFile(vocab_file, "w") as f:
-      f.write("dummy content")
-
-    hub_destination = os.path.join(self.get_temp_dir(), "hub")
-    export_tfhub.export_bert_tfhub(bert_config, model_checkpoint_path,
-                                   hub_destination, vocab_file)
-    return hub_destination
-
-  def test_task_with_hub(self):
-    hub_module_url = self._export_bert_tfhub()
-    config = tagging.TaggingConfig(
-        hub_module_url=hub_module_url,
-        network=self._encoder_config,
-        num_classes=4,
-        train_data=self._train_data_config)
-    self._run_task(config)
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/spaces/Nehal07/Text-Colour-Changes/app.py b/spaces/Nehal07/Text-Colour-Changes/app.py
deleted file mode 100644
index 1c43fac67d343b4d199dc1577737770f39dc010c..0000000000000000000000000000000000000000
--- a/spaces/Nehal07/Text-Colour-Changes/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import streamlit as st
-
-# Function to change text color based on user input
-def change_text_color(text, color):
-    # Apply HTML styling to change text color, set font size, and add a border
-    styled_text = f'<div style="color:{color}; font-size: 32px; font-weight: bold; border: 2px solid {color}; padding: 10px;">{text}</div>'
-    return styled_text
-
-# Streamlit app
-st.title("Text Color Changer")
-
-# Input text
-user_text = st.text_input("Enter your text:")
-
-# Predefined color selection
-st.subheader("Select text color:")
-
-# Expanded list of predefined color options
-color_options = [
-    "red", "blue", "green", "orange", "yellow", "purple", "pink", "brown", "gray",
-    "cyan", "magenta", "violet", " indigo", "lime", "olive", "teal", "navy", "maroon",
-    "aquamarine", "azure", "beige", "black", "fuchsia", "silver", "white",
-    "lavender", "turquoise", "plum", "salmon", "sienna", "tomato", "wheat", "orchid",
-    "thistle", "peru", "royalblue", "dodgerblue", "darkslategray", "darkseagreen",
-    "darkred", "darkorange", "darkolivegreen", "darkmagenta", "darkkhaki", "darkgray",
-    "darkcyan", "darkblue", "darkgoldenrod", "mediumblue", "mediumorchid",
-    "cornflowerblue", "chocolate", "coral", "crimson", "forestgreen", "gainsboro",
-    "goldenrod", "hotpink", "ivory", "khaki", "lawngreen", "lightcyan", "lightgrey",
-    "lightpink", "lightsalmon", "lightseagreen", "lightskyblue", "lightslategray",
-    "mediumaquamarine", "mediumslateblue", "mediumspringgreen", "midnightblue",
-    "mistyrose", "moccasin", "navajowhite", "oldlace", "olivedrab", "orangered",
-    "palevioletred", "peachpuff", "rebeccapurple", "rosybrown", "seagreen",
-    "seashell", "slateblue", "slategray", "springgreen", "steelblue", "tan",
-    "transparent", "wheat"
-]
-
-# User can select a predefined color or input a custom color code
-selected_color = st.selectbox("Select a color:", color_options + ["Custom"])
-
-if selected_color == "Custom":
-    custom_color_code = st.color_picker("Choose a custom color:")
-    selected_color = custom_color_code
-
-# Apply text color change and display
-styled_text = change_text_color(user_text, selected_color)
-st.markdown(styled_text, unsafe_allow_html=True)
-
-# Add a separate custom button
-if st.button("Customize Text Color"):
-    selected_color = st.color_picker("Choose a custom color:")
-    styled_text = change_text_color(user_text, selected_color)
-    st.markdown(styled_text, unsafe_allow_html=True)
diff --git a/spaces/Next7years/CatHeiHei_v1/app.py b/spaces/Next7years/CatHeiHei_v1/app.py
deleted file mode 100644
index 12e9b2224d92a3b7473579f8f80a56d856992468..0000000000000000000000000000000000000000
--- a/spaces/Next7years/CatHeiHei_v1/app.py
+++ /dev/null
@@ -1,330 +0,0 @@
-import gradio as gr
-from PIL import Image  
-import re
-import os
-import io
-import base64
-# import requests
-
-from diffusers import StableDiffusionPipeline
-import torch
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-
-model_id = "Next7years/stable-diffusion-v1-5-CatHeiHei-v1"
-device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
-print("info: running device type: " + device.type )
-
-#word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
-#word_list = word_list_dataset["train"]['text']
-default_negative_prompt="ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face, blurry, draft, grainy"
-example_dir = "prompt_examples"
-is_gpu_busy = False
-'''
-def infer(prompt):
-    global is_gpu_busy
-    samples = 4
-    steps = 50
-    scale = 7.5
-    #for filter in word_list:
-    #    if re.search(rf"\b{filter}\b", prompt):
-    #        raise gr.Error("Unsafe content found. Please try again with different prompts.")
-        
-    images = []
-    url = os.getenv('JAX_BACKEND_URL')
-    payload = {'prompt': prompt}
-    images_request = requests.post(url, json = payload)
-    for image in images_request.json()["images"]:
-        image_b64 = (f"data:image/jpeg;base64,{image}")
-        images.append(image_b64)
-    
-    return images
-'''
-
-def infer(prompt):
-    samples = 4
-    steps = 50
-    scale = 7.5
-    if device.type == "cuda" or device.type == "mps":   
-        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-    else:
-        pipe = StableDiffusionPipeline.from_pretrained(model_id)
-    pipe = pipe.to(device)
-    images = []
-    results = pipe(prompt, negative_prompt=default_negative_prompt, num_images_per_prompt=samples, num_inference_steps=steps, guidance_scale=scale).images
-    print(results)
-    for image in results:
-        jpeg_image = io.BytesIO()
-        image.save(jpeg_image, format='JPEG')
-        
-        base64_image = base64.b64encode(jpeg_image.getvalue()).decode('utf-8')
-        image_b64 = (f"data:image/jpeg;base64,{base64_image}")
-        #print(image_b64)
-        images.append(image_b64)
-
-    return images
-    
-css = """
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: black;
-            background: black;
-        }
-        input[type='range'] {
-            accent-color: black;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-btn {
-            font-size: .7rem !important;
-            line-height: 19px;
-            margin-top: 12px;
-            margin-bottom: 12px;
-            padding: 2px 8px;
-            border-radius: 14px !important;
-        }
-        #advanced-options {
-            display: none;
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-        #container-advanced-btns{
-            display: flex;
-            flex-wrap: wrap;
-            justify-content: space-between;
-            align-items: center;
-        }
-        .animate-spin {
-            animation: spin 1s linear infinite;
-        }
-        @keyframes spin {
-            from {
-                transform: rotate(0deg);
-            }
-            to {
-                transform: rotate(360deg);
-            }
-        }
-        #share-btn-container {
-            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-        }
-        #share-btn {
-            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
-        }
-        #share-btn * {
-            all: unset;
-        }
-        .gr-form{
-            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
-        }
-        #prompt-container{
-            gap: 0;
-        }
-        #share-btn-container div:nth-child(-n+2){
-        width: auto !important;
-        min-height: 0px !important;
-        } 
-"""
-
-block = gr.Blocks(css=css)
-
-def read_files_from_directory(directory):
-    file_contents = []
-    for filename in os.listdir(directory):
-        if filename.endswith(".txt"):
-            file_path = os.path.join(directory, filename)
-            with open(file_path, 'r') as f:
-                content = f.read()
-                file_contents.append([content])
-    return file_contents
-
-examples = read_files_from_directory(example_dir)
-
-metadata = [
-    {"title": "Positive Example",
-     "description": "A positive example input.",
-     "thumbnail": "https://example.com/images/positive.jpg",
-     "label": "Positive"},
-    {"title": "Negative Example",
-     "description": "A negative example input.",
-     "thumbnail": "https://example.com/images/negative.jpg",
-     "label": "Negative"}
-]
-
-
-with block:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 650px; margin: 0 auto; padding-top: 7px;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 12px;">
-                   Welcome to CatHeiHei v1 Model
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px">
-                 
-                We're excited to open-source this unique AI model, designed specifically to generate images of the world-famous Cat HeiHei. 
-                Our goal is to foster creativity and collaboration within the community, and we can't wait to see the amazing artwork you'll create!
-       
-                <p>Follow us on Instagram: 
-                    <a href="https://www.instagram.com/cat_heihei/" style="display: inline;">
-                        <img src="https://www.instagram.com/static/images/ico/favicon-192.png/68d99ba29cc8.png" alt="Instagram Logo" style="display: inline; width: 20px; height: 20px; margin-right: 5px;">
-                        @cat_heihei
-                    </a>
-                </p>
-              </p>
-            </div>
-        """
-    )
-    with gr.Group():
-        with gr.Box():
-            with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Enter your prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    elem_id="prompt-text-input",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                    full_width=False,
-                )
-
-        gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-
-        with gr.Group(elem_id="container-advanced-btns"):
-            advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
-            with gr.Group(elem_id="share-btn-container"):
-                community_icon = gr.HTML(community_icon_html)
-                loading_icon = gr.HTML(loading_icon_html)
-                share_button = gr.Button("Share to community", elem_id="share-btn")
-
-        with gr.Row(elem_id="advanced-options"):
-            gr.Markdown("Advanced settings are temporarily unavailable")
-            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
-            steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
-            scale = gr.Slider(
-                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
-            )
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=2147483647,
-                step=1,
-                randomize=True,
-            )
-
-        ex = gr.Examples(examples=examples, label="Example prompt to generate CatHeiHei", fn=infer, inputs=text, outputs=[gallery], cache_examples=False, postprocess=False)
-        ex.dataset.headers = [""]
-
-        text.submit(infer, inputs=text, outputs=[gallery], postprocess=False)
-        btn.click(infer, inputs=text, outputs=[gallery], postprocess=False)
-        
-        advanced_button.click(
-            None,
-            [],
-            text,
-            _js="""
-            () => {
-                const options = document.querySelector("body > gradio-app").querySelector("#advanced-options");
-                options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none";
-            }""",
-        )
-        share_button.click(
-            None,
-            [],
-            [],
-            _js=share_js,
-        )
-        gr.HTML(
-            """
-                <div class="footer" style="padding-top: 20px;">
-                    <p>Thanks for using the <b>Cat HeiHei </b> Customized Model! We appreciate your support and creativity. Share your feedback, suggestions, and content ideas by messaging us on Instagram 
-                    <a href="https://www.instagram.com/cat_heihei/" style="display: inline;">
-                        <img src="https://www.instagram.com/static/images/ico/favicon-192.png/68d99ba29cc8.png" alt="Instagram Logo" style="display: inline; width: 20px; height: 20px; margin-right: 5px;">
-                        @cat_heihei
-                    </a>. 
-                    Let's make the Cat HeiHei community a fun, creative space for all! Happy creating! 🐾💖
-                    </p>
-                </div>
-           """
-        )
-
-block.queue(concurrency_count=40, max_size=20).launch(max_threads=150)
-
-
diff --git a/spaces/Norod78/distilgpt2_TextIteratorStreamer/README.md b/spaces/Norod78/distilgpt2_TextIteratorStreamer/README.md
deleted file mode 100644
index a8f50b4fd90eb0fed29d0098598da40400d60098..0000000000000000000000000000000000000000
--- a/spaces/Norod78/distilgpt2_TextIteratorStreamer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Distilgpt2 TextIteratorStreamer
-emoji: 💦
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py
deleted file mode 100644
index d878278475fb24cf6b97d66d784e657567f5aa80..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-for file in os.listdir(os.path.dirname(__file__)):
-    if file.endswith(".py") and not file.startswith("_"):
-        task_name = file[: file.find(".py")]
-        importlib.import_module("examples.speech_text_joint_to_text.tasks." + task_name)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/encoders/bytes.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/encoders/bytes.py
deleted file mode 100644
index f88f8f6929f5b6bdb0db470be9ebedf8fe1f752d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/encoders/bytes.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from fairseq.data.encoders import register_bpe
-from fairseq.data.encoders.byte_utils import (
-    SPACE,
-    SPACE_ESCAPE,
-    byte_encode,
-    smart_byte_decode,
-)
-
-
-@register_bpe("bytes")
-class Bytes(object):
-    def __init__(self, *unused):
-        pass
-
-    @staticmethod
-    def add_args(parser):
-        pass
-
-    @staticmethod
-    def encode(x: str) -> str:
-        encoded = byte_encode(x)
-        escaped = encoded.replace(SPACE, SPACE_ESCAPE)
-        return SPACE.join(list(escaped))
-
-    @staticmethod
-    def decode(x: str) -> str:
-        unescaped = x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE)
-        return smart_byte_decode(unescaped)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_dictionary.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_dictionary.py
deleted file mode 100644
index dc9d71b3c722ce3066e182d4b237b2a72999d4d0..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_dictionary.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import io
-import os
-import string
-import tempfile
-import unittest
-
-import torch
-from fairseq import tokenizer
-from fairseq.data import Dictionary
-
-
-class TestDictionary(unittest.TestCase):
-    def test_finalize(self):
-        txt = [
-            "A B C D",
-            "B C D",
-            "C D",
-            "D",
-        ]
-        ref_ids1 = list(
-            map(
-                torch.IntTensor,
-                [
-                    [4, 5, 6, 7, 2],
-                    [5, 6, 7, 2],
-                    [6, 7, 2],
-                    [7, 2],
-                ],
-            )
-        )
-        ref_ids2 = list(
-            map(
-                torch.IntTensor,
-                [
-                    [7, 6, 5, 4, 2],
-                    [6, 5, 4, 2],
-                    [5, 4, 2],
-                    [4, 2],
-                ],
-            )
-        )
-
-        # build dictionary
-        d = Dictionary()
-        for line in txt:
-            d.encode_line(line, add_if_not_exist=True)
-
-        def get_ids(dictionary):
-            ids = []
-            for line in txt:
-                ids.append(dictionary.encode_line(line, add_if_not_exist=False))
-            return ids
-
-        def assertMatch(ids, ref_ids):
-            for toks, ref_toks in zip(ids, ref_ids):
-                self.assertEqual(toks.size(), ref_toks.size())
-                self.assertEqual(0, (toks != ref_toks).sum().item())
-
-        ids = get_ids(d)
-        assertMatch(ids, ref_ids1)
-
-        # check finalized dictionary
-        d.finalize()
-        finalized_ids = get_ids(d)
-        assertMatch(finalized_ids, ref_ids2)
-
-        # write to disk and reload
-        with tempfile.NamedTemporaryFile(mode="w") as tmp_dict:
-            d.save(tmp_dict.name)
-            d = Dictionary.load(tmp_dict.name)
-            reload_ids = get_ids(d)
-            assertMatch(reload_ids, ref_ids2)
-            assertMatch(finalized_ids, reload_ids)
-
-    def test_overwrite(self):
-        # for example, Camembert overwrites <unk>, <s> and </s>
-        dict_file = io.StringIO(
-            "<unk> 999 #fairseq:overwrite\n"
-            "<s> 999 #fairseq:overwrite\n"
-            "</s> 999 #fairseq:overwrite\n"
-            ", 999\n"
-            "▁de 999\n"
-        )
-        d = Dictionary()
-        d.add_from_file(dict_file)
-        self.assertEqual(d.index("<pad>"), 1)
-        self.assertEqual(d.index("foo"), 3)
-        self.assertEqual(d.index("<unk>"), 4)
-        self.assertEqual(d.index("<s>"), 5)
-        self.assertEqual(d.index("</s>"), 6)
-        self.assertEqual(d.index(","), 7)
-        self.assertEqual(d.index("▁de"), 8)
-
-    def test_no_overwrite(self):
-        # for example, Camembert overwrites <unk>, <s> and </s>
-        dict_file = io.StringIO(
-            "<unk> 999\n" "<s> 999\n" "</s> 999\n" ", 999\n" "▁de 999\n"
-        )
-        d = Dictionary()
-        with self.assertRaisesRegex(RuntimeError, "Duplicate"):
-            d.add_from_file(dict_file)
-
-    def test_space(self):
-        # for example, character models treat space as a symbol
-        dict_file = io.StringIO("  999\n" "a 999\n" "b 999\n")
-        d = Dictionary()
-        d.add_from_file(dict_file)
-        self.assertEqual(d.index(" "), 4)
-        self.assertEqual(d.index("a"), 5)
-        self.assertEqual(d.index("b"), 6)
-
-    def test_add_file_to_dict(self):
-        counts = {}
-        num_lines = 100
-        per_line = 10
-        with tempfile.TemporaryDirectory("test_sampling") as data_dir:
-            filename = os.path.join(data_dir, "dummy.txt")
-            with open(filename, "w", encoding="utf-8") as data:
-                for c in string.ascii_letters:
-                    line = f"{c} " * per_line
-                    for _ in range(num_lines):
-                        data.write(f"{line}\n")
-                    counts[c] = per_line * num_lines
-                    per_line += 5
-
-            dict = Dictionary()
-            Dictionary.add_file_to_dictionary(
-                filename, dict, tokenizer.tokenize_line, 10
-            )
-            dict.finalize(threshold=0, nwords=-1, padding_factor=8)
-
-            for c in string.ascii_letters:
-                count = dict.get_count(dict.index(c))
-                self.assertEqual(
-                    counts[c], count, f"{c} count is {count} but should be {counts[c]}"
-                )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/logging/metrics.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/logging/metrics.py
deleted file mode 100644
index 58c2fb64e186ed9d5e9a06c73194d98a21bb7560..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/logging/metrics.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-A standalone module for aggregating metrics.
-
-Metrics can be logged from anywhere using the `log_*` functions defined
-in this module. The logged values will be aggregated dynamically based
-on the aggregation context in which the logging occurs. See the
-:func:`aggregate` context manager for more details.
-"""
-
-import contextlib
-import uuid
-from collections import defaultdict
-from typing import Callable, List, Optional
-
-from .meters import *
-
-
-# Aggregation contexts are considered "active" when inside the scope
-# created by the :func:`aggregate` context manager.
-_aggregators = OrderedDict()
-_active_aggregators = OrderedDict()
-_active_aggregators_cnt = defaultdict(lambda: 0)
-
-
-def reset() -> None:
-    """Reset all metrics aggregators."""
-    _aggregators.clear()
-    _active_aggregators.clear()
-    _active_aggregators_cnt.clear()
-
-    # The "default" aggregator observes all logged values.
-    _aggregators["default"] = MetersDict()
-    _active_aggregators["default"] = _aggregators["default"]
-    _active_aggregators_cnt["default"] = 1
-
-
-reset()
-
-
-@contextlib.contextmanager
-def aggregate(name: Optional[str] = None, new_root: bool = False):
-    """Context manager to aggregate metrics under a given name.
-
-    Aggregations can be nested. If *new_root* is ``False``, then logged
-    metrics will be recorded along the entire stack of nested
-    aggregators, including a global "default" aggregator. If *new_root*
-    is ``True``, then this aggregator will be the root of a new
-    aggregation stack, thus bypassing any parent aggregators.
-
-    Note that aggregation contexts are uniquely identified by their
-    *name* (e.g., train, valid). Creating a context with an existing
-    name will reuse the corresponding :class:`MetersDict` instance.
-    If no name is given, then a temporary aggregator will be created.
-
-    Usage::
-
-        with metrics.aggregate("train"):
-            for step, batch in enumerate(epoch):
-                with metrics.aggregate("train_inner") as agg:
-                    metrics.log_scalar("loss", get_loss(batch))
-                    if step % log_interval == 0:
-                        print(agg.get_smoothed_value("loss"))
-                        agg.reset()
-        print(metrics.get_smoothed_values("train")["loss"])
-
-    Args:
-        name (str): name of the aggregation. Defaults to a
-            random/temporary name if not given explicitly.
-        new_root (bool): make this aggregation the root of a new
-            aggregation stack.
-    """
-    if name is None:
-        # generate a temporary name
-        name = str(uuid.uuid4())
-        assert name not in _aggregators
-        agg = MetersDict()
-    else:
-        assert name != "default"
-        agg = _aggregators.setdefault(name, MetersDict())
-
-    if new_root:
-        backup_aggregators = _active_aggregators.copy()
-        _active_aggregators.clear()
-        backup_aggregators_cnt = _active_aggregators_cnt.copy()
-        _active_aggregators_cnt.clear()
-
-    _active_aggregators[name] = agg
-    _active_aggregators_cnt[name] += 1
-
-    yield agg
-
-    _active_aggregators_cnt[name] -= 1
-    if _active_aggregators_cnt[name] == 0 and name in _active_aggregators:
-        del _active_aggregators[name]
-
-    if new_root:
-        _active_aggregators.clear()
-        _active_aggregators.update(backup_aggregators)
-        _active_aggregators_cnt.clear()
-        _active_aggregators_cnt.update(backup_aggregators_cnt)
-
-
-def get_active_aggregators() -> List[MetersDict]:
-    return list(_active_aggregators.values())
-
-
-def log_scalar(
-    key: str,
-    value: float,
-    weight: float = 1,
-    priority: int = 10,
-    round: Optional[int] = None,
-):
-    """Log a scalar value.
-
-    Args:
-        key (str): name of the field to log
-        value (float): value to log
-        weight (float): weight that this value contributes to the average.
-            A weight of 0 will always log the latest value.
-        priority (int): smaller values are logged earlier in the output
-        round (Optional[int]): number of digits to round to when displaying
-    """
-    for agg in get_active_aggregators():
-        if key not in agg:
-            agg.add_meter(key, AverageMeter(round=round), priority)
-        agg[key].update(value, weight)
-
-def log_scalar_sum(
-    key: str,
-    value: float,
-    priority: int = 10,
-    round: Optional[int] = None,
-):
-    """Log a scalar value that is summed for reporting.
-
-    Args:
-        key (str): name of the field to log
-        value (float): value to log
-        priority (int): smaller values are logged earlier in the output
-        round (Optional[int]): number of digits to round to when displaying
-    """
-    for agg in get_active_aggregators():
-        if key not in agg:
-            agg.add_meter(key, SumMeter(round=round), priority)
-        agg[key].update(value)
-
-
-def log_derived(key: str, fn: Callable[[MetersDict], float], priority: int = 20):
-    """Log a scalar value derived from other meters.
-
-    Args:
-        key (str): name of the field to log
-        fn (Callable[[MetersDict], float]): function that takes a single
-            argument *meters* and returns the derived value
-        priority (int): smaller values are logged earlier in the output
-    """
-    for agg in get_active_aggregators():
-        if key not in agg:
-            agg.add_meter(key, MetersDict._DerivedMeter(fn), priority)
-
-
-def log_speed(
-    key: str,
-    value: float,
-    priority: int = 30,
-    round: Optional[int] = None,
-):
-    """Log the rate of some quantity per second.
-
-    Args:
-        key (str): name of the field to log
-        value (float): value to log
-        priority (int): smaller values are logged earlier in the output
-        round (Optional[int]): number of digits to round to when displaying
-    """
-    for agg in get_active_aggregators():
-        if key not in agg:
-            agg.add_meter(key, TimeMeter(round=round), priority)
-            agg[key].reset()  # reset meter on the first call
-        else:
-            agg[key].update(value)
-
-
-def log_start_time(key: str, priority: int = 40, round: Optional[int] = None):
-    """Log the duration of some event in seconds.
-
-    The duration will be computed once :func:`log_stop_time` is called.
-
-    Args:
-        key (str): name of the field to log
-        priority (int): smaller values are logged earlier in the output
-        round (Optional[int]): number of digits to round to when displaying
-    """
-    for agg in get_active_aggregators():
-        if key not in agg:
-            agg.add_meter(key, StopwatchMeter(round=round), priority)
-        agg[key].start()
-
-
-def log_stop_time(key: str, weight: float = 0.0, prehook=None):
-    """Log the duration of some event in seconds.
-
-    The duration will be computed since :func:`log_start_time` was called.
-    Set weight > 0 to report the average time instead of the sum.
-
-    Args:
-        key (str): name of the field to log
-        weight (float): weight that this time contributes to the average
-        prehook (function, no arguments): will be called before the timer
-        is stopped. For example, use prehook=torch.cuda.synchronize to
-        make sure all gpu operations are done before timer is stopped.
-    """
-    for agg in get_active_aggregators():
-        if key in agg:
-            agg[key].stop(weight, prehook)
-
-
-def log_custom(
-    new_meter_fn: Callable[[], Meter],
-    key: str,
-    *args,
-    priority: int = 50,
-    **kwargs,
-):
-    """Log using a custom Meter.
-
-    Any extra *args* or *kwargs* will be passed through to the Meter's
-    *update* method.
-
-    Args:
-        new_meter_fn (Callable[[], Meter]): function that returns a new
-            Meter instance
-        key (str): name of the field to log
-        priority (int): smaller values are logged earlier in the output
-    """
-    for agg in get_active_aggregators():
-        if key not in agg:
-            agg.add_meter(key, new_meter_fn(), priority)
-        agg[key].update(*args, **kwargs)
-
-
-def reset_meter(name: str, key: str) -> None:
-    """Reset Meter instance aggregated under a given *name* and *key*."""
-    meter = get_meter(name, key)
-    if meter is not None:
-        meter.reset()
-
-
-def reset_meters(name: str) -> None:
-    """Reset Meter instances aggregated under a given *name*."""
-    meters = get_meters(name)
-    if meters is not None:
-        meters.reset()
-
-
-def get_meter(name: str, key: str) -> Meter:
-    """Get a single Meter instance aggregated under *name* and *key*.
-
-    Returns:
-        Meter or None if no metrics have been logged under *name* and *key*.
-    """
-    if name not in _aggregators:
-        return None
-    return _aggregators[name].get(key, None)
-
-
-def get_meters(name: str) -> MetersDict:
-    """Get Meter instances aggregated under a given *name*.
-
-    Returns:
-        MetersDict or None if no metrics have been logged under *name*.
-    """
-    return _aggregators.get(name, None)
-
-
-def get_smoothed_value(name: str, key: str) -> float:
-    """Get a single smoothed value.
-
-    Raises:
-        KeyError: if no metrics have been logged under *name* and *key*.
-    """
-    return _aggregators[name].get_smoothed_value(key)
-
-
-def get_smoothed_values(name: str) -> Dict[str, float]:
-    """Get smoothed values aggregated under a given *name*.
-
-    Raises:
-        KeyError: if no metrics have been logged under *name*.
-    """
-    return _aggregators[name].get_smoothed_values()
-
-
-def state_dict():
-    return OrderedDict([(name, agg.state_dict()) for name, agg in _aggregators.items()])
-
-
-def load_state_dict(state_dict):
-    for name, agg_state in state_dict.items():
-        _aggregators[name] = MetersDict()
-        _aggregators[name].load_state_dict(agg_state)
-
-
-def xla_metrics_report():
-    try:
-        import torch_xla.debug.metrics as met
-        print(met.metrics_report())
-    except ImportError:
-        return
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp
deleted file mode 100644
index ece47a8d908b93cec102743070c9057986d39d3f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#include <torch/extension.h>
-#include <vector>
-
-std::vector<at::Tensor>
-lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l);
-
-std::vector<at::Tensor> lightconv_cuda_backward(
-    at::Tensor gradOutput,
-    int padding_l,
-    at::Tensor input,
-    at::Tensor filters);
-
-#define CHECK_CUDA(x) \
-  AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
-  AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
-  CHECK_CUDA(x);       \
-  CHECK_CONTIGUOUS(x)
-
-std::vector<at::Tensor>
-lightconv_forward(at::Tensor input, at::Tensor filters, int padding_l) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(filters);
-
-  return lightconv_cuda_forward(input, filters, padding_l);
-}
-
-std::vector<at::Tensor> lightconv_backward(
-    at::Tensor gradOutput,
-    int padding_l,
-    at::Tensor input,
-    at::Tensor filters) {
-  CHECK_INPUT(gradOutput);
-  CHECK_INPUT(input);
-  CHECK_INPUT(filters);
-
-  return lightconv_cuda_backward(gradOutput, padding_l, input, filters);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("forward", &lightconv_forward, "lighconv forward (CUDA)");
-  m.def("backward", &lightconv_backward, "lighconv backward (CUDA)");
-}
diff --git a/spaces/ORI-Muchim/PowerTTS/monotonic_align/__init__.py b/spaces/ORI-Muchim/PowerTTS/monotonic_align/__init__.py
deleted file mode 100644
index 40b6f64aa116c74cac2f6a33444c9eeea2fdb38c..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/PowerTTS/monotonic_align/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba optimized version.
-    neg_cent: [b, t_t, t_s]
-    mask: [b, t_t, t_s]
-    """
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)
-
diff --git a/spaces/Olivier-Truong/faster-whisper-webui-v2/cli.py b/spaces/Olivier-Truong/faster-whisper-webui-v2/cli.py
deleted file mode 100644
index 66e8b9480b4b8c93eedabd19af0757bfde580732..0000000000000000000000000000000000000000
--- a/spaces/Olivier-Truong/faster-whisper-webui-v2/cli.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import argparse
-import os
-import pathlib
-from urllib.parse import urlparse
-import warnings
-import numpy as np
-
-import torch
-from app import VadOptions, WhisperTranscriber
-from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
-from src.diarization.diarization import Diarization
-from src.download import download_url
-from src.languages import get_language_names
-
-from src.utils import optional_float, optional_int, str2bool
-from src.whisper.whisperFactory import create_whisper_container
-
-def cli():
-    app_config = ApplicationConfig.create_default()
-    whisper_models = app_config.get_model_names()
-
-    # For the CLI, we fallback to saving the output to the current directory
-    output_dir = app_config.output_dir if app_config.output_dir is not None else "."
-
-    # Environment variable overrides
-    default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", app_config.whisper_implementation)
-
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("audio", nargs="+", type=str, \
-                        help="audio file(s) to transcribe")
-    parser.add_argument("--model", default=app_config.default_model_name, choices=whisper_models, \
-                        help="name of the Whisper model to use") # medium
-    parser.add_argument("--model_dir", type=str, default=app_config.model_dir, \
-                        help="the path to save model files; uses ~/.cache/whisper by default")
-    parser.add_argument("--device", default=app_config.device, \
-                        help="device to use for PyTorch inference")
-    parser.add_argument("--output_dir", "-o", type=str, default=output_dir, \
-                        help="directory to save the outputs")
-    parser.add_argument("--verbose", type=str2bool, default=app_config.verbose, \
-                        help="whether to print out the progress and debug messages")
-    parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\
-                        help="the Whisper implementation to use")
-                        
-    parser.add_argument("--task", type=str, default=app_config.task, choices=["transcribe", "translate"], \
-                        help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
-    parser.add_argument("--language", type=str, default=app_config.language, choices=sorted(get_language_names()), \
-                        help="language spoken in the audio, specify None to perform language detection")
-
-    parser.add_argument("--vad", type=str, default=app_config.default_vad, choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], \
-                        help="The voice activity detection algorithm to use") # silero-vad
-    parser.add_argument("--vad_initial_prompt_mode", type=str, default=app_config.vad_initial_prompt_mode, choices=VAD_INITIAL_PROMPT_MODE_VALUES, \
-                        help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment
-    parser.add_argument("--vad_merge_window", type=optional_float, default=app_config.vad_merge_window, \
-                        help="The window size (in seconds) to merge voice segments")
-    parser.add_argument("--vad_max_merge_size", type=optional_float, default=app_config.vad_max_merge_size,\
-                         help="The maximum size (in seconds) of a voice segment")
-    parser.add_argument("--vad_padding", type=optional_float, default=app_config.vad_padding, \
-                        help="The padding (in seconds) to add to each voice segment")
-    parser.add_argument("--vad_prompt_window", type=optional_float, default=app_config.vad_prompt_window, \
-                        help="The window size of the prompt to pass to Whisper")
-    parser.add_argument("--vad_cpu_cores", type=int, default=app_config.vad_cpu_cores, \
-                        help="The number of CPU cores to use for VAD pre-processing.") # 1
-    parser.add_argument("--vad_parallel_devices", type=str, default=app_config.vad_parallel_devices, \
-                        help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # ""
-    parser.add_argument("--auto_parallel", type=bool, default=app_config.auto_parallel, \
-                        help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False
-
-    parser.add_argument("--temperature", type=float, default=app_config.temperature, \
-                        help="temperature to use for sampling")
-    parser.add_argument("--best_of", type=optional_int, default=app_config.best_of, \
-                        help="number of candidates when sampling with non-zero temperature")
-    parser.add_argument("--beam_size", type=optional_int, default=app_config.beam_size, \
-                        help="number of beams in beam search, only applicable when temperature is zero")
-    parser.add_argument("--patience", type=float, default=app_config.patience, \
-                        help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
-    parser.add_argument("--length_penalty", type=float, default=app_config.length_penalty, \
-                        help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple lengt normalization by default")
-
-    parser.add_argument("--suppress_tokens", type=str, default=app_config.suppress_tokens, \
-                        help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
-    parser.add_argument("--initial_prompt", type=str, default=app_config.initial_prompt, \
-                        help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--condition_on_previous_text", type=str2bool, default=app_config.condition_on_previous_text, \
-                        help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
-    parser.add_argument("--fp16", type=str2bool, default=app_config.fp16, \
-                        help="whether to perform inference in fp16; True by default")
-    parser.add_argument("--compute_type", type=str, default=app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \
-                        help="the compute type to use for inference")
-
-    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=app_config.temperature_increment_on_fallback, \
-                        help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
-    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=app_config.compression_ratio_threshold, \
-                        help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
-    parser.add_argument("--logprob_threshold", type=optional_float, default=app_config.logprob_threshold, \
-                        help="if the average log probability is lower than this value, treat the decoding as failed")
-    parser.add_argument("--no_speech_threshold", type=optional_float, default=app_config.no_speech_threshold, \
-                        help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
-
-    parser.add_argument("--word_timestamps", type=str2bool, default=app_config.word_timestamps, 
-                        help="(experimental) extract word-level timestamps and refine the results based on them")
-    parser.add_argument("--prepend_punctuations", type=str, default=app_config.prepend_punctuations, 
-                        help="if word_timestamps is True, merge these punctuation symbols with the next word")
-    parser.add_argument("--append_punctuations", type=str, default=app_config.append_punctuations, 
-                        help="if word_timestamps is True, merge these punctuation symbols with the previous word")
-    parser.add_argument("--highlight_words", type=str2bool, default=app_config.highlight_words,
-                        help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
-    parser.add_argument("--threads", type=optional_int, default=0, 
-                        help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
-
-    # Diarization
-    parser.add_argument('--auth_token', type=str, default=app_config.auth_token, help='HuggingFace API Token (optional)')
-    parser.add_argument("--diarization", type=str2bool, default=app_config.diarization, \
-                        help="whether to perform speaker diarization")
-    parser.add_argument("--diarization_num_speakers", type=int, default=app_config.diarization_speakers, help="Number of speakers")
-    parser.add_argument("--diarization_min_speakers", type=int, default=app_config.diarization_min_speakers, help="Minimum number of speakers")
-    parser.add_argument("--diarization_max_speakers", type=int, default=app_config.diarization_max_speakers, help="Maximum number of speakers")
-
-    args = parser.parse_args().__dict__
-    model_name: str = args.pop("model")
-    model_dir: str = args.pop("model_dir")
-    output_dir: str = args.pop("output_dir")
-    device: str = args.pop("device")
-    os.makedirs(output_dir, exist_ok=True)
-
-    if (threads := args.pop("threads")) > 0:
-        torch.set_num_threads(threads)
-
-    whisper_implementation = args.pop("whisper_implementation")
-    print(f"Using {whisper_implementation} for Whisper")
-
-    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
-        warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
-        args["language"] = "en"
-
-    temperature = args.pop("temperature")
-    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
-    if temperature_increment_on_fallback is not None:
-        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
-    else:
-        temperature = [temperature]
-
-    vad = args.pop("vad")
-    vad_initial_prompt_mode = args.pop("vad_initial_prompt_mode")
-    vad_merge_window = args.pop("vad_merge_window")
-    vad_max_merge_size = args.pop("vad_max_merge_size")
-    vad_padding = args.pop("vad_padding")
-    vad_prompt_window = args.pop("vad_prompt_window")
-    vad_cpu_cores = args.pop("vad_cpu_cores")
-    auto_parallel = args.pop("auto_parallel")
-    
-    compute_type = args.pop("compute_type")
-    highlight_words = args.pop("highlight_words")
-
-    auth_token = args.pop("auth_token")
-    diarization = args.pop("diarization")
-    num_speakers = args.pop("diarization_num_speakers")
-    min_speakers = args.pop("diarization_min_speakers")
-    max_speakers = args.pop("diarization_max_speakers")
-    
-    transcriber = WhisperTranscriber(delete_uploaded_files=False, vad_cpu_cores=vad_cpu_cores, app_config=app_config)
-    transcriber.set_parallel_devices(args.pop("vad_parallel_devices"))
-    transcriber.set_auto_parallel(auto_parallel)
-
-    if diarization:
-        transcriber.set_diarization(auth_token=auth_token, enable_daemon_process=False, num_speakers=num_speakers, min_speakers=min_speakers, max_speakers=max_speakers)
-
-    model = create_whisper_container(whisper_implementation=whisper_implementation, model_name=model_name, 
-                                     device=device, compute_type=compute_type, download_root=model_dir, models=app_config.models)
-    
-    if (transcriber._has_parallel_devices()):
-        print("Using parallel devices:", transcriber.parallel_device_list)
-
-    for audio_path in args.pop("audio"):
-        sources = []
-
-        # Detect URL and download the audio
-        if (uri_validator(audio_path)):
-            # Download from YouTube/URL directly
-            for source_path in  download_url(audio_path, maxDuration=-1, destinationDirectory=output_dir, playlistItems=None):
-                source_name = os.path.basename(source_path)
-                sources.append({ "path": source_path, "name": source_name })
-        else:
-            sources.append({ "path": audio_path, "name": os.path.basename(audio_path) })
-
-        for source in sources:
-            source_path = source["path"]
-            source_name = source["name"]
-
-            vadOptions = VadOptions(vad, vad_merge_window, vad_max_merge_size, vad_padding, vad_prompt_window, 
-                                    VadInitialPromptMode.from_string(vad_initial_prompt_mode))
-
-            result = transcriber.transcribe_file(model, source_path, temperature=temperature, vadOptions=vadOptions, **args)
-            
-            transcriber.write_result(result, source_name, output_dir, highlight_words)
-
-    transcriber.close()
-
-def uri_validator(x):
-    try:
-        result = urlparse(x)
-        return all([result.scheme, result.netloc])
-    except:
-        return False
-
-if __name__ == '__main__':
-    cli()
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py
deleted file mode 100644
index cbb32e19ea518eee84941b20f58d1054e84d1937..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import dataclasses
-import logging
-from collections import abc
-from typing import Any
-
-from detectron2.utils.registry import _convert_target_to_string, locate
-
-__all__ = ["dump_dataclass", "instantiate"]
-
-
-def dump_dataclass(obj: Any):
-    """
-    Dump a dataclass recursively into a dict that can be later instantiated.
-
-    Args:
-        obj: a dataclass object
-
-    Returns:
-        dict
-    """
-    assert dataclasses.is_dataclass(obj) and not isinstance(
-        obj, type
-    ), "dump_dataclass() requires an instance of a dataclass."
-    ret = {"_target_": _convert_target_to_string(type(obj))}
-    for f in dataclasses.fields(obj):
-        v = getattr(obj, f.name)
-        if dataclasses.is_dataclass(v):
-            v = dump_dataclass(v)
-        if isinstance(v, (list, tuple)):
-            v = [dump_dataclass(x) if dataclasses.is_dataclass(x) else x for x in v]
-        ret[f.name] = v
-    return ret
-
-
-def instantiate(cfg):
-    """
-    Recursively instantiate objects defined in dictionaries by
-    "_target_" and arguments.
-
-    Args:
-        cfg: a dict-like object with "_target_" that defines the caller, and
-            other keys that define the arguments
-
-    Returns:
-        object instantiated by cfg
-    """
-    from omegaconf import ListConfig
-
-    if isinstance(cfg, ListConfig):
-        lst = [instantiate(x) for x in cfg]
-        return ListConfig(lst, flags={"allow_objects": True})
-    if isinstance(cfg, list):
-        # Specialize for list, because many classes take
-        # list[objects] as arguments, such as ResNet, DatasetMapper
-        return [instantiate(x) for x in cfg]
-
-    if isinstance(cfg, abc.Mapping) and "_target_" in cfg:
-        # conceptually equivalent to hydra.utils.instantiate(cfg) with _convert_=all,
-        # but faster: https://github.com/facebookresearch/hydra/issues/1200
-        cfg = {k: instantiate(v) for k, v in cfg.items()}
-        cls = cfg.pop("_target_")
-        cls = instantiate(cls)
-
-        if isinstance(cls, str):
-            cls_name = cls
-            cls = locate(cls_name)
-            assert cls is not None, cls_name
-        else:
-            try:
-                cls_name = cls.__module__ + "." + cls.__qualname__
-            except Exception:
-                # target could be anything, so the above could fail
-                cls_name = str(cls)
-        assert callable(cls), f"_target_ {cls} does not define a callable object"
-        try:
-            return cls(**cfg)
-        except TypeError:
-            logger = logging.getLogger(__name__)
-            logger.error(f"Error when instantiating {cls_name}!")
-            raise
-    return cfg  # return as-is if don't know what to do
diff --git a/spaces/OptimalScale/Robin-33b/lmflow/models/text_regression_model.py b/spaces/OptimalScale/Robin-33b/lmflow/models/text_regression_model.py
deleted file mode 100644
index 285cbc4f4c515adf742ec4f5bc17a43f8e5e3057..0000000000000000000000000000000000000000
--- a/spaces/OptimalScale/Robin-33b/lmflow/models/text_regression_model.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-"""
-A model maps "text_only" data to float.
-"""
-
-from lmflow.models.regression_model import RegressionModel
-from lmflow.datasets.dataset import Dataset
-
-
-class TextRegressionModel(RegressionModel):
-    r"""
-    Initializes a TextRegressionModel instance.
-
-    Parameters
-    ------------
-
-    model_args : 
-        Model arguments such as model name, path, revision, etc.
-
-    args : Optional.
-        Positional arguments.
-
-    kwargs : Optional.
-        Keyword arguments.    
-    """
-
-    def __init__(
-        self,
-        model_args,
-        *args,
-        **kwargs
-    ):
-        """
-        Initializes a TextRegressionModel instance.
-        :param model_args: dictionary with model arguments such as model name, path, revision, etc.
-        """
-        self.inference_func = None
-
-
-    def register_inference_function(self, inference_func):
-        """
-        Registers a regression function.
-        """
-        self.inference_func = inference_func
-
-
-    def inference(self, inputs: Dataset):
-        """
-        Gets regression results of a given dataset.
-
-        :inputs: Dataset object, only accept type "text_only".
-        """
-        if self.inference_func is not None:
-            return self.inference_func(inputs)
-        else:
-            pass
diff --git a/spaces/Ordenador/classify-text-with-bert-hate-speech/app.py b/spaces/Ordenador/classify-text-with-bert-hate-speech/app.py
deleted file mode 100644
index 08bc093dbec99175cadaa5ec6f8446aa6207222a..0000000000000000000000000000000000000000
--- a/spaces/Ordenador/classify-text-with-bert-hate-speech/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import gradio as gr
-from official.nlp.optimization import AdamWeightDecay, WarmUp
-import tensorflow as tf
-import tensorflow_hub as hub
-import tensorflow_text as text
-import numpy as np
-np.set_printoptions(suppress=True)
-
-labels = [
-    "hate speech",
-    "offensive language",
-    "neither"
-]
-
-
-with tf.keras.utils.custom_object_scope({'AdamWeightDecay': AdamWeightDecay(), 'WarmUp': WarmUp}):
-    classifier_model = tf.keras.models.load_model('classifier_model.h5',
-                                                  custom_objects={'KerasLayer': hub.KerasLayer})
-
-
-def run_model(text):
-    prediction = classifier_model.predict([text])[0]
-    confidences = {labels[i]: float(prediction[i]) for i in range(len(labels))}
-    return confidences
-
-
-examples = [
-    ["This is wonderful!"],
-]
-
-short_description = "This application classifies text into three categories: hate speech, offensive language, and neither, using a deep learning model trained on the Hate Speech and Offensive Language Dataset. Enter a sentence and the model will predict its category."
-
-hate_speech = gr.Interface(
-    fn=run_model,
-    inputs=gr.Textbox(lines=5,
-                      placeholder="Enter a sentence here...",
-                      label="Input Text"),
-    outputs=gr.outputs.Label(),
-    examples=examples,
-    title="Hate Speech and Offensive Language Classifier",
-    description=short_description
-)
-
-hate_speech.launch()
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/utils.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/utils.go
deleted file mode 100644
index e3c7cc2f88895117a481ce1d02e5962441bb8355..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/utils.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/spinner.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/spinner.py
deleted file mode 100644
index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/spinner.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""A simple spinner module"""
-import itertools
-import sys
-import threading
-import time
-
-
-class Spinner:
-    """A simple spinner class"""
-
-    def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
-        """Initialize the spinner class
-
-        Args:
-            message (str): The message to display.
-            delay (float): The delay between each spinner update.
-        """
-        self.spinner = itertools.cycle(["-", "/", "|", "\\"])
-        self.delay = delay
-        self.message = message
-        self.running = False
-        self.spinner_thread = None
-
-    def spin(self) -> None:
-        """Spin the spinner"""
-        while self.running:
-            sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
-            sys.stdout.flush()
-            time.sleep(self.delay)
-            sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
-
-    def __enter__(self):
-        """Start the spinner"""
-        self.running = True
-        self.spinner_thread = threading.Thread(target=self.spin)
-        self.spinner_thread.start()
-
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
-        """Stop the spinner
-
-        Args:
-            exc_type (Exception): The exception type.
-            exc_value (Exception): The exception value.
-            exc_traceback (Exception): The exception traceback.
-        """
-        self.running = False
-        if self.spinner_thread is not None:
-            self.spinner_thread.join()
-        sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
-        sys.stdout.flush()
-
-    def update_message(self, new_message, delay=0.1):
-        """Update the spinner message
-        Args:
-            new_message (str): New message to display
-            delay: Delay in seconds before updating the message
-        """
-        time.sleep(delay)
-        sys.stdout.write(
-            f"\r{' ' * (len(self.message) + 2)}\r"
-        )  # Clear the current message
-        sys.stdout.flush()
-        self.message = new_message
diff --git a/spaces/Qiukai/gpt/crazy_functions/test_project/cpp/cppipc/queue.h b/spaces/Qiukai/gpt/crazy_functions/test_project/cpp/cppipc/queue.h
deleted file mode 100644
index a21f3446e06b5826af7b554c8a7d9c5d80848b62..0000000000000000000000000000000000000000
--- a/spaces/Qiukai/gpt/crazy_functions/test_project/cpp/cppipc/queue.h
+++ /dev/null
@@ -1,216 +0,0 @@
-#pragma once
-
-#include <type_traits>
-#include <new>
-#include <utility>  // [[since C++14]]: std::exchange
-#include <algorithm>
-#include <atomic>
-#include <tuple>
-#include <thread>
-#include <chrono>
-#include <string>
-#include <cassert>  // assert
-
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/rw_lock.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-
-namespace ipc {
-namespace detail {
-
-class queue_conn {
-protected:
-    circ::cc_t connected_ = 0;
-    shm::handle elems_h_;
-
-    template <typename Elems>
-    Elems* open(char const * name) {
-        if (name == nullptr || name[0] == '\0') {
-            ipc::error("fail open waiter: name is empty!\n");
-            return nullptr;
-        }
-        if (!elems_h_.acquire(name, sizeof(Elems))) {
-            return nullptr;
-        }
-        auto elems = static_cast<Elems*>(elems_h_.get());
-        if (elems == nullptr) {
-            ipc::error("fail acquire elems: %s\n", name);
-            return nullptr;
-        }
-        elems->init();
-        return elems;
-    }
-
-    void close() {
-        elems_h_.release();
-    }
-
-public:
-    queue_conn() = default;
-    queue_conn(const queue_conn&) = delete;
-    queue_conn& operator=(const queue_conn&) = delete;
-
-    bool connected() const noexcept {
-        return connected_ != 0;
-    }
-
-    circ::cc_t connected_id() const noexcept {
-        return connected_;
-    }
-
-    template <typename Elems>
-    auto connect(Elems* elems) noexcept
-                         /*needs 'optional' here*/
-     -> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {
-        if (elems == nullptr) return {};
-        // if it's already connected, just return
-        if (connected()) return {connected(), false, 0};
-        connected_ = elems->connect_receiver();
-        return {connected(), true, elems->cursor()};
-    }
-
-    template <typename Elems>
-    bool disconnect(Elems* elems) noexcept {
-        if (elems == nullptr) return false;
-        // if it's already disconnected, just return false
-        if (!connected()) return false;
-        elems->disconnect_receiver(std::exchange(connected_, 0));
-        return true;
-    }
-};
-
-template <typename Elems>
-class queue_base : public queue_conn {
-    using base_t = queue_conn;
-
-public:
-    using elems_t  = Elems;
-    using policy_t = typename elems_t::policy_t;
-
-protected:
-    elems_t * elems_ = nullptr;
-    decltype(std::declval<elems_t>().cursor()) cursor_ = 0;
-    bool sender_flag_ = false;
-
-public:
-    using base_t::base_t;
-
-    queue_base() = default;
-
-    explicit queue_base(char const * name)
-        : queue_base{} {
-        elems_ = open<elems_t>(name);
-    }
-
-    explicit queue_base(elems_t * elems) noexcept
-        : queue_base{} {
-        assert(elems != nullptr);
-        elems_ = elems;
-    }
-
-    /* not virtual */ ~queue_base() {
-        base_t::close();
-    }
-
-    elems_t       * elems()       noexcept { return elems_; }
-    elems_t const * elems() const noexcept { return elems_; }
-
-    bool ready_sending() noexcept {
-        if (elems_ == nullptr) return false;
-        return sender_flag_ || (sender_flag_ = elems_->connect_sender());
-    }
-
-    void shut_sending() noexcept {
-        if (elems_ == nullptr) return;
-        if (!sender_flag_) return;
-        elems_->disconnect_sender();
-    }
-
-    bool connect() noexcept {
-        auto tp = base_t::connect(elems_);
-        if (std::get<0>(tp) && std::get<1>(tp)) {
-            cursor_ = std::get<2>(tp);
-            return true;
-        }
-        return std::get<0>(tp);
-    }
-
-    bool disconnect() noexcept {
-        return base_t::disconnect(elems_);
-    }
-
-    std::size_t conn_count() const noexcept {
-        return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();
-    }
-
-    bool valid() const noexcept {
-        return elems_ != nullptr;
-    }
-
-    bool empty() const noexcept {
-        return !valid() || (cursor_ == elems_->cursor());
-    }
-
-    template <typename T, typename F, typename... P>
-    bool push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
-        });
-    }
-
-    template <typename T, typename F, typename... P>
-    bool force_push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->force_push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
-        });
-    }
-
-    template <typename T, typename F>
-    bool pop(T& item, F&& out) {
-        if (elems_ == nullptr) {
-            return false;
-        }
-        return elems_->pop(this, &(this->cursor_), [&item](void* p) {
-            ::new (&item) T(std::move(*static_cast<T*>(p)));
-        }, std::forward<F>(out));
-    }
-};
-
-} // namespace detail
-
-template <typename T, typename Policy>
-class queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {
-    using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;
-
-public:
-    using value_t = T;
-
-    using base_t::base_t;
-
-    template <typename... P>
-    bool push(P&&... params) {
-        return base_t::template push<T>(std::forward<P>(params)...);
-    }
-
-    template <typename... P>
-    bool force_push(P&&... params) {
-        return base_t::template force_push<T>(std::forward<P>(params)...);
-    }
-
-    bool pop(T& item) {
-        return base_t::pop(item, [](bool) {});
-    }
-
-    template <typename F>
-    bool pop(T& item, F&& out) {
-        return base_t::pop(item, std::forward<F>(out));
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/Realcat/image-matching-webui/hloc/pipelines/Cambridge/__init__.py b/spaces/Realcat/image-matching-webui/hloc/pipelines/Cambridge/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/models/transformer/layers/layer_scale.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/models/transformer/layers/layer_scale.py
deleted file mode 100644
index ca5daa52bd81d3581adeb2198ea5b7dba2a3aea1..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/models/transformer/layers/layer_scale.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
-
-from typing import Union
-
-import torch
-from torch import Tensor
-from torch import nn
-
-
-class LayerScale(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        init_values: Union[float, Tensor] = 1e-5,
-        inplace: bool = False,
-    ) -> None:
-        super().__init__()
-        self.inplace = inplace
-        self.gamma = nn.Parameter(init_values * torch.ones(dim))
-
-    def forward(self, x: Tensor) -> Tensor:
-        return x.mul_(self.gamma) if self.inplace else x * self.gamma
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/colorspace.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/colorspace.py
deleted file mode 100644
index 814533952fdfda23d67cb6a3073692d8c1156add..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/colorspace.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-
-
-def imconvert(img, src, dst):
-    """Convert an image from the src colorspace to dst colorspace.
-
-    Args:
-        img (ndarray): The input image.
-        src (str): The source colorspace, e.g., 'rgb', 'hsv'.
-        dst (str): The destination colorspace, e.g., 'rgb', 'hsv'.
-
-    Returns:
-        ndarray: The converted image.
-    """
-    code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}')
-    out_img = cv2.cvtColor(img, code)
-    return out_img
-
-
-def bgr2gray(img, keepdim=False):
-    """Convert a BGR image to grayscale image.
-
-    Args:
-        img (ndarray): The input image.
-        keepdim (bool): If False (by default), then return the grayscale image
-            with 2 dims, otherwise 3 dims.
-
-    Returns:
-        ndarray: The converted grayscale image.
-    """
-    out_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    if keepdim:
-        out_img = out_img[..., None]
-    return out_img
-
-
-def rgb2gray(img, keepdim=False):
-    """Convert a RGB image to grayscale image.
-
-    Args:
-        img (ndarray): The input image.
-        keepdim (bool): If False (by default), then return the grayscale image
-            with 2 dims, otherwise 3 dims.
-
-    Returns:
-        ndarray: The converted grayscale image.
-    """
-    out_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-    if keepdim:
-        out_img = out_img[..., None]
-    return out_img
-
-
-def gray2bgr(img):
-    """Convert a grayscale image to BGR image.
-
-    Args:
-        img (ndarray): The input image.
-
-    Returns:
-        ndarray: The converted BGR image.
-    """
-    img = img[..., None] if img.ndim == 2 else img
-    out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-    return out_img
-
-
-def gray2rgb(img):
-    """Convert a grayscale image to RGB image.
-
-    Args:
-        img (ndarray): The input image.
-
-    Returns:
-        ndarray: The converted RGB image.
-    """
-    img = img[..., None] if img.ndim == 2 else img
-    out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-    return out_img
-
-
-def _convert_input_type_range(img):
-    """Convert the type and range of the input image.
-
-    It converts the input image to np.float32 type and range of [0, 1].
-    It is mainly used for pre-processing the input image in colorspace
-    conversion functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with type of np.float32 and range of
-            [0, 1].
-    """
-    img_type = img.dtype
-    img = img.astype(np.float32)
-    if img_type == np.float32:
-        pass
-    elif img_type == np.uint8:
-        img /= 255.
-    else:
-        raise TypeError('The img type should be np.float32 or np.uint8, '
-                        f'but got {img_type}')
-    return img
-
-
-def _convert_output_type_range(img, dst_type):
-    """Convert the type and range of the image according to dst_type.
-
-    It converts the image to desired type and range. If `dst_type` is np.uint8,
-    images will be converted to np.uint8 type with range [0, 255]. If
-    `dst_type` is np.float32, it converts the image to np.float32 type with
-    range [0, 1].
-    It is mainly used for post-processing images in colorspace conversion
-    functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The image to be converted with np.float32 type and
-            range [0, 255].
-        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
-            converts the image to np.uint8 type with range [0, 255]. If
-            dst_type is np.float32, it converts the image to np.float32 type
-            with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with desired type and range.
-    """
-    if dst_type not in (np.uint8, np.float32):
-        raise TypeError('The dst_type should be np.float32 or np.uint8, '
-                        f'but got {dst_type}')
-    if dst_type == np.uint8:
-        img = img.round()
-    else:
-        img /= 255.
-    return img.astype(dst_type)
-
-
-def rgb2ycbcr(img, y_only=False):
-    """Convert a RGB image to YCbCr image.
-
-    This function produces the same results as Matlab's `rgb2ycbcr` function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                  [24.966, 112.0, -18.214]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def bgr2ycbcr(img, y_only=False):
-    """Convert a BGR image to YCbCr image.
-
-    The bgr version of rgb2ycbcr.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2rgb(img):
-    """Convert a YCbCr image to RGB image.
-
-    This function produces the same results as Matlab's ycbcr2rgb function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted RGB image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
-                              [0, -0.00153632, 0.00791071],
-                              [0.00625893, -0.00318811, 0]]) * 255.0 + [
-                                  -222.921, 135.576, -276.836
-                              ]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2bgr(img):
-    """Convert a YCbCr image to BGR image.
-
-    The bgr version of ycbcr2rgb.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted BGR image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
-                              [0.00791071, -0.00153632, 0],
-                              [0, -0.00318811, 0.00625893]]) * 255.0 + [
-                                  -276.836, 135.576, -222.921
-                              ]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def convert_color_factory(src, dst):
-
-    code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}')
-
-    def convert_color(img):
-        out_img = cv2.cvtColor(img, code)
-        return out_img
-
-    convert_color.__doc__ = f"""Convert a {src.upper()} image to {dst.upper()}
-        image.
-
-    Args:
-        img (ndarray or str): The input image.
-
-    Returns:
-        ndarray: The converted {dst.upper()} image.
-    """
-
-    return convert_color
-
-
-bgr2rgb = convert_color_factory('bgr', 'rgb')
-
-rgb2bgr = convert_color_factory('rgb', 'bgr')
-
-bgr2hsv = convert_color_factory('bgr', 'hsv')
-
-hsv2bgr = convert_color_factory('hsv', 'bgr')
-
-bgr2hls = convert_color_factory('bgr', 'hls')
-
-hls2bgr = convert_color_factory('hls', 'bgr')
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/io.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/io.py
deleted file mode 100644
index d3fa2e8cc06b1a7b0b69de6406980b15d61a1e5d..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/io.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import io
-import os.path as osp
-from pathlib import Path
-
-import cv2
-import numpy as np
-from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION,
-                 IMREAD_UNCHANGED)
-
-from annotator.uniformer.mmcv.utils import check_file_exist, is_str, mkdir_or_exist
-
-try:
-    from turbojpeg import TJCS_RGB, TJPF_BGR, TJPF_GRAY, TurboJPEG
-except ImportError:
-    TJCS_RGB = TJPF_GRAY = TJPF_BGR = TurboJPEG = None
-
-try:
-    from PIL import Image, ImageOps
-except ImportError:
-    Image = None
-
-try:
-    import tifffile
-except ImportError:
-    tifffile = None
-
-jpeg = None
-supported_backends = ['cv2', 'turbojpeg', 'pillow', 'tifffile']
-
-imread_flags = {
-    'color': IMREAD_COLOR,
-    'grayscale': IMREAD_GRAYSCALE,
-    'unchanged': IMREAD_UNCHANGED,
-    'color_ignore_orientation': IMREAD_IGNORE_ORIENTATION | IMREAD_COLOR,
-    'grayscale_ignore_orientation':
-    IMREAD_IGNORE_ORIENTATION | IMREAD_GRAYSCALE
-}
-
-imread_backend = 'cv2'
-
-
-def use_backend(backend):
-    """Select a backend for image decoding.
-
-    Args:
-        backend (str): The image decoding backend type. Options are `cv2`,
-        `pillow`, `turbojpeg` (see https://github.com/lilohuang/PyTurboJPEG)
-        and `tifffile`. `turbojpeg` is faster but it only supports `.jpeg`
-        file format.
-    """
-    assert backend in supported_backends
-    global imread_backend
-    imread_backend = backend
-    if imread_backend == 'turbojpeg':
-        if TurboJPEG is None:
-            raise ImportError('`PyTurboJPEG` is not installed')
-        global jpeg
-        if jpeg is None:
-            jpeg = TurboJPEG()
-    elif imread_backend == 'pillow':
-        if Image is None:
-            raise ImportError('`Pillow` is not installed')
-    elif imread_backend == 'tifffile':
-        if tifffile is None:
-            raise ImportError('`tifffile` is not installed')
-
-
-def _jpegflag(flag='color', channel_order='bgr'):
-    channel_order = channel_order.lower()
-    if channel_order not in ['rgb', 'bgr']:
-        raise ValueError('channel order must be either "rgb" or "bgr"')
-
-    if flag == 'color':
-        if channel_order == 'bgr':
-            return TJPF_BGR
-        elif channel_order == 'rgb':
-            return TJCS_RGB
-    elif flag == 'grayscale':
-        return TJPF_GRAY
-    else:
-        raise ValueError('flag must be "color" or "grayscale"')
-
-
-def _pillow2array(img, flag='color', channel_order='bgr'):
-    """Convert a pillow image to numpy array.
-
-    Args:
-        img (:obj:`PIL.Image.Image`): The image loaded using PIL
-        flag (str): Flags specifying the color type of a loaded image,
-            candidates are 'color', 'grayscale' and 'unchanged'.
-            Default to 'color'.
-        channel_order (str): The channel order of the output image array,
-            candidates are 'bgr' and 'rgb'. Default to 'bgr'.
-
-    Returns:
-        np.ndarray: The converted numpy array
-    """
-    channel_order = channel_order.lower()
-    if channel_order not in ['rgb', 'bgr']:
-        raise ValueError('channel order must be either "rgb" or "bgr"')
-
-    if flag == 'unchanged':
-        array = np.array(img)
-        if array.ndim >= 3 and array.shape[2] >= 3:  # color image
-            array[:, :, :3] = array[:, :, (2, 1, 0)]  # RGB to BGR
-    else:
-        # Handle exif orientation tag
-        if flag in ['color', 'grayscale']:
-            img = ImageOps.exif_transpose(img)
-        # If the image mode is not 'RGB', convert it to 'RGB' first.
-        if img.mode != 'RGB':
-            if img.mode != 'LA':
-                # Most formats except 'LA' can be directly converted to RGB
-                img = img.convert('RGB')
-            else:
-                # When the mode is 'LA', the default conversion will fill in
-                #  the canvas with black, which sometimes shadows black objects
-                #  in the foreground.
-                #
-                # Therefore, a random color (124, 117, 104) is used for canvas
-                img_rgba = img.convert('RGBA')
-                img = Image.new('RGB', img_rgba.size, (124, 117, 104))
-                img.paste(img_rgba, mask=img_rgba.split()[3])  # 3 is alpha
-        if flag in ['color', 'color_ignore_orientation']:
-            array = np.array(img)
-            if channel_order != 'rgb':
-                array = array[:, :, ::-1]  # RGB to BGR
-        elif flag in ['grayscale', 'grayscale_ignore_orientation']:
-            img = img.convert('L')
-            array = np.array(img)
-        else:
-            raise ValueError(
-                'flag must be "color", "grayscale", "unchanged", '
-                f'"color_ignore_orientation" or "grayscale_ignore_orientation"'
-                f' but got {flag}')
-    return array
-
-
-def imread(img_or_path, flag='color', channel_order='bgr', backend=None):
-    """Read an image.
-
-    Args:
-        img_or_path (ndarray or str or Path): Either a numpy array or str or
-            pathlib.Path. If it is a numpy array (loaded image), then
-            it will be returned as is.
-        flag (str): Flags specifying the color type of a loaded image,
-            candidates are `color`, `grayscale`, `unchanged`,
-            `color_ignore_orientation` and `grayscale_ignore_orientation`.
-            By default, `cv2` and `pillow` backend would rotate the image
-            according to its EXIF info unless called with `unchanged` or
-            `*_ignore_orientation` flags. `turbojpeg` and `tifffile` backend
-            always ignore image's EXIF info regardless of the flag.
-            The `turbojpeg` backend only supports `color` and `grayscale`.
-        channel_order (str): Order of channel, candidates are `bgr` and `rgb`.
-        backend (str | None): The image decoding backend type. Options are
-            `cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`.
-            If backend is None, the global imread_backend specified by
-            ``mmcv.use_backend()`` will be used. Default: None.
-
-    Returns:
-        ndarray: Loaded image array.
-    """
-
-    if backend is None:
-        backend = imread_backend
-    if backend not in supported_backends:
-        raise ValueError(f'backend: {backend} is not supported. Supported '
-                         "backends are 'cv2', 'turbojpeg', 'pillow'")
-    if isinstance(img_or_path, Path):
-        img_or_path = str(img_or_path)
-
-    if isinstance(img_or_path, np.ndarray):
-        return img_or_path
-    elif is_str(img_or_path):
-        check_file_exist(img_or_path,
-                         f'img file does not exist: {img_or_path}')
-        if backend == 'turbojpeg':
-            with open(img_or_path, 'rb') as in_file:
-                img = jpeg.decode(in_file.read(),
-                                  _jpegflag(flag, channel_order))
-                if img.shape[-1] == 1:
-                    img = img[:, :, 0]
-            return img
-        elif backend == 'pillow':
-            img = Image.open(img_or_path)
-            img = _pillow2array(img, flag, channel_order)
-            return img
-        elif backend == 'tifffile':
-            img = tifffile.imread(img_or_path)
-            return img
-        else:
-            flag = imread_flags[flag] if is_str(flag) else flag
-            img = cv2.imread(img_or_path, flag)
-            if flag == IMREAD_COLOR and channel_order == 'rgb':
-                cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
-            return img
-    else:
-        raise TypeError('"img" must be a numpy array or a str or '
-                        'a pathlib.Path object')
-
-
-def imfrombytes(content, flag='color', channel_order='bgr', backend=None):
-    """Read an image from bytes.
-
-    Args:
-        content (bytes): Image bytes got from files or other streams.
-        flag (str): Same as :func:`imread`.
-        backend (str | None): The image decoding backend type. Options are
-            `cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the
-            global imread_backend specified by ``mmcv.use_backend()`` will be
-            used. Default: None.
-
-    Returns:
-        ndarray: Loaded image array.
-    """
-
-    if backend is None:
-        backend = imread_backend
-    if backend not in supported_backends:
-        raise ValueError(f'backend: {backend} is not supported. Supported '
-                         "backends are 'cv2', 'turbojpeg', 'pillow'")
-    if backend == 'turbojpeg':
-        img = jpeg.decode(content, _jpegflag(flag, channel_order))
-        if img.shape[-1] == 1:
-            img = img[:, :, 0]
-        return img
-    elif backend == 'pillow':
-        buff = io.BytesIO(content)
-        img = Image.open(buff)
-        img = _pillow2array(img, flag, channel_order)
-        return img
-    else:
-        img_np = np.frombuffer(content, np.uint8)
-        flag = imread_flags[flag] if is_str(flag) else flag
-        img = cv2.imdecode(img_np, flag)
-        if flag == IMREAD_COLOR and channel_order == 'rgb':
-            cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
-        return img
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
-    """Write image to file.
-
-    Args:
-        img (ndarray): Image array to be written.
-        file_path (str): Image file path.
-        params (None or list): Same as opencv :func:`imwrite` interface.
-        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
-            whether to create it automatically.
-
-    Returns:
-        bool: Successful or not.
-    """
-    if auto_mkdir:
-        dir_name = osp.abspath(osp.dirname(file_path))
-        mkdir_or_exist(dir_name)
-    return cv2.imwrite(file_path, img, params)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/__init__.py
deleted file mode 100644
index 04011130435cf9fdfadeb821919046b1bddab7d4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/__init__.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from .atss import ATSS
-from .base import BaseDetector
-from .cascade_rcnn import CascadeRCNN
-from .cornernet import CornerNet
-from .detr import DETR
-from .fast_rcnn import FastRCNN
-from .faster_rcnn import FasterRCNN
-from .fcos import FCOS
-from .fovea import FOVEA
-from .fsaf import FSAF
-from .gfl import GFL
-from .grid_rcnn import GridRCNN
-from .htc import HybridTaskCascade
-from .kd_one_stage import KnowledgeDistillationSingleStageDetector
-from .mask_rcnn import MaskRCNN
-from .mask_scoring_rcnn import MaskScoringRCNN
-from .nasfcos import NASFCOS
-from .paa import PAA
-from .point_rend import PointRend
-from .reppoints_detector import RepPointsDetector
-from .retinanet import RetinaNet
-from .rpn import RPN
-from .scnet import SCNet
-from .single_stage import SingleStageDetector
-from .sparse_rcnn import SparseRCNN
-from .trident_faster_rcnn import TridentFasterRCNN
-from .two_stage import TwoStageDetector
-from .vfnet import VFNet
-from .yolact import YOLACT
-from .yolo import YOLOV3
-
-__all__ = [
-    'ATSS', 'BaseDetector', 'SingleStageDetector',
-    'KnowledgeDistillationSingleStageDetector', 'TwoStageDetector', 'RPN',
-    'FastRCNN', 'FasterRCNN', 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade',
-    'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector',
-    'FOVEA', 'FSAF', 'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA',
-    'YOLOV3', 'YOLACT', 'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN',
-    'SCNet'
-]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/pipelines/transforms.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/pipelines/transforms.py
deleted file mode 100644
index 94e869b252ef6d8b43604add2bbc02f034614bfb..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/pipelines/transforms.py
+++ /dev/null
@@ -1,889 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import deprecated_api_warning, is_tuple_of
-from numpy import random
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class Resize(object):
-    """Resize images & seg.
-
-    This transform resizes the input image to some scale. If the input dict
-    contains the key "scale", then the scale in the input dict is used,
-    otherwise the specified scale in the init method is used.
-
-    ``img_scale`` can be None, a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 4 multiscale modes:
-
-    - ``ratio_range is not None``:
-    1. When img_scale is None, img_scale is the shape of image in results
-        (img_scale = results['img'].shape[:2]) and the image is resized based
-        on the original size. (mode 1)
-    2. When img_scale is a tuple (single-scale), randomly sample a ratio from
-        the ratio range and multiply it with the image scale. (mode 2)
-
-    - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
-    scale from the a range. (mode 3)
-
-    - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
-    scale from multiple scales. (mode 4)
-
-    Args:
-        img_scale (tuple or list[tuple]): Images scales for resizing.
-        multiscale_mode (str): Either "range" or "value".
-        ratio_range (tuple[float]): (min_ratio, max_ratio)
-        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
-            image.
-    """
-
-    def __init__(self,
-                 img_scale=None,
-                 multiscale_mode='range',
-                 ratio_range=None,
-                 keep_ratio=True):
-        if img_scale is None:
-            self.img_scale = None
-        else:
-            if isinstance(img_scale, list):
-                self.img_scale = img_scale
-            else:
-                self.img_scale = [img_scale]
-            assert mmcv.is_list_of(self.img_scale, tuple)
-
-        if ratio_range is not None:
-            # mode 1: given img_scale=None and a range of image ratio
-            # mode 2: given a scale and a range of image ratio
-            assert self.img_scale is None or len(self.img_scale) == 1
-        else:
-            # mode 3 and 4: given multiple scales or a range of scales
-            assert multiscale_mode in ['value', 'range']
-
-        self.multiscale_mode = multiscale_mode
-        self.ratio_range = ratio_range
-        self.keep_ratio = keep_ratio
-
-    @staticmethod
-    def random_select(img_scales):
-        """Randomly select an img_scale from given candidates.
-
-        Args:
-            img_scales (list[tuple]): Images scales for selection.
-
-        Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
-                where ``img_scale`` is the selected image scale and
-                ``scale_idx`` is the selected index in the given candidates.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple)
-        scale_idx = np.random.randint(len(img_scales))
-        img_scale = img_scales[scale_idx]
-        return img_scale, scale_idx
-
-    @staticmethod
-    def random_sample(img_scales):
-        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
-
-        Args:
-            img_scales (list[tuple]): Images scale range for sampling.
-                There must be two tuples in img_scales, which specify the lower
-                and upper bound of image scales.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where
-                ``img_scale`` is sampled scale and None is just a placeholder
-                to be consistent with :func:`random_select`.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
-        img_scale_long = [max(s) for s in img_scales]
-        img_scale_short = [min(s) for s in img_scales]
-        long_edge = np.random.randint(
-            min(img_scale_long),
-            max(img_scale_long) + 1)
-        short_edge = np.random.randint(
-            min(img_scale_short),
-            max(img_scale_short) + 1)
-        img_scale = (long_edge, short_edge)
-        return img_scale, None
-
-    @staticmethod
-    def random_sample_ratio(img_scale, ratio_range):
-        """Randomly sample an img_scale when ``ratio_range`` is specified.
-
-        A ratio will be randomly sampled from the range specified by
-        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
-        generate sampled scale.
-
-        Args:
-            img_scale (tuple): Images scale base to multiply with ratio.
-            ratio_range (tuple[float]): The minimum and maximum ratio to scale
-                the ``img_scale``.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and
-                None is just a placeholder to be consistent with
-                :func:`random_select`.
-        """
-
-        assert isinstance(img_scale, tuple) and len(img_scale) == 2
-        min_ratio, max_ratio = ratio_range
-        assert min_ratio <= max_ratio
-        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
-        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
-        return scale, None
-
-    def _random_scale(self, results):
-        """Randomly sample an img_scale according to ``ratio_range`` and
-        ``multiscale_mode``.
-
-        If ``ratio_range`` is specified, a ratio will be sampled and be
-        multiplied with ``img_scale``.
-        If multiple scales are specified by ``img_scale``, a scale will be
-        sampled according to ``multiscale_mode``.
-        Otherwise, single scale will be used.
-
-        Args:
-            results (dict): Result dict from :obj:`dataset`.
-
-        Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into
-                ``results``, which would be used by subsequent pipelines.
-        """
-
-        if self.ratio_range is not None:
-            if self.img_scale is None:
-                h, w = results['img'].shape[:2]
-                scale, scale_idx = self.random_sample_ratio((w, h),
-                                                            self.ratio_range)
-            else:
-                scale, scale_idx = self.random_sample_ratio(
-                    self.img_scale[0], self.ratio_range)
-        elif len(self.img_scale) == 1:
-            scale, scale_idx = self.img_scale[0], 0
-        elif self.multiscale_mode == 'range':
-            scale, scale_idx = self.random_sample(self.img_scale)
-        elif self.multiscale_mode == 'value':
-            scale, scale_idx = self.random_select(self.img_scale)
-        else:
-            raise NotImplementedError
-
-        results['scale'] = scale
-        results['scale_idx'] = scale_idx
-
-    def _resize_img(self, results):
-        """Resize images with ``results['scale']``."""
-        if self.keep_ratio:
-            img, scale_factor = mmcv.imrescale(
-                results['img'], results['scale'], return_scale=True)
-            # the w_scale and h_scale has minor difference
-            # a real fix should be done in the mmcv.imrescale in the future
-            new_h, new_w = img.shape[:2]
-            h, w = results['img'].shape[:2]
-            w_scale = new_w / w
-            h_scale = new_h / h
-        else:
-            img, w_scale, h_scale = mmcv.imresize(
-                results['img'], results['scale'], return_scale=True)
-        scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                dtype=np.float32)
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['pad_shape'] = img.shape  # in case that there is no padding
-        results['scale_factor'] = scale_factor
-        results['keep_ratio'] = self.keep_ratio
-
-    def _resize_seg(self, results):
-        """Resize semantic segmentation map with ``results['scale']``."""
-        for key in results.get('seg_fields', []):
-            if self.keep_ratio:
-                gt_seg = mmcv.imrescale(
-                    results[key], results['scale'], interpolation='nearest')
-            else:
-                gt_seg = mmcv.imresize(
-                    results[key], results['scale'], interpolation='nearest')
-            results[key] = gt_seg
-
-    def __call__(self, results):
-        """Call function to resize images, bounding boxes, masks, semantic
-        segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
-                'keep_ratio' keys are added into result dict.
-        """
-
-        if 'scale' not in results:
-            self._random_scale(results)
-        self._resize_img(results)
-        self._resize_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(img_scale={self.img_scale}, '
-                     f'multiscale_mode={self.multiscale_mode}, '
-                     f'ratio_range={self.ratio_range}, '
-                     f'keep_ratio={self.keep_ratio})')
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomFlip(object):
-    """Flip the image & seg.
-
-    If the input dict contains the key "flip", then the flag will be used,
-    otherwise it will be randomly decided by a ratio specified in the init
-    method.
-
-    Args:
-        prob (float, optional): The flipping probability. Default: None.
-        direction(str, optional): The flipping direction. Options are
-            'horizontal' and 'vertical'. Default: 'horizontal'.
-    """
-
-    @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip')
-    def __init__(self, prob=None, direction='horizontal'):
-        self.prob = prob
-        self.direction = direction
-        if prob is not None:
-            assert prob >= 0 and prob <= 1
-        assert direction in ['horizontal', 'vertical']
-
-    def __call__(self, results):
-        """Call function to flip bounding boxes, masks, semantic segmentation
-        maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added into
-                result dict.
-        """
-
-        if 'flip' not in results:
-            flip = True if np.random.rand() < self.prob else False
-            results['flip'] = flip
-        if 'flip_direction' not in results:
-            results['flip_direction'] = self.direction
-        if results['flip']:
-            # flip image
-            results['img'] = mmcv.imflip(
-                results['img'], direction=results['flip_direction'])
-
-            # flip segs
-            for key in results.get('seg_fields', []):
-                # use copy() to make numpy stride positive
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction']).copy()
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(prob={self.prob})'
-
-
-@PIPELINES.register_module()
-class Pad(object):
-    """Pad the image & mask.
-
-    There are two padding modes: (1) pad to a fixed size and (2) pad to the
-    minimum size that is divisible by some number.
-    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
-
-    Args:
-        size (tuple, optional): Fixed padding size.
-        size_divisor (int, optional): The divisor of padded size.
-        pad_val (float, optional): Padding value. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-    """
-
-    def __init__(self,
-                 size=None,
-                 size_divisor=None,
-                 pad_val=0,
-                 seg_pad_val=255):
-        self.size = size
-        self.size_divisor = size_divisor
-        self.pad_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        # only one of size and size_divisor should be valid
-        assert size is not None or size_divisor is not None
-        assert size is None or size_divisor is None
-
-    def _pad_img(self, results):
-        """Pad images according to ``self.size``."""
-        if self.size is not None:
-            padded_img = mmcv.impad(
-                results['img'], shape=self.size, pad_val=self.pad_val)
-        elif self.size_divisor is not None:
-            padded_img = mmcv.impad_to_multiple(
-                results['img'], self.size_divisor, pad_val=self.pad_val)
-        results['img'] = padded_img
-        results['pad_shape'] = padded_img.shape
-        results['pad_fixed_size'] = self.size
-        results['pad_size_divisor'] = self.size_divisor
-
-    def _pad_seg(self, results):
-        """Pad masks according to ``results['pad_shape']``."""
-        for key in results.get('seg_fields', []):
-            results[key] = mmcv.impad(
-                results[key],
-                shape=results['pad_shape'][:2],
-                pad_val=self.seg_pad_val)
-
-    def __call__(self, results):
-        """Call function to pad images, masks, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Updated result dict.
-        """
-
-        self._pad_img(results)
-        self._pad_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
-                    f'pad_val={self.pad_val})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Normalize(object):
-    """Normalize the image.
-
-    Added key is "img_norm_cfg".
-
-    Args:
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB,
-            default is true.
-    """
-
-    def __init__(self, mean, std, to_rgb=True):
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-        self.to_rgb = to_rgb
-
-    def __call__(self, results):
-        """Call function to normalize images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Normalized results, 'img_norm_cfg' key is added into
-                result dict.
-        """
-
-        results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
-                                          self.to_rgb)
-        results['img_norm_cfg'] = dict(
-            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
-                    f'{self.to_rgb})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Rerange(object):
-    """Rerange the image pixel value.
-
-    Args:
-        min_value (float or int): Minimum value of the reranged image.
-            Default: 0.
-        max_value (float or int): Maximum value of the reranged image.
-            Default: 255.
-    """
-
-    def __init__(self, min_value=0, max_value=255):
-        assert isinstance(min_value, float) or isinstance(min_value, int)
-        assert isinstance(max_value, float) or isinstance(max_value, int)
-        assert min_value < max_value
-        self.min_value = min_value
-        self.max_value = max_value
-
-    def __call__(self, results):
-        """Call function to rerange images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-        Returns:
-            dict: Reranged results.
-        """
-
-        img = results['img']
-        img_min_value = np.min(img)
-        img_max_value = np.max(img)
-
-        assert img_min_value < img_max_value
-        # rerange to [0, 1]
-        img = (img - img_min_value) / (img_max_value - img_min_value)
-        # rerange to [min_value, max_value]
-        img = img * (self.max_value - self.min_value) + self.min_value
-        results['img'] = img
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class CLAHE(object):
-    """Use CLAHE method to process the image.
-
-    See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
-    Graphics Gems, 1994:474-485.` for more information.
-
-    Args:
-        clip_limit (float): Threshold for contrast limiting. Default: 40.0.
-        tile_grid_size (tuple[int]): Size of grid for histogram equalization.
-            Input image will be divided into equally sized rectangular tiles.
-            It defines the number of tiles in row and column. Default: (8, 8).
-    """
-
-    def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
-        assert isinstance(clip_limit, (float, int))
-        self.clip_limit = clip_limit
-        assert is_tuple_of(tile_grid_size, int)
-        assert len(tile_grid_size) == 2
-        self.tile_grid_size = tile_grid_size
-
-    def __call__(self, results):
-        """Call function to Use CLAHE method process images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        for i in range(results['img'].shape[2]):
-            results['img'][:, :, i] = mmcv.clahe(
-                np.array(results['img'][:, :, i], dtype=np.uint8),
-                self.clip_limit, self.tile_grid_size)
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(clip_limit={self.clip_limit}, '\
-                    f'tile_grid_size={self.tile_grid_size})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCrop(object):
-    """Random crop the image & seg.
-
-    Args:
-        crop_size (tuple): Expected size after cropping, (h, w).
-        cat_max_ratio (float): The maximum ratio that single category could
-            occupy.
-    """
-
-    def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
-        assert crop_size[0] > 0 and crop_size[1] > 0
-        self.crop_size = crop_size
-        self.cat_max_ratio = cat_max_ratio
-        self.ignore_index = ignore_index
-
-    def get_crop_bbox(self, img):
-        """Randomly get a crop bounding box."""
-        margin_h = max(img.shape[0] - self.crop_size[0], 0)
-        margin_w = max(img.shape[1] - self.crop_size[1], 0)
-        offset_h = np.random.randint(0, margin_h + 1)
-        offset_w = np.random.randint(0, margin_w + 1)
-        crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
-        crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
-
-        return crop_y1, crop_y2, crop_x1, crop_x2
-
-    def crop(self, img, crop_bbox):
-        """Crop from ``img``"""
-        crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
-        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
-        return img
-
-    def __call__(self, results):
-        """Call function to randomly crop images, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-
-        img = results['img']
-        crop_bbox = self.get_crop_bbox(img)
-        if self.cat_max_ratio < 1.:
-            # Repeat 10 times
-            for _ in range(10):
-                seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
-                labels, cnt = np.unique(seg_temp, return_counts=True)
-                cnt = cnt[labels != self.ignore_index]
-                if len(cnt) > 1 and np.max(cnt) / np.sum(
-                        cnt) < self.cat_max_ratio:
-                    break
-                crop_bbox = self.get_crop_bbox(img)
-
-        # crop the image
-        img = self.crop(img, crop_bbox)
-        img_shape = img.shape
-        results['img'] = img
-        results['img_shape'] = img_shape
-
-        # crop semantic seg
-        for key in results.get('seg_fields', []):
-            results[key] = self.crop(results[key], crop_bbox)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(crop_size={self.crop_size})'
-
-
-@PIPELINES.register_module()
-class RandomRotate(object):
-    """Rotate the image & seg.
-
-    Args:
-        prob (float): The rotation probability.
-        degree (float, tuple[float]): Range of degrees to select from. If
-            degree is a number instead of tuple like (min, max),
-            the range of degree will be (``-degree``, ``+degree``)
-        pad_val (float, optional): Padding value of image. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-        center (tuple[float], optional): Center point (w, h) of the rotation in
-            the source image. If not specified, the center of the image will be
-            used. Default: None.
-        auto_bound (bool): Whether to adjust the image size to cover the whole
-            rotated image. Default: False
-    """
-
-    def __init__(self,
-                 prob,
-                 degree,
-                 pad_val=0,
-                 seg_pad_val=255,
-                 center=None,
-                 auto_bound=False):
-        self.prob = prob
-        assert prob >= 0 and prob <= 1
-        if isinstance(degree, (float, int)):
-            assert degree > 0, f'degree {degree} should be positive'
-            self.degree = (-degree, degree)
-        else:
-            self.degree = degree
-        assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
-                                      f'tuple of (min, max)'
-        self.pal_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        self.center = center
-        self.auto_bound = auto_bound
-
-    def __call__(self, results):
-        """Call function to rotate image, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Rotated results.
-        """
-
-        rotate = True if np.random.rand() < self.prob else False
-        degree = np.random.uniform(min(*self.degree), max(*self.degree))
-        if rotate:
-            # rotate image
-            results['img'] = mmcv.imrotate(
-                results['img'],
-                angle=degree,
-                border_value=self.pal_val,
-                center=self.center,
-                auto_bound=self.auto_bound)
-
-            # rotate segs
-            for key in results.get('seg_fields', []):
-                results[key] = mmcv.imrotate(
-                    results[key],
-                    angle=degree,
-                    border_value=self.seg_pad_val,
-                    center=self.center,
-                    auto_bound=self.auto_bound,
-                    interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(prob={self.prob}, ' \
-                    f'degree={self.degree}, ' \
-                    f'pad_val={self.pal_val}, ' \
-                    f'seg_pad_val={self.seg_pad_val}, ' \
-                    f'center={self.center}, ' \
-                    f'auto_bound={self.auto_bound})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RGB2Gray(object):
-    """Convert RGB image to grayscale image.
-
-    This transform calculate the weighted mean of input image channels with
-    ``weights`` and then expand the channels to ``out_channels``. When
-    ``out_channels`` is None, the number of output channels is the same as
-    input channels.
-
-    Args:
-        out_channels (int): Expected number of output channels after
-            transforming. Default: None.
-        weights (tuple[float]): The weights to calculate the weighted mean.
-            Default: (0.299, 0.587, 0.114).
-    """
-
-    def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
-        assert out_channels is None or out_channels > 0
-        self.out_channels = out_channels
-        assert isinstance(weights, tuple)
-        for item in weights:
-            assert isinstance(item, (float, int))
-        self.weights = weights
-
-    def __call__(self, results):
-        """Call function to convert RGB image to grayscale image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with grayscale image.
-        """
-        img = results['img']
-        assert len(img.shape) == 3
-        assert img.shape[2] == len(self.weights)
-        weights = np.array(self.weights).reshape((1, 1, -1))
-        img = (img * weights).sum(2, keepdims=True)
-        if self.out_channels is None:
-            img = img.repeat(weights.shape[2], axis=2)
-        else:
-            img = img.repeat(self.out_channels, axis=2)
-
-        results['img'] = img
-        results['img_shape'] = img.shape
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(out_channels={self.out_channels}, ' \
-                    f'weights={self.weights})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class AdjustGamma(object):
-    """Using gamma correction to process the image.
-
-    Args:
-        gamma (float or int): Gamma value used in gamma correction.
-            Default: 1.0.
-    """
-
-    def __init__(self, gamma=1.0):
-        assert isinstance(gamma, float) or isinstance(gamma, int)
-        assert gamma > 0
-        self.gamma = gamma
-        inv_gamma = 1.0 / gamma
-        self.table = np.array([(i / 255.0)**inv_gamma * 255
-                               for i in np.arange(256)]).astype('uint8')
-
-    def __call__(self, results):
-        """Call function to process the image with gamma correction.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        results['img'] = mmcv.lut_transform(
-            np.array(results['img'], dtype=np.uint8), self.table)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(gamma={self.gamma})'
-
-
-@PIPELINES.register_module()
-class SegRescale(object):
-    """Rescale semantic segmentation maps.
-
-    Args:
-        scale_factor (float): The scale factor of the final output.
-    """
-
-    def __init__(self, scale_factor=1):
-        self.scale_factor = scale_factor
-
-    def __call__(self, results):
-        """Call function to scale the semantic segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with semantic segmentation map scaled.
-        """
-        for key in results.get('seg_fields', []):
-            if self.scale_factor != 1:
-                results[key] = mmcv.imrescale(
-                    results[key], self.scale_factor, interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
-
-
-@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
-    """Apply photometric distortion to image sequentially, every transformation
-    is applied with a probability of 0.5. The position of random contrast is in
-    second or second to last.
-
-    1. random brightness
-    2. random contrast (mode 0)
-    3. convert color from BGR to HSV
-    4. random saturation
-    5. random hue
-    6. convert color from HSV to BGR
-    7. random contrast (mode 1)
-
-    Args:
-        brightness_delta (int): delta of brightness.
-        contrast_range (tuple): range of contrast.
-        saturation_range (tuple): range of saturation.
-        hue_delta (int): delta of hue.
-    """
-
-    def __init__(self,
-                 brightness_delta=32,
-                 contrast_range=(0.5, 1.5),
-                 saturation_range=(0.5, 1.5),
-                 hue_delta=18):
-        self.brightness_delta = brightness_delta
-        self.contrast_lower, self.contrast_upper = contrast_range
-        self.saturation_lower, self.saturation_upper = saturation_range
-        self.hue_delta = hue_delta
-
-    def convert(self, img, alpha=1, beta=0):
-        """Multiple with alpha and add beat with clip."""
-        img = img.astype(np.float32) * alpha + beta
-        img = np.clip(img, 0, 255)
-        return img.astype(np.uint8)
-
-    def brightness(self, img):
-        """Brightness distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                beta=random.uniform(-self.brightness_delta,
-                                    self.brightness_delta))
-        return img
-
-    def contrast(self, img):
-        """Contrast distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                alpha=random.uniform(self.contrast_lower, self.contrast_upper))
-        return img
-
-    def saturation(self, img):
-        """Saturation distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :, 1] = self.convert(
-                img[:, :, 1],
-                alpha=random.uniform(self.saturation_lower,
-                                     self.saturation_upper))
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def hue(self, img):
-        """Hue distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :,
-                0] = (img[:, :, 0].astype(int) +
-                      random.randint(-self.hue_delta, self.hue_delta)) % 180
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def __call__(self, results):
-        """Call function to perform photometric distortion on images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images distorted.
-        """
-
-        img = results['img']
-        # random brightness
-        img = self.brightness(img)
-
-        # mode == 0 --> do random contrast first
-        # mode == 1 --> do random contrast last
-        mode = random.randint(2)
-        if mode == 1:
-            img = self.contrast(img)
-
-        # random saturation
-        img = self.saturation(img)
-
-        # random hue
-        img = self.hue(img)
-
-        # random contrast
-        if mode == 0:
-            img = self.contrast(img)
-
-        results['img'] = img
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(brightness_delta={self.brightness_delta}, '
-                     f'contrast_range=({self.contrast_lower}, '
-                     f'{self.contrast_upper}), '
-                     f'saturation_range=({self.saturation_lower}, '
-                     f'{self.saturation_upper}), '
-                     f'hue_delta={self.hue_delta})')
-        return repr_str
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/losses/utils.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/losses/utils.py
deleted file mode 100644
index 85aec9f3045240c3de96a928324ae8f5c3aebe8b..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/losses/utils.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import functools
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch.nn.functional as F
-
-
-def get_class_weight(class_weight):
-    """Get class weight for loss function.
-
-    Args:
-        class_weight (list[float] | str | None): If class_weight is a str,
-            take it as a file name and read from it.
-    """
-    if isinstance(class_weight, str):
-        # take it as a file path
-        if class_weight.endswith('.npy'):
-            class_weight = np.load(class_weight)
-        else:
-            # pkl, json or yaml
-            class_weight = mmcv.load(class_weight)
-
-    return class_weight
-
-
-def reduce_loss(loss, reduction):
-    """Reduce loss as specified.
-
-    Args:
-        loss (Tensor): Elementwise loss tensor.
-        reduction (str): Options are "none", "mean" and "sum".
-
-    Return:
-        Tensor: Reduced loss tensor.
-    """
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, elementwise_mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.mean()
-    elif reduction_enum == 2:
-        return loss.sum()
-
-
-def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
-    """Apply element-wise weight and reduce loss.
-
-    Args:
-        loss (Tensor): Element-wise loss.
-        weight (Tensor): Element-wise weights.
-        reduction (str): Same as built-in losses of PyTorch.
-        avg_factor (float): Avarage factor when computing the mean of losses.
-
-    Returns:
-        Tensor: Processed loss values.
-    """
-    # if weight is specified, apply element-wise weight
-    if weight is not None:
-        assert weight.dim() == loss.dim()
-        if weight.dim() > 1:
-            assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
-        loss = loss * weight
-
-    # if avg_factor is not specified, just reduce the loss
-    if avg_factor is None:
-        loss = reduce_loss(loss, reduction)
-    else:
-        # if reduction is mean, then average the loss by avg_factor
-        if reduction == 'mean':
-            loss = loss.sum() / avg_factor
-        # if reduction is 'none', then do nothing, otherwise raise an error
-        elif reduction != 'none':
-            raise ValueError('avg_factor can not be used with reduction="sum"')
-    return loss
-
-
-def weighted_loss(loss_func):
-    """Create a weighted version of a given loss function.
-
-    To use this decorator, the loss function must have the signature like
-    `loss_func(pred, target, **kwargs)`. The function only needs to compute
-    element-wise loss without any reduction. This decorator will add weight
-    and reduction arguments to the function. The decorated function will have
-    the signature like `loss_func(pred, target, weight=None, reduction='mean',
-    avg_factor=None, **kwargs)`.
-
-    :Example:
-
-    >>> import torch
-    >>> @weighted_loss
-    >>> def l1_loss(pred, target):
-    >>>     return (pred - target).abs()
-
-    >>> pred = torch.Tensor([0, 2, 3])
-    >>> target = torch.Tensor([1, 1, 1])
-    >>> weight = torch.Tensor([1, 0, 1])
-
-    >>> l1_loss(pred, target)
-    tensor(1.3333)
-    >>> l1_loss(pred, target, weight)
-    tensor(1.)
-    >>> l1_loss(pred, target, reduction='none')
-    tensor([1., 1., 2.])
-    >>> l1_loss(pred, target, weight, avg_factor=2)
-    tensor(1.5000)
-    """
-
-    @functools.wraps(loss_func)
-    def wrapper(pred,
-                target,
-                weight=None,
-                reduction='mean',
-                avg_factor=None,
-                **kwargs):
-        # get element-wise loss
-        loss = loss_func(pred, target, **kwargs)
-        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
-        return loss
-
-    return wrapper
diff --git a/spaces/Rongjiehuang/GenerSpeech/tasks/tts/pe.py b/spaces/Rongjiehuang/GenerSpeech/tasks/tts/pe.py
deleted file mode 100644
index 3880c80d0820c36e044c00bd38a07fd3cce73323..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/GenerSpeech/tasks/tts/pe.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-
-import torch
-import numpy as np
-import os
-
-from tasks.base_task import BaseDataset
-from tasks.tts.fs2 import FastSpeech2Task
-from modules.fastspeech.pe import PitchExtractor
-import utils
-from utils.indexed_datasets import IndexedDataset
-from utils.hparams import hparams
-from utils.plot import f0_to_figure
-from utils.pitch_utils import norm_interp_f0, denorm_f0
-
-
-class PeDataset(BaseDataset):
-    def __init__(self, prefix, shuffle=False):
-        super().__init__(shuffle)
-        self.data_dir = hparams['binary_data_dir']
-        self.prefix = prefix
-        self.hparams = hparams
-        self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
-        self.indexed_ds = None
-
-        # pitch stats
-        f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy'
-        if os.path.exists(f0_stats_fn):
-            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn)
-            hparams['f0_mean'] = float(hparams['f0_mean'])
-            hparams['f0_std'] = float(hparams['f0_std'])
-        else:
-            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None
-
-        if prefix == 'test':
-            if hparams['num_test_samples'] > 0:
-                self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids']
-                self.sizes = [self.sizes[i] for i in self.avail_idxs]
-
-    def _get_item(self, index):
-        if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
-            index = self.avail_idxs[index]
-        if self.indexed_ds is None:
-            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
-        return self.indexed_ds[index]
-
-    def __getitem__(self, index):
-        hparams = self.hparams
-        item = self._get_item(index)
-        max_frames = hparams['max_frames']
-        spec = torch.Tensor(item['mel'])[:max_frames]
-        # mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
-        f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
-        pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
-        # print(item.keys(), item['mel'].shape, spec.shape)
-        sample = {
-            "id": index,
-            "item_name": item['item_name'],
-            "text": item['txt'],
-            "mel": spec,
-            "pitch": pitch,
-            "f0": f0,
-            "uv": uv,
-            # "mel2ph": mel2ph,
-            # "mel_nonpadding": spec.abs().sum(-1) > 0,
-        }
-        return sample
-
-    def collater(self, samples):
-        if len(samples) == 0:
-            return {}
-        id = torch.LongTensor([s['id'] for s in samples])
-        item_names = [s['item_name'] for s in samples]
-        text = [s['text'] for s in samples]
-        f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
-        pitch = utils.collate_1d([s['pitch'] for s in samples])
-        uv = utils.collate_1d([s['uv'] for s in samples])
-        mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
-        mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
-        # mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
-        #     if samples[0]['mel2ph'] is not None else None
-        # mel_nonpaddings = utils.collate_1d([s['mel_nonpadding'].float() for s in samples], 0.0)
-
-        batch = {
-            'id': id,
-            'item_name': item_names,
-            'nsamples': len(samples),
-            'text': text,
-            'mels': mels,
-            'mel_lengths': mel_lengths,
-            'pitch': pitch,
-            # 'mel2ph': mel2ph,
-            # 'mel_nonpaddings': mel_nonpaddings,
-            'f0': f0,
-            'uv': uv,
-        }
-        return batch
-
-
-class PitchExtractionTask(FastSpeech2Task):
-    def __init__(self):
-        super().__init__()
-        self.dataset_cls = PeDataset
-
-    def build_tts_model(self):
-        self.model = PitchExtractor(conv_layers=hparams['pitch_extractor_conv_layers'])
-
-    # def build_scheduler(self, optimizer):
-    #     return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5)
-    def _training_step(self, sample, batch_idx, _):
-        loss_output = self.run_model(self.model, sample)
-        total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
-        loss_output['batch_size'] = sample['mels'].size()[0]
-        return total_loss, loss_output
-
-    def validation_step(self, sample, batch_idx):
-        outputs = {}
-        outputs['losses'] = {}
-        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=True)
-        outputs['total_loss'] = sum(outputs['losses'].values())
-        outputs['nsamples'] = sample['nsamples']
-        outputs = utils.tensors_to_scalars(outputs)
-        if batch_idx < hparams['num_valid_plots']:
-            self.plot_pitch(batch_idx, model_out, sample)
-        return outputs
-
-    def run_model(self, model, sample, return_output=False, infer=False):
-        f0 = sample['f0']
-        uv = sample['uv']
-        output = model(sample['mels'])
-        losses = {}
-        self.add_pitch_loss(output, sample, losses)
-        if not return_output:
-            return losses
-        else:
-            return losses, output
-
-    def plot_pitch(self, batch_idx, model_out, sample):
-        gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
-        self.logger.experiment.add_figure(
-            f'f0_{batch_idx}',
-            f0_to_figure(gt_f0[0], None, model_out['f0_denorm_pred'][0]),
-            self.global_step)
-
-    def add_pitch_loss(self, output, sample, losses):
-        # mel2ph = sample['mel2ph']  # [B, T_s]
-        mel = sample['mels']
-        f0 = sample['f0']
-        uv = sample['uv']
-        # nonpadding = (mel2ph != 0).float() if hparams['pitch_type'] == 'frame' \
-        #     else (sample['txt_tokens'] != 0).float()
-        nonpadding = (mel.abs().sum(-1) > 0).float()  # sample['mel_nonpaddings']
-        # print(nonpadding[0][-8:], nonpadding.shape)
-        self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding)
\ No newline at end of file
diff --git a/spaces/Rules99/Bioinformatics_Project/README.md b/spaces/Rules99/Bioinformatics_Project/README.md
deleted file mode 100644
index 88b33c9abd45aab7065f94ec7048d1380a527371..0000000000000000000000000000000000000000
--- a/spaces/Rules99/Bioinformatics_Project/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: Bioinfo's Project
-emoji: 🧬
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-# Configuration
-`title`: _string_  
-Display title for the Space
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/SERER/VITS-Umamusume-voice-synthesizer/text/sanskrit.py b/spaces/SERER/VITS-Umamusume-voice-synthesizer/text/sanskrit.py
deleted file mode 100644
index 0223aaac384a2f850f5bc20651fc18eb964607d0..0000000000000000000000000000000000000000
--- a/spaces/SERER/VITS-Umamusume-voice-synthesizer/text/sanskrit.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import re
-from indic_transliteration import sanscript
-
-
-# List of (iast, ipa) pairs:
-_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('a', 'ə'),
-    ('ā', 'aː'),
-    ('ī', 'iː'),
-    ('ū', 'uː'),
-    ('ṛ', 'ɹ`'),
-    ('ṝ', 'ɹ`ː'),
-    ('ḷ', 'l`'),
-    ('ḹ', 'l`ː'),
-    ('e', 'eː'),
-    ('o', 'oː'),
-    ('k', 'k⁼'),
-    ('k⁼h', 'kʰ'),
-    ('g', 'g⁼'),
-    ('g⁼h', 'gʰ'),
-    ('ṅ', 'ŋ'),
-    ('c', 'ʧ⁼'),
-    ('ʧ⁼h', 'ʧʰ'),
-    ('j', 'ʥ⁼'),
-    ('ʥ⁼h', 'ʥʰ'),
-    ('ñ', 'n^'),
-    ('ṭ', 't`⁼'),
-    ('t`⁼h', 't`ʰ'),
-    ('ḍ', 'd`⁼'),
-    ('d`⁼h', 'd`ʰ'),
-    ('ṇ', 'n`'),
-    ('t', 't⁼'),
-    ('t⁼h', 'tʰ'),
-    ('d', 'd⁼'),
-    ('d⁼h', 'dʰ'),
-    ('p', 'p⁼'),
-    ('p⁼h', 'pʰ'),
-    ('b', 'b⁼'),
-    ('b⁼h', 'bʰ'),
-    ('y', 'j'),
-    ('ś', 'ʃ'),
-    ('ṣ', 's`'),
-    ('r', 'ɾ'),
-    ('l̤', 'l`'),
-    ('h', 'ɦ'),
-    ("'", ''),
-    ('~', '^'),
-    ('ṃ', '^')
-]]
-
-
-def devanagari_to_ipa(text):
-    text = text.replace('ॐ', 'ओम्')
-    text = re.sub(r'\s*।\s*$', '.', text)
-    text = re.sub(r'\s*।\s*', ', ', text)
-    text = re.sub(r'\s*॥', '.', text)
-    text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
-    for regex, replacement in _iast_to_ipa:
-        text = re.sub(regex, replacement, text)
-    text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
-                  [:-1]+'h'+x.group(1)+'*', text)
-    return text
diff --git a/spaces/SeViLA/SeViLA/lavis/common/optims.py b/spaces/SeViLA/SeViLA/lavis/common/optims.py
deleted file mode 100644
index 28b68645d509eedbafb47e8efa213ba6e2cc63f3..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/common/optims.py
+++ /dev/null
@@ -1,117 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import math
-
-from lavis.common.registry import registry
-
-
-@registry.register_lr_scheduler("linear_warmup_step_lr")
-class LinearWarmupStepLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        min_lr,
-        init_lr,
-        decay_rate=1,
-        warmup_start_lr=-1,
-        warmup_steps=0,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.min_lr = min_lr
-
-        self.decay_rate = decay_rate
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        if cur_epoch == 0:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            step_lr_schedule(
-                epoch=cur_epoch,
-                optimizer=self.optimizer,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-                decay_rate=self.decay_rate,
-            )
-
-
-@registry.register_lr_scheduler("linear_warmup_cosine_lr")
-class LinearWarmupCosineLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        min_lr,
-        init_lr,
-        warmup_steps=0,
-        warmup_start_lr=-1,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.min_lr = min_lr
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        # assuming the warmup iters less than one epoch
-        if cur_epoch == 0:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            cosine_lr_schedule(
-                epoch=cur_epoch,
-                optimizer=self.optimizer,
-                max_epoch=self.max_epoch,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-            )
-
-
-def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
-    """Decay the learning rate"""
-    lr = (init_lr - min_lr) * 0.5 * (
-        1.0 + math.cos(math.pi * epoch / max_epoch)
-    ) + min_lr
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr):
-    """Warmup the learning rate"""
-    lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max(max_step, 1))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate):
-    """Decay the learning rate"""
-    lr = max(min_lr, init_lr * (decay_rate**epoch))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
diff --git a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/models/GroundingDINO/groundingdino.py b/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/models/GroundingDINO/groundingdino.py
deleted file mode 100644
index 052df6220595a1b39b7e2aea37ca4872d113dfd2..0000000000000000000000000000000000000000
--- a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/models/GroundingDINO/groundingdino.py
+++ /dev/null
@@ -1,395 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR model and criterion classes.
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# ------------------------------------------------------------------------
-import copy
-from typing import List
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torchvision.ops.boxes import nms
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-
-from groundingdino.util import box_ops, get_tokenlizer
-from groundingdino.util.misc import (
-    NestedTensor,
-    accuracy,
-    get_world_size,
-    interpolate,
-    inverse_sigmoid,
-    is_dist_avail_and_initialized,
-    nested_tensor_from_tensor_list,
-)
-from groundingdino.util.utils import get_phrases_from_posmap
-from groundingdino.util.visualizer import COCOVisualizer
-from groundingdino.util.vl_utils import create_positive_map_from_span
-
-from ..registry import MODULE_BUILD_FUNCS
-from .backbone import build_backbone
-from .bertwarper import (
-    BertModelWarper,
-    generate_masks_with_special_tokens,
-    generate_masks_with_special_tokens_and_transfer_map,
-)
-from .transformer import build_transformer
-from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
-
-
-class GroundingDINO(nn.Module):
-    """This is the Cross-Attention Detector module that performs object detection"""
-
-    def __init__(
-        self,
-        backbone,
-        transformer,
-        num_queries,
-        aux_loss=False,
-        iter_update=False,
-        query_dim=2,
-        num_feature_levels=1,
-        nheads=8,
-        # two stage
-        two_stage_type="no",  # ['no', 'standard']
-        dec_pred_bbox_embed_share=True,
-        two_stage_class_embed_share=True,
-        two_stage_bbox_embed_share=True,
-        num_patterns=0,
-        dn_number=100,
-        dn_box_noise_scale=0.4,
-        dn_label_noise_ratio=0.5,
-        dn_labelbook_size=100,
-        text_encoder_type="bert-base-uncased",
-        sub_sentence_present=True,
-        max_text_len=256,
-    ):
-        """Initializes the model.
-        Parameters:
-            backbone: torch module of the backbone to be used. See backbone.py
-            transformer: torch module of the transformer architecture. See transformer.py
-            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
-                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
-            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
-        """
-        super().__init__()
-        self.num_queries = num_queries
-        self.transformer = transformer
-        self.hidden_dim = hidden_dim = transformer.d_model
-        self.num_feature_levels = num_feature_levels
-        self.nheads = nheads
-        self.max_text_len = 256
-        self.sub_sentence_present = sub_sentence_present
-
-        # setting query dim
-        self.query_dim = query_dim
-        assert query_dim == 4
-
-        # for dn training
-        self.num_patterns = num_patterns
-        self.dn_number = dn_number
-        self.dn_box_noise_scale = dn_box_noise_scale
-        self.dn_label_noise_ratio = dn_label_noise_ratio
-        self.dn_labelbook_size = dn_labelbook_size
-
-        # bert
-        self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
-        self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
-        self.bert.pooler.dense.weight.requires_grad_(False)
-        self.bert.pooler.dense.bias.requires_grad_(False)
-        self.bert = BertModelWarper(bert_model=self.bert)
-
-        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
-        nn.init.constant_(self.feat_map.bias.data, 0)
-        nn.init.xavier_uniform_(self.feat_map.weight.data)
-        # freeze
-
-        # special tokens
-        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
-
-        # prepare input projection layers
-        if num_feature_levels > 1:
-            num_backbone_outs = len(backbone.num_channels)
-            input_proj_list = []
-            for _ in range(num_backbone_outs):
-                in_channels = backbone.num_channels[_]
-                input_proj_list.append(
-                    nn.Sequential(
-                        nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
-                        nn.GroupNorm(32, hidden_dim),
-                    )
-                )
-            for _ in range(num_feature_levels - num_backbone_outs):
-                input_proj_list.append(
-                    nn.Sequential(
-                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
-                        nn.GroupNorm(32, hidden_dim),
-                    )
-                )
-                in_channels = hidden_dim
-            self.input_proj = nn.ModuleList(input_proj_list)
-        else:
-            assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
-            self.input_proj = nn.ModuleList(
-                [
-                    nn.Sequential(
-                        nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
-                        nn.GroupNorm(32, hidden_dim),
-                    )
-                ]
-            )
-
-        self.backbone = backbone
-        self.aux_loss = aux_loss
-        self.box_pred_damping = box_pred_damping = None
-
-        self.iter_update = iter_update
-        assert iter_update, "Why not iter_update?"
-
-        # prepare pred layers
-        self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
-        # prepare class & box embed
-        _class_embed = ContrastiveEmbed()
-
-        _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
-        nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
-        nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
-
-        if dec_pred_bbox_embed_share:
-            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
-        else:
-            box_embed_layerlist = [
-                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
-            ]
-        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
-        self.bbox_embed = nn.ModuleList(box_embed_layerlist)
-        self.class_embed = nn.ModuleList(class_embed_layerlist)
-        self.transformer.decoder.bbox_embed = self.bbox_embed
-        self.transformer.decoder.class_embed = self.class_embed
-
-        # two stage
-        self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
-        if two_stage_type != "no":
-            if two_stage_bbox_embed_share:
-                assert dec_pred_bbox_embed_share
-                self.transformer.enc_out_bbox_embed = _bbox_embed
-            else:
-                self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
-
-            if two_stage_class_embed_share:
-                assert dec_pred_bbox_embed_share
-                self.transformer.enc_out_class_embed = _class_embed
-            else:
-                self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
-
-            self.refpoint_embed = None
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        # init input_proj
-        for proj in self.input_proj:
-            nn.init.xavier_uniform_(proj[0].weight, gain=1)
-            nn.init.constant_(proj[0].bias, 0)
-
-    def init_ref_points(self, use_num_queries):
-        self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
-
-    def forward(self, samples: NestedTensor, targets: List = None, **kw):
-        """The forward expects a NestedTensor, which consists of:
-           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
-           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
-
-        It returns a dict with the following elements:
-           - "pred_logits": the classification logits (including no-object) for all queries.
-                            Shape= [batch_size x num_queries x num_classes]
-           - "pred_boxes": The normalized boxes coordinates for all queries, represented as
-                           (center_x, center_y, width, height). These values are normalized in [0, 1],
-                           relative to the size of each individual image (disregarding possible padding).
-                           See PostProcess for information on how to retrieve the unnormalized bounding box.
-           - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
-                            dictionnaries containing the two above keys for each decoder layer.
-        """
-        if targets is None:
-            captions = kw["captions"]
-        else:
-            captions = [t["caption"] for t in targets]
-        len(captions)
-
-        # encoder texts
-        tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
-            samples.device
-        )
-        (
-            text_self_attention_masks,
-            position_ids,
-            cate_to_token_mask_list,
-        ) = generate_masks_with_special_tokens_and_transfer_map(
-            tokenized, self.specical_tokens, self.tokenizer
-        )
-
-        if text_self_attention_masks.shape[1] > self.max_text_len:
-            text_self_attention_masks = text_self_attention_masks[
-                :, : self.max_text_len, : self.max_text_len
-            ]
-            position_ids = position_ids[:, : self.max_text_len]
-            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
-            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
-            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
-
-        # extract text embeddings
-        if self.sub_sentence_present:
-            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
-            tokenized_for_encoder["attention_mask"] = text_self_attention_masks
-            tokenized_for_encoder["position_ids"] = position_ids
-        else:
-            # import ipdb; ipdb.set_trace()
-            tokenized_for_encoder = tokenized
-
-        bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768
-
-        encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
-        text_token_mask = tokenized.attention_mask.bool()  # bs, 195
-        # text_token_mask: True for nomask, False for mask
-        # text_self_attention_masks: True for nomask, False for mask
-
-        if encoded_text.shape[1] > self.max_text_len:
-            encoded_text = encoded_text[:, : self.max_text_len, :]
-            text_token_mask = text_token_mask[:, : self.max_text_len]
-            position_ids = position_ids[:, : self.max_text_len]
-            text_self_attention_masks = text_self_attention_masks[
-                :, : self.max_text_len, : self.max_text_len
-            ]
-
-        text_dict = {
-            "encoded_text": encoded_text,  # bs, 195, d_model
-            "text_token_mask": text_token_mask,  # bs, 195
-            "position_ids": position_ids,  # bs, 195
-            "text_self_attention_masks": text_self_attention_masks,  # bs, 195,195
-        }
-
-        # import ipdb; ipdb.set_trace()
-
-        if isinstance(samples, (list, torch.Tensor)):
-            samples = nested_tensor_from_tensor_list(samples)
-        features, poss = self.backbone(samples)
-
-        srcs = []
-        masks = []
-        for l, feat in enumerate(features):
-            src, mask = feat.decompose()
-            srcs.append(self.input_proj[l](src))
-            masks.append(mask)
-            assert mask is not None
-        if self.num_feature_levels > len(srcs):
-            _len_srcs = len(srcs)
-            for l in range(_len_srcs, self.num_feature_levels):
-                if l == _len_srcs:
-                    src = self.input_proj[l](features[-1].tensors)
-                else:
-                    src = self.input_proj[l](srcs[-1])
-                m = samples.mask
-                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
-                pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
-                srcs.append(src)
-                masks.append(mask)
-                poss.append(pos_l)
-
-        input_query_bbox = input_query_label = attn_mask = dn_meta = None
-        hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
-            srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
-        )
-
-        # deformable-detr-like anchor update
-        outputs_coord_list = []
-        for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
-            zip(reference[:-1], self.bbox_embed, hs)
-        ):
-            layer_delta_unsig = layer_bbox_embed(layer_hs)
-            layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
-            layer_outputs_unsig = layer_outputs_unsig.sigmoid()
-            outputs_coord_list.append(layer_outputs_unsig)
-        outputs_coord_list = torch.stack(outputs_coord_list)
-
-        # output
-        outputs_class = torch.stack(
-            [
-                layer_cls_embed(layer_hs, text_dict)
-                for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
-            ]
-        )
-        out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
-
-        # # for intermediate outputs
-        # if self.aux_loss:
-        #     out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
-
-        # # for encoder output
-        # if hs_enc is not None:
-        #     # prepare intermediate outputs
-        #     interm_coord = ref_enc[-1]
-        #     interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
-        #     out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
-        #     out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
-
-        return out
-
-    @torch.jit.unused
-    def _set_aux_loss(self, outputs_class, outputs_coord):
-        # this is a workaround to make torchscript happy, as torchscript
-        # doesn't support dictionary with non-homogeneous values, such
-        # as a dict having both a Tensor and a list.
-        return [
-            {"pred_logits": a, "pred_boxes": b}
-            for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
-        ]
-
-
-@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
-def build_groundingdino(args):
-
-    backbone = build_backbone(args)
-    transformer = build_transformer(args)
-
-    dn_labelbook_size = args.dn_labelbook_size
-    dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
-    sub_sentence_present = args.sub_sentence_present
-
-    model = GroundingDINO(
-        backbone,
-        transformer,
-        num_queries=args.num_queries,
-        aux_loss=True,
-        iter_update=True,
-        query_dim=4,
-        num_feature_levels=args.num_feature_levels,
-        nheads=args.nheads,
-        dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
-        two_stage_type=args.two_stage_type,
-        two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
-        two_stage_class_embed_share=args.two_stage_class_embed_share,
-        num_patterns=args.num_patterns,
-        dn_number=0,
-        dn_box_noise_scale=args.dn_box_noise_scale,
-        dn_label_noise_ratio=args.dn_label_noise_ratio,
-        dn_labelbook_size=dn_labelbook_size,
-        text_encoder_type=args.text_encoder_type,
-        sub_sentence_present=sub_sentence_present,
-        max_text_len=args.max_text_len,
-    )
-
-    return model
diff --git a/spaces/Solis/Solis/llm_src/utils/cot/get_prompt.py b/spaces/Solis/Solis/llm_src/utils/cot/get_prompt.py
deleted file mode 100644
index 2384a6a1784c147004d241e1c7003b33f721dc69..0000000000000000000000000000000000000000
--- a/spaces/Solis/Solis/llm_src/utils/cot/get_prompt.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import random
-
-def get_prompt(num_cases=8):
-    """
-    select prompt examples from COT
-    """
-    COT_PROMPT = "Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\nA: We start with 15 trees. Later we have 21 trees. The difference must be the number of trees they planted. So, they must have planted 21 - 15 = 6 trees. The answer is 6.\n\nQ: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\nA: There are 3 cars in the parking lot already. 2 more arrive. Now there are 3 + 2 = 5 cars. The answer is 5.\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\nA: Leah had 32 chocolates and Leah's sister had 42. That means there were originally 32 + 42 = 74 chocolates. 35 have been eaten. So in total they still have 74 - 35 = 39 chocolates. The answer is 39.\n\nQ: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\nA: Jason had 20 lollipops. Since he only has 12 now, he must have given the rest to Denny. The number of lollipops he has given to Denny must have been 20 - 12 = 8 lollipops. The answer is 8.\n\nQ: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\nA: He has 5 toys. He got 2 from mom, so after that he has 5 + 2 = 7 toys. Then he got 2 more from dad, so in total he has 7 + 2 = 9 toys. The answer is 9.\n\nQ: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\nA: There are 4 days from monday to thursday. 5 computers were added each day. That means in total 4 * 5 = 20 computers were added. There were 9 computers in the beginning, so now there are 9 + 20 = 29 computers. The answer is 29.\n\nQ: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\nA: Michael initially had 58 balls. He lost 23 on Tuesday, so after that he has 58 - 23 = 35 balls. On Wednesday he lost 2 more so now he has 35 - 2 = 33 balls. The answer is 33.\n\nQ: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\nA: She bought 5 bagels for $3 each. This means she spent 5 * $3 = $15 on the bagels. She had $23 in beginning, so now she has $23 - $15 = $8. The answer is 8."
-
-    COT_prompt_list = COT_PROMPT.split('\n\n')
-    random.shuffle(COT_prompt_list)
-    COT_prompt = '\n\n'.join(COT_prompt_list[:num_cases]) + '\n\n'
-
-    return COT_prompt
-
-
-if __name__ == "__main__":
-    print(get_prompt())
\ No newline at end of file
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py
deleted file mode 100644
index 64ad3f8c77afe1ab5908e407ad14d4879e1b1ad1..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-    launcher.bind_(conditioner='clapemb2music')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    cache_path = {'conditioners.description.clap.cache_path':
-                  '/fsx-audio-craft-llm/jadecopet/experiments/audiocraft/caches/clap_embed_music'}
-    text_wav_training_opt = {'conditioners.description.clap.text_p': 0.5}
-
-    launcher.bind_(fsdp)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        launcher()
-        launcher(text_wav_training_opt)
-        launcher(cache_path)
-        launcher(cache_path, text_wav_training_opt)
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/modules/transformer.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/modules/transformer.py
deleted file mode 100644
index 048c06dfbb0ab4167afce95dffb73dcc343c2344..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/modules/transformer.py
+++ /dev/null
@@ -1,747 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Transformer model, with streaming support, xformer attention support
-and easy causal attention with a potentially finite receptive field.
-
-See `StreamingTransformer` for more information.
-
-Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
-"""
-
-import typing as tp
-
-from einops import rearrange
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint as torch_checkpoint
-from xformers import ops
-
-from .rope import RotaryEmbedding
-from .streaming import StreamingModule
-
-_efficient_attention_backend: str = 'torch'
-
-
-def set_efficient_attention_backend(backend: str = 'torch'):
-    # Using torch by default, it seems a bit faster on older P100 GPUs (~20% faster).
-    global _efficient_attention_backend
-    assert _efficient_attention_backend in ['xformers', 'torch']
-    _efficient_attention_backend = backend
-
-
-def _get_attention_time_dimension() -> int:
-    if _efficient_attention_backend == 'torch':
-        return 2
-    else:
-        return 1
-
-
-def _is_profiled() -> bool:
-    # Return true if we are currently running with a xformers profiler activated.
-    try:
-        from xformers.profiler import profiler
-    except ImportError:
-        return False
-    return profiler._Profiler._CURRENT_PROFILER is not None
-
-
-def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
-    """Create normalization module for transformer encoder layer.
-
-    Args:
-        norm_type (str): Normalization method.
-        dim (int): Dimension of the normalized layer.
-        **kwargs (dict): Additional parameters for normalization layer.
-    Returns:
-        nn.Module: Normalization module.
-    """
-    if norm_type == 'layer_norm':
-        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
-    else:
-        raise ValueError(f"Unknown norm type: {norm_type}")
-
-
-def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
-                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
-    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
-
-    Args:
-        positions (torch.Tensor): LongTensor of positions.
-        dim (int): Dimension of the embedding.
-        max_period (float): Maximum period of the cosine/sine functions.
-        dtype (torch.dtype or str): dtype to use to generate the embedding.
-    Returns:
-        torch.Tensor: Sinusoidal positional embedding.
-    """
-    # We aim for BTC format
-    assert dim % 2 == 0
-    half_dim = dim // 2
-    positions = positions.to(dtype)
-    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
-    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
-    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
-    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
-
-
-def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers."""
-    if n_rep == 1:
-        return x
-    if _efficient_attention_backend == 'torch':
-        bs, n_kv_heads, slen, head_dim = x.shape
-        return (
-            x[:, :, None, :, :]
-            .expand(bs, n_kv_heads, n_rep, slen, head_dim)
-            .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
-        )
-    else:
-        bs, slen, n_kv_heads, head_dim = x.shape
-        return (
-            x[:, :, :, None, :]
-            .expand(bs, slen, n_kv_heads, n_rep, head_dim)
-            .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
-        )
-
-
-class LayerScale(nn.Module):
-    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
-    This rescales diagonally the residual outputs close to 0, with a learnt scale.
-
-    Args:
-        channels (int): Number of channels.
-        init (float): Initial scale.
-        channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
-        device (torch.device or str, optional): Device on which to initialize the module.
-        dtype (torch.dtype, optional): dtype to use to initialize the module.
-    """
-    def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
-                 device=None, dtype=None):
-        super().__init__()
-        self.channel_last = channel_last
-        self.scale = nn.Parameter(
-            torch.full((channels,), init,
-                       requires_grad=True, device=device, dtype=dtype))
-
-    def forward(self, x: torch.Tensor):
-        if self.channel_last:
-            return self.scale * x
-        else:
-            return self.scale[:, None] * x
-
-
-class StreamingMultiheadAttention(StreamingModule):
-    """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
-
-    Args:
-        embed_dim (int): Dimension to project to.
-        num_heads (int): Number of heads.
-        dropout (float): Dropout level.
-        bias (bool): Use bias in projections.
-        causal (bool): Causal mask applied automatically.
-        past_context (int, optional): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        rope (`RotaryEmbedding`, optional): Rope embedding to use.
-        cross_attention: Should be true when used as a cross attention.
-            All keys and values must be available at once, streaming is only for the queries.
-            Cannot be used with `causal` or `rope` (as it wouldn't make sens to
-            interpret the time steps in the keys relative to those in the queries).
-        safe_streaming (bool): Bug fix, will go away with xformers update.
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device, optional): Device on which to initialize.
-        dtype (torch.dtype, optional): dtype to use.
-    """
-    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
-                 safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
-                 device=None, dtype=None):
-        super().__init__()
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        if past_context is not None:
-            assert causal
-
-        self.embed_dim = embed_dim
-        self.causal = causal
-        self.past_context = past_context
-        self.memory_efficient = memory_efficient
-        self.attention_as_float32 = attention_as_float32
-        self.rope = rope
-        self.cross_attention = cross_attention
-        self.safe_streaming = safe_streaming
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.kv_repeat = kv_repeat
-        if cross_attention:
-            assert not causal, "Causal cannot work with cross attention."
-            assert rope is None, "Rope cannot work with cross attention."
-
-        if memory_efficient:
-            _verify_xformers_memory_efficient_compat()
-
-        self.custom = _is_custom(custom, memory_efficient)
-        if self.custom:
-            out_dim = embed_dim
-            assert num_heads % kv_repeat == 0
-            assert not cross_attention or kv_repeat == 1
-            num_kv = num_heads // kv_repeat
-            kv_dim = (embed_dim // num_heads) * num_kv
-            out_dim += 2 * kv_dim
-            in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
-            # We try to follow the default PyTorch MHA convention, to easily compare results.
-            self.in_proj_weight = in_proj.weight
-            self.in_proj_bias = in_proj.bias
-            if bias:
-                self.in_proj_bias.data.zero_()  # Following Pytorch convention
-            self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
-            if bias:
-                self.out_proj.bias.data.zero_()
-        else:
-            assert not qk_layer_norm
-            assert kv_repeat == 1
-            self.mha = nn.MultiheadAttention(
-                embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
-                **factory_kwargs)
-        self.qk_layer_norm = qk_layer_norm
-        if qk_layer_norm:
-            assert self.custom
-            assert kv_repeat == 1
-            ln_dim = embed_dim
-            self.q_layer_norm = nn.LayerNorm(ln_dim)
-            self.k_layer_norm = nn.LayerNorm(ln_dim)
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        if not self.custom:
-            # Support compat with regular MHA
-            keys = [n for n, _ in self.mha.named_parameters()]
-            for key in keys:
-                if prefix + key in state_dict:
-                    state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
-        # Return a causal mask, accounting for potentially stored past keys/values
-        # We actually return a bias for the attention score, as this has the same
-        # convention both in the builtin MHA in Pytorch, and Xformers functions.
-        time_dim = _get_attention_time_dimension()
-        if self.memory_efficient:
-            from xformers.ops import LowerTriangularMask
-            if current_steps == 1:
-                # If we only have one step, then we do not need a mask.
-                return None
-            elif 'past_keys' in self._streaming_state:
-                raise RuntimeError("Not supported at the moment")
-            else:
-                # Then we can safely use a lower triangular mask
-                return LowerTriangularMask()
-        if self._streaming_state:
-            past_keys = self._streaming_state['past_keys']
-            past_steps = past_keys.shape[time_dim]
-        else:
-            past_steps = 0
-
-        queries_pos = torch.arange(
-            past_steps, current_steps + past_steps, device=device).view(-1, 1)
-        keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
-        delta = queries_pos - keys_pos
-        valid = delta >= 0
-        if self.past_context is not None:
-            valid &= (delta <= self.past_context)
-        return torch.where(
-            valid,
-            torch.zeros([], device=device, dtype=dtype),
-            torch.full([], float('-inf'), device=device, dtype=dtype))
-
-    def _complete_kv(self, k, v):
-        time_dim = _get_attention_time_dimension()
-        if self.cross_attention:
-            # With cross attention we assume all keys and values
-            # are already available, and streaming is with respect
-            # to the queries only.
-            return k, v
-        # Complete the key/value pair using the streaming state.
-        if self._streaming_state:
-            pk = self._streaming_state['past_keys']
-            nk = torch.cat([pk, k], dim=time_dim)
-            if v is k:
-                nv = nk
-            else:
-                pv = self._streaming_state['past_values']
-                nv = torch.cat([pv, v], dim=time_dim)
-        else:
-            nk = k
-            nv = v
-
-        assert nk.shape[time_dim] == nv.shape[time_dim]
-        offset = 0
-        if self.past_context is not None:
-            offset = max(0, nk.shape[time_dim] - self.past_context)
-        if self._is_streaming:
-            self._streaming_state['past_keys'] = nk[:, offset:]
-            if v is not k:
-                self._streaming_state['past_values'] = nv[:, offset:]
-            if 'offset' in self._streaming_state:
-                self._streaming_state['offset'] += offset
-            else:
-                self._streaming_state['offset'] = torch.tensor(0)
-        return nk, nv
-
-    def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
-        # TODO: fix and verify layout.
-        assert _efficient_attention_backend == 'xformers', "Rope not supported with torch attn."
-        # Apply rope embeddings to query and key tensors.
-        assert self.rope is not None
-        if 'past_keys' in self._streaming_state:
-            past_keys_offset = self._streaming_state['past_keys'].shape[1]
-        else:
-            past_keys_offset = 0
-        if 'offset' in self._streaming_state:
-            past_context_offset = int(self._streaming_state['offset'].item())
-        else:
-            past_context_offset = 0
-        streaming_offset = past_context_offset + past_keys_offset
-        return self.rope.rotate_qk(query, key, start=streaming_offset)
-
-    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
-                key_padding_mask=None, need_weights=False, attn_mask=None,
-                average_attn_weights=True, is_causal=False):
-        assert attn_mask is None
-        assert not is_causal, ("New param added in torch 2.0.1 not supported, "
-                               "use the causal args in the constructor.")
-
-        time_dim = _get_attention_time_dimension()
-        if time_dim == 2:
-            layout = "b h t d"
-        else:
-            layout = "b t h d"
-        dtype = query.dtype
-        if self._is_streaming:
-            assert self.causal or self.cross_attention, \
-                "Streaming only available for causal or cross attention"
-
-        if self.causal:
-            # At the moment we specialize only for the self-attention case.
-            assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
-
-        if self.custom:
-            # custom implementation
-            assert need_weights is False
-            assert key_padding_mask is None
-            if self.cross_attention:
-                # Different queries, keys, values, we have to spit manually the weights
-                # before applying the linear.
-                dim = self.in_proj_weight.shape[0] // 3
-                if self.in_proj_bias is None:
-                    bias_q, bias_k, bias_v = None, None, None
-                else:
-                    bias_q = self.in_proj_bias[:dim]
-                    bias_k = self.in_proj_bias[dim: 2 * dim]
-                    bias_v = self.in_proj_bias[2 * dim:]
-                q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
-                # todo: when streaming, we could actually save k, v and check the shape actually match.
-                k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
-                v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
-                if self.qk_layer_norm is True:
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                q, k, v = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k, v]]
-            else:
-                if not _is_profiled():
-                    # profiling breaks that propertysomehow.
-                    assert query is key, "specialized implementation"
-                    assert value is key, "specialized implementation"
-                projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
-                if self.kv_repeat == 1:
-                    if time_dim == 2:
-                        bound_layout = "b h p t d"
-                    else:
-                        bound_layout = "b t p h d"
-                    packed = rearrange(projected, f"b t (p h d) -> {bound_layout}", p=3, h=self.num_heads)
-                    q, k, v = ops.unbind(packed, dim=2)
-                else:
-                    embed_dim = self.embed_dim
-                    per_head_dim = (embed_dim // self.num_heads)
-                    kv_heads = self.num_heads // self.kv_repeat
-                    q = projected[:, :, :embed_dim]
-                    start = embed_dim
-                    end = start + per_head_dim * kv_heads
-                    k = projected[:, :, start: end]
-                    v = projected[:, :, end:]
-                    q = rearrange(q, f"b t (h d) -> {layout}", h=self.num_heads)
-                    k = rearrange(k, f"b t (h d) -> {layout}", h=kv_heads)
-                    v = rearrange(v, f"b t (h d) -> {layout}", h=kv_heads)
-
-                if self.qk_layer_norm is True:
-                    assert self.kv_repeat == 1
-                    q, k = [rearrange(x, f"{layout} -> b t (h d)") for x in [q, k]]
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                    q, k = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k]]
-                if self.rope:
-                    q, k = self._apply_rope(q, k)
-                k, v = self._complete_kv(k, v)
-                if self.kv_repeat > 1:
-                    k = expand_repeated_kv(k, self.kv_repeat)
-                    v = expand_repeated_kv(v, self.kv_repeat)
-            if self.attention_as_float32:
-                q, k, v = [x.float() for x in [q, k, v]]
-            if self.memory_efficient:
-                p = self.dropout if self.training else 0
-                if _efficient_attention_backend == 'torch':
-                    x = torch.nn.functional.scaled_dot_product_attention(
-                        q, k, v, is_causal=attn_mask is not None, dropout_p=p)
-                else:
-                    x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
-            else:
-                # We include the dot product as float32, for consistency
-                # with the other implementations that include that step
-                # as part of the attention. Note that when using `autocast`,
-                # the einsums would be done as bfloat16, but the softmax
-                # would be done as bfloat16, so `attention_as_float32` will
-                # extend a bit the range of operations done in float32,
-                # although this should make no difference.
-                q = q / q.shape[-1] ** 0.5
-                key_layout = layout.replace('t', 'k')
-                query_layout = layout
-                if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
-                    with torch.autocast(device_type=q.device.type, dtype=torch.float32):
-                        pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
-                else:
-                    pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
-                if attn_mask is not None:
-                    pre_w = pre_w + attn_mask
-                w = torch.softmax(pre_w, dim=-1)
-                w = F.dropout(w, self.dropout, training=self.training).to(v)
-                # Key and value have the same format.
-                x = torch.einsum(f"b h t k, {key_layout} -> {layout}", w, v)
-            x = x.to(dtype)
-            x = rearrange(x, f"{layout} -> b t (h d)", h=self.num_heads)
-            x = self.out_proj(x)
-        else:
-            key, value = self._complete_kv(key, value)
-            if self.attention_as_float32:
-                query, key, value = [x.float() for x in [query, key, value]]
-            x, _ = self.mha(
-                query, key, value, key_padding_mask,
-                need_weights, attn_mask, average_attn_weights)
-            x = x.to(dtype)
-
-        return x, None
-
-
-class StreamingTransformerLayer(nn.TransformerEncoderLayer):
-    """TransformerLayer with Streaming / Causal support.
-    This also integrates cross_attention, when passing `cross_attention=True`,
-    rather than having two separate classes like in PyTorch.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int, optional): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
-        qk_layer_norm_cross (bool): Same for the cross attention.
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-            Cross attention will use the default MHA, as it typically won't require
-            special treatment.
-        layer_scale (float, optional): If not None, LayerScale will be used with
-            the given value as initial scale.
-        rope (`RotaryEmbedding`, optional): Rope embedding to use.
-        attention_dropout (float, optional): If not None, separate the value of the dimension dropout
-            in FFN and of the attention dropout.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device, optional): Device on which to initialize.
-        dtype (torch.dtype, optional): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
-                 bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
-                 past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
-                 kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
-        super().__init__(d_model, num_heads, dim_feedforward, dropout,
-                         device=device, dtype=dtype, batch_first=True, **kwargs)
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        # Redefine self_attn to our streaming multi-head attention
-        attn_kwargs: tp.Dict[str, tp.Any] = {
-            'embed_dim': d_model,
-            'num_heads': num_heads,
-            'dropout': dropout if attention_dropout is None else attention_dropout,
-            'bias': bias_attn,
-            'custom': custom,
-            'memory_efficient': memory_efficient,
-            'attention_as_float32': attention_as_float32,
-        }
-        self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
-            causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
-            kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs)  # type: ignore
-        # Redefine feedforward layers to expose bias parameter
-        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
-        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
-
-        self.layer_scale_1: nn.Module
-        self.layer_scale_2: nn.Module
-        if layer_scale is None:
-            self.layer_scale_1 = nn.Identity()
-            self.layer_scale_2 = nn.Identity()
-        else:
-            self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
-            self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
-
-        self.cross_attention: tp.Optional[nn.Module] = None
-        if cross_attention:
-            self.cross_attention = StreamingMultiheadAttention(
-                cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
-                **attn_kwargs, **factory_kwargs)
-            # Norm and dropout
-            self.dropout_cross = nn.Dropout(dropout)
-            # eps value matching that used in PyTorch reference implementation.
-            self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
-            self.layer_scale_cross: nn.Module
-            if layer_scale is None:
-                self.layer_scale_cross = nn.Identity()
-            else:
-                self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
-        self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-        self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-
-    def _cross_attention_block(self, src: torch.Tensor,
-                               cross_attention_src: torch.Tensor) -> torch.Tensor:
-        assert self.cross_attention is not None
-        # queries are from src, keys and values from cross_attention_src.
-        x = self.cross_attention(
-            src, cross_attention_src, cross_attention_src, need_weights=False)[0]
-        return self.dropout_cross(x)  # type: ignore
-
-    def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None,  # type: ignore
-                src_key_padding_mask: tp.Optional[torch.Tensor] = None,
-                cross_attention_src: tp.Optional[torch.Tensor] = None):
-        if self.cross_attention is None:
-            assert cross_attention_src is None
-        else:
-            assert cross_attention_src is not None
-        x = src
-        if self.norm_first:
-            x = x + self.layer_scale_1(
-                self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
-            if cross_attention_src is not None:
-                x = x + self.layer_scale_cross(
-                    self._cross_attention_block(
-                        self.norm_cross(x), cross_attention_src))
-            x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
-        else:
-            x = self.norm1(x + self.layer_scale_1(
-                self._sa_block(x, src_mask, src_key_padding_mask)))
-            if cross_attention_src is not None:
-                x = self.norm_cross(
-                    x + self.layer_scale_cross(
-                        self._cross_attention_block(src, cross_attention_src)))
-            x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
-        return x
-
-
-class StreamingTransformer(StreamingModule):
-    """Transformer with Streaming / Causal support.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int, optional): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-        layer_scale (float, optional): If not None, LayerScale will be used
-            with the given value as initial scale.
-        positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
-        max_period (float): Maximum period of the time embedding.
-        positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
-        xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
-        lr (float, optional): learning rate override through the `make_optim_group` API.
-        weight_decay (float, optional): Weight_decay override through the `make_optim_group` API.
-        layer_class: (subclass of `StreamingTransformerLayer): class to use
-            to initialize the layers, allowing further customization outside of AudioCraft.
-        checkpointing (str): Checkpointing strategy to reduce memory usage.
-            No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
-            if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
-            minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
-            a policy for opting-out some operations of the checkpointing like
-            linear layers and attention, providing a middle ground between speed and memory.
-        device (torch.device, optional): Device on which to initialize.
-        dtype (torch.dtype, optional): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
-                 dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None,
-                 custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
-                 xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
-                 layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
-                 checkpointing: str = 'none', device=None, dtype=None, **kwargs):
-        super().__init__()
-        assert d_model % num_heads == 0
-
-        self.positional_embedding = positional_embedding
-        self.max_period = max_period
-        self.positional_scale = positional_scale
-        self.weight_decay = weight_decay
-        self.lr = lr
-
-        assert positional_embedding in ['sin', 'rope', 'sin_rope']
-        self.rope: tp.Optional[RotaryEmbedding] = None
-        if self.positional_embedding in ['rope', 'sin_rope']:
-            assert _is_custom(custom, memory_efficient)
-            self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
-                                        xpos=xpos, scale=positional_scale, device=device)
-
-        self.checkpointing = checkpointing
-
-        assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
-        if self.checkpointing.startswith('xformers'):
-            _verify_xformers_internal_compat()
-
-        self.layers = nn.ModuleList()
-        for idx in range(num_layers):
-            self.layers.append(
-                layer_class(
-                    d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
-                    dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
-                    causal=causal, past_context=past_context, custom=custom,
-                    memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
-                    cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
-                    device=device, dtype=dtype, **kwargs))
-
-        if self.checkpointing != 'none':
-            for layer in self.layers:
-                # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
-                # backward hook inside of FSDP...
-                layer._magma_checkpointed = True  # type: ignore
-                assert layer.layer_drop == 0., "Need further checking"  # type: ignore
-
-    def _apply_layer(self, layer, *args, **kwargs):
-        method = self.checkpointing
-        if method == 'none':
-            return layer(*args, **kwargs)
-        elif method == 'torch':
-            return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
-        elif method.startswith('xformers'):
-            from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
-            if method == 'xformers_default':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "xformers.efficient_attention_forward_cutlass.default",
-                    "xformers_flash.flash_fwd.default",
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            elif method == 'xformers_mm':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            else:
-                raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
-            policy_fn = _get_default_policy(allow_list)
-            return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
-        else:
-            raise ValueError(f"Checkpointing method {method} is unknown.")
-
-    def forward(self, x: torch.Tensor, *args, **kwargs):
-        B, T, C = x.shape
-
-        if 'offsets' in self._streaming_state:
-            offsets = self._streaming_state['offsets']
-        else:
-            offsets = torch.zeros(B, dtype=torch.long, device=x.device)
-
-        if self.positional_embedding in ['sin', 'sin_rope']:
-            positions = torch.arange(T, device=x.device).view(1, -1, 1)
-            positions = positions + offsets.view(-1, 1, 1)
-            pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
-            x = x + self.positional_scale * pos_emb
-
-        for layer in self.layers:
-            x = self._apply_layer(layer, x, *args, **kwargs)
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return x
-
-    def make_optim_group(self):
-        group = {"params": list(self.parameters())}
-        if self.lr is not None:
-            group["lr"] = self.lr
-        if self.weight_decay is not None:
-            group["weight_decay"] = self.weight_decay
-        return group
-
-
-# special attention related function
-
-def _verify_xformers_memory_efficient_compat():
-    try:
-        from xformers.ops import memory_efficient_attention, LowerTriangularMask  # noqa
-    except ImportError:
-        raise ImportError(
-            "xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _verify_xformers_internal_compat():
-    try:
-        from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy  # noqa
-    except ImportError:
-        raise ImportError(
-            "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _is_custom(custom: bool, memory_efficient: bool):
-    return custom or memory_efficient
diff --git a/spaces/SuYuanS/AudioCraft_Plus/scripts/resample_dataset.py b/spaces/SuYuanS/AudioCraft_Plus/scripts/resample_dataset.py
deleted file mode 100644
index af5288712b8d2cde2d9814c747275e69f6e970c8..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/scripts/resample_dataset.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Resampling script.
-"""
-import argparse
-from pathlib import Path
-import shutil
-import typing as tp
-
-import submitit
-import tqdm
-
-from audiocraft.data.audio import audio_read, audio_write
-from audiocraft.data.audio_dataset import load_audio_meta, find_audio_files
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.environment import AudioCraftEnvironment
-
-
-def read_txt_files(path: tp.Union[str, Path]):
-    with open(args.files_path) as f:
-        lines = [line.rstrip() for line in f]
-        print(f"Read {len(lines)} in .txt")
-        lines = [line for line in lines if Path(line).suffix not in ['.json', '.txt', '.csv']]
-        print(f"Filtered and keep {len(lines)} from .txt")
-        return lines
-
-
-def read_egs_files(path: tp.Union[str, Path]):
-    path = Path(path)
-    if path.is_dir():
-        if (path / 'data.jsonl').exists():
-            path = path / 'data.jsonl'
-        elif (path / 'data.jsonl.gz').exists():
-            path = path / 'data.jsonl.gz'
-        else:
-            raise ValueError("Don't know where to read metadata from in the dir. "
-                             "Expecting either a data.jsonl or data.jsonl.gz file but none found.")
-    meta = load_audio_meta(path)
-    return [m.path for m in meta]
-
-
-def process_dataset(args, n_shards: int, node_index: int, task_index: tp.Optional[int] = None):
-    if task_index is None:
-        env = submitit.JobEnvironment()
-        task_index = env.global_rank
-    shard_index = node_index * args.tasks_per_node + task_index
-
-    if args.files_path is None:
-        lines = [m.path for m in find_audio_files(args.root_path, resolve=False, progress=True, workers=8)]
-    else:
-        files_path = Path(args.files_path)
-        if files_path.suffix == '.txt':
-            print(f"Reading file list from .txt file: {args.files_path}")
-            lines = read_txt_files(args.files_path)
-        else:
-            print(f"Reading file list from egs: {args.files_path}")
-            lines = read_egs_files(args.files_path)
-
-    total_files = len(lines)
-    print(
-        f"Total of {total_files} processed with {n_shards} shards. " +
-        f"Current idx = {shard_index} -> {total_files // n_shards} files to process"
-    )
-    for idx, line in tqdm.tqdm(enumerate(lines)):
-
-        # skip if not part of this shard
-        if idx % n_shards != shard_index:
-            continue
-
-        path = str(AudioCraftEnvironment.apply_dataset_mappers(line))
-        root_path = str(args.root_path)
-        if not root_path.endswith('/'):
-            root_path += '/'
-        assert path.startswith(str(root_path)), \
-            f"Mismatch between path and provided root: {path} VS {root_path}"
-
-        try:
-            metadata_path = Path(path).with_suffix('.json')
-            out_path = args.out_path / path[len(root_path):]
-            out_metadata_path = out_path.with_suffix('.json')
-            out_done_token = out_path.with_suffix('.done')
-
-            # don't reprocess existing files
-            if out_done_token.exists():
-                continue
-
-            print(idx, out_path, path)
-            mix, sr = audio_read(path)
-            mix_channels = args.channels if args.channels is not None and args.channels > 0 else mix.size(0)
-            # enforce simple stereo
-            out_channels = mix_channels
-            if out_channels > 2:
-                print(f"Mix has more than two channels: {out_channels}, enforcing 2 channels")
-                out_channels = 2
-            out_sr = args.sample_rate if args.sample_rate is not None else sr
-            out_wav = convert_audio(mix, sr, out_sr, out_channels)
-            audio_write(out_path.with_suffix(''), out_wav, sample_rate=out_sr,
-                        format=args.format, normalize=False, strategy='clip')
-            if metadata_path.exists():
-                shutil.copy(metadata_path, out_metadata_path)
-            else:
-                print(f"No metadata found at {str(metadata_path)}")
-            out_done_token.touch()
-        except Exception as e:
-            print(f"Error processing file line: {line}, {e}")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Resample dataset with SLURM.")
-    parser.add_argument(
-        "--log_root",
-        type=Path,
-        default=Path.home() / 'tmp' / 'resample_logs',
-    )
-    parser.add_argument(
-        "--files_path",
-        type=Path,
-        help="List of files to process, either .txt (one file per line) or a jsonl[.gz].",
-    )
-    parser.add_argument(
-        "--root_path",
-        type=Path,
-        required=True,
-        help="When rewriting paths, this will be the prefix to remove.",
-    )
-    parser.add_argument(
-        "--out_path",
-        type=Path,
-        required=True,
-        help="When rewriting paths, `root_path` will be replaced by this.",
-    )
-    parser.add_argument("--xp_name", type=str, default="shutterstock")
-    parser.add_argument(
-        "--nodes",
-        type=int,
-        default=4,
-    )
-    parser.add_argument(
-        "--tasks_per_node",
-        type=int,
-        default=20,
-    )
-    parser.add_argument(
-        "--cpus_per_task",
-        type=int,
-        default=4,
-    )
-    parser.add_argument(
-        "--memory_gb",
-        type=int,
-        help="Memory in GB."
-    )
-    parser.add_argument(
-        "--format",
-        type=str,
-        default="wav",
-    )
-    parser.add_argument(
-        "--sample_rate",
-        type=int,
-        default=32000,
-    )
-    parser.add_argument(
-        "--channels",
-        type=int,
-    )
-    parser.add_argument(
-        "--partition",
-        default='learnfair',
-    )
-    parser.add_argument("--qos")
-    parser.add_argument("--account")
-    parser.add_argument("--timeout", type=int, default=4320)
-    parser.add_argument('--debug', action='store_true', help='debug mode (local run)')
-    args = parser.parse_args()
-    n_shards = args.tasks_per_node * args.nodes
-    if args.files_path is None:
-        print("Warning: --files_path not provided, not recommended when processing more than 10k files.")
-    if args.debug:
-        print("Debugging mode")
-        process_dataset(args, n_shards=n_shards, node_index=0, task_index=0)
-    else:
-
-        log_folder = Path(args.log_root) / args.xp_name / '%j'
-        print(f"Logging to: {log_folder}")
-        log_folder.parent.mkdir(parents=True, exist_ok=True)
-        executor = submitit.AutoExecutor(folder=str(log_folder))
-        if args.qos:
-            executor.update_parameters(slurm_partition=args.partition, slurm_qos=args.qos, slurm_account=args.account)
-        else:
-            executor.update_parameters(slurm_partition=args.partition)
-        executor.update_parameters(
-            slurm_job_name=args.xp_name, timeout_min=args.timeout,
-            cpus_per_task=args.cpus_per_task, tasks_per_node=args.tasks_per_node, nodes=1)
-        if args.memory_gb:
-            executor.update_parameters(mem=f'{args.memory_gb}GB')
-        jobs = []
-        with executor.batch():
-            for node_index in range(args.nodes):
-                job = executor.submit(process_dataset, args, n_shards=n_shards, node_index=node_index)
-                jobs.append(job)
-        for job in jobs:
-            print(f"Waiting on job {job.job_id}")
-            job.results()
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/data/datasets/__init__.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/data/datasets/__init__.py
deleted file mode 100644
index 59ce30713f63d056107b2a06ecd434eb27a30b7d..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/data/datasets/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from . import (
-    register_ade20k_panoptic,
-    register_cityscapes_panoptic,
-    register_coco_panoptic_annos_semseg,
-    register_ade20k_instance,
-    register_coco_panoptic2instance,
-)
diff --git a/spaces/TEnngal/bingo/src/lib/hooks/chat-history.ts b/spaces/TEnngal/bingo/src/lib/hooks/chat-history.ts
deleted file mode 100644
index c6fbf3fecfa86fe553f56acc8253236b8f22a775..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/lib/hooks/chat-history.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { zip } from 'lodash-es'
-import { ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { Storage } from '../storage'
-
-/**
- * conversations:$botId => Conversation[]
- * conversation:$botId:$cid:messages => ChatMessageModel[]
- */
-
-interface Conversation {
-  id: string
-  createdAt: number
-}
-
-type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] }
-
-async function loadHistoryConversations(botId: BotId): Promise<Conversation[]> {
-  const key = `conversations:${botId}`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
-  const conversations = await loadHistoryConversations(botId)
-  const newConversations = conversations.filter((c) => c.id !== cid)
-  await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise<ChatMessageModel[]> {
-  const key = `conversation:${botId}:${cid}:messages`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
-  const conversations = await loadHistoryConversations(botId)
-  if (!conversations.some((c) => c.id === cid)) {
-    conversations.unshift({ id: cid, createdAt: Date.now() })
-    await Storage.set({ [`conversations:${botId}`]: conversations })
-  }
-  const key = `conversation:${botId}:${cid}:messages`
-  await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise<ConversationWithMessages[]> {
-  const conversations = await loadHistoryConversations(botId)
-  const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
-  return zip(conversations, messagesList).map(([c, messages]) => ({
-    id: c!.id,
-    createdAt: c!.createdAt,
-    messages: messages!,
-  }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
-  const messages = await loadConversationMessages(botId, conversationId)
-  const newMessages = messages.filter((m) => m.id !== messageId)
-  await setConversationMessages(botId, conversationId, newMessages)
-  if (!newMessages.length) {
-    await deleteHistoryConversation(botId, conversationId)
-  }
-}
diff --git a/spaces/TFEH/Streamlit_demo/README.md b/spaces/TFEH/Streamlit_demo/README.md
deleted file mode 100644
index 8f7fe11fc5d36104763acf5fde0a1ab8ba9d6390..0000000000000000000000000000000000000000
--- a/spaces/TFEH/Streamlit_demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit Demo
-emoji: 📊
-colorFrom: yellow
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/monotonic_align/__init__.py b/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/monotonic_align/__init__.py
deleted file mode 100644
index aed94600a6b01f4322b371b0c57d5b05713c4dac..0000000000000000000000000000000000000000
--- a/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/monotonic_align/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/egg_link.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/egg_link.py
deleted file mode 100644
index eb57ed1519f82adb79a3d2377e1f286df9d8ef6b..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/egg_link.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import os
-import re
-import sys
-from typing import List, Optional
-
-from pip._internal.locations import site_packages, user_site
-from pip._internal.utils.virtualenv import (
-    running_under_virtualenv,
-    virtualenv_no_global,
-)
-
-__all__ = [
-    "egg_link_path_from_sys_path",
-    "egg_link_path_from_location",
-]
-
-
-def _egg_link_name(raw_name: str) -> str:
-    """
-    Convert a Name metadata value to a .egg-link name, by applying
-    the same substitution as pkg_resources's safe_name function.
-    Note: we cannot use canonicalize_name because it has a different logic.
-    """
-    return re.sub("[^A-Za-z0-9.]+", "-", raw_name) + ".egg-link"
-
-
-def egg_link_path_from_sys_path(raw_name: str) -> Optional[str]:
-    """
-    Look for a .egg-link file for project name, by walking sys.path.
-    """
-    egg_link_name = _egg_link_name(raw_name)
-    for path_item in sys.path:
-        egg_link = os.path.join(path_item, egg_link_name)
-        if os.path.isfile(egg_link):
-            return egg_link
-    return None
-
-
-def egg_link_path_from_location(raw_name: str) -> Optional[str]:
-    """
-    Return the path for the .egg-link file if it exists, otherwise, None.
-
-    There's 3 scenarios:
-    1) not in a virtualenv
-       try to find in site.USER_SITE, then site_packages
-    2) in a no-global virtualenv
-       try to find in site_packages
-    3) in a yes-global virtualenv
-       try to find in site_packages, then site.USER_SITE
-       (don't look in global location)
-
-    For #1 and #3, there could be odd cases, where there's an egg-link in 2
-    locations.
-
-    This method will just return the first one found.
-    """
-    sites: List[str] = []
-    if running_under_virtualenv():
-        sites.append(site_packages)
-        if not virtualenv_no_global() and user_site:
-            sites.append(user_site)
-    else:
-        if user_site:
-            sites.append(user_site)
-        sites.append(site_packages)
-
-    egg_link_name = _egg_link_name(raw_name)
-    for site in sites:
-        egglink = os.path.join(site, egg_link_name)
-        if os.path.isfile(egglink):
-            return egglink
-    return None
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/install.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/install.py
deleted file mode 100644
index 73caacb049de312e75b3ff11e885e9ffb0f87b8f..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/install.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from distutils.errors import DistutilsArgError
-import inspect
-import glob
-import platform
-import distutils.command.install as orig
-
-import setuptools
-from ..warnings import SetuptoolsDeprecationWarning, SetuptoolsWarning
-
-# Prior to numpy 1.9, NumPy relies on the '_install' name, so provide it for
-# now. See https://github.com/pypa/setuptools/issues/199/
-_install = orig.install
-
-
-class install(orig.install):
-    """Use easy_install to install the package, w/dependencies"""
-
-    user_options = orig.install.user_options + [
-        ('old-and-unmanageable', None, "Try not to use this!"),
-        ('single-version-externally-managed', None,
-         "used by system package builders to create 'flat' eggs"),
-    ]
-    boolean_options = orig.install.boolean_options + [
-        'old-and-unmanageable', 'single-version-externally-managed',
-    ]
-    new_commands = [
-        ('install_egg_info', lambda self: True),
-        ('install_scripts', lambda self: True),
-    ]
-    _nc = dict(new_commands)
-
-    def initialize_options(self):
-        SetuptoolsDeprecationWarning.emit(
-            "setup.py install is deprecated.",
-            """
-            Please avoid running ``setup.py`` directly.
-            Instead, use pypa/build, pypa/installer or other
-            standards-based tools.
-            """,
-            see_url="https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html",
-            # TODO: Document how to bootstrap setuptools without install
-            #       (e.g. by unziping the wheel file)
-            #       and then add a due_date to this warning.
-        )
-
-        orig.install.initialize_options(self)
-        self.old_and_unmanageable = None
-        self.single_version_externally_managed = None
-
-    def finalize_options(self):
-        orig.install.finalize_options(self)
-        if self.root:
-            self.single_version_externally_managed = True
-        elif self.single_version_externally_managed:
-            if not self.root and not self.record:
-                raise DistutilsArgError(
-                    "You must specify --record or --root when building system"
-                    " packages"
-                )
-
-    def handle_extra_path(self):
-        if self.root or self.single_version_externally_managed:
-            # explicit backward-compatibility mode, allow extra_path to work
-            return orig.install.handle_extra_path(self)
-
-        # Ignore extra_path when installing an egg (or being run by another
-        # command without --root or --single-version-externally-managed
-        self.path_file = None
-        self.extra_dirs = ''
-
-    def run(self):
-        # Explicit request for old-style install?  Just do it
-        if self.old_and_unmanageable or self.single_version_externally_managed:
-            return orig.install.run(self)
-
-        if not self._called_from_setup(inspect.currentframe()):
-            # Run in backward-compatibility mode to support bdist_* commands.
-            orig.install.run(self)
-        else:
-            self.do_egg_install()
-
-    @staticmethod
-    def _called_from_setup(run_frame):
-        """
-        Attempt to detect whether run() was called from setup() or by another
-        command.  If called by setup(), the parent caller will be the
-        'run_command' method in 'distutils.dist', and *its* caller will be
-        the 'run_commands' method.  If called any other way, the
-        immediate caller *might* be 'run_command', but it won't have been
-        called by 'run_commands'. Return True in that case or if a call stack
-        is unavailable. Return False otherwise.
-        """
-        if run_frame is None:
-            msg = "Call stack not available. bdist_* commands may fail."
-            SetuptoolsWarning.emit(msg)
-            if platform.python_implementation() == 'IronPython':
-                msg = "For best results, pass -X:Frames to enable call stack."
-                SetuptoolsWarning.emit(msg)
-            return True
-
-        frames = inspect.getouterframes(run_frame)
-        for frame in frames[2:4]:
-            caller, = frame[:1]
-            info = inspect.getframeinfo(caller)
-            caller_module = caller.f_globals.get('__name__', '')
-
-            if caller_module == "setuptools.dist" and info.function == "run_command":
-                # Starting from v61.0.0 setuptools overwrites dist.run_command
-                continue
-
-            return (
-                caller_module == 'distutils.dist'
-                and info.function == 'run_commands'
-            )
-
-    def do_egg_install(self):
-
-        easy_install = self.distribution.get_command_class('easy_install')
-
-        cmd = easy_install(
-            self.distribution, args="x", root=self.root, record=self.record,
-        )
-        cmd.ensure_finalized()  # finalize before bdist_egg munges install cmd
-        cmd.always_copy_from = '.'  # make sure local-dir eggs get installed
-
-        # pick up setup-dir .egg files only: no .egg-info
-        cmd.package_index.scan(glob.glob('*.egg'))
-
-        self.run_command('bdist_egg')
-        args = [self.distribution.get_command_obj('bdist_egg').egg_output]
-
-        if setuptools.bootstrap_install_from:
-            # Bootstrap self-installation of setuptools
-            args.insert(0, setuptools.bootstrap_install_from)
-
-        cmd.args = args
-        cmd.run(show_deprecation=False)
-        setuptools.bootstrap_install_from = None
-
-
-# XXX Python 3.1 doesn't see _nc if this is inside the class
-install.sub_commands = (
-    [cmd for cmd in orig.install.sub_commands if cmd[0] not in install._nc] +
-    install.new_commands
-)
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/shared.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/shared.py
deleted file mode 100644
index 2d0f7bf3999064a68f28a1207d65a2de7ae98c0a..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/shared.py
+++ /dev/null
@@ -1,1034 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import collections
-import contextlib
-import copy
-import functools
-import logging
-import numpy as np
-import os
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-from unittest import mock
-import caffe2.python.utils as putils
-import torch
-import torch.nn.functional as F
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core, net_drawer, workspace
-from torch.nn.functional import interpolate as interp
-
-logger = logging.getLogger(__name__)
-
-
-# ==== torch/utils_toffee/cast.py =======================================
-
-
-def to_device(t, device_str):
-    """
-    This function is a replacement of .to(another_device) such that it allows the
-    casting to be traced properly by explicitly calling the underlying copy ops.
-    It also avoids introducing unncessary op when casting to the same device.
-    """
-    src = t.device
-    dst = torch.device(device_str)
-
-    if src == dst:
-        return t
-    elif src.type == "cuda" and dst.type == "cpu":
-        return torch.ops._caffe2.CopyGPUToCPU(t)
-    elif src.type == "cpu" and dst.type == "cuda":
-        return torch.ops._caffe2.CopyCPUToGPU(t)
-    else:
-        raise RuntimeError("Can't cast tensor from device {} to device {}".format(src, dst))
-
-
-# ==== torch/utils_toffee/interpolate.py =======================================
-
-
-# Note: borrowed from vision/detection/fair/detectron/detectron/modeling/detector.py
-def BilinearInterpolation(tensor_in, up_scale):
-    assert up_scale % 2 == 0, "Scale should be even"
-
-    def upsample_filt(size):
-        factor = (size + 1) // 2
-        if size % 2 == 1:
-            center = factor - 1
-        else:
-            center = factor - 0.5
-
-        og = np.ogrid[:size, :size]
-        return (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)
-
-    kernel_size = int(up_scale) * 2
-    bil_filt = upsample_filt(kernel_size)
-
-    dim = int(tensor_in.shape[1])
-    kernel = np.zeros((dim, dim, kernel_size, kernel_size), dtype=np.float32)
-    kernel[range(dim), range(dim), :, :] = bil_filt
-
-    tensor_out = F.conv_transpose2d(
-        tensor_in,
-        weight=to_device(torch.Tensor(kernel), tensor_in.device),
-        bias=None,
-        stride=int(up_scale),
-        padding=int(up_scale / 2),
-    )
-
-    return tensor_out
-
-
-# NOTE: ONNX is incompatible with traced torch.nn.functional.interpolate if
-# using dynamic `scale_factor` rather than static `size`. (T43166860)
-# NOTE: Caffe2 Int8 conversion might not be able to quantize `size` properly.
-def onnx_compatibale_interpolate(
-    input, size=None, scale_factor=None, mode="nearest", align_corners=None
-):
-    # NOTE: The input dimensions are interpreted in the form:
-    # `mini-batch x channels x [optional depth] x [optional height] x width`.
-    if size is None and scale_factor is not None:
-        if input.dim() == 4:
-            if isinstance(scale_factor, (int, float)):
-                height_scale, width_scale = (scale_factor, scale_factor)
-            else:
-                assert isinstance(scale_factor, (tuple, list))
-                assert len(scale_factor) == 2
-                height_scale, width_scale = scale_factor
-
-            assert not align_corners, "No matching C2 op for align_corners == True"
-            if mode == "nearest":
-                return torch.ops._caffe2.ResizeNearest(
-                    input, order="NCHW", width_scale=width_scale, height_scale=height_scale
-                )
-            elif mode == "bilinear":
-                logger.warning(
-                    "Use F.conv_transpose2d for bilinear interpolate"
-                    " because there's no such C2 op, this may cause significant"
-                    " slowdown and the boundary pixels won't be as same as"
-                    " using F.interpolate due to padding."
-                )
-                assert height_scale == width_scale
-                return BilinearInterpolation(input, up_scale=height_scale)
-        logger.warning("Output size is not static, it might cause ONNX conversion issue")
-
-    return interp(input, size, scale_factor, mode, align_corners)
-
-
-@contextlib.contextmanager
-def mock_torch_nn_functional_interpolate():
-    if torch.onnx.is_in_onnx_export():
-        with mock.patch(
-            "torch.nn.functional.interpolate", side_effect=onnx_compatibale_interpolate
-        ):
-            yield
-    else:
-        yield
-
-
-# ==== torch/utils_caffe2/ws_utils.py ==========================================
-
-
-class ScopedWS(object):
-    def __init__(self, ws_name, is_reset, is_cleanup=False):
-        self.ws_name = ws_name
-        self.is_reset = is_reset
-        self.is_cleanup = is_cleanup
-        self.org_ws = ""
-
-    def __enter__(self):
-        self.org_ws = workspace.CurrentWorkspace()
-        if self.ws_name is not None:
-            workspace.SwitchWorkspace(self.ws_name, True)
-        if self.is_reset:
-            workspace.ResetWorkspace()
-
-        return workspace
-
-    def __exit__(self, *args):
-        if self.is_cleanup:
-            workspace.ResetWorkspace()
-        if self.ws_name is not None:
-            workspace.SwitchWorkspace(self.org_ws)
-
-
-def fetch_any_blob(name):
-    bb = None
-    try:
-        bb = workspace.FetchBlob(name)
-    except TypeError:
-        bb = workspace.FetchInt8Blob(name)
-    except Exception as e:
-        logger.error("Get blob {} error: {}".format(name, e))
-
-    return bb
-
-
-# ==== torch/utils_caffe2/protobuf.py ==========================================
-
-
-def get_pb_arg(pb, arg_name):
-    for x in pb.arg:
-        if x.name == arg_name:
-            return x
-    return None
-
-
-def get_pb_arg_valf(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return arg.f if arg is not None else default_val
-
-
-def get_pb_arg_floats(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return list(map(float, arg.floats)) if arg is not None else default_val
-
-
-def get_pb_arg_ints(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return list(map(int, arg.ints)) if arg is not None else default_val
-
-
-def get_pb_arg_vali(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return arg.i if arg is not None else default_val
-
-
-def get_pb_arg_vals(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return arg.s if arg is not None else default_val
-
-
-def get_pb_arg_valstrings(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return list(arg.strings) if arg is not None else default_val
-
-
-def check_set_pb_arg(pb, arg_name, arg_attr, arg_value, allow_override=False):
-    arg = get_pb_arg(pb, arg_name)
-    if arg is None:
-        arg = putils.MakeArgument(arg_name, arg_value)
-        assert hasattr(arg, arg_attr)
-        pb.arg.extend([arg])
-    if allow_override and getattr(arg, arg_attr) != arg_value:
-        logger.warning(
-            "Override argument {}: {} -> {}".format(arg_name, getattr(arg, arg_attr), arg_value)
-        )
-        setattr(arg, arg_attr, arg_value)
-    else:
-        assert arg is not None
-        assert getattr(arg, arg_attr) == arg_value, "Existing value {}, new value {}".format(
-            getattr(arg, arg_attr), arg_value
-        )
-
-
-def _create_const_fill_op_from_numpy(name, tensor, device_option=None):
-    assert type(tensor) == np.ndarray
-    kTypeNameMapper = {
-        np.dtype("float32"): "GivenTensorFill",
-        np.dtype("int32"): "GivenTensorIntFill",
-        np.dtype("int64"): "GivenTensorInt64Fill",
-        np.dtype("uint8"): "GivenTensorStringFill",
-    }
-
-    args_dict = {}
-    if tensor.dtype == np.dtype("uint8"):
-        args_dict.update({"values": [str(tensor.data)], "shape": [1]})
-    else:
-        args_dict.update({"values": tensor, "shape": tensor.shape})
-
-    if device_option is not None:
-        args_dict["device_option"] = device_option
-
-    return core.CreateOperator(kTypeNameMapper[tensor.dtype], [], [name], **args_dict)
-
-
-def _create_const_fill_op_from_c2_int8_tensor(name, int8_tensor):
-    assert type(int8_tensor) == workspace.Int8Tensor
-    kTypeNameMapper = {
-        np.dtype("int32"): "Int8GivenIntTensorFill",
-        np.dtype("uint8"): "Int8GivenTensorFill",
-    }
-
-    tensor = int8_tensor.data
-    assert tensor.dtype in [np.dtype("uint8"), np.dtype("int32")]
-    values = tensor.tobytes() if tensor.dtype == np.dtype("uint8") else tensor
-
-    return core.CreateOperator(
-        kTypeNameMapper[tensor.dtype],
-        [],
-        [name],
-        values=values,
-        shape=tensor.shape,
-        Y_scale=int8_tensor.scale,
-        Y_zero_point=int8_tensor.zero_point,
-    )
-
-
-def create_const_fill_op(
-    name: str,
-    blob: Union[np.ndarray, workspace.Int8Tensor],
-    device_option: Optional[caffe2_pb2.DeviceOption] = None,
-) -> caffe2_pb2.OperatorDef:
-    """
-    Given a blob object, return the Caffe2 operator that creates this blob
-    as constant. Currently support NumPy tensor and Caffe2 Int8Tensor.
-    """
-
-    tensor_type = type(blob)
-    assert tensor_type in [
-        np.ndarray,
-        workspace.Int8Tensor,
-    ], 'Error when creating const fill op for "{}", unsupported blob type: {}'.format(
-        name, type(blob)
-    )
-
-    if tensor_type == np.ndarray:
-        return _create_const_fill_op_from_numpy(name, blob, device_option)
-    elif tensor_type == workspace.Int8Tensor:
-        assert device_option is None
-        return _create_const_fill_op_from_c2_int8_tensor(name, blob)
-
-
-def construct_init_net_from_params(
-    params: Dict[str, Any], device_options: Optional[Dict[str, caffe2_pb2.DeviceOption]] = None
-) -> caffe2_pb2.NetDef:
-    """
-    Construct the init_net from params dictionary
-    """
-    init_net = caffe2_pb2.NetDef()
-    device_options = device_options or {}
-    for name, blob in params.items():
-        if isinstance(blob, str):
-            logger.warning(
-                (
-                    "Blob {} with type {} is not supported in generating init net,"
-                    " skipped.".format(name, type(blob))
-                )
-            )
-            continue
-        init_net.op.extend(
-            [create_const_fill_op(name, blob, device_option=device_options.get(name, None))]
-        )
-        init_net.external_output.append(name)
-    return init_net
-
-
-def get_producer_map(ssa):
-    """
-    Return dict from versioned blob to (i, j),
-        where i is index of producer op, j is the index of output of that op.
-    """
-    producer_map = {}
-    for i in range(len(ssa)):
-        outputs = ssa[i][1]
-        for j, outp in enumerate(outputs):
-            producer_map[outp] = (i, j)
-    return producer_map
-
-
-def get_consumer_map(ssa):
-    """
-    Return dict from versioned blob to list of (i, j),
-        where i is index of consumer op, j is the index of input of that op.
-    """
-    consumer_map = collections.defaultdict(list)
-    for i in range(len(ssa)):
-        inputs = ssa[i][0]
-        for j, inp in enumerate(inputs):
-            consumer_map[inp].append((i, j))
-    return consumer_map
-
-
-def get_params_from_init_net(
-    init_net: caffe2_pb2.NetDef,
-) -> [Dict[str, Any], Dict[str, caffe2_pb2.DeviceOption]]:
-    """
-    Take the output blobs from init_net by running it.
-    Outputs:
-        params: dict from blob name to numpy array
-        device_options: dict from blob name to the device option of its creating op
-    """
-    # NOTE: this assumes that the params is determined by producer op with the
-    # only exception be CopyGPUToCPU which is CUDA op but returns CPU tensor.
-    def _get_device_option(producer_op):
-        if producer_op.type == "CopyGPUToCPU":
-            return caffe2_pb2.DeviceOption()
-        else:
-            return producer_op.device_option
-
-    with ScopedWS("__get_params_from_init_net__", is_reset=True, is_cleanup=True) as ws:
-        ws.RunNetOnce(init_net)
-        params = {b: fetch_any_blob(b) for b in init_net.external_output}
-    ssa, versions = core.get_ssa(init_net)
-    producer_map = get_producer_map(ssa)
-    device_options = {
-        b: _get_device_option(init_net.op[producer_map[(b, versions[b])][0]])
-        for b in init_net.external_output
-    }
-    return params, device_options
-
-
-def _updater_raise(op, input_types, output_types):
-    raise RuntimeError(
-        "Failed to apply updater for op {} given input_types {} and"
-        " output_types {}".format(op, input_types, output_types)
-    )
-
-
-def _generic_status_identifier(
-    predict_net: caffe2_pb2.NetDef,
-    status_updater: Callable,
-    known_status: Dict[Tuple[str, int], Any],
-) -> Dict[Tuple[str, int], Any]:
-    """
-    Statically infer the status of each blob, the status can be such as device type
-        (CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here
-        is versioned blob (Tuple[str, int]) in the format compatible with ssa.
-    Inputs:
-        predict_net: the caffe2 network
-        status_updater: a callable, given an op and the status of its input/output,
-            it returns the updated status of input/output. `None` is used for
-            representing unknown status.
-        known_status: a dict containing known status, used as initialization.
-    Outputs:
-        A dict mapping from versioned blob to its status
-    """
-    ssa, versions = core.get_ssa(predict_net)
-    versioned_ext_input = [(b, 0) for b in predict_net.external_input]
-    versioned_ext_output = [(b, versions[b]) for b in predict_net.external_output]
-    all_versioned_blobs = set().union(*[set(x[0] + x[1]) for x in ssa])
-
-    allowed_vbs = all_versioned_blobs.union(versioned_ext_input).union(versioned_ext_output)
-    assert all(k in allowed_vbs for k in known_status)
-    assert all(v is not None for v in known_status.values())
-    _known_status = copy.deepcopy(known_status)
-
-    def _check_and_update(key, value):
-        assert value is not None
-        if key in _known_status:
-            if not _known_status[key] == value:
-                raise RuntimeError(
-                    "Confilict status for {}, existing status {}, new status {}".format(
-                        key, _known_status[key], value
-                    )
-                )
-        _known_status[key] = value
-
-    def _update_i(op, ssa_i):
-        versioned_inputs = ssa_i[0]
-        versioned_outputs = ssa_i[1]
-
-        inputs_status = [_known_status.get(b, None) for b in versioned_inputs]
-        outputs_status = [_known_status.get(b, None) for b in versioned_outputs]
-
-        new_inputs_status, new_outputs_status = status_updater(op, inputs_status, outputs_status)
-
-        for versioned_blob, status in zip(
-            versioned_inputs + versioned_outputs, new_inputs_status + new_outputs_status
-        ):
-            if status is not None:
-                _check_and_update(versioned_blob, status)
-
-    for op, ssa_i in zip(predict_net.op, ssa):
-        _update_i(op, ssa_i)
-    for op, ssa_i in zip(reversed(predict_net.op), reversed(ssa)):
-        _update_i(op, ssa_i)
-
-    # NOTE: This strictly checks all the blob from predict_net must be assgined
-    # a known status. However sometimes it's impossible (eg. having deadend op),
-    # we may relax this constraint if
-    for k in all_versioned_blobs:
-        if k not in _known_status:
-            raise NotImplementedError(
-                "Can not infer the status for {}. Currently only support the case where"
-                " a single forward and backward pass can identify status for all blobs.".format(k)
-            )
-
-    return _known_status
-
-
-def infer_device_type(
-    predict_net: caffe2_pb2.NetDef,
-    known_status: Dict[Tuple[str, int], Any],
-    device_name_style: str = "caffe2",
-) -> Dict[Tuple[str, int], str]:
-    """Return the device type ("cpu" or "gpu"/"cuda") of each (versioned) blob"""
-
-    assert device_name_style in ["caffe2", "pytorch"]
-    _CPU_STR = "cpu"
-    _GPU_STR = "gpu" if device_name_style == "caffe2" else "cuda"
-
-    def _copy_cpu_to_gpu_updater(op, input_types, output_types):
-        if input_types[0] == _GPU_STR or output_types[0] == _CPU_STR:
-            _updater_raise(op, input_types, output_types)
-        return ([_CPU_STR], [_GPU_STR])
-
-    def _copy_gpu_to_cpu_updater(op, input_types, output_types):
-        if input_types[0] == _CPU_STR or output_types[0] == _GPU_STR:
-            _updater_raise(op, input_types, output_types)
-        return ([_GPU_STR], [_CPU_STR])
-
-    def _other_ops_updater(op, input_types, output_types):
-        non_none_types = [x for x in input_types + output_types if x is not None]
-        if len(non_none_types) > 0:
-            the_type = non_none_types[0]
-            if not all(x == the_type for x in non_none_types):
-                _updater_raise(op, input_types, output_types)
-        else:
-            the_type = None
-        return ([the_type for _ in op.input], [the_type for _ in op.output])
-
-    def _device_updater(op, *args, **kwargs):
-        return {
-            "CopyCPUToGPU": _copy_cpu_to_gpu_updater,
-            "CopyGPUToCPU": _copy_gpu_to_cpu_updater,
-        }.get(op.type, _other_ops_updater)(op, *args, **kwargs)
-
-    return _generic_status_identifier(predict_net, _device_updater, known_status)
-
-
-# ==== torch/utils_caffe2/vis.py ===============================================
-
-
-def _modify_blob_names(ops, blob_rename_f):
-    ret = []
-
-    def _replace_list(blob_list, replaced_list):
-        del blob_list[:]
-        blob_list.extend(replaced_list)
-
-    for x in ops:
-        cur = copy.deepcopy(x)
-        _replace_list(cur.input, list(map(blob_rename_f, cur.input)))
-        _replace_list(cur.output, list(map(blob_rename_f, cur.output)))
-        ret.append(cur)
-
-    return ret
-
-
-def _rename_blob(name, blob_sizes, blob_ranges):
-    def _list_to_str(bsize):
-        ret = ", ".join([str(x) for x in bsize])
-        ret = "[" + ret + "]"
-        return ret
-
-    ret = name
-    if blob_sizes is not None and name in blob_sizes:
-        ret += "\n" + _list_to_str(blob_sizes[name])
-    if blob_ranges is not None and name in blob_ranges:
-        ret += "\n" + _list_to_str(blob_ranges[name])
-
-    return ret
-
-
-# graph_name could not contain word 'graph'
-def save_graph(net, file_name, graph_name="net", op_only=True, blob_sizes=None, blob_ranges=None):
-    blob_rename_f = functools.partial(_rename_blob, blob_sizes=blob_sizes, blob_ranges=blob_ranges)
-    return save_graph_base(net, file_name, graph_name, op_only, blob_rename_f)
-
-
-def save_graph_base(net, file_name, graph_name="net", op_only=True, blob_rename_func=None):
-    graph = None
-    ops = net.op
-    if blob_rename_func is not None:
-        ops = _modify_blob_names(ops, blob_rename_func)
-    if not op_only:
-        graph = net_drawer.GetPydotGraph(ops, graph_name, rankdir="TB")
-    else:
-        graph = net_drawer.GetPydotGraphMinimal(
-            ops, graph_name, rankdir="TB", minimal_dependency=True
-        )
-
-    try:
-        par_dir = os.path.dirname(file_name)
-        if not os.path.exists(par_dir):
-            os.makedirs(par_dir)
-
-        format = os.path.splitext(os.path.basename(file_name))[-1]
-        if format == ".png":
-            graph.write_png(file_name)
-        elif format == ".pdf":
-            graph.write_pdf(file_name)
-        elif format == ".svg":
-            graph.write_svg(file_name)
-        else:
-            print("Incorrect format {}".format(format))
-    except Exception as e:
-        print("Error when writing graph to image {}".format(e))
-
-    return graph
-
-
-# ==== torch/utils_toffee/aten_to_caffe2.py ====================================
-
-
-def group_norm_replace_aten_with_caffe2(predict_net: caffe2_pb2.NetDef):
-    """
-    For ONNX exported model, GroupNorm will be represented as ATen op,
-        this can be a drop in replacement from ATen to GroupNorm
-    """
-    count = 0
-    for op in predict_net.op:
-        if op.type == "ATen":
-            op_name = get_pb_arg_vals(op, "operator", None)  # return byte in py3
-            if op_name and op_name.decode() == "group_norm":
-                op.arg.remove(get_pb_arg(op, "operator"))
-
-                if get_pb_arg_vali(op, "cudnn_enabled", None):
-                    op.arg.remove(get_pb_arg(op, "cudnn_enabled"))
-
-                num_groups = get_pb_arg_vali(op, "num_groups", None)
-                if num_groups is not None:
-                    op.arg.remove(get_pb_arg(op, "num_groups"))
-                    check_set_pb_arg(op, "group", "i", num_groups)
-
-                op.type = "GroupNorm"
-                count += 1
-    if count > 1:
-        logger.info("Replaced {} ATen operator to GroupNormOp".format(count))
-
-
-# ==== torch/utils_toffee/alias.py =============================================
-
-
-def alias(x, name, is_backward=False):
-    if not torch.onnx.is_in_onnx_export():
-        return x
-    assert isinstance(x, torch.Tensor)
-    return torch.ops._caffe2.AliasWithName(x, name, is_backward=is_backward)
-
-
-def fuse_alias_placeholder(predict_net, init_net):
-    """Remove AliasWithName placeholder and rename the input/output of it"""
-    # First we finish all the re-naming
-    for i, op in enumerate(predict_net.op):
-        if op.type == "AliasWithName":
-            assert len(op.input) == 1
-            assert len(op.output) == 1
-            name = get_pb_arg_vals(op, "name", None).decode()
-            is_backward = bool(get_pb_arg_vali(op, "is_backward", 0))
-            rename_op_input(predict_net, init_net, i, 0, name, from_producer=is_backward)
-            rename_op_output(predict_net, i, 0, name)
-
-    # Remove AliasWithName, should be very safe since it's a non-op
-    new_ops = []
-    for op in predict_net.op:
-        if op.type != "AliasWithName":
-            new_ops.append(op)
-        else:
-            # safety check
-            assert op.input == op.output
-            assert op.input[0] == op.arg[0].s.decode()
-    del predict_net.op[:]
-    predict_net.op.extend(new_ops)
-
-
-# ==== torch/utils_caffe2/graph_transform.py ===================================
-
-
-class IllegalGraphTransformError(ValueError):
-    """When a graph transform function call can't be executed."""
-
-
-def _rename_versioned_blob_in_proto(
-    proto: caffe2_pb2.NetDef,
-    old_name: str,
-    new_name: str,
-    version: int,
-    ssa: List[Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]],
-    start_versions: Dict[str, int],
-    end_versions: Dict[str, int],
-):
-    """In given proto, rename all blobs with matched version"""
-    # Operater list
-    for op, i_th_ssa in zip(proto.op, ssa):
-        versioned_inputs, versioned_outputs = i_th_ssa
-        for i in range(len(op.input)):
-            if versioned_inputs[i] == (old_name, version):
-                op.input[i] = new_name
-        for i in range(len(op.output)):
-            if versioned_outputs[i] == (old_name, version):
-                op.output[i] = new_name
-    # external_input
-    if start_versions.get(old_name, 0) == version:
-        for i in range(len(proto.external_input)):
-            if proto.external_input[i] == old_name:
-                proto.external_input[i] = new_name
-    # external_output
-    if end_versions.get(old_name, 0) == version:
-        for i in range(len(proto.external_output)):
-            if proto.external_output[i] == old_name:
-                proto.external_output[i] = new_name
-
-
-def rename_op_input(
-    predict_net: caffe2_pb2.NetDef,
-    init_net: caffe2_pb2.NetDef,
-    op_id: int,
-    input_id: int,
-    new_name: str,
-    from_producer: bool = False,
-):
-    """
-    Rename the op_id-th operator in predict_net, change it's input_id-th input's
-        name to the new_name. It also does automatic re-route and change
-        external_input and init_net if necessary.
-    - It requires the input is only consumed by this op.
-    - This function modifies predict_net and init_net in-place.
-    - When from_producer is enable, this also updates other operators that consumes
-        the same input. Be cautious because may trigger unintended behavior.
-    """
-    assert isinstance(predict_net, caffe2_pb2.NetDef)
-    assert isinstance(init_net, caffe2_pb2.NetDef)
-
-    init_net_ssa, init_net_versions = core.get_ssa(init_net)
-    predict_net_ssa, predict_net_versions = core.get_ssa(
-        predict_net, copy.deepcopy(init_net_versions)
-    )
-
-    versioned_inputs, versioned_outputs = predict_net_ssa[op_id]
-    old_name, version = versioned_inputs[input_id]
-
-    if from_producer:
-        producer_map = get_producer_map(predict_net_ssa)
-        if not (old_name, version) in producer_map:
-            raise NotImplementedError(
-                "Can't find producer, the input {} is probably from"
-                " init_net, this is not supported yet.".format(old_name)
-            )
-        producer = producer_map[(old_name, version)]
-        rename_op_output(predict_net, producer[0], producer[1], new_name)
-        return
-
-    def contain_targets(op_ssa):
-        return (old_name, version) in op_ssa[0]
-
-    is_consumer = [contain_targets(op_ssa) for op_ssa in predict_net_ssa]
-    if sum(is_consumer) > 1:
-        raise IllegalGraphTransformError(
-            (
-                "Input '{}' of operator(#{}) are consumed by other ops, please use"
-                + " rename_op_output on the producer instead. Offending op: \n{}"
-            ).format(old_name, op_id, predict_net.op[op_id])
-        )
-
-    # update init_net
-    _rename_versioned_blob_in_proto(
-        init_net, old_name, new_name, version, init_net_ssa, {}, init_net_versions
-    )
-    # update predict_net
-    _rename_versioned_blob_in_proto(
-        predict_net,
-        old_name,
-        new_name,
-        version,
-        predict_net_ssa,
-        init_net_versions,
-        predict_net_versions,
-    )
-
-
-def rename_op_output(predict_net: caffe2_pb2.NetDef, op_id: int, output_id: int, new_name: str):
-    """
-    Rename the op_id-th operator in predict_net, change it's output_id-th input's
-        name to the new_name. It also does automatic re-route and change
-        external_output and if necessary.
-    - It allows multiple consumers of its output.
-    - This function modifies predict_net in-place, doesn't need init_net.
-    """
-    assert isinstance(predict_net, caffe2_pb2.NetDef)
-
-    ssa, blob_versions = core.get_ssa(predict_net)
-
-    versioned_inputs, versioned_outputs = ssa[op_id]
-    old_name, version = versioned_outputs[output_id]
-
-    # update predict_net
-    _rename_versioned_blob_in_proto(
-        predict_net, old_name, new_name, version, ssa, {}, blob_versions
-    )
-
-
-def get_sub_graph_external_input_output(
-    predict_net: caffe2_pb2.NetDef, sub_graph_op_indices: List[int]
-) -> Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]:
-    """
-    Return the list of external input/output of sub-graph,
-    each element is tuple of the name and corresponding version in predict_net.
-
-    external input/output is defined the same way as caffe2 NetDef.
-    """
-    ssa, versions = core.get_ssa(predict_net)
-
-    all_inputs = []
-    all_outputs = []
-    for op_id in sub_graph_op_indices:
-        all_inputs += [inp for inp in ssa[op_id][0] if inp not in all_inputs]
-        all_outputs += list(ssa[op_id][1])  # ssa output won't repeat
-
-    # for versioned blobs, external inputs are just those blob in all_inputs
-    # but not in all_outputs
-    ext_inputs = [inp for inp in all_inputs if inp not in all_outputs]
-
-    # external outputs are essentially outputs of this subgraph that are used
-    # outside of this sub-graph (including predict_net.external_output)
-    all_other_inputs = sum(
-        (ssa[i][0] for i in range(len(ssa)) if i not in sub_graph_op_indices),
-        [(outp, versions[outp]) for outp in predict_net.external_output],
-    )
-    ext_outputs = [outp for outp in all_outputs if outp in set(all_other_inputs)]
-
-    return ext_inputs, ext_outputs
-
-
-class DiGraph:
-    """A DAG representation of caffe2 graph, each vertice is a versioned blob."""
-
-    def __init__(self):
-        self.vertices = set()
-        self.graph = collections.defaultdict(list)
-
-    def add_edge(self, u, v):
-        self.graph[u].append(v)
-        self.vertices.add(u)
-        self.vertices.add(v)
-
-    # grab from https://www.geeksforgeeks.org/find-paths-given-source-destination/
-    def get_all_paths(self, s, d):
-        visited = {k: False for k in self.vertices}
-        path = []
-        all_paths = []
-
-        def _get_all_paths_util(graph, u, d, visited, path):
-            visited[u] = True
-            path.append(u)
-            if u == d:
-                all_paths.append(copy.deepcopy(path))
-            else:
-                for i in graph[u]:
-                    if not visited[i]:
-                        _get_all_paths_util(graph, i, d, visited, path)
-            path.pop()
-            visited[u] = False
-
-        _get_all_paths_util(self.graph, s, d, visited, path)
-        return all_paths
-
-    @staticmethod
-    def from_ssa(ssa):
-        graph = DiGraph()
-        for op_id in range(len(ssa)):
-            for inp in ssa[op_id][0]:
-                for outp in ssa[op_id][1]:
-                    graph.add_edge(inp, outp)
-        return graph
-
-
-def _get_dependency_chain(ssa, versioned_target, versioned_source):
-    """
-    Return the index list of relevant operator to produce target blob from source blob,
-        if there's no dependency, return empty list.
-    """
-
-    # finding all paths between nodes can be O(N!), thus we can only search
-    # in the subgraph using the op starting from the first consumer of source blob
-    # to the producer of the target blob.
-    consumer_map = get_consumer_map(ssa)
-    producer_map = get_producer_map(ssa)
-    start_op = min(x[0] for x in consumer_map[versioned_source]) - 15
-    end_op = (
-        producer_map[versioned_target][0] + 15 if versioned_target in producer_map else start_op
-    )
-    sub_graph_ssa = ssa[start_op : end_op + 1]
-    if len(sub_graph_ssa) > 30:
-        logger.warning(
-            "Subgraph bebetween {} and {} is large (from op#{} to op#{}), it"
-            " might take non-trival time to find all paths between them.".format(
-                versioned_source, versioned_target, start_op, end_op
-            )
-        )
-
-    dag = DiGraph.from_ssa(sub_graph_ssa)
-    paths = dag.get_all_paths(versioned_source, versioned_target)  # include two ends
-    ops_in_paths = [[producer_map[blob][0] for blob in path[1:]] for path in paths]
-    return sorted(set().union(*[set(ops) for ops in ops_in_paths]))
-
-
-def identify_reshape_sub_graph(predict_net: caffe2_pb2.NetDef) -> List[List[int]]:
-    """
-    Idenfity the reshape sub-graph in a protobuf.
-    The reshape sub-graph is defined as matching the following pattern:
-
-    (input_blob) -> Op_1 -> ... -> Op_N -> (new_shape) -─┐
-        └-------------------------------------------> Reshape -> (output_blob)
-
-    Return:
-        List of sub-graphs, each sub-graph is represented as a list of indices
-        of the relavent ops, [Op_1, Op_2, ..., Op_N, Reshape]
-    """
-
-    ssa, _ = core.get_ssa(predict_net)
-
-    ret = []
-    for i, op in enumerate(predict_net.op):
-        if op.type == "Reshape":
-            assert len(op.input) == 2
-            input_ssa = ssa[i][0]
-            data_source = input_ssa[0]
-            shape_source = input_ssa[1]
-            op_indices = _get_dependency_chain(ssa, shape_source, data_source)
-            ret.append(op_indices + [i])
-    return ret
-
-
-def remove_reshape_for_fc(predict_net, params):
-    """
-    In PyTorch nn.Linear has to take 2D tensor, this often leads to reshape
-        a 4D tensor to 2D by calling .view(). However this (dynamic) reshaping
-        doesn't work well with ONNX and Int8 tools, and cause using extra
-        ops (eg. ExpandDims) that might not be available on mobile.
-    Luckily Caffe2 supports 4D tensor for FC, so we can remove those reshape
-        after exporting ONNX model.
-    """
-    from caffe2.python import core
-
-    # find all reshape sub-graph that can be removed, which is now all Reshape
-    # sub-graph whose output is only consumed by FC.
-    # TODO: to make it safer, we may need the actually value to better determine
-    # if a Reshape before FC is removable.
-    reshape_sub_graphs = identify_reshape_sub_graph(predict_net)
-    sub_graphs_to_remove = []
-    for reshape_sub_graph in reshape_sub_graphs:
-        reshape_op_id = reshape_sub_graph[-1]
-        assert predict_net.op[reshape_op_id].type == "Reshape"
-        ssa, _ = core.get_ssa(predict_net)
-        reshape_output = ssa[reshape_op_id][1][0]
-        consumers = [i for i in range(len(ssa)) if reshape_output in ssa[i][0]]
-        if all(predict_net.op[consumer].type == "FC" for consumer in consumers):
-            # safety check if the sub-graph is isolated, for this reshape sub-graph,
-            # it means it has one non-param external input and one external output.
-            ext_inputs, ext_outputs = get_sub_graph_external_input_output(
-                predict_net, reshape_sub_graph
-            )
-            non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0]
-            if len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1:
-                sub_graphs_to_remove.append(reshape_sub_graph)
-
-    # perform removing subgraph by:
-    # 1: rename the Reshape's output to its input, then the graph can be
-    #   seen as in-place itentify, meaning whose external input/output are the same.
-    # 2: simply remove those ops.
-    remove_op_ids = []
-    params_to_remove = []
-    for sub_graph in sub_graphs_to_remove:
-        logger.info(
-            "Remove Reshape sub-graph:\n{}".format(
-                "".join(["(#{:>4})\n{}".format(i, predict_net.op[i]) for i in sub_graph])
-            )
-        )
-        reshape_op_id = sub_graph[-1]
-        new_reshap_output = predict_net.op[reshape_op_id].input[0]
-        rename_op_output(predict_net, reshape_op_id, 0, new_reshap_output)
-        ext_inputs, ext_outputs = get_sub_graph_external_input_output(predict_net, sub_graph)
-        non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0]
-        params_ext_inputs = [inp for inp in ext_inputs if inp[1] == 0]
-        assert len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1
-        assert ext_outputs[0][0] == non_params_ext_inputs[0][0]
-        assert ext_outputs[0][1] == non_params_ext_inputs[0][1] + 1
-        remove_op_ids.extend(sub_graph)
-        params_to_remove.extend(params_ext_inputs)
-
-    predict_net = copy.deepcopy(predict_net)
-    new_ops = [op for i, op in enumerate(predict_net.op) if i not in remove_op_ids]
-    del predict_net.op[:]
-    predict_net.op.extend(new_ops)
-    for versioned_params in params_to_remove:
-        name = versioned_params[0]
-        logger.info("Remove params: {} from init_net and predict_net.external_input".format(name))
-        del params[name]
-        predict_net.external_input.remove(name)
-
-    return predict_net, params
-
-
-def fuse_copy_between_cpu_and_gpu(predict_net: caffe2_pb2.NetDef):
-    """
-    In-place fuse extra copy ops between cpu/gpu for the following case:
-        a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1
-                        -CopyBToA> c2 -NextOp2-> d2
-    The fused network will look like:
-        a -NextOp1-> d1
-          -NextOp2-> d2
-    """
-
-    _COPY_OPS = ["CopyCPUToGPU", "CopyGPUToCPU"]
-
-    def _fuse_once(predict_net):
-        ssa, blob_versions = core.get_ssa(predict_net)
-        consumer_map = get_consumer_map(ssa)
-        versioned_external_output = [
-            (name, blob_versions[name]) for name in predict_net.external_output
-        ]
-
-        for op_id, op in enumerate(predict_net.op):
-            if op.type in _COPY_OPS:
-                fw_copy_versioned_output = ssa[op_id][1][0]
-                consumer_ids = [x[0] for x in consumer_map[fw_copy_versioned_output]]
-                reverse_op_type = _COPY_OPS[1 - _COPY_OPS.index(op.type)]
-
-                is_fusable = (
-                    len(consumer_ids) > 0
-                    and fw_copy_versioned_output not in versioned_external_output
-                    and all(
-                        predict_net.op[_op_id].type == reverse_op_type
-                        and ssa[_op_id][1][0] not in versioned_external_output
-                        for _op_id in consumer_ids
-                    )
-                )
-
-                if is_fusable:
-                    for rv_copy_op_id in consumer_ids:
-                        # making each NextOp uses "a" directly and removing Copy ops
-                        rs_copy_versioned_output = ssa[rv_copy_op_id][1][0]
-                        next_op_id, inp_id = consumer_map[rs_copy_versioned_output][0]
-                        predict_net.op[next_op_id].input[inp_id] = op.input[0]
-                    # remove CopyOps
-                    new_ops = [
-                        op
-                        for i, op in enumerate(predict_net.op)
-                        if i != op_id and i not in consumer_ids
-                    ]
-                    del predict_net.op[:]
-                    predict_net.op.extend(new_ops)
-                    return True
-
-        return False
-
-    # _fuse_once returns False is nothing can be fused
-    while _fuse_once(predict_net):
-        pass
-
-
-def remove_dead_end_ops(net_def: caffe2_pb2.NetDef):
-    """remove ops if its output is not used or not in external_output"""
-    ssa, versions = core.get_ssa(net_def)
-    versioned_external_output = [(name, versions[name]) for name in net_def.external_output]
-    consumer_map = get_consumer_map(ssa)
-    removed_op_ids = set()
-
-    def _is_dead_end(versioned_blob):
-        return not (
-            versioned_blob in versioned_external_output
-            or (
-                len(consumer_map[versioned_blob]) > 0
-                and all(x[0] not in removed_op_ids for x in consumer_map[versioned_blob])
-            )
-        )
-
-    for i, ssa_i in reversed(list(enumerate(ssa))):
-        versioned_outputs = ssa_i[1]
-        if all(_is_dead_end(outp) for outp in versioned_outputs):
-            removed_op_ids.add(i)
-
-    # simply removing those deadend ops should have no effect to external_output
-    new_ops = [op for i, op in enumerate(net_def.op) if i not in removed_op_ids]
-    del net_def.op[:]
-    net_def.op.extend(new_ops)
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/__init__.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/__init__.py
deleted file mode 100644
index 9020c2df23e2af280b7bb168b996ae9eaf312eb8..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py
deleted file mode 100644
index b93b7ae6ca633fa8686c86f82e7d55ac5ca62bc7..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.structures import Boxes, RotatedBoxes
-from detectron2.utils.testing import random_boxes
-
-logger = logging.getLogger(__name__)
-
-
-class TestROIPooler(unittest.TestCase):
-    def _test_roialignv2_roialignrotated_match(self, device):
-        pooler_resolution = 14
-        canonical_level = 4
-        canonical_scale_factor = 2 ** canonical_level
-        pooler_scales = (1.0 / canonical_scale_factor,)
-        sampling_ratio = 0
-
-        N, C, H, W = 2, 4, 10, 8
-        N_rois = 10
-        std = 11
-        mean = 0
-        feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
-
-        features = [feature.to(device)]
-
-        rois = []
-        rois_rotated = []
-        for _ in range(N):
-            boxes = random_boxes(N_rois, W * canonical_scale_factor)
-            rotated_boxes = torch.zeros(N_rois, 5)
-            rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
-            rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
-            rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
-            rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
-            rois.append(Boxes(boxes).to(device))
-            rois_rotated.append(RotatedBoxes(rotated_boxes).to(device))
-
-        roialignv2_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type="ROIAlignV2",
-        )
-
-        roialignv2_out = roialignv2_pooler(features, rois)
-
-        roialignrotated_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type="ROIAlignRotated",
-        )
-
-        roialignrotated_out = roialignrotated_pooler(features, rois_rotated)
-
-        self.assertTrue(torch.allclose(roialignv2_out, roialignrotated_out, atol=1e-4))
-
-    def test_roialignv2_roialignrotated_match_cpu(self):
-        self._test_roialignv2_roialignrotated_match(device="cpu")
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_roialignv2_roialignrotated_match_cuda(self):
-        self._test_roialignv2_roialignrotated_match(device="cuda")
-
-    def _test_scriptability(self, device):
-        pooler_resolution = 14
-        canonical_level = 4
-        canonical_scale_factor = 2 ** canonical_level
-        pooler_scales = (1.0 / canonical_scale_factor,)
-        sampling_ratio = 0
-
-        N, C, H, W = 2, 4, 10, 8
-        N_rois = 10
-        std = 11
-        mean = 0
-        feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
-
-        features = [feature.to(device)]
-
-        rois = []
-        for _ in range(N):
-            boxes = random_boxes(N_rois, W * canonical_scale_factor)
-
-            rois.append(Boxes(boxes).to(device))
-
-        roialignv2_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type="ROIAlignV2",
-        )
-
-        roialignv2_out = roialignv2_pooler(features, rois)
-        scripted_roialignv2_out = torch.jit.script(roialignv2_pooler)(features, rois)
-        self.assertTrue(torch.equal(roialignv2_out, scripted_roialignv2_out))
-
-    def test_scriptability_cpu(self):
-        self._test_scriptability(device="cpu")
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_scriptability_gpu(self):
-        self._test_scriptability(device="cuda")
-
-    def test_no_images(self):
-        N, C, H, W = 0, 32, 32, 32
-        feature = torch.rand(N, C, H, W) - 0.5
-        features = [feature]
-        pooler = ROIPooler(
-            output_size=14, scales=(1.0,), sampling_ratio=0.0, pooler_type="ROIAlignV2"
-        )
-        output = pooler.forward(features, [])
-        self.assertEqual(output.shape, (0, C, 14, 14))
-
-    def test_roi_pooler_tracing(self):
-        class Model(torch.nn.Module):
-            def __init__(self, roi):
-                super(Model, self).__init__()
-                self.roi = roi
-
-            def forward(self, x, boxes):
-                return self.roi(x, [Boxes(boxes)])
-
-        pooler_resolution = 14
-        canonical_level = 4
-        canonical_scale_factor = 2 ** canonical_level
-        pooler_scales = (1.0 / canonical_scale_factor, 0.5 / canonical_scale_factor)
-        sampling_ratio = 0
-
-        N, C, H, W = 1, 4, 10, 8
-        N_rois = 10
-        std = 11
-        mean = 0
-        feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
-        feature = [feature, feature]
-
-        rois = random_boxes(N_rois, W * canonical_scale_factor)
-        # Add one larger box so that this level has only one box.
-        # This may trigger the bug https://github.com/pytorch/pytorch/issues/49852
-        # that we shall workaround.
-        rois = torch.cat([rois, torch.tensor([[0, 0, 448, 448]])])
-
-        model = Model(
-            ROIPooler(
-                output_size=pooler_resolution,
-                scales=pooler_scales,
-                sampling_ratio=sampling_ratio,
-                pooler_type="ROIAlign",
-            )
-        )
-
-        with torch.no_grad():
-            func = torch.jit.trace(model, (feature, rois))
-            o = func(feature, rois)
-            self.assertEqual(o.shape, (11, 4, 14, 14))
-            o = func(feature, rois[:5])
-            self.assertEqual(o.shape, (5, 4, 14, 14))
-            o = func(feature, random_boxes(20, W * canonical_scale_factor))
-            self.assertEqual(o.shape, (20, 4, 14, 14))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/Thorsten-Voice/demo/app.py b/spaces/Thorsten-Voice/demo/app.py
deleted file mode 100644
index caffe106b612615475968707af6d07a9074fad28..0000000000000000000000000000000000000000
--- a/spaces/Thorsten-Voice/demo/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import streamlit as st
-import tempfile
-from typing import Optional
-from TTS.config import load_config
-import numpy as np
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-from PIL import Image
-
-image = Image.open('Thorsten-Voice_transparent.png')
-st.image(image)
-
-st.title("Thorsten-Voice - einfach ausprobieren")
-st.markdown("* 🇩🇪 Eine qualititativ hochwertige, deutsche, künstliche Stimme, die offline erzeugt werden kann, sollte jedem Projekt kostenlos und ohne lizenzrechtliche Einschränkungen zur Verfügung stehen.")
-st.markdown("* 🇺🇸 A high-quality German artificial voice that can be generated offline should be available to any project free of charge and without any licensing restrictions.")
-st.markdown("* Ein ⭐ ist gerne gesehen: https://github.com/thorstenMueller/Thorsten-Voice")
-st.markdown("* **Danke für's Abonnieren 😊: https://www.youtube.com/@ThorstenMueller**")
-st.markdown("* Die Webseite des Projektes lautet: https://www.Thorsten-Voice.de")
-
-text = st.text_area("Zu sprechender Text",max_chars=250)
-model = st.radio("Welches Thorsten-Voice Modell möchtest Du testen?",
-    ('Thorsten-VITS','Thorsten-DDC'))
-
-if text:
-    # Load Thorsten-Voice TTS/Vocoder models
-    # Thanks to Coqui for inspiration and code snipplets :)
-    manager = ModelManager()
-    
-    if model == "Thorsten-VITS":
-        model_path, config_path, model_item = manager.download_model("tts_models/de/thorsten/vits")
-        code = '''pip install tts==0.8.0
-tts-server --model_name tts_models/de/thorsten/vits
-http://localhost:5002 im Browser öffnen'''
-    
-    if model == "Thorsten-DDC":
-        model_path, config_path, model_item = manager.download_model("tts_models/de/thorsten/tacotron2-DDC")
-        code = '''pip install tts==0.8.0
-tts-server --model_name tts_models/de/thorsten/tacotron2-DDC
-http://localhost:5002 im Browser öffnen'''
-
-    vocoder_name: Optional[str] = model_item["default_vocoder"]
-    vocoder_path = None
-    vocoder_config_path = None
-    if vocoder_name is not None:
-        vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
-
-    synthesizer = Synthesizer(
-        model_path, config_path, None, None, vocoder_path, vocoder_config_path,)
-
-    wav = synthesizer.tts(text)
-    filename = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-    synthesizer.save_wav(wav, filename)
-    
-    audio_file = open(filename.name, 'rb')
-    audio_bytes = audio_file.read()
-
-    st.audio(audio_bytes,format="audio/wav")
-
-    st.header("Wie gefällt Dir meine freie Stimme?")
-    st.markdown("Lass es mich gerne wissen - https://twitter.com/ThorstenVoice - Danke.")
-
-    st.header("Thorsten-Voice lokal ausführen:")
-    st.code(code, language='shell')
-
-# TODO: st.write("Mit diesen Kommandos kannst Du die Stimme lokal nutzen")
-   
\ No newline at end of file
diff --git a/spaces/UdayPrasad/mnist_classification/app.py b/spaces/UdayPrasad/mnist_classification/app.py
deleted file mode 100644
index ebba7608d1fc1d2b49463d67726c68317edb71ee..0000000000000000000000000000000000000000
--- a/spaces/UdayPrasad/mnist_classification/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import scipy
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-from keras.models import load_model
-import pickle
-
-def mnist_prediction(test_image, model='KNN'):
-  test_image_flatten = test_image.reshape((-1, 28*28))
-  if model == 'KNN':
-    with open('KNN_best_model_final.pkl', 'rb') as file:
-      knn_loaded = pickle.load(file)
-    ans = knn_loaded.predict(test_image_flatten)
-    return ans[0]
-  elif model == 'SoftMax':
-    with open('softmax_best_model_final.pkl', 'rb') as file:
-      softmax_model_loaded = pickle.load(file)
-    ans = softmax_model_loaded.predict(test_image_flatten)
-    return ans[0]
-  elif model == 'Deep Neural Network':
-    dnn_model = load_model("deep_nn_model_final.h5")
-    ans_prediction = dnn_model.predict(np.asarray(test_image_flatten))
-    ans = np.argmax(ans_prediction)
-    return ans
-  elif model == 'CNN':
-    cnn_model = load_model("cnn_model_final.h5")
-    ans_prediction = cnn_model.predict(np.asarray([test_image]))
-    ans = np.argmax(ans_prediction)
-    return ans
-  elif model == 'SVM':
-    with open('svm_best_model_final.pkl', 'rb') as file:
-      svm_model_loaded = pickle.load(file)
-    ans = svm_model_loaded.predict(test_image_flatten)
-    return ans[0]
-  elif model == 'Decision Tree':
-    with open('tree_model_final.pkl', 'rb') as file:
-      tree_model_loaded = pickle.load(file)
-    ans = tree_model_loaded.predict(test_image_flatten)
-    return ans[0]
-  elif model == 'Random Forest':
-    with open('forest_model_final.pkl', 'rb') as file:
-      forest_model_loaded = pickle.load(file)
-    ans = forest_model_loaded.predict(test_image_flatten)
-    return ans[0]
-  return "Not found"
-
-input_image = gr.inputs.Image(shape=(28, 28), image_mode='L')
-input_model = gr.inputs.Dropdown(['KNN', 'SoftMax', 'Deep Neural Network', 'CNN', 'SVM', 'Decision Tree', 'Random Forest'])
-
-output_label = gr.outputs.Textbox(label="Predicted Digit")
-
-gr.Interface(fn=mnist_prediction, 
-                         inputs = [input_image, input_model], 
-                         outputs = output_label,
-                         title = "MNIST classification", 
-                         ).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/User1342/WatchTower/README.md b/spaces/User1342/WatchTower/README.md
deleted file mode 100644
index 7fbf12980e3330f64ce95f7fced06ee6a12befa4..0000000000000000000000000000000000000000
--- a/spaces/User1342/WatchTower/README.md
+++ /dev/null
@@ -1,26 +0,0 @@
----
-title: "WatchTower"
-emoji: "🗼"
-colorFrom: "black"
-colorTo: "blue"
-sdk: "gradio"
-sdk_version: 3.9
-app_file: app.py
-pinned: true
----
-
-**Note: Use WatchTower via the embedded interface at [HF.space](https://hf.space/embed/User1342/WatchTower/?)!**
-
-## Files
-- app.py - Gradio web interface for WatchTower.
-- radical_tweet_aggregator.py - Pinpoint Wrapper and Tweepy integration for aggregating and assigning extremism scores to user posts.
-- predictor.py - The Pinpoint wrapper script. Also see /Pinpoint and the respective Github repo for the Pinpoint codebase.
-
-## Tweet Attribution and Bias
-Please Note: If an account is blocked by Pinpoint, that is not to say that the author(s) of this tooling deem such a user to be violent or extremist, instead it is simply a result of the training the machine learning model has undergone and a result of the analysis on the last 10 tweets of the user in question. WatchTower is undergoing tuning to ensure it provides a fair and an as un-bias as possible assessment of Tweets, feedback is always welcome.
-
-## Model Statistics
-Pinpoint has several configurations, the configuration used by WatchTower has the following stats: 0.827 (accuracy), 0.676 (recall), 0.739 (precision), 0.706 (f-measure).
-
-## Constraints
-The Twitter APIv2 rate limits blocking requests to a 'User rate limit (User context): 15 requests per 15-minute window per each authenticated user'. This will pose issues to WatchTower when blocking larger numbers of users and will cause the tooling to hang or crash completely. A fix would be to change hosting and have a running service in the background that performs the blocking tasks adhering to the rate limiting. 
\ No newline at end of file
diff --git a/spaces/VK123/ChatGPT4/app.py b/spaces/VK123/ChatGPT4/app.py
deleted file mode 100644
index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000
--- a/spaces/VK123/ChatGPT4/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Huggingface provided GPT4 OpenAI API Key 
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-#Inferenec function
-def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-    print(f"system message is ^^ {system_msg}")
-    if system_msg.strip() == '':
-        initial_message = [{"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = []
-    else:
-        initial_message= [{"role": "system", "content": system_msg},
-                   {"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = [{"role": "system", "content": system_msg},]
-        
-    if chat_counter == 0 :
-        payload = {
-        "model": "gpt-4",
-        "messages": initial_message , 
-        "temperature" : 1.0,
-        "top_p":1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-        print(f"chat_counter - {chat_counter}")
-    else: #if chat_counter != 0 :
-        messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
-        for data in chatbot:
-          user = {}
-          user["role"] = "user" 
-          user["content"] = data[0] 
-          assistant = {}
-          assistant["role"] = "assistant" 
-          assistant["content"] = data[1]
-          messages.append(user)
-          messages.append(assistant)
-        temp = {}
-        temp["role"] = "user" 
-        temp["content"] = inputs
-        messages.append(temp)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,}
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"Logging : payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"Logging : response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
-#Resetting to blank
-def reset_textbox():
-    return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
-    return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
-    return gr.update(visible=True)
-
-title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
-
-#display message for themes feature
-theme_addon_msg = """<center>🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple <code>theme.push_to_hub()</code>. 
-<br>🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - <a href="https://huggingface.co/Gradio-Themes" target="_blank">Gradio-Themes-Party🎨</a> 🏆</center>
-"""
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. 
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-#Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
-                      text_size=gr.themes.sizes.text_lg)                
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
-                      theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
-    gr.HTML(theme_addon_msg)
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-
-    with gr.Column(elem_id = "col_container"):
-        #GPT4 API Key is provided by Huggingface 
-        with gr.Accordion(label="System message:", open=False):
-            system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="")
-            accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
-        chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        state = gr.State([]) 
-        with gr.Row():
-            with gr.Column(scale=7):
-                b1 = gr.Button().style(full_width=True)
-            with gr.Column(scale=3):
-                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
-    
-        #top_p, temperature
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    #Event handling
-    inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    
-    inputs.submit(set_visible_false, [], [system_msg])
-    b1.click(set_visible_false, [], [system_msg])
-    inputs.submit(set_visible_true, [], [accordion_msg])
-    b1.click(set_visible_true, [], [accordion_msg])
-    
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-
-    #Examples 
-    with gr.Accordion(label="Examples for System message:", open=False):
-        gr.Examples(
-                examples = [["""You are an AI programming assistant.
-        
-                - Follow the user's requirements carefully and to the letter.
-                - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
-                - Then output the code in a single code block.
-                - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
-                ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
-                ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
-                ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
-                ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
-                ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
-                ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
-                ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
-                ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
-                ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
-                ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
-                ["You are a helpful assistant that provides detailed and accurate information."],
-                ["You are an assistant that speaks like Shakespeare."],
-                ["You are a friendly assistant who uses casual language and humor."],
-                ["You are a financial advisor who gives expert advice on investments and budgeting."],
-                ["You are a health and fitness expert who provides advice on nutrition and exercise."],
-                ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
-                ["You are a movie critic who shares insightful opinions on films and their themes."],
-                ["You are a history enthusiast who loves to discuss historical events and figures."],
-                ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
-                ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
-                inputs = system_msg,)
-        
-demo.queue(max_size=99, concurrency_count=20).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Vaibhav-vinci/NewSpace/app.py b/spaces/Vaibhav-vinci/NewSpace/app.py
deleted file mode 100644
index 57cd273c5ff93b7207ad5128b69e27253c602ca3..0000000000000000000000000000000000000000
--- a/spaces/Vaibhav-vinci/NewSpace/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import streamlit as st
-from datetime import date
-
-import yfinance as yf
-from prophet import Prophet
-from prophet.plot import plot_plotly
-from plotly import graph_objs as go
-
-START = "2015-01-01"
-TODAY = date.today().strftime("%Y-%m-%d")
-
-st.title('Stock Forecast App')
-
-stocks = ('GOOG', 'AAPL', 'MSFT', 'GME')
-selected_stock = st.selectbox('Select dataset for prediction', stocks)
-
-n_years = st.slider('Years of prediction:', 1, 4)
-period = n_years * 365
-
-
-@st.cache_data
-def load_data(ticker):
-    data = yf.download(ticker, START, TODAY)
-    data.reset_index(inplace=True)
-    return data
-
-	
-data_load_state = st.text('Loading data...')
-data = load_data(selected_stock)
-data_load_state.text('Loading data... done!')
-
-st.subheader('Raw data')
-st.write(data.tail())
-
-# Plot raw data
-def plot_raw_data():
-	fig = go.Figure()
-	fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="stock_open"))
-	fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="stock_close"))
-	fig.layout.update(title_text='Time Series data with Rangeslider', xaxis_rangeslider_visible=True)
-	st.plotly_chart(fig)
-	
-plot_raw_data()
-
-# Predict forecast with Prophet.
-df_train = data[['Date','Close']]
-df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
-
-m = Prophet()
-m.fit(df_train)
-future = m.make_future_dataframe(periods=period)
-forecast = m.predict(future)
-
-# Show and plot forecast
-st.subheader('Forecast data')
-st.write(forecast.tail())
-    
-st.write(f'Forecast plot for {n_years} years')
-fig1 = plot_plotly(m, forecast)
-st.plotly_chart(fig1)
-
-st.write("Forecast components")
-fig2 = m.plot_components(forecast)
-st.write(fig2)
\ No newline at end of file
diff --git a/spaces/Web3Daily/WebGPT3/style.css b/spaces/Web3Daily/WebGPT3/style.css
deleted file mode 100644
index 9a11087420bc5fee72e55663e2cfc786c2866cdc..0000000000000000000000000000000000000000
--- a/spaces/Web3Daily/WebGPT3/style.css
+++ /dev/null
@@ -1,7 +0,0 @@
-body {
-  background-color: #f0f0f0;
-}
-.gradio_submit_button {
-    background-color: #DF3840;
-    color: white;
-}
diff --git a/spaces/XzJosh/JM-Bert-VITS2/losses.py b/spaces/XzJosh/JM-Bert-VITS2/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/JM-Bert-VITS2/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/Yaroslav1234/PublicComment.AI/README.md b/spaces/Yaroslav1234/PublicComment.AI/README.md
deleted file mode 100644
index 58f24c7384fe43dae10bddb97e48f1aca583bb16..0000000000000000000000000000000000000000
--- a/spaces/Yaroslav1234/PublicComment.AI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PublicComment.AI
-emoji: 🐨
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/__init__.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/__init__.py
deleted file mode 100644
index 4e648e632d55c70f160d49630378d202fbde4e45..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .compat import downgrade_config, upgrade_config
-from .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable
-from .instantiate import instantiate
-from .lazy import LazyCall, LazyConfig
-
-__all__ = [
-    "CfgNode",
-    "get_cfg",
-    "global_cfg",
-    "set_global_cfg",
-    "downgrade_config",
-    "upgrade_config",
-    "configurable",
-    "instantiate",
-    "LazyCall",
-    "LazyConfig",
-]
-
-
-from detectron2.utils.env import fixup_module_metadata
-
-fixup_module_metadata(__name__, globals(), __all__)
-del fixup_module_metadata
diff --git a/spaces/Yuankai/ChatReviewer/app.py b/spaces/Yuankai/ChatReviewer/app.py
deleted file mode 100644
index 7452bc5c1f1c9bcae4d9b991c2c4138f17fbac65..0000000000000000000000000000000000000000
--- a/spaces/Yuankai/ChatReviewer/app.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import numpy as np
-import os
-import re
-import jieba
-from io import BytesIO
-import datetime
-import time
-import openai, tenacity
-import argparse
-import configparser
-import json
-import tiktoken
-import PyPDF2
-import gradio
-
-
-def contains_chinese(text):
-    for ch in text:
-        if u'\u4e00' <= ch <= u'\u9fff':
-            return True
-    return False
-
-def insert_sentence(text, sentence, interval):
-    lines = text.split('\n')
-    new_lines = []
-
-    for line in lines:
-        if contains_chinese(line):
-            words = list(jieba.cut(line))
-            separator = ''
-        else:
-            words = line.split()
-            separator = ' '
-
-        new_words = []
-        count = 0
-
-        for word in words:
-            new_words.append(word)
-            count += 1
-
-            if count % interval == 0:
-                new_words.append(sentence)
-
-        new_lines.append(separator.join(new_words))
-
-    return '\n'.join(new_lines)
-    
-# 定义Reviewer类
-class Reviewer:
-    # 初始化方法，设置属性
-    def __init__(self, api, review_format, paper_pdf, language):
-        self.api = api
-        self.review_format = review_format
-
-        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 for your review:" + text[:input_text_index] 
-        messages=[
-                {"role": "system", "content": "You are a professional reviewer. Now I will give you a paper. You need to give a complete review opinion according to the following requirements and format:"+ self.review_format +" Must be output 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 
-        result = insert_sentence(result, '**Generated by ChatGPT, no copying allowed!**', 25)
-        result += "\n\n⚠伦理声明/Ethics statement：\n--禁止直接复制生成的评论用于任何论文审稿工作！\n--Direct copying of generated comments for any paper review work is prohibited!"
-        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)
-        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, review_format, paper_pdf, language):  
-    start_time = time.time()
-    if not api or not review_format 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, review_format, 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 = "🤖ChatReviewer🤖"
-# 描述
-
-description = '''<div align='left'>
-
-<strong>ChatReviewer是一款基于ChatGPT-3.5的API开发的智能论文分析与建议助手。</strong>其用途如下：
-
-⭐️对论文的优缺点进行快速总结和分析，提高科研人员的文献阅读和理解的效率，紧跟研究前沿。
-
-⭐️对自己的论文进行分析，根据ChatReviewer生成的改进建议进行查漏补缺，进一步提高自己的论文质量。
-
-如果觉得很卡，可以点击右上角的Duplicate this Space，把ChatReviewer复制到你自己的Space中！（🈲：禁止直接复制生成的评论用于任何论文审稿工作！）
-
-本项目的[Github](https://github.com/nishiwen1214/ChatReviewer)，欢迎Star和Fork，也欢迎大佬赞助让本项目快速成长！💗（[获取Api Key](https://chatgpt.cn.obiscr.com/blog/posts/2023/How-to-get-api-key/)）
-</div>
-'''
-
-# 创建Gradio界面
-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=5,
-        label="请输入特定的分析要求和格式(否则为默认格式)",
-        default="""* Overall Review
-Please briefly summarize the main points and contributions of this paper.
-xxx
-* Paper Strength 
-Please provide a list of the strengths of this paper, including but not limited to: innovative and practical methodology, insightful empirical findings or in-depth theoretical analysis, 
-well-structured review of relevant literature, and any other factors that may make the paper valuable to readers. (Maximum length: 2,000 characters) 
-(1) xxx
-(2) xxx
-(3) xxx
-* Paper Weakness 
-Please provide a numbered list of your main concerns regarding this paper (so authors could respond to the concerns individually). 
-These may include, but are not limited to: inadequate implementation details for reproducing the study, limited evaluation and ablation studies for the proposed method, 
-correctness of the theoretical analysis or experimental results, lack of comparisons or discussions with widely-known baselines in the field, lack of clarity in exposition, 
-or any other factors that may impede the reader's understanding or benefit from the paper. Please kindly refrain from providing a general assessment of the paper's novelty without providing detailed explanations. (Maximum length: 2,000 characters) 
-(1) xxx
-(2) xxx
-(3) xxx
-* Questions To Authors And Suggestions For Rebuttal 
-Please provide a numbered list of specific and clear questions that pertain to the details of the proposed method, evaluation setting, or additional results that would aid in supporting the authors' claims. 
-The questions should be formulated in a manner that, after the authors have answered them during the rebuttal, it would enable a more thorough assessment of the paper's quality. (Maximum length: 2,000 characters)
-*Overall score (1-10)
-The paper is scored on a scale of 1-10, with 10 being the full mark, and 6 stands for borderline accept. Then give the reason for your rating.
-xxx"""
-    ),
-    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_reviewer_gui = gradio.Interface(fn=main,
-                                 inputs=inp,
-                                 outputs = [gradio.Textbox(lines=25, label="分析结果"), gradio.Textbox(lines=2, label="资源统计")],
-                                 title=title,
-                                 description=description)
-
-# Start server
-chat_reviewer_gui .launch(quiet=True, show_api=False)
\ No newline at end of file
diff --git a/spaces/Yuliang/ICON/lib/renderer/gl/normal_render.py b/spaces/Yuliang/ICON/lib/renderer/gl/normal_render.py
deleted file mode 100644
index e4eb41c1564719636e606714cc01bbed9356ff21..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/renderer/gl/normal_render.py
+++ /dev/null
@@ -1,97 +0,0 @@
-'''
-MIT License
-
-Copyright (c) 2019 Shunsuke Saito, Zeng Huang, and Ryota Natsume
-
-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 numpy as np
-import math
-
-from .framework import *
-from .norm_render import NormRender
-
-
-class NormalRender(NormRender):
-    def __init__(self, width=1600, height=1200, name='Normal Renderer'):
-        NormRender.__init__(self,
-                            width,
-                            height,
-                            name,
-                            program_files=['normal.vs', 'normal.fs'])
-
-        self.norm_buffer = glGenBuffers(1)
-
-        self.norm_data = None
-
-    def set_normal_mesh(self, vertices, faces, norms, face_normals):
-        NormRender.set_mesh(self, vertices, faces)
-
-        self.norm_data = norms[face_normals.reshape([-1])]
-
-        glBindBuffer(GL_ARRAY_BUFFER, self.norm_buffer)
-        glBufferData(GL_ARRAY_BUFFER, self.norm_data, GL_STATIC_DRAW)
-
-        glBindBuffer(GL_ARRAY_BUFFER, 0)
-
-    def euler_to_rot_mat(self, r_x, r_y, r_z):
-        R_x = np.array([[1, 0, 0], [0, math.cos(r_x), -math.sin(r_x)],
-                        [0, math.sin(r_x), math.cos(r_x)]])
-
-        R_y = np.array([[math.cos(r_y), 0, math.sin(r_y)], [0, 1, 0],
-                        [-math.sin(r_y), 0, math.cos(r_y)]])
-
-        R_z = np.array([[math.cos(r_z), -math.sin(r_z), 0],
-                        [math.sin(r_z), math.cos(r_z), 0], [0, 0, 1]])
-
-        R = np.dot(R_z, np.dot(R_y, R_x))
-
-        return R
-
-    def draw(self):
-        self.draw_init()
-
-        glUseProgram(self.program)
-        glUniformMatrix4fv(self.model_mat_unif, 1, GL_FALSE,
-                           self.model_view_matrix.transpose())
-        glUniformMatrix4fv(self.persp_mat_unif, 1, GL_FALSE,
-                           self.projection_matrix.transpose())
-
-        # Handle vertex buffer
-        glBindBuffer(GL_ARRAY_BUFFER, self.vertex_buffer)
-
-        glEnableVertexAttribArray(0)
-        glVertexAttribPointer(0, self.vertex_dim, GL_DOUBLE, GL_FALSE, 0, None)
-
-        # Handle normal buffer
-        glBindBuffer(GL_ARRAY_BUFFER, self.norm_buffer)
-
-        glEnableVertexAttribArray(1)
-        glVertexAttribPointer(1, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-        glDrawArrays(GL_TRIANGLES, 0, self.n_vertices)
-
-        glDisableVertexAttribArray(1)
-        glDisableVertexAttribArray(0)
-
-        glBindBuffer(GL_ARRAY_BUFFER, 0)
-
-        glUseProgram(0)
-
-        self.draw_end()
diff --git a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/static/js/main.666d1b34.chunk.js b/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/static/js/main.666d1b34.chunk.js
deleted file mode 100644
index c9641dd90cef4d6d87b76c5e0113ff5903fde4c6..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/static/js/main.666d1b34.chunk.js
+++ /dev/null
@@ -1,2 +0,0 @@
-(this.webpackJsonpstreamlit_cookie_manager=this.webpackJsonpstreamlit_cookie_manager||[]).push([[0],{5:function(e,n,o){"use strict";o.r(n);var a=o(2),t=(window.parent||window).document,c=null;a.a.events.addEventListener(a.a.RENDER_EVENT,(function(e){var n,o=e.detail;n=o.args.queue,Object.keys(n).forEach((function(e){var o=n[e];if(null===o.value)t.cookie="".concat(encodeURIComponent(e),"=; max-age=0; path=").concat(encodeURIComponent(o.path));else{var a=new Date(o.expires_at);t.cookie="".concat(encodeURIComponent(e),"=").concat(encodeURIComponent(o.value),";")+" expires=".concat(a.toUTCString(),";")+" path=".concat(encodeURIComponent(o.path),";")}}));var i=t.cookie;c===i||o.args.saveOnly||(a.a.setComponentValue(i),c=i)})),a.a.setComponentReady(),a.a.setFrameHeight(0)}},[[5,1,2]]]);
-//# sourceMappingURL=main.666d1b34.chunk.js.map
\ No newline at end of file
diff --git a/spaces/aaronespasa/deepfake-detection/app.py b/spaces/aaronespasa/deepfake-detection/app.py
deleted file mode 100644
index 11dbdc16f719399c68191981e664b950d2de6221..0000000000000000000000000000000000000000
--- a/spaces/aaronespasa/deepfake-detection/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import torch
-import torch.nn.functional as F
-from facenet_pytorch import MTCNN, InceptionResnetV1
-import os
-import numpy as np
-from PIL import Image
-import zipfile
-import cv2
-from pytorch_grad_cam import GradCAM
-from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
-from pytorch_grad_cam.utils.image import show_cam_on_image
-
-with zipfile.ZipFile("examples.zip","r") as zip_ref:
-    zip_ref.extractall(".")
-
-DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-
-mtcnn = MTCNN(
-    select_largest=False,
-    post_process=False,
-    device=DEVICE
-).to(DEVICE).eval()
-
-model = InceptionResnetV1(
-    pretrained="vggface2",
-    classify=True,
-    num_classes=1,
-    device=DEVICE
-)
-
-checkpoint = torch.load("resnetinceptionv1_epoch_32.pth", map_location=torch.device('cpu'))
-model.load_state_dict(checkpoint['model_state_dict'])
-model.to(DEVICE)
-model.eval()
-
-EXAMPLES_FOLDER = 'examples'
-examples_names = os.listdir(EXAMPLES_FOLDER)
-examples = []
-for example_name in examples_names:
-    example_path = os.path.join(EXAMPLES_FOLDER, example_name)
-    label = example_name.split('_')[0]
-    example = {
-        'path': example_path,
-        'label': label
-    }
-    examples.append(example)
-np.random.shuffle(examples) # shuffle
-
-def predict(input_image:Image.Image, true_label:str):
-    """Predict the label of the input_image"""
-    face = mtcnn(input_image)
-    if face is None:
-        raise Exception('No face detected')
-    face = face.unsqueeze(0) # add the batch dimension
-    face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False)
-    
-    # convert the face into a numpy array to be able to plot it
-    prev_face = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
-    prev_face = prev_face.astype('uint8')
-
-    face = face.to(DEVICE)
-    face = face.to(torch.float32)
-    face = face / 255.0
-    face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
-
-    target_layers=[model.block8.branch1[-1]]
-    use_cuda = True if torch.cuda.is_available() else False
-    cam = GradCAM(model=model, target_layers=target_layers, use_cuda=use_cuda)
-    targets = [ClassifierOutputTarget(0)]
-
-    grayscale_cam = cam(input_tensor=face, targets=targets, eigen_smooth=True)
-    grayscale_cam = grayscale_cam[0, :]
-    visualization = show_cam_on_image(face_image_to_plot, grayscale_cam, use_rgb=True)
-    face_with_mask = cv2.addWeighted(prev_face, 1, visualization, 0.5, 0)
-
-    with torch.no_grad():
-        output = torch.sigmoid(model(face).squeeze(0))
-        prediction = "real" if output.item() < 0.5 else "fake"
-        
-        real_prediction = 1 - output.item()
-        fake_prediction = output.item()
-        
-        confidences = {
-            'real': real_prediction,
-            'fake': fake_prediction
-        }
-    return confidences, true_label, face_with_mask
-
-interface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.inputs.Image(label="Input Image", type="pil"),
-        "text"
-    ],
-    outputs=[
-        gr.outputs.Label(label="Class"),
-        "text",
-        gr.outputs.Image(label="Face with Explainability")
-    ],
-    examples=[[examples[i]["path"], examples[i]["label"]] for i in range(10)]
-).launch()
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/assign_score_withk.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/assign_score_withk.py
deleted file mode 100644
index 4906adaa2cffd1b46912fbe7d4f87ef2f9fa0012..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/assign_score_withk.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['assign_score_withk_forward', 'assign_score_withk_backward'])
-
-
-class AssignScoreWithK(Function):
-    r"""Perform weighted sum to generate output features according to scores.
-    Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/
-    scene_seg/lib/paconv_lib/src/gpu>`_.
-
-    This is a memory-efficient CUDA implementation of assign_scores operation,
-    which first transform all point features with weight bank, then assemble
-    neighbor features with ``knn_idx`` and perform weighted sum of ``scores``.
-
-    See the `paper <https://arxiv.org/pdf/2103.14635.pdf>`_ appendix Sec. D for
-        more detailed descriptions.
-
-    Note:
-        This implementation assumes using ``neighbor`` kernel input, which is
-            (point_features - center_features, point_features).
-        See https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/model/
-        pointnet2/paconv.py#L128 for more details.
-    """
-
-    @staticmethod
-    def forward(ctx,
-                scores,
-                point_features,
-                center_features,
-                knn_idx,
-                aggregate='sum'):
-        """
-        Args:
-            scores (torch.Tensor): (B, npoint, K, M), predicted scores to
-                aggregate weight matrices in the weight bank.
-                ``npoint`` is the number of sampled centers.
-                ``K`` is the number of queried neighbors.
-                ``M`` is the number of weight matrices in the weight bank.
-            point_features (torch.Tensor): (B, N, M, out_dim)
-                Pre-computed point features to be aggregated.
-            center_features (torch.Tensor): (B, N, M, out_dim)
-                Pre-computed center features to be aggregated.
-            knn_idx (torch.Tensor): (B, npoint, K), index of sampled kNN.
-                We assume the first idx in each row is the idx of the center.
-            aggregate (str, optional): Aggregation method.
-                Can be 'sum', 'avg' or 'max'. Defaults: 'sum'.
-
-        Returns:
-            torch.Tensor: (B, out_dim, npoint, K), the aggregated features.
-        """
-        agg = {'sum': 0, 'avg': 1, 'max': 2}
-
-        B, N, M, out_dim = point_features.size()
-        _, npoint, K, _ = scores.size()
-
-        output = point_features.new_zeros((B, out_dim, npoint, K))
-        ext_module.assign_score_withk_forward(
-            point_features.contiguous(),
-            center_features.contiguous(),
-            scores.contiguous(),
-            knn_idx.contiguous(),
-            output,
-            B=B,
-            N0=N,
-            N1=npoint,
-            M=M,
-            K=K,
-            O=out_dim,
-            aggregate=agg[aggregate])
-
-        ctx.save_for_backward(output, point_features, center_features, scores,
-                              knn_idx)
-        ctx.agg = agg[aggregate]
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        """
-        Args:
-            grad_out (torch.Tensor): (B, out_dim, npoint, K)
-
-        Returns:
-            grad_scores (torch.Tensor): (B, npoint, K, M)
-            grad_point_features (torch.Tensor): (B, N, M, out_dim)
-            grad_center_features (torch.Tensor): (B, N, M, out_dim)
-        """
-        _, point_features, center_features, scores, knn_idx = ctx.saved_tensors
-
-        agg = ctx.agg
-
-        B, N, M, out_dim = point_features.size()
-        _, npoint, K, _ = scores.size()
-
-        grad_point_features = point_features.new_zeros(point_features.shape)
-        grad_center_features = center_features.new_zeros(center_features.shape)
-        grad_scores = scores.new_zeros(scores.shape)
-
-        ext_module.assign_score_withk_backward(
-            grad_out.contiguous(),
-            point_features.contiguous(),
-            center_features.contiguous(),
-            scores.contiguous(),
-            knn_idx.contiguous(),
-            grad_point_features,
-            grad_center_features,
-            grad_scores,
-            B=B,
-            N0=N,
-            N1=npoint,
-            M=M,
-            K=K,
-            O=out_dim,
-            aggregate=agg)
-
-        return grad_scores, grad_point_features, \
-            grad_center_features, None, None
-
-
-assign_score_withk = AssignScoreWithK.apply
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
-    def __init__(self,
-                 by_epoch=True,
-                 warmup=None,
-                 warmup_iters=0,
-                 warmup_ratio=0.9):
-        # validate the "warmup" argument
-        if warmup is not None:
-            if warmup not in ['constant', 'linear', 'exp']:
-                raise ValueError(
-                    f'"{warmup}" is not a supported type for warming up, valid'
-                    ' types are "constant" and "linear"')
-        if warmup is not None:
-            assert warmup_iters > 0, \
-                '"warmup_iters" must be a positive integer'
-            assert 0 < warmup_ratio <= 1.0, \
-                '"warmup_momentum" must be in range (0,1]'
-
-        self.by_epoch = by_epoch
-        self.warmup = warmup
-        self.warmup_iters = warmup_iters
-        self.warmup_ratio = warmup_ratio
-
-        self.base_momentum = []  # initial momentum for all param groups
-        self.regular_momentum = [
-        ]  # expected momentum if no warming up is performed
-
-    def _set_momentum(self, runner, momentum_groups):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                for param_group, mom in zip(optim.param_groups,
-                                            momentum_groups[k]):
-                    if 'momentum' in param_group.keys():
-                        param_group['momentum'] = mom
-                    elif 'betas' in param_group.keys():
-                        param_group['betas'] = (mom, param_group['betas'][1])
-        else:
-            for param_group, mom in zip(runner.optimizer.param_groups,
-                                        momentum_groups):
-                if 'momentum' in param_group.keys():
-                    param_group['momentum'] = mom
-                elif 'betas' in param_group.keys():
-                    param_group['betas'] = (mom, param_group['betas'][1])
-
-    def get_momentum(self, runner, base_momentum):
-        raise NotImplementedError
-
-    def get_regular_momentum(self, runner):
-        if isinstance(runner.optimizer, dict):
-            momentum_groups = {}
-            for k in runner.optimizer.keys():
-                _momentum_group = [
-                    self.get_momentum(runner, _base_momentum)
-                    for _base_momentum in self.base_momentum[k]
-                ]
-                momentum_groups.update({k: _momentum_group})
-            return momentum_groups
-        else:
-            return [
-                self.get_momentum(runner, _base_momentum)
-                for _base_momentum in self.base_momentum
-            ]
-
-    def get_warmup_momentum(self, cur_iters):
-
-        def _get_warmup_momentum(cur_iters, regular_momentum):
-            if self.warmup == 'constant':
-                warmup_momentum = [
-                    _momentum / self.warmup_ratio
-                    for _momentum in self.regular_momentum
-                ]
-            elif self.warmup == 'linear':
-                k = (1 - cur_iters / self.warmup_iters) * (1 -
-                                                           self.warmup_ratio)
-                warmup_momentum = [
-                    _momentum / (1 - k) for _momentum in self.regular_mom
-                ]
-            elif self.warmup == 'exp':
-                k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
-                warmup_momentum = [
-                    _momentum / k for _momentum in self.regular_mom
-                ]
-            return warmup_momentum
-
-        if isinstance(self.regular_momentum, dict):
-            momentum_groups = {}
-            for key, regular_momentum in self.regular_momentum.items():
-                momentum_groups[key] = _get_warmup_momentum(
-                    cur_iters, regular_momentum)
-            return momentum_groups
-        else:
-            return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
-    def before_run(self, runner):
-        # NOTE: when resuming from a checkpoint,
-        # if 'initial_momentum' is not saved,
-        # it will be set according to the optimizer params
-        if isinstance(runner.optimizer, dict):
-            self.base_momentum = {}
-            for k, optim in runner.optimizer.items():
-                for group in optim.param_groups:
-                    if 'momentum' in group.keys():
-                        group.setdefault('initial_momentum', group['momentum'])
-                    else:
-                        group.setdefault('initial_momentum', group['betas'][0])
-                _base_momentum = [
-                    group['initial_momentum'] for group in optim.param_groups
-                ]
-                self.base_momentum.update({k: _base_momentum})
-        else:
-            for group in runner.optimizer.param_groups:
-                if 'momentum' in group.keys():
-                    group.setdefault('initial_momentum', group['momentum'])
-                else:
-                    group.setdefault('initial_momentum', group['betas'][0])
-            self.base_momentum = [
-                group['initial_momentum']
-                for group in runner.optimizer.param_groups
-            ]
-
-    def before_train_epoch(self, runner):
-        if not self.by_epoch:
-            return
-        self.regular_mom = self.get_regular_momentum(runner)
-        self._set_momentum(runner, self.regular_mom)
-
-    def before_train_iter(self, runner):
-        cur_iter = runner.iter
-        if not self.by_epoch:
-            self.regular_mom = self.get_regular_momentum(runner)
-            if self.warmup is None or cur_iter >= self.warmup_iters:
-                self._set_momentum(runner, self.regular_mom)
-            else:
-                warmup_momentum = self.get_warmup_momentum(cur_iter)
-                self._set_momentum(runner, warmup_momentum)
-        elif self.by_epoch:
-            if self.warmup is None or cur_iter > self.warmup_iters:
-                return
-            elif cur_iter == self.warmup_iters:
-                self._set_momentum(runner, self.regular_mom)
-            else:
-                warmup_momentum = self.get_warmup_momentum(cur_iter)
-                self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
-    """Step momentum scheduler with min value clipping.
-
-    Args:
-        step (int | list[int]): Step to decay the momentum. If an int value is
-            given, regard it as the decay interval. If a list is given, decay
-            momentum at these steps.
-        gamma (float, optional): Decay momentum ratio. Default: 0.5.
-        min_momentum (float, optional): Minimum momentum value to keep. If
-            momentum after decay is lower than this value, it will be clipped
-            accordingly. If None is given, we don't perform lr clipping.
-            Default: None.
-    """
-
-    def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
-        if isinstance(step, list):
-            assert mmcv.is_list_of(step, int)
-            assert all([s > 0 for s in step])
-        elif isinstance(step, int):
-            assert step > 0
-        else:
-            raise TypeError('"step" must be a list or integer')
-        self.step = step
-        self.gamma = gamma
-        self.min_momentum = min_momentum
-        super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
-    def get_momentum(self, runner, base_momentum):
-        progress = runner.epoch if self.by_epoch else runner.iter
-
-        # calculate exponential term
-        if isinstance(self.step, int):
-            exp = progress // self.step
-        else:
-            exp = len(self.step)
-            for i, s in enumerate(self.step):
-                if progress < s:
-                    exp = i
-                    break
-
-        momentum = base_momentum * (self.gamma**exp)
-        if self.min_momentum is not None:
-            # clip to a minimum value
-            momentum = max(momentum, self.min_momentum)
-        return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
-    def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
-        assert (min_momentum is None) ^ (min_momentum_ratio is None)
-        self.min_momentum = min_momentum
-        self.min_momentum_ratio = min_momentum_ratio
-        super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
-    def get_momentum(self, runner, base_momentum):
-        if self.by_epoch:
-            progress = runner.epoch
-            max_progress = runner.max_epochs
-        else:
-            progress = runner.iter
-            max_progress = runner.max_iters
-        if self.min_momentum_ratio is not None:
-            target_momentum = base_momentum * self.min_momentum_ratio
-        else:
-            target_momentum = self.min_momentum
-        return annealing_cos(base_momentum, target_momentum,
-                             progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
-    """Cyclic momentum Scheduler.
-
-    Implement the cyclical momentum scheduler policy described in
-    https://arxiv.org/pdf/1708.07120.pdf
-
-    This momentum scheduler usually used together with the CyclicLRUpdater
-    to improve the performance in the 3D detection area.
-
-    Attributes:
-        target_ratio (tuple[float]): Relative ratio of the lowest momentum and
-            the highest momentum to the initial momentum.
-        cyclic_times (int): Number of cycles during training
-        step_ratio_up (float): The ratio of the increasing process of momentum
-            in  the total cycle.
-        by_epoch (bool): Whether to update momentum by epoch.
-    """
-
-    def __init__(self,
-                 by_epoch=False,
-                 target_ratio=(0.85 / 0.95, 1),
-                 cyclic_times=1,
-                 step_ratio_up=0.4,
-                 **kwargs):
-        if isinstance(target_ratio, float):
-            target_ratio = (target_ratio, target_ratio / 1e5)
-        elif isinstance(target_ratio, tuple):
-            target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
-                if len(target_ratio) == 1 else target_ratio
-        else:
-            raise ValueError('target_ratio should be either float '
-                             f'or tuple, got {type(target_ratio)}')
-
-        assert len(target_ratio) == 2, \
-            '"target_ratio" must be list or tuple of two floats'
-        assert 0 <= step_ratio_up < 1.0, \
-            '"step_ratio_up" must be in range [0,1)'
-
-        self.target_ratio = target_ratio
-        self.cyclic_times = cyclic_times
-        self.step_ratio_up = step_ratio_up
-        self.momentum_phases = []  # init momentum_phases
-        # currently only support by_epoch=False
-        assert not by_epoch, \
-            'currently only support "by_epoch" = False'
-        super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
-    def before_run(self, runner):
-        super(CyclicMomentumUpdaterHook, self).before_run(runner)
-        # initiate momentum_phases
-        # total momentum_phases are separated as up and down
-        max_iter_per_phase = runner.max_iters // self.cyclic_times
-        iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
-        self.momentum_phases.append(
-            [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
-        self.momentum_phases.append([
-            iter_up_phase, max_iter_per_phase, max_iter_per_phase,
-            self.target_ratio[0], self.target_ratio[1]
-        ])
-
-    def get_momentum(self, runner, base_momentum):
-        curr_iter = runner.iter
-        for (start_iter, end_iter, max_iter_per_phase, start_ratio,
-             end_ratio) in self.momentum_phases:
-            curr_iter %= max_iter_per_phase
-            if start_iter <= curr_iter < end_iter:
-                progress = curr_iter - start_iter
-                return annealing_cos(base_momentum * start_ratio,
-                                     base_momentum * end_ratio,
-                                     progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
-    """OneCycle momentum Scheduler.
-
-    This momentum scheduler usually used together with the OneCycleLrUpdater
-    to improve the performance.
-
-    Args:
-        base_momentum (float or list): Lower momentum boundaries in the cycle
-            for each parameter group. Note that momentum is cycled inversely
-            to learning rate; at the peak of a cycle, momentum is
-            'base_momentum' and learning rate is 'max_lr'.
-            Default: 0.85
-        max_momentum (float or list): Upper momentum boundaries in the cycle
-            for each parameter group. Functionally,
-            it defines the cycle amplitude (max_momentum - base_momentum).
-            Note that momentum is cycled inversely
-            to learning rate; at the start of a cycle, momentum is
-            'max_momentum' and learning rate is 'base_lr'
-            Default: 0.95
-        pct_start (float): The percentage of the cycle (in number of steps)
-            spent increasing the learning rate.
-            Default: 0.3
-        anneal_strategy (str): {'cos', 'linear'}
-            Specifies the annealing strategy: 'cos' for cosine annealing,
-            'linear' for linear annealing.
-            Default: 'cos'
-        three_phase (bool): If three_phase is True, use a third phase of the
-            schedule to annihilate the learning rate according to
-            final_div_factor instead of modifying the second phase (the first
-            two phases will be symmetrical about the step indicated by
-            pct_start).
-            Default: False
-    """
-
-    def __init__(self,
-                 base_momentum=0.85,
-                 max_momentum=0.95,
-                 pct_start=0.3,
-                 anneal_strategy='cos',
-                 three_phase=False,
-                 **kwargs):
-        # validate by_epoch, currently only support by_epoch=False
-        if 'by_epoch' not in kwargs:
-            kwargs['by_epoch'] = False
-        else:
-            assert not kwargs['by_epoch'], \
-                'currently only support "by_epoch" = False'
-        if not isinstance(base_momentum, (float, list, dict)):
-            raise ValueError('base_momentum must be the type among of float,'
-                             'list or dict.')
-        self._base_momentum = base_momentum
-        if not isinstance(max_momentum, (float, list, dict)):
-            raise ValueError('max_momentum must be the type among of float,'
-                             'list or dict.')
-        self._max_momentum = max_momentum
-        # validate pct_start
-        if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
-            raise ValueError('Expected float between 0 and 1 pct_start, but '
-                             f'got {pct_start}')
-        self.pct_start = pct_start
-        # validate anneal_strategy
-        if anneal_strategy not in ['cos', 'linear']:
-            raise ValueError('anneal_strategy must by one of "cos" or '
-                             f'"linear", instead got {anneal_strategy}')
-        elif anneal_strategy == 'cos':
-            self.anneal_func = annealing_cos
-        elif anneal_strategy == 'linear':
-            self.anneal_func = annealing_linear
-        self.three_phase = three_phase
-        self.momentum_phases = []  # init momentum_phases
-        super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
-    def before_run(self, runner):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                if ('momentum' not in optim.defaults
-                        and 'betas' not in optim.defaults):
-                    raise ValueError('optimizer must support momentum with'
-                                     'option enabled')
-                self.use_beta1 = 'betas' in optim.defaults
-                _base_momentum = format_param(k, optim, self._base_momentum)
-                _max_momentum = format_param(k, optim, self._max_momentum)
-                for group, b_momentum, m_momentum in zip(
-                        optim.param_groups, _base_momentum, _max_momentum):
-                    if self.use_beta1:
-                        _, beta2 = group['betas']
-                        group['betas'] = (m_momentum, beta2)
-                    else:
-                        group['momentum'] = m_momentum
-                    group['base_momentum'] = b_momentum
-                    group['max_momentum'] = m_momentum
-        else:
-            optim = runner.optimizer
-            if ('momentum' not in optim.defaults
-                    and 'betas' not in optim.defaults):
-                raise ValueError('optimizer must support momentum with'
-                                 'option enabled')
-            self.use_beta1 = 'betas' in optim.defaults
-            k = type(optim).__name__
-            _base_momentum = format_param(k, optim, self._base_momentum)
-            _max_momentum = format_param(k, optim, self._max_momentum)
-            for group, b_momentum, m_momentum in zip(optim.param_groups,
-                                                     _base_momentum,
-                                                     _max_momentum):
-                if self.use_beta1:
-                    _, beta2 = group['betas']
-                    group['betas'] = (m_momentum, beta2)
-                else:
-                    group['momentum'] = m_momentum
-                group['base_momentum'] = b_momentum
-                group['max_momentum'] = m_momentum
-
-        if self.three_phase:
-            self.momentum_phases.append({
-                'end_iter':
-                float(self.pct_start * runner.max_iters) - 1,
-                'start_momentum':
-                'max_momentum',
-                'end_momentum':
-                'base_momentum'
-            })
-            self.momentum_phases.append({
-                'end_iter':
-                float(2 * self.pct_start * runner.max_iters) - 2,
-                'start_momentum':
-                'base_momentum',
-                'end_momentum':
-                'max_momentum'
-            })
-            self.momentum_phases.append({
-                'end_iter': runner.max_iters - 1,
-                'start_momentum': 'max_momentum',
-                'end_momentum': 'max_momentum'
-            })
-        else:
-            self.momentum_phases.append({
-                'end_iter':
-                float(self.pct_start * runner.max_iters) - 1,
-                'start_momentum':
-                'max_momentum',
-                'end_momentum':
-                'base_momentum'
-            })
-            self.momentum_phases.append({
-                'end_iter': runner.max_iters - 1,
-                'start_momentum': 'base_momentum',
-                'end_momentum': 'max_momentum'
-            })
-
-    def _set_momentum(self, runner, momentum_groups):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                for param_group, mom in zip(optim.param_groups,
-                                            momentum_groups[k]):
-                    if 'momentum' in param_group.keys():
-                        param_group['momentum'] = mom
-                    elif 'betas' in param_group.keys():
-                        param_group['betas'] = (mom, param_group['betas'][1])
-        else:
-            for param_group, mom in zip(runner.optimizer.param_groups,
-                                        momentum_groups):
-                if 'momentum' in param_group.keys():
-                    param_group['momentum'] = mom
-                elif 'betas' in param_group.keys():
-                    param_group['betas'] = (mom, param_group['betas'][1])
-
-    def get_momentum(self, runner, param_group):
-        curr_iter = runner.iter
-        start_iter = 0
-        for i, phase in enumerate(self.momentum_phases):
-            end_iter = phase['end_iter']
-            if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
-                pct = (curr_iter - start_iter) / (end_iter - start_iter)
-                momentum = self.anneal_func(
-                    param_group[phase['start_momentum']],
-                    param_group[phase['end_momentum']], pct)
-                break
-            start_iter = end_iter
-        return momentum
-
-    def get_regular_momentum(self, runner):
-        if isinstance(runner.optimizer, dict):
-            momentum_groups = {}
-            for k, optim in runner.optimizer.items():
-                _momentum_group = [
-                    self.get_momentum(runner, param_group)
-                    for param_group in optim.param_groups
-                ]
-                momentum_groups.update({k: _momentum_group})
-            return momentum_groups
-        else:
-            momentum_groups = []
-            for param_group in runner.optimizer.param_groups:
-                momentum_groups.append(self.get_momentum(runner, param_group))
-            return momentum_groups
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/path.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/path.py
deleted file mode 100644
index 7dab4b3041413b1432b0f434b8b14783097d33c6..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/path.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import os.path as osp
-from pathlib import Path
-
-from .misc import is_str
-
-
-def is_filepath(x):
-    return is_str(x) or isinstance(x, Path)
-
-
-def fopen(filepath, *args, **kwargs):
-    if is_str(filepath):
-        return open(filepath, *args, **kwargs)
-    elif isinstance(filepath, Path):
-        return filepath.open(*args, **kwargs)
-    raise ValueError('`filepath` should be a string or a Path')
-
-
-def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
-    if not osp.isfile(filename):
-        raise FileNotFoundError(msg_tmpl.format(filename))
-
-
-def mkdir_or_exist(dir_name, mode=0o777):
-    if dir_name == '':
-        return
-    dir_name = osp.expanduser(dir_name)
-    os.makedirs(dir_name, mode=mode, exist_ok=True)
-
-
-def symlink(src, dst, overwrite=True, **kwargs):
-    if os.path.lexists(dst) and overwrite:
-        os.remove(dst)
-    os.symlink(src, dst, **kwargs)
-
-
-def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True):
-    """Scan a directory to find the interested files.
-
-    Args:
-        dir_path (str | obj:`Path`): Path of the directory.
-        suffix (str | tuple(str), optional): File suffix that we are
-            interested in. Default: None.
-        recursive (bool, optional): If set to True, recursively scan the
-            directory. Default: False.
-        case_sensitive (bool, optional) : If set to False, ignore the case of
-            suffix. Default: True.
-
-    Returns:
-        A generator for all the interested files with relative paths.
-    """
-    if isinstance(dir_path, (str, Path)):
-        dir_path = str(dir_path)
-    else:
-        raise TypeError('"dir_path" must be a string or Path object')
-
-    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-        raise TypeError('"suffix" must be a string or tuple of strings')
-
-    if suffix is not None and not case_sensitive:
-        suffix = suffix.lower() if isinstance(suffix, str) else tuple(
-            item.lower() for item in suffix)
-
-    root = dir_path
-
-    def _scandir(dir_path, suffix, recursive, case_sensitive):
-        for entry in os.scandir(dir_path):
-            if not entry.name.startswith('.') and entry.is_file():
-                rel_path = osp.relpath(entry.path, root)
-                _rel_path = rel_path if case_sensitive else rel_path.lower()
-                if suffix is None or _rel_path.endswith(suffix):
-                    yield rel_path
-            elif recursive and os.path.isdir(entry.path):
-                # scan recursively if entry.path is a directory
-                yield from _scandir(entry.path, suffix, recursive,
-                                    case_sensitive)
-
-    return _scandir(dir_path, suffix, recursive, case_sensitive)
-
-
-def find_vcs_root(path, markers=('.git', )):
-    """Finds the root directory (including itself) of specified markers.
-
-    Args:
-        path (str): Path of directory or file.
-        markers (list[str], optional): List of file or directory names.
-
-    Returns:
-        The directory contained one of the markers or None if not found.
-    """
-    if osp.isfile(path):
-        path = osp.dirname(path)
-
-    prev, cur = None, osp.abspath(osp.expanduser(path))
-    while cur != prev:
-        if any(osp.exists(osp.join(cur, marker)) for marker in markers):
-            return cur
-        prev, cur = cur, osp.split(cur)[0]
-    return None
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/dense_heads/rpn_test_mixin.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/dense_heads/rpn_test_mixin.py
deleted file mode 100644
index 4ce5c66f82595f496e6e55719c1caee75150d568..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/dense_heads/rpn_test_mixin.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import sys
-
-from mmdet.core import merge_aug_proposals
-
-if sys.version_info >= (3, 7):
-    from mmdet.utils.contextmanagers import completed
-
-
-class RPNTestMixin(object):
-    """Test methods of RPN."""
-
-    if sys.version_info >= (3, 7):
-
-        async def async_simple_test_rpn(self, x, img_metas):
-            sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
-            async with completed(
-                    __name__, 'rpn_head_forward',
-                    sleep_interval=sleep_interval):
-                rpn_outs = self(x)
-
-            proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-            return proposal_list
-
-    def simple_test_rpn(self, x, img_metas):
-        """Test without augmentation.
-
-        Args:
-            x (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-            img_metas (list[dict]): Meta info of each image.
-
-        Returns:
-            list[Tensor]: Proposals of each image.
-        """
-        rpn_outs = self(x)
-        proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-        return proposal_list
-
-    def aug_test_rpn(self, feats, img_metas):
-        samples_per_gpu = len(img_metas[0])
-        aug_proposals = [[] for _ in range(samples_per_gpu)]
-        for x, img_meta in zip(feats, img_metas):
-            proposal_list = self.simple_test_rpn(x, img_meta)
-            for i, proposals in enumerate(proposal_list):
-                aug_proposals[i].append(proposals)
-        # reorganize the order of 'img_metas' to match the dimensions
-        # of 'aug_proposals'
-        aug_img_metas = []
-        for i in range(samples_per_gpu):
-            aug_img_meta = []
-            for j in range(len(img_metas)):
-                aug_img_meta.append(img_metas[j][i])
-            aug_img_metas.append(aug_img_meta)
-        # after merging, proposals will be rescaled to the original image size
-        merged_proposals = [
-            merge_aug_proposals(proposals, aug_img_meta, self.test_cfg)
-            for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas)
-        ]
-        return merged_proposals
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/yolo_neck.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/yolo_neck.py
deleted file mode 100644
index c2f9b9ef3859796c284c16ad1a92fe41ecbed613..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/yolo_neck.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# Copyright (c) 2019 Western Digital Corporation or its affiliates.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule
-
-from ..builder import NECKS
-
-
-class DetectionBlock(nn.Module):
-    """Detection block in YOLO neck.
-
-    Let out_channels = n, the DetectionBlock contains:
-    Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer.
-    The first 6 ConvLayers are formed the following way:
-        1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n.
-    The Conv2D layer is 1x1x255.
-    Some block will have branch after the fifth ConvLayer.
-    The input channel is arbitrary (in_channels)
-
-    Args:
-        in_channels (int): The number of input channels.
-        out_channels (int): The number of output channels.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-            Default: dict(type='BN', requires_grad=True)
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='LeakyReLU', negative_slope=0.1).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):
-        super(DetectionBlock, self).__init__()
-        double_out_channels = out_channels * 2
-
-        # shortcut
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-        self.conv1 = ConvModule(in_channels, out_channels, 1, **cfg)
-        self.conv2 = ConvModule(
-            out_channels, double_out_channels, 3, padding=1, **cfg)
-        self.conv3 = ConvModule(double_out_channels, out_channels, 1, **cfg)
-        self.conv4 = ConvModule(
-            out_channels, double_out_channels, 3, padding=1, **cfg)
-        self.conv5 = ConvModule(double_out_channels, out_channels, 1, **cfg)
-
-    def forward(self, x):
-        tmp = self.conv1(x)
-        tmp = self.conv2(tmp)
-        tmp = self.conv3(tmp)
-        tmp = self.conv4(tmp)
-        out = self.conv5(tmp)
-        return out
-
-
-@NECKS.register_module()
-class YOLOV3Neck(nn.Module):
-    """The neck of YOLOV3.
-
-    It can be treated as a simplified version of FPN. It
-    will take the result from Darknet backbone and do some upsampling and
-    concatenation. It will finally output the detection result.
-
-    Note:
-        The input feats should be from top to bottom.
-            i.e., from high-lvl to low-lvl
-        But YOLOV3Neck will process them in reversed order.
-            i.e., from bottom (high-lvl) to top (low-lvl)
-
-    Args:
-        num_scales (int): The number of scales / stages.
-        in_channels (int): The number of input channels.
-        out_channels (int): The number of output channels.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-            Default: dict(type='BN', requires_grad=True)
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='LeakyReLU', negative_slope=0.1).
-    """
-
-    def __init__(self,
-                 num_scales,
-                 in_channels,
-                 out_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):
-        super(YOLOV3Neck, self).__init__()
-        assert (num_scales == len(in_channels) == len(out_channels))
-        self.num_scales = num_scales
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-
-        # shortcut
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        # To support arbitrary scales, the code looks awful, but it works.
-        # Better solution is welcomed.
-        self.detect1 = DetectionBlock(in_channels[0], out_channels[0], **cfg)
-        for i in range(1, self.num_scales):
-            in_c, out_c = self.in_channels[i], self.out_channels[i]
-            self.add_module(f'conv{i}', ConvModule(in_c, out_c, 1, **cfg))
-            # in_c + out_c : High-lvl feats will be cat with low-lvl feats
-            self.add_module(f'detect{i+1}',
-                            DetectionBlock(in_c + out_c, out_c, **cfg))
-
-    def forward(self, feats):
-        assert len(feats) == self.num_scales
-
-        # processed from bottom (high-lvl) to top (low-lvl)
-        outs = []
-        out = self.detect1(feats[-1])
-        outs.append(out)
-
-        for i, x in enumerate(reversed(feats[:-1])):
-            conv = getattr(self, f'conv{i+1}')
-            tmp = conv(out)
-
-            # Cat with low-lvl feats
-            tmp = F.interpolate(tmp, scale_factor=2)
-            tmp = torch.cat((tmp, x), 1)
-
-            detect = getattr(self, f'detect{i+2}')
-            out = detect(tmp)
-            outs.append(out)
-
-        return tuple(outs)
-
-    def init_weights(self):
-        """Initialize the weights of module."""
-        # init is done in ConvModule
-        pass
diff --git a/spaces/abidlabs/mic_or_file/README.md b/spaces/abidlabs/mic_or_file/README.md
deleted file mode 100644
index 4ab6fb0fc99205d16be3aeddf201c221554f2317..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/mic_or_file/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Mic Or File
-emoji: 💻
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/adirik/stylemc-demo/encoder4editing/models/stylegan2/op/upfirdn2d.cpp b/spaces/adirik/stylemc-demo/encoder4editing/models/stylegan2/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/encoder4editing/models/stylegan2/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
-                            int up_x, int up_y, int down_x, int down_y,
-                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
-                        int up_x, int up_y, int down_x, int down_y,
-                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(kernel);
-
-    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/aijack/jojo/e4e/configs/paths_config.py b/spaces/aijack/jojo/e4e/configs/paths_config.py
deleted file mode 100644
index 4604f6063b8125364a52a492de52fcc54004f373..0000000000000000000000000000000000000000
--- a/spaces/aijack/jojo/e4e/configs/paths_config.py
+++ /dev/null
@@ -1,28 +0,0 @@
-dataset_paths = {
-	#  Face Datasets (In the paper: FFHQ - train, CelebAHQ - test)
-	'ffhq': '',
-	'celeba_test': '',
-
-	#  Cars Dataset (In the paper: Stanford cars)
-	'cars_train': '',
-	'cars_test': '',
-
-	#  Horse Dataset (In the paper: LSUN Horse)
-	'horse_train': '',
-	'horse_test': '',
-
-	#  Church Dataset (In the paper: LSUN Church)
-	'church_train': '',
-	'church_test': '',
-
-	#  Cats Dataset (In the paper: LSUN Cat)
-	'cats_train': '',
-	'cats_test': ''
-}
-
-model_paths = {
-	'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
-	'ir_se50': 'pretrained_models/model_ir_se50.pth',
-	'shape_predictor': 'pretrained_models/shape_predictor_68_face_landmarks.dat',
-	'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth'
-}
diff --git a/spaces/aijack/jojo/e4e/datasets/inference_dataset.py b/spaces/aijack/jojo/e4e/datasets/inference_dataset.py
deleted file mode 100644
index fb577d7b538d634f27013c2784d2ea32143154cb..0000000000000000000000000000000000000000
--- a/spaces/aijack/jojo/e4e/datasets/inference_dataset.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from torch.utils.data import Dataset
-from PIL import Image
-from utils import data_utils
-
-
-class InferenceDataset(Dataset):
-
-	def __init__(self, root, opts, transform=None, preprocess=None):
-		self.paths = sorted(data_utils.make_dataset(root))
-		self.transform = transform
-		self.preprocess = preprocess
-		self.opts = opts
-
-	def __len__(self):
-		return len(self.paths)
-
-	def __getitem__(self, index):
-		from_path = self.paths[index]
-		if self.preprocess is not None:
-			from_im = self.preprocess(from_path)
-		else:
-			from_im = Image.open(from_path).convert('RGB')
-		if self.transform:
-			from_im = self.transform(from_im)
-		return from_im
diff --git a/spaces/akhaliq/Music_Source_Separation/scripts/0_download_datasets/musdb18.sh b/spaces/akhaliq/Music_Source_Separation/scripts/0_download_datasets/musdb18.sh
deleted file mode 100644
index 58ba01624b45a2f2d75cba4e3f1612833fc68d4b..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Music_Source_Separation/scripts/0_download_datasets/musdb18.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-MUSDB18_DATASET_DIR=${1:-"./datasets/musdb18"}	# The first argument is dataset directory.
-
-echo "MUSDB18_DATASET_DIR=${MUSDB18_DATASET_DIR}"
-
-# Set up paths.
-mkdir -p $MUSDB18_DATASET_DIR
-cd $MUSDB18_DATASET_DIR
-
-# Download dataset from Zenodo.
-echo "The dataset link is at https://zenodo.org/record/1117372"
-
-wget -O "musdb18.zip" "https://zenodo.org/record/1117372/files/musdb18.zip?download=1"
-
-# Unzip dataset.
-unzip "musdb18.zip"
-
-# The downloaded MUSDB18 dataset looks like:
-# ./datasets/musdb18
-# ├── train (100 files)
-# │   ├── 'A Classic Education - NightOwl.stem.mp4'
-# │   └── ...
-# ├── test (50 files)
-# │   ├── 'Al James - Schoolboy Facination.stem.mp4'
-# │   └── ...
-# └── README.md
\ No newline at end of file
diff --git a/spaces/akhaliq/PaintTransformer/train/util/html.py b/spaces/akhaliq/PaintTransformer/train/util/html.py
deleted file mode 100644
index cc3262a1eafda34842e4dbad47bb6ba72f0c5a68..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/PaintTransformer/train/util/html.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import dominate
-from dominate.tags import meta, h3, table, tr, td, p, a, img, br
-import os
-
-
-class HTML:
-    """This HTML class allows us to save images and write texts into a single HTML file.
-
-     It consists of functions such as <add_header> (add a text header to the HTML file),
-     <add_images> (add a row of images to the HTML file), and <save> (save the HTML to the disk).
-     It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API.
-    """
-
-    def __init__(self, web_dir, title, refresh=0):
-        """Initialize the HTML classes
-
-        Parameters:
-            web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_dir>/index.html; images will be saved at <web_dir/images/
-            title (str)   -- the webpage name
-            refresh (int) -- how often the website refresh itself; if 0; no refreshing
-        """
-        self.title = title
-        self.web_dir = web_dir
-        self.img_dir = os.path.join(self.web_dir, 'images')
-        if not os.path.exists(self.web_dir):
-            os.makedirs(self.web_dir)
-        if not os.path.exists(self.img_dir):
-            os.makedirs(self.img_dir)
-
-        self.doc = dominate.document(title=title)
-        if refresh > 0:
-            with self.doc.head:
-                meta(http_equiv="refresh", content=str(refresh))
-
-    def get_image_dir(self):
-        """Return the directory that stores images"""
-        return self.img_dir
-
-    def add_header(self, text):
-        """Insert a header to the HTML file
-
-        Parameters:
-            text (str) -- the header text
-        """
-        with self.doc:
-            h3(text)
-
-    def add_images(self, ims, txts, links, width=400):
-        """add images to the HTML file
-
-        Parameters:
-            ims (str list)   -- a list of image paths
-            txts (str list)  -- a list of image names shown on the website
-            links (str list) --  a list of hyperref links; when you click an image, it will redirect you to a new page
-        """
-        self.t = table(border=1, style="table-layout: fixed;")  # Insert a table
-        self.doc.add(self.t)
-        with self.t:
-            with tr():
-                for im, txt, link in zip(ims, txts, links):
-                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
-                        with p():
-                            with a(href=os.path.join('images', link)):
-                                img(style="width:%dpx" % width, src=os.path.join('images', im))
-                            br()
-                            p(txt)
-
-    def save(self):
-        """save the current content to the HMTL file"""
-        html_file = '%s/index.html' % self.web_dir
-        f = open(html_file, 'wt')
-        f.write(self.doc.render())
-        f.close()
-
-
-if __name__ == '__main__':  # we show an example usage here.
-    html = HTML('web/', 'test_html')
-    html.add_header('hello world')
-
-    ims, txts, links = [], [], []
-    for n in range(4):
-        ims.append('image_%d.png' % n)
-        txts.append('text_%d' % n)
-        links.append('image_%d.png' % n)
-    html.add_images(ims, txts, links)
-    html.save()
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/models/index.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/models/index.py
deleted file mode 100644
index b94c32511f0cda2363bfc4f29c9c8bfcc7101f9b..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/models/index.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import urllib.parse
-
-
-class PackageIndex:
-    """Represents a Package Index and provides easier access to endpoints"""
-
-    __slots__ = ["url", "netloc", "simple_url", "pypi_url", "file_storage_domain"]
-
-    def __init__(self, url: str, file_storage_domain: str) -> None:
-        super().__init__()
-        self.url = url
-        self.netloc = urllib.parse.urlsplit(url).netloc
-        self.simple_url = self._url_for_path("simple")
-        self.pypi_url = self._url_for_path("pypi")
-
-        # This is part of a temporary hack used to block installs of PyPI
-        # packages which depend on external urls only necessary until PyPI can
-        # block such packages themselves
-        self.file_storage_domain = file_storage_domain
-
-    def _url_for_path(self, path: str) -> str:
-        return urllib.parse.urljoin(self.url, path)
-
-
-PyPI = PackageIndex("https://pypi.org/", file_storage_domain="files.pythonhosted.org")
-TestPyPI = PackageIndex(
-    "https://test.pypi.org/", file_storage_domain="test-files.pythonhosted.org"
-)
diff --git a/spaces/algomuffin/jojo_fork/e4e/criteria/lpips/utils.py b/spaces/algomuffin/jojo_fork/e4e/criteria/lpips/utils.py
deleted file mode 100644
index 3d15a0983775810ef6239c561c67939b2b9ee3b5..0000000000000000000000000000000000000000
--- a/spaces/algomuffin/jojo_fork/e4e/criteria/lpips/utils.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from collections import OrderedDict
-
-import torch
-
-
-def normalize_activation(x, eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
-    return x / (norm_factor + eps)
-
-
-def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
-    # build url
-    url = 'https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/' \
-        + f'master/lpips/weights/v{version}/{net_type}.pth'
-
-    # download
-    old_state_dict = torch.hub.load_state_dict_from_url(
-        url, progress=True,
-        map_location=None if torch.cuda.is_available() else torch.device('cpu')
-    )
-
-    # rename keys
-    new_state_dict = OrderedDict()
-    for key, val in old_state_dict.items():
-        new_key = key
-        new_key = new_key.replace('lin', '')
-        new_key = new_key.replace('model.', '')
-        new_state_dict[new_key] = val
-
-    return new_state_dict
diff --git a/spaces/ali-ghamdan/deoldify/fastai/vision/transform.py b/spaces/ali-ghamdan/deoldify/fastai/vision/transform.py
deleted file mode 100644
index f6b444b542ec1ac5dc53d6337edf666597fecd17..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/vision/transform.py
+++ /dev/null
@@ -1,346 +0,0 @@
-"Image transformations for data augmentation. All transforms are done on the tensor level"
-from ..torch_core import *
-from .image import *
-from .image import _affine_mult
-
-__all__ = ['brightness', 'contrast', 'crop', 'crop_pad', 'cutout', 'dihedral', 'dihedral_affine', 'flip_affine', 'flip_lr',
-           'get_transforms', 'jitter', 'pad', 'perspective_warp', 'rand_pad', 'rand_crop', 'rand_zoom', 'rgb_randomize', 'rotate', 'skew', 'squish',
-           'rand_resize_crop', 'symmetric_warp', 'tilt', 'zoom', 'zoom_crop']
-
-_pad_mode_convert = {'reflection':'reflect', 'zeros':'constant', 'border':'replicate'}
-
-#NB: Although TfmLighting etc can be used as decorators, that doesn't work in Windows,
-#    so we do it manually for now.
-
-def _brightness(x, change:uniform):
-    "Apply `change` in brightness of image `x`."
-    return x.add_(scipy.special.logit(change))
-brightness = TfmLighting(_brightness)
-
-def _contrast(x, scale:log_uniform):
-    "Apply `scale` to contrast of image `x`."
-    return x.mul_(scale)
-contrast = TfmLighting(_contrast)
-
-def _rotate(degrees:uniform):
-    "Rotate image by `degrees`."
-    angle = degrees * math.pi / 180
-    return [[float(cos(angle)), float(-sin(angle)), 0.],
-            [float(sin(angle)),  float(cos(angle)), 0.],
-            [0.        ,  0.        , 1.]]
-rotate = TfmAffine(_rotate)
-
-def _get_zoom_mat(sw:float, sh:float, c:float, r:float)->AffineMatrix:
-    "`sw`,`sh` scale width,height - `c`,`r` focus col,row."
-    return [[sw, 0,  c],
-            [0, sh,  r],
-            [0,  0, 1.]]
-
-def _zoom(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
-    "Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
-    s = 1-1/scale
-    col_c = s * (2*col_pct - 1)
-    row_c = s * (2*row_pct - 1)
-    return _get_zoom_mat(1/scale, 1/scale, col_c, row_c)
-zoom = TfmAffine(_zoom)
-
-def _squish(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
-    "Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
-    if scale <= 1:
-        col_c = (1-scale) * (2*col_pct - 1)
-        return _get_zoom_mat(scale, 1, col_c, 0.)
-    else:
-        row_c = (1-1/scale) * (2*row_pct - 1)
-        return _get_zoom_mat(1, 1/scale, 0., row_c)
-squish = TfmAffine(_squish)
-
-def _jitter(c, magnitude:uniform):
-    "Replace pixels by random neighbors at `magnitude`."
-    c.flow.add_((torch.rand_like(c.flow)-0.5)*magnitude*2)
-    return c
-jitter = TfmCoord(_jitter)
-
-def _flip_lr(x):
-    "Flip `x` horizontally."
-    #return x.flip(2)
-    if isinstance(x, ImagePoints):
-        x.flow.flow[...,0] *= -1
-        return x
-    return tensor(np.ascontiguousarray(np.array(x)[...,::-1]))
-flip_lr = TfmPixel(_flip_lr)
-
-def _flip_affine() -> TfmAffine:
-    "Flip `x` horizontally."
-    return [[-1, 0, 0.],
-            [0,  1, 0],
-            [0,  0, 1.]]
-flip_affine = TfmAffine(_flip_affine)
-
-def _dihedral(x, k:partial(uniform_int,0,7)):
-    "Randomly flip `x` image based on `k`."
-    flips=[]
-    if k&1: flips.append(1)
-    if k&2: flips.append(2)
-    if flips: x = torch.flip(x,flips)
-    if k&4: x = x.transpose(1,2)
-    return x.contiguous()
-dihedral = TfmPixel(_dihedral)
-
-def _dihedral_affine(k:partial(uniform_int,0,7)):
-    "Randomly flip `x` image based on `k`."
-    x = -1 if k&1 else 1
-    y = -1 if k&2 else 1
-    if k&4: return [[0, x, 0.],
-                    [y, 0, 0],
-                    [0, 0, 1.]]
-    return [[x, 0, 0.],
-            [0, y, 0],
-            [0, 0, 1.]]
-dihedral_affine = TfmAffine(_dihedral_affine)
-
-def _pad_coord(x, row_pad:int, col_pad:int, mode='zeros'):
-    #TODO: implement other padding modes than zeros?
-    h,w = x.size
-    pad = torch.Tensor([w/(w + 2*col_pad), h/(h + 2*row_pad)])
-    x.flow = FlowField((h+2*row_pad, w+2*col_pad) , x.flow.flow * pad[None])
-    return x
-
-def _pad_default(x, padding:int, mode='reflection'):
-    "Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border')."
-    mode = _pad_mode_convert[mode]
-    return F.pad(x[None], (padding,)*4, mode=mode)[0]
-
-def _pad_image_points(x, padding:int, mode='reflection'):
-    return _pad_coord(x, padding, padding, mode)
-
-def _pad(x, padding:int, mode='reflection'):
-    f_pad = _pad_image_points if isinstance(x, ImagePoints) else  _pad_default
-    return f_pad(x, padding, mode)
-
-pad = TfmPixel(_pad, order=-10)
-
-def _cutout(x, n_holes:uniform_int=1, length:uniform_int=40):
-    "Cut out `n_holes` number of square holes of size `length` in image at random locations."
-    h,w = x.shape[1:]
-    for n in range(n_holes):
-        h_y = np.random.randint(0, h)
-        h_x = np.random.randint(0, w)
-        y1 = int(np.clip(h_y - length / 2, 0, h))
-        y2 = int(np.clip(h_y + length / 2, 0, h))
-        x1 = int(np.clip(h_x - length / 2, 0, w))
-        x2 = int(np.clip(h_x + length / 2, 0, w))
-        x[:, y1:y2, x1:x2] = 0
-    return x
-
-cutout = TfmPixel(_cutout, order=20)
-
-def _rgb_randomize(x, channel:int=None, thresh:float=0.3):
-    "Randomize one of the channels of the input image"
-    if channel is None: channel = np.random.randint(0, x.shape[0] - 1)
-    x[channel] = torch.rand(x.shape[1:]) * np.random.uniform(0, thresh)
-    return x
-
-rgb_randomize = TfmPixel(_rgb_randomize)
-
-def _minus_epsilon(row_pct:float, col_pct:float, eps:float=1e-7):
-    if row_pct==1.: row_pct -= 1e-7
-    if col_pct==1.: col_pct -= 1e-7
-    return row_pct,col_pct
-
-def _crop_default(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):
-    "Crop `x` to `size` pixels. `row_pct`,`col_pct` select focal point of crop."
-    rows,cols = tis2hw(size)
-    row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
-    row = int((x.size(1)-rows+1) * row_pct)
-    col = int((x.size(2)-cols+1) * col_pct)
-    return x[:, row:row+rows, col:col+cols].contiguous()
-
-def _crop_image_points(x, size, row_pct=0.5, col_pct=0.5):
-    h,w = x.size
-    rows,cols = tis2hw(size)
-    row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
-    x.flow.flow.mul_(torch.Tensor([w/cols, h/rows])[None])
-    row = int((h-rows+1) * row_pct)
-    col = int((w-cols+1) * col_pct)
-    x.flow.flow.add_(-1 + torch.Tensor([w/cols-2*col/cols, h/rows-2*row/rows])[None])
-    x.size = (rows, cols)
-    return x
-
-def _crop(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):
-    f_crop = _crop_image_points if isinstance(x, ImagePoints) else _crop_default
-    return f_crop(x, size, row_pct, col_pct)
-
-crop = TfmPixel(_crop)
-
-def _crop_pad_default(x, size, padding_mode='reflection', row_pct:uniform = 0.5, col_pct:uniform = 0.5):
-    "Crop and pad tfm - `row_pct`,`col_pct` sets focal point."
-    padding_mode = _pad_mode_convert[padding_mode]
-    size = tis2hw(size)
-    if x.shape[1:] == torch.Size(size): return x
-    rows,cols = size
-    row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
-    if x.size(1)<rows or x.size(2)<cols:
-        row_pad = max((rows-x.size(1)+1)//2, 0)
-        col_pad = max((cols-x.size(2)+1)//2, 0)
-        x = F.pad(x[None], (col_pad,col_pad,row_pad,row_pad), mode=padding_mode)[0]
-    row = int((x.size(1)-rows+1)*row_pct)
-    col = int((x.size(2)-cols+1)*col_pct)
-    x = x[:, row:row+rows, col:col+cols]
-    return x.contiguous() # without this, get NaN later - don't know why
-
-def _crop_pad_image_points(x, size, padding_mode='reflection', row_pct = 0.5, col_pct = 0.5):
-    size = tis2hw(size)
-    rows,cols = size
-    if x.size[0]<rows or x.size[1]<cols:
-        row_pad = max((rows-x.size[0]+1)//2, 0)
-        col_pad = max((cols-x.size[1]+1)//2, 0)
-        x = _pad_coord(x, row_pad, col_pad)
-    return crop(x,(rows,cols), row_pct, col_pct)
-
-def _crop_pad(x, size, padding_mode='reflection', row_pct:uniform = 0.5, col_pct:uniform = 0.5):
-    f_crop_pad = _crop_pad_image_points if isinstance(x, ImagePoints) else _crop_pad_default
-    return f_crop_pad(x, size, padding_mode, row_pct, col_pct)
-
-crop_pad = TfmCrop(_crop_pad)
-
-def _image_maybe_add_crop_pad(img, tfms):
-    tfm_names = [tfm.__name__ for tfm in tfms]
-    return [crop_pad()] + tfms if 'crop_pad' not in tfm_names else tfms
-Image._maybe_add_crop_pad = _image_maybe_add_crop_pad
-
-rand_pos = {'row_pct':(0,1), 'col_pct':(0,1)}
-
-def rand_pad(padding:int, size:int, mode:str='reflection'):
-    "Fixed `mode` `padding` and random crop of `size`"
-    return [pad(padding=padding,mode=mode),
-            crop(size=size, **rand_pos)]
-
-def rand_zoom(scale:uniform=1.0, p:float=1.):
-    "Randomized version of `zoom`."
-    return zoom(scale=scale, **rand_pos, p=p)
-
-def rand_crop(*args, padding_mode='reflection', p:float=1.):
-    "Randomized version of `crop_pad`."
-    return crop_pad(*args, **rand_pos, padding_mode=padding_mode, p=p)
-
-def zoom_crop(scale:float, do_rand:bool=False, p:float=1.0):
-    "Randomly zoom and/or crop."
-    zoom_fn = rand_zoom if do_rand else zoom
-    crop_fn = rand_crop if do_rand else crop_pad
-    return [zoom_fn(scale=scale, p=p), crop_fn()]
-
-def _find_coeffs(orig_pts:Points, targ_pts:Points)->Tensor:
-    "Find 8 coeff mentioned [here](https://web.archive.org/web/20150222120106/xenia.media.mit.edu/~cwren/interpolator/)."
-    matrix = []
-    #The equations we'll need to solve.
-    for p1, p2 in zip(targ_pts, orig_pts):
-        matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])
-        matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])
-
-    A = FloatTensor(matrix)
-    B = FloatTensor(orig_pts).view(8, 1)
-    #The 8 scalars we seek are solution of AX = B
-    return torch.linalg.solve(A,B)[:,0]
-
-def _apply_perspective(coords:FlowField, coeffs:Points)->FlowField:
-    "Transform `coords` with `coeffs`."
-    size = coords.flow.size()
-    #compress all the dims expect the last one ang adds ones, coords become N * 3
-    coords.flow = coords.flow.view(-1,2)
-    #Transform the coeffs in a 3*3 matrix with a 1 at the bottom left
-    coeffs = torch.cat([coeffs, FloatTensor([1])]).view(3,3)
-    coords.flow = torch.addmm(coeffs[:,2], coords.flow, coeffs[:,:2].t())
-    coords.flow.mul_(1/coords.flow[:,2].unsqueeze(1))
-    coords.flow = coords.flow[:,:2].view(size)
-    return coords
-
-_orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
-
-def _do_perspective_warp(c:FlowField, targ_pts:Points, invert=False):
-    "Apply warp to `targ_pts` from `_orig_pts` to `c` `FlowField`."
-    if invert: return _apply_perspective(c, _find_coeffs(targ_pts, _orig_pts))
-    return _apply_perspective(c, _find_coeffs(_orig_pts, targ_pts))
-
-def _perspective_warp(c, magnitude:partial(uniform,size=8)=0, invert=False):
-    "Apply warp of `magnitude` to `c`."
-    magnitude = magnitude.view(4,2)
-    targ_pts = [[x+m for x,m in zip(xs, ms)] for xs, ms in zip(_orig_pts, magnitude)]
-    return _do_perspective_warp(c, targ_pts, invert)
-perspective_warp = TfmCoord(_perspective_warp)
-
-def _symmetric_warp(c, magnitude:partial(uniform,size=4)=0, invert=False):
-    "Apply symmetric warp of `magnitude` to `c`."
-    m = listify(magnitude, 4)
-    targ_pts = [[-1-m[3],-1-m[1]], [-1-m[2],1+m[1]], [1+m[3],-1-m[0]], [1+m[2],1+m[0]]]
-    return _do_perspective_warp(c, targ_pts, invert)
-symmetric_warp = TfmCoord(_symmetric_warp)
-
-def _tilt(c, direction:uniform_int, magnitude:uniform=0, invert=False):
-    "Tilt `c` field with random `direction` and `magnitude`."
-    orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
-    if direction == 0:   targ_pts = [[-1,-1], [-1,1], [1,-1-magnitude], [1,1+magnitude]]
-    elif direction == 1: targ_pts = [[-1,-1-magnitude], [-1,1+magnitude], [1,-1], [1,1]]
-    elif direction == 2: targ_pts = [[-1,-1], [-1-magnitude,1], [1,-1], [1+magnitude,1]]
-    elif direction == 3: targ_pts = [[-1-magnitude,-1], [-1,1], [1+magnitude,-1], [1,1]]
-    coeffs = _find_coeffs(targ_pts, _orig_pts) if invert else _find_coeffs(_orig_pts, targ_pts)
-    return _apply_perspective(c, coeffs)
-tilt = TfmCoord(_tilt)
-
-def _skew(c, direction:uniform_int, magnitude:uniform=0, invert=False):
-    "Skew `c` field with random `direction` and `magnitude`."
-    orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
-    if direction == 0:   targ_pts = [[-1-magnitude,-1], [-1,1], [1,-1], [1,1]]
-    elif direction == 1: targ_pts = [[-1,-1-magnitude], [-1,1], [1,-1], [1,1]]
-    elif direction == 2: targ_pts = [[-1,-1], [-1-magnitude,1], [1,-1], [1,1]]
-    elif direction == 3: targ_pts = [[-1,-1], [-1,1+magnitude], [1,-1], [1,1]]
-    elif direction == 4: targ_pts = [[-1,-1], [-1,1], [1+magnitude,-1], [1,1]]
-    elif direction == 5: targ_pts = [[-1,-1], [-1,1], [1,-1-magnitude], [1,1]]
-    elif direction == 6: targ_pts = [[-1,-1], [-1,1], [1,-1], [1+magnitude,1]]
-    elif direction == 7: targ_pts = [[-1,-1], [-1,1], [1,-1], [1,1+magnitude]]
-    coeffs = _find_coeffs(targ_pts, _orig_pts) if invert else _find_coeffs(_orig_pts, targ_pts)
-    return _apply_perspective(c, coeffs)
-skew = TfmCoord(_skew)
-
-def get_transforms(do_flip:bool=True, flip_vert:bool=False, max_rotate:float=10., max_zoom:float=1.1,
-                   max_lighting:float=0.2, max_warp:float=0.2, p_affine:float=0.75,
-                   p_lighting:float=0.75, xtra_tfms:Optional[Collection[Transform]]=None)->Collection[Transform]:
-    "Utility func to easily create a list of flip, rotate, `zoom`, warp, lighting transforms."
-    res = [rand_crop()]
-    if do_flip:    res.append(dihedral_affine() if flip_vert else flip_lr(p=0.5))
-    if max_warp:   res.append(symmetric_warp(magnitude=(-max_warp,max_warp), p=p_affine))
-    if max_rotate: res.append(rotate(degrees=(-max_rotate,max_rotate), p=p_affine))
-    if max_zoom>1: res.append(rand_zoom(scale=(1.,max_zoom), p=p_affine))
-    if max_lighting:
-        res.append(brightness(change=(0.5*(1-max_lighting), 0.5*(1+max_lighting)), p=p_lighting))
-        res.append(contrast(scale=(1-max_lighting, 1/(1-max_lighting)), p=p_lighting))
-    #       train                   , valid
-    return (res + listify(xtra_tfms), [crop_pad()])
-
-def _compute_zs_mat(sz:TensorImageSize, scale:float, squish:float,
-                   invert:bool, row_pct:float, col_pct:float)->AffineMatrix:
-    "Utility routine to compute zoom/squish matrix."
-    orig_ratio = math.sqrt(sz[1]/sz[0])
-    for s,r,i in zip(scale,squish, invert):
-        s,r = 1/math.sqrt(s),math.sqrt(r)
-        if s * r <= 1 and s / r <= 1: #Test if we are completely inside the picture
-            w,h = (s/r, s*r) if i else (s*r,s/r)
-            col_c = (1-w) * (2*col_pct - 1)
-            row_c = (1-h) * (2*row_pct - 1)
-            return _get_zoom_mat(w, h, col_c, row_c)
-
-    #Fallback, hack to emulate a center crop without cropping anything yet.
-    if orig_ratio > 1: return _get_zoom_mat(1/orig_ratio**2, 1, 0, 0.)
-    else:              return _get_zoom_mat(1, orig_ratio**2, 0, 0.)
-
-def _zoom_squish(c, scale:uniform=1.0, squish:uniform=1.0, invert:rand_bool=False,
-                row_pct:uniform=0.5, col_pct:uniform=0.5):
-    #This is intended for scale, squish and invert to be of size 10 (or whatever) so that the transform
-    #can try a few zoom/squishes before falling back to center crop (like torchvision.RandomResizedCrop)
-    m = _compute_zs_mat(c.size, scale, squish, invert, row_pct, col_pct)
-    return _affine_mult(c, FloatTensor(m))
-zoom_squish = TfmCoord(_zoom_squish)
-
-def rand_resize_crop(size:int, max_scale:float=2., ratios:Tuple[float,float]=(0.75,1.33)):
-    "Randomly resize and crop the image to a ratio in `ratios` after a zoom of `max_scale`."
-    return [zoom_squish(scale=(1.,max_scale,8), squish=(*ratios,8), invert=(0.5,8), row_pct=(0.,1.), col_pct=(0.,1.)),
-            crop(size=size)]
diff --git a/spaces/ali-ghamdan/gfp-Gans/gfpgan/archs/stylegan2_clean_arch.py b/spaces/ali-ghamdan/gfp-Gans/gfpgan/archs/stylegan2_clean_arch.py
deleted file mode 100644
index 9e2ee94e50401b95e4c9997adef5581d521d725f..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/gfp-Gans/gfpgan/archs/stylegan2_clean_arch.py
+++ /dev/null
@@ -1,368 +0,0 @@
-import math
-import random
-import torch
-from basicsr.archs.arch_util import default_init_weights
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-
-class NormStyleCode(nn.Module):
-
-    def forward(self, x):
-        """Normalize the style codes.
-
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-
-    There is no bias in ModulatedConv2d.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-        eps (float): A value added to the denominator for numerical stability. Default: 1e-8.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 num_style_feat,
-                 demodulate=True,
-                 sample_mode=None,
-                 eps=1e-8):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        # modulation inside each modulated conv
-        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
-        # initialization
-        default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
-            math.sqrt(in_channels * kernel_size**2))
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
-
-        # upsample or downsample if necessary
-        if self.sample_mode == 'upsample':
-            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
-        elif self.sample_mode == 'downsample':
-            x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, '
-                f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})')
-
-
-class StyleConv(nn.Module):
-    """Style conv used in StyleGAN2.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-    """
-
-    def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
-        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # add bias
-        out = out + self.bias
-        # activation
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB (image space) from features.
-
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(self, in_channels, num_style_feat, upsample=True):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.modulated_conv = ModulatedConv2d(
-            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-@ARCH_REGISTRY.register()
-class StyleGAN2GeneratorClean(nn.Module):
-    """Clean version of StyleGAN2 Generator.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1):
-        super(StyleGAN2GeneratorClean, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.extend(
-                [nn.Linear(num_style_feat, num_style_feat, bias=True),
-                 nn.LeakyReLU(negative_slope=0.2, inplace=True)])
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-        # initialization
-        default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu')
-
-        # channel list
-        channels = {
-            '4': int(512 * narrow),
-            '8': int(512 * narrow),
-            '16': int(512 * narrow),
-            '32': int(512 * narrow),
-            '64': int(256 * channel_multiplier * narrow),
-            '128': int(128 * channel_multiplier * narrow),
-            '256': int(64 * channel_multiplier * narrow),
-            '512': int(32 * channel_multiplier * narrow),
-            '1024': int(16 * channel_multiplier * narrow)
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels['4'], size=4)
-        self.style_conv1 = StyleConv(
-            channels['4'],
-            channels['4'],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None)
-        self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False)
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels['4']
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2**((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode='upsample'))
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None))
-            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(self,
-                styles,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorClean.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
diff --git a/spaces/all-things-vits/CLIPGroundingExplainability/clip_grounding/utils/paths.py b/spaces/all-things-vits/CLIPGroundingExplainability/clip_grounding/utils/paths.py
deleted file mode 100644
index 0a405d4faf9b58ef5e2c7d60f70971894e9ceb96..0000000000000000000000000000000000000000
--- a/spaces/all-things-vits/CLIPGroundingExplainability/clip_grounding/utils/paths.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""Path helpers for the relfm project."""
-from os.path import join, abspath, dirname
-
-
-REPO_PATH = dirname(dirname(dirname(abspath(__file__))))
-DATA_ROOT = join(REPO_PATH, "data")
-
-DATASET_ROOTS = {
-    "PNG": join(DATA_ROOT, "panoptic_narrative_grounding"),
-}
diff --git a/spaces/allknowingroger/huggingface/assets/index-2ec67419.js b/spaces/allknowingroger/huggingface/assets/index-2ec67419.js
deleted file mode 100644
index 7a337577ec608ff9ec3f54efda2234fafb2cb68b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/huggingface/assets/index-2ec67419.js
+++ /dev/null
@@ -1,41 +0,0 @@
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- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var tr=Symbol.for("react.element"),Uc=Symbol.for("react.portal"),Vc=Symbol.for("react.fragment"),Bc=Symbol.for("react.strict_mode"),Qc=Symbol.for("react.profiler"),Hc=Symbol.for("react.provider"),Wc=Symbol.for("react.context"),Kc=Symbol.for("react.forward_ref"),Yc=Symbol.for("react.suspense"),Xc=Symbol.for("react.memo"),Gc=Symbol.for("react.lazy"),Qo=Symbol.iterator;function Zc(e){return e===null||typeof e!="object"?null:(e=Qo&&e[Qo]||e["@@iterator"],typeof e=="function"?e:null)}var ts={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},ns=Object.assign,rs={};function cn(e,t,n){this.props=e,this.context=t,this.refs=rs,this.updater=n||ts}cn.prototype.isReactComponent={};cn.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};cn.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function ls(){}ls.prototype=cn.prototype;function Wi(e,t,n){this.props=e,this.context=t,this.refs=rs,this.updater=n||ts}var Ki=Wi.prototype=new ls;Ki.constructor=Wi;ns(Ki,cn.prototype);Ki.isPureReactComponent=!0;var Ho=Array.isArray,is=Object.prototype.hasOwnProperty,Yi={current:null},os={key:!0,ref:!0,__self:!0,__source:!0};function us(e,t,n){var r,l={},i=null,o=null;if(t!=null)for(r in t.ref!==void 0&&(o=t.ref),t.key!==void 0&&(i=""+t.key),t)is.call(t,r)&&!os.hasOwnProperty(r)&&(l[r]=t[r]);var u=arguments.length-2;if(u===1)l.children=n;else if(1<u){for(var s=Array(u),c=0;c<u;c++)s[c]=arguments[c+2];l.children=s}if(e&&e.defaultProps)for(r in u=e.defaultProps,u)l[r]===void 0&&(l[r]=u[r]);return{$$typeof:tr,type:e,key:i,ref:o,props:l,_owner:Yi.current}}function qc(e,t){return{$$typeof:tr,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function Xi(e){return typeof e=="object"&&e!==null&&e.$$typeof===tr}function Jc(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var Wo=/\/+/g;function _l(e,t){return typeof e=="object"&&e!==null&&e.key!=null?Jc(""+e.key):t.toString(36)}function jr(e,t,n,r,l){var i=typeof e;(i==="undefined"||i==="boolean")&&(e=null);var o=!1;if(e===null)o=!0;else switch(i){case"string":case"number":o=!0;break;case"object":switch(e.$$typeof){case tr:case Uc:o=!0}}if(o)return o=e,l=l(o),e=r===""?"."+_l(o,0):r,Ho(l)?(n="",e!=null&&(n=e.replace(Wo,"$&/")+"/"),jr(l,t,n,"",function(c){return c})):l!=null&&(Xi(l)&&(l=qc(l,n+(!l.key||o&&o.key===l.key?"":(""+l.key).replace(Wo,"$&/")+"/")+e)),t.push(l)),1;if(o=0,r=r===""?".":r+":",Ho(e))for(var u=0;u<e.length;u++){i=e[u];var s=r+_l(i,u);o+=jr(i,t,n,s,l)}else if(s=Zc(e),typeof s=="function")for(e=s.call(e),u=0;!(i=e.next()).done;)i=i.value,s=r+_l(i,u++),o+=jr(i,t,n,s,l);else if(i==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). 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- * @license React
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diff --git a/spaces/almakedon/faster-whisper-webui/src/segments.py b/spaces/almakedon/faster-whisper-webui/src/segments.py
deleted file mode 100644
index ec2650dceade5d0b2022264f6419115eab085aea..0000000000000000000000000000000000000000
--- a/spaces/almakedon/faster-whisper-webui/src/segments.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Any, Dict, List
-
-import copy
-
-def merge_timestamps(timestamps: List[Dict[str, Any]], merge_window: float = 5, max_merge_size: float = 30, padding_left: float = 1, padding_right: float = 1):
-    result = []
-
-    if len(timestamps) == 0:
-        return result
-    if max_merge_size is None:
-        return timestamps
-
-    if padding_left is None:
-        padding_left = 0
-    if padding_right is None:
-        padding_right = 0
-
-    processed_time = 0
-    current_segment = None
-
-    for i in range(len(timestamps)):
-        next_segment = timestamps[i]
-
-        delta = next_segment['start'] - processed_time
-
-        # Note that segments can still be longer than the max merge size, they just won't be merged in that case
-        if current_segment is None or (merge_window is not None and delta > merge_window) \
-                 or next_segment['end'] - current_segment['start'] > max_merge_size:
-            # Finish the current segment
-            if current_segment is not None:
-                # Add right padding
-                finish_padding = min(padding_right, delta / 2) if delta < padding_left + padding_right else padding_right
-                current_segment['end'] += finish_padding
-                delta -= finish_padding
-
-                result.append(current_segment)
-
-            # Start a new segment
-            current_segment = copy.deepcopy(next_segment)
-
-            # Pad the segment
-            current_segment['start'] = current_segment['start'] - min(padding_left, delta)
-            processed_time = current_segment['end']
-
-        else:
-            # Merge the segment
-            current_segment['end'] = next_segment['end']
-            processed_time = current_segment['end']
-        
-    # Add the last segment
-    if current_segment is not None:
-        current_segment['end'] += padding_right
-        result.append(current_segment)
-    
-    return result
\ No newline at end of file
diff --git a/spaces/amankishore/sjc/guided_diffusion/fp16_util.py b/spaces/amankishore/sjc/guided_diffusion/fp16_util.py
deleted file mode 100644
index d599568f3197bcc236e9ae617829fa060640795f..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/guided_diffusion/fp16_util.py
+++ /dev/null
@@ -1,237 +0,0 @@
-"""
-Helpers to train with 16-bit precision.
-"""
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
-
-# from . import logger
-
-INITIAL_LOG_LOSS_SCALE = 20.0
-
-
-def convert_module_to_f16(l):
-    """
-    Convert primitive modules to float16.
-    """
-    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
-        l.weight.data = l.weight.data.half()
-        if l.bias is not None:
-            l.bias.data = l.bias.data.half()
-
-
-def convert_module_to_f32(l):
-    """
-    Convert primitive modules to float32, undoing convert_module_to_f16().
-    """
-    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
-        l.weight.data = l.weight.data.float()
-        if l.bias is not None:
-            l.bias.data = l.bias.data.float()
-
-
-def make_master_params(param_groups_and_shapes):
-    """
-    Copy model parameters into a (differently-shaped) list of full-precision
-    parameters.
-    """
-    master_params = []
-    for param_group, shape in param_groups_and_shapes:
-        master_param = nn.Parameter(
-            _flatten_dense_tensors(
-                [param.detach().float() for (_, param) in param_group]
-            ).view(shape)
-        )
-        master_param.requires_grad = True
-        master_params.append(master_param)
-    return master_params
-
-
-def model_grads_to_master_grads(param_groups_and_shapes, master_params):
-    """
-    Copy the gradients from the model parameters into the master parameters
-    from make_master_params().
-    """
-    for master_param, (param_group, shape) in zip(
-        master_params, param_groups_and_shapes
-    ):
-        master_param.grad = _flatten_dense_tensors(
-            [param_grad_or_zeros(param) for (_, param) in param_group]
-        ).view(shape)
-
-
-def master_params_to_model_params(param_groups_and_shapes, master_params):
-    """
-    Copy the master parameter data back into the model parameters.
-    """
-    # Without copying to a list, if a generator is passed, this will
-    # silently not copy any parameters.
-    for master_param, (param_group, _) in zip(master_params, param_groups_and_shapes):
-        for (_, param), unflat_master_param in zip(
-            param_group, unflatten_master_params(param_group, master_param.view(-1))
-        ):
-            param.detach().copy_(unflat_master_param)
-
-
-def unflatten_master_params(param_group, master_param):
-    return _unflatten_dense_tensors(master_param, [param for (_, param) in param_group])
-
-
-def get_param_groups_and_shapes(named_model_params):
-    named_model_params = list(named_model_params)
-    scalar_vector_named_params = (
-        [(n, p) for (n, p) in named_model_params if p.ndim <= 1],
-        (-1),
-    )
-    matrix_named_params = (
-        [(n, p) for (n, p) in named_model_params if p.ndim > 1],
-        (1, -1),
-    )
-    return [scalar_vector_named_params, matrix_named_params]
-
-
-def master_params_to_state_dict(
-    model, param_groups_and_shapes, master_params, use_fp16
-):
-    if use_fp16:
-        state_dict = model.state_dict()
-        for master_param, (param_group, _) in zip(
-            master_params, param_groups_and_shapes
-        ):
-            for (name, _), unflat_master_param in zip(
-                param_group, unflatten_master_params(param_group, master_param.view(-1))
-            ):
-                assert name in state_dict
-                state_dict[name] = unflat_master_param
-    else:
-        state_dict = model.state_dict()
-        for i, (name, _value) in enumerate(model.named_parameters()):
-            assert name in state_dict
-            state_dict[name] = master_params[i]
-    return state_dict
-
-
-def state_dict_to_master_params(model, state_dict, use_fp16):
-    if use_fp16:
-        named_model_params = [
-            (name, state_dict[name]) for name, _ in model.named_parameters()
-        ]
-        param_groups_and_shapes = get_param_groups_and_shapes(named_model_params)
-        master_params = make_master_params(param_groups_and_shapes)
-    else:
-        master_params = [state_dict[name] for name, _ in model.named_parameters()]
-    return master_params
-
-
-def zero_master_grads(master_params):
-    for param in master_params:
-        param.grad = None
-
-
-def zero_grad(model_params):
-    for param in model_params:
-        # Taken from https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer.add_param_group
-        if param.grad is not None:
-            param.grad.detach_()
-            param.grad.zero_()
-
-
-def param_grad_or_zeros(param):
-    if param.grad is not None:
-        return param.grad.data.detach()
-    else:
-        return th.zeros_like(param)
-
-
-class MixedPrecisionTrainer:
-    def __init__(
-        self,
-        *,
-        model,
-        use_fp16=False,
-        fp16_scale_growth=1e-3,
-        initial_lg_loss_scale=INITIAL_LOG_LOSS_SCALE,
-    ):
-        self.model = model
-        self.use_fp16 = use_fp16
-        self.fp16_scale_growth = fp16_scale_growth
-
-        self.model_params = list(self.model.parameters())
-        self.master_params = self.model_params
-        self.param_groups_and_shapes = None
-        self.lg_loss_scale = initial_lg_loss_scale
-
-        if self.use_fp16:
-            self.param_groups_and_shapes = get_param_groups_and_shapes(
-                self.model.named_parameters()
-            )
-            self.master_params = make_master_params(self.param_groups_and_shapes)
-            self.model.convert_to_fp16()
-
-    def zero_grad(self):
-        zero_grad(self.model_params)
-
-    def backward(self, loss: th.Tensor):
-        if self.use_fp16:
-            loss_scale = 2 ** self.lg_loss_scale
-            (loss * loss_scale).backward()
-        else:
-            loss.backward()
-
-    def optimize(self, opt: th.optim.Optimizer):
-        if self.use_fp16:
-            return self._optimize_fp16(opt)
-        else:
-            return self._optimize_normal(opt)
-
-    def _optimize_fp16(self, opt: th.optim.Optimizer):
-        logger.logkv_mean("lg_loss_scale", self.lg_loss_scale)
-        model_grads_to_master_grads(self.param_groups_and_shapes, self.master_params)
-        grad_norm, param_norm = self._compute_norms(grad_scale=2 ** self.lg_loss_scale)
-        if check_overflow(grad_norm):
-            self.lg_loss_scale -= 1
-            logger.log(f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}")
-            zero_master_grads(self.master_params)
-            return False
-
-        logger.logkv_mean("grad_norm", grad_norm)
-        logger.logkv_mean("param_norm", param_norm)
-
-        for p in self.master_params:
-            p.grad.mul_(1.0 / (2 ** self.lg_loss_scale))
-        opt.step()
-        zero_master_grads(self.master_params)
-        master_params_to_model_params(self.param_groups_and_shapes, self.master_params)
-        self.lg_loss_scale += self.fp16_scale_growth
-        return True
-
-    def _optimize_normal(self, opt: th.optim.Optimizer):
-        grad_norm, param_norm = self._compute_norms()
-        logger.logkv_mean("grad_norm", grad_norm)
-        logger.logkv_mean("param_norm", param_norm)
-        opt.step()
-        return True
-
-    def _compute_norms(self, grad_scale=1.0):
-        grad_norm = 0.0
-        param_norm = 0.0
-        for p in self.master_params:
-            with th.no_grad():
-                param_norm += th.norm(p, p=2, dtype=th.float32).item() ** 2
-                if p.grad is not None:
-                    grad_norm += th.norm(p.grad, p=2, dtype=th.float32).item() ** 2
-        return np.sqrt(grad_norm) / grad_scale, np.sqrt(param_norm)
-
-    def master_params_to_state_dict(self, master_params):
-        return master_params_to_state_dict(
-            self.model, self.param_groups_and_shapes, master_params, self.use_fp16
-        )
-
-    def state_dict_to_master_params(self, state_dict):
-        return state_dict_to_master_params(self.model, state_dict, self.use_fp16)
-
-
-def check_overflow(value):
-    return (value == float("inf")) or (value == -float("inf")) or (value != value)
diff --git a/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/datasets/png_utils.py b/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/datasets/png_utils.py
deleted file mode 100644
index 277d9d8f2071236d41c83ec9c8e7c29cc321cee3..0000000000000000000000000000000000000000
--- a/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/datasets/png_utils.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""Helper functions for Panoptic Narrative Grounding."""
-
-import os
-from os.path import join, isdir, exists
-from typing import List
-
-import torch
-from PIL import Image
-from skimage import io
-import numpy as np
-import textwrap
-import matplotlib.pyplot as plt
-from matplotlib import transforms
-from imgaug.augmentables.segmaps import SegmentationMapsOnImage
-
-
-def rainbow_text(x,y,ls,lc,fig, ax,**kw):
-    """
-    Take a list of strings ``ls`` and colors ``lc`` and place them next to each
-    other, with text ls[i] being shown in color lc[i].
-    
-    Ref: https://stackoverflow.com/questions/9169052/partial-coloring-of-text-in-matplotlib
-    """
-    t = ax.transAxes
-
-    for s,c in zip(ls,lc):
-        
-        text = ax.text(x,y,s+" ",color=c, transform=t, **kw)
-        text.draw(fig.canvas.get_renderer())
-        ex = text.get_window_extent()
-        t = transforms.offset_copy(text._transform, x=ex.width, units='dots')
-
-
-def find_first_index_greater_than(elements, key):
-    return next(x[0] for x in enumerate(elements) if x[1] > key)
-
-
-def split_caption_phrases(caption_phrases, colors, max_char_in_a_line=50):
-    char_lengths = np.cumsum([len(x) for x in caption_phrases])
-    thresholds = [max_char_in_a_line * i for i in range(1, 1 + char_lengths[-1] // max_char_in_a_line)]
-
-    utt_per_line = []
-    col_per_line = []
-    start_index = 0
-    for t in thresholds:
-        index = find_first_index_greater_than(char_lengths, t)
-        utt_per_line.append(caption_phrases[start_index:index])
-        col_per_line.append(colors[start_index:index])
-        start_index = index
-
-    return utt_per_line, col_per_line
-
-
-def show_image_and_caption(image: Image, caption_phrases: list, colors: list = None):
-
-    if colors is None:
-        colors = ["black" for _ in range(len(caption_phrases))]
-
-    fig, axes = plt.subplots(1, 2, figsize=(15, 4))
-
-    ax = axes[0]
-    ax.imshow(image)
-    ax.set_xticks([])
-    ax.set_yticks([])
-
-    ax = axes[1]
-    utt_per_line, col_per_line = split_caption_phrases(caption_phrases, colors, max_char_in_a_line=50)
-    y = 0.7
-    for U, C in zip(utt_per_line, col_per_line):
-        rainbow_text(
-            0., y,
-            U,
-            C,
-            size=15, ax=ax, fig=fig,
-            horizontalalignment='left',
-            verticalalignment='center',
-        )
-        y -= 0.11
-
-    ax.axis("off")
-
-    fig.tight_layout()
-    plt.show()
-
-
-def show_images_and_caption(
-        images: List,
-        caption_phrases: list,
-        colors: list = None,
-        image_xlabels: List=[],
-        figsize=None,
-        show=False,
-        xlabelsize=14,
-    ):
-
-    if colors is None:
-        colors = ["black" for _ in range(len(caption_phrases))]
-    caption_phrases[0] = caption_phrases[0].capitalize()
-
-    if figsize is None:
-        figsize = (5 * len(images) + 8, 4)
-    
-    if image_xlabels is None:
-        image_xlabels = ["" for _ in range(len(images))]
-
-    fig, axes = plt.subplots(1, len(images) + 1, figsize=figsize)
-
-    for i, image in enumerate(images):
-        ax = axes[i]
-        ax.imshow(image)
-        ax.set_xticks([])
-        ax.set_yticks([])
-        ax.set_xlabel(image_xlabels[i], fontsize=xlabelsize)
-
-    ax = axes[-1]
-    utt_per_line, col_per_line = split_caption_phrases(caption_phrases, colors, max_char_in_a_line=40)
-    y = 0.7
-    for U, C in zip(utt_per_line, col_per_line):
-        rainbow_text(
-            0., y,
-            U,
-            C,
-            size=23, ax=ax, fig=fig,
-            horizontalalignment='left',
-            verticalalignment='center',
-            # weight='bold'
-        )
-        y -= 0.11
-
-    ax.axis("off")
-
-    fig.tight_layout()
-    
-    if show:
-        plt.show()
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/delightful_tts/train_delightful_tts.py b/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/delightful_tts/train_delightful_tts.py
deleted file mode 100644
index 81e40c84aed772f768f91218b45446e669c2aa91..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/delightful_tts/train_delightful_tts.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.delightful_tts import DelightfulTTS, DelightfulTtsArgs, VocoderConfig
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio.processor import AudioProcessor
-
-data_path = ""
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-dataset_config = BaseDatasetConfig(
-    dataset_name="ljspeech", formatter="ljspeech", meta_file_train="metadata.csv", path=data_path
-)
-
-audio_config = DelightfulTtsAudioConfig()
-model_args = DelightfulTtsArgs()
-
-vocoder_config = VocoderConfig()
-
-delightful_tts_config = DelightfulTTSConfig(
-    run_name="delightful_tts_ljspeech",
-    run_description="Train like in delightful tts paper.",
-    model_args=model_args,
-    audio=audio_config,
-    vocoder=vocoder_config,
-    batch_size=32,
-    eval_batch_size=16,
-    num_loader_workers=10,
-    num_eval_loader_workers=10,
-    precompute_num_workers=10,
-    batch_group_size=2,
-    compute_input_seq_cache=True,
-    compute_f0=True,
-    f0_cache_path=os.path.join(output_path, "f0_cache"),
-    run_eval=True,
-    test_delay_epochs=-1,
-    epochs=1000,
-    text_cleaner="english_cleaners",
-    use_phonemes=True,
-    phoneme_language="en-us",
-    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
-    print_step=50,
-    print_eval=False,
-    mixed_precision=True,
-    output_path=output_path,
-    datasets=[dataset_config],
-    start_by_longest=False,
-    eval_split_size=0.1,
-    binary_align_loss_alpha=0.0,
-    use_attn_priors=False,
-    lr_gen=4e-1,
-    lr=4e-1,
-    lr_disc=4e-1,
-    max_text_len=130,
-)
-
-tokenizer, config = TTSTokenizer.init_from_config(delightful_tts_config)
-
-ap = AudioProcessor.init_from_config(config)
-
-
-train_samples, eval_samples = load_tts_samples(
-    dataset_config,
-    eval_split=True,
-    eval_split_max_size=config.eval_split_max_size,
-    eval_split_size=config.eval_split_size,
-)
-
-model = DelightfulTTS(ap=ap, config=config, tokenizer=tokenizer, speaker_manager=None)
-
-trainer = Trainer(
-    TrainerArgs(),
-    config,
-    output_path,
-    model=model,
-    train_samples=train_samples,
-    eval_samples=eval_samples,
-)
-
-trainer.fit()
diff --git a/spaces/arundevops47/chatbot-with-langchain-and-pinecone/app.py b/spaces/arundevops47/chatbot-with-langchain-and-pinecone/app.py
deleted file mode 100644
index e11ae901aca8a392bc3e598eda9b6b839bcbdec6..0000000000000000000000000000000000000000
--- a/spaces/arundevops47/chatbot-with-langchain-and-pinecone/app.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import streamlit as st
-import pinecone
-from langchain.llms import OpenAI
-from langchain.prompts import PromptTemplate
-from langchain.vectorstores import Pinecone
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.chains.question_answering import load_qa_chain
-from langchain.memory import ConversationBufferMemory, CombinedMemory, ConversationKGMemory
-
-if "generated" not in st.session_state:
-    st.session_state["generated"] = []
-if "past" not in st.session_state:
-    st.session_state["past"] = []
-if "input" not in st.session_state:
-    st.session_state["input"] = ""
-if "stored_session" not in st.session_state:
-    st.session_state["stored_session"] = []
-
-# Define function to get user input
-def get_text():
-    """
-    Get user input text.
-    Returns:
-        str: The text entered by the user.
-    """
-    input_text = st.text_input("You:", st.session_state["input"], key="input",
-                               placeholder="Enter your message here...", label_visibility='hidden')
-    return input_text
-
-def reset_entity_memory():
-    """
-    Resets the entity memory to its initial state.
-    """
-    st.session_state["entity_memory"] = CombinedMemory(memories=[KG, CBM])
-
-def new_chat():
-    """
-    Clears session state and starts a new chat.
-    """
-    save = []
-    for i in range(len(st.session_state['generated'])-1, -1, -1):
-        save.append("User:" + st.session_state["past"][i])
-        save.append("Bot:" + st.session_state["generated"][i]['output_text'])      
-    st.session_state["stored_session"].append(save)
-    st.session_state["generated"] = []
-    st.session_state["past"] = []
-    st.session_state["input"] = ""
-    if "entity_memory" in st.session_state:
-        reset_entity_memory()
-
-# Set up the Streamlit app layout
-st.title("ChatBot with Pinecone")
-st.markdown(
-        ''' 
-        > :black[**A Chat Bot that queries your own corpus in Pinecone.**]
-        ''')
-# st.markdown(" > Powered by -  🦜 LangChain + OpenAI + Streamlit")
-
-openai_api = st.sidebar.text_input("OpenAI API Key", type="password")
-pinecone_api = st.sidebar.text_input("Pinecone API Key", type="password")
-pinecone_env = st.sidebar.text_input("Pinecone Environment")
-pinecone_index = st.sidebar.text_input("Pinecone Index")
-MODEL = st.sidebar.selectbox("Model", ["gpt-4","gpt-3.5-turbo", "text-davinci-003"])
-
-if openai_api and pinecone_api and pinecone_env and pinecone_index:
-
-    # Create Pinecone Instance
-    pinecone.init(api_key=pinecone_api, environment=pinecone_env)
-
-    # Create OpenAI Instance
-    llm = OpenAI(
-        temperature=0, 
-        openai_api_key=openai_api,
-        model_name=MODEL,
-        max_tokens=2500,
-    )
-    
-    # Create a ConversationEntityMemory object if not already created
-    if 'entity_memory' not in st.session_state:
-            KG = ConversationKGMemory(llm=llm, input_key="human_input")
-            CBM = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
-            st.session_state["entity_memory"] = CombinedMemory(memories=[KG, CBM])
-    
-
-    # Set Template
-    # template = """You are a chatbot having a conversation with a human.
-    # Given the following extracted parts of a long document and a question, create a final answer.
-    # {context}
-    # Human: {human_input}
-    # Chatbot:"""
-
-    template = """As an advanced AI chatbot, you are engaged in a detailed conversation with a human user. You have access to a vast knowledge base stored in Pinecone's vector database. Your task is to leverage this information to provide comprehensive, accurate, and helpful responses. Given the following segments extracted from an extensive document and a user's question, analyze the context, draw specific information from the Pinecone memory, and formulate a well-rounded answer that not only addresses the query but also provides additional relevant information that could be beneficial to the user.
-    Context: {context}
-    Human: {human_input}
-    Your Response as Chatbot:"""
-
-    # Set the Prompt
-    prompt = PromptTemplate(
-        input_variables=["human_input", "context"], 
-        template=template
-    )
-            
-    # Get Context
-    embeddings = OpenAIEmbeddings(openai_api_key=openai_api)
-    docsearch = Pinecone.from_existing_index(pinecone_index, embedding=embeddings)
-    # Get user input
-
-else:
-    st.error("Please enter your API keys in the sidebar.")
-
-input_text = get_text()
-
-if input_text:
-    # Fetch docs using user input for cosine similarity
-    docs = docsearch.similarity_search(input_text, k=6)
-
-    # # Get Response
-    chain = load_qa_chain(OpenAI(temperature=0, openai_api_key=openai_api), chain_type="stuff", memory=st.session_state["entity_memory"], prompt=prompt, verbose=True)
-    # Generate the output using user input and store it in the session state
-    output = chain({"input_documents": docs, "human_input": input_text}, return_only_outputs=True)
-
-    st.session_state.past.append(input_text)
-    st.session_state.generated.append(output)
-
-    with st.expander("Conversation"):
-        for i in range(len(st.session_state["generated"])-1, -1, -1):
-            st.info(st.session_state["past"][i])
-            st.success(st.session_state["generated"][i]['output_text'])
-
-# # Create button to start a new chat
-# st.sidebar.button("New Chat", on_click=new_chat, type="primary")
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/IcoImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/IcoImagePlugin.py
deleted file mode 100644
index 17b9855a0a583461e5c72a935708debcc6501092..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/IcoImagePlugin.py
+++ /dev/null
@@ -1,355 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Windows Icon support for PIL
-#
-# History:
-#       96-05-27 fl     Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-# This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis
-# <casadebender@gmail.com>.
-# https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki
-#
-# Icon format references:
-#   * https://en.wikipedia.org/wiki/ICO_(file_format)
-#   * https://msdn.microsoft.com/en-us/library/ms997538.aspx
-
-
-import warnings
-from io import BytesIO
-from math import ceil, log
-
-from . import BmpImagePlugin, Image, ImageFile, PngImagePlugin
-from ._binary import i16le as i16
-from ._binary import i32le as i32
-from ._binary import o8
-from ._binary import o16le as o16
-from ._binary import o32le as o32
-
-#
-# --------------------------------------------------------------------
-
-_MAGIC = b"\0\0\1\0"
-
-
-def _save(im, fp, filename):
-    fp.write(_MAGIC)  # (2+2)
-    bmp = im.encoderinfo.get("bitmap_format") == "bmp"
-    sizes = im.encoderinfo.get(
-        "sizes",
-        [(16, 16), (24, 24), (32, 32), (48, 48), (64, 64), (128, 128), (256, 256)],
-    )
-    frames = []
-    provided_ims = [im] + im.encoderinfo.get("append_images", [])
-    width, height = im.size
-    for size in sorted(set(sizes)):
-        if size[0] > width or size[1] > height or size[0] > 256 or size[1] > 256:
-            continue
-
-        for provided_im in provided_ims:
-            if provided_im.size != size:
-                continue
-            frames.append(provided_im)
-            if bmp:
-                bits = BmpImagePlugin.SAVE[provided_im.mode][1]
-                bits_used = [bits]
-                for other_im in provided_ims:
-                    if other_im.size != size:
-                        continue
-                    bits = BmpImagePlugin.SAVE[other_im.mode][1]
-                    if bits not in bits_used:
-                        # Another image has been supplied for this size
-                        # with a different bit depth
-                        frames.append(other_im)
-                        bits_used.append(bits)
-            break
-        else:
-            # TODO: invent a more convenient method for proportional scalings
-            frame = provided_im.copy()
-            frame.thumbnail(size, Image.Resampling.LANCZOS, reducing_gap=None)
-            frames.append(frame)
-    fp.write(o16(len(frames)))  # idCount(2)
-    offset = fp.tell() + len(frames) * 16
-    for frame in frames:
-        width, height = frame.size
-        # 0 means 256
-        fp.write(o8(width if width < 256 else 0))  # bWidth(1)
-        fp.write(o8(height if height < 256 else 0))  # bHeight(1)
-
-        bits, colors = BmpImagePlugin.SAVE[frame.mode][1:] if bmp else (32, 0)
-        fp.write(o8(colors))  # bColorCount(1)
-        fp.write(b"\0")  # bReserved(1)
-        fp.write(b"\0\0")  # wPlanes(2)
-        fp.write(o16(bits))  # wBitCount(2)
-
-        image_io = BytesIO()
-        if bmp:
-            frame.save(image_io, "dib")
-
-            if bits != 32:
-                and_mask = Image.new("1", size)
-                ImageFile._save(
-                    and_mask, image_io, [("raw", (0, 0) + size, 0, ("1", 0, -1))]
-                )
-        else:
-            frame.save(image_io, "png")
-        image_io.seek(0)
-        image_bytes = image_io.read()
-        if bmp:
-            image_bytes = image_bytes[:8] + o32(height * 2) + image_bytes[12:]
-        bytes_len = len(image_bytes)
-        fp.write(o32(bytes_len))  # dwBytesInRes(4)
-        fp.write(o32(offset))  # dwImageOffset(4)
-        current = fp.tell()
-        fp.seek(offset)
-        fp.write(image_bytes)
-        offset = offset + bytes_len
-        fp.seek(current)
-
-
-def _accept(prefix):
-    return prefix[:4] == _MAGIC
-
-
-class IcoFile:
-    def __init__(self, buf):
-        """
-        Parse image from file-like object containing ico file data
-        """
-
-        # check magic
-        s = buf.read(6)
-        if not _accept(s):
-            raise SyntaxError("not an ICO file")
-
-        self.buf = buf
-        self.entry = []
-
-        # Number of items in file
-        self.nb_items = i16(s, 4)
-
-        # Get headers for each item
-        for i in range(self.nb_items):
-            s = buf.read(16)
-
-            icon_header = {
-                "width": s[0],
-                "height": s[1],
-                "nb_color": s[2],  # No. of colors in image (0 if >=8bpp)
-                "reserved": s[3],
-                "planes": i16(s, 4),
-                "bpp": i16(s, 6),
-                "size": i32(s, 8),
-                "offset": i32(s, 12),
-            }
-
-            # See Wikipedia
-            for j in ("width", "height"):
-                if not icon_header[j]:
-                    icon_header[j] = 256
-
-            # See Wikipedia notes about color depth.
-            # We need this just to differ images with equal sizes
-            icon_header["color_depth"] = (
-                icon_header["bpp"]
-                or (
-                    icon_header["nb_color"] != 0
-                    and ceil(log(icon_header["nb_color"], 2))
-                )
-                or 256
-            )
-
-            icon_header["dim"] = (icon_header["width"], icon_header["height"])
-            icon_header["square"] = icon_header["width"] * icon_header["height"]
-
-            self.entry.append(icon_header)
-
-        self.entry = sorted(self.entry, key=lambda x: x["color_depth"])
-        # ICO images are usually squares
-        # self.entry = sorted(self.entry, key=lambda x: x['width'])
-        self.entry = sorted(self.entry, key=lambda x: x["square"])
-        self.entry.reverse()
-
-    def sizes(self):
-        """
-        Get a list of all available icon sizes and color depths.
-        """
-        return {(h["width"], h["height"]) for h in self.entry}
-
-    def getentryindex(self, size, bpp=False):
-        for (i, h) in enumerate(self.entry):
-            if size == h["dim"] and (bpp is False or bpp == h["color_depth"]):
-                return i
-        return 0
-
-    def getimage(self, size, bpp=False):
-        """
-        Get an image from the icon
-        """
-        return self.frame(self.getentryindex(size, bpp))
-
-    def frame(self, idx):
-        """
-        Get an image from frame idx
-        """
-
-        header = self.entry[idx]
-
-        self.buf.seek(header["offset"])
-        data = self.buf.read(8)
-        self.buf.seek(header["offset"])
-
-        if data[:8] == PngImagePlugin._MAGIC:
-            # png frame
-            im = PngImagePlugin.PngImageFile(self.buf)
-            Image._decompression_bomb_check(im.size)
-        else:
-            # XOR + AND mask bmp frame
-            im = BmpImagePlugin.DibImageFile(self.buf)
-            Image._decompression_bomb_check(im.size)
-
-            # change tile dimension to only encompass XOR image
-            im._size = (im.size[0], int(im.size[1] / 2))
-            d, e, o, a = im.tile[0]
-            im.tile[0] = d, (0, 0) + im.size, o, a
-
-            # figure out where AND mask image starts
-            bpp = header["bpp"]
-            if 32 == bpp:
-                # 32-bit color depth icon image allows semitransparent areas
-                # PIL's DIB format ignores transparency bits, recover them.
-                # The DIB is packed in BGRX byte order where X is the alpha
-                # channel.
-
-                # Back up to start of bmp data
-                self.buf.seek(o)
-                # extract every 4th byte (eg. 3,7,11,15,...)
-                alpha_bytes = self.buf.read(im.size[0] * im.size[1] * 4)[3::4]
-
-                # convert to an 8bpp grayscale image
-                mask = Image.frombuffer(
-                    "L",  # 8bpp
-                    im.size,  # (w, h)
-                    alpha_bytes,  # source chars
-                    "raw",  # raw decoder
-                    ("L", 0, -1),  # 8bpp inverted, unpadded, reversed
-                )
-            else:
-                # get AND image from end of bitmap
-                w = im.size[0]
-                if (w % 32) > 0:
-                    # bitmap row data is aligned to word boundaries
-                    w += 32 - (im.size[0] % 32)
-
-                # the total mask data is
-                # padded row size * height / bits per char
-
-                total_bytes = int((w * im.size[1]) / 8)
-                and_mask_offset = header["offset"] + header["size"] - total_bytes
-
-                self.buf.seek(and_mask_offset)
-                mask_data = self.buf.read(total_bytes)
-
-                # convert raw data to image
-                mask = Image.frombuffer(
-                    "1",  # 1 bpp
-                    im.size,  # (w, h)
-                    mask_data,  # source chars
-                    "raw",  # raw decoder
-                    ("1;I", int(w / 8), -1),  # 1bpp inverted, padded, reversed
-                )
-
-                # now we have two images, im is XOR image and mask is AND image
-
-            # apply mask image as alpha channel
-            im = im.convert("RGBA")
-            im.putalpha(mask)
-
-        return im
-
-
-##
-# Image plugin for Windows Icon files.
-
-
-class IcoImageFile(ImageFile.ImageFile):
-    """
-    PIL read-only image support for Microsoft Windows .ico files.
-
-    By default the largest resolution image in the file will be loaded. This
-    can be changed by altering the 'size' attribute before calling 'load'.
-
-    The info dictionary has a key 'sizes' that is a list of the sizes available
-    in the icon file.
-
-    Handles classic, XP and Vista icon formats.
-
-    When saving, PNG compression is used. Support for this was only added in
-    Windows Vista. If you are unable to view the icon in Windows, convert the
-    image to "RGBA" mode before saving.
-
-    This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis
-    <casadebender@gmail.com>.
-    https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki
-    """
-
-    format = "ICO"
-    format_description = "Windows Icon"
-
-    def _open(self):
-        self.ico = IcoFile(self.fp)
-        self.info["sizes"] = self.ico.sizes()
-        self.size = self.ico.entry[0]["dim"]
-        self.load()
-
-    @property
-    def size(self):
-        return self._size
-
-    @size.setter
-    def size(self, value):
-        if value not in self.info["sizes"]:
-            raise ValueError("This is not one of the allowed sizes of this image")
-        self._size = value
-
-    def load(self):
-        if self.im is not None and self.im.size == self.size:
-            # Already loaded
-            return Image.Image.load(self)
-        im = self.ico.getimage(self.size)
-        # if tile is PNG, it won't really be loaded yet
-        im.load()
-        self.im = im.im
-        self.mode = im.mode
-        if im.size != self.size:
-            warnings.warn("Image was not the expected size")
-
-            index = self.ico.getentryindex(self.size)
-            sizes = list(self.info["sizes"])
-            sizes[index] = im.size
-            self.info["sizes"] = set(sizes)
-
-            self.size = im.size
-
-    def load_seek(self):
-        # Flag the ImageFile.Parser so that it
-        # just does all the decode at the end.
-        pass
-
-
-#
-# --------------------------------------------------------------------
-
-
-Image.register_open(IcoImageFile.format, IcoImageFile, _accept)
-Image.register_save(IcoImageFile.format, _save)
-Image.register_extension(IcoImageFile.format, ".ico")
-
-Image.register_mime(IcoImageFile.format, "image/x-icon")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/ansi_test.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/ansi_test.py
deleted file mode 100644
index 0a20c80f882066e0e1323b0c7f61e22913c32e35..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/ansi_test.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main
-
-from ..ansi import Back, Fore, Style
-from ..ansitowin32 import AnsiToWin32
-
-stdout_orig = sys.stdout
-stderr_orig = sys.stderr
-
-
-class AnsiTest(TestCase):
-
-    def setUp(self):
-        # sanity check: stdout should be a file or StringIO object.
-        # It will only be AnsiToWin32 if init() has previously wrapped it
-        self.assertNotEqual(type(sys.stdout), AnsiToWin32)
-        self.assertNotEqual(type(sys.stderr), AnsiToWin32)
-
-    def tearDown(self):
-        sys.stdout = stdout_orig
-        sys.stderr = stderr_orig
-
-
-    def testForeAttributes(self):
-        self.assertEqual(Fore.BLACK, '\033[30m')
-        self.assertEqual(Fore.RED, '\033[31m')
-        self.assertEqual(Fore.GREEN, '\033[32m')
-        self.assertEqual(Fore.YELLOW, '\033[33m')
-        self.assertEqual(Fore.BLUE, '\033[34m')
-        self.assertEqual(Fore.MAGENTA, '\033[35m')
-        self.assertEqual(Fore.CYAN, '\033[36m')
-        self.assertEqual(Fore.WHITE, '\033[37m')
-        self.assertEqual(Fore.RESET, '\033[39m')
-
-        # Check the light, extended versions.
-        self.assertEqual(Fore.LIGHTBLACK_EX, '\033[90m')
-        self.assertEqual(Fore.LIGHTRED_EX, '\033[91m')
-        self.assertEqual(Fore.LIGHTGREEN_EX, '\033[92m')
-        self.assertEqual(Fore.LIGHTYELLOW_EX, '\033[93m')
-        self.assertEqual(Fore.LIGHTBLUE_EX, '\033[94m')
-        self.assertEqual(Fore.LIGHTMAGENTA_EX, '\033[95m')
-        self.assertEqual(Fore.LIGHTCYAN_EX, '\033[96m')
-        self.assertEqual(Fore.LIGHTWHITE_EX, '\033[97m')
-
-
-    def testBackAttributes(self):
-        self.assertEqual(Back.BLACK, '\033[40m')
-        self.assertEqual(Back.RED, '\033[41m')
-        self.assertEqual(Back.GREEN, '\033[42m')
-        self.assertEqual(Back.YELLOW, '\033[43m')
-        self.assertEqual(Back.BLUE, '\033[44m')
-        self.assertEqual(Back.MAGENTA, '\033[45m')
-        self.assertEqual(Back.CYAN, '\033[46m')
-        self.assertEqual(Back.WHITE, '\033[47m')
-        self.assertEqual(Back.RESET, '\033[49m')
-
-        # Check the light, extended versions.
-        self.assertEqual(Back.LIGHTBLACK_EX, '\033[100m')
-        self.assertEqual(Back.LIGHTRED_EX, '\033[101m')
-        self.assertEqual(Back.LIGHTGREEN_EX, '\033[102m')
-        self.assertEqual(Back.LIGHTYELLOW_EX, '\033[103m')
-        self.assertEqual(Back.LIGHTBLUE_EX, '\033[104m')
-        self.assertEqual(Back.LIGHTMAGENTA_EX, '\033[105m')
-        self.assertEqual(Back.LIGHTCYAN_EX, '\033[106m')
-        self.assertEqual(Back.LIGHTWHITE_EX, '\033[107m')
-
-
-    def testStyleAttributes(self):
-        self.assertEqual(Style.DIM, '\033[2m')
-        self.assertEqual(Style.NORMAL, '\033[22m')
-        self.assertEqual(Style.BRIGHT, '\033[1m')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/enum_util.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/enum_util.py
deleted file mode 100644
index dda9027dffa70d1c9760c211f8e27efe49cd54fb..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/enum_util.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from ._contourpy import FillType, LineType, ZInterp
-
-
-def as_fill_type(fill_type):
-    """Coerce a FillType or string value to a FillType.
-
-    Args:
-        fill_type (FillType or str): Value to convert.
-
-    Return:
-        FillType: Converted value.
-    """
-    if isinstance(fill_type, str):
-        fill_type = FillType.__members__[fill_type]
-    return fill_type
-
-
-def as_line_type(line_type):
-    """Coerce a LineType or string value to a LineType.
-
-    Args:
-        line_type (LineType or str): Value to convert.
-
-    Return:
-        LineType: Converted value.
-    """
-    if isinstance(line_type, str):
-        line_type = LineType.__members__[line_type]
-    return line_type
-
-
-def as_z_interp(z_interp):
-    """Coerce a ZInterp or string value to a ZInterp.
-
-    Args:
-        z_interp (ZInterp or str): Value to convert.
-
-    Return:
-        ZInterp: Converted value.
-    """
-    if isinstance(z_interp, str):
-        z_interp = ZInterp.__members__[z_interp]
-    return z_interp
diff --git a/spaces/asdasdasdasd/Face-forgery-detection/extract_landmarks.py b/spaces/asdasdasdasd/Face-forgery-detection/extract_landmarks.py
deleted file mode 100644
index 1c52b1385959338293021d4a6dbe6c61808337e7..0000000000000000000000000000000000000000
--- a/spaces/asdasdasdasd/Face-forgery-detection/extract_landmarks.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import numpy as np
-import cv2
-from landmark_utils import detect_frames_track
-
-def detect_track(video):
-    vidcap = cv2.VideoCapture(video)
-    frames = []
-    while True:
-        success, image = vidcap.read()
-        if success:
-            frames.append(image)
-        else:
-            break
-    raw_data = detect_frames_track(frames)
-
-    vidcap.release()
-    return np.array(raw_data)
-
-def extract_landmark(video):
-    raw_data = detect_track(video)
-    if len(raw_data) == 0:
-        print("No face detected", video)
-    else:
-        np.savetxt(video + ".txt", raw_data, fmt='%1.5f')
-        path = video + ".txt"
-    return path
-
-def get_data_for_test(path, fake, block):  # fake:manipulated=1 original=0
-    file = path
-    x = []
-    x_diff = []
-    y = []
-
-    video_y = []
-    count_y = {}
-    sample_to_video = []
-
-    # for file in tqdm(files):
-    vectors = np.loadtxt(file)
-    print("vectors = ",vectors)
-    video_y.append(fake)
-
-    for i in range(0, vectors.shape[0] - block, block):
-        vec = vectors[i:i + block, :]
-        x.append(vec)
-        vec_next = vectors[i + 1:i + block, :]
-        vec_next = np.pad(vec_next, ((0, 1), (0, 0)), 'constant', constant_values=(0, 0))
-        vec_diff = (vec_next - vec)[:block - 1, :]
-        x_diff.append(vec_diff)
-
-        y.append(fake)
-
-        # Dict for counting number of samples in video
-        if file not in count_y:
-            count_y[file] = 1
-        else:
-            count_y[file] += 1
-
-        sample_to_video.append(file)
-    return np.array(x), np.array(x_diff), np.array(y), np.array(video_y), np.array(sample_to_video), count_y
-
-def merge_video_prediction(mix_prediction, s2v, vc):
-    prediction_video = []
-    pre_count = {}
-    for p, v_label in zip(mix_prediction, s2v):
-        p_bi = 0
-        if p >= 0.5:
-            p_bi = 1
-        if v_label in pre_count:
-            pre_count[v_label] += p_bi
-        else:
-            pre_count[v_label] = p_bi
-    for key in pre_count.keys():
-        prediction_video.append(pre_count[key] / vc[key])
-    return prediction_video
\ No newline at end of file
diff --git a/spaces/awacke1/AdventureGame/app.py b/spaces/awacke1/AdventureGame/app.py
deleted file mode 100644
index a982b4d789bc2b5e5801d6b605dab0252de9ac83..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AdventureGame/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import streamlit as st
-import random
-
-class Adventure:
-    def __init__(self, name):
-        self.name = name
-        self.health = 100
-        self.gold = 0
-        self.victory = False
-
-    def fight(self, monster_name, monster_attack):
-        self.health -= monster_attack
-        if self.health <= 0:
-            st.write("You have been defeated by the {}.".format(monster_name))
-            st.write("Your adventure has come to an end.")
-            self.victory = False
-        else:
-            st.write("You fought the {} and took {} damage. Your health is now {}.".format(
-                monster_name, monster_attack, self.health))
-            self.victory = True
-
-    def move(self, direction):
-        if direction == "North":
-            st.write("You move North.")
-        elif direction == "South":
-            st.write("You move South.")
-        elif direction == "East":
-            st.write("You move East.")
-        elif direction == "West":
-            st.write("You move West.")
-        else:
-            st.write("Invalid direction. Please choose North, South, East, or West.")
-
-def play_game():
-    name = st.text_input("Enter your name:")
-    adventure = Adventure(name)
-    st.write("Welcome to the dungeon, {}.".format(adventure.name))
-
-    while adventure.victory==False:
-        count=1
-        #direction = st.selectbox('Choose your next move:', ('North', 'South', 'East', 'West'), key = count)
-        #direction = st.selectbox("Choose your next move:", ("North", "South", "East", "West"), key = count)
-        count = count + 1
-        adventure.move("North")
-
-        if random.random() > 0.7:
-            monster_name = "Goblin"
-            monster_attack = random.randint(10, 25)
-            st.write("A {} appears and attacks you.".format(monster_name))
-            adventure.fight(monster_name, monster_attack)
-
-if __name__ == "__main__":
-    play_game()
\ No newline at end of file
diff --git a/spaces/awacke1/ChatGPTStreamlit4/app.py b/spaces/awacke1/ChatGPTStreamlit4/app.py
deleted file mode 100644
index 9402065c73bef1a037b37956352603043aed7688..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPTStreamlit4/app.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import streamlit as st
-import openai
-import os
-import base64
-import glob
-import json
-import mistune
-import pytz
-import math
-import requests
-
-from datetime import datetime
-from openai import ChatCompletion
-from xml.etree import ElementTree as ET
-from bs4 import BeautifulSoup
-from collections import deque
-from audio_recorder_streamlit import audio_recorder
-
-openai.api_key = os.getenv('OPENAI_KEY')
-st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
-
-menu = ["htm", "txt", "md", "py"]
-choice = st.sidebar.selectbox("Output File Type:", menu)
-model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
-
-def generate_filename(prompt, file_type):
-    central = pytz.timezone('US/Central')
-    safe_date_time = datetime.now(central).strftime("%m%d_%I%M")  
-    safe_prompt = "".join(x for x in prompt if x.isalnum())[:45]
-    return f"{safe_date_time}_{safe_prompt}.{file_type}"
-
-def chat_with_model(prompt, document_section):
-    model = model_choice
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.append({'role': 'user', 'content': prompt})
-    if len(document_section)>0:
-        conversation.append({'role': 'assistant', 'content': document_section})
-    response = openai.ChatCompletion.create(model=model, messages=conversation)
-    return response
-    #return response['choices'][0]['message']['content']
-
-def transcribe_audio(openai_key, file_path, model):
-    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
-    headers = {
-        "Authorization": f"Bearer {openai_key}",
-    }
-    with open(file_path, 'rb') as f:
-        data = {'file': f}
-        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
-    if response.status_code == 200:
-        st.write(response.json())
-        response2 = chat_with_model(response.json().get('text'), '')
-        st.write('Responses:')
-        #st.write(response)
-        st.write(response2)
-        return response.json().get('text')
-    else:
-        st.write(response.json())
-        st.error("Error in API call.")
-        return None
-
-def save_and_play_audio(audio_recorder):
-    audio_bytes = audio_recorder()
-    if audio_bytes:
-        filename = generate_filename("Recording", "wav")
-        with open(filename, 'wb') as f:
-            f.write(audio_bytes)
-        st.audio(audio_bytes, format="audio/wav")
-        return filename
-    return None
-
-def create_file(filename, prompt, response):
-    if filename.endswith(".txt"):
-        with open(filename, 'w') as file:
-            file.write(f"Prompt:\n{prompt}\nResponse:\n{response}")
-    elif filename.endswith(".htm"):
-        with open(filename, 'w') as file:
-            file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")
-    elif filename.endswith(".md"):
-        with open(filename, 'w') as file:
-            file.write(f"# Prompt:\n{prompt}\n# Response:\n{response}")
-def truncate_document(document, length):
-    return document[:length]
-def divide_document(document, max_length):
-    return [document[i:i+max_length] for i in range(0, len(document), max_length)]
-def get_table_download_link(file_path):
-    with open(file_path, 'r') as file:
-        data = file.read()
-    b64 = base64.b64encode(data.encode()).decode()  
-    file_name = os.path.basename(file_path)
-    ext = os.path.splitext(file_name)[1]  # get the file extension
-    if ext == '.txt':
-        mime_type = 'text/plain'
-    elif ext == '.htm':
-        mime_type = 'text/html'
-    elif ext == '.md':
-        mime_type = 'text/markdown'
-    else:
-        mime_type = 'application/octet-stream'  # general binary data type
-    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
-    return href
-
-    
-
-# Audio, transcribe, GPT:
-filename = save_and_play_audio(audio_recorder)
-if filename is not None:
-    transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
-    st.write(transcription)
-    gptOutput = chat_with_model(transcription, '') # push transcript through as prompt
-    filename = generate_filename(transcription, choice)
-    create_file(filename, transcription, gptOutput)
-    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-
-
-
-
-def CompressXML(xml_text):
-    root = ET.fromstring(xml_text)
-    for elem in list(root.iter()):
-        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
-            elem.parent.remove(elem)
-    return ET.tostring(root, encoding='unicode', method="xml")
-    
-def read_file_content(file,max_length):
-    if file.type == "application/json":
-        content = json.load(file)
-        return str(content)
-    elif file.type == "text/html" or file.type == "text/htm":
-        content = BeautifulSoup(file, "html.parser")
-        return content.text
-    elif file.type == "application/xml" or file.type == "text/xml":
-        tree = ET.parse(file)
-        root = tree.getroot()
-        xml = CompressXML(ET.tostring(root, encoding='unicode'))
-        return xml
-    elif file.type == "text/markdown" or file.type == "text/md":
-        md = mistune.create_markdown()
-        content = md(file.read().decode())
-        return content
-    elif file.type == "text/plain":
-        return file.getvalue().decode()
-    else:
-        return ""
-
-def main():
-    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
-
-    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
-    with collength:
-        #max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4.  8x=96000 for gpt4-32k.
-        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
-    with colupload:
-        uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "html", "htm", "md", "txt"])
-    
-    document_sections = deque()
-    document_responses = {}
-
-    if uploaded_file is not None:
-        file_content = read_file_content(uploaded_file, max_length)
-        document_sections.extend(divide_document(file_content, max_length))
-
-    if len(document_sections) > 0:
-        
-        if st.button("👁️ View Upload"):
-            st.markdown("**Sections of the uploaded file:**")
-            for i, section in enumerate(list(document_sections)):
-                st.markdown(f"**Section {i+1}**\n{section}")
-        
-        st.markdown("**Chat with the model:**")
-        for i, section in enumerate(list(document_sections)):
-            if i in document_responses:
-                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
-            else:
-                if st.button(f"Chat about Section {i+1}"):
-                    st.write('Reasoning with your inputs...')
-                    response = chat_with_model(user_prompt, section)
-                    st.write('Response:')
-                    st.write(response)
-                    document_responses[i] = response
-                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
-                    create_file(filename, user_prompt, response)
-                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    if st.button('💬 Chat'):
-        st.write('Reasoning with your inputs...')
-        response = chat_with_model(user_prompt, ''.join(list(document_sections)))
-        st.write('Response:')
-        st.write(response)
-        
-        filename = generate_filename(user_prompt, choice)
-        create_file(filename, user_prompt, response)
-        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    all_files = glob.glob("*.*")
-    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
-    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
-    
-    for file in all_files:
-        col1, col3 = st.sidebar.columns([5,1])  # adjust the ratio as needed
-        with col1:
-            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
-        with col3:
-            if st.button("🗑", key="delete_"+file):
-                os.remove(file)
-                st.experimental_rerun()
-            
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/awacke1/Pandas-Gamification-Mechanics/backup-app.py b/spaces/awacke1/Pandas-Gamification-Mechanics/backup-app.py
deleted file mode 100644
index a0c1dfedd98cb4a0ec39e88e68adfc3a07429eb7..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Pandas-Gamification-Mechanics/backup-app.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import streamlit as st
-import random
-import pandas as pd
-from datetime import datetime
-
-# Define the game mechanics
-
-def generate_scenario():
-    scenarios = ['🦸 You are a superhero saving the world from a meteorite', 
-                 '🏴‍☠️ You are a pirate searching for treasure on a deserted island', 
-                 '👨‍🍳 You are a chef trying to win a cooking competition', 
-                 '🕵️ You are a detective solving a murder case']
-    return random.choice(scenarios)
-
-def calculate_score(slider_values):
-    bluffing_score, deduction_score, humor_score, memory_score, roleplay_score = slider_values
-
-    total_score = bluffing_score + deduction_score + humor_score + memory_score + roleplay_score
-    return total_score
-
-def play_game(slider_values):
-    scenario = generate_scenario()
-    st.write('🎭 Act out the following scenario: ' + scenario)
-    total_score = calculate_score(slider_values)
-    st.write('🎯 Your total score is: ' + str(total_score))
-
-    # Save game history to a dataframe
-    game_history_df = pd.DataFrame({'Scenario': [scenario],
-                                    'Bluffing': [slider_values[0]],
-                                    'Deduction': [slider_values[1]],
-                                    'Humor': [slider_values[2]],
-                                    'Memory': [slider_values[3]],
-                                    'Roleplay': [slider_values[4]],
-                                    'Total Score': [total_score]})
-
-    # Append to existing game history
-    try:
-        existing_game_history = pd.read_csv('game_history.csv')
-        game_history_df = pd.concat([existing_game_history, game_history_df], ignore_index=True)
-    except:
-        pass
-
-    return game_history_df
-
-def save_game_history(game_history_df):
-    game_history_df.to_csv('game_history.csv', index=False)
-    st.write('📝 Game history saved!')
-    st.write(game_history_df)
-
-def save_simulation_results(simulation_results_df):
-    filename = datetime.now().strftime('%Y-%m-%d %H-%M-%S') + '.csv'
-    simulation_results_df.to_csv(filename, index=False)
-    st.write('📝 Simulation results saved!')
-    st.write(simulation_results_df)
-
-def run_simulations(num_simulations):
-    total_scores = []
-    simulation_results_df = pd.DataFrame(columns=['Scenario', 'Bluffing', 'Deduction', 'Humor', 'Memory', 'Roleplay', 'Total Score'])
-    for i in range(num_simulations):
-        slider_values = [random.randint(1, 10) for i in range(5)]
-        total_score = calculate_score(slider_values)
-        total_scores.append(total_score)
-        scenario = generate_scenario()
-        simulation_results_df = simulation_results_df.append({'Scenario': scenario,
-                                                              'Bluffing': slider_values[0],
-                                                              'Deduction': slider_values[1],
-                                                              'Humor': slider_values[2],
-                                                              'Memory': slider_values[3],
-                                                              'Roleplay': slider_values[4],
-                                                              'Total Score': total_score}, ignore_index=True)
-    st.write('🎲 Average score from ' + str(num_simulations) + ' simulations: ' + str(sum(total_scores)/len(total_scores)))
-    st.write(simulation_results_df)
-    save_simulation_results(simulation_results_df)
-
-
-# Define the Streamlit app
-
-st.set_page_config(layout="wide")
-st.title('🎭 Acting Game Mechanics')
-st.write('🎯 Welcome to the Acting Game Mechanics! This game measures your ability to bluff, deduce, use humor, remember details, and role-play. Drag the sliders to the left or right to adjust each skill, and click 🎭 Play to act out a scenario and receive a score.')
-
-with st.sidebar:
-    slider_values = [st.slider('🃏 Bluffing', 1, 10, 5),
-    st.slider('🕵️ Deduction', 1, 10, 5),
-    st.slider('😂 Humor', 1, 10, 5),
-    st.slider('🧠 Memory', 1, 10, 5),
-    st.slider('👥 Roleplay', 1, 10, 5)]
-
-if st.button('🎭 Play'):
-    game_history_df = play_game(slider_values)
-    save_game_history(game_history_df)
-
-if st.button('🎲 Run simulations'):
-    num_simulations = st.slider('🔁 Number of simulations', 1, 100000, 1000)
-    run_simulations(num_simulations)
-
-if st.button('📝 Show all game history'):
-    try:
-        game_history_df = pd.read_csv('game_history.csv')
-        st.write(game_history_df)
-    except:
-        st.write('No game history found')
-
-if st.button('📝 Download game history'):
-    try:
-        game_history_df = pd.read_csv('game_history.csv')
-        filename = 'game_history_' + datetime.now().strftime('%Y-%m-%d %H-%M-%S') + '.csv'
-        game_history_df.to_csv(filename, index=False)
-        st.write('📝 Game history downloaded!')
-        st.write(game_history_df)
-    except:
-        st.write('No game history found')
-
-if st.button('📝 Download simulation results'):
-    try:
-        simulation_results_df = pd.read_csv('simulation_results.csv')
-        filename = 'simulation_results_' + datetime.now().strftime('%Y-%m-%d %H-%M-%S') + '.csv'
-        simulation_results_df.to_csv(filename, index=False)
-        st.write('📝 Simulation results downloaded!')
-        st.write(simulation_results_df)
-    except:
-        st.write('No simulation results found')
-
-st.markdown("""
-🎭 Acting Game Mechanics 
-🎯🃏 Bluffing
-🕵️ Deduction
-😂 Humor
-🧠 Memory
-👥 Roleplay
-
-🎭 Acting Game Mechanics:
-
-Characterization: Act out your character's traits, emotions, and personality.
-Improvisation: Think on your feet and come up with responses to unexpected situations.
-Scripted Dialogue: Deliver lines from a pre-written script or engage in scripted conversations.
-For more information on Acting Game Mechanics, you can visit the Wikipedia page on Role-playing game mechanics: https://en.wikipedia.org/wiki/Acting
-
-🎯🃏 Bluffing:
-
-Lying: Convince others that your false information is true.
-Concealment: Hide your true intentions or actions.
-Misdirection: Lead others to believe something different from what you are doing.
-For more information on Bluffing, you can visit the Wikipedia page on Bluff (poker): https://en.wikipedia.org/wiki/Bluff_(poker)
-
-🕵️ Deduction:
-
-Clue Analysis: Examine clues to draw conclusions about a mystery.
-Logical Reasoning: Use deductive reasoning to arrive at the correct solution.
-Pattern Recognition: Recognize and match patterns to uncover hidden information.
-For more information on Deduction, you can visit the Wikipedia page on Deduction game: https://en.wikipedia.org/wiki/Deduction
-
-😂 Humor:
-
-Parody: Use humorous exaggeration or imitation to make fun of a subject.
-Puns: Play with words to create humorous meanings.
-Satire: Use irony, sarcasm, and ridicule to critique a topic or individual.
-For more information on Humor, you can visit the Wikipedia page on Humor: https://en.wikipedia.org/wiki/Humor
-
-🧠 Memory:
-
-Recall: Remember information from previous events or interactions.
-Recognition: Identify information you have seen or heard before.
-Memorization: Commit information to memory for future use.
-For more information on Memory, you can visit the Wikipedia page on Memory game: https://en.wikipedia.org/wiki/Memory
-
-👥 Roleplay:
-
-Character Creation: Develop a unique character with specific traits and abilities.
-Storytelling: Create a narrative with the characters and the world they inhabit.
-Collaborative Play: Work with others to create an immersive experience.
-For more information on Roleplay, you can visit the Wikipedia page on Role-playing game: https://en.wikipedia.org/wiki/Role-playing_game
-
-""")
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/NodeBuilder.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/NodeBuilder.js
deleted file mode 100644
index d80a961fa397642e62267b9519b0364f79b1848b..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/NodeBuilder.js
+++ /dev/null
@@ -1,968 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { NodeUniform } from './NodeUniform.js';
-import { NodeUtils } from './NodeUtils.js';
-import { NodeLib } from './NodeLib.js';
-import { FunctionNode } from './FunctionNode.js';
-import { ConstNode } from './ConstNode.js';
-import { StructNode } from './StructNode.js';
-import { Vector2Node } from '../inputs/Vector2Node.js';
-import { Vector3Node } from '../inputs/Vector3Node.js';
-import { Vector4Node } from '../inputs/Vector4Node.js';
-import { TextureNode } from '../inputs/TextureNode.js';
-import { CubeTextureNode } from '../inputs/CubeTextureNode.js';
-
-var elements = NodeUtils.elements,
-	constructors = [ 'float', 'vec2', 'vec3', 'vec4' ],
-	convertFormatToType = {
-		float: 'f',
-		vec2: 'v2',
-		vec3: 'v3',
-		vec4: 'v4',
-		mat4: 'v4',
-		int: 'i',
-		bool: 'b'
-	},
-	convertTypeToFormat = {
-		t: 'sampler2D',
-		tc: 'samplerCube',
-		b: 'bool',
-		i: 'int',
-		f: 'float',
-		c: 'vec3',
-		v2: 'vec2',
-		v3: 'vec3',
-		v4: 'vec4',
-		m3: 'mat3',
-		m4: 'mat4'
-	};
-
-function NodeBuilder() {
-
-	this.slots = [];
-	this.caches = [];
-	this.contexts = [];
-
-	this.keywords = {};
-
-	this.nodeData = {};
-
-	this.requires = {
-		uv: [],
-		color: [],
-		lights: false,
-		fog: false
-	};
-
-	this.includes = {
-		consts: [],
-		functions: [],
-		structs: []
-	};
-
-	this.attributes = {};
-
-	this.prefixCode = [
-		"#ifdef TEXTURE_LOD_EXT",
-
-		"	#define texCube(a, b) textureCube(a, b)",
-		"	#define texCubeBias(a, b, c) textureCubeLodEXT(a, b, c)",
-
-		"	#define tex2D(a, b) texture2D(a, b)",
-		"	#define tex2DBias(a, b, c) texture2DLodEXT(a, b, c)",
-
-		"#else",
-
-		"	#define texCube(a, b) textureCube(a, b)",
-		"	#define texCubeBias(a, b, c) textureCube(a, b, c)",
-
-		"	#define tex2D(a, b) texture2D(a, b)",
-		"	#define tex2DBias(a, b, c) texture2D(a, b, c)",
-
-		"#endif",
-
-		"#include <packing>",
-		"#include <common>"
-
-	].join( "\n" );
-
-	this.parsCode = {
-		vertex: '',
-		fragment: ''
-	};
-
-	this.code = {
-		vertex: '',
-		fragment: ''
-	};
-
-	this.nodeCode = {
-		vertex: '',
-		fragment: ''
-	};
-
-	this.resultCode = {
-		vertex: '',
-		fragment: ''
-	};
-
-	this.finalCode = {
-		vertex: '',
-		fragment: ''
-	};
-
-	this.inputs = {
-		uniforms: {
-			list: [],
-			vertex: [],
-			fragment: []
-		},
-		vars: {
-			varying: [],
-			vertex: [],
-			fragment: []
-		}
-	};
-
-	// send to material
-
-	this.defines = {};
-
-	this.uniforms = {};
-
-	this.extensions = {};
-
-	this.updaters = [];
-
-	this.nodes = [];
-
-	// --
-
-	this.parsing = false;
-	this.optimize = true;
-
-}
-
-NodeBuilder.prototype = {
-
-	constructor: NodeBuilder,
-
-	build: function ( vertex, fragment ) {
-
-		this.buildShader( 'vertex', vertex );
-		this.buildShader( 'fragment', fragment );
-
-		if ( this.requires.uv[ 0 ] ) {
-
-			this.addVaryCode( 'varying vec2 vUv;' );
-
-			this.addVertexFinalCode( 'vUv = uv;' );
-
-		}
-
-		if ( this.requires.uv[ 1 ] ) {
-
-			this.addVaryCode( 'varying vec2 vUv2;' );
-			this.addVertexParsCode( 'attribute vec2 uv2;' );
-
-			this.addVertexFinalCode( 'vUv2 = uv2;' );
-
-		}
-
-		if ( this.requires.color[ 0 ] ) {
-
-			this.addVaryCode( 'varying vec4 vColor;' );
-			this.addVertexParsCode( 'attribute vec4 color;' );
-
-			this.addVertexFinalCode( 'vColor = color;' );
-
-		}
-
-		if ( this.requires.color[ 1 ] ) {
-
-			this.addVaryCode( 'varying vec4 vColor2;' );
-			this.addVertexParsCode( 'attribute vec4 color2;' );
-
-			this.addVertexFinalCode( 'vColor2 = color2;' );
-
-		}
-
-		if ( this.requires.position ) {
-
-			this.addVaryCode( 'varying vec3 vPosition;' );
-
-			this.addVertexFinalCode( 'vPosition = transformed;' );
-
-		}
-
-		if ( this.requires.worldPosition ) {
-
-			this.addVaryCode( 'varying vec3 vWPosition;' );
-
-			this.addVertexFinalCode( 'vWPosition = ( modelMatrix * vec4( transformed, 1.0 ) ).xyz;' );
-
-		}
-
-		if ( this.requires.normal ) {
-
-			this.addVaryCode( 'varying vec3 vObjectNormal;' );
-
-			this.addVertexFinalCode( 'vObjectNormal = normal;' );
-
-		}
-
-		if ( this.requires.worldNormal ) {
-
-			this.addVaryCode( 'varying vec3 vWNormal;' );
-
-			this.addVertexFinalCode( 'vWNormal = ( modelMatrix * vec4( objectNormal, 0.0 ) ).xyz;' );
-
-		}
-
-		return this;
-
-	},
-
-	buildShader: function ( shader, node ) {
-
-		this.resultCode[ shader ] = node.build( this.setShader( shader ), 'v4' );
-
-	},
-
-	setMaterial: function ( material, renderer ) {
-
-		this.material = material;
-		this.renderer = renderer;
-
-		this.requires.lights = material.lights;
-		this.requires.fog = material.fog;
-
-		this.mergeDefines( material.defines );
-
-		return this;
-
-	},
-
-	addFlow: function ( slot, cache, context ) {
-
-		return this.addSlot( slot ).addCache( cache ).addContext( context );
-
-	},
-
-	removeFlow: function () {
-
-		return this.removeSlot().removeCache().removeContext();
-
-	},
-
-	addCache: function ( name ) {
-
-		this.cache = name || '';
-		this.caches.push( this.cache );
-
-		return this;
-
-	},
-
-	removeCache: function () {
-
-		this.caches.pop();
-		this.cache = this.caches[ this.caches.length - 1 ] || '';
-
-		return this;
-
-	},
-
-	addContext: function ( context ) {
-
-		this.context = Object.assign( {}, this.context, context );
-		this.contexts.push( this.context );
-
-		return this;
-
-	},
-
-	removeContext: function () {
-
-		this.contexts.pop();
-		this.context = this.contexts[ this.contexts.length - 1 ] || {};
-
-		return this;
-
-	},
-
-	addSlot: function ( name ) {
-
-		this.slot = name || '';
-		this.slots.push( this.slot );
-
-		return this;
-
-	},
-
-	removeSlot: function () {
-
-		this.slots.pop();
-		this.slot = this.slots[ this.slots.length - 1 ] || '';
-
-		return this;
-
-	},
-
-
-	addVertexCode: function ( code ) {
-
-		this.addCode( code, 'vertex' );
-
-	},
-
-	addFragmentCode: function ( code ) {
-
-		this.addCode( code, 'fragment' );
-
-	},
-
-	addCode: function ( code, shader ) {
-
-		this.code[ shader || this.shader ] += code + '\n';
-
-	},
-
-
-	addVertexNodeCode: function ( code ) {
-
-		this.addNodeCode( code, 'vertex' );
-
-	},
-
-	addFragmentNodeCode: function ( code ) {
-
-		this.addNodeCode( code, 'fragment' );
-
-	},
-
-	addNodeCode: function ( code, shader ) {
-
-		this.nodeCode[ shader || this.shader ] += code + '\n';
-
-	},
-
-	clearNodeCode: function ( shader ) {
-
-		shader = shader || this.shader;
-
-		var code = this.nodeCode[ shader ];
-
-		this.nodeCode[ shader ] = '';
-
-		return code;
-
-	},
-
-	clearVertexNodeCode: function ( ) {
-
-		return this.clearNodeCode( 'vertex' );
-
-	},
-
-	clearFragmentNodeCode: function ( ) {
-
-		return this.clearNodeCode( 'fragment' );
-
-	},
-
-	addVertexFinalCode: function ( code ) {
-
-		this.addFinalCode( code, 'vertex' );
-
-	},
-
-	addFragmentFinalCode: function ( code ) {
-
-		this.addFinalCode( code, 'fragment' );
-
-	},
-
-	addFinalCode: function ( code, shader ) {
-
-		this.finalCode[ shader || this.shader ] += code + '\n';
-
-	},
-
-
-	addVertexParsCode: function ( code ) {
-
-		this.addParsCode( code, 'vertex' );
-
-	},
-
-	addFragmentParsCode: function ( code ) {
-
-		this.addParsCode( code, 'fragment' );
-
-	},
-
-	addParsCode: function ( code, shader ) {
-
-		this.parsCode[ shader || this.shader ] += code + '\n';
-
-	},
-
-
-	addVaryCode: function ( code ) {
-
-		this.addVertexParsCode( code );
-		this.addFragmentParsCode( code );
-
-	},
-
-
-	isCache: function ( name ) {
-
-		return this.caches.indexOf( name ) !== - 1;
-
-	},
-
-	isSlot: function ( name ) {
-
-		return this.slots.indexOf( name ) !== - 1;
-
-	},
-
-	define: function ( name, value ) {
-
-		this.defines[ name ] = value === undefined ? 1 : value;
-
-	},
-
-	isDefined: function ( name ) {
-
-		return this.defines[ name ] !== undefined;
-
-	},
-
-	getVar: function ( uuid, type, ns, shader = 'varying', prefix = 'V', label = '' ) {
-
-		var vars = this.getVars( shader ),
-			data = vars[ uuid ];
-
-		if ( ! data ) {
-
-			var index = vars.length,
-				name = ns ? ns : 'node' + prefix + index + ( label ? '_' + label : '' );
-
-			data = { name: name, type: type };
-
-			vars.push( data );
-			vars[ uuid ] = data;
-
-		}
-
-		return data;
-
-	},
-
-	getTempVar: function ( uuid, type, ns, label ) {
-
-		return this.getVar( uuid, type, ns, this.shader, 'T', label );
-
-	},
-
-	getAttribute: function ( name, type ) {
-
-		if ( ! this.attributes[ name ] ) {
-
-			var varying = this.getVar( name, type );
-
-			this.addVertexParsCode( 'attribute ' + type + ' ' + name + ';' );
-			this.addVertexFinalCode( varying.name + ' = ' + name + ';' );
-
-			this.attributes[ name ] = { varying: varying, name: name, type: type };
-
-		}
-
-		return this.attributes[ name ];
-
-	},
-
-	getCode: function ( shader ) {
-
-		return [
-			this.prefixCode,
-			this.parsCode[ shader ],
-			this.getVarListCode( this.getVars( 'varying' ), 'varying' ),
-			this.getVarListCode( this.inputs.uniforms[ shader ], 'uniform' ),
-			this.getIncludesCode( 'consts', shader ),
-			this.getIncludesCode( 'structs', shader ),
-			this.getIncludesCode( 'functions', shader ),
-			'void main() {',
-			this.getVarListCode( this.getVars( shader ) ),
-			this.code[ shader ],
-			this.resultCode[ shader ],
-			this.finalCode[ shader ],
-			'}'
-		].join( "\n" );
-
-	},
-
-	getVarListCode: function ( vars, prefix ) {
-
-		prefix = prefix || '';
-
-		var code = '';
-
-		for ( var i = 0, l = vars.length; i < l; ++ i ) {
-
-			var nVar = vars[ i ],
-				type = nVar.type,
-				name = nVar.name;
-
-			var formatType = this.getFormatByType( type );
-
-			if ( formatType === undefined ) {
-
-				throw new Error( "Node pars " + formatType + " not found." );
-
-			}
-
-			code += prefix + ' ' + formatType + ' ' + name + ';\n';
-
-		}
-
-		return code;
-
-	},
-
-	getVars: function ( shader ) {
-
-		return this.inputs.vars[ shader || this.shader ];
-
-	},
-
-	getNodeData: function ( node ) {
-
-		var uuid = node.isNode ? node.uuid : node;
-
-		return this.nodeData[ uuid ] = this.nodeData[ uuid ] || {};
-
-	},
-
-	createUniform: function ( shader, type, node, ns, needsUpdate, label ) {
-
-		var uniforms = this.inputs.uniforms,
-			index = uniforms.list.length;
-
-		var uniform = new NodeUniform( {
-			type: type,
-			name: ns ? ns : 'nodeU' + index + ( label ? '_' + label : '' ),
-			node: node,
-			needsUpdate: needsUpdate
-		} );
-
-		uniforms.list.push( uniform );
-
-		uniforms[ shader ].push( uniform );
-		uniforms[ shader ][ uniform.name ] = uniform;
-
-		this.uniforms[ uniform.name ] = uniform;
-
-		return uniform;
-
-	},
-
-	createVertexUniform: function ( type, node, ns, needsUpdate, label ) {
-
-		return this.createUniform( 'vertex', type, node, ns, needsUpdate, label );
-
-	},
-
-	createFragmentUniform: function ( type, node, ns, needsUpdate, label ) {
-
-		return this.createUniform( 'fragment', type, node, ns, needsUpdate, label );
-
-	},
-
-	include: function ( node, parent, source ) {
-
-		var includesStruct;
-
-		node = typeof node === 'string' ? NodeLib.get( node ) : node;
-
-		if ( this.context.include === false ) {
-
-			return node.name;
-
-		}
-
-
-		if ( node instanceof FunctionNode ) {
-
-			includesStruct = this.includes.functions;
-
-		} else if ( node instanceof ConstNode ) {
-
-			includesStruct = this.includes.consts;
-
-		} else if ( node instanceof StructNode ) {
-
-			includesStruct = this.includes.structs;
-
-		}
-
-		var includes = includesStruct[ this.shader ] = includesStruct[ this.shader ] || [];
-
-		if ( node ) {
-
-			var included = includes[ node.name ];
-
-			if ( ! included ) {
-
-				included = includes[ node.name ] = {
-					node: node,
-					deps: []
-				};
-
-				includes.push( included );
-
-				included.src = node.build( this, 'source' );
-
-			}
-
-			if ( node instanceof FunctionNode && parent && includes[ parent.name ] && includes[ parent.name ].deps.indexOf( node ) == - 1 ) {
-
-				includes[ parent.name ].deps.push( node );
-
-				if ( node.includes && node.includes.length ) {
-
-					var i = 0;
-
-					do {
-
-						this.include( node.includes[ i ++ ], parent );
-
-					} while ( i < node.includes.length );
-
-				}
-
-			}
-
-			if ( source ) {
-
-				included.src = source;
-
-			}
-
-			return node.name;
-
-		} else {
-
-			throw new Error( "Include not found." );
-
-		}
-
-	},
-
-	colorToVectorProperties: function ( color ) {
-
-		return color.replace( 'r', 'x' ).replace( 'g', 'y' ).replace( 'b', 'z' ).replace( 'a', 'w' );
-
-	},
-
-	colorToVector: function ( color ) {
-
-		return color.replace( /c/g, 'v3' );
-
-	},
-
-	getIncludes: function ( type, shader ) {
-
-		return this.includes[ type ][ shader || this.shader ];
-
-	},
-
-	getIncludesCode: function () {
-
-		function sortByPosition( a, b ) {
-
-			return a.deps.length - b.deps.length;
-
-		}
-
-		return function getIncludesCode( type, shader ) {
-
-			var includes = this.getIncludes( type, shader );
-
-			if ( ! includes ) return '';
-
-			var code = '',
-				includes = includes.sort( sortByPosition );
-
-			for ( var i = 0; i < includes.length; i ++ ) {
-
-				if ( includes[ i ].src ) code += includes[ i ].src + '\n';
-
-			}
-
-			return code;
-
-		};
-
-	}(),
-
-	getConstructorFromLength: function ( len ) {
-
-		return constructors[ len - 1 ];
-
-	},
-
-	isTypeMatrix: function ( format ) {
-
-		return /^m/.test( format );
-
-	},
-
-	getTypeLength: function ( type ) {
-
-		if ( type === 'f' ) return 1;
-
-		return parseInt( this.colorToVector( type ).substr( 1 ) );
-
-	},
-
-	getTypeFromLength: function ( len ) {
-
-		if ( len === 1 ) return 'f';
-
-		return 'v' + len;
-
-	},
-
-	findNode: function () {
-
-		for ( var i = 0; i < arguments.length; i ++ ) {
-
-			var nodeCandidate = arguments[ i ];
-
-			if ( nodeCandidate !== undefined && nodeCandidate.isNode ) {
-
-				return nodeCandidate;
-
-			}
-
-		}
-
-	},
-
-	resolve: function () {
-
-		for ( var i = 0; i < arguments.length; i ++ ) {
-
-			var nodeCandidate = arguments[ i ];
-
-			if ( nodeCandidate !== undefined ) {
-
-				if ( nodeCandidate.isNode ) {
-
-					return nodeCandidate;
-
-				} else if ( nodeCandidate.isTexture ) {
-
-					switch ( nodeCandidate.mapping ) {
-
-						case THREE.CubeReflectionMapping:
-						case THREE.CubeRefractionMapping:
-
-							return new CubeTextureNode( nodeCandidate );
-
-							break;
-
-						case THREE.CubeUVReflectionMapping:
-						case THREE.CubeUVRefractionMapping:
-
-							return new TextureCubeNode( new TextureNode( nodeCandidate ) );
-
-							break;
-
-						default:
-
-							return new TextureNode( nodeCandidate );
-
-					}
-
-				} else if ( nodeCandidate.isVector2 ) {
-
-					return new Vector2Node( nodeCandidate );
-
-				} else if ( nodeCandidate.isVector3 ) {
-
-					return new Vector3Node( nodeCandidate );
-
-				} else if ( nodeCandidate.isVector4 ) {
-
-					return new Vector4Node( nodeCandidate );
-
-				}
-
-			}
-
-		}
-
-	},
-
-	format: function ( code, from, to ) {
-
-		var typeToType = this.colorToVector( to + ' <- ' + from );
-
-		switch ( typeToType ) {
-
-			case 'f <- v2' : return code + '.x';
-			case 'f <- v3' : return code + '.x';
-			case 'f <- v4' : return code + '.x';
-			case 'f <- i' :
-			case 'f <- b' :	return 'float( ' + code + ' )';
-
-			case 'v2 <- f' : return 'vec2( ' + code + ' )';
-			case 'v2 <- v3': return code + '.xy';
-			case 'v2 <- v4': return code + '.xy';
-			case 'v2 <- i' :
-			case 'v2 <- b' : return 'vec2( float( ' + code + ' ) )';
-
-			case 'v3 <- f' : return 'vec3( ' + code + ' )';
-			case 'v3 <- v2': return 'vec3( ' + code + ', 0.0 )';
-			case 'v3 <- v4': return code + '.xyz';
-			case 'v3 <- i' :
-			case 'v3 <- b' : return 'vec2( float( ' + code + ' ) )';
-
-			case 'v4 <- f' : return 'vec4( ' + code + ' )';
-			case 'v4 <- v2': return 'vec4( ' + code + ', 0.0, 1.0 )';
-			case 'v4 <- v3': return 'vec4( ' + code + ', 1.0 )';
-			case 'v4 <- i' :
-			case 'v4 <- b' : return 'vec4( float( ' + code + ' ) )';
-
-			case 'i <- f' :
-			case 'i <- b' : return 'int( ' + code + ' )';
-			case 'i <- v2' : return 'int( ' + code + '.x )';
-			case 'i <- v3' : return 'int( ' + code + '.x )';
-			case 'i <- v4' : return 'int( ' + code + '.x )';
-
-			case 'b <- f' : return '( ' + code + ' != 0.0 )';
-			case 'b <- v2' : return '( ' + code + ' != vec2( 0.0 ) )';
-			case 'b <- v3' : return '( ' + code + ' != vec3( 0.0 ) )';
-			case 'b <- v4' : return '( ' + code + ' != vec4( 0.0 ) )';
-			case 'b <- i' : return '( ' + code + ' != 0 )';
-
-		}
-
-		return code;
-
-	},
-
-	getTypeByFormat: function ( format ) {
-
-		return convertFormatToType[ format ] || format;
-
-	},
-
-	getFormatByType: function ( type ) {
-
-		return convertTypeToFormat[ type ] || type;
-
-	},
-
-	getUuid: function ( uuid, useCache ) {
-
-		useCache = useCache !== undefined ? useCache : true;
-
-		if ( useCache && this.cache ) uuid = this.cache + '-' + uuid;
-
-		return uuid;
-
-	},
-
-	getElementByIndex: function ( index ) {
-
-		return elements[ index ];
-
-	},
-
-	getIndexByElement: function ( elm ) {
-
-		return elements.indexOf( elm );
-
-	},
-
-	isShader: function ( shader ) {
-
-		return this.shader === shader;
-
-	},
-
-	setShader: function ( shader ) {
-
-		this.shader = shader;
-
-		return this;
-
-	},
-
-	mergeDefines: function ( defines ) {
-
-		for ( var name in defines ) {
-
-			this.defines[ name ] = defines[ name ];
-
-		}
-
-		return this.defines;
-
-	},
-
-	mergeUniform: function ( uniforms ) {
-
-		for ( var name in uniforms ) {
-
-			this.uniforms[ name ] = uniforms[ name ];
-
-		}
-
-		return this.uniforms;
-
-	},
-
-	getTextureEncodingFromMap: function ( map, gammaOverrideLinear ) {
-
-		gammaOverrideLinear = gammaOverrideLinear !== undefined ? gammaOverrideLinear : this.context.gamma && ( this.renderer ? this.renderer.gammaInput : false );
-
-		var encoding;
-
-		if ( ! map ) {
-
-			encoding = THREE.LinearEncoding;
-
-		} else if ( map.isTexture ) {
-
-			encoding = map.encoding;
-
-		} else if ( map.isWebGLRenderTarget ) {
-
-			console.warn( "THREE.WebGLPrograms.getTextureEncodingFromMap: don't use render targets as textures. Use their .texture property instead." );
-			encoding = map.texture.encoding;
-
-		}
-
-		// add backwards compatibility for WebGLRenderer.gammaInput/gammaOutput parameter, should probably be removed at some point.
-		if ( encoding === THREE.LinearEncoding && gammaOverrideLinear ) {
-
-			encoding = THREE.GammaEncoding;
-
-		}
-
-		return encoding;
-
-	}
-
-};
-
-export { NodeBuilder };
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/NodeLib.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/NodeLib.js
deleted file mode 100644
index 63795db72b98971a0d206a3cf602dd69b68b858c..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/NodeLib.js
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-var NodeLib = {
-
-	nodes: {},
-	keywords: {},
-
-	add: function ( node ) {
-
-		this.nodes[ node.name ] = node;
-
-	},
-
-	addKeyword: function ( name, callback, cache ) {
-
-		cache = cache !== undefined ? cache : true;
-
-		this.keywords[ name ] = { callback: callback, cache: cache };
-
-	},
-
-	remove: function ( node ) {
-
-		delete this.nodes[ node.name ];
-
-	},
-
-	removeKeyword: function ( name ) {
-
-		delete this.keywords[ name ];
-
-	},
-
-	get: function ( name ) {
-
-		return this.nodes[ name ];
-
-	},
-
-	getKeyword: function ( name, material ) {
-
-		return this.keywords[ name ].callback.call( this, material );
-
-	},
-
-	getKeywordData: function ( name ) {
-
-		return this.keywords[ name ];
-
-	},
-
-	contains: function ( name ) {
-
-		return this.nodes[ name ] != undefined;
-
-	},
-
-	containsKeyword: function ( name ) {
-
-		return this.keywords[ name ] != undefined;
-
-	}
-
-};
-
-export { NodeLib };
diff --git a/spaces/basicv8vc/learning-rate-scheduler-online/README.md b/spaces/basicv8vc/learning-rate-scheduler-online/README.md
deleted file mode 100644
index 0d68175555c980ce29cd0cad5971671d8505f8d5..0000000000000000000000000000000000000000
--- a/spaces/basicv8vc/learning-rate-scheduler-online/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Learning Rate Scheduler Online
-emoji: 🔥
-colorFrom: yellow
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.9.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/bhanuprakash99/mygenAIAvatarSpeech/README.md b/spaces/bhanuprakash99/mygenAIAvatarSpeech/README.md
deleted file mode 100644
index 3962aa482e80e4da629c9e2cdd06907a0936e4da..0000000000000000000000000000000000000000
--- a/spaces/bhanuprakash99/mygenAIAvatarSpeech/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MygenAIAvatarSpeech
-emoji: 🔥
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bigjoker/stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py b/spaces/bigjoker/stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py
deleted file mode 100644
index 29ec16018858f4210f00c83a6e18c0cb7adb5e40..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import torch
-import gradio as gr
-
-import lora
-import extra_networks_lora
-import ui_extra_networks_lora
-from modules import script_callbacks, ui_extra_networks, extra_networks, shared
-
-
-def unload():
-    torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
-    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
-
-
-def before_ui():
-    ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
-    extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
-
-
-if not hasattr(torch.nn, 'Linear_forward_before_lora'):
-    torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
-
-if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
-    torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
-
-torch.nn.Linear.forward = lora.lora_Linear_forward
-torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
-
-script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
-script_callbacks.on_script_unloaded(unload)
-script_callbacks.on_before_ui(before_ui)
-
-
-shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
-    "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
-
-}))
diff --git a/spaces/bioriAsaeru/text-to-voice/Adobe Premiere Pro Cs6 Family Serial Number List !!BETTER!!.md b/spaces/bioriAsaeru/text-to-voice/Adobe Premiere Pro Cs6 Family Serial Number List !!BETTER!!.md
deleted file mode 100644
index d59d41b946ce66c24112cd4adde7ef54642f4a44..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Adobe Premiere Pro Cs6 Family Serial Number List !!BETTER!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Adobe premiere pro cs6 family serial number list</h2><br /><p><b><b>Download File</b> ->->->-> <a href="https://urloso.com/2uyOb9">https://urloso.com/2uyOb9</a></b></p><br /><br />
-
-This document was uploaded by user and they confirmed that they have the permission to share it. If you are author or own the copyright of this book, please ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Blue Dragon Xbox 360 Rom 13.md b/spaces/bioriAsaeru/text-to-voice/Blue Dragon Xbox 360 Rom 13.md
deleted file mode 100644
index ec40408caaf2913cfb2be04f960e3a41aa09874b..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Blue Dragon Xbox 360 Rom 13.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-
-<h1>How to Play Blue Dragon on Xbox 360 Emulator</h1>
-<p>Blue Dragon is a role-playing video game developed by Mistwalker and Artoon and published by Microsoft Game Studios exclusively for the Xbox 360. It was released in Japan in 2006 and worldwide in 2007. The game follows a group of young adventurers who can use their shadows to transform into powerful creatures. The game features a turn-based combat system and a colorful cel-shaded art style.</p>
-<p>If you want to play Blue Dragon on your PC, you will need an Xbox 360 emulator and a ROM file of the game. A ROM file is a copy of the game data that can be read by an emulator. An emulator is a software that mimics the hardware and software of a console, allowing you to play its games on your PC.</p>
-<h2>Blue Dragon Xbox 360 Rom 13</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://urloso.com/2uyQ9G">https://urloso.com/2uyQ9G</a></b></p><br /><br />
-<p>One of the most popular Xbox 360 emulators is Xenia, which can run many Xbox 360 games with varying degrees of compatibility and performance. You can download Xenia from its official website or its GitHub page. You will also need a powerful PC with a modern CPU and GPU to run Xenia smoothly.</p>
-<p>To get a ROM file of Blue Dragon, you will need to own a physical copy of the game and rip it from your Xbox 360 console using a compatible DVD drive. Alternatively, you can search for Blue Dragon ROMs online, but be aware that downloading ROMs from unauthorized sources may be illegal in your country. You will also need to make sure that the ROM file matches the region of your emulator.</p>
-<p>Blue Dragon is composed of three discs, so you will need three ROM files for each disc. The ROM files are usually in ISO format, which is a disc image file that contains all the data of the disc. You can use a software like WinRAR or 7-Zip to extract the ISO files from compressed archives.</p>
-<p>Once you have Xenia and the Blue Dragon ROM files, you can follow these steps to play the game:</p>
-<ol>
-<li>Launch Xenia and go to File > Open.</li>
-<li>Browse to the folder where you saved the Blue Dragon ROM files and select the first disc (Blue Dragon (USA, Europe) (En,Fr) (Disc 1).iso).</li>
-<li>The game should start loading. You may see some graphical glitches or errors, but they should not affect the gameplay.</li>
-<li>When prompted to switch discs, go to File > Open again and select the next disc (Blue Dragon (USA, Europe) (En,Fr) (Disc 2).iso or Blue Dragon (USA, Europe) (En,Fr) (Disc 3).iso).</li>
-<li>Enjoy playing Blue Dragon on your PC!</li>
-</ol>
-<p>If you have any questions or issues with Xenia or Blue Dragon, you can check out their official websites or forums for more information and support.</p>
-
-<p>Blue Dragon is one of the most acclaimed Xbox 360 games, receiving positive reviews from critics and fans alike. The game was praised for its engaging story, charming characters, beautiful graphics, and epic soundtrack. The game was also nominated for several awards, including the Best RPG of 2007 by IGN and GameSpot.</p>
-<p>The game was created by some of the most influential figures in the Japanese RPG industry, such as Hironobu Sakaguchi, the creator of Final Fantasy; Akira Toriyama, the creator of Dragon Ball; and Nobuo Uematsu, the composer of Final Fantasy. The game also features the voice talents of famous actors, such as Tara Strong, Yuri Lowenthal, and Liam O'Brien.</p>
-<p></p>
-<p>Blue Dragon is a classic JRPG that fans of the genre should not miss. The game offers a rich and immersive world to explore, a captivating story to follow, and a fun and strategic combat system to master. The game also has a lot of content and replay value, with multiple endings, side quests, mini-games, and secrets to discover.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Gta8gamefreeNEW! Downloadfullversionforpc.md b/spaces/bioriAsaeru/text-to-voice/Gta8gamefreeNEW! Downloadfullversionforpc.md
deleted file mode 100644
index 8f40159278908f5fc2a7ddcb4e3331633d784847..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Gta8gamefreeNEW! Downloadfullversionforpc.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>gta8gamefreedownloadfullversionforpc</h2><br /><p><b><b>Download Zip</b> &hArr; <a href="https://urloso.com/2uyPaN">https://urloso.com/2uyPaN</a></b></p><br /><br />
-<br />
-gta8gamefreedownloadfullversionforpc · Street Fighter 33s000 Chd.epub · 7Loader By Orbit30 VRDS v1 1 · Initial D 5th Stage 1080p Torrent. 좋아요공감. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/HD Online Player (watch [HOT] Full Movie Peccato Veniale 197).md b/spaces/bioriAsaeru/text-to-voice/HD Online Player (watch [HOT] Full Movie Peccato Veniale 197).md
deleted file mode 100644
index ae4928d5800541e966cbdfdb1df8f4761ca7b5e2..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HD Online Player (watch [HOT] Full Movie Peccato Veniale 197).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (watch full movie peccato veniale 197)</h2><br /><p><b><b>Download</b> &#10026; <a href="https://urloso.com/2uyOsD">https://urloso.com/2uyOsD</a></b></p><br /><br />
-<br />
-Peccato veniale with english subtitles laura antonelli watch online hight quality video. ... HD. 3,2k. 1:37:05. Грех, достойный прощения / peccato veniale (1974). 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bobmunzir/meta-llama-Llama-2-70b-hf/Dockerfile b/spaces/bobmunzir/meta-llama-Llama-2-70b-hf/Dockerfile
deleted file mode 100644
index fb4d04336ede050357a8846aba48ef5c42f13f88..0000000000000000000000000000000000000000
--- a/spaces/bobmunzir/meta-llama-Llama-2-70b-hf/Dockerfile
+++ /dev/null
@@ -1,121 +0,0 @@
-ARG MODEL_NAME
-ARG MODEL_PARAMS
-ARG APP_COLOR
-ARG APP_NAME
-
-
-FROM node:19 as chatui-builder
-ARG MODEL_NAME
-ARG MODEL_PARAMS
-ARG APP_COLOR
-ARG APP_NAME
-
-WORKDIR /app
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    git gettext && \
-    rm -rf /var/lib/apt/lists/*
-
-
-RUN git clone https://github.com/huggingface/chat-ui.git
-
-WORKDIR /app/chat-ui
-
-
-COPY .env.local.template .env.local.template
-
-RUN mkdir defaults
-ADD defaults /defaults
-RUN chmod -R 777 /defaults
-RUN --mount=type=secret,id=MONGODB_URL,mode=0444 \
-    MODEL_NAME="${MODEL_NAME:="$(cat /defaults/MODEL_NAME)"}" && export MODEL_NAME \
-    && MODEL_PARAMS="${MODEL_PARAMS:="$(cat /defaults/MODEL_PARAMS)"}" && export MODEL_PARAMS \
-    && APP_COLOR="${APP_COLOR:="$(cat /defaults/APP_COLOR)"}" && export APP_COLOR \
-    && APP_NAME="${APP_NAME:="$(cat /defaults/APP_NAME)"}" && export APP_NAME \
-    && MONGODB_URL=$(cat /run/secrets/MONGODB_URL > /dev/null | grep '^' || cat /defaults/MONGODB_URL) && export MONGODB_URL && \
-    echo "${MONGODB_URL}" && \
-    envsubst < ".env.local.template" > ".env.local" \ 
-    && rm .env.local.template
-
-
-
-RUN --mount=type=cache,target=/app/.npm \
-    npm set cache /app/.npm && \
-    npm ci
-
-RUN npm run build
-
-FROM ghcr.io/huggingface/text-generation-inference:0.9.4
-
-ARG MODEL_NAME
-ARG MODEL_PARAMS
-ARG APP_COLOR
-ARG APP_NAME
-
-ENV TZ=Europe/Paris \
-    PORT=3000
-
-
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    gnupg \
-    curl \
-    gettext && \
-    rm -rf /var/lib/apt/lists/*
-COPY entrypoint.sh.template entrypoint.sh.template
-
-RUN mkdir defaults
-ADD defaults /defaults
-RUN chmod -R 777 /defaults
-
-RUN --mount=type=secret,id=MONGODB_URL,mode=0444 \
-    MODEL_NAME="${MODEL_NAME:="$(cat /defaults/MODEL_NAME)"}" && export MODEL_NAME \
-    && MODEL_PARAMS="${MODEL_PARAMS:="$(cat /defaults/MODEL_PARAMS)"}" && export MODEL_PARAMS \
-    && APP_COLOR="${APP_COLOR:="$(cat /defaults/APP_COLOR)"}" && export APP_COLOR \
-    && APP_NAME="${APP_NAME:="$(cat /defaults/APP_NAME)"}" && export APP_NAME \
-    && MONGODB_URL=$(cat /run/secrets/MONGODB_URL > /dev/null | grep '^' || cat /defaults/MONGODB_URL) && export MONGODB_URL &&  \
-    envsubst < "entrypoint.sh.template" > "entrypoint.sh" \
-    && rm entrypoint.sh.template
-
-
-RUN curl -fsSL https://pgp.mongodb.com/server-6.0.asc | \
-    gpg -o /usr/share/keyrings/mongodb-server-6.0.gpg \
-    --dearmor
-
-RUN echo "deb [ arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-6.0.gpg ] https://repo.mongodb.org/apt/ubuntu focal/mongodb-org/6.0 multiverse" | tee /etc/apt/sources.list.d/mongodb-org-6.0.list
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    mongodb-org && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN mkdir -p /data/db
-RUN chown -R 1000:1000 /data
-
-RUN curl -fsSL https://deb.nodesource.com/setup_19.x | /bin/bash -
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    nodejs && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN mkdir /app
-RUN chown -R 1000:1000 /app
-
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-RUN npm config set prefix /home/user/.local
-RUN npm install -g pm2
-
-COPY --from=chatui-builder --chown=1000 /app/chat-ui/node_modules /app/node_modules
-COPY --from=chatui-builder --chown=1000 /app/chat-ui/package.json /app/package.json
-COPY --from=chatui-builder --chown=1000 /app/chat-ui/build /app/build
-
-ENTRYPOINT ["/bin/bash"]
-CMD ["entrypoint.sh"]
-
-
diff --git a/spaces/bright1/Sepsis-Prediction-API/src/app/static/styles.css b/spaces/bright1/Sepsis-Prediction-API/src/app/static/styles.css
deleted file mode 100644
index 6d9c6083bde844a5edadfc1e174b076bd8e2f7d2..0000000000000000000000000000000000000000
--- a/spaces/bright1/Sepsis-Prediction-API/src/app/static/styles.css
+++ /dev/null
@@ -1,7 +0,0 @@
-h1 {
-    color:rgb(81, 146, 43);
-}
-
-p {
-    font-weight: 600;
-}
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_matcher.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_matcher.py
deleted file mode 100644
index 6eb2db0c24b117337c431e9ef00a85a3bced71b9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_matcher.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import unittest
-from typing import List
-import torch
-
-from detectron2.config import get_cfg
-from detectron2.modeling.matcher import Matcher
-
-
-class TestMatcher(unittest.TestCase):
-    def test_scriptability(self):
-        cfg = get_cfg()
-        anchor_matcher = Matcher(
-            cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True
-        )
-        match_quality_matrix = torch.tensor(
-            [[0.15, 0.45, 0.2, 0.6], [0.3, 0.65, 0.05, 0.1], [0.05, 0.4, 0.25, 0.4]]
-        )
-        expected_matches = torch.tensor([1, 1, 2, 0])
-        expected_match_labels = torch.tensor([-1, 1, 0, 1], dtype=torch.int8)
-
-        matches, match_labels = anchor_matcher(match_quality_matrix)
-        self.assertTrue(torch.allclose(matches, expected_matches))
-        self.assertTrue(torch.allclose(match_labels, expected_match_labels))
-
-        # nonzero_tuple must be import explicitly to let jit know what it is.
-        # https://github.com/pytorch/pytorch/issues/38964
-        from detectron2.layers import nonzero_tuple  # noqa F401
-
-        def f(thresholds: List[float], labels: List[int]):
-            return Matcher(thresholds, labels, allow_low_quality_matches=True)
-
-        scripted_anchor_matcher = torch.jit.script(f)(
-            cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS
-        )
-        matches, match_labels = scripted_anchor_matcher(match_quality_matrix)
-        self.assertTrue(torch.allclose(matches, expected_matches))
-        self.assertTrue(torch.allclose(match_labels, expected_match_labels))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/cadige/03-Streamlit-Video/README.md b/spaces/cadige/03-Streamlit-Video/README.md
deleted file mode 100644
index a3b5020ab776eb1285f8b4934afea1c28a02b428..0000000000000000000000000000000000000000
--- a/spaces/cadige/03-Streamlit-Video/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 03 Streamlit Video
-emoji: 📉
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/helpers.py b/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/helpers.py
deleted file mode 100644
index b51fdf97141407fcc1c9d249a086ddbfd042469f..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/helpers.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from collections import namedtuple
-import torch
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
-	def forward(self, input):
-		return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
-	norm = torch.norm(input, 2, axis, True)
-	output = torch.div(input, norm)
-	return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
-	""" A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
-	return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
-	if num_layers == 50:
-		blocks = [
-			get_block(in_channel=64, depth=64, num_units=3),
-			get_block(in_channel=64, depth=128, num_units=4),
-			get_block(in_channel=128, depth=256, num_units=14),
-			get_block(in_channel=256, depth=512, num_units=3)
-		]
-	elif num_layers == 100:
-		blocks = [
-			get_block(in_channel=64, depth=64, num_units=3),
-			get_block(in_channel=64, depth=128, num_units=13),
-			get_block(in_channel=128, depth=256, num_units=30),
-			get_block(in_channel=256, depth=512, num_units=3)
-		]
-	elif num_layers == 152:
-		blocks = [
-			get_block(in_channel=64, depth=64, num_units=3),
-			get_block(in_channel=64, depth=128, num_units=8),
-			get_block(in_channel=128, depth=256, num_units=36),
-			get_block(in_channel=256, depth=512, num_units=3)
-		]
-	else:
-		raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
-	return blocks
-
-
-class SEModule(Module):
-	def __init__(self, channels, reduction):
-		super(SEModule, self).__init__()
-		self.avg_pool = AdaptiveAvgPool2d(1)
-		self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
-		self.relu = ReLU(inplace=True)
-		self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
-		self.sigmoid = Sigmoid()
-
-	def forward(self, x):
-		module_input = x
-		x = self.avg_pool(x)
-		x = self.fc1(x)
-		x = self.relu(x)
-		x = self.fc2(x)
-		x = self.sigmoid(x)
-		return module_input * x
-
-
-class bottleneck_IR(Module):
-	def __init__(self, in_channel, depth, stride):
-		super(bottleneck_IR, self).__init__()
-		if in_channel == depth:
-			self.shortcut_layer = MaxPool2d(1, stride)
-		else:
-			self.shortcut_layer = Sequential(
-				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-				BatchNorm2d(depth)
-			)
-		self.res_layer = Sequential(
-			BatchNorm2d(in_channel),
-			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
-			Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
-		)
-
-	def forward(self, x):
-		shortcut = self.shortcut_layer(x)
-		res = self.res_layer(x)
-		return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
-	def __init__(self, in_channel, depth, stride):
-		super(bottleneck_IR_SE, self).__init__()
-		if in_channel == depth:
-			self.shortcut_layer = MaxPool2d(1, stride)
-		else:
-			self.shortcut_layer = Sequential(
-				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-				BatchNorm2d(depth)
-			)
-		self.res_layer = Sequential(
-			BatchNorm2d(in_channel),
-			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
-			PReLU(depth),
-			Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
-			BatchNorm2d(depth),
-			SEModule(depth, 16)
-		)
-
-	def forward(self, x):
-		shortcut = self.shortcut_layer(x)
-		res = self.res_layer(x)
-		return res + shortcut
diff --git a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/task_planning.py b/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/task_planning.py
deleted file mode 100644
index 480fccddf0220a0e6962dd8b33b74da936fd8d82..0000000000000000000000000000000000000000
--- a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/task_planning.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import logging
-
-from langchain import LLMChain
-from langchain.llms.base import BaseLLM
-from langchain.prompts import load_prompt
-
-from hugginggpt.exceptions import TaskPlanningException, wrap_exceptions
-from hugginggpt.history import ConversationHistory
-from hugginggpt.llm_factory import LLM_MAX_TOKENS, count_tokens
-from hugginggpt.resources import get_prompt_resource
-from hugginggpt.task_parsing import Task, parse_tasks
-
-logger = logging.getLogger(__name__)
-
-MAIN_PROMPT_TOKENS = 800
-MAX_HISTORY_TOKENS = LLM_MAX_TOKENS - MAIN_PROMPT_TOKENS
-
-
-@wrap_exceptions(TaskPlanningException, "Failed to plan tasks")
-def plan_tasks(
-    user_input: str, history: ConversationHistory, llm: BaseLLM
-) -> list[Task]:
-    """Use LLM agent to plan tasks in order solve user request."""
-    logger.info("Starting task planning")
-    task_planning_prompt_template = load_prompt(
-        get_prompt_resource("task-planning-few-shot-prompt.json")
-    )
-    llm_chain = LLMChain(prompt=task_planning_prompt_template, llm=llm)
-    history_truncated = truncate_history(history)
-    output = llm_chain.predict(
-        user_input=user_input, history=history_truncated, stop=["<im_end>"]
-    )
-    logger.info(f"Task planning raw output: {output}")
-    tasks = parse_tasks(output)
-    return tasks
-
-
-def truncate_history(history: ConversationHistory) -> ConversationHistory:
-    """Truncate history to fit within the max token limit for the task planning LLM"""
-    example_prompt_template = load_prompt(
-        get_prompt_resource("task-planning-example-prompt.json")
-    )
-    token_counter = 0
-    n_messages = 0
-    # Iterate through history backwards in pairs, to ensure most recent messages are kept
-    for i in range(0, len(history), 2):
-        user_message = history[-(i + 2)]
-        assistant_message = history[-(i + 1)]
-        # Turn messages into LLM prompt string
-        history_text = example_prompt_template.format(
-            example_input=user_message["content"],
-            example_output=assistant_message["content"],
-        )
-        n_tokens = count_tokens(history_text)
-        if token_counter + n_tokens <= MAX_HISTORY_TOKENS:
-            n_messages += 2
-            token_counter += n_tokens
-        else:
-            break
-    start = len(history) - n_messages
-    return history[start:]
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/McIdasImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/McIdasImagePlugin.py
deleted file mode 100644
index 17c008b9a6a1218f6e51add4fda83acb92ee06ce..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/McIdasImagePlugin.py
+++ /dev/null
@@ -1,75 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Basic McIdas support for PIL
-#
-# History:
-# 1997-05-05 fl  Created (8-bit images only)
-# 2009-03-08 fl  Added 16/32-bit support.
-#
-# Thanks to Richard Jones and Craig Swank for specs and samples.
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import struct
-
-from . import Image, ImageFile
-
-
-def _accept(s):
-    return s[:8] == b"\x00\x00\x00\x00\x00\x00\x00\x04"
-
-
-##
-# Image plugin for McIdas area images.
-
-
-class McIdasImageFile(ImageFile.ImageFile):
-    format = "MCIDAS"
-    format_description = "McIdas area file"
-
-    def _open(self):
-        # parse area file directory
-        s = self.fp.read(256)
-        if not _accept(s) or len(s) != 256:
-            msg = "not an McIdas area file"
-            raise SyntaxError(msg)
-
-        self.area_descriptor_raw = s
-        self.area_descriptor = w = [0] + list(struct.unpack("!64i", s))
-
-        # get mode
-        if w[11] == 1:
-            mode = rawmode = "L"
-        elif w[11] == 2:
-            # FIXME: add memory map support
-            mode = "I"
-            rawmode = "I;16B"
-        elif w[11] == 4:
-            # FIXME: add memory map support
-            mode = "I"
-            rawmode = "I;32B"
-        else:
-            msg = "unsupported McIdas format"
-            raise SyntaxError(msg)
-
-        self.mode = mode
-        self._size = w[10], w[9]
-
-        offset = w[34] + w[15]
-        stride = w[15] + w[10] * w[11] * w[14]
-
-        self.tile = [("raw", (0, 0) + self.size, offset, (rawmode, stride, 1))]
-
-
-# --------------------------------------------------------------------
-# registry
-
-Image.register_open(McIdasImageFile.format, McIdasImageFile, _accept)
-
-# no default extension
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/cookiejar.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/cookiejar.py
deleted file mode 100644
index 6c88b47e3583430e05ea671af5b6da2a557073ec..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/cookiejar.py
+++ /dev/null
@@ -1,415 +0,0 @@
-import asyncio
-import contextlib
-import datetime
-import os  # noqa
-import pathlib
-import pickle
-import re
-from collections import defaultdict
-from http.cookies import BaseCookie, Morsel, SimpleCookie
-from typing import (  # noqa
-    DefaultDict,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Set,
-    Tuple,
-    Union,
-    cast,
-)
-
-from yarl import URL
-
-from .abc import AbstractCookieJar, ClearCookiePredicate
-from .helpers import is_ip_address, next_whole_second
-from .typedefs import LooseCookies, PathLike, StrOrURL
-
-__all__ = ("CookieJar", "DummyCookieJar")
-
-
-CookieItem = Union[str, "Morsel[str]"]
-
-
-class CookieJar(AbstractCookieJar):
-    """Implements cookie storage adhering to RFC 6265."""
-
-    DATE_TOKENS_RE = re.compile(
-        r"[\x09\x20-\x2F\x3B-\x40\x5B-\x60\x7B-\x7E]*"
-        r"(?P<token>[\x00-\x08\x0A-\x1F\d:a-zA-Z\x7F-\xFF]+)"
-    )
-
-    DATE_HMS_TIME_RE = re.compile(r"(\d{1,2}):(\d{1,2}):(\d{1,2})")
-
-    DATE_DAY_OF_MONTH_RE = re.compile(r"(\d{1,2})")
-
-    DATE_MONTH_RE = re.compile(
-        "(jan)|(feb)|(mar)|(apr)|(may)|(jun)|(jul)|" "(aug)|(sep)|(oct)|(nov)|(dec)",
-        re.I,
-    )
-
-    DATE_YEAR_RE = re.compile(r"(\d{2,4})")
-
-    MAX_TIME = datetime.datetime.max.replace(tzinfo=datetime.timezone.utc)
-
-    MAX_32BIT_TIME = datetime.datetime.utcfromtimestamp(2**31 - 1)
-
-    def __init__(
-        self,
-        *,
-        unsafe: bool = False,
-        quote_cookie: bool = True,
-        treat_as_secure_origin: Union[StrOrURL, List[StrOrURL], None] = None,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> None:
-        super().__init__(loop=loop)
-        self._cookies: DefaultDict[Tuple[str, str], SimpleCookie[str]] = defaultdict(
-            SimpleCookie
-        )
-        self._host_only_cookies: Set[Tuple[str, str]] = set()
-        self._unsafe = unsafe
-        self._quote_cookie = quote_cookie
-        if treat_as_secure_origin is None:
-            treat_as_secure_origin = []
-        elif isinstance(treat_as_secure_origin, URL):
-            treat_as_secure_origin = [treat_as_secure_origin.origin()]
-        elif isinstance(treat_as_secure_origin, str):
-            treat_as_secure_origin = [URL(treat_as_secure_origin).origin()]
-        else:
-            treat_as_secure_origin = [
-                URL(url).origin() if isinstance(url, str) else url.origin()
-                for url in treat_as_secure_origin
-            ]
-        self._treat_as_secure_origin = treat_as_secure_origin
-        self._next_expiration = next_whole_second()
-        self._expirations: Dict[Tuple[str, str, str], datetime.datetime] = {}
-        # #4515: datetime.max may not be representable on 32-bit platforms
-        self._max_time = self.MAX_TIME
-        try:
-            self._max_time.timestamp()
-        except OverflowError:
-            self._max_time = self.MAX_32BIT_TIME
-
-    def save(self, file_path: PathLike) -> None:
-        file_path = pathlib.Path(file_path)
-        with file_path.open(mode="wb") as f:
-            pickle.dump(self._cookies, f, pickle.HIGHEST_PROTOCOL)
-
-    def load(self, file_path: PathLike) -> None:
-        file_path = pathlib.Path(file_path)
-        with file_path.open(mode="rb") as f:
-            self._cookies = pickle.load(f)
-
-    def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None:
-        if predicate is None:
-            self._next_expiration = next_whole_second()
-            self._cookies.clear()
-            self._host_only_cookies.clear()
-            self._expirations.clear()
-            return
-
-        to_del = []
-        now = datetime.datetime.now(datetime.timezone.utc)
-        for (domain, path), cookie in self._cookies.items():
-            for name, morsel in cookie.items():
-                key = (domain, path, name)
-                if (
-                    key in self._expirations and self._expirations[key] <= now
-                ) or predicate(morsel):
-                    to_del.append(key)
-
-        for domain, path, name in to_del:
-            self._host_only_cookies.discard((domain, name))
-            key = (domain, path, name)
-            if key in self._expirations:
-                del self._expirations[(domain, path, name)]
-            self._cookies[(domain, path)].pop(name, None)
-
-        next_expiration = min(self._expirations.values(), default=self._max_time)
-        try:
-            self._next_expiration = next_expiration.replace(
-                microsecond=0
-            ) + datetime.timedelta(seconds=1)
-        except OverflowError:
-            self._next_expiration = self._max_time
-
-    def clear_domain(self, domain: str) -> None:
-        self.clear(lambda x: self._is_domain_match(domain, x["domain"]))
-
-    def __iter__(self) -> "Iterator[Morsel[str]]":
-        self._do_expiration()
-        for val in self._cookies.values():
-            yield from val.values()
-
-    def __len__(self) -> int:
-        return sum(1 for i in self)
-
-    def _do_expiration(self) -> None:
-        self.clear(lambda x: False)
-
-    def _expire_cookie(
-        self, when: datetime.datetime, domain: str, path: str, name: str
-    ) -> None:
-        self._next_expiration = min(self._next_expiration, when)
-        self._expirations[(domain, path, name)] = when
-
-    def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None:
-        """Update cookies."""
-        hostname = response_url.raw_host
-
-        if not self._unsafe and is_ip_address(hostname):
-            # Don't accept cookies from IPs
-            return
-
-        if isinstance(cookies, Mapping):
-            cookies = cookies.items()
-
-        for name, cookie in cookies:
-            if not isinstance(cookie, Morsel):
-                tmp: SimpleCookie[str] = SimpleCookie()
-                tmp[name] = cookie  # type: ignore[assignment]
-                cookie = tmp[name]
-
-            domain = cookie["domain"]
-
-            # ignore domains with trailing dots
-            if domain.endswith("."):
-                domain = ""
-                del cookie["domain"]
-
-            if not domain and hostname is not None:
-                # Set the cookie's domain to the response hostname
-                # and set its host-only-flag
-                self._host_only_cookies.add((hostname, name))
-                domain = cookie["domain"] = hostname
-
-            if domain.startswith("."):
-                # Remove leading dot
-                domain = domain[1:]
-                cookie["domain"] = domain
-
-            if hostname and not self._is_domain_match(domain, hostname):
-                # Setting cookies for different domains is not allowed
-                continue
-
-            path = cookie["path"]
-            if not path or not path.startswith("/"):
-                # Set the cookie's path to the response path
-                path = response_url.path
-                if not path.startswith("/"):
-                    path = "/"
-                else:
-                    # Cut everything from the last slash to the end
-                    path = "/" + path[1 : path.rfind("/")]
-                cookie["path"] = path
-
-            max_age = cookie["max-age"]
-            if max_age:
-                try:
-                    delta_seconds = int(max_age)
-                    try:
-                        max_age_expiration = datetime.datetime.now(
-                            datetime.timezone.utc
-                        ) + datetime.timedelta(seconds=delta_seconds)
-                    except OverflowError:
-                        max_age_expiration = self._max_time
-                    self._expire_cookie(max_age_expiration, domain, path, name)
-                except ValueError:
-                    cookie["max-age"] = ""
-
-            else:
-                expires = cookie["expires"]
-                if expires:
-                    expire_time = self._parse_date(expires)
-                    if expire_time:
-                        self._expire_cookie(expire_time, domain, path, name)
-                    else:
-                        cookie["expires"] = ""
-
-            self._cookies[(domain, path)][name] = cookie
-
-        self._do_expiration()
-
-    def filter_cookies(
-        self, request_url: URL = URL()
-    ) -> Union["BaseCookie[str]", "SimpleCookie[str]"]:
-        """Returns this jar's cookies filtered by their attributes."""
-        self._do_expiration()
-        request_url = URL(request_url)
-        filtered: Union["SimpleCookie[str]", "BaseCookie[str]"] = (
-            SimpleCookie() if self._quote_cookie else BaseCookie()
-        )
-        hostname = request_url.raw_host or ""
-        request_origin = URL()
-        with contextlib.suppress(ValueError):
-            request_origin = request_url.origin()
-
-        is_not_secure = (
-            request_url.scheme not in ("https", "wss")
-            and request_origin not in self._treat_as_secure_origin
-        )
-
-        for cookie in self:
-            name = cookie.key
-            domain = cookie["domain"]
-
-            # Send shared cookies
-            if not domain:
-                filtered[name] = cookie.value
-                continue
-
-            if not self._unsafe and is_ip_address(hostname):
-                continue
-
-            if (domain, name) in self._host_only_cookies:
-                if domain != hostname:
-                    continue
-            elif not self._is_domain_match(domain, hostname):
-                continue
-
-            if not self._is_path_match(request_url.path, cookie["path"]):
-                continue
-
-            if is_not_secure and cookie["secure"]:
-                continue
-
-            # It's critical we use the Morsel so the coded_value
-            # (based on cookie version) is preserved
-            mrsl_val = cast("Morsel[str]", cookie.get(cookie.key, Morsel()))
-            mrsl_val.set(cookie.key, cookie.value, cookie.coded_value)
-            filtered[name] = mrsl_val
-
-        return filtered
-
-    @staticmethod
-    def _is_domain_match(domain: str, hostname: str) -> bool:
-        """Implements domain matching adhering to RFC 6265."""
-        if hostname == domain:
-            return True
-
-        if not hostname.endswith(domain):
-            return False
-
-        non_matching = hostname[: -len(domain)]
-
-        if not non_matching.endswith("."):
-            return False
-
-        return not is_ip_address(hostname)
-
-    @staticmethod
-    def _is_path_match(req_path: str, cookie_path: str) -> bool:
-        """Implements path matching adhering to RFC 6265."""
-        if not req_path.startswith("/"):
-            req_path = "/"
-
-        if req_path == cookie_path:
-            return True
-
-        if not req_path.startswith(cookie_path):
-            return False
-
-        if cookie_path.endswith("/"):
-            return True
-
-        non_matching = req_path[len(cookie_path) :]
-
-        return non_matching.startswith("/")
-
-    @classmethod
-    def _parse_date(cls, date_str: str) -> Optional[datetime.datetime]:
-        """Implements date string parsing adhering to RFC 6265."""
-        if not date_str:
-            return None
-
-        found_time = False
-        found_day = False
-        found_month = False
-        found_year = False
-
-        hour = minute = second = 0
-        day = 0
-        month = 0
-        year = 0
-
-        for token_match in cls.DATE_TOKENS_RE.finditer(date_str):
-
-            token = token_match.group("token")
-
-            if not found_time:
-                time_match = cls.DATE_HMS_TIME_RE.match(token)
-                if time_match:
-                    found_time = True
-                    hour, minute, second = (int(s) for s in time_match.groups())
-                    continue
-
-            if not found_day:
-                day_match = cls.DATE_DAY_OF_MONTH_RE.match(token)
-                if day_match:
-                    found_day = True
-                    day = int(day_match.group())
-                    continue
-
-            if not found_month:
-                month_match = cls.DATE_MONTH_RE.match(token)
-                if month_match:
-                    found_month = True
-                    assert month_match.lastindex is not None
-                    month = month_match.lastindex
-                    continue
-
-            if not found_year:
-                year_match = cls.DATE_YEAR_RE.match(token)
-                if year_match:
-                    found_year = True
-                    year = int(year_match.group())
-
-        if 70 <= year <= 99:
-            year += 1900
-        elif 0 <= year <= 69:
-            year += 2000
-
-        if False in (found_day, found_month, found_year, found_time):
-            return None
-
-        if not 1 <= day <= 31:
-            return None
-
-        if year < 1601 or hour > 23 or minute > 59 or second > 59:
-            return None
-
-        return datetime.datetime(
-            year, month, day, hour, minute, second, tzinfo=datetime.timezone.utc
-        )
-
-
-class DummyCookieJar(AbstractCookieJar):
-    """Implements a dummy cookie storage.
-
-    It can be used with the ClientSession when no cookie processing is needed.
-
-    """
-
-    def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None) -> None:
-        super().__init__(loop=loop)
-
-    def __iter__(self) -> "Iterator[Morsel[str]]":
-        while False:
-            yield None
-
-    def __len__(self) -> int:
-        return 0
-
-    def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None:
-        pass
-
-    def clear_domain(self, domain: str) -> None:
-        pass
-
-    def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None:
-        pass
-
-    def filter_cookies(self, request_url: URL) -> "BaseCookie[str]":
-        return SimpleCookie()
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/modeling/test_roi_heads.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/modeling/test_roi_heads.py
deleted file mode 100644
index 86360e1e36bf2e2d969db426eb11e54318a95385..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/modeling/test_roi_heads.py
+++ /dev/null
@@ -1,323 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-from copy import deepcopy
-import torch
-from torch import nn
-
-from detectron2 import model_zoo
-from detectron2.config import get_cfg
-from detectron2.export.torchscript_patch import (
-    freeze_training_mode,
-    patch_builtin_len,
-    patch_instances,
-)
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.proposal_generator.build import build_proposal_generator
-from detectron2.modeling.roi_heads import (
-    FastRCNNConvFCHead,
-    KRCNNConvDeconvUpsampleHead,
-    MaskRCNNConvUpsampleHead,
-    StandardROIHeads,
-    build_roi_heads,
-)
-from detectron2.projects import point_rend
-from detectron2.structures import BitMasks, Boxes, ImageList, Instances, RotatedBoxes
-from detectron2.utils.events import EventStorage
-from detectron2.utils.testing import assert_instances_allclose, random_boxes
-
-logger = logging.getLogger(__name__)
-
-"""
-Make sure the losses of ROIHeads/RPN do not change, to avoid
-breaking the forward logic by mistake.
-This relies on assumption that pytorch's RNG is stable.
-"""
-
-
-class ROIHeadsTest(unittest.TestCase):
-    def test_roi_heads(self):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
-        cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
-        cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
-        cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
-        cfg.MODEL.MASK_ON = True
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)}
-
-        image_shape = (15, 15)
-        gt_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
-        gt_instance0 = Instances(image_shape)
-        gt_instance0.gt_boxes = Boxes(gt_boxes0)
-        gt_instance0.gt_classes = torch.tensor([2, 1])
-        gt_instance0.gt_masks = BitMasks(torch.rand((2,) + image_shape) > 0.5)
-        gt_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32)
-        gt_instance1 = Instances(image_shape)
-        gt_instance1.gt_boxes = Boxes(gt_boxes1)
-        gt_instance1.gt_classes = torch.tensor([1, 2])
-        gt_instance1.gt_masks = BitMasks(torch.rand((2,) + image_shape) > 0.5)
-        gt_instances = [gt_instance0, gt_instance1]
-
-        proposal_generator = build_proposal_generator(cfg, feature_shape)
-        roi_heads = StandardROIHeads(cfg, feature_shape)
-
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(images, features, gt_instances)
-            _, detector_losses = roi_heads(images, features, proposals, gt_instances)
-
-        detector_losses.update(proposal_losses)
-        expected_losses = {
-            "loss_cls": 4.5253729820251465,
-            "loss_box_reg": 0.009785720147192478,
-            "loss_mask": 0.693184494972229,
-            "loss_rpn_cls": 0.08186662942171097,
-            "loss_rpn_loc": 0.1104838103055954,
-        }
-        succ = all(
-            torch.allclose(detector_losses[name], torch.tensor(expected_losses.get(name, 0.0)))
-            for name in detector_losses.keys()
-        )
-        self.assertTrue(
-            succ,
-            "Losses has changed! New losses: {}".format(
-                {k: v.item() for k, v in detector_losses.items()}
-            ),
-        )
-
-    def test_rroi_heads(self):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN"
-        cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator"
-        cfg.MODEL.ROI_HEADS.NAME = "RROIHeads"
-        cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
-        cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
-        cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
-        cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead"
-        cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignRotated"
-        cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1)
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)}
-
-        image_shape = (15, 15)
-        gt_boxes0 = torch.tensor([[2, 2, 2, 2, 30], [4, 4, 4, 4, 0]], dtype=torch.float32)
-        gt_instance0 = Instances(image_shape)
-        gt_instance0.gt_boxes = RotatedBoxes(gt_boxes0)
-        gt_instance0.gt_classes = torch.tensor([2, 1])
-        gt_boxes1 = torch.tensor([[1.5, 5.5, 1, 3, 0], [8.5, 4, 3, 2, -50]], dtype=torch.float32)
-        gt_instance1 = Instances(image_shape)
-        gt_instance1.gt_boxes = RotatedBoxes(gt_boxes1)
-        gt_instance1.gt_classes = torch.tensor([1, 2])
-        gt_instances = [gt_instance0, gt_instance1]
-
-        proposal_generator = build_proposal_generator(cfg, feature_shape)
-        roi_heads = build_roi_heads(cfg, feature_shape)
-
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(images, features, gt_instances)
-            _, detector_losses = roi_heads(images, features, proposals, gt_instances)
-
-        detector_losses.update(proposal_losses)
-        expected_losses = {
-            "loss_cls": 4.365657806396484,
-            "loss_box_reg": 0.0015851043863222003,
-            "loss_rpn_cls": 0.2427729219198227,
-            "loss_rpn_loc": 0.3646621108055115,
-        }
-        succ = all(
-            torch.allclose(detector_losses[name], torch.tensor(expected_losses.get(name, 0.0)))
-            for name in detector_losses.keys()
-        )
-        self.assertTrue(
-            succ,
-            "Losses has changed! New losses: {}".format(
-                {k: v.item() for k, v in detector_losses.items()}
-            ),
-        )
-
-    def test_box_head_scriptability(self):
-        input_shape = ShapeSpec(channels=1024, height=14, width=14)
-        box_features = torch.randn(4, 1024, 14, 14)
-
-        box_head = FastRCNNConvFCHead(
-            input_shape, conv_dims=[512, 512], fc_dims=[1024, 1024]
-        ).eval()
-        script_box_head = torch.jit.script(box_head)
-
-        origin_output = box_head(box_features)
-        script_output = script_box_head(box_features)
-        self.assertTrue(torch.equal(origin_output, script_output))
-
-    def test_mask_head_scriptability(self):
-        input_shape = ShapeSpec(channels=1024)
-        mask_features = torch.randn(4, 1024, 14, 14)
-
-        image_shapes = [(10, 10), (15, 15)]
-        pred_instance0 = Instances(image_shapes[0])
-        pred_classes0 = torch.tensor([1, 2, 3], dtype=torch.int64)
-        pred_instance0.pred_classes = pred_classes0
-        pred_instance1 = Instances(image_shapes[1])
-        pred_classes1 = torch.tensor([4], dtype=torch.int64)
-        pred_instance1.pred_classes = pred_classes1
-
-        mask_head = MaskRCNNConvUpsampleHead(
-            input_shape, num_classes=80, conv_dims=[256, 256]
-        ).eval()
-        # pred_instance will be in-place changed during the inference
-        # process of `MaskRCNNConvUpsampleHead`
-        origin_outputs = mask_head(mask_features, deepcopy([pred_instance0, pred_instance1]))
-
-        fields = {"pred_masks": torch.Tensor, "pred_classes": torch.Tensor}
-        with freeze_training_mode(mask_head), patch_instances(fields) as NewInstances:
-            sciript_mask_head = torch.jit.script(mask_head)
-            pred_instance0 = NewInstances.from_instances(pred_instance0)
-            pred_instance1 = NewInstances.from_instances(pred_instance1)
-            script_outputs = sciript_mask_head(mask_features, [pred_instance0, pred_instance1])
-
-        for origin_ins, script_ins in zip(origin_outputs, script_outputs):
-            assert_instances_allclose(origin_ins, script_ins, rtol=0)
-
-    def test_keypoint_head_scriptability(self):
-        input_shape = ShapeSpec(channels=1024, height=14, width=14)
-        keypoint_features = torch.randn(4, 1024, 14, 14)
-
-        image_shapes = [(10, 10), (15, 15)]
-        pred_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6], [1, 5, 2, 8]], dtype=torch.float32)
-        pred_instance0 = Instances(image_shapes[0])
-        pred_instance0.pred_boxes = Boxes(pred_boxes0)
-        pred_boxes1 = torch.tensor([[7, 3, 10, 5]], dtype=torch.float32)
-        pred_instance1 = Instances(image_shapes[1])
-        pred_instance1.pred_boxes = Boxes(pred_boxes1)
-
-        keypoint_head = KRCNNConvDeconvUpsampleHead(
-            input_shape, num_keypoints=17, conv_dims=[512, 512]
-        ).eval()
-        origin_outputs = keypoint_head(
-            keypoint_features, deepcopy([pred_instance0, pred_instance1])
-        )
-
-        fields = {
-            "pred_boxes": Boxes,
-            "pred_keypoints": torch.Tensor,
-            "pred_keypoint_heatmaps": torch.Tensor,
-        }
-        with freeze_training_mode(keypoint_head), patch_instances(fields) as NewInstances:
-            script_keypoint_head = torch.jit.script(keypoint_head)
-            pred_instance0 = NewInstances.from_instances(pred_instance0)
-            pred_instance1 = NewInstances.from_instances(pred_instance1)
-            script_outputs = script_keypoint_head(
-                keypoint_features, [pred_instance0, pred_instance1]
-            )
-
-        for origin_ins, script_ins in zip(origin_outputs, script_outputs):
-            assert_instances_allclose(origin_ins, script_ins, rtol=0)
-
-    def test_StandardROIHeads_scriptability(self):
-        cfg = get_cfg()
-        cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
-        cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
-        cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
-        cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
-        cfg.MODEL.MASK_ON = True
-        cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.01
-        cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.01
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)}
-
-        roi_heads = StandardROIHeads(cfg, feature_shape).eval()
-
-        proposal0 = Instances(image_sizes[0])
-        proposal_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
-        proposal0.proposal_boxes = Boxes(proposal_boxes0)
-        proposal0.objectness_logits = torch.tensor([0.5, 0.7], dtype=torch.float32)
-
-        proposal1 = Instances(image_sizes[1])
-        proposal_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32)
-        proposal1.proposal_boxes = Boxes(proposal_boxes1)
-        proposal1.objectness_logits = torch.tensor([0.1, 0.9], dtype=torch.float32)
-        proposals = [proposal0, proposal1]
-
-        pred_instances, _ = roi_heads(images, features, proposals)
-        fields = {
-            "objectness_logits": torch.Tensor,
-            "proposal_boxes": Boxes,
-            "pred_classes": torch.Tensor,
-            "scores": torch.Tensor,
-            "pred_masks": torch.Tensor,
-            "pred_boxes": Boxes,
-            "pred_keypoints": torch.Tensor,
-            "pred_keypoint_heatmaps": torch.Tensor,
-        }
-        with freeze_training_mode(roi_heads), patch_instances(fields) as new_instances:
-            proposal0 = new_instances.from_instances(proposal0)
-            proposal1 = new_instances.from_instances(proposal1)
-            proposals = [proposal0, proposal1]
-            scripted_rot_heads = torch.jit.script(roi_heads)
-            scripted_pred_instances, _ = scripted_rot_heads(images, features, proposals)
-
-        for instance, scripted_instance in zip(pred_instances, scripted_pred_instances):
-            assert_instances_allclose(instance, scripted_instance, rtol=0)
-
-    def test_PointRend_mask_head_tracing(self):
-        cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
-        point_rend.add_pointrend_config(cfg)
-        cfg.MODEL.ROI_HEADS.IN_FEATURES = ["p2", "p3"]
-        cfg.MODEL.ROI_MASK_HEAD.NAME = "PointRendMaskHead"
-        cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE = ""
-        cfg.MODEL.ROI_MASK_HEAD.POINT_HEAD_ON = True
-        chan = 256
-        head = point_rend.PointRendMaskHead(
-            cfg,
-            {
-                "p2": ShapeSpec(channels=chan, stride=4),
-                "p3": ShapeSpec(channels=chan, stride=8),
-            },
-        )
-
-        def gen_inputs(h, w, N):
-            p2 = torch.rand(1, chan, h, w)
-            p3 = torch.rand(1, chan, h // 2, w // 2)
-            boxes = random_boxes(N, max_coord=h)
-            return p2, p3, boxes
-
-        class Wrap(nn.ModuleDict):
-            def forward(self, p2, p3, boxes):
-                features = {
-                    "p2": p2,
-                    "p3": p3,
-                }
-                inst = Instances((p2.shape[2] * 4, p2.shape[3] * 4))
-                inst.pred_boxes = Boxes(boxes)
-                inst.pred_classes = torch.zeros(inst.__len__(), dtype=torch.long)
-                out = self.head(features, [inst])[0]
-                return out.pred_masks
-
-        model = Wrap({"head": head})
-        model.eval()
-        with torch.no_grad(), patch_builtin_len():
-            traced = torch.jit.trace(model, gen_inputs(302, 208, 20))
-            inputs = gen_inputs(100, 120, 30)
-            out_eager = model(*inputs)
-            out_trace = traced(*inputs)
-            self.assertTrue(torch.allclose(out_eager, out_trace))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/chasetank/Visual-GPT-3.5-Turbo/README.md b/spaces/chasetank/Visual-GPT-3.5-Turbo/README.md
deleted file mode 100644
index d0618008e694979743945a6ca8a968509804fc62..0000000000000000000000000000000000000000
--- a/spaces/chasetank/Visual-GPT-3.5-Turbo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Visual Chatgpt
-emoji: 🎨
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: osl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chendl/compositional_test/app.py b/spaces/chendl/compositional_test/app.py
deleted file mode 100644
index d385779de44358f8f232f12bb548084bede147be..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/app.py
+++ /dev/null
@@ -1,465 +0,0 @@
-import os
-import sys
-from pathlib import Path
-os.system("python -m pip install --upgrade pip")
-os.system("cd multimodal && pip install .")
-os.system("cd multimodal/YOLOX && pip install .")
-import numpy as np
-import torch
-from PIL import Image
-import tempfile
-
-import string
-import cv2
-
-import gradio as gr
-import torch
-from PIL import Image
-from huggingface_hub import hf_hub_download, login
-
-from open_flamingo.src.factory import create_model_and_transforms
-from open_flamingo.chat.conversation import ChatBOT, CONV_VISION
-
-sys.path.append(str(Path(__file__).parent.parent.parent))
-TEMP_FILE_DIR = Path(__file__).parent / 'temp'
-TEMP_FILE_DIR.mkdir(parents=True, exist_ok=True)
-
-SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue.
-
-You can duplicate and use it with a paid private GPU.
-
-<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Vision-CAIR/minigpt4?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a>
-
-Alternatively, you can also use the demo on our [project page](https://compositionalvlm.github.io/).
-'''
-
-flamingo, image_processor, tokenizer, vis_embed_size = create_model_and_transforms(
-    "ViT-L-14",
-    "datacomp_xl_s13b_b90k",
-    "EleutherAI/pythia-1.4b",
-    "EleutherAI/pythia-1.4b",
-    location_token_num=1000,
-    lora=False,
-    lora_r=16,
-    use_sam=None,
-    add_visual_token=True,
-    use_format_v2=True,
-    add_box=True,
-    add_pe=False,
-    add_relation=False,
-    enhance_data=False,
-)
-
-model_name = "pythiaS"
-checkpoint_path = hf_hub_download("chendl/compositional_test", "pythiaS.pt")
-checkpoint = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
-model_state_dict = {}
-for key in checkpoint.keys():
-    model_state_dict[key.replace("module.", "")] = checkpoint[key]
-if "vision_encoder.logit_scale" in model_state_dict:
-    # previous checkpoint has some unnecessary weights
-    del model_state_dict["vision_encoder.logit_scale"]
-    del model_state_dict["vision_encoder.visual.proj"]
-    del model_state_dict["vision_encoder.visual.ln_post.weight"]
-    del model_state_dict["vision_encoder.visual.ln_post.bias"]
-flamingo.load_state_dict(model_state_dict, strict=True)
-chat = ChatBOT(flamingo, image_processor, tokenizer, vis_embed_size,model_name)
-
-
-def get_outputs(
-        model,
-        batch_images,
-        attention_mask,
-        max_generation_length,
-        min_generation_length,
-        num_beams,
-        length_penalty,
-        input_ids,
-        image_start_index_list=None,
-        image_nums=None,
-        bad_words_ids=None,
-):
-    #  and torch.cuda.amp.autocast(dtype=torch.float16)
-    with torch.inference_mode():
-        outputs = model(
-            vision_x=batch_images,
-            lang_x=input_ids,
-            attention_mask=attention_mask,
-            labels=None,
-            image_nums=image_nums,
-            image_start_index_list=image_start_index_list,
-            added_bbox_list=None,
-            add_box=False,
-        )
-        # outputs = model.generate(
-        #     batch_images,
-        #     input_ids,
-        #     attention_mask=attention_mask,
-        #     max_new_tokens=max_generation_length,
-        #     min_length=min_generation_length,
-        #     num_beams=num_beams,
-        #     length_penalty=length_penalty,
-        #     image_start_index_list=image_start_index_list,
-        #     image_nums=image_nums,
-        #     bad_words_ids=bad_words_ids,
-        # )
-
-    return outputs
-
-
-def generate(
-        idx,
-        image,
-        text,
-        vis_embed_size=256,
-        rank=0,
-        world_size=1,
-):
-    if image is None:
-        raise gr.Error("Please upload an image.")
-    flamingo.eval()
-    loc_token_ids = []
-    for i in range(1000):
-        loc_token_ids.append(int(tokenizer(f"<loc_{i}>", add_special_tokens=False)["input_ids"][-1]))
-    media_token_id = tokenizer("<|#image#|>", add_special_tokens=False)["input_ids"][-1]
-    endofmedia_token_id = tokenizer("<|#endofimage#|>", add_special_tokens=False)["input_ids"][-1]
-    pad_token_id = tokenizer(tokenizer.pad_token, add_special_tokens=False)["input_ids"][-1]
-    bos_token_id = tokenizer(tokenizer.bos_token, add_special_tokens=False)["input_ids"][-1]
-    prebox_token_id = tokenizer("<|#prebox#|>", add_special_tokens=False)["input_ids"][-1]
-
-    image_ori = image
-    image = image.convert("RGB")
-    width = image.width
-    height = image.height
-    image = image.resize((224, 224))
-    batch_images = image_processor(image).unsqueeze(0).unsqueeze(1).unsqueeze(0)
-    if idx == 1:
-        prompt = [
-            f"{tokenizer.bos_token}<|#image#|>{tokenizer.pad_token * vis_embed_size}<|#endofimage#|><|#object#|> {text.rstrip('.').strip()}<|#endofobject#|><|#visual#|>"]
-        bad_words_ids = None
-        max_generation_length = 5
-    else:
-        prompt = [f"<|#image#|>{tokenizer.pad_token * vis_embed_size}<|#endofimage#|>{text.rstrip('.')}"]
-        bad_words_ids = loc_word_ids
-        max_generation_length = 30
-    encodings = tokenizer(
-        prompt,
-        padding="longest",
-        truncation=True,
-        return_tensors="pt",
-        max_length=2000,
-    )
-    input_ids = encodings["input_ids"]
-    attention_mask = encodings["attention_mask"]
-    image_start_index_list = ((input_ids == media_token_id).nonzero(as_tuple=True)[-1] + 1).tolist()
-    image_start_index_list = [[x] for x in image_start_index_list]
-    image_nums = [1] * len(input_ids)
-    outputs = get_outputs(
-        model=flamingo,
-        batch_images=batch_images,
-        attention_mask=attention_mask,
-        max_generation_length=max_generation_length,
-        min_generation_length=4,
-        num_beams=1,
-        length_penalty=1.0,
-        input_ids=input_ids,
-        bad_words_ids=bad_words_ids,
-        image_start_index_list=image_start_index_list,
-        image_nums=image_nums,
-    )
-
-    boxes = outputs["boxes"]
-    scores = outputs["scores"]
-    if len(scores) > 0:
-        box = boxes[scores.argmax()] / 224
-    print(f"{box}")
-
-    if idx == 1:
-        open_cv_image = np.array(image_ori)
-        # Convert RGB to BGR
-        open_cv_image = open_cv_image[:, :, ::-1].copy()
-        box = box * [width, height, width, height]
-        # for box in boxes:
-        open_cv_image = cv2.rectangle(open_cv_image, box[:2].astype(int), box[2:].astype(int), (255, 0, 0), 2)
-        out_image = Image.fromarray(cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB))
-        return f"Output:{box}", out_image
-    elif idx == 2:
-        gen_text = tokenizer.batch_decode(outputs)
-        return (f"Question: {text.strip()} Answer: {gen_text}")
-    else:
-        gen_text = tokenizer.batch_decode(outputs)
-        return (f"Output:{gen_text}")
-
-
-title = """<h1 align="center">Demo of Compositional-VLM</h1>"""
-description = """<h3>This is the demo of Compositional-VLM. Upload your images and start chatting!</h3>"""
-article = """<div style='display:flex; gap: 0.25rem; '><a href='https://vis-www.cs.umass.edu/CoVLM/'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/UMass-Foundation-Model/CoVLM'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://arxiv.org/abs/2311.03354'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
-"""
-
-
-# TODO show examples below
-
-# ========================================
-#             Gradio Setting
-# ========================================
-
-def gradio_reset(chat_state, img_list):
-    if chat_state is not None:
-        chat_state = []
-    if img_list is not None:
-        img_list = []
-    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first',
-                                                                    interactive=False), gr.update(
-        value="Upload & Start Chat", interactive=True), chat_state, img_list
-
-
-def build_image(image):
-    if image is None:
-        return None
-    # res = draw_bounding_boxes(image=image, boxes=boxes_to_draw, colors=color_to_draw, width=8)
-    from torchvision.transforms import ToPILImage
-    # res = ToPILImage()(res)
-    _, path = tempfile.mkstemp(suffix='.jpg', dir=TEMP_FILE_DIR)
-    image.save(path)
-
-    return path
-
-
-def upload_img(gr_img, text_input, chat_state, chatbot):
-    if gr_img is None:
-        return None, None, gr.update(interactive=True), chat_state, None
-    chat_state = []
-    img_list = []
-    path = build_image(gr_img)
-    chatbot = chatbot + [[(path,), None]]
-    llm_message = chat.upload_img(gr_img, chat_state, img_list)
-    return gr.update(interactive=False), gr.Textbox(placeholder='Type and press Enter', interactive=True), gr.update(
-        value="Start Chatting", interactive=False), chat_state, img_list, chatbot
-
-
-def gradio_ask(user_message, chatbot, chat_state, radio):
-    # if len(user_message) == 0:
-    #     return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
-
-    chat.ask(user_message, chat_state, radio)
-    chatbot = chatbot + [[user_message, None]]
-    return chatbot, chat_state
-
-
-def generate_ans(user_message, chatbot, chat_state, img_list, radio, text, num_beams, temperature):
-    # if len(user_message) == 0:
-    #     return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
-
-    chat.ask(user_message, chat_state, radio)
-    chatbot = chatbot + [[user_message, None]]
-    # return chatbot, chat_state
-    image = None
-    llm_message, image = \
-        chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature,
-                    max_length=2000, radio=radio, text_input=text)
-
-    chatbot[-1][1] = llm_message
-    if chat_state[-1]["from"] == "gpt":
-        chat_state[-1]["value"] = llm_message
-    if image == None:
-        return "", chatbot, chat_state, img_list
-    else:
-        path = build_image(image)
-        chatbot = chatbot + [[None, (path,)]]
-        return "", chatbot, chat_state, img_list
-
-
-def gradio_answer(chatbot, chat_state, img_list, radio, text, num_beams, temperature):
-    image = None
-    llm_message, image = \
-        chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature,
-                    max_length=2000, radio=radio, text_input=text)
-
-    chatbot[-1][1] = llm_message
-    if chat_state[-1]["from"] == "gpt":
-        chat_state[-1]["value"] = llm_message
-    if image == None:
-        return "", chatbot, chat_state, img_list
-    else:
-        path = build_image(image)
-        chatbot = chatbot + [[None, (path,)]]
-        return "", chatbot, chat_state, img_list
-
-
-task_template = {
-    "Cap": "Summarize the content of the photo <image>.",
-    "VQA": "For this image <image>, I want a simple and direct answer to my question: <question>",
-    "REC": "Can you point out <expr> in the image <image> and provide the coordinates of its location?",
-    "GC": "Can you give me a description of the region <boxes> in image <image>?",
-    "Advanced": "<question>",
-}
-
-with gr.Blocks() as demo:
-    gr.Markdown(title)
-    gr.Markdown(SHARED_UI_WARNING)
-    gr.Markdown(description)
-    gr.Markdown(article)
-
-    with gr.Row():
-        with gr.Column(scale=0.5):
-            image = gr.Image(type="pil")
-            upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
-            clear = gr.Button("Restart")
-            radio = gr.Radio(
-                ["Cap", "VQA", "REC", "Advanced"], label="Task Template", value='Cap',
-            )
-
-            num_beams = gr.Slider(
-                minimum=1,
-                maximum=5,
-                value=1,
-                step=1,
-                interactive=True,
-                label="beam search numbers)",
-            )
-
-            temperature = gr.Slider(
-                minimum=0.1,
-                maximum=2.0,
-                value=1.0,
-                step=0.1,
-                interactive=True,
-                label="Temperature",
-            )
-
-        with gr.Column():
-            chat_state = gr.State()
-            img_list = gr.State()
-            chatbot = gr.Chatbot(label='Compositional-VLM')
-
-            # template = gr.Textbox(label='Template', show_label=True, lines=1, interactive=False,
-            #                       value='Provide a comprehensive description of the image <image> and specify the positions of any mentioned objects in square brackets.')
-            # text_input = gr.Textbox(label='<question>', show_label=True, placeholder="Please upload your image first， then input...", lines=3,
-            #                         value=None, visible=False, interactive=False)
-            # with gr.Row():
-            text_input = gr.Textbox(label='User', placeholder='Please upload your image first, then input...',
-                                    interactive=False)
-            # submit_button = gr.Button(value="Submit", interactive=True, variant="primary")
-
-    upload_button.click(upload_img, [image, text_input, chat_state, chatbot],
-                        [image, text_input, upload_button, chat_state, img_list, chatbot])
-    # submit_button.click(gradio_ask, [text_input, chatbot, chat_state,radio], [chatbot, chat_state]).then(
-    #     gradio_answer, [chatbot, chat_state, img_list,  radio, text_input,num_beams, temperature], [text_input,chatbot, chat_state, img_list]
-    # )
-
-    text_input.submit(generate_ans,
-                      [text_input, chatbot, chat_state, img_list, radio, text_input, num_beams, temperature],
-                      [text_input, chatbot, chat_state, img_list])
-
-    # text_input.submit(gradio_ask, [text_input, chatbot, chat_state, radio], [chatbot, chat_state]).then(
-    #     gradio_answer, [chatbot, chat_state, img_list, radio, text_input, num_beams, temperature],
-    #     [text_input, chatbot, chat_state, img_list]
-    # )
-    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list],
-                queue=False)
-
-demo.launch(share=True)
-# 
-# with gr.Blocks() as demo:
-#     gr.Markdown(
-#         """
-#     🍜 Object Centric Pretraining Demo  
-#     In this demo we showcase the in-context learning and grounding capabilities of the Object-Centric Pretrained model, a large multimodal model. Note that we add two additional demonstrations to the ones presented to improve the demo experience.
-#     The model is trained on an interleaved mixture of text, images and bounding box and is able to generate text conditioned on sequences of images/text.
-#     """
-#     )
-# 
-#     with gr.Accordion("See terms and conditions"):
-#         gr.Markdown(
-#             """**Please read the following information carefully before proceeding.**This demo does NOT store any personal information on its users, and it does NOT store user queries.""")
-# 
-#     with gr.Tab("📷 Image Captioning"):
-#         with gr.Row():
-# 
-# 
-#             query_image = gr.Image(type="pil")
-#         with gr.Row():
-#             chat_input = gr.Textbox(lines=1, label="Chat Input")
-#         text_output = gr.Textbox(value="Output:", label="Model output")
-# 
-#         run_btn = gr.Button("Run model")
-# 
-# 
-# 
-#         def on_click_fn(img,text): return generate(0, img, text)
-# 
-#         run_btn.click(on_click_fn, inputs=[query_image,chat_input], outputs=[text_output])
-# 
-#     with gr.Tab("🦓 Grounding"):
-#         with gr.Row():
-#             with gr.Column(scale=1):
-#                 query_image = gr.Image(type="pil")
-#             with gr.Column(scale=1):
-#                 out_image = gr.Image(type="pil")
-#         with gr.Row():
-#             chat_input = gr.Textbox(lines=1, label="Chat Input")
-#         text_output = gr.Textbox(value="Output:", label="Model output")
-# 
-#         run_btn = gr.Button("Run model")
-# 
-# 
-#         def on_click_fn(img, text): return generate(1, img, text)
-# 
-# 
-#         run_btn.click(on_click_fn, inputs=[query_image, chat_input], outputs=[text_output, out_image])
-# 
-#     with gr.Tab("🔢 Counting objects"):
-#         with gr.Row():
-#             query_image = gr.Image(type="pil")
-#         with gr.Row():
-#             chat_input = gr.Textbox(lines=1, label="Chat Input")
-#         text_output = gr.Textbox(value="Output:", label="Model output")
-# 
-#         run_btn = gr.Button("Run model")
-# 
-# 
-#         def on_click_fn(img,text): return generate(0, img, text)
-# 
-# 
-#         run_btn.click(on_click_fn, inputs=[query_image, chat_input], outputs=[text_output])
-# 
-#     with gr.Tab("🕵️ Visual Question Answering"):
-#         with gr.Row():
-#             query_image = gr.Image(type="pil")
-#         with gr.Row():
-#             question = gr.Textbox(lines=1, label="Question")
-#         text_output = gr.Textbox(value="Output:", label="Model output")
-# 
-#         run_btn = gr.Button("Run model")
-# 
-# 
-#         def on_click_fn(img, txt): return generate(2, img, txt)
-# 
-# 
-#         run_btn.click(
-#             on_click_fn, inputs=[query_image, question], outputs=[text_output]
-#         )
-# 
-#     with gr.Tab("🌎 Custom"):
-#         gr.Markdown(
-#             """### Customize the demonstration by uploading your own images and text samples. 
-#                     ### **Note: Any text prompt you use will be prepended with an 'Output:', so you don't need to include it in your prompt.**"""
-#         )
-#         with gr.Row():
-#             query_image = gr.Image(type="pil")
-#         with gr.Row():
-#             question = gr.Textbox(lines=1, label="Question")
-#         text_output = gr.Textbox(value="Output:", label="Model output")
-# 
-#         run_btn = gr.Button("Run model")
-# 
-# 
-#         def on_click_fn(img, txt): return generate(2, img, txt)
-# 
-# 
-#         run_btn.click(
-#             on_click_fn, inputs=[query_image, question], outputs=[text_output]
-#         )
-# 
-# demo.queue(concurrency_count=1)
-# demo.launch()
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/movement-pruning/emmental/modules/__init__.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/movement-pruning/emmental/modules/__init__.py
deleted file mode 100644
index 761a6343d6b5466bddadbaa927436c2d6351d67f..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/movement-pruning/emmental/modules/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
-from .masked_nn import MaskedLinear
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/feature_extraction_utils.py b/spaces/chendl/compositional_test/transformers/src/transformers/feature_extraction_utils.py
deleted file mode 100644
index e41ff8af54a61922eda88ef99d30e22e874e4da7..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/feature_extraction_utils.py
+++ /dev/null
@@ -1,608 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
- Feature extraction saving/loading class for common feature extractors.
-"""
-
-import copy
-import json
-import os
-from collections import UserDict
-from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
-
-import numpy as np
-
-from .dynamic_module_utils import custom_object_save
-from .utils import (
-    FEATURE_EXTRACTOR_NAME,
-    PushToHubMixin,
-    TensorType,
-    cached_file,
-    copy_func,
-    download_url,
-    is_flax_available,
-    is_jax_tensor,
-    is_numpy_array,
-    is_offline_mode,
-    is_remote_url,
-    is_tf_available,
-    is_torch_available,
-    is_torch_device,
-    is_torch_dtype,
-    logging,
-    requires_backends,
-)
-
-
-if TYPE_CHECKING:
-    if is_torch_available():
-        import torch  # noqa
-
-
-logger = logging.get_logger(__name__)
-
-PreTrainedFeatureExtractor = Union["SequenceFeatureExtractor"]  # noqa: F821
-
-
-class BatchFeature(UserDict):
-    r"""
-    Holds the output of the [`~SequenceFeatureExtractor.pad`] and feature extractor specific `__call__` methods.
-
-    This class is derived from a python dictionary and can be used as a dictionary.
-
-    Args:
-        data (`dict`):
-            Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask',
-            etc.).
-        tensor_type (`Union[None, str, TensorType]`, *optional*):
-            You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
-            initialization.
-    """
-
-    def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None):
-        super().__init__(data)
-        self.convert_to_tensors(tensor_type=tensor_type)
-
-    def __getitem__(self, item: str) -> Union[Any]:
-        """
-        If the key is a string, returns the value of the dict associated to `key` ('input_values', 'attention_mask',
-        etc.).
-        """
-        if isinstance(item, str):
-            return self.data[item]
-        else:
-            raise KeyError("Indexing with integers is not available when using Python based feature extractors")
-
-    def __getattr__(self, item: str):
-        try:
-            return self.data[item]
-        except KeyError:
-            raise AttributeError
-
-    def __getstate__(self):
-        return {"data": self.data}
-
-    def __setstate__(self, state):
-        if "data" in state:
-            self.data = state["data"]
-
-    # Copied from transformers.tokenization_utils_base.BatchEncoding.keys
-    def keys(self):
-        return self.data.keys()
-
-    # Copied from transformers.tokenization_utils_base.BatchEncoding.values
-    def values(self):
-        return self.data.values()
-
-    # Copied from transformers.tokenization_utils_base.BatchEncoding.items
-    def items(self):
-        return self.data.items()
-
-    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
-        """
-        Convert the inner content to tensors.
-
-        Args:
-            tensor_type (`str` or [`~utils.TensorType`], *optional*):
-                The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
-                `None`, no modification is done.
-        """
-        if tensor_type is None:
-            return self
-
-        # Convert to TensorType
-        if not isinstance(tensor_type, TensorType):
-            tensor_type = TensorType(tensor_type)
-
-        # Get a function reference for the correct framework
-        if tensor_type == TensorType.TENSORFLOW:
-            if not is_tf_available():
-                raise ImportError(
-                    "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."
-                )
-            import tensorflow as tf
-
-            as_tensor = tf.constant
-            is_tensor = tf.is_tensor
-        elif tensor_type == TensorType.PYTORCH:
-            if not is_torch_available():
-                raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
-            import torch  # noqa
-
-            def as_tensor(value):
-                if isinstance(value, (list, tuple)) and len(value) > 0 and isinstance(value[0], np.ndarray):
-                    value = np.array(value)
-                return torch.tensor(value)
-
-            is_tensor = torch.is_tensor
-        elif tensor_type == TensorType.JAX:
-            if not is_flax_available():
-                raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.")
-            import jax.numpy as jnp  # noqa: F811
-
-            as_tensor = jnp.array
-            is_tensor = is_jax_tensor
-        else:
-            as_tensor = np.asarray
-            is_tensor = is_numpy_array
-
-        # Do the tensor conversion in batch
-        for key, value in self.items():
-            try:
-                if not is_tensor(value):
-                    tensor = as_tensor(value)
-
-                    self[key] = tensor
-            except:  # noqa E722
-                if key == "overflowing_values":
-                    raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
-                raise ValueError(
-                    "Unable to create tensor, you should probably activate padding "
-                    "with 'padding=True' to have batched tensors with the same length."
-                )
-
-        return self
-
-    def to(self, *args, **kwargs) -> "BatchFeature":
-        """
-        Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
-        different `dtypes` and sending the `BatchFeature` to a different `device`.
-
-        Args:
-            args (`Tuple`):
-                Will be passed to the `to(...)` function of the tensors.
-            kwargs (`Dict`, *optional*):
-                Will be passed to the `to(...)` function of the tensors.
-
-        Returns:
-            [`BatchFeature`]: The same instance after modification.
-        """
-        requires_backends(self, ["torch"])
-        import torch  # noqa
-
-        new_data = {}
-        device = kwargs.get("device")
-        # Check if the args are a device or a dtype
-        if device is None and len(args) > 0:
-            # device should be always the first argument
-            arg = args[0]
-            if is_torch_dtype(arg):
-                # The first argument is a dtype
-                pass
-            elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
-                device = arg
-            else:
-                # it's something else
-                raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
-        # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
-        for k, v in self.items():
-            # check if v is a floating point
-            if torch.is_floating_point(v):
-                # cast and send to device
-                new_data[k] = v.to(*args, **kwargs)
-            elif device is not None:
-                new_data[k] = v.to(device=device)
-            else:
-                new_data[k] = v
-        self.data = new_data
-        return self
-
-
-class FeatureExtractionMixin(PushToHubMixin):
-    """
-    This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature
-    extractors.
-    """
-
-    _auto_class = None
-
-    def __init__(self, **kwargs):
-        """Set elements of `kwargs` as attributes."""
-        # Pop "processor_class" as it should be saved as private attribute
-        self._processor_class = kwargs.pop("processor_class", None)
-        # Additional attributes without default values
-        for key, value in kwargs.items():
-            try:
-                setattr(self, key, value)
-            except AttributeError as err:
-                logger.error(f"Can't set {key} with value {value} for {self}")
-                raise err
-
-    def _set_processor_class(self, processor_class: str):
-        """Sets processor class as an attribute."""
-        self._processor_class = processor_class
-
-    @classmethod
-    def from_pretrained(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
-    ) -> PreTrainedFeatureExtractor:
-        r"""
-        Instantiate a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a feature extractor, *e.g.* a
-        derived class of [`SequenceFeatureExtractor`].
-
-        Args:
-            pretrained_model_name_or_path (`str` or `os.PathLike`):
-                This can be either:
-
-                - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
-                  huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
-                  namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
-                - a path to a *directory* containing a feature extractor file saved using the
-                  [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] method, e.g.,
-                  `./my_model_directory/`.
-                - a path or url to a saved feature extractor JSON *file*, e.g.,
-                  `./my_model_directory/preprocessor_config.json`.
-            cache_dir (`str` or `os.PathLike`, *optional*):
-                Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
-                standard cache should not be used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force to (re-)download the feature extractor files and override the cached versions
-                if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received file. Attempts to resume the download if such a file
-                exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
-            use_auth_token (`str` or `bool`, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
-                the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-
-
-                <Tip>
-
-                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".
-
-                </Tip>
-
-            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
-                If `False`, then this function returns just the final feature extractor object. If `True`, then this
-                functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
-                consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
-                `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
-            kwargs (`Dict[str, Any]`, *optional*):
-                The values in kwargs of any keys which are feature extractor attributes will be used to override the
-                loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
-                controlled by the `return_unused_kwargs` keyword parameter.
-
-        Returns:
-            A feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`].
-
-        Examples:
-
-        ```python
-        # We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a
-        # derived class: *Wav2Vec2FeatureExtractor*
-        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "facebook/wav2vec2-base-960h"
-        )  # Download feature_extraction_config from huggingface.co and cache.
-        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "./test/saved_model/"
-        )  # E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')*
-        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("./test/saved_model/preprocessor_config.json")
-        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False
-        )
-        assert feature_extractor.return_attention_mask is False
-        feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
-            "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False, return_unused_kwargs=True
-        )
-        assert feature_extractor.return_attention_mask is False
-        assert unused_kwargs == {"foo": False}
-        ```"""
-        feature_extractor_dict, kwargs = cls.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
-
-        return cls.from_dict(feature_extractor_dict, **kwargs)
-
-    def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
-        """
-        Save a feature_extractor object to the directory `save_directory`, so that it can be re-loaded using the
-        [`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`] class method.
-
-        Args:
-            save_directory (`str` or `os.PathLike`):
-                Directory where the feature extractor JSON file will be saved (will be created if it does not exist).
-            push_to_hub (`bool`, *optional*, defaults to `False`):
-                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
-                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
-                namespace).
-            kwargs:
-                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
-        """
-        if os.path.isfile(save_directory):
-            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
-
-        os.makedirs(save_directory, exist_ok=True)
-
-        if push_to_hub:
-            commit_message = kwargs.pop("commit_message", None)
-            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
-            repo_id = self._create_repo(repo_id, **kwargs)
-            files_timestamps = self._get_files_timestamps(save_directory)
-
-        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
-        # loaded from the Hub.
-        if self._auto_class is not None:
-            custom_object_save(self, save_directory, config=self)
-
-        # If we save using the predefined names, we can load using `from_pretrained`
-        output_feature_extractor_file = os.path.join(save_directory, FEATURE_EXTRACTOR_NAME)
-
-        self.to_json_file(output_feature_extractor_file)
-        logger.info(f"Feature extractor saved in {output_feature_extractor_file}")
-
-        if push_to_hub:
-            self._upload_modified_files(
-                save_directory,
-                repo_id,
-                files_timestamps,
-                commit_message=commit_message,
-                token=kwargs.get("use_auth_token"),
-            )
-
-        return [output_feature_extractor_file]
-
-    @classmethod
-    def get_feature_extractor_dict(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
-    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
-        """
-        From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
-        feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`] using `from_dict`.
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`):
-                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
-
-        Returns:
-            `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the feature extractor object.
-        """
-        cache_dir = kwargs.pop("cache_dir", None)
-        force_download = kwargs.pop("force_download", False)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        revision = kwargs.pop("revision", None)
-
-        from_pipeline = kwargs.pop("_from_pipeline", None)
-        from_auto_class = kwargs.pop("_from_auto", False)
-
-        user_agent = {"file_type": "feature extractor", "from_auto_class": from_auto_class}
-        if from_pipeline is not None:
-            user_agent["using_pipeline"] = from_pipeline
-
-        if is_offline_mode() and not local_files_only:
-            logger.info("Offline mode: forcing local_files_only=True")
-            local_files_only = True
-
-        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-        is_local = os.path.isdir(pretrained_model_name_or_path)
-        if os.path.isdir(pretrained_model_name_or_path):
-            feature_extractor_file = os.path.join(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME)
-        if os.path.isfile(pretrained_model_name_or_path):
-            resolved_feature_extractor_file = pretrained_model_name_or_path
-            is_local = True
-        elif is_remote_url(pretrained_model_name_or_path):
-            feature_extractor_file = pretrained_model_name_or_path
-            resolved_feature_extractor_file = download_url(pretrained_model_name_or_path)
-        else:
-            feature_extractor_file = FEATURE_EXTRACTOR_NAME
-            try:
-                # Load from local folder or from cache or download from model Hub and cache
-                resolved_feature_extractor_file = cached_file(
-                    pretrained_model_name_or_path,
-                    feature_extractor_file,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    resume_download=resume_download,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    user_agent=user_agent,
-                    revision=revision,
-                )
-            except EnvironmentError:
-                # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
-                # the original exception.
-                raise
-            except Exception:
-                # For any other exception, we throw a generic error.
-                raise EnvironmentError(
-                    f"Can't load feature extractor for '{pretrained_model_name_or_path}'. If you were trying to load"
-                    " it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
-                    f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
-                    f" directory containing a {FEATURE_EXTRACTOR_NAME} file"
-                )
-
-        try:
-            # Load feature_extractor dict
-            with open(resolved_feature_extractor_file, "r", encoding="utf-8") as reader:
-                text = reader.read()
-            feature_extractor_dict = json.loads(text)
-
-        except json.JSONDecodeError:
-            raise EnvironmentError(
-                f"It looks like the config file at '{resolved_feature_extractor_file}' is not a valid JSON file."
-            )
-
-        if is_local:
-            logger.info(f"loading configuration file {resolved_feature_extractor_file}")
-        else:
-            logger.info(
-                f"loading configuration file {feature_extractor_file} from cache at {resolved_feature_extractor_file}"
-            )
-
-        return feature_extractor_dict, kwargs
-
-    @classmethod
-    def from_dict(cls, feature_extractor_dict: Dict[str, Any], **kwargs) -> PreTrainedFeatureExtractor:
-        """
-        Instantiates a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a Python dictionary of
-        parameters.
-
-        Args:
-            feature_extractor_dict (`Dict[str, Any]`):
-                Dictionary that will be used to instantiate the feature extractor object. Such a dictionary can be
-                retrieved from a pretrained checkpoint by leveraging the
-                [`~feature_extraction_utils.FeatureExtractionMixin.to_dict`] method.
-            kwargs (`Dict[str, Any]`):
-                Additional parameters from which to initialize the feature extractor object.
-
-        Returns:
-            [`~feature_extraction_utils.FeatureExtractionMixin`]: The feature extractor object instantiated from those
-            parameters.
-        """
-        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
-
-        feature_extractor = cls(**feature_extractor_dict)
-
-        # Update feature_extractor with kwargs if needed
-        to_remove = []
-        for key, value in kwargs.items():
-            if hasattr(feature_extractor, key):
-                setattr(feature_extractor, key, value)
-                to_remove.append(key)
-        for key in to_remove:
-            kwargs.pop(key, None)
-
-        logger.info(f"Feature extractor {feature_extractor}")
-        if return_unused_kwargs:
-            return feature_extractor, kwargs
-        else:
-            return feature_extractor
-
-    def to_dict(self) -> Dict[str, Any]:
-        """
-        Serializes this instance to a Python dictionary.
-
-        Returns:
-            `Dict[str, Any]`: Dictionary of all the attributes that make up this feature extractor instance.
-        """
-        output = copy.deepcopy(self.__dict__)
-        output["feature_extractor_type"] = self.__class__.__name__
-
-        return output
-
-    @classmethod
-    def from_json_file(cls, json_file: Union[str, os.PathLike]) -> PreTrainedFeatureExtractor:
-        """
-        Instantiates a feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`] from the path to
-        a JSON file of parameters.
-
-        Args:
-            json_file (`str` or `os.PathLike`):
-                Path to the JSON file containing the parameters.
-
-        Returns:
-            A feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`]: The feature_extractor
-            object instantiated from that JSON file.
-        """
-        with open(json_file, "r", encoding="utf-8") as reader:
-            text = reader.read()
-        feature_extractor_dict = json.loads(text)
-        return cls(**feature_extractor_dict)
-
-    def to_json_string(self) -> str:
-        """
-        Serializes this instance to a JSON string.
-
-        Returns:
-            `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
-        """
-        dictionary = self.to_dict()
-
-        for key, value in dictionary.items():
-            if isinstance(value, np.ndarray):
-                dictionary[key] = value.tolist()
-
-        # make sure private name "_processor_class" is correctly
-        # saved as "processor_class"
-        _processor_class = dictionary.pop("_processor_class", None)
-        if _processor_class is not None:
-            dictionary["processor_class"] = _processor_class
-
-        return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
-        """
-        Save this instance to a JSON file.
-
-        Args:
-            json_file_path (`str` or `os.PathLike`):
-                Path to the JSON file in which this feature_extractor instance's parameters will be saved.
-        """
-        with open(json_file_path, "w", encoding="utf-8") as writer:
-            writer.write(self.to_json_string())
-
-    def __repr__(self):
-        return f"{self.__class__.__name__} {self.to_json_string()}"
-
-    @classmethod
-    def register_for_auto_class(cls, auto_class="AutoFeatureExtractor"):
-        """
-        Register this class with a given auto class. This should only be used for custom feature extractors as the ones
-        in the library are already mapped with `AutoFeatureExtractor`.
-
-        <Tip warning={true}>
-
-        This API is experimental and may have some slight breaking changes in the next releases.
-
-        </Tip>
-
-        Args:
-            auto_class (`str` or `type`, *optional*, defaults to `"AutoFeatureExtractor"`):
-                The auto class to register this new feature extractor with.
-        """
-        if not isinstance(auto_class, str):
-            auto_class = auto_class.__name__
-
-        import transformers.models.auto as auto_module
-
-        if not hasattr(auto_module, auto_class):
-            raise ValueError(f"{auto_class} is not a valid auto class.")
-
-        cls._auto_class = auto_class
-
-
-FeatureExtractionMixin.push_to_hub = copy_func(FeatureExtractionMixin.push_to_hub)
-if FeatureExtractionMixin.push_to_hub.__doc__ is not None:
-    FeatureExtractionMixin.push_to_hub.__doc__ = FeatureExtractionMixin.push_to_hub.__doc__.format(
-        object="feature extractor", object_class="AutoFeatureExtractor", object_files="feature extractor file"
-    )
diff --git a/spaces/cherry0021/lab-ni-doc/add-to-swarm.sh b/spaces/cherry0021/lab-ni-doc/add-to-swarm.sh
deleted file mode 100644
index 7865a871ddb642ac9574452ef8b4d8e8a43e6615..0000000000000000000000000000000000000000
--- a/spaces/cherry0021/lab-ni-doc/add-to-swarm.sh
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env bash
-cd $(cd -P -- "$(dirname -- "$0")" && pwd -P)
-
-# Fetching port and network as input
-PORT=8888
-REGISTRY=5000
-while [[ "$#" -gt 0 ]]; do case $1 in
-  -p|--port) PORT="$2"; shift;;
-  -r|--registry) REGISTRY="$2"; shift;;
-  -n|--network) NETWORK="$2"; shift;;
-#  -u|--uglify) uglify=1;;
-  *) echo "Unknown parameter passed: $1"; exit 1;;
-esac; shift; done
-
-# Check if arguments are valid
-if [[ $PORT != [0-9][0-9][0-9][0-9]* ]]; then
-    echo "Given port is not valid."
-    echo "Usage: $0 -p [port] -n [docker-network] -r [registry-port] # ports must be an integer with 4 or more digits."
-    exit 21
-fi
-
-if [[ $REGISTRY != [0-9][0-9][0-9][0-9]* ]]; then
-    echo "Given registry port is not valid."
-    echo "Usage: $0 -p [port] -n [docker-network] -r [registry-port] # ports must be an integer with 4 or more digits."
-    exit 21
-fi
-
-if [[ $NETWORK == "" ]]; then
-    echo "No docker network was provided to which this gpu-jupyter should be added to."
-    echo "Usage: $0 -p [port] -n [docker-network] -r [registry-port] # ports must be an integer with 4 or more digits."
-    exit 22
-fi
-result=$(docker network ls)
-if [[ "$result" != *" $NETWORK "* ]]; then
-    echo "Could not find network $NETWORK. Please provide a valid docker network."
-    echo "Please select a network:"
-    docker network ls
-    exit 23
-fi
-
-# starting in swarm
-export HOSTNAME=$(hostname)
-export JUPYTER_PORT=$PORT
-export REGISTRY_PORT=$REGISTRY
-export JUPYTER_NETWORK=$NETWORK
-echo "Adding gpu-jupyter to the swarm on the node $HOSTNAME in the network $NETWORK on port $PORT and registry to port $REGISTRY."
-
-# substitute the blueprint docker-compose-swarm with the environment variables and stack deploy it.
-envsubst < docker-compose-swarm.yml > .docker-compose-swarm.yml.envsubst
-docker-compose -f .docker-compose-swarm.yml.envsubst build
-docker-compose -f .docker-compose-swarm.yml.envsubst push
-docker stack deploy --compose-file .docker-compose-swarm.yml.envsubst gpu
-rm .docker-compose-swarm.yml.envsubst
-
-echo
-echo "Added gpu-jupyter to docker swarm $NETWORK on port $JUPYTER_PORT."
-echo "See 'docker service ps gpu_gpu-jupyter' for status info."
-echo "See 'docker service logs -f gpu_gpu-jupyter' for logs."
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/codingstatemachine.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/codingstatemachine.py
deleted file mode 100644
index 8ed4a8773b8404c2705aa8728e5fd692362ba168..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/codingstatemachine.py
+++ /dev/null
@@ -1,90 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-import logging
-
-from .codingstatemachinedict import CodingStateMachineDict
-from .enums import MachineState
-
-
-class CodingStateMachine:
-    """
-    A state machine to verify a byte sequence for a particular encoding. For
-    each byte the detector receives, it will feed that byte to every active
-    state machine available, one byte at a time. The state machine changes its
-    state based on its previous state and the byte it receives. There are 3
-    states in a state machine that are of interest to an auto-detector:
-
-    START state: This is the state to start with, or a legal byte sequence
-                 (i.e. a valid code point) for character has been identified.
-
-    ME state:  This indicates that the state machine identified a byte sequence
-               that is specific to the charset it is designed for and that
-               there is no other possible encoding which can contain this byte
-               sequence. This will to lead to an immediate positive answer for
-               the detector.
-
-    ERROR state: This indicates the state machine identified an illegal byte
-                 sequence for that encoding. This will lead to an immediate
-                 negative answer for this encoding. Detector will exclude this
-                 encoding from consideration from here on.
-    """
-
-    def __init__(self, sm: CodingStateMachineDict) -> None:
-        self._model = sm
-        self._curr_byte_pos = 0
-        self._curr_char_len = 0
-        self._curr_state = MachineState.START
-        self.active = True
-        self.logger = logging.getLogger(__name__)
-        self.reset()
-
-    def reset(self) -> None:
-        self._curr_state = MachineState.START
-
-    def next_state(self, c: int) -> int:
-        # for each byte we get its class
-        # if it is first byte, we also get byte length
-        byte_class = self._model["class_table"][c]
-        if self._curr_state == MachineState.START:
-            self._curr_byte_pos = 0
-            self._curr_char_len = self._model["char_len_table"][byte_class]
-        # from byte's class and state_table, we get its next state
-        curr_state = self._curr_state * self._model["class_factor"] + byte_class
-        self._curr_state = self._model["state_table"][curr_state]
-        self._curr_byte_pos += 1
-        return self._curr_state
-
-    def get_current_charlen(self) -> int:
-        return self._curr_char_len
-
-    def get_coding_state_machine(self) -> str:
-        return self._model["name"]
-
-    @property
-    def language(self) -> str:
-        return self._model["language"]
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/qtPen.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/qtPen.py
deleted file mode 100644
index eb13d03d2f611de4ce0b29ce3995f85e8f9e491a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/qtPen.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from fontTools.pens.basePen import BasePen
-
-
-__all__ = ["QtPen"]
-
-
-class QtPen(BasePen):
-    def __init__(self, glyphSet, path=None):
-        BasePen.__init__(self, glyphSet)
-        if path is None:
-            from PyQt5.QtGui import QPainterPath
-
-            path = QPainterPath()
-        self.path = path
-
-    def _moveTo(self, p):
-        self.path.moveTo(*p)
-
-    def _lineTo(self, p):
-        self.path.lineTo(*p)
-
-    def _curveToOne(self, p1, p2, p3):
-        self.path.cubicTo(*p1, *p2, *p3)
-
-    def _qCurveToOne(self, p1, p2):
-        self.path.quadTo(*p1, *p2)
-
-    def _closePath(self):
-        self.path.closeSubpath()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/_version.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/_version.py
deleted file mode 100644
index 1a1666e0bb91f6574664078281c9f90a5c195c19..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/_version.py
+++ /dev/null
@@ -1,21 +0,0 @@
-
-# This file was generated by 'versioneer.py' (0.20) from
-# revision-control system data, or from the parent directory name of an
-# unpacked source archive. Distribution tarballs contain a pre-generated copy
-# of this file.
-
-import json
-
-version_json = '''
-{
- "date": "2023-06-09T13:30:57-0400",
- "dirty": false,
- "error": null,
- "full-revisionid": "9a1e624022f3ad39071de5b17bafa23214b8662b",
- "version": "2023.6.0"
-}
-'''  # END VERSION_JSON
-
-
-def get_versions():
-    return json.loads(version_json)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-03d58ab8.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-03d58ab8.css
deleted file mode 100644
index c02568c42d3cf011dc008a256fdece5721dbccab..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-03d58ab8.css
+++ /dev/null
@@ -1 +0,0 @@
-.hide.svelte-ydeks8{display:none}
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-9da94804.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-9da94804.css
deleted file mode 100644
index 79d901421a55ea578fdaf2c50c84e8fafcea8c41..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-9da94804.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-1gww5xe{display:flex;position:absolute;justify-content:center;align-items:center;border-radius:var(--radius-sm);background-color:#000c;padding:var(--size-1) .4rem;color:#fff;font-size:var(--text-sm)}span.svelte-1gww5xe{display:inline-block;margin-right:var(--size-1);border-radius:var(--radius-xs);width:var(--size-3);height:var(--size-3)}.wrap.svelte-1mjxput{margin-top:var(--size-3)}.legend.svelte-1mjxput{display:flex;justify-content:center;align-items:center;color:var(--body-text-color)}.legend-item.svelte-1mjxput{display:flex;align-items:center;gap:var(--spacing-sm);margin-right:var(--size-2);margin-left:var(--size-2)}.legend-box.svelte-1mjxput{display:inline-block;border-radius:var(--radius-xs);width:var(--size-3);height:var(--size-3)}svg.svelte-1mjxput{width:var(--size-full)}.label-text.svelte-1mjxput{fill:var(--body-text-color);font-size:var(--text-sm);font-family:var(--font-mono)}.main-label.svelte-1mjxput{display:flex;justify-content:center;align-items:center;color:var(--body-text-color)}.chart.svelte-etmurc{display:flex;display:relative;justify-content:center;align-items:center;background:var(--background-fill-primary);width:var(--size-full);height:var(--size-64)}
diff --git a/spaces/cihyFjudo/fairness-paper-search/Partition Magic 8.05 Serial Full Version A Powerful and Reliable Utility Software for Windows 2000 and XP.md b/spaces/cihyFjudo/fairness-paper-search/Partition Magic 8.05 Serial Full Version A Powerful and Reliable Utility Software for Windows 2000 and XP.md
deleted file mode 100644
index 0236ebf6304f33bfd967fca0485579463fab75e1..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Partition Magic 8.05 Serial Full Version A Powerful and Reliable Utility Software for Windows 2000 and XP.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<p>Powerful partition magic software for Windows 11/10/7 is exactly what you need. And this is what we are about to discuss and recommend the best Windows partition magic tool to assist you in performing disk partitioning operations successfully.</p>
-<p>To ensure that all Windows users could apply the partition magic software to execute all disk management tasks, it's essential that you learn what is an all-in-one partition magic software for Windows 11/10 users. And here is a list that you can check through and define if the current tool suits your need:</p>
-<h2>Partition Magic 8.05 Serial Full Version</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://tinurli.com/2uwjIf">https://tinurli.com/2uwjIf</a></b></p><br /><br />
-<p>As reliable free partition magic software, EaseUS partition magic software has helped millions of people manage their disks easily and quickly. You can now free download the professional <strong>partition magic full version</strong> for Windows to help you resize partition, extend system disk, update HDD, and migrate OS to SSD with ease.</p>
-<p>Partition Magic, the product of Symantec, is very famous for partition management under Windows 98, Windows 2000, and Windows XP. It was acquired by Symantec in 2003. Norton Partition Magic has not been upgraded since 2009. The Symantec website states "Sorry, we no longer offer Norton Partition Magic." The application is incompatible with Windows Vista and later versions.</p>
-<p>Which is your best alternative for Partition Magic? If search "Partition Magic Alternative" online, Google will present you with a full list of recommended partition manager software on the search result page.</p>
-<p>Though it's been a truth that computers are becoming smarter and quite a lot of jobs can be done by computers. However, unavoidable problems such as partitioning cases still need to be fixed by human beings. Partition magic software, which is known as a professional partition tool, is designed for helping users to resize partitions, extend system drives, merge or combine partitions, upgrade HDD or migrate OS to SDD.</p>
-<p>If you are facing partition problems in your computer, partition magic can help. How to get professional partition magic software? EaseUS software makes it simple and easy for you to free download professional partition magic software now. Yes, it is possible to<strong> free download professional partition magic full version </strong>software! How? Let's find the answer now.</p>
-<p>EaseUS software provides the best partition magic software - EaseUS Partition Master for great Windows users to fix complex partitioning problems with ease. Don't ever think about trying cracked partition manager software or pirated partition magic software to manage your computer partitions. It is not a wise way to protect data and privacy. In order to protect your personal data, prevent unexpected troubles such as system crash or virus attack from happening to your PC, you are highly recommended to download copyrighted partition magic software - EaseUS Partition Master.</p>
-<p>EaseUS software makes it free for all users to download its full version of partition magic software - EaseUS Partition Master. You can search EaseUS Partition Master and free download it online or directly click the below download button to get it right now.</p>
-<p>After downloading it, you can install this partition magic software into your computer. In order to get the full version of EaseUS Partition Master, you need to do one more thing which is to activate it with serial key. If you need a free version of this software, feel free to download it by clicking the below download button. It's 100% risk-free and clean.</p>
-<p></p>
-<p>The program runs on pre-Vista Microsoft Windows operating systems including Windows 2000 and Windows XP, but the application is incompatible with Windows Vista and later versions (although Microsoft added resizing). In any of these cases, existing partitions can be resized without loss of data.</p>
-<p>PartitionMagic is capable of resizing NTFS, FAT16 or FAT32 partitions without data loss, and can copy and move partitions, including to other disks. It also has various other features, including being able to convert between FAT16, FAT32 and NTFS, modify the cluster size of FAT16, FAT32 and NTFS filesystems, and merge adjacent FAT or NTFS filesystems (all without data loss, though some NTFS-only metadata is lost on conversion to FAT). Additionally, it had somewhat limited support for ext2 and ext3 partitions. PartitionMagic was the first commercial product of its kind containing patented technology.[3]</p>
-<p>The stable version of PartitionMagic 8.05 also included a rescue floppy disk with an additional DOS version of PartitionMagic. The DOS version (which included DR-DOS or MS-DOS) came on one 2.88 MB or two 1.44 MB floppy disks.[4][5]</p>
-<p>Disk partition software is helpful to most of Windows computer users, with which you can create, delete, format, resize, merge, convert, copy, defrag partitions, etc. PowerQuest PartitionMagic is the first choice in the past ten years, it is so famous that many people still search or talk about it nowadays. In fact, PartitionMagic doesn't support Windows XP SP2 and all subsequent versions. Some people are wondering if there's a <strong>Partition Magic portable</strong> for Windows 11/10/8/7, I'll give you the answer in this article.</p>
-<p><strong>PartitionMagic 8.0.5</strong> is the last version released under Symantec on May 5, 2004. It supports Windows XP, but is not full compatible with XP SP2/SP3. From Windows Vista, boot manager is changed and new file system is added, so PartitionMagic does not support Windows Vista and subsequent versions.</p>
-<p>However, many PartitionMagic fans still search by Google or ask in technical forums whether there's <strong>Partition Magic portable</strong> version. I found some Partition Magic portable EXE format tools, but they always report error and even cannot enter the main window. Even if you find a portable version that can run in Windows 11/10/8/7, do not use it to shrink or extend partitions. Because boot manager, file system and many other place are different with old Windows XP. Obviously, your disk partition will be damaged if you do that modification. There are PartitionMagic alternative, even free of charge.</p>
-<p>It's time to say goodbye to Symantec/Norton Partition Magic, there's better choice now. As the safest partition software, NIUBI Partition Editor has free version for Windows 11/10/8/7/Vista/XP (32 & 64 bit) home users. It is similar but much more powerful than Norton PartitionMagic.</p>
-<p>Partition Magic is a powerful hard drive partitioning program, which runs on Windows Operating System including Windows 2000, Windows XP. As of December 8, 2009, Partition Magic no longer updates. Seeing that Partition Magic stops updating, many users consider downloading Partition Magic torrent and crack to manage the hard disk in Windows 10/8/7. Nowadays, you can also search "Partition Magic free download with crack torrent", "download Partition Magic crack torrent", "partition magic 8.0 full version with crack free download" on the Internet to get your hard disk managed.</p>
-<p>There are many tools for disk partitioning in the market. Which is the best alternative to Partition Magic 8.0 full version? At the moment, the most important thing is to make sure to download the partition tool from a well-known software company. AOMEI Partition Assistant Professional provides advanced operations to manage hard disk in Windows XP, Vista, 7, 8, 8.1, 10, 11, including Windows To Go Creator, MBR to GPT Converter, Extend Partition Wizard, Check Partition, Merge Partitions, Allocate Free Space, etc. Moreover, AOMEI Partition Assistant is more flexible to use than Partition Magic.</p>
-<p>Thus, try the best alternative to Partition Magic full version - AOMEI Partition Assistant to manage the hard disk. The below text is introducing how to extend partition with AOMEI Partition Assistant</p>
-<p>Wait for a few minutes, these operations will be completed. As a result, the free space on the J partition has been transferred to the E partition. Besides, you can also extend partition with unallocated space on the same disk, it turns out the <strong>Extend Partition Wizard</strong> is a very useful function. The alternative to Partition Magic 8.0 full version-AOMEI Partition Assistant also supports other main features, like migrating OS to SSD, making bootable media, disk copy, partition recovery, etc. And its Server edition can offer an all-around service for Windows Server users.</p>
-<p>Do you feel frustrated in managing disk and partition in Windows 11/10/8/7? Actually, most Windows users have the same feeling as you have. <strong>MiniTool Portable partition magic</strong> is the best choice for you.</p>
-<p>As Windows built-in Disk Management tool still has many shortages in functions and operations, many Windows users are finding better ways to manage disk and partition: "If only I have an excellent portable partition magic that can manage disk and partition easily on PC without downloading!"</p>
-<p>Although MiniTool cuts off the installing process, the features of portable partition manager keep coincident with Partition Wizard Free Edition because it comes right from the latter one. What you can find in Partition Wizard Free Edition also appears in the portable version. (View full features)</p>
-<p>Well, we have prepared a package in consideration of users' convenience. With this package, you can bring it to anywhere you reach, and deal with the disk & partition issues at home and your workplace; or help your friends to get out of their trouble. Download portable partition magic!</p>
-<p>Despite this portable partition magic, MiniTool Partition Wizard offers another five editions to satisfy various user demands. Just choose the one that is most suitable for you. To know their differences, please visit Edition Comparison.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cjayic/soft-vc-widowmaker/README.md b/spaces/cjayic/soft-vc-widowmaker/README.md
deleted file mode 100644
index 2887f477825d950b50f2db990da5d0292d6bd452..0000000000000000000000000000000000000000
--- a/spaces/cjayic/soft-vc-widowmaker/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Soft-VC Widowmaker
-emoji: 🕷️
-colorFrom: black
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cleanmaster/akagi-sovits3/inference/slicer.py b/spaces/cleanmaster/akagi-sovits3/inference/slicer.py
deleted file mode 100644
index 35a888b906e7df8634cfdcec914f650c6cefd26a..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/akagi-sovits3/inference/slicer.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import time
-
-import numpy as np
-import torch
-import torchaudio
-from scipy.ndimage import maximum_filter1d, uniform_filter1d
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-# @timeit
-def _window_maximum(arr, win_sz):
-    return maximum_filter1d(arr, size=win_sz)[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1]
-
-
-# @timeit
-def _window_rms(arr, win_sz):
-    filtered = np.sqrt(uniform_filter1d(np.power(arr, 2), win_sz) - np.power(uniform_filter1d(arr, win_sz), 2))
-    return filtered[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1]
-
-
-def level2db(levels, eps=1e-12):
-    return 20 * np.log10(np.clip(levels, a_min=eps, a_max=1))
-
-
-def _apply_slice(audio, begin, end):
-    if len(audio.shape) > 1:
-        return audio[:, begin: end]
-    else:
-        return audio[begin: end]
-
-
-class Slicer:
-    def __init__(self,
-                 sr: int,
-                 db_threshold: float = -40,
-                 min_length: int = 5000,
-                 win_l: int = 300,
-                 win_s: int = 20,
-                 max_silence_kept: int = 500):
-        self.db_threshold = db_threshold
-        self.min_samples = round(sr * min_length / 1000)
-        self.win_ln = round(sr * win_l / 1000)
-        self.win_sn = round(sr * win_s / 1000)
-        self.max_silence = round(sr * max_silence_kept / 1000)
-        if not self.min_samples >= self.win_ln >= self.win_sn:
-            raise ValueError('The following condition must be satisfied: min_length >= win_l >= win_s')
-        if not self.max_silence >= self.win_sn:
-            raise ValueError('The following condition must be satisfied: max_silence_kept >= win_s')
-
-    @timeit
-    def slice(self, audio):
-        samples = audio
-        if samples.shape[0] <= self.min_samples:
-            return {"0": {"slice": False, "split_time": f"0,{len(audio)}"}}
-        # get absolute amplitudes
-        abs_amp = np.abs(samples - np.mean(samples))
-        # calculate local maximum with large window
-        win_max_db = level2db(_window_maximum(abs_amp, win_sz=self.win_ln))
-        sil_tags = []
-        left = right = 0
-        while right < win_max_db.shape[0]:
-            if win_max_db[right] < self.db_threshold:
-                right += 1
-            elif left == right:
-                left += 1
-                right += 1
-            else:
-                if left == 0:
-                    split_loc_l = left
-                else:
-                    sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2)
-                    rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn))
-                    split_win_l = left + np.argmin(rms_db_left)
-                    split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn])
-                if len(sil_tags) != 0 and split_loc_l - sil_tags[-1][1] < self.min_samples and right < win_max_db.shape[
-                    0] - 1:
-                    right += 1
-                    left = right
-                    continue
-                if right == win_max_db.shape[0] - 1:
-                    split_loc_r = right + self.win_ln
-                else:
-                    sil_right_n = min(self.max_silence, (right + self.win_ln - left) // 2)
-                    rms_db_right = level2db(_window_rms(samples[right + self.win_ln - sil_right_n: right + self.win_ln],
-                                                        win_sz=self.win_sn))
-                    split_win_r = right + self.win_ln - sil_right_n + np.argmin(rms_db_right)
-                    split_loc_r = split_win_r + np.argmin(abs_amp[split_win_r: split_win_r + self.win_sn])
-                sil_tags.append((split_loc_l, split_loc_r))
-                right += 1
-                left = right
-        if left != right:
-            sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2)
-            rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn))
-            split_win_l = left + np.argmin(rms_db_left)
-            split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn])
-            sil_tags.append((split_loc_l, samples.shape[0]))
-        if len(sil_tags) == 0:
-            return {"0": {"slice": False, "split_time": f"0,{len(audio)}"}}
-        else:
-            chunks = []
-            # 第一段静音并非从头开始，补上有声片段
-            if sil_tags[0][0]:
-                chunks.append({"slice": False, "split_time": f"0,{sil_tags[0][0]}"})
-            for i in range(0, len(sil_tags)):
-                # 标识有声片段（跳过第一段）
-                if i:
-                    chunks.append({"slice": False, "split_time": f"{sil_tags[i - 1][1]},{sil_tags[i][0]}"})
-                # 标识所有静音片段
-                chunks.append({"slice": True, "split_time": f"{sil_tags[i][0]},{sil_tags[i][1]}"})
-            # 最后一段静音并非结尾，补上结尾片段
-            if sil_tags[-1][1] != len(audio):
-                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1]},{len(audio)}"})
-            chunk_dict = {}
-            for i in range(len(chunks)):
-                chunk_dict[str(i)] = chunks[i]
-            return chunk_dict
-
-
-def cut(audio_path, db_thresh=-30, min_len=5000, win_l=300, win_s=20, max_sil_kept=500):
-    audio, sr = torchaudio.load(audio_path)
-    if len(audio.shape) == 2 and audio.shape[1] >= 2:
-        audio = torch.mean(audio, dim=0).unsqueeze(0)
-    audio = audio.cpu().numpy()[0]
-
-    slicer = Slicer(
-        sr=sr,
-        db_threshold=db_thresh,
-        min_length=min_len,
-        win_l=win_l,
-        win_s=win_s,
-        max_silence_kept=max_sil_kept
-    )
-    chunks = slicer.slice(audio)
-    return chunks
-
-
-def chunks2audio(audio_path, chunks):
-    chunks = dict(chunks)
-    audio, sr = torchaudio.load(audio_path)
-    if len(audio.shape) == 2 and audio.shape[1] >= 2:
-        audio = torch.mean(audio, dim=0).unsqueeze(0)
-    audio = audio.cpu().numpy()[0]
-    result = []
-    for k, v in chunks.items():
-        tag = v["split_time"].split(",")
-        result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
-    return result, sr
-
-
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/GimpGradientFile.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/GimpGradientFile.py
deleted file mode 100644
index 8e801be0b8a3c373e3cbd274a10f0da57edb5e70..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/GimpGradientFile.py
+++ /dev/null
@@ -1,137 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read (and render) GIMP gradient files
-#
-# History:
-#       97-08-23 fl     Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-Stuff to translate curve segments to palette values (derived from
-the corresponding code in GIMP, written by Federico Mena Quintero.
-See the GIMP distribution for more information.)
-"""
-
-
-from math import log, pi, sin, sqrt
-
-from ._binary import o8
-
-EPSILON = 1e-10
-""""""  # Enable auto-doc for data member
-
-
-def linear(middle, pos):
-    if pos <= middle:
-        if middle < EPSILON:
-            return 0.0
-        else:
-            return 0.5 * pos / middle
-    else:
-        pos = pos - middle
-        middle = 1.0 - middle
-        if middle < EPSILON:
-            return 1.0
-        else:
-            return 0.5 + 0.5 * pos / middle
-
-
-def curved(middle, pos):
-    return pos ** (log(0.5) / log(max(middle, EPSILON)))
-
-
-def sine(middle, pos):
-    return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0
-
-
-def sphere_increasing(middle, pos):
-    return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2)
-
-
-def sphere_decreasing(middle, pos):
-    return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2)
-
-
-SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing]
-""""""  # Enable auto-doc for data member
-
-
-class GradientFile:
-    gradient = None
-
-    def getpalette(self, entries=256):
-        palette = []
-
-        ix = 0
-        x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
-
-        for i in range(entries):
-            x = i / (entries - 1)
-
-            while x1 < x:
-                ix += 1
-                x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
-
-            w = x1 - x0
-
-            if w < EPSILON:
-                scale = segment(0.5, 0.5)
-            else:
-                scale = segment((xm - x0) / w, (x - x0) / w)
-
-            # expand to RGBA
-            r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5))
-            g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5))
-            b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5))
-            a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5))
-
-            # add to palette
-            palette.append(r + g + b + a)
-
-        return b"".join(palette), "RGBA"
-
-
-class GimpGradientFile(GradientFile):
-    """File handler for GIMP's gradient format."""
-
-    def __init__(self, fp):
-        if fp.readline()[:13] != b"GIMP Gradient":
-            msg = "not a GIMP gradient file"
-            raise SyntaxError(msg)
-
-        line = fp.readline()
-
-        # GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do
-        if line.startswith(b"Name: "):
-            line = fp.readline().strip()
-
-        count = int(line)
-
-        gradient = []
-
-        for i in range(count):
-            s = fp.readline().split()
-            w = [float(x) for x in s[:11]]
-
-            x0, x1 = w[0], w[2]
-            xm = w[1]
-            rgb0 = w[3:7]
-            rgb1 = w[7:11]
-
-            segment = SEGMENTS[int(s[11])]
-            cspace = int(s[12])
-
-            if cspace != 0:
-                msg = "cannot handle HSV colour space"
-                raise OSError(msg)
-
-            gradient.append((x0, x1, xm, rgb0, rgb1, segment))
-
-        self.gradient = gradient
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ansi.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ansi.c
deleted file mode 100644
index c1e31266ec3732d600156c98d9da5e149e0d13a3..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ansi.c
+++ /dev/null
@@ -1,493 +0,0 @@
-/*
- * ASCII/ANSI art decoder
- * Copyright (c) 2010 Peter Ross <pross@xvid.org>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * ASCII/ANSI art decoder
- */
-
-#include "libavutil/common.h"
-#include "libavutil/frame.h"
-#include "libavutil/xga_font_data.h"
-#include "avcodec.h"
-#include "cga_data.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-#define ATTR_BOLD         0x01  /**< Bold/Bright-foreground (mode 1) */
-#define ATTR_FAINT        0x02  /**< Faint (mode 2) */
-#define ATTR_ITALICS      0x04  /**< Italics (mode 3) */
-#define ATTR_UNDERLINE    0x08  /**< Underline (mode 4) */
-#define ATTR_BLINK        0x10  /**< Blink/Bright-background (mode 5) */
-#define ATTR_REVERSE      0x40  /**< Reverse (mode 7) */
-#define ATTR_CONCEALED    0x80  /**< Concealed (mode 8) */
-
-#define DEFAULT_FG_COLOR     7  /**< CGA color index */
-#define DEFAULT_BG_COLOR     0
-#define DEFAULT_SCREEN_MODE  3  /**< 80x25 */
-
-#define FONT_WIDTH           8  /**< Font width */
-
-/** map ansi color index to cga palette index */
-static const uint8_t ansi_to_cga[16] = {
-    0,  4,  2,  6,  1,  5,  3, 7, 8, 12, 10, 14,  9, 13, 11, 15
-};
-
-typedef struct AnsiContext {
-    AVFrame *frame;
-    int x;                /**< x cursor position (pixels) */
-    int y;                /**< y cursor position (pixels) */
-    int sx;               /**< saved x cursor position (pixels) */
-    int sy;               /**< saved y cursor position (pixels) */
-    const uint8_t* font;  /**< font */
-    int font_height;      /**< font height */
-    int attributes;       /**< attribute flags */
-    int fg;               /**< foreground color */
-    int bg;               /**< background color */
-    int first_frame;
-
-    /* ansi parser state machine */
-    enum {
-        STATE_NORMAL = 0,
-        STATE_ESCAPE,
-        STATE_CODE,
-        STATE_MUSIC_PREAMBLE
-    } state;
-#define MAX_NB_ARGS 4
-    int args[MAX_NB_ARGS];
-    int nb_args;          /**< number of arguments (may exceed MAX_NB_ARGS) */
-} AnsiContext;
-
-static av_cold int decode_init(AVCodecContext *avctx)
-{
-    AnsiContext *s = avctx->priv_data;
-    avctx->pix_fmt = AV_PIX_FMT_PAL8;
-
-    /* defaults */
-    s->font        = avpriv_vga16_font;
-    s->font_height = 16;
-    s->fg          = DEFAULT_FG_COLOR;
-    s->bg          = DEFAULT_BG_COLOR;
-
-    if (!avctx->width || !avctx->height) {
-        int ret = ff_set_dimensions(avctx, 80 << 3, 25 << 4);
-        if (ret < 0)
-            return ret;
-    } else if (avctx->width % FONT_WIDTH || avctx->height % s->font_height) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid dimensions %d %d\n", avctx->width, avctx->height);
-        return AVERROR(EINVAL);
-    }
-
-    s->frame = av_frame_alloc();
-    if (!s->frame)
-        return AVERROR(ENOMEM);
-
-    return 0;
-}
-
-static void set_palette(uint32_t *pal)
-{
-    int r, g, b;
-    memcpy(pal, ff_cga_palette, 16 * 4);
-    pal += 16;
-#define COLOR(x) ((x) * 40 + 55)
-    for (r = 0; r < 6; r++)
-        for (g = 0; g < 6; g++)
-            for (b = 0; b < 6; b++)
-                *pal++ = 0xFF000000 | (COLOR(r) << 16) | (COLOR(g) << 8) | COLOR(b);
-#define GRAY(x) ((x) * 10 + 8)
-    for (g = 0; g < 24; g++)
-        *pal++ = 0xFF000000 | (GRAY(g) << 16) | (GRAY(g) << 8) | GRAY(g);
-}
-
-static void hscroll(AVCodecContext *avctx)
-{
-    AnsiContext *s = avctx->priv_data;
-    int i;
-
-    if (s->y <= avctx->height - 2*s->font_height) {
-        s->y += s->font_height;
-        return;
-    }
-
-    i = 0;
-    for (; i < avctx->height - s->font_height; i++)
-        memcpy(s->frame->data[0] + i * s->frame->linesize[0],
-               s->frame->data[0] + (i + s->font_height) * s->frame->linesize[0],
-               avctx->width);
-    for (; i < avctx->height; i++)
-        memset(s->frame->data[0] + i * s->frame->linesize[0],
-            DEFAULT_BG_COLOR, avctx->width);
-}
-
-static void erase_line(AVCodecContext * avctx, int xoffset, int xlength)
-{
-    AnsiContext *s = avctx->priv_data;
-    int i;
-    for (i = 0; i < s->font_height; i++)
-        memset(s->frame->data[0] + (s->y + i)*s->frame->linesize[0] + xoffset,
-            DEFAULT_BG_COLOR, xlength);
-}
-
-static void erase_screen(AVCodecContext *avctx)
-{
-    AnsiContext *s = avctx->priv_data;
-    int i;
-    for (i = 0; i < avctx->height; i++)
-        memset(s->frame->data[0] + i * s->frame->linesize[0], DEFAULT_BG_COLOR, avctx->width);
-    s->x = s->y = 0;
-}
-
-/**
- * Draw character to screen
- */
-static void draw_char(AVCodecContext *avctx, int c)
-{
-    AnsiContext *s = avctx->priv_data;
-    int fg = s->fg;
-    int bg = s->bg;
-
-    if ((s->attributes & ATTR_BOLD))
-        fg += 8;
-    if ((s->attributes & ATTR_BLINK))
-        bg += 8;
-    if ((s->attributes & ATTR_REVERSE))
-        FFSWAP(int, fg, bg);
-    if ((s->attributes & ATTR_CONCEALED))
-        fg = bg;
-    ff_draw_pc_font(s->frame->data[0] + s->y * s->frame->linesize[0] + s->x,
-                    s->frame->linesize[0], s->font, s->font_height, c, fg, bg);
-    s->x += FONT_WIDTH;
-    if (s->x > avctx->width - FONT_WIDTH) {
-        s->x = 0;
-        hscroll(avctx);
-    }
-}
-
-/**
- * Execute ANSI escape code
- * @return 0 on success, negative on error
- */
-static int execute_code(AVCodecContext * avctx, int c)
-{
-    AnsiContext *s = avctx->priv_data;
-    int ret, i;
-    int width  = avctx->width;
-    int height = avctx->height;
-
-    switch(c) {
-    case 'A': //Cursor Up
-        s->y = FFMAX(s->y - (s->nb_args > 0 ? s->args[0]*s->font_height : s->font_height), 0);
-        break;
-    case 'B': //Cursor Down
-        s->y = FFMIN(s->y + (s->nb_args > 0 ? s->args[0]*s->font_height : s->font_height), avctx->height - s->font_height);
-        break;
-    case 'C': //Cursor Right
-        s->x = FFMIN(s->x + (s->nb_args > 0 ? s->args[0]*FONT_WIDTH : FONT_WIDTH), avctx->width  - FONT_WIDTH);
-        break;
-    case 'D': //Cursor Left
-        s->x = FFMAX(s->x - (s->nb_args > 0 ? s->args[0]*FONT_WIDTH : FONT_WIDTH), 0);
-        break;
-    case 'H': //Cursor Position
-    case 'f': //Horizontal and Vertical Position
-        s->y = s->nb_args > 0 ? av_clip((s->args[0] - 1)*s->font_height, 0, avctx->height - s->font_height) : 0;
-        s->x = s->nb_args > 1 ? av_clip((s->args[1] - 1)*FONT_WIDTH,     0, avctx->width  - FONT_WIDTH) : 0;
-        break;
-    case 'h': //set screen mode
-    case 'l': //reset screen mode
-        if (s->nb_args < 2)
-            s->args[0] = DEFAULT_SCREEN_MODE;
-        switch(s->args[0]) {
-        case 0: case 1: case 4: case 5: case 13: case 19: //320x200 (25 rows)
-            s->font = avpriv_cga_font;
-            s->font_height = 8;
-            width  = 40<<3;
-            height = 25<<3;
-            break;
-        case 2: case 3: //640x400 (25 rows)
-            s->font = avpriv_vga16_font;
-            s->font_height = 16;
-            width  = 80<<3;
-            height = 25<<4;
-            break;
-        case 6: case 14: //640x200 (25 rows)
-            s->font = avpriv_cga_font;
-            s->font_height = 8;
-            width  = 80<<3;
-            height = 25<<3;
-            break;
-        case 7: //set line wrapping
-            break;
-        case 15: case 16: //640x350 (43 rows)
-            s->font = avpriv_cga_font;
-            s->font_height = 8;
-            width  = 80<<3;
-            height = 43<<3;
-            break;
-        case 17: case 18: //640x480 (60 rows)
-            s->font = avpriv_cga_font;
-            s->font_height = 8;
-            width  = 80<<3;
-            height = 60<<4;
-            break;
-        default:
-            avpriv_request_sample(avctx, "Unsupported screen mode");
-        }
-        s->x = av_clip(s->x, 0, width  - FONT_WIDTH);
-        s->y = av_clip(s->y, 0, height - s->font_height);
-        if (width != avctx->width || height != avctx->height) {
-            av_frame_unref(s->frame);
-            ret = ff_set_dimensions(avctx, width, height);
-            if (ret < 0)
-                return ret;
-            if ((ret = ff_get_buffer(avctx, s->frame,
-                                     AV_GET_BUFFER_FLAG_REF)) < 0)
-                return ret;
-            s->frame->pict_type           = AV_PICTURE_TYPE_I;
-            s->frame->palette_has_changed = 1;
-            set_palette((uint32_t *)s->frame->data[1]);
-            erase_screen(avctx);
-        } else if (c == 'l') {
-            erase_screen(avctx);
-        }
-        break;
-    case 'J': //Erase in Page
-        switch (s->args[0]) {
-        case 0:
-            erase_line(avctx, s->x, avctx->width - s->x);
-            if (s->y < avctx->height - s->font_height)
-                memset(s->frame->data[0] + (s->y + s->font_height)*s->frame->linesize[0],
-                    DEFAULT_BG_COLOR, (avctx->height - s->y - s->font_height)*s->frame->linesize[0]);
-            break;
-        case 1:
-            erase_line(avctx, 0, s->x);
-            if (s->y > 0)
-                memset(s->frame->data[0], DEFAULT_BG_COLOR, s->y * s->frame->linesize[0]);
-            break;
-        case 2:
-            erase_screen(avctx);
-        }
-        break;
-    case 'K': //Erase in Line
-        switch(s->args[0]) {
-        case 0:
-            erase_line(avctx, s->x, avctx->width - s->x);
-            break;
-        case 1:
-            erase_line(avctx, 0, s->x);
-            break;
-        case 2:
-            erase_line(avctx, 0, avctx->width);
-        }
-        break;
-    case 'm': //Select Graphics Rendition
-        if (s->nb_args == 0) {
-            s->nb_args = 1;
-            s->args[0] = 0;
-        }
-        for (i = 0; i < FFMIN(s->nb_args, MAX_NB_ARGS); i++) {
-            int m = s->args[i];
-            if (m == 0) {
-                s->attributes = 0;
-                s->fg = DEFAULT_FG_COLOR;
-                s->bg = DEFAULT_BG_COLOR;
-            } else if (m == 1 || m == 2 || m == 3 || m == 4 || m == 5 || m == 7 || m == 8) {
-                s->attributes |= 1 << (m - 1);
-            } else if (m >= 30 && m <= 37) {
-                s->fg = ansi_to_cga[m - 30];
-            } else if (m == 38 && i + 2 < FFMIN(s->nb_args, MAX_NB_ARGS) && s->args[i + 1] == 5 && s->args[i + 2] < 256) {
-                int index = s->args[i + 2];
-                s->fg = index < 16 ? ansi_to_cga[index] : index;
-                i += 2;
-            } else if (m == 39) {
-                s->fg = ansi_to_cga[DEFAULT_FG_COLOR];
-            } else if (m >= 40 && m <= 47) {
-                s->bg = ansi_to_cga[m - 40];
-            } else if (m == 48 && i + 2 < FFMIN(s->nb_args, MAX_NB_ARGS) && s->args[i + 1] == 5 && s->args[i + 2] < 256) {
-                int index = s->args[i + 2];
-                s->bg = index < 16 ? ansi_to_cga[index] : index;
-                i += 2;
-            } else if (m == 49) {
-                s->fg = ansi_to_cga[DEFAULT_BG_COLOR];
-            } else {
-                avpriv_request_sample(avctx, "Unsupported rendition parameter");
-            }
-        }
-        break;
-    case 'n': //Device Status Report
-    case 'R': //report current line and column
-        /* ignore */
-        break;
-    case 's': //Save Cursor Position
-        s->sx = s->x;
-        s->sy = s->y;
-        break;
-    case 'u': //Restore Cursor Position
-        s->x = av_clip(s->sx, 0, avctx->width  - FONT_WIDTH);
-        s->y = av_clip(s->sy, 0, avctx->height - s->font_height);
-        break;
-    default:
-        avpriv_request_sample(avctx, "Unknown escape code");
-        break;
-    }
-    s->x = av_clip(s->x, 0, avctx->width  - FONT_WIDTH);
-    s->y = av_clip(s->y, 0, avctx->height - s->font_height);
-    return 0;
-}
-
-static int decode_frame(AVCodecContext *avctx, AVFrame *rframe,
-                        int *got_frame, AVPacket *avpkt)
-{
-    AnsiContext *s = avctx->priv_data;
-    const uint8_t *buf = avpkt->data;
-    int buf_size = avpkt->size;
-    const uint8_t *buf_end   = buf+buf_size;
-    int ret, i, count;
-
-    if ((ret = ff_reget_buffer(avctx, s->frame, 0)) < 0)
-        return ret;
-    if (!avctx->frame_num) {
-        for (i=0; i<avctx->height; i++)
-            memset(s->frame->data[0]+ i*s->frame->linesize[0], 0, avctx->width);
-        memset(s->frame->data[1], 0, AVPALETTE_SIZE);
-    }
-
-    s->frame->pict_type           = AV_PICTURE_TYPE_I;
-    s->frame->palette_has_changed = 1;
-    set_palette((uint32_t *)s->frame->data[1]);
-    if (!s->first_frame) {
-        erase_screen(avctx);
-        s->first_frame = 1;
-    }
-
-    while(buf < buf_end) {
-        switch(s->state) {
-        case STATE_NORMAL:
-            switch (buf[0]) {
-            case 0x00: //NUL
-            case 0x07: //BEL
-            case 0x1A: //SUB
-                /* ignore */
-                break;
-            case 0x08: //BS
-                s->x = FFMAX(s->x - 1, 0);
-                break;
-            case 0x09: //HT
-                i = s->x / FONT_WIDTH;
-                count = ((i + 8) & ~7) - i;
-                for (i = 0; i < count; i++)
-                    draw_char(avctx, ' ');
-                break;
-            case 0x0A: //LF
-                hscroll(avctx);
-            case 0x0D: //CR
-                s->x = 0;
-                break;
-            case 0x0C: //FF
-                erase_screen(avctx);
-                break;
-            case 0x1B: //ESC
-                s->state = STATE_ESCAPE;
-                break;
-            default:
-                draw_char(avctx, buf[0]);
-            }
-            break;
-        case STATE_ESCAPE:
-            if (buf[0] == '[') {
-                s->state   = STATE_CODE;
-                s->nb_args = 0;
-                s->args[0] = -1;
-            } else {
-                s->state = STATE_NORMAL;
-                draw_char(avctx, 0x1B);
-                continue;
-            }
-            break;
-        case STATE_CODE:
-            switch(buf[0]) {
-            case '0': case '1': case '2': case '3': case '4':
-            case '5': case '6': case '7': case '8': case '9':
-                if (s->nb_args < MAX_NB_ARGS && s->args[s->nb_args] < 6553)
-                    s->args[s->nb_args] = FFMAX(s->args[s->nb_args], 0) * 10 + buf[0] - '0';
-                break;
-            case ';':
-                if (s->nb_args < MAX_NB_ARGS)
-                    s->nb_args++;
-                if (s->nb_args < MAX_NB_ARGS)
-                    s->args[s->nb_args] = 0;
-                break;
-            case 'M':
-                s->state = STATE_MUSIC_PREAMBLE;
-                break;
-            case '=': case '?':
-                /* ignore */
-                break;
-            default:
-                if (s->nb_args > MAX_NB_ARGS)
-                    av_log(avctx, AV_LOG_WARNING, "args overflow (%i)\n", s->nb_args);
-                if (s->nb_args < MAX_NB_ARGS && s->args[s->nb_args] >= 0)
-                    s->nb_args++;
-                if ((ret = execute_code(avctx, buf[0])) < 0)
-                    return ret;
-                s->state = STATE_NORMAL;
-            }
-            break;
-        case STATE_MUSIC_PREAMBLE:
-            if (buf[0] == 0x0E || buf[0] == 0x1B)
-                s->state = STATE_NORMAL;
-            /* ignore music data */
-            break;
-        }
-        buf++;
-    }
-
-    *got_frame = 1;
-    if ((ret = av_frame_ref(rframe, s->frame)) < 0)
-        return ret;
-    return buf_size;
-}
-
-static av_cold int decode_close(AVCodecContext *avctx)
-{
-    AnsiContext *s = avctx->priv_data;
-
-    av_frame_free(&s->frame);
-    return 0;
-}
-
-static const FFCodecDefault ansi_defaults[] = {
-    { "max_pixels", "640*480" },
-    { NULL },
-};
-
-const FFCodec ff_ansi_decoder = {
-    .p.name         = "ansi",
-    CODEC_LONG_NAME("ASCII/ANSI art"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_ANSI,
-    .priv_data_size = sizeof(AnsiContext),
-    .init           = decode_init,
-    .close          = decode_close,
-    FF_CODEC_DECODE_CB(decode_frame),
-    .p.capabilities = AV_CODEC_CAP_DR1,
-    .defaults       = ansi_defaults,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cabac.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cabac.h
deleted file mode 100644
index 38d06b2842e89a8070f11059697b4b097d979213..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cabac.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * H.26L/H.264/AVC/JVT/14496-10/... encoder/decoder
- * Copyright (c) 2003 Michael Niedermayer <michaelni@gmx.at>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * Context Adaptive Binary Arithmetic Coder.
- */
-
-#ifndef AVCODEC_CABAC_H
-#define AVCODEC_CABAC_H
-
-#include <stdint.h>
-
-extern const uint8_t ff_h264_cabac_tables[512 + 4*2*64 + 4*64 + 63];
-#define H264_NORM_SHIFT_OFFSET 0
-#define H264_LPS_RANGE_OFFSET 512
-#define H264_MLPS_STATE_OFFSET 1024
-#define H264_LAST_COEFF_FLAG_OFFSET_8x8_OFFSET 1280
-
-#define CABAC_BITS 16
-#define CABAC_MASK ((1<<CABAC_BITS)-1)
-
-typedef struct CABACContext{
-    int low;
-    int range;
-    const uint8_t *bytestream_start;
-    const uint8_t *bytestream;
-    const uint8_t *bytestream_end;
-}CABACContext;
-
-int ff_init_cabac_decoder(CABACContext *c, const uint8_t *buf, int buf_size);
-
-#endif /* AVCODEC_CABAC_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Incredibox Icon Series APK and Create Your Own Blinding Lights Remix.md b/spaces/congsaPfin/Manga-OCR/logs/Download Incredibox Icon Series APK and Create Your Own Blinding Lights Remix.md
deleted file mode 100644
index ab3f6051d6700bc1c9c6240796354b277a64e10a..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Incredibox Icon Series APK and Create Your Own Blinding Lights Remix.md	
+++ /dev/null
@@ -1,121 +0,0 @@
-
-<h1>Incredibox Icon Series APK: A Fun and Interactive Music Experience</h1>
-<p>Do you love music? Do you enjoy creating your own tunes with the help of a merry crew of beatboxers? Do you want to try a modded version of the popular music app Incredibox that features icons inspired by famous singers and songs? If you answered yes to any of these questions, then you should check out Incredibox Icon Series APK, a fun and interactive music experience that will unleash your creativity and entertain you for hours.</p>
-<h2>incredibox icon series apk</h2><br /><p><b><b>Download File</b> &#9193; <a href="https://urlca.com/2uO7LN">https://urlca.com/2uO7LN</a></b></p><br /><br />
-<h2>What is Incredibox Icon Series APK?</h2>
-<p>Incredibox Icon Series APK is a music app that lets you create your own music with the help of a merry crew of beatboxers. You can choose your musical style among 8 impressive atmospheres and start to lay down, record and share your mix. You can also drag and drop icons onto the avatars to make them sing and unlock animated choruses that will enhance your tune.</p>
-<p>Incredibox Icon Series APK is also a modded version of the original Incredibox app that features icons inspired by famous singers and songs. For example, you can find icons that resemble The Weeknd, Michael Jackson, Billie Eilish, Ed Sheeran, Bruno Mars, Lady Gaga, Justin Bieber, Ariana Grande, and more. You can also find icons that represent songs like Blinding Lights, Thriller, Bad Guy, Shape of You, Uptown Funk, Poker Face, Baby, Thank U Next, and more. You can mix and match these icons to create unique and original music.</p>
-<p>Incredibox Icon Series APK is also a free download that works on Android devices. You can download it from the link provided by RB - SERIES on YouTube, who is one of the creators of this mod. You can also watch his videos to see some examples of his mixes using this mod.</p>
-<h2>What are the features of Incredibox Icon Series APK?</h2>
-<p>Incredibox Icon Series APK has many features that make it a fun and interactive music experience. Here are some of them:</p>
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-incredibox icon series v1 blinding lights - all bonuses - green screen - youtube[3](https://www.youtube.com/watch?v=t4xqnrUvCqo)<br />
-how to download and install incredibox - blinding lights - mod - youtube[2](https://www.youtube.com/watch?v=BJLfiu5yeh0)<br />
-how to download and install incrediobox - blinding lights - mod - mediafire[10](https://www.mediafire.com/file/pb4f7ffybxit4pp/Incredibox+-+Icon+Series.apk/file)<br />
-how to download and install incrediobox - blinding lights - mod - rb-series[8](https://youtube.com/channel/UCC3ezszbZQgZ8fJQzK9xY6w)<br />
-how to download and install incrediobox - blinding lights - mod - discord[6](https://discord.gg/amteBFEfQF)<br />
-how to download and install incrediobox - blinding lights - mod - twitter[7](https://twitter.com/REDBUNNYSERIES?s=09)<br />
-how to download and install incrediobox - blinding lights - mod - instagram[11](https://www.instagram.com/rb_series.official/)<br />
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-how to download and install incrediobox - blinding lights - mod - beatbox[2](https://www.youtube.com/watch?v=BJLfiu5yeh0)</p>
-<ul>
-<li>You can choose your musical style among 8 impressive atmospheres and start to lay down, record and share your mix. Each atmosphere has its own theme, mood, color, sound, and icons. You can choose from Alpha, Little Miss, Sunrise, The Love, Brazil, Alive, Jeevan, and Dystopia.</li>
-<li>You can drag and drop icons onto the avatars to make them sing and unlock animated choruses. Each icon represents a different sound element, such as beats, melodies, effects, voices, and more. You can also find icons that are inspired by famous singers and songs, as mentioned before. You can combine up to 7 icons to create your mix.</li>
-<li>You can save and share your mix with other users and vote for your favorites. You can access the Mix tab to see the latest mixes created by other users. You can also join the Top 50 chart if your mix gets enough votes. You can also share your mix on social media platforms like Facebook, Twitter, Instagram, and more.</li>
-</ul>
-<h2>How to download and install Incredibox Icon Series APK?</h2>
-<p>Downloading and installing Incredibox Icon Series APK is easy and simple. Just follow these steps:</p>
-<ol>
-<li>Visit the link provided by RB - SERIES on YouTube. This link will take you to a Google Drive folder where you can find the APK file.</li>
-<li>Allow installation from unknown sources on your device settings. This is necessary because this app is not available on the official Google Play Store.</li>
-<li>Open the downloaded file and follow the instructions. The installation process should take only a few minutes.</li>
-</ol>
-<h2>How to play Incredibox Icon Series APK?</h2>
-<p>Playing Incredibox Icon Series APK is fun and easy. Just follow these steps:</p>
-<ol>
-<li>Launch the app and select an atmosphere. You can swipe left or right to see the different atmospheres available.</li>
-<li>Drag and drop icons onto the avatars to make them sing. You can see the icons at the bottom of the screen. You can also swipe left or right to see more icons. You can combine up to 7 icons to create your mix.</li>
-<li>Find the right sound combos to unlock animated choruses. When you find a good combo, you will see a star icon appear on the top right corner of the screen. Tap on it to unlock an animated chorus that will add more flavor to your mix.</li>
-<li>Record and save your mix. When you are happy with your mix, you can tap on the record button on the top left corner of the screen. You can then name your mix and save it on your device or on the cloud.</li>
-<li>Share your mix with other users and vote for others. You can access the Mix tab to see the latest mixes created by other users. You can also join the Top 50 chart if your mix gets enough votes. You can also share your mix on social media platforms like Facebook, Twitter, Instagram, and more.</li>
-</ol>
-<h2>What are some tips and tricks for Incredibox Icon Series APK?</h2>
-<p>If you want to improve your skills and have more fun with Incredibox Icon Series APK, here are some tips and tricks that you can try:</p>
-<ul>
-<li>Experiment with different icons and atmospheres to find your style. There are many possibilities and combinations that you can explore with this app. Don't be afraid to try new things and see what works for you.</li>
-<li>Watch some videos of other users' mixes for inspiration  . You can learn a lot from watching how other users create their mixes using this app. You can also get some ideas for your own mixes and discover new sound combos that you might not have thought of before.</li>
-<li>Use the shuffle button to randomize your mix. If you are feeling stuck or bored with your mix, you can use the shuffle button on the top right corner of the screen to change it up. This will randomly rearrange the icons on the avatars and create a new mix for you.</li>
-<li>Use the mute button to silence some avatars. If you want to focus on one sound element or reduce some noise in your mix, you can use the mute button on the bottom right corner of each avatar to silence it. This will allow you to hear only the sounds that you want.</li>
-<li>Use the solo button to isolate one avatar's sound. If you want to hear how one sound element sounds by itself or enhance it in your mix, you can use the solo button on the bottom left corner of each avatar to isolate it. This will mute all other avatars except for the one that you selected.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Incredibox Icon Series APK is a fun and interactive music experience that will unleash your creativity and entertain you for hours. You can create your own music with the help of a merry crew of beatboxers and drag and drop icons inspired by famous singers and songs. You can also save and share your mix with other users and vote for your favorites. You can also download and install this app for free on your Android device. If you are looking for a fun and interactive music experience, you should give Incredibox Icon Series APK a try.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Incredibox Icon Series APK:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Incredibox Icon Series APK safe to download and use?</td>
-<td>Yes, Incredibox Icon Series APK is safe to download and use. It does not contain any viruses or malware. However, you should always download it from a trusted source and scan it with an antivirus before installing it.</td>
-</tr>
-<tr>
-<td>Is Incredibox Icon Series APK legal to use?</td>
-<td>Yes, Incredibox Icon Series APK is legal to use. It is a modded version of the original Incredibox app that does not infringe on any copyrights or trademarks. However, you should always respect the rights of the original creators and not use this app for commercial purposes.</td>
-</tr>
-<tr>
-<td>Does Incredibox Icon Series APK require an internet connection?</td>
-<td>No, Incredibox Icon Series APK does not require an internet connection. You can use this app offline and create your own music without any interruptions. However, you will need an internet connection if you want to save your mix on the cloud or share it with other users.</td>
-</tr>
-<tr>
-<td>Can I use Incredibox Icon Series APK on other devices besides Android?</td>
-<td>No, Incredibox Icon Series APK is only compatible with Android devices. It will not work on iOS, Windows, Mac, or any other devices. If you want to use the original Incredibox app on other devices, you can visit their official website and download it from there.</td>
-</tr>
-<tr>
-<td>Can I request new icons or atmospheres for Incredibox Icon Series APK?</td>
-<td>Yes, you can request new icons or atmospheres for Incredibox Icon Series APK. You can contact the creators of this mod, RB - SERIES, on their YouTube channel or their Instagram account. They are always open to suggestions and feedback from their users.</td>
-</tr>
-</table></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Free Fire Mod APK Unlimited Diamonds Auto Aim and More in One Zip File.md b/spaces/congsaPfin/Manga-OCR/logs/Free Fire Mod APK Unlimited Diamonds Auto Aim and More in One Zip File.md
deleted file mode 100644
index 8243d3c40e97a0a368c89ab232a1fd209660eb9b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Free Fire Mod APK Unlimited Diamonds Auto Aim and More in One Zip File.md	
+++ /dev/null
@@ -1,81 +0,0 @@
-<br />
-<h1>Free Fire Mod Apk Unlimited Diamonds Zip: Everything You Need to Know</h1>
- <p>Free Fire is one of the most popular battle royale games on mobile, with over 500 million downloads on Google Play Store. The game offers a thrilling survival experience where you have to fight against 49 other players on a remote island. You can choose from a variety of characters, weapons, vehicles, and game modes to suit your playstyle.</p>
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- <p>However, some players are not satisfied with the official version of the game and want to get an edge over their opponents. That's why they look for modified versions of the game that offer some extra features and benefits. One such version is free fire mod apk unlimited diamonds zip, which claims to provide unlimited diamonds, coins, health, ammo, and more.</p>
- <p>But what exactly is free fire mod apk unlimited diamonds zip and how does it work? Is it safe to use or does it come with some risks? And what precautions should you take before downloading and installing it? In this article, we will answer all these questions and more. Read on to find out everything you need to know about free fire mod apk unlimited diamonds zip.</p>
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- <p>Free fire mod apk unlimited diamonds zip is a modified application of the official Free Fire game that offers some pro features. It's just like a cracked or hacked version of Free Fire in which you can instantly locate enemies behind walls or in buildings as well as kill them easily by auto aimbot feature.</p>
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- <p>We hope this article has helped you understand everything you need to know about free fire mod apk unlimited diamonds zip. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
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-<h1>FNAF 1 Mod APK 2.0 1: How to Download and Play the Ultimate Horror Game on Your Android Device</h1>
- <p>If you are a fan of horror games, you must have heard of Five Nights at Freddy's, or FNAF for short. It is one of the most popular and terrifying games ever created, with millions of downloads and fans worldwide. But did you know that there is a modded version of FNAF 1 that you can play on your Android device? It is called FNAF 1 Mod APK 2.0 1, and it is a modified version of the original game that adds new features, enhancements, and challenges to make it even more scary and fun. In this article, we will tell you everything you need to know about FNAF 1 Mod APK 2.0 1, including what it is, how to download and install it, how to play it, and why you should play it.</p>
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- <h2>What is FNAF 1 Mod APK 2.0 1?</h2>
- <p>FNAF 1 Mod APK 2.0 1 is a modified version of the original Five Nights at Freddy's game that was released in 2014 for PC and later for mobile devices. The original game is a point-and-click survival horror game that takes place in a fictional pizza restaurant called Freddy Fazbear's Pizza, where the player takes the role of a night security guard who must survive five nights (and a bonus sixth night) from midnight to 6 AM without being attacked by the animatronic characters that roam in free mode at night. The game relies on visuals and sounds to create a chilling atmosphere and a sense of fear. The game has a dark and humorous tone, and it became a huge success, spawning a media franchise with several sequels, spin-offs, books, merchandise, and a film adaptation in development.</p>
- <p>The modded version of FNAF 1, however, adds new features and enhancements to the original game that make it more interesting and challenging. Some of these features are:</p>
- <ul>
-<li>Unlocked all nights, modes, cheats, and extras</li>
-<li>Unlimited power for the doors, lights, and cameras</li>
-<li>Customizable difficulty for each animatronic character</li>
-<li>New graphics and sounds for the game</li>
-<li>New jump scares and Easter eggs for the game</li>
-<li>Compatible with all Android devices</li>
-</ul>
- <p>With these features, FNAF 1 Mod APK 2.0 1 offers a new experience for FNAF fans who want to enjoy the game on their Android devices with more options and challenges.</p>
- <h2>How to download and install FNAF 1 Mod APK 2.0 1 on your Android device?</h2>
- <p>If you want to download and install FNAF 1 Mod APK 2.0 1 on your Android device, you need to follow these simple steps:</p>
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- <h3>Step 1: Enable unknown sources</h3>
- <p <p>Since FNAF 1 Mod APK 2.0 1 is not available on the official Google Play Store, you need to enable the option to install apps from unknown sources on your Android device. To do this, go to your device's settings, then security, and then toggle on the unknown sources option. This will allow you to install apps from sources other than the Google Play Store.</p>
- <h3>Step 2: Download the APK file</h3>
- <p>Next, you need to download the APK file of FNAF 1 Mod APK 2.0 1 from a reliable source. You can use the link below to download the file directly to your device. The file size is about 50 MB, so make sure you have enough storage space and a stable internet connection.</p>
- <p><a href="">Download FNAF 1 Mod APK 2.0 1 here</a></p>
- <h3>Step 3: Install the APK file</h3>
- <p>Once you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your device's file manager and tap on it. You will see a prompt asking you to confirm the installation. Tap on install and wait for the process to complete. It may take a few seconds or minutes depending on your device's performance.</p>
- <h3>Step 4: Launch the game and enjoy</h3>
- <p>After the installation is done, you can launch the game by tapping on its icon on your device's home screen or app drawer. You will see the game's logo and a loading screen. Then, you will be taken to the main menu of the game, where you can select your preferred night, mode, cheat, or extra. You can also access the settings and adjust the sound, brightness, and language of the game. Now, you are ready to play FNAF 1 Mod APK 2.0 1 on your Android device and have a thrilling experience.</p>
- <h2>How to play FNAF 1 Mod APK 2.0 1 on your Android device?</h2>
- <p>If you have played the original FNAF 1 game before, you will be familiar with the gameplay and the objective of FNAF 1 Mod APK 2.0 1. However, if you are new to the game or want a refresher, here is how to play FNAF 1 Mod APK 2.0 1 on your Android device:</p>
- <h3>The gameplay and the objective</h3>
- <p>The gameplay of FNAF 1 Mod APK 2.0 1 is similar to the original game, but with some differences and additions. The game is divided into six nights (and a bonus seventh night) that last from midnight to 6 AM in-game time. Each night has a different level of difficulty and challenge. The objective of the game is to survive each night without being attacked by the animatronic characters that roam around the restaurant at night.</p>
- <p>To do this, you have to monitor their movements using a security camera system that covers various locations in the restaurant. You also have access to two doors on each side of your office that you can close or open to prevent or allow the animatronics from entering your office. However, closing the doors consumes power, which is limited and cannot be replenished until the next night. If you run out of power before 6 AM, you will be left in darkness and vulnerable to attacks.</p>
- <p>In addition to the doors, you also have lights that you can use to check if any animatronic is near your door or not. You also have a music box that you can use to soothe one of the animatronics that is sensitive to noise. However, using these tools also consumes power, so you have to use them wisely and sparingly.</p>
- <h3>The animatronics and their behavior</h3>
- <p>The animatronics are the main antagonists of FNAF 1 Mod APK 2.0 1 and they are four in total: Freddy Fazbear, Bonnie the Bunny, Chica the Chicken, and Foxy the Pirate Fox. They are robotic mascots that entertain children during the day, but at night they become hostile and murderous towards anyone they encounter.</p>
- <p>Each animatronic has a different appearance, personality, and behavior pattern that you have to learn and adapt to in order to survive their attacks. Here is a brief description of each animatronic:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-<th>Behavior</th>
-</tr>
-<tr>
-<td>Freddy Fazbear</td>
-<td>The leader of the band and the main mascot of Freddy Fazbear's Pizza. He is a brown bear wearing a black bowtie and a top hat.</td> <td>He is the most cunning and unpredictable of the animatronics. He usually stays in the dark and moves when you are not looking. He can also disable your cameras and sneak into your office without making any noise.</td>
-</tr>
-<tr>
-<td>Bonnie the Bunny</td>
-<td>The guitarist of the band and the first animatronic to move. He is a purple bunny wearing a red bowtie.</td>
-<td>He is the most aggressive and active of the animatronics. He usually moves from the left side of the restaurant and tries to enter your office through the left door. He can also stare at you through the window or the camera.</td>
-</tr>
-<tr>
-<td>Chica the Chicken</td>
-<td>The backup singer of the band and the second animatronic to move. She is a yellow chicken wearing a bib that says "Let's Eat!"</td>
-<td>She is the most noisy and hungry of the animatronics. She usually moves from the right side of the restaurant and tries to enter your office through the right door. She can also hold a cupcake in her hand or make loud noises in the kitchen.</td>
-</tr>
-<tr>
-<td>Foxy the Pirate Fox</td>
-<td>The out-of-order animatronic and the hidden character of the game. He is a red fox wearing an eye patch and a hook.</td>
-<td>He is the most mysterious and dangerous of the animatronics. He usually stays in a closed curtain in Pirate Cove, but if you don't check on him frequently, he will run towards your office and attack you through the left door. He can also peek out of his curtain or sing a pirate song.</td>
-</tr>
-</table>
- <p>You have to keep an eye on each animatronic using your cameras and close your doors when they are near your office. However, you cannot close both doors at the same time, as this will drain your power faster. You also have to be careful not to look at them for too long, as this will make them more aggressive and active.</p>
- <h3>The tools and the power</h3>
- <p>As mentioned before, you have access to some tools that can help you survive each night, such as doors, lights, cameras, and a music box. However, these tools are not unlimited and they consume power, which is limited and cannot be replenished until the next night. You have to manage your power wisely and use your tools sparingly, as running out of power before 6 AM will result in a game over.</p>
- <p>In FNAF 1 Mod APK 2.0 1, however, you have an option to unlock unlimited power for your tools, which means you can use them as much as you want without worrying about running out of power. This can make the game easier or harder depending on how you use it. For example, you can close both doors all night and be safe from any attack, but this will also make the game boring and less challenging. On the other hand, you can use your lights and cameras more often and see more of what is happening in the restaurant, but this will also make the game more scary and tense.</p>
- <h3>The tips and tricks</h3>
- <p>If you want to play FNAF 1 Mod APK 2.0 1 on your Android device and survive each night, here are some tips and tricks that can help you:</p>
- <ul>
-<li>Check your cameras frequently, but not too frequently. This will help you keep track of where each animatronic is and when they are near your office. However, checking your cameras too often will make them more active and aggressive, so try to balance your camera usage.</li>
-<li>Close your doors only when necessary. This will help you save power and prevent any attack from entering your office. However, closing your doors too early or too late will waste power or expose you to danger, so try to time your door usage.</li>
-<li>Use your lights wisely. This will help you see if any animatronic is near your door or not. However, using your lights too much will consume power and attract their attention, so try to use your lights sparingly.</li>
-<li>Use your music box smartly. This will help you soothe one of the animatronics that is sensitive to noise and prevent it from attacking you. However, using your music box too much will consume power and annoy other animatronics, so try to use your music box moderately.</li>
-</li>
-<li>Explore the extras and the Easter eggs. This will help you discover more about the game and its secrets. You can unlock some extras that can show you some behind-the-scenes information, such as the animatronics' blueprints, the newspaper clippings, the developer's commentary, etc. You can also find some Easter eggs that can reveal some hidden details, such as the Golden Freddy poster, the phone guy's messages, the endoskeleton in the backstage, etc.</li>
-</ul>
- <h2>Why should you play FNAF 1 Mod APK 2.0 1 on your Android device?</h2>
- <p>If you are still wondering why you should play FNAF 1 Mod APK 2.0 1 on your Android device, here are some reasons that can convince you:</p>
- <h3>The advantages of playing the modded version</h3>
- <p>Playing FNAF 1 Mod APK 2.0 1 on your Android device has some advantages over playing the original game or other versions of the game. Some of these advantages are:</p>
- <ul>
-<li>You can play the game for free without paying any money or watching any ads.</li>
-<li>You can play the game offline without needing any internet connection or data usage.</li>
-<li>You can play the game on any Android device without worrying about compatibility or performance issues.</li>
-<li>You can play the game with more features and options that can enhance your gameplay and enjoyment.</li>
-<li>You can play the game with more challenges and surprises that can test your skills and nerves.</li>
-</ul>
- <h3>The reviews and ratings of the game</h3>
- <p>Playing FNAF 1 Mod APK 2.0 1 on your Android device also has some positive reviews and ratings from other players who have tried it. Some of these reviews and ratings are:</p>
- <ul>
-<li>"This is the best mod ever! It makes the game more fun and scary at the same time. I love how I can customize the difficulty and use cheats to make it easier or harder. The graphics and sounds are also amazing. I recommend this mod to anyone who loves FNAF."</li>
-<li>"I have played all the FNAF games, but this one is my favorite. It has everything I want in a horror game: suspense, jump scares, humor, mystery, and more. The mod adds so much to the original game that it feels like a new game. I can't get enough of it."</li>
-<li>"This mod is awesome! It gives me more control and freedom over how I want to play the game. I can choose which night, mode, cheat, or extra I want to play. I can also change the difficulty level for each animatronic to make it more challenging or easy. The mod also has some new graphics and sounds that make it more realistic and immersive."</li>
-</ul>
- <h3>The challenges and rewards of the game</h3>
- <p>Playing FNAF 1 Mod APK 2.0 1 on your Android device also has some challenges and rewards that can motivate you to play more and improve your gameplay. Some of these challenges and rewards are:</p>
- <ul>
-<li>You can challenge yourself to complete all six nights (and a bonus seventh night) with different difficulty levels and modes.</li>
-<li>You can challenge yourself to find all the Easter eggs and secrets that are hidden in the game.</li>
-<li>You can challenge yourself to unlock all the cheats and extras that are available in the game.</li>
-<li>You can reward yourself with a sense of accomplishment and satisfaction after completing each night or finding each Easter egg or secret.</li>
-<li>You can reward yourself with a sense of fun and excitement after experiencing each jump scare or surprise.</li>
-<li>You can reward yourself with a sense of curiosity and wonder after learning more about the game and its story.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, FNAF 1 Mod APK 2.0 1 is a modified version of the original Five Nights at Freddy's game that adds new features, enhancements, and challenges to make it more scary and fun. It is a point-and-click survival horror game that takes place in a fictional pizza restaurant where you have to survive five nights (and a bonus sixth night) from midnight to 6 AM without being attacked by the animatronic characters that roam in free mode at night. You have to monitor their movements using a security camera system and use some tools such as doors, lights, cameras, and a music box to prevent or allow their attacks. You also have an option to unlock unlimited power for your tools, customize your difficulty level for each animatronic, unlock some cheats and extras, and find some Easter eggs and secrets in the game.</p>
- <p>If you want to download and install FNAF 1 Mod APK 2.0 1 on your Android device, you can follow the steps that we have explained in this article. You can also check out the reviews and ratings of the game from other players who have tried it. You can also challenge yourself to play the game with different difficulty levels and modes, and discover more about the game and its secrets. FNAF 1 Mod APK 2.0 1 is a game that will keep you on the edge of your seat and give you a thrilling and terrifying experience. If you are a fan of horror games, you should definitely try FNAF 1 Mod APK 2.0 1 on your Android device and see for yourself.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers about FNAF 1 Mod APK 2.0 1:</p>
- <ul>
-<li>Q: Is FNAF 1 Mod APK 2.0 1 safe to download and install?</li>
-<li>A: Yes, FNAF 1 Mod APK 2.0 1 is safe to download and install, as long as you use a reliable source and enable unknown sources on your device. However, you should always be careful when downloading and installing any app from unknown sources, as they may contain viruses or malware that can harm your device or data.</li>
-<li>Q: Is FNAF 1 Mod APK 2.0 1 compatible with all Android devices?</li>
-<li>A: Yes, FNAF 1 Mod APK 2.0 1 is compatible with all Android devices that run on Android 4.0 or higher. However, some devices may have different performance or compatibility issues depending on their specifications or settings.</li>
-<li>Q: How can I update FNAF 1 Mod APK 2.0 1 to the latest version?</li>
-<li>A: To update FNAF 1 Mod APK 2.0 1 to the latest version, you need to download and install the latest APK file from the same source that you used before. You can also check for updates from within the game's settings.</li>
-<li>Q: How can I uninstall FNAF 1 Mod APK 2.0 1 from my device?</li>
-<li>A: To uninstall FNAF 1 Mod APK 2.0 1 from your device, you need to go to your device's settings, then apps, then find and select FNAF 1 Mod APK 2.0 1, then tap on uninstall and confirm.</li>
-<li>Q: How can I contact the developer of FNAF 1 Mod APK 2.0 1?</li>
-<li>A: To contact the developer of FNAF 1 Mod APK 2.0 1, you can use the email address that is provided in the game's settings or in the source that you downloaded the game from.</li>
-</ul></p> 401be4b1e0<br />
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-<h1>LibreOffice Download 5: A Free and Powerful Office Suite</h1>
-<p>If you are looking for a free and versatile office suite that can handle all your document, spreadsheet, presentation, and database needs, you might want to consider LibreOffice. LibreOffice is an open source software that is compatible with Microsoft Office and other popular formats. In this article, we will show you how to download LibreOffice 5, the latest stable version of this amazing software, and how to use its features to boost your productivity and creativity.</p>
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- <h2>LibreOffice Features</h2>
-<p>LibreOffice includes six applications that cover the most common office tasks:</p>
-<ul>
-<li><strong>Writer</strong>: A word processor that lets you create and edit text documents with graphics, tables, charts, and more. You can also export your documents as PDFs or e-books.</li>
-<li><strong>Calc</strong>: A spreadsheet program that allows you to perform calculations, analyze data, and create charts and graphs. You can also use formulas, functions, macros, and pivot tables.</li>
-<li><strong>Impress</strong>: A presentation software that helps you create slideshows with animations, transitions, multimedia, and interactive elements. You can also record narrations and export your presentations as videos.</li>
-<li><strong>Draw</strong>: A vector graphics and diagramming tool that enables you to draw shapes, logos, flowcharts, maps, and more. You can also edit images and PDFs.</li>
-<li><strong>Base</strong>: A database management system that lets you create and manage databases, forms, queries, and reports. You can also connect to external databases and import or export data.</li>
-<li><strong>Math</strong>: A formula editor that allows you to create and edit mathematical equations and symbols. You can also insert them into other LibreOffice documents.</li>
-<li><strong>Charts</strong>: A charting component that helps you create and customize charts from data in LibreOffice documents. You can also insert them into other LibreOffice documents.</li>
-</ul>
- <h2>LibreOffice vs Microsoft Office</h2>
-<p>LibreOffice is often compared to Microsoft Office, the most popular office suite in the world. How do they differ? Here are some key aspects to consider:</p>
- <h3>Price</h3>
-<p>LibreOffice is completely free to download and use for any purpose. You don't need to pay for a license or a subscription. You can also modify the source code if you want to. Microsoft Office requires you to buy a one-time license or a monthly or yearly subscription. The price depends on the version and the number of devices you want to use it on.</p>
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- <h3>Programs</h3>
-<p>LibreOffice offers six programs that cover the main office functions. Microsoft Office offers more programs depending on the edition you choose. For example, Microsoft Office 365 includes Word, Excel, PowerPoint, OneNote, Outlook, Publisher, Access, Skype for Business, OneDrive (with 1 TB storage), SharePoint Online (for business users), Exchange Online (for business users), Teams (for business users), Yammer (for business users), Sway (for personal users), Forms (for personal users), Stream (for personal users), To Do (for personal users), Planner (for personal users), Power Automate (for personal users), Power Apps (for personal users), Power BI (for personal users), Whiteboard (for personal users), MyAnalytics (for personal users), Bookings (for business users), Invoicing (for business users), MileIQ (for business users), Listings (for business users), Connections (for business users), Business Center (for business users).</p>
- <h3>File Compatibility</h3>
-<p>LibreOffice supports a wide range of file formats including Microsoft Office formats such as DOCX, XLSX, PPTX. It also supports the Open Document Format (ODF), which is an international standard for office documents. It also supports PDF, HTML, EPUB, and many other formats. Microsoft Office supports its own formats and some other formats such as ODF, PDF, HTML, but not all of them. Some features or formatting may be lost or changed when converting between different formats.</p>
- <h3>User Support</h3>
-<p>LibreOffice is supported by a community of volunteers and developers who provide online documentation, forums, mailing lists, chat rooms, and bug reports. You can also find tutorials, guides, books, and courses from third-party sources. Microsoft Office is supported by Microsoft and its partners who provide online help, phone support, chat support, forums, blogs, and feedback channels. You can also find tutorials, guides, books, and courses from third-party sources.</p>
- <h2>How to Download LibreOffice 5</h2>
-<p>If you are ready to try LibreOffice 5, here are the steps you need to follow:</p>
- <h3>System Requirements</h3>
-<p>Before you download LibreOffice 5, make sure your computer meets the minimum system requirements:</p>
-<table>
-<tr>
-<th>Operating System</th>
-<th>Memory (RAM)</th>
-<th>Hard Disk Space</th>
-<th>Processor</th>
-</tr>
-<tr>
-<td>Windows Vista SP2 or later</td>
-<td>512 MB</td>
-<td>1.5 GB</td>
-<td>Pentium 4 or later</td>
-</tr>
-<tr>
-<td>Mac OS X 10.8 or later</td>
-<td>512 MB</td>
-<td>800 MB</td>
-<td>Intel processor</td>
-</tr>
-<tr>
-<td>Linux kernel version 2.6.18 or later with glibc2 version 2.5 or later and libstdc++6 version 4.1.1 or later</td>
-<td>512 MB</td>
-<td>550 MB</td>
-<td>x86 compatible processor</td>
-</tr>
-</table>
- <h3>Download Links</h3>
-<p>You can download LibreOffice 5 from the official website: <a href="">https://www.libreoffice.org/download/download/</a>. You can choose the version that matches your operating system and language. You can also download the help files and the SDK (software development kit) if you need them.</p>
- <h3>Installation Steps</h3>
-<p>After you download the file, you need to run it to start the installation process. The steps may vary depending on your operating system, but they are generally easy and straightforward. You can follow the instructions on the screen and customize the settings as you wish. You can also check the online installation guides for more details: <a href="">https://www.libreoffice.org/get-help/installation/</a>.</p>
- <h2>How to Use LibreOffice 5</h2>
-<p>Once you have installed LibreOffice 5, you can start using it right away. Here are some basic tips to help you get started:</p>
- <h3>Basic Tips</h3>
-<ul>
-<li>To launch LibreOffice 5, you can click on the desktop icon or the start menu icon. You will see the LibreOffice Start Center where you can choose which application you want to use or open an existing document.</li>
-<li>To create a new document, you can click on the File menu and select New. You will see a list of templates that you can use or you can choose a blank document.</li>
-<li>To open an existing document, you can click on the File menu and select Open. You will see a dialog box where you can browse your computer or your cloud storage and select the file you want to open.</li>
-<li>To save a document, you can click on the File menu and select Save or Save As. You will see a dialog box where you can choose the location and the format of your file.</li>
-<li>To edit a document, you can use the toolbar icons, the menu options, or the keyboard shortcuts to perform various actions such as cut, copy, paste, undo, redo, format, insert, etc.</li>
-<li>To print a document, you can click on the File menu and select Print. You will see a dialog box where you can adjust the print settings and preview your document before printing.</li>
-<li>To close a document or an application, you can click on the File menu and select Close or Exit.</li>
-<li>To get help or support, you can click on the Help menu and select LibreOffice Help or Online Help. You will see a window where you can search for topics or browse categories of information.</li>
-</ul>
- <h3>Online Resources</h3>
-<p>If you want to learn more about LibreOffice 5 and its features, you can check out these online resources:</ <ul>
-<li><a href="">https://www.libreoffice.org/discover/libreoffice/</a>: The official website of LibreOffice where you can find news, downloads, documentation, and more.</li>
-<li><a href="">https://documentation.libreoffice.org/en/english-documentation/</a>: The official documentation of LibreOffice where you can find user guides, tutorials, FAQs, and more.</li>
-<li><a href="">https://ask.libreoffice.org/en/questions/</a>: The official question and answer site of LibreOffice where you can ask and answer questions about LibreOffice.</li>
-<li><a href="">https://wiki.documentfoundation.org/Main_Page</a>: The official wiki of the Document Foundation, the organization behind LibreOffice, where you can find information about the project, the community, the development, and more.</li>
-<li><a href="">https://www.youtube.com/user/libreoffice</a>: The official YouTube channel of LibreOffice where you can watch videos about LibreOffice features, tips, and tutorials.</li>
-</ul>
- <h3>Extensions</h3>
-<p>If you want to enhance LibreOffice 5 with additional functionality, you can install extensions that add new features or improve existing ones. Extensions are add-ons that are developed by third-party developers and users. You can find and download extensions from the official extension site: <a href="">https://extensions.libreoffice.org/</a>. You can also install extensions from the Tools menu by selecting Extension Manager. You will see a dialog box where you can add, remove, enable, or disable extensions.</p>
- <h2>Conclusion</h2>
-<p>LibreOffice 5 is a free and powerful office suite that can help you create and edit documents, spreadsheets, presentations, and more. It is compatible with Microsoft Office and other popular formats. It also offers a range of features and options that let you customize your work and express your creativity. You can download LibreOffice 5 from the official website and install it on your computer easily. You can also use online resources and extensions to learn more about LibreOffice 5 and enhance its functionality. If you are looking for a free and versatile office suite, LibreOffice 5 is a great choice for you.</p>
- <h2>FAQs</h2>
-<ul>
-<li><strong>Q: Is LibreOffice 5 safe to download and use?</strong></li>
-<li>A: Yes, LibreOffice 5 is safe to download and use. It is an open source software that is developed and tested by a community of volunteers and developers. It does not contain any malware or spyware. However, you should always download LibreOffice 5 from the official website or a trusted source to avoid any potential risks.</li>
-<li><strong>Q: How do I update LibreOffice 5 to the latest version?</strong></li>
-<li>A: You can update LibreOffice 5 to the latest version by downloading and installing the new version from the official website. You can also check for updates from the Help menu by selecting Check for Updates. You will see a dialog box that will tell you if there is a new version available or not.</li>
-<li><strong>Q: How do I uninstall LibreOffice 5 from my computer?</strong></li>
-<li>A: You can uninstall LibreOffice 5 from your computer by following the instructions for your operating system. For Windows, you can go to the Control Panel and select Programs and Features. Then you can find LibreOffice 5 in the list of programs and click on Uninstall. For Mac OS X, you can go to the Applications folder and drag the LibreOffice icon to the Trash. For Linux, you can use your package manager or terminal to remove LibreOffice 5.</li>
-<li><strong>Q: How do I contact LibreOffice 5 support?</strong></li>
-<li>A: You can contact LibreOffice 5 support by using the online resources mentioned above. You can also join the mailing lists or chat rooms of LibreOffice 5 where you can communicate with other users and developers. You can also report bugs or suggest improvements on the bug tracker or the feedback site of LibreOffice 5.</li>
-<li><strong>Q: How do I donate to LibreOffice 5?</strong></li>
-<li>A: You can donate to LibreOffice 5 by visiting the donation page of the Document Foundation: <a href="">https://www.libreoffice.org/donate/</a>. You can choose the amount and the method of your donation. Your donation will help support the development and maintenance of LibreOffice 5 and its community.</li>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Netflix 7.1 2 APK Download - The Most Popular Streaming Service.md b/spaces/congsaPfin/Manga-OCR/logs/Netflix 7.1 2 APK Download - The Most Popular Streaming Service.md
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-<h1>Netflix 7.1 2 APK Download: How to Watch Netflix on Any Android Device</h1>
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/layers/lambda_layer.py b/spaces/cooelf/Multimodal-CoT/timm/models/layers/lambda_layer.py
deleted file mode 100644
index 2d1027a18146f3171724a45d82107c77e1297e5c..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/layers/lambda_layer.py
+++ /dev/null
@@ -1,84 +0,0 @@
-""" Lambda Layer
-
-Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
-    - https://arxiv.org/abs/2102.08602
-
-@misc{2102.08602,
-Author = {Irwan Bello},
-Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
-Year = {2021},
-}
-
-Status:
-This impl is a WIP. Code snippets in the paper were used as reference but
-good chance some details are missing/wrong.
-
-I've only implemented local lambda conv based pos embeddings.
-
-For a PyTorch impl that includes other embedding options checkout
-https://github.com/lucidrains/lambda-networks
-
-Hacked together by / Copyright 2021 Ross Wightman
-"""
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from .weight_init import trunc_normal_
-
-
-class LambdaLayer(nn.Module):
-    """Lambda Layer w/ lambda conv position embedding
-
-    Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
-        - https://arxiv.org/abs/2102.08602
-    """
-    def __init__(
-            self,
-            dim, dim_out=None, stride=1, num_heads=4, dim_head=16, r=7, qkv_bias=False):
-        super().__init__()
-        self.dim = dim
-        self.dim_out = dim_out or dim
-        self.dim_k = dim_head  # query depth 'k'
-        self.num_heads = num_heads
-        assert self.dim_out % num_heads == 0, ' should be divided by num_heads'
-        self.dim_v = self.dim_out // num_heads  # value depth 'v'
-        self.r = r  # relative position neighbourhood (lambda conv kernel size)
-
-        self.qkv = nn.Conv2d(
-            dim,
-            num_heads * dim_head + dim_head + self.dim_v,
-            kernel_size=1, bias=qkv_bias)
-        self.norm_q = nn.BatchNorm2d(num_heads * dim_head)
-        self.norm_v = nn.BatchNorm2d(self.dim_v)
-
-        # NOTE currently only supporting the local lambda convolutions for positional
-        self.conv_lambda = nn.Conv3d(1, dim_head, (r, r, 1), padding=(r // 2, r // 2, 0))
-
-        self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
-
-    def reset_parameters(self):
-        trunc_normal_(self.qkv.weight, std=self.dim ** -0.5)
-        trunc_normal_(self.conv_lambda.weight, std=self.dim_k ** -0.5)
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        M = H * W
-
-        qkv = self.qkv(x)
-        q, k, v = torch.split(qkv, [
-            self.num_heads * self.dim_k, self.dim_k, self.dim_v], dim=1)
-        q = self.norm_q(q).reshape(B, self.num_heads, self.dim_k, M).transpose(-1, -2)  # B, num_heads, M, K
-        v = self.norm_v(v).reshape(B, self.dim_v, M).transpose(-1, -2)  # B, M, V
-        k = F.softmax(k.reshape(B, self.dim_k, M), dim=-1)  # B, K, M
-
-        content_lam = k @ v  # B, K, V
-        content_out = q @ content_lam.unsqueeze(1)  # B, num_heads, M, V
-
-        position_lam = self.conv_lambda(v.reshape(B, 1, H, W, self.dim_v))  # B, H, W, V, K
-        position_lam = position_lam.reshape(B, 1, self.dim_k, H * W, self.dim_v).transpose(2, 3)  # B, 1, M, K, V
-        position_out = (q.unsqueeze(-2) @ position_lam).squeeze(-2)  # B, num_heads, M, V
-
-        out = (content_out + position_out).transpose(3, 1).reshape(B, C, H, W)  # B, C (num_heads * V), H, W
-        out = self.pool(out)
-        return out
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/tnt.py b/spaces/cooelf/Multimodal-CoT/timm/models/tnt.py
deleted file mode 100644
index 8186cc4aea0c53c5a6217e3cdd0b9193bb6d1359..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/tnt.py
+++ /dev/null
@@ -1,268 +0,0 @@
-""" Transformer in Transformer (TNT) in PyTorch
-
-A PyTorch implement of TNT as described in
-'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
-
-The official mindspore code is released and available at
-https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
-"""
-import math
-import torch
-import torch.nn as nn
-from functools import partial
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.models.helpers import build_model_with_cfg
-from timm.models.layers import Mlp, DropPath, trunc_normal_
-from timm.models.layers.helpers import to_2tuple
-from timm.models.registry import register_model
-from timm.models.vision_transformer import resize_pos_embed
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
-        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'pixel_embed.proj', 'classifier': 'head',
-        **kwargs
-    }
-
-
-default_cfgs = {
-    'tnt_s_patch16_224': _cfg(
-        url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
-        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
-    ),
-    'tnt_b_patch16_224': _cfg(
-        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
-    ),
-}
-
-
-class Attention(nn.Module):
-    """ Multi-Head Attention
-    """
-    def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.num_heads = num_heads
-        head_dim = hidden_dim // num_heads
-        self.head_dim = head_dim
-        self.scale = head_dim ** -0.5
-
-        self.qk = nn.Linear(dim, hidden_dim * 2, bias=qkv_bias)
-        self.v = nn.Linear(dim, dim, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop, inplace=True)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop, inplace=True)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
-        q, k = qk[0], qk[1]   # make torchscript happy (cannot use tensor as tuple)
-        v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-    """ TNT Block
-    """
-    def __init__(self, dim, in_dim, num_pixel, num_heads=12, in_num_head=4, mlp_ratio=4.,
-            qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        # Inner transformer
-        self.norm_in = norm_layer(in_dim)
-        self.attn_in = Attention(
-            in_dim, in_dim, num_heads=in_num_head, qkv_bias=qkv_bias,
-            attn_drop=attn_drop, proj_drop=drop)
-        
-        self.norm_mlp_in = norm_layer(in_dim)
-        self.mlp_in = Mlp(in_features=in_dim, hidden_features=int(in_dim * 4),
-            out_features=in_dim, act_layer=act_layer, drop=drop)
-        
-        self.norm1_proj = norm_layer(in_dim)
-        self.proj = nn.Linear(in_dim * num_pixel, dim, bias=True)
-        # Outer transformer
-        self.norm_out = norm_layer(dim)
-        self.attn_out = Attention(
-            dim, dim, num_heads=num_heads, qkv_bias=qkv_bias,
-            attn_drop=attn_drop, proj_drop=drop)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        
-        self.norm_mlp = norm_layer(dim)
-        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio),
-            out_features=dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, pixel_embed, patch_embed):
-        # inner
-        pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed)))
-        pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
-        # outer
-        B, N, C = patch_embed.size()
-        patch_embed[:, 1:] = patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1))
-        patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed)))
-        patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed)))
-        return pixel_embed, patch_embed
-
-
-class PixelEmbed(nn.Module):
-    """ Image to Pixel Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        # grid_size property necessary for resizing positional embedding
-        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
-        num_patches = (self.grid_size[0]) * (self.grid_size[1])
-        self.img_size = img_size
-        self.num_patches = num_patches
-        self.in_dim = in_dim
-        new_patch_size = [math.ceil(ps / stride) for ps in patch_size]
-        self.new_patch_size = new_patch_size
-
-        self.proj = nn.Conv2d(in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
-        self.unfold = nn.Unfold(kernel_size=new_patch_size, stride=new_patch_size)
-
-    def forward(self, x, pixel_pos):
-        B, C, H, W = x.shape
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x)
-        x = self.unfold(x)
-        x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size[0], self.new_patch_size[1])
-        x = x + pixel_pos
-        x = x.reshape(B * self.num_patches, self.in_dim, -1).transpose(1, 2)
-        return x
-
-
-class TNT(nn.Module):
-    """ Transformer in Transformer - https://arxiv.org/abs/2103.00112
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, in_dim=48, depth=12,
-                 num_heads=12, in_num_head=4, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
-                 drop_path_rate=0., norm_layer=nn.LayerNorm, first_stride=4):
-        super().__init__()
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-
-        self.pixel_embed = PixelEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, in_dim=in_dim, stride=first_stride)
-        num_patches = self.pixel_embed.num_patches
-        self.num_patches = num_patches
-        new_patch_size = self.pixel_embed.new_patch_size
-        num_pixel = new_patch_size[0] * new_patch_size[1]
-        
-        self.norm1_proj = norm_layer(num_pixel * in_dim)
-        self.proj = nn.Linear(num_pixel * in_dim, embed_dim)
-        self.norm2_proj = norm_layer(embed_dim)
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        self.patch_pos = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        self.pixel_pos = nn.Parameter(torch.zeros(1, in_dim, new_patch_size[0], new_patch_size[1]))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-        blocks = []
-        for i in range(depth):
-            blocks.append(Block(
-                dim=embed_dim, in_dim=in_dim, num_pixel=num_pixel, num_heads=num_heads, in_num_head=in_num_head,
-                mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate,
-                drop_path=dpr[i], norm_layer=norm_layer))
-        self.blocks = nn.ModuleList(blocks)
-        self.norm = norm_layer(embed_dim)
-
-        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-        trunc_normal_(self.cls_token, std=.02)
-        trunc_normal_(self.patch_pos, std=.02)
-        trunc_normal_(self.pixel_pos, std=.02)
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'patch_pos', 'pixel_pos', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        B = x.shape[0]
-        pixel_embed = self.pixel_embed(x, self.pixel_pos)
-        
-        patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1))))
-        patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1)
-        patch_embed = patch_embed + self.patch_pos
-        patch_embed = self.pos_drop(patch_embed)
-
-        for blk in self.blocks:
-            pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
-
-        patch_embed = self.norm(patch_embed)
-        return patch_embed[:, 0]
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def checkpoint_filter_fn(state_dict, model):
-    """ convert patch embedding weight from manual patchify + linear proj to conv"""
-    if state_dict['patch_pos'].shape != model.patch_pos.shape:
-        state_dict['patch_pos'] = resize_pos_embed(state_dict['patch_pos'],
-            model.patch_pos, getattr(model, 'num_tokens', 1), model.pixel_embed.grid_size)
-    return state_dict
-
-
-def _create_tnt(variant, pretrained=False, **kwargs):
-    if kwargs.get('features_only', None):
-        raise RuntimeError('features_only not implemented for Vision Transformer models.')
-
-    model = build_model_with_cfg(
-        TNT, variant, pretrained,
-        default_cfg=default_cfgs[variant],
-        pretrained_filter_fn=checkpoint_filter_fn,
-        **kwargs)
-    return model
-
-
-@register_model
-def tnt_s_patch16_224(pretrained=False, **kwargs):
-    model_cfg = dict(
-        patch_size=16, embed_dim=384, in_dim=24, depth=12, num_heads=6, in_num_head=4,
-        qkv_bias=False, **kwargs)
-    model = _create_tnt('tnt_s_patch16_224', pretrained=pretrained, **model_cfg)
-    return model
-
-
-@register_model
-def tnt_b_patch16_224(pretrained=False, **kwargs):
-    model_cfg = dict(
-        patch_size=16, embed_dim=640, in_dim=40, depth=12, num_heads=10, in_num_head=4,
-        qkv_bias=False, **kwargs)
-    model = _create_tnt('tnt_b_patch16_224', pretrained=pretrained, **model_cfg)
-    return model
diff --git a/spaces/cooelf/Multimodal-CoT/timm/utils/misc.py b/spaces/cooelf/Multimodal-CoT/timm/utils/misc.py
deleted file mode 100644
index 39c0097c60ed602547f832f1f8dafbe37f156064..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/utils/misc.py
+++ /dev/null
@@ -1,18 +0,0 @@
-""" Misc utils
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import re
-
-
-def natural_key(string_):
-    """See http://www.codinghorror.com/blog/archives/001018.html"""
-    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
-
-
-def add_bool_arg(parser, name, default=False, help=''):
-    dest_name = name.replace('-', '_')
-    group = parser.add_mutually_exclusive_group(required=False)
-    group.add_argument('--' + name, dest=dest_name, action='store_true', help=help)
-    group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help)
-    parser.set_defaults(**{dest_name: default})
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/saconv.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/saconv.py
deleted file mode 100644
index b4ee3978e097fca422805db4e31ae481006d7971..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/saconv.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init
-from annotator.uniformer.mmcv.ops.deform_conv import deform_conv2d
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
-
-
-@CONV_LAYERS.register_module(name='SAC')
-class SAConv2d(ConvAWS2d):
-    """SAC (Switchable Atrous Convolution)
-
-    This is an implementation of SAC in DetectoRS
-    (https://arxiv.org/pdf/2006.02334.pdf).
-
-    Args:
-        in_channels (int): Number of channels in the input image
-        out_channels (int): Number of channels produced by the convolution
-        kernel_size (int or tuple): Size of the convolving kernel
-        stride (int or tuple, optional): Stride of the convolution. Default: 1
-        padding (int or tuple, optional): Zero-padding added to both sides of
-            the input. Default: 0
-        padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
-            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
-        dilation (int or tuple, optional): Spacing between kernel elements.
-            Default: 1
-        groups (int, optional): Number of blocked connections from input
-            channels to output channels. Default: 1
-        bias (bool, optional): If ``True``, adds a learnable bias to the
-            output. Default: ``True``
-        use_deform: If ``True``, replace convolution with deformable
-            convolution. Default: ``False``.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 bias=True,
-                 use_deform=False):
-        super().__init__(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups,
-            bias=bias)
-        self.use_deform = use_deform
-        self.switch = nn.Conv2d(
-            self.in_channels, 1, kernel_size=1, stride=stride, bias=True)
-        self.weight_diff = nn.Parameter(torch.Tensor(self.weight.size()))
-        self.pre_context = nn.Conv2d(
-            self.in_channels, self.in_channels, kernel_size=1, bias=True)
-        self.post_context = nn.Conv2d(
-            self.out_channels, self.out_channels, kernel_size=1, bias=True)
-        if self.use_deform:
-            self.offset_s = nn.Conv2d(
-                self.in_channels,
-                18,
-                kernel_size=3,
-                padding=1,
-                stride=stride,
-                bias=True)
-            self.offset_l = nn.Conv2d(
-                self.in_channels,
-                18,
-                kernel_size=3,
-                padding=1,
-                stride=stride,
-                bias=True)
-        self.init_weights()
-
-    def init_weights(self):
-        constant_init(self.switch, 0, bias=1)
-        self.weight_diff.data.zero_()
-        constant_init(self.pre_context, 0)
-        constant_init(self.post_context, 0)
-        if self.use_deform:
-            constant_init(self.offset_s, 0)
-            constant_init(self.offset_l, 0)
-
-    def forward(self, x):
-        # pre-context
-        avg_x = F.adaptive_avg_pool2d(x, output_size=1)
-        avg_x = self.pre_context(avg_x)
-        avg_x = avg_x.expand_as(x)
-        x = x + avg_x
-        # switch
-        avg_x = F.pad(x, pad=(2, 2, 2, 2), mode='reflect')
-        avg_x = F.avg_pool2d(avg_x, kernel_size=5, stride=1, padding=0)
-        switch = self.switch(avg_x)
-        # sac
-        weight = self._get_weight(self.weight)
-        zero_bias = torch.zeros(
-            self.out_channels, device=weight.device, dtype=weight.dtype)
-
-        if self.use_deform:
-            offset = self.offset_s(avg_x)
-            out_s = deform_conv2d(x, offset, weight, self.stride, self.padding,
-                                  self.dilation, self.groups, 1)
-        else:
-            if (TORCH_VERSION == 'parrots'
-                    or digit_version(TORCH_VERSION) < digit_version('1.5.0')):
-                out_s = super().conv2d_forward(x, weight)
-            elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
-                # bias is a required argument of _conv_forward in torch 1.8.0
-                out_s = super()._conv_forward(x, weight, zero_bias)
-            else:
-                out_s = super()._conv_forward(x, weight)
-        ori_p = self.padding
-        ori_d = self.dilation
-        self.padding = tuple(3 * p for p in self.padding)
-        self.dilation = tuple(3 * d for d in self.dilation)
-        weight = weight + self.weight_diff
-        if self.use_deform:
-            offset = self.offset_l(avg_x)
-            out_l = deform_conv2d(x, offset, weight, self.stride, self.padding,
-                                  self.dilation, self.groups, 1)
-        else:
-            if (TORCH_VERSION == 'parrots'
-                    or digit_version(TORCH_VERSION) < digit_version('1.5.0')):
-                out_l = super().conv2d_forward(x, weight)
-            elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
-                # bias is a required argument of _conv_forward in torch 1.8.0
-                out_l = super()._conv_forward(x, weight, zero_bias)
-            else:
-                out_l = super()._conv_forward(x, weight)
-
-        out = switch * out_s + (1 - switch) * out_l
-        self.padding = ori_p
-        self.dilation = ori_d
-        # post-context
-        avg_x = F.adaptive_avg_pool2d(out, output_size=1)
-        avg_x = self.post_context(avg_x)
-        avg_x = avg_x.expand_as(out)
-        out = out + avg_x
-        return out
diff --git a/spaces/cozyanduofen/bingo/src/components/chat-image.tsx b/spaces/cozyanduofen/bingo/src/components/chat-image.tsx
deleted file mode 100644
index 05ecc9771eada27a0f2d160bb01cba170d37bb09..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/components/chat-image.tsx
+++ /dev/null
@@ -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>
-  )
-}
diff --git a/spaces/cryddd/junelee-wizard-vicuna-13b/README.md b/spaces/cryddd/junelee-wizard-vicuna-13b/README.md
deleted file mode 100644
index 5c7981bd475caed8d54c32388eb757af8b2ae3de..0000000000000000000000000000000000000000
--- a/spaces/cryddd/junelee-wizard-vicuna-13b/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Junelee Wizard Vicuna 13b
-emoji: 😻
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/__init__.py b/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/__init__.py
deleted file mode 100644
index c4fa60fdf4ef37f8153776ea20c69ee9bdfa5244..0000000000000000000000000000000000000000
--- a/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .parallel_tracker import ParallelTracker
\ No newline at end of file
diff --git a/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/matchers/single_stage_matcher.py b/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/matchers/single_stage_matcher.py
deleted file mode 100644
index ab2dfb9d1cb73f952929f1f45439a135aa3e75d0..0000000000000000000000000000000000000000
--- a/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/matchers/single_stage_matcher.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-from typing import List, Tuple, Dict
-from ..core.matching import linear_assignment
-from ..core.tracklet import Tracklet
-from .distances import DistCosine
-
-class SingleStageMatcher():
-    def __init__(self,
-                 dist_high_cfg: Dict,
-                 dist_low_cfg: Dict,
-                 match_thr: float):
-        """ Perform matching with high score detection boxes and low score detection boxes in a single step
-
-        Args:
-            - dist_high_cfg (Dict): distance fucntion to compute the matching score between tracklets and high score detection boxes
-            - dist_low_cfg (Dict): distance function to compute the matching score between tracklets and low score detection boxes. This function should be stricter than dist_high_cfg as low_detection is less reliable than high_detection. 
-            - match_thr (float): matching distance threshold. Lower value means stricter matching.
-        """
-        self.dist_high = DistCosine(**dist_high_cfg)
-        self.dist_low  = DistCosine(**dist_low_cfg)
-        self.match_thr = match_thr
-        
-    def __call__(self, 
-                 tracks: List[Tracklet], 
-                 dets_high: List[Tracklet],
-                 dets_low: List[Tracklet]) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """ Associate with tracklets with detection boxes"""
-        dist_high = self.dist_high(tracks, dets_high)
-        dist_low = self.dist_low(tracks, dets_low)
-        if np.prod(dist_high.shape) >0 and np.prod(dist_low.shape) >0:
-            beta = dist_high.max()/dist_low.max()
-            dists = np.concatenate([dist_high,beta*dist_low],axis=1)
-        else:
-            if np.prod(dist_high.shape) >0:
-                dists = dist_high
-            else:
-                dists = dist_low
-        matches_idx, unmatched_tracks_idx, unmatched_dets_idx = linear_assignment(dists, thresh=self.match_thr)
-        return matches_idx, unmatched_tracks_idx, unmatched_dets_idx
\ No newline at end of file
diff --git a/spaces/daffyshaci/bert-keyword-extraction/README.md b/spaces/daffyshaci/bert-keyword-extraction/README.md
deleted file mode 100644
index c30e632d99a3d7af364e2c921c515b158b676ff4..0000000000000000000000000000000000000000
--- a/spaces/daffyshaci/bert-keyword-extraction/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bert Keyword Extraction
-emoji: 📚
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dandan4272/hand_gesture_rec/model/stgcn/__init__.py b/spaces/dandan4272/hand_gesture_rec/model/stgcn/__init__.py
deleted file mode 100644
index 994bbfe80f0e9ab578b26bce59278134278991de..0000000000000000000000000000000000000000
--- a/spaces/dandan4272/hand_gesture_rec/model/stgcn/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-
-from .stgcn import *
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/stapled.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/stapled.py
deleted file mode 100644
index 1b2862e3eac2ae6f18212d312e7cc7c2acdf0c5c..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/stapled.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Callable, Generic, Mapping, Sequence, TypeVar
-
-from ..abc import (
-    ByteReceiveStream,
-    ByteSendStream,
-    ByteStream,
-    Listener,
-    ObjectReceiveStream,
-    ObjectSendStream,
-    ObjectStream,
-    TaskGroup,
-)
-
-T_Item = TypeVar("T_Item")
-T_Stream = TypeVar("T_Stream")
-
-
-@dataclass(eq=False)
-class StapledByteStream(ByteStream):
-    """
-    Combines two byte streams into a single, bidirectional byte stream.
-
-    Extra attributes will be provided from both streams, with the receive stream providing the
-    values in case of a conflict.
-
-    :param ByteSendStream send_stream: the sending byte stream
-    :param ByteReceiveStream receive_stream: the receiving byte stream
-    """
-
-    send_stream: ByteSendStream
-    receive_stream: ByteReceiveStream
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        return await self.receive_stream.receive(max_bytes)
-
-    async def send(self, item: bytes) -> None:
-        await self.send_stream.send(item)
-
-    async def send_eof(self) -> None:
-        await self.send_stream.aclose()
-
-    async def aclose(self) -> None:
-        await self.send_stream.aclose()
-        await self.receive_stream.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return {
-            **self.send_stream.extra_attributes,
-            **self.receive_stream.extra_attributes,
-        }
-
-
-@dataclass(eq=False)
-class StapledObjectStream(Generic[T_Item], ObjectStream[T_Item]):
-    """
-    Combines two object streams into a single, bidirectional object stream.
-
-    Extra attributes will be provided from both streams, with the receive stream providing the
-    values in case of a conflict.
-
-    :param ObjectSendStream send_stream: the sending object stream
-    :param ObjectReceiveStream receive_stream: the receiving object stream
-    """
-
-    send_stream: ObjectSendStream[T_Item]
-    receive_stream: ObjectReceiveStream[T_Item]
-
-    async def receive(self) -> T_Item:
-        return await self.receive_stream.receive()
-
-    async def send(self, item: T_Item) -> None:
-        await self.send_stream.send(item)
-
-    async def send_eof(self) -> None:
-        await self.send_stream.aclose()
-
-    async def aclose(self) -> None:
-        await self.send_stream.aclose()
-        await self.receive_stream.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return {
-            **self.send_stream.extra_attributes,
-            **self.receive_stream.extra_attributes,
-        }
-
-
-@dataclass(eq=False)
-class MultiListener(Generic[T_Stream], Listener[T_Stream]):
-    """
-    Combines multiple listeners into one, serving connections from all of them at once.
-
-    Any MultiListeners in the given collection of listeners will have their listeners moved into
-    this one.
-
-    Extra attributes are provided from each listener, with each successive listener overriding any
-    conflicting attributes from the previous one.
-
-    :param listeners: listeners to serve
-    :type listeners: Sequence[Listener[T_Stream]]
-    """
-
-    listeners: Sequence[Listener[T_Stream]]
-
-    def __post_init__(self) -> None:
-        listeners: list[Listener[T_Stream]] = []
-        for listener in self.listeners:
-            if isinstance(listener, MultiListener):
-                listeners.extend(listener.listeners)
-                del listener.listeners[:]  # type: ignore[attr-defined]
-            else:
-                listeners.append(listener)
-
-        self.listeners = listeners
-
-    async def serve(
-        self, handler: Callable[[T_Stream], Any], task_group: TaskGroup | None = None
-    ) -> None:
-        from .. import create_task_group
-
-        async with create_task_group() as tg:
-            for listener in self.listeners:
-                tg.start_soon(listener.serve, handler, task_group)
-
-    async def aclose(self) -> None:
-        for listener in self.listeners:
-            await listener.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        attributes: dict = {}
-        for listener in self.listeners:
-            attributes.update(listener.extra_attributes)
-
-        return attributes
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/fuse.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/fuse.py
deleted file mode 100644
index cdf742a52cfaa1ccbb37a9d053cf428831e59b19..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/fuse.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import argparse
-import logging
-import os
-import stat
-import threading
-import time
-from errno import EIO, ENOENT
-
-from fuse import FUSE, FuseOSError, LoggingMixIn, Operations
-
-from fsspec import __version__
-from fsspec.core import url_to_fs
-
-logger = logging.getLogger("fsspec.fuse")
-
-
-class FUSEr(Operations):
-    def __init__(self, fs, path, ready_file=False):
-        self.fs = fs
-        self.cache = {}
-        self.root = path.rstrip("/") + "/"
-        self.counter = 0
-        logger.info("Starting FUSE at %s", path)
-        self._ready_file = ready_file
-
-    def getattr(self, path, fh=None):
-        logger.debug("getattr %s", path)
-        if self._ready_file and path in ["/.fuse_ready", ".fuse_ready"]:
-            return {"type": "file", "st_size": 5}
-
-        path = "".join([self.root, path.lstrip("/")]).rstrip("/")
-        try:
-            info = self.fs.info(path)
-        except FileNotFoundError:
-            raise FuseOSError(ENOENT)
-
-        data = {"st_uid": info.get("uid", 1000), "st_gid": info.get("gid", 1000)}
-        perm = info.get("mode", 0o777)
-
-        if info["type"] != "file":
-            data["st_mode"] = stat.S_IFDIR | perm
-            data["st_size"] = 0
-            data["st_blksize"] = 0
-        else:
-            data["st_mode"] = stat.S_IFREG | perm
-            data["st_size"] = info["size"]
-            data["st_blksize"] = 5 * 2**20
-            data["st_nlink"] = 1
-        data["st_atime"] = info["atime"] if "atime" in info else time.time()
-        data["st_ctime"] = info["ctime"] if "ctime" in info else time.time()
-        data["st_mtime"] = info["mtime"] if "mtime" in info else time.time()
-        return data
-
-    def readdir(self, path, fh):
-        logger.debug("readdir %s", path)
-        path = "".join([self.root, path.lstrip("/")])
-        files = self.fs.ls(path, False)
-        files = [os.path.basename(f.rstrip("/")) for f in files]
-        return [".", ".."] + files
-
-    def mkdir(self, path, mode):
-        path = "".join([self.root, path.lstrip("/")])
-        self.fs.mkdir(path)
-        return 0
-
-    def rmdir(self, path):
-        path = "".join([self.root, path.lstrip("/")])
-        self.fs.rmdir(path)
-        return 0
-
-    def read(self, path, size, offset, fh):
-        logger.debug("read %s", (path, size, offset))
-        if self._ready_file and path in ["/.fuse_ready", ".fuse_ready"]:
-            # status indicator
-            return b"ready"
-
-        f = self.cache[fh]
-        f.seek(offset)
-        out = f.read(size)
-        return out
-
-    def write(self, path, data, offset, fh):
-        logger.debug("write %s", (path, offset))
-        f = self.cache[fh]
-        f.seek(offset)
-        f.write(data)
-        return len(data)
-
-    def create(self, path, flags, fi=None):
-        logger.debug("create %s", (path, flags))
-        fn = "".join([self.root, path.lstrip("/")])
-        self.fs.touch(fn)  # OS will want to get attributes immediately
-        f = self.fs.open(fn, "wb")
-        self.cache[self.counter] = f
-        self.counter += 1
-        return self.counter - 1
-
-    def open(self, path, flags):
-        logger.debug("open %s", (path, flags))
-        fn = "".join([self.root, path.lstrip("/")])
-        if flags % 2 == 0:
-            # read
-            mode = "rb"
-        else:
-            # write/create
-            mode = "wb"
-        self.cache[self.counter] = self.fs.open(fn, mode)
-        self.counter += 1
-        return self.counter - 1
-
-    def truncate(self, path, length, fh=None):
-        fn = "".join([self.root, path.lstrip("/")])
-        if length != 0:
-            raise NotImplementedError
-        # maybe should be no-op since open with write sets size to zero anyway
-        self.fs.touch(fn)
-
-    def unlink(self, path):
-        fn = "".join([self.root, path.lstrip("/")])
-        try:
-            self.fs.rm(fn, False)
-        except (OSError, FileNotFoundError):
-            raise FuseOSError(EIO)
-
-    def release(self, path, fh):
-        try:
-            if fh in self.cache:
-                f = self.cache[fh]
-                f.close()
-                self.cache.pop(fh)
-        except Exception as e:
-            print(e)
-        return 0
-
-    def chmod(self, path, mode):
-        if hasattr(self.fs, "chmod"):
-            path = "".join([self.root, path.lstrip("/")])
-            return self.fs.chmod(path, mode)
-        raise NotImplementedError
-
-
-def run(
-    fs,
-    path,
-    mount_point,
-    foreground=True,
-    threads=False,
-    ready_file=False,
-    ops_class=FUSEr,
-):
-    """Mount stuff in a local directory
-
-    This uses fusepy to make it appear as if a given path on an fsspec
-    instance is in fact resident within the local file-system.
-
-    This requires that fusepy by installed, and that FUSE be available on
-    the system (typically requiring a package to be installed with
-    apt, yum, brew, etc.).
-
-    Parameters
-    ----------
-    fs: file-system instance
-        From one of the compatible implementations
-    path: str
-        Location on that file-system to regard as the root directory to
-        mount. Note that you typically should include the terminating "/"
-        character.
-    mount_point: str
-        An empty directory on the local file-system where the contents of
-        the remote path will appear.
-    foreground: bool
-        Whether or not calling this function will block. Operation will
-        typically be more stable if True.
-    threads: bool
-        Whether or not to create threads when responding to file operations
-        within the mounter directory. Operation will typically be more
-        stable if False.
-    ready_file: bool
-        Whether the FUSE process is ready. The ``.fuse_ready`` file will
-        exist in the ``mount_point`` directory if True. Debugging purpose.
-    ops_class: FUSEr or Subclass of FUSEr
-        To override the default behavior of FUSEr. For Example, logging
-        to file.
-
-    """
-    func = lambda: FUSE(
-        ops_class(fs, path, ready_file=ready_file),
-        mount_point,
-        nothreads=not threads,
-        foreground=foreground,
-    )
-    if not foreground:
-        th = threading.Thread(target=func)
-        th.daemon = True
-        th.start()
-        return th
-    else:  # pragma: no cover
-        try:
-            func()
-        except KeyboardInterrupt:
-            pass
-
-
-def main(args):
-    """Mount filesystem from chained URL to MOUNT_POINT.
-
-    Examples:
-
-    python3 -m fsspec.fuse memory /usr/share /tmp/mem
-
-    python3 -m fsspec.fuse local /tmp/source /tmp/local \\
-            -l /tmp/fsspecfuse.log
-
-    You can also mount chained-URLs and use special settings:
-
-    python3 -m fsspec.fuse 'filecache::zip::file://data.zip' \\
-            / /tmp/zip \\
-            -o 'filecache-cache_storage=/tmp/simplecache'
-
-    You can specify the type of the setting by using `[int]` or `[bool]`,
-    (`true`, `yes`, `1` represents the Boolean value `True`):
-
-    python3 -m fsspec.fuse 'simplecache::ftp://ftp1.at.proftpd.org' \\
-            /historic/packages/RPMS /tmp/ftp \\
-            -o 'simplecache-cache_storage=/tmp/simplecache' \\
-            -o 'simplecache-check_files=false[bool]' \\
-            -o 'ftp-listings_expiry_time=60[int]' \\
-            -o 'ftp-username=anonymous' \\
-            -o 'ftp-password=xieyanbo'
-    """
-
-    class RawDescriptionArgumentParser(argparse.ArgumentParser):
-        def format_help(self):
-            usage = super(RawDescriptionArgumentParser, self).format_help()
-            parts = usage.split("\n\n")
-            parts[1] = self.description.rstrip()
-            return "\n\n".join(parts)
-
-    parser = RawDescriptionArgumentParser(prog="fsspec.fuse", description=main.__doc__)
-    parser.add_argument("--version", action="version", version=__version__)
-    parser.add_argument("url", type=str, help="fs url")
-    parser.add_argument("source_path", type=str, help="source directory in fs")
-    parser.add_argument("mount_point", type=str, help="local directory")
-    parser.add_argument(
-        "-o",
-        "--option",
-        action="append",
-        help="Any options of protocol included in the chained URL",
-    )
-    parser.add_argument(
-        "-l", "--log-file", type=str, help="Logging FUSE debug info (Default: '')"
-    )
-    parser.add_argument(
-        "-f",
-        "--foreground",
-        action="store_false",
-        help="Running in foreground or not (Default: False)",
-    )
-    parser.add_argument(
-        "-t",
-        "--threads",
-        action="store_false",
-        help="Running with threads support (Default: False)",
-    )
-    parser.add_argument(
-        "-r",
-        "--ready-file",
-        action="store_false",
-        help="The `.fuse_ready` file will exist after FUSE is ready. "
-        "(Debugging purpose, Default: False)",
-    )
-    args = parser.parse_args(args)
-
-    kwargs = {}
-    for item in args.option or []:
-        key, sep, value = item.partition("=")
-        if not sep:
-            parser.error(message="Wrong option: {!r}".format(item))
-        val = value.lower()
-        if val.endswith("[int]"):
-            value = int(value[: -len("[int]")])
-        elif val.endswith("[bool]"):
-            value = val[: -len("[bool]")] in ["1", "yes", "true"]
-
-        if "-" in key:
-            fs_name, setting_name = key.split("-", 1)
-            if fs_name in kwargs:
-                kwargs[fs_name][setting_name] = value
-            else:
-                kwargs[fs_name] = {setting_name: value}
-        else:
-            kwargs[key] = value
-
-    if args.log_file:
-        logging.basicConfig(
-            level=logging.DEBUG,
-            filename=args.log_file,
-            format="%(asctime)s %(message)s",
-        )
-
-        class LoggingFUSEr(FUSEr, LoggingMixIn):
-            pass
-
-        fuser = LoggingFUSEr
-    else:
-        fuser = FUSEr
-
-    fs, url_path = url_to_fs(args.url, **kwargs)
-    logger.debug("Mounting %s to %s", url_path, str(args.mount_point))
-    run(
-        fs,
-        args.source_path,
-        args.mount_point,
-        foreground=args.foreground,
-        threads=args.threads,
-        ready_file=args.ready_file,
-        ops_class=fuser,
-    )
-
-
-if __name__ == "__main__":
-    import sys
-
-    main(sys.argv[1:])
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/_generated/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/_generated/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_nbagg.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_nbagg.py
deleted file mode 100644
index f4ee4b1179f3fd90884262ad16de4408654d6316..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_nbagg.py
+++ /dev/null
@@ -1,243 +0,0 @@
-"""Interactive figures in the IPython notebook."""
-# Note: There is a notebook in
-# lib/matplotlib/backends/web_backend/nbagg_uat.ipynb to help verify
-# that changes made maintain expected behaviour.
-
-from base64 import b64encode
-import io
-import json
-import pathlib
-import uuid
-
-from ipykernel.comm import Comm
-from IPython.display import display, Javascript, HTML
-
-from matplotlib import is_interactive
-from matplotlib._pylab_helpers import Gcf
-from matplotlib.backend_bases import _Backend, CloseEvent, NavigationToolbar2
-from .backend_webagg_core import (
-    FigureCanvasWebAggCore, FigureManagerWebAgg, NavigationToolbar2WebAgg)
-from .backend_webagg_core import (  # noqa: F401 # pylint: disable=W0611
-    TimerTornado, TimerAsyncio)
-
-
-def connection_info():
-    """
-    Return a string showing the figure and connection status for the backend.
-
-    This is intended as a diagnostic tool, and not for general use.
-    """
-    result = [
-        '{fig} - {socket}'.format(
-            fig=(manager.canvas.figure.get_label()
-                 or "Figure {}".format(manager.num)),
-            socket=manager.web_sockets)
-        for manager in Gcf.get_all_fig_managers()
-    ]
-    if not is_interactive():
-        result.append(f'Figures pending show: {len(Gcf.figs)}')
-    return '\n'.join(result)
-
-
-_FONT_AWESOME_CLASSES = {  # font-awesome 4 names
-    'home': 'fa fa-home',
-    'back': 'fa fa-arrow-left',
-    'forward': 'fa fa-arrow-right',
-    'zoom_to_rect': 'fa fa-square-o',
-    'move': 'fa fa-arrows',
-    'download': 'fa fa-floppy-o',
-    None: None
-}
-
-
-class NavigationIPy(NavigationToolbar2WebAgg):
-
-    # Use the standard toolbar items + download button
-    toolitems = [(text, tooltip_text,
-                  _FONT_AWESOME_CLASSES[image_file], name_of_method)
-                 for text, tooltip_text, image_file, name_of_method
-                 in (NavigationToolbar2.toolitems +
-                     (('Download', 'Download plot', 'download', 'download'),))
-                 if image_file in _FONT_AWESOME_CLASSES]
-
-
-class FigureManagerNbAgg(FigureManagerWebAgg):
-    _toolbar2_class = ToolbarCls = NavigationIPy
-
-    def __init__(self, canvas, num):
-        self._shown = False
-        super().__init__(canvas, num)
-
-    @classmethod
-    def create_with_canvas(cls, canvas_class, figure, num):
-        canvas = canvas_class(figure)
-        manager = cls(canvas, num)
-        if is_interactive():
-            manager.show()
-            canvas.draw_idle()
-
-        def destroy(event):
-            canvas.mpl_disconnect(cid)
-            Gcf.destroy(manager)
-
-        cid = canvas.mpl_connect('close_event', destroy)
-        return manager
-
-    def display_js(self):
-        # XXX How to do this just once? It has to deal with multiple
-        # browser instances using the same kernel (require.js - but the
-        # file isn't static?).
-        display(Javascript(FigureManagerNbAgg.get_javascript()))
-
-    def show(self):
-        if not self._shown:
-            self.display_js()
-            self._create_comm()
-        else:
-            self.canvas.draw_idle()
-        self._shown = True
-        # plt.figure adds an event which makes the figure in focus the active
-        # one. Disable this behaviour, as it results in figures being put as
-        # the active figure after they have been shown, even in non-interactive
-        # mode.
-        if hasattr(self, '_cidgcf'):
-            self.canvas.mpl_disconnect(self._cidgcf)
-        if not is_interactive():
-            from matplotlib._pylab_helpers import Gcf
-            Gcf.figs.pop(self.num, None)
-
-    def reshow(self):
-        """
-        A special method to re-show the figure in the notebook.
-
-        """
-        self._shown = False
-        self.show()
-
-    @property
-    def connected(self):
-        return bool(self.web_sockets)
-
-    @classmethod
-    def get_javascript(cls, stream=None):
-        if stream is None:
-            output = io.StringIO()
-        else:
-            output = stream
-        super().get_javascript(stream=output)
-        output.write((pathlib.Path(__file__).parent
-                      / "web_backend/js/nbagg_mpl.js")
-                     .read_text(encoding="utf-8"))
-        if stream is None:
-            return output.getvalue()
-
-    def _create_comm(self):
-        comm = CommSocket(self)
-        self.add_web_socket(comm)
-        return comm
-
-    def destroy(self):
-        self._send_event('close')
-        # need to copy comms as callbacks will modify this list
-        for comm in list(self.web_sockets):
-            comm.on_close()
-        self.clearup_closed()
-
-    def clearup_closed(self):
-        """Clear up any closed Comms."""
-        self.web_sockets = {socket for socket in self.web_sockets
-                            if socket.is_open()}
-
-        if len(self.web_sockets) == 0:
-            CloseEvent("close_event", self.canvas)._process()
-
-    def remove_comm(self, comm_id):
-        self.web_sockets = {socket for socket in self.web_sockets
-                            if socket.comm.comm_id != comm_id}
-
-
-class FigureCanvasNbAgg(FigureCanvasWebAggCore):
-    manager_class = FigureManagerNbAgg
-
-
-class CommSocket:
-    """
-    Manages the Comm connection between IPython and the browser (client).
-
-    Comms are 2 way, with the CommSocket being able to publish a message
-    via the send_json method, and handle a message with on_message. On the
-    JS side figure.send_message and figure.ws.onmessage do the sending and
-    receiving respectively.
-
-    """
-    def __init__(self, manager):
-        self.supports_binary = None
-        self.manager = manager
-        self.uuid = str(uuid.uuid4())
-        # Publish an output area with a unique ID. The javascript can then
-        # hook into this area.
-        display(HTML("<div id=%r></div>" % self.uuid))
-        try:
-            self.comm = Comm('matplotlib', data={'id': self.uuid})
-        except AttributeError as err:
-            raise RuntimeError('Unable to create an IPython notebook Comm '
-                               'instance. Are you in the IPython '
-                               'notebook?') from err
-        self.comm.on_msg(self.on_message)
-
-        manager = self.manager
-        self._ext_close = False
-
-        def _on_close(close_message):
-            self._ext_close = True
-            manager.remove_comm(close_message['content']['comm_id'])
-            manager.clearup_closed()
-
-        self.comm.on_close(_on_close)
-
-    def is_open(self):
-        return not (self._ext_close or self.comm._closed)
-
-    def on_close(self):
-        # When the socket is closed, deregister the websocket with
-        # the FigureManager.
-        if self.is_open():
-            try:
-                self.comm.close()
-            except KeyError:
-                # apparently already cleaned it up?
-                pass
-
-    def send_json(self, content):
-        self.comm.send({'data': json.dumps(content)})
-
-    def send_binary(self, blob):
-        if self.supports_binary:
-            self.comm.send({'blob': 'image/png'}, buffers=[blob])
-        else:
-            # The comm is ASCII, so we send the image in base64 encoded data
-            # URL form.
-            data = b64encode(blob).decode('ascii')
-            data_uri = "data:image/png;base64,{0}".format(data)
-            self.comm.send({'data': data_uri})
-
-    def on_message(self, message):
-        # The 'supports_binary' message is relevant to the
-        # websocket itself.  The other messages get passed along
-        # to matplotlib as-is.
-
-        # Every message has a "type" and a "figure_id".
-        message = json.loads(message['content']['data'])
-        if message['type'] == 'closing':
-            self.on_close()
-            self.manager.clearup_closed()
-        elif message['type'] == 'supports_binary':
-            self.supports_binary = message['value']
-        else:
-            self.manager.handle_json(message)
-
-
-@_Backend.export
-class _BackendNbAgg(_Backend):
-    FigureCanvas = FigureCanvasNbAgg
-    FigureManager = FigureManagerNbAgg
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/models/attention_flax.py b/spaces/declare-lab/tango/diffusers/src/diffusers/models/attention_flax.py
deleted file mode 100644
index 1a47d728c2f911cbc24e353f8c500909c960f159..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/models/attention_flax.py
+++ /dev/null
@@ -1,302 +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 flax.linen as nn
-import jax.numpy as jnp
-
-
-class FlaxAttention(nn.Module):
-    r"""
-    A Flax multi-head attention module as described in: https://arxiv.org/abs/1706.03762
-
-    Parameters:
-        query_dim (:obj:`int`):
-            Input hidden states dimension
-        heads (:obj:`int`, *optional*, defaults to 8):
-            Number of heads
-        dim_head (:obj:`int`, *optional*, defaults to 64):
-            Hidden states dimension inside each head
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-
-    """
-    query_dim: int
-    heads: int = 8
-    dim_head: int = 64
-    dropout: float = 0.0
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        inner_dim = self.dim_head * self.heads
-        self.scale = self.dim_head**-0.5
-
-        # Weights were exported with old names {to_q, to_k, to_v, to_out}
-        self.query = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_q")
-        self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_k")
-        self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_v")
-
-        self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out_0")
-
-    def reshape_heads_to_batch_dim(self, tensor):
-        batch_size, seq_len, dim = tensor.shape
-        head_size = self.heads
-        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
-        tensor = jnp.transpose(tensor, (0, 2, 1, 3))
-        tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
-        return tensor
-
-    def reshape_batch_dim_to_heads(self, tensor):
-        batch_size, seq_len, dim = tensor.shape
-        head_size = self.heads
-        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
-        tensor = jnp.transpose(tensor, (0, 2, 1, 3))
-        tensor = tensor.reshape(batch_size // head_size, seq_len, dim * head_size)
-        return tensor
-
-    def __call__(self, hidden_states, context=None, deterministic=True):
-        context = hidden_states if context is None else context
-
-        query_proj = self.query(hidden_states)
-        key_proj = self.key(context)
-        value_proj = self.value(context)
-
-        query_states = self.reshape_heads_to_batch_dim(query_proj)
-        key_states = self.reshape_heads_to_batch_dim(key_proj)
-        value_states = self.reshape_heads_to_batch_dim(value_proj)
-
-        # compute attentions
-        attention_scores = jnp.einsum("b i d, b j d->b i j", query_states, key_states)
-        attention_scores = attention_scores * self.scale
-        attention_probs = nn.softmax(attention_scores, axis=2)
-
-        # attend to values
-        hidden_states = jnp.einsum("b i j, b j d -> b i d", attention_probs, value_states)
-        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
-        hidden_states = self.proj_attn(hidden_states)
-        return hidden_states
-
-
-class FlaxBasicTransformerBlock(nn.Module):
-    r"""
-    A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in:
-    https://arxiv.org/abs/1706.03762
-
-
-    Parameters:
-        dim (:obj:`int`):
-            Inner hidden states dimension
-        n_heads (:obj:`int`):
-            Number of heads
-        d_head (:obj:`int`):
-            Hidden states dimension inside each head
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        only_cross_attention (`bool`, defaults to `False`):
-            Whether to only apply cross attention.
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    dim: int
-    n_heads: int
-    d_head: int
-    dropout: float = 0.0
-    only_cross_attention: bool = False
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        # self attention (or cross_attention if only_cross_attention is True)
-        self.attn1 = FlaxAttention(self.dim, self.n_heads, self.d_head, self.dropout, dtype=self.dtype)
-        # cross attention
-        self.attn2 = FlaxAttention(self.dim, self.n_heads, self.d_head, self.dropout, dtype=self.dtype)
-        self.ff = FlaxFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype)
-        self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
-        self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
-        self.norm3 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
-
-    def __call__(self, hidden_states, context, deterministic=True):
-        # self attention
-        residual = hidden_states
-        if self.only_cross_attention:
-            hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic)
-        else:
-            hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic)
-        hidden_states = hidden_states + residual
-
-        # cross attention
-        residual = hidden_states
-        hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic)
-        hidden_states = hidden_states + residual
-
-        # feed forward
-        residual = hidden_states
-        hidden_states = self.ff(self.norm3(hidden_states), deterministic=deterministic)
-        hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-class FlaxTransformer2DModel(nn.Module):
-    r"""
-    A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:
-    https://arxiv.org/pdf/1506.02025.pdf
-
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input number of channels
-        n_heads (:obj:`int`):
-            Number of heads
-        d_head (:obj:`int`):
-            Hidden states dimension inside each head
-        depth (:obj:`int`, *optional*, defaults to 1):
-            Number of transformers block
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        use_linear_projection (`bool`, defaults to `False`): tbd
-        only_cross_attention (`bool`, defaults to `False`): tbd
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    n_heads: int
-    d_head: int
-    depth: int = 1
-    dropout: float = 0.0
-    use_linear_projection: bool = False
-    only_cross_attention: bool = False
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-5)
-
-        inner_dim = self.n_heads * self.d_head
-        if self.use_linear_projection:
-            self.proj_in = nn.Dense(inner_dim, dtype=self.dtype)
-        else:
-            self.proj_in = nn.Conv(
-                inner_dim,
-                kernel_size=(1, 1),
-                strides=(1, 1),
-                padding="VALID",
-                dtype=self.dtype,
-            )
-
-        self.transformer_blocks = [
-            FlaxBasicTransformerBlock(
-                inner_dim,
-                self.n_heads,
-                self.d_head,
-                dropout=self.dropout,
-                only_cross_attention=self.only_cross_attention,
-                dtype=self.dtype,
-            )
-            for _ in range(self.depth)
-        ]
-
-        if self.use_linear_projection:
-            self.proj_out = nn.Dense(inner_dim, dtype=self.dtype)
-        else:
-            self.proj_out = nn.Conv(
-                inner_dim,
-                kernel_size=(1, 1),
-                strides=(1, 1),
-                padding="VALID",
-                dtype=self.dtype,
-            )
-
-    def __call__(self, hidden_states, context, deterministic=True):
-        batch, height, width, channels = hidden_states.shape
-        residual = hidden_states
-        hidden_states = self.norm(hidden_states)
-        if self.use_linear_projection:
-            hidden_states = hidden_states.reshape(batch, height * width, channels)
-            hidden_states = self.proj_in(hidden_states)
-        else:
-            hidden_states = self.proj_in(hidden_states)
-            hidden_states = hidden_states.reshape(batch, height * width, channels)
-
-        for transformer_block in self.transformer_blocks:
-            hidden_states = transformer_block(hidden_states, context, deterministic=deterministic)
-
-        if self.use_linear_projection:
-            hidden_states = self.proj_out(hidden_states)
-            hidden_states = hidden_states.reshape(batch, height, width, channels)
-        else:
-            hidden_states = hidden_states.reshape(batch, height, width, channels)
-            hidden_states = self.proj_out(hidden_states)
-
-        hidden_states = hidden_states + residual
-        return hidden_states
-
-
-class FlaxFeedForward(nn.Module):
-    r"""
-    Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's
-    [`FeedForward`] class, with the following simplifications:
-    - The activation function is currently hardcoded to a gated linear unit from:
-    https://arxiv.org/abs/2002.05202
-    - `dim_out` is equal to `dim`.
-    - The number of hidden dimensions is hardcoded to `dim * 4` in [`FlaxGELU`].
-
-    Parameters:
-        dim (:obj:`int`):
-            Inner hidden states dimension
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    dim: int
-    dropout: float = 0.0
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        # The second linear layer needs to be called
-        # net_2 for now to match the index of the Sequential layer
-        self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)
-        self.net_2 = nn.Dense(self.dim, dtype=self.dtype)
-
-    def __call__(self, hidden_states, deterministic=True):
-        hidden_states = self.net_0(hidden_states)
-        hidden_states = self.net_2(hidden_states)
-        return hidden_states
-
-
-class FlaxGEGLU(nn.Module):
-    r"""
-    Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from
-    https://arxiv.org/abs/2002.05202.
-
-    Parameters:
-        dim (:obj:`int`):
-            Input hidden states dimension
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    dim: int
-    dropout: float = 0.0
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        inner_dim = self.dim * 4
-        self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype)
-
-    def __call__(self, hidden_states, deterministic=True):
-        hidden_states = self.proj(hidden_states)
-        hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)
-        return hidden_linear * nn.gelu(hidden_gelu)
diff --git a/spaces/declare-lab/tango/diffusers/utils/check_doc_toc.py b/spaces/declare-lab/tango/diffusers/utils/check_doc_toc.py
deleted file mode 100644
index ff9285c63f16865d0b7a7e6672ee93552b15f77a..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/utils/check_doc_toc.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-from collections import defaultdict
-
-import yaml
-
-
-PATH_TO_TOC = "docs/source/en/_toctree.yml"
-
-
-def clean_doc_toc(doc_list):
-    """
-    Cleans the table of content of the model documentation by removing duplicates and sorting models alphabetically.
-    """
-    counts = defaultdict(int)
-    overview_doc = []
-    new_doc_list = []
-    for doc in doc_list:
-        if "local" in doc:
-            counts[doc["local"]] += 1
-
-        if doc["title"].lower() == "overview":
-            overview_doc.append({"local": doc["local"], "title": doc["title"]})
-        else:
-            new_doc_list.append(doc)
-
-    doc_list = new_doc_list
-    duplicates = [key for key, value in counts.items() if value > 1]
-
-    new_doc = []
-    for duplicate_key in duplicates:
-        titles = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key})
-        if len(titles) > 1:
-            raise ValueError(
-                f"{duplicate_key} is present several times in the documentation table of content at "
-                "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
-                "others."
-            )
-        # Only add this once
-        new_doc.append({"local": duplicate_key, "title": titles[0]})
-
-    # Add none duplicate-keys
-    new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1])
-    new_doc = sorted(new_doc, key=lambda s: s["title"].lower())
-
-    # "overview" gets special treatment and is always first
-    if len(overview_doc) > 1:
-        raise ValueError("{doc_list} has two 'overview' docs which is not allowed.")
-
-    overview_doc.extend(new_doc)
-
-    # Sort
-    return overview_doc
-
-
-def check_scheduler_doc(overwrite=False):
-    with open(PATH_TO_TOC, encoding="utf-8") as f:
-        content = yaml.safe_load(f.read())
-
-    # Get to the API doc
-    api_idx = 0
-    while content[api_idx]["title"] != "API":
-        api_idx += 1
-    api_doc = content[api_idx]["sections"]
-
-    # Then to the model doc
-    scheduler_idx = 0
-    while api_doc[scheduler_idx]["title"] != "Schedulers":
-        scheduler_idx += 1
-
-    scheduler_doc = api_doc[scheduler_idx]["sections"]
-    new_scheduler_doc = clean_doc_toc(scheduler_doc)
-
-    diff = False
-    if new_scheduler_doc != scheduler_doc:
-        diff = True
-        if overwrite:
-            api_doc[scheduler_idx]["sections"] = new_scheduler_doc
-
-    if diff:
-        if overwrite:
-            content[api_idx]["sections"] = api_doc
-            with open(PATH_TO_TOC, "w", encoding="utf-8") as f:
-                f.write(yaml.dump(content, allow_unicode=True))
-        else:
-            raise ValueError(
-                "The model doc part of the table of content is not properly sorted, run `make style` to fix this."
-            )
-
-
-def check_pipeline_doc(overwrite=False):
-    with open(PATH_TO_TOC, encoding="utf-8") as f:
-        content = yaml.safe_load(f.read())
-
-    # Get to the API doc
-    api_idx = 0
-    while content[api_idx]["title"] != "API":
-        api_idx += 1
-    api_doc = content[api_idx]["sections"]
-
-    # Then to the model doc
-    pipeline_idx = 0
-    while api_doc[pipeline_idx]["title"] != "Pipelines":
-        pipeline_idx += 1
-
-    diff = False
-    pipeline_docs = api_doc[pipeline_idx]["sections"]
-    new_pipeline_docs = []
-
-    # sort sub pipeline docs
-    for pipeline_doc in pipeline_docs:
-        if "section" in pipeline_doc:
-            sub_pipeline_doc = pipeline_doc["section"]
-            new_sub_pipeline_doc = clean_doc_toc(sub_pipeline_doc)
-            if overwrite:
-                pipeline_doc["section"] = new_sub_pipeline_doc
-        new_pipeline_docs.append(pipeline_doc)
-
-    # sort overall pipeline doc
-    new_pipeline_docs = clean_doc_toc(new_pipeline_docs)
-
-    if new_pipeline_docs != pipeline_docs:
-        diff = True
-        if overwrite:
-            api_doc[pipeline_idx]["sections"] = new_pipeline_docs
-
-    if diff:
-        if overwrite:
-            content[api_idx]["sections"] = api_doc
-            with open(PATH_TO_TOC, "w", encoding="utf-8") as f:
-                f.write(yaml.dump(content, allow_unicode=True))
-        else:
-            raise ValueError(
-                "The model doc part of the table of content is not properly sorted, run `make style` to fix this."
-            )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
-    args = parser.parse_args()
-
-    check_scheduler_doc(args.fix_and_overwrite)
-    check_pipeline_doc(args.fix_and_overwrite)
diff --git a/spaces/decodemai/business_tech_ideas/README.md b/spaces/decodemai/business_tech_ideas/README.md
deleted file mode 100644
index 155222611dcb20ace7da5dfc905f43a230a6e52e..0000000000000000000000000000000000000000
--- a/spaces/decodemai/business_tech_ideas/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Business Tech Ideas
-emoji: 🔥
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: cc-by-nc-nd-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/derek-thomas/disc-golf-simulator/shotshaper/projectile.py b/spaces/derek-thomas/disc-golf-simulator/shotshaper/projectile.py
deleted file mode 100644
index 9ddb4b2dc0ee1d517c472e3ac00f523f8c9ed584..0000000000000000000000000000000000000000
--- a/spaces/derek-thomas/disc-golf-simulator/shotshaper/projectile.py
+++ /dev/null
@@ -1,607 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Oct 19 18:29:10 2021
-
-@author: 2913452
-
-TODO:
-    - height of athlete, optimal angle shot put
-        - note that biomech can influence
-          how much force an athlete can emit for each angle
-    - 
-"""
-
-from abc import ABC, abstractmethod
-from scipy.integrate import solve_ivp
-from scipy.interpolate import interp1d
-from .transforms import T_12, T_23, T_34, T_14, T_41, T_31
-import matplotlib.pyplot as pl
-from numpy import exp,matmul,pi,sqrt,arctan2,radians,degrees,sin,cos,array,concatenate,linspace,zeros_like,cross,zeros,argmin
-from numpy.linalg import norm
-from . import environment
-import os
-import yaml
-
-T_END = 60
-N_STEP = 200
-
-def hit_ground(t, y, *args): 
-    return y[2]
-
-def stopped(t, y, *args):
-    U = norm(y[3:6])
-    return U - 1e-4
-
-class Shot:
-    def __init__(self,t,x,v,att=None):
-        self.time = t
-        self.position = x
-        self.velocity = v
-        if att is not None:
-            self.attitude = att
-        
-
-class _Projectile(ABC):
-    def __init__(self):
-        pass
-   
-    def _shoot(self, advance_function, y0, *args):
-        hit_ground.terminal = True
-        hit_ground.direction = -1
-        stopped.terminal = True
-        stopped.direction = -1
-        
-        sol = solve_ivp(advance_function,[0,T_END],y0,
-                        dense_output=True,args=args,
-                        method='RK45',
-                        events=(hit_ground,stopped))
-        
-        t = linspace(0,sol.t[-1],N_STEP)
-        
-        f = sol.sol(t)
-        pos = array([f[0],f[1],f[2]])
-        vel = array([f[3],f[4],f[5]])
-        
-        if len(f) <= 6:
-            shot = Shot(t, pos, vel)
-        else:    
-            att = array([f[6],f[7],f[8]])
-            shot = Shot(t, pos, vel, att)
-        
-        return shot
-         
-    @abstractmethod
-    def advance(self,t,vec,*args):
-        """
-        :param float T: Thrust
-        :param float Q: Torque
-        :param float P: Power
-        :return: Right hand side of kinematic equations for a projectile
-        :rtype: array
-        """
-        
-class _Particle(_Projectile):
-    def __init__(self):
-        super().__init__()
-        
-        self.g = environment.g
-        
-    def initialize_shot(self, **kwargs):
-        kwargs.setdefault('yaw', 0.0) 
-        
-        pitch = radians(kwargs["pitch"])
-        yaw = radians(kwargs["yaw"])
-        U = kwargs["speed"]
-        xy = cos(pitch)
-        u = U*xy*cos(yaw)
-        w = U*sin(pitch)
-        v = U*xy*sin(-yaw)
-        if "position" in kwargs:
-            x,y,z = kwargs["position"]
-        else:
-            x = 0.
-            y = 0.
-            z = 0.
-        
-        y0 = array((x,y,z,u,v,w))
-        return y0
-            
-    def shoot(self, **kwargs):
-
-        y0 = self.initialize_shot(**kwargs)
-        shot = self._shoot(self.advance, y0)
-        
-        return shot
-        
-    def gravity_force(self, x=None):
-        if x is None:
-            return array((0,0,environment.g))
-        else:
-            # Messy way to return g array...
-            f = zeros_like(x)
-            f[0,:] = 0
-            f[1,:] = 0
-            f[2,:] = environment.g
-            return f
-        
-    def advance(self, t, vec, *args):
-        # x, y, z, u, v, w = vec
-        x = vec[0:3]
-        u = vec[3:6]
-        
-        f = self.gravity_force()
-        
-        return concatenate((u,f))
-        
-    
-class _SphericalParticleAirResistance(_Particle):
-    def __init__(self, mass, diameter):
-        super().__init__()
-        
-        self.mass = mass
-        self.diameter = diameter
-        self.radius = 0.5*diameter
-        self.area = 0.25*pi*diameter**2
-        self.volume = 4./3.*pi*self.radius**3
-        
-    
-    def air_resistance_force(self, U, Cd):
-        
-        f = -0.5*environment.rho*self.area*Cd*norm(U)*U/self.mass
-        #f = -0.5*environment.rho*self.area*Cd*Umag*U/self.mass
-        
-        return f
-    
-    def advance(self, t, vec, *args):
-        x = vec[0:3]
-        u = vec[3:6]
-        
-        Cd = self.drag_coefficient(norm(u))
-        
-        f = self.air_resistance_force(u, Cd) \
-          + self.gravity_force()
-        
-        return concatenate((u,f))
-       
-        
-    def reynolds_number(self, velocity):
-        """
-        Reynolds number, non-dimensional number giving the 
-        ratio of inertial forces to viscous forces. Used
-        for calculating the drag coefficient.
-        
-        :param float velocity: Velocity seen by particle
-        :return: Reynolds number
-        :rtype: float
-        
-        """
-        return environment.rho*velocity*self.diameter/environment.mu
-    
-    def drag_coefficient(self, velocity):
-        """
-        Drag coefficient for sphere, empirical curve fit
-        taken from:
-        
-        F. A. Morrison, An Introduction to Fluid Mechanics, (Cambridge
-        University Press, New York, 2013). This correlation appears in
-        Figure 8.13 on page 625. 
-
-        The full formula is:
-
-        .. math::
-            F = \\frac{2}{\\pi}\\cos^{-1}e^{-f} \\\\
-            f = \\frac{B}{2}\\frac{R-r}{r\\sin\\phi}
-
-
-        :param float velocity: Velocity seen by particle
-        :return: Drag coefficient
-        :rtype: float
-        """
-    
-        Re = self.reynolds_number(velocity)
-        
-        if Re <= 0:
-            return 1e30
-        
-        tmp1 = Re/5.0
-        tmp2 = Re/2.63e5
-        tmp3 = Re/1e6
-        
-        Cd = 24.0/Re \
-           + 2.6*tmp1/(1 + tmp1**1.52) \
-           + 0.411*tmp2**-7.94/(1 + tmp2**-8) \
-           + 0.25*tmp3/(1 + tmp3) 
-           
-        return Cd
-    
-
-class _SphericalParticleAirResistanceSpin(_SphericalParticleAirResistance):
-    def __init__(self, mass, diameter):
-        super().__init__(mass, diameter)
-        
-        
-    def lift_coefficient(self, Umag, omega):
-        # TODO - complex dependency on Re. For now,
-        #        assume constant
-        return 0.9
-        
-    def shoot(self, **kwargs):
-        y0 = self.initialize_shot(**kwargs)
-        spin = array((kwargs["spin"]))
-        
-        shot = self._shoot(self.advance, y0, spin)
-        
-        return shot        
-    
-    def spin_force(self,U,spin):
-        
-        Umag = norm(U)
-        omega = norm(spin)
-        
-        Cl = self.lift_coefficient(Umag, omega)
-        
-        if U.ndim == 1:
-            f = Cl*pi*self.radius**3*environment.rho*cross(spin, U)/self.mass
-        else:
-            # Messy way to return spin array for post-processing
-            f = zeros_like(U)
-            for i in range(U.shape[1]):
-                f[:,i] = Cl*pi*self.radius**3*environment.rho*cross(spin, U[:,i])/self.mass
-        
-        return f
-    
-    def advance(self, t, vec, spin):
-        x = vec[0:3]
-        u = vec[3:6]
-        
-        Cd = self.drag_coefficient(norm(u), norm(spin))
-        
-        f = self.air_resistance_force(u, Cd) \
-          + self.gravity_force() \
-          + self.spin_force(u,spin)
-        
-        return concatenate((u,f))
-
-
-class ShotPutBall(_SphericalParticleAirResistance):
-    """
-    Note that diameter can vary 110 mm to 130mm
-    and 95 mm to 110 mm
-    """
-    def __init__(self, weight_class):
-        
-        if weight_class == 'M':
-            mass = 7.26
-            diameter = 0.11
-        elif weight_class == 'F':
-            mass = 4.0
-            diameter = 0.095
-            
-        super().__init__(mass, diameter)
-        
-        
-class SoccerBall(_SphericalParticleAirResistanceSpin):
-    """
-    Note that diameter can vary 110 mm to 130mm
-    and 95 mm to 110 mm
-    """
-    def __init__(self, mass=0.430, diameter=0.22):
-                    
-        super(SoccerBall, self).__init__(mass, diameter)
-    
-    def drag_coefficient(self, velocity, omega):
-        # Texture, sewing pattern and spin will alter
-        # the drag coefficient.
-        # Here, use correlation from
-        
-        # Goff, J. E., & Carré, M. J. (2010). Soccer ball lift 
-        # coefficients via trajectory analysis. 
-        # European Journal of Physics, 31(4), 775.
-        
-        
-        vc = 12.19
-        vs = 1.309
-        
-        S = omega*self.radius/velocity;
-        if S > 0.05 and velocity > vc:
-            Cd = 0.4127*S**0.3056;
-        else:
-            Cd = 0.155 + 0.346 / (1 + exp((velocity - vc)/vs))
-        
-        return Cd
-    
-    def lift_coefficient(self, Umag, omega):
-        # TODO - complex dependency on Re and spin, skin texture etc
-        return 0.9
- 
-class TableTennisBall(SoccerBall):
-    """
-    
-    """
-    def __init__(self):
-        
-        mass = 2.7e-3   
-        diameter = 40e-3 
-            
-        super(TableTennisBall, self).__init__(mass, diameter)
-        
-        
-class DiscGolfDisc(_Projectile):
-    def __init__(self, name, mass=0.175):
-        this_dir = os.path.dirname(os.path.abspath(__file__))
-        path = os.path.join(this_dir, 'discs', name + '.yaml')
-    
-        self.name = name
-        
-        with open(path, 'r') as f:
-            data = yaml.load(f, Loader=yaml.FullLoader)
-            
-            self.diameter = data['diameter']
-            self.mass = mass
-            self.weight = environment.g*mass
-            self.area = pi*self.diameter**2/4.0
-            self.I_xy = mass*data['J_xy']
-            self.I_z = mass*data['J_z']
-            
-            a = array(data['alpha'])
-            cl = array(data['Cl'])
-            cd = array(data['Cd'])
-            cm = array(data['Cm'])
-            
-        
-        self._alpha,self._Cl,self._Cd,self._Cm = self._flip(a,cl,cd,cm)
-        kind = 'linear'
-        self.Cl_func = interp1d(self._alpha, self._Cl, kind=kind)
-        self.Cd_func = interp1d(self._alpha, self._Cd, kind=kind)
-        self.Cm_func = interp1d(self._alpha, self._Cm, kind=kind)
-        
-    def _flip(self,a,cl,cd,cm):
-        """
-        Data given from -90 deg to 90 deg.
-        Expand to -180 to 180 using symmetry considerations.
-        """
-        n = len(a)
-
-        idx = argmin(abs(a))
-        a2 = zeros(2*n)
-        cl2 = zeros(2*n)
-        cd2 = zeros(2*n)
-        cm2 = zeros(2*n)
-        
-        a2[idx:idx+n] = a[:]
-        cl2[idx:idx+n] = cl[:]
-        cd2[idx:idx+n] = cd[:]
-        cm2[idx:idx+n] = cm[:]
-        for i in range(idx):
-            a2[i] = -(180 + a[idx-i])
-            cl2[i] = -cl[idx-i]
-            cd2[i] =  cd[idx-i]
-            cm2[i] = -cm[idx-i]
-        
-        for i in range(idx+n,2*n):
-            a2[i] = 180 - a[idx+n-i-2]
-            cl2[i] = -cl[idx+n-i-2]
-            cd2[i] =  cd[idx+n-i-2]
-            cm2[i] = -cm[idx+n-i-2]
-        
-        return a2,cl2,cd2,cm2
-        
-    def _normalize_angle(self, alpha):
-        """
-        Ensure that the angle fulfils :math:`-\\pi < \\alpha < \\pi`
-
-        :param float alpha: Angle in radians
-        :return: Normalized angle
-        :rtype: float
-        """
-
-        return arctan2(sin(alpha), cos(alpha))
-    
-    def Cd(self, alpha): 
-        """
-        Provide drag coefficent for a given angle of attack.
-
-        :param float alpha: Angle in radians
-        :return: Drag coefficient
-        :rtype: float
-        """
-        
-        # NB! The stored data uses degrees for the angle
-        return self.Cd_func(degrees(self._normalize_angle(alpha)))
-
-    def Cl(self, alpha): 
-        """
-        Provide drag coefficent for a given angle of attack.
-
-        :param float alpha: Angle in radians
-        :return: Drag coefficient
-        :rtype: float
-        """
-        
-        # NB! The stored data uses degrees for the angle
-        return self.Cl_func(degrees(self._normalize_angle(alpha)))
-
-    def Cm(self, alpha): 
-        """
-        Provide coefficent of moment for a given angle of attack.
-
-        :param float alpha: Angle in radians
-        :return: Coefficient of moment
-        :rtype: float
-        """
-    
-        # NB! The stored data uses degrees for the angle
-        return self.Cm_func(degrees(self._normalize_angle(alpha)))
-
-
-    def plot_coeffs(self, color='k'):
-        """
-        Utility function to quickly explore disc coefficients.
-
-        :param string color: Matplotlib color key. Default value is k, i.e. black.
-        """
-        pl.plot(self._alpha, self._Cl, 'C0-o',label='$C_L$')
-        pl.plot(self._alpha, self._Cd, 'C1-o',label='$C_D$')
-        pl.plot(self._alpha, 3*self._Cm, 'C2-o',label='$C_M$')
-        
-        a = linspace(-pi,pi,200)
-        #pl.plot(degrees(a), self.Cl(a), 'C0-',label='$C_L$')
-        #pl.plot(degrees(a), self.Cd(a), 'C1-',label='$C_D$')
-        #pl.plot(degrees(a), 3*self.Cm(a), 'C2-',label='$C_M$')
-        
-        pl.xlabel('Angle of attack ($^\circ$)')
-        pl.ylabel('Aerodynamic coefficients (-)')
-        pl.legend(loc='upper left')
-        ax = pl.gca()
-        ax2 = pl.gca().twinx()
-        ax2.set_ylabel("Aerodynamic efficiency, $C_L/C_D$")
-        pl.plot(self._alpha, self._Cl/self._Cd, 'C3-.',label='$C_L/C_D$')
-        ax2.legend(loc='upper right')
-        
-        return ax,ax2
-    
-    
-    def empirical_spin(self, speed):
-        # Simple empirical formula for spin rate, based on curve-fitting
-        # data from:
-        # https://www.dgcoursereview.com/dgr/forums/viewtopic.php?f=2&t=7097
-        #omega = -0.257*speed**2 + 15.338*speed
-
-        # Alternatively, experiments indicate a linear relationship,
-        omega = 5.2*speed
-
-        return omega
-
-            
-    
-    def initialize_shot(self, **kwargs):
-        U = kwargs["speed"]
-        
-        kwargs.setdefault('yaw', 0.0) 
-        #kwargs.setdefault('omega', self.empirical_spin(U)) 
-        
-        pitch = radians(kwargs["pitch"])
-        yaw = radians(kwargs["yaw"])
-        omega = kwargs["omega"]
-        
-        # phi, theta
-        roll_angle = radians(kwargs["roll_angle"]) # phi
-        nose_angle = radians(kwargs["nose_angle"]) # theta
-        # psi, rotation around z irrelevant for starting position
-        #      since the disc is symmetric
-        
-        # Initialize position
-        if "position" in kwargs:
-            x,y,z = kwargs["position"]
-        else:
-            x = 0.
-            y = 0.
-            z = 0.
-        
-        # Initialize velocity
-        xy = cos(pitch)
-        u = U*xy*cos(yaw)
-        v = U*xy*sin(-yaw)
-        w = U*sin(pitch)
-        
-        # Initialize angles
-        attitude = array([roll_angle, nose_angle, 0])
-        # The initial orientation of the disc must also account for the
-        # angle of the throw itself, i.e. the launch angle. 
-        attitude += matmul(T_12(attitude), array((0, pitch, 0)))
-        
-        #attitude = matmul(T_23(yaw),attitude)
-        #attitude += matmul(T_12(attitude), array((0, pitch, 0)))
-        phi, theta, psi = attitude
-        y0 = array((x,y,z,u,v,w,phi,theta,psi))
-        return y0, omega
-            
-    def shoot(self, **kwargs):
-
-        y0, omega = self.initialize_shot(**kwargs)
-               
-        shot = self._shoot(self.advance, y0, omega)
-        
-        return shot
-    
-    def post_process(self, s, omega):
-        n = len(s.time)
-        alphas = zeros(n)
-        betas = zeros(n)
-        lifts = zeros(n)
-        drags = zeros(n)
-        moms = zeros(n)
-        rolls = zeros(n)
-        for i in range(n):
-            x = s.position[:,i]
-            u = s.velocity[:,i]
-            a = s.attitude[:,i]
-            
-            alpha, beta, Fd, Fl, M, g4 = self.forces(x, u, a, omega)
-            
-            alphas[i] = alpha
-            betas[i] = beta
-            lifts[i] = Fl
-            drags[i] = Fd
-            moms[i] = M
-            rolls[i] = -M/(omega*(self.I_xy - self.I_z))
-        
-        arc_length = norm(s.position, axis=0)
-        return arc_length,degrees(alphas),degrees(betas),lifts,drags,moms,degrees(rolls)
-            
-    def forces(self, x, u, a, omega):
-        # Velocity in body axes
-        urel = u - environment.wind_abl(x[2])
-        u2 = matmul(T_12(a), urel)
-        # Side slip angle is the angle between the x and y velocity
-        beta = -arctan2(u2[1], u2[0])
-        # Velocity in zero side slip axes
-        u3 = matmul(T_23(beta), u2)
-        # Angle of attack is the angle between 
-        # vertical and horizontal velocity
-        alpha = -arctan2(u3[2], u3[0])
-        # Velocity in wind system, where forces are to be calculated
-        u4 = matmul(T_34(alpha), u3)
-        
-        # Convert gravitational force from Earth to Wind axes
-        g = array((0, 0, self.mass*environment.g))
-        g4 = T_14(g, a, beta, alpha)
-        
-        # Aerodynamic forces
-        q = 0.5*environment.rho*u4[0]**2
-        S = self.area
-        D = self.diameter
-        
-        Fd = q*S*self.Cd(alpha)
-        Fl = q*S*self.Cl(alpha)
-        M  = q*S*D*self.Cm(alpha)
-        
-        return alpha, beta, Fd, Fl, M, g4
-        
-    def advance(self, t, vec, omega):
-        x = vec[0:3]
-        u = vec[3:6]
-        a = vec[6:9]
-        
-        alpha, beta, Fd, Fl, M, g4 = self.forces(x, u, a, omega)
-        
-        m = self.mass
-        # Calculate accelerations
-        dudt = (-Fd + g4[0])/m
-        dvdt =        g4[1]/m
-        dwdt = ( Fl + g4[2])/m
-        acc4 = array((dudt,dvdt,dwdt))
-        # Roll rate acts around x-axis (in axes 3: zero side slip axes)
-        dphidt = -M/(omega*(self.I_xy - self.I_z))
-        # Other angular rotations are ignored, assume zero wobble
-        angvel3 = array((dphidt, 0, 0))
-        
-        acc1 = T_41(acc4, a, beta, alpha)
-        angvel1 = T_31(angvel3, a, beta)
-        
-        return concatenate((u,acc1,angvel1)) 
-
-    
-    
-
-    
diff --git a/spaces/derful/Chatgpt-academic/show_math.py b/spaces/derful/Chatgpt-academic/show_math.py
deleted file mode 100644
index 80fa881d1c2ace5813f75b5d8a19ca056a8bfa4f..0000000000000000000000000000000000000000
--- a/spaces/derful/Chatgpt-academic/show_math.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# This program is written by: https://github.com/polarwinkel/mdtex2html
-
-from latex2mathml.converter import convert as tex2mathml
-import re
-
-incomplete = '<font style="color:orange;" class="tooltip">&#9888;<span class="tooltiptext">formula incomplete</span></font>'
-convError = '<font style="color:red" class="tooltip">&#9888;<span class="tooltiptext">LaTeX-convert-error</span></font>'
-
-def convert(mdtex, extensions=[], splitParagraphs=True):
-    ''' converts recursively the Markdown-LaTeX-mixture to HTML with MathML '''
-    found = False
-    # handle all paragraphs separately (prevents aftereffects)
-    if splitParagraphs:
-        parts = re.split("\n\n", mdtex)
-        result = ''
-        for part in parts:
-            result += convert(part, extensions, splitParagraphs=False)
-        return result
-    # find first $$-formula:
-    parts = re.split('\${2}', mdtex, 2)
-    if len(parts)>1:
-        found = True
-        result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
-        try:
-            result += '<div class="blockformula">'+tex2mathml(parts[1])+'</div>\n'
-        except:
-            result += '<div class="blockformula">'+convError+'</div>'
-        if len(parts)==3:
-            result += convert(parts[2], extensions, splitParagraphs=False)
-        else:
-            result += '<div class="blockformula">'+incomplete+'</div>'
-    # else find first $-formulas:
-    else:
-        parts = re.split('\${1}', mdtex, 2)
-    if len(parts)>1 and not found:
-        found = True
-        try:
-            mathml = tex2mathml(parts[1])
-        except:
-            mathml = convError
-        if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
-            parts[0]=parts[0]+'&#x200b;'
-        if len(parts)==3:
-            result = convert(parts[0]+mathml+parts[2], extensions, splitParagraphs=False)
-        else:
-            result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
-    # else find first \[..\]-equation:
-    else:
-        parts = re.split(r'\\\[', mdtex, 1)
-    if len(parts)>1 and not found:
-        found = True
-        result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
-        parts = re.split(r'\\\]', parts[1], 1)
-        try:
-            result += '<div class="blockformula">'+tex2mathml(parts[0])+'</div>\n'
-        except:
-            result += '<div class="blockformula">'+convError+'</div>'
-        if len(parts)==2:
-            result += convert(parts[1], extensions, splitParagraphs=False)
-        else:
-            result += '<div class="blockformula">'+incomplete+'</div>'
-    # else find first \(..\)-equation:
-    else:
-        parts = re.split(r'\\\(', mdtex, 1)
-    if len(parts)>1 and not found:
-        found = True
-        subp = re.split(r'\\\)', parts[1], 1)
-        try:
-            mathml = tex2mathml(subp[0])
-        except:
-            mathml = convError
-        if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
-            parts[0]=parts[0]+'&#x200b;'
-        if len(subp)==2:
-            result = convert(parts[0]+mathml+subp[1], extensions, splitParagraphs=False)
-        else:
-            result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
-    if not found:
-        result = mdtex
-    return result
diff --git a/spaces/derinsu/Background_Generator/U-2-Net/images/xx.py b/spaces/derinsu/Background_Generator/U-2-Net/images/xx.py
deleted file mode 100644
index fd858d0a09515aa69cfd648cde531b007761a602..0000000000000000000000000000000000000000
--- a/spaces/derinsu/Background_Generator/U-2-Net/images/xx.py
+++ /dev/null
@@ -1 +0,0 @@
-# DNM
diff --git a/spaces/dgottfrid/clipcluster/app.py b/spaces/dgottfrid/clipcluster/app.py
deleted file mode 100644
index e2c3eb1544c0a5508c9fd2ffa56fbf8b2deb5d1e..0000000000000000000000000000000000000000
--- a/spaces/dgottfrid/clipcluster/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import gradio as gr
-from PIL import Image
-
-from sklearn.cluster import KMeans
-import torch
-import clip
-import numpy as np
-
-
-# load model
-model, preprocess = clip.load("ViT-B/32")
-
-
-def pp_image(image):
-    i = preprocess(image).unsqueeze(0)
-    return model.encode_image(i).detach().numpy().squeeze(0)
-
-
-def image_cluster(images, count):
-    # out = Image.open(str(i) for i in list(enumerate(images))[0].name)
-    embeddings = []
-    names = []
-    for img in enumerate(images):
-        names.append(img[0])
-        embeddings.append(pp_image(Image.open(img[1].name)))
-
-    cluster = KMeans(n_clusters=int(count), n_init="auto")
-    cluster.fit(np.array(embeddings))
-
-    out = "\n".join(
-        [f"{names[i]} cluster {cluster.labels_[i]}" for i in range(len(names))]
-    )
-
-    return out
-
-
-iface = gr.Interface(
-    fn=image_cluster,
-    inputs=[
-        gr.File(file_count="multiple", label="Upload Images"),
-        gr.Number(4, label="Number of clusters"),
-    ],
-    outputs="text",
-    title="Image Clustering with OpenAI CLIP",
-    description="Cluster a bunch of images using the CLIP embeddings",
-)
-
-iface.launch()
diff --git a/spaces/diacanFperku/AutoGPT/Descargar Historia De Chile Walterio Millar Pdf 70 !!LINK!!.md b/spaces/diacanFperku/AutoGPT/Descargar Historia De Chile Walterio Millar Pdf 70 !!LINK!!.md
deleted file mode 100644
index 10335c964195b5b2a7fcd00612243b652d32af49..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Descargar Historia De Chile Walterio Millar Pdf 70 !!LINK!!.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-<h1>Descargar Historia de Chile Walterio Millar PDF 70: Una Guía Completa</h1>
-<p>¿Te interesa la historia de Chile y quieres leer el libro de Walterio Millar? ¿Quieres descargar el PDF de la edición 70, la más actualizada y completa? Entonces estás en el lugar correcto. En este artículo te vamos a mostrar cómo puedes descargar Historia de Chile Walterio Millar PDF 70 de forma fácil y segura.</p>
-<p>Historia de Chile Walterio Millar es un libro clásico y reconocido que narra la historia de Chile desde sus orígenes hasta el siglo XXI. Es una obra que combina rigor histórico con un estilo ameno y didáctico, ideal para estudiantes, profesores y aficionados a la historia. El libro cuenta con más de 800 páginas, ilustraciones, mapas, cronologías y bibliografía.</p>
-<h2>descargar historia de chile walterio millar pdf 70</h2><br /><p><b><b>Download Zip</b> &#10027;&#10027;&#10027; <a href="https://gohhs.com/2uFTUU">https://gohhs.com/2uFTUU</a></b></p><br /><br />
-<p>La edición 70 es la más reciente y actualizada, publicada en el año 2019. Incluye nuevos capítulos sobre la transición democrática, los gobiernos de la Concertación, el Bicentenario y los desafíos del siglo XXI. Además, incorpora las últimas investigaciones y fuentes históricas disponibles.</p>
-<p>Para descargar Historia de Chile Walterio Millar PDF 70 solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Ingresa al sitio web oficial de la editorial Zig-Zag, que es la encargada de publicar el libro. Puedes hacer clic <a href="https://www.zigzag.cl/libro/historia-de-chile-70-edicion/">aquí</a> para ir directamente.</li>
-<li>Busca el libro en el catálogo o usa el buscador para encontrarlo por su título o autor.</li>
-<li>Selecciona la opción "Comprar eBook" y añade el libro a tu carrito de compras.</li>
-<li>Regístrate o inicia sesión con tu cuenta de usuario y completa los datos de pago. Puedes pagar con tarjeta de crédito, débito o PayPal.</li>
-<li>Una vez confirmado el pago, recibirás un correo electrónico con el enlace para descargar el PDF del libro. También podrás acceder al PDF desde tu biblioteca virtual en el sitio web.</li>
-</ol>
-<p>Así de fácil es descargar Historia de Chile Walterio Millar PDF 70. Ahora podrás disfrutar de este excelente libro en tu computadora, tablet o celular, y aprender más sobre la historia de Chile. Recuerda que al comprar el eBook estás apoyando al autor y a la editorial, y respetando los derechos de autor.</p>
-<p>Esperamos que este artículo te haya sido útil y que hayas podido descargar Historia de Chile Walterio Millar PDF 70 sin problemas. Si te gustó, compártelo con tus amigos y déjanos un comentario. ¡Gracias por leernos!</p>
-
-<p>Si quieres saber más sobre el autor de Historia de Chile Walterio Millar PDF 70, te contamos que Walterio Millar Sánchez (1925-2009) fue un destacado historiador, profesor y escritor chileno. Se especializó en la historia de América Latina y de Chile, y publicó más de 30 libros y numerosos artículos académicos. Fue miembro de la Academia Chilena de la Historia y recibió varios premios y reconocimientos por su labor.</p>
-<p>Entre sus obras más importantes se encuentran Historia del Mundo Contemporáneo (1958), Historia de América (1966), Historia de Chile (1974), Chile en el Siglo XX (1983) y Chile Ayer y Hoy (1999). Su libro Historia de Chile es considerado un referente obligado para el estudio de la historia nacional, y ha sido reeditado y actualizado varias veces desde su primera publicación.</p>
-<p>Walterio Millar fue un historiador comprometido con la educación y la difusión de la historia. Su estilo narrativo era claro, sencillo y atractivo, lo que le permitió llegar a un amplio público. Su obra ha sido utilizada por generaciones de estudiantes y profesores, y sigue vigente hasta el día de hoy.</p>
-<p></p>
-<p>Si quieres conocer más sobre Walterio Millar y su obra, te recomendamos visitar su página web oficial <a href="http://www.walteriomillar.cl/">aquí</a>, donde podrás encontrar su biografía, sus libros, sus artículos y sus entrevistas. También puedes seguir su página de Facebook <a href="https://www.facebook.com/walteriomillaroficial/">aquí</a>, donde podrás estar al tanto de las novedades y los eventos relacionados con su legado.</p>
-<p>Esperamos que este artículo te haya ayudado a descargar Historia de Chile Walterio Millar PDF 70 y a conocer más sobre su autor. Si te gustó, no olvides compartirlo con tus amigos y dejarnos un comentario. ¡Hasta la próxima!</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Dr. Dolittle 5 DVDRip !EXCLUSIVE!.md b/spaces/diacanFperku/AutoGPT/Dr. Dolittle 5 DVDRip !EXCLUSIVE!.md
deleted file mode 100644
index 79de89e6b4117b9726b6ddce6b73318d6758c64a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Dr. Dolittle 5 DVDRip !EXCLUSIVE!.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<h2>Dr. Dolittle 5 DVDRip</h2><br /><p><b><b>Download File</b> - <a href="https://gohhs.com/2uFVyn">https://gohhs.com/2uFVyn</a></b></p><br /><br />
-
-ADULT. DOLITHLIBROWS. BROKSAFFEDOWN. MOD REVIEW.The Saint George Fire Department and our response partners are receiving requests for assistance in the aftermath of Hurricane Michael. All requests for assistance are being coordinated through the Governor’s Office of Emergency Services.
-
-The storm is slowly changing course to our fire departments and the number of requests for assistance continue to rise. This is impacting the ability of our response partners to address immediate needs and with every day that goes by there are more and more needs that must be addressed.
-
-We want to express our appreciation for the donations that have come in from our friends and neighbors who have answered the call to help and we thank you in advance for your continued support as this storm moves through the state of Florida.So after a bit of messing around in the forests of woods, me and Roy came across some abandoned ruins, probably a quarry or something, as well as a small shack and at that point he started getting weird about ghosts.
-
-Most likely he was around 10 at this point, he would not walk over any of the rock piles he found unless I insisted, he was scared of the monsters that lurked there. Anyway, I think we eventually figured out that we were on a abandoned mountain road, the shack could be a safe house, and the monsters were feral dogs.
-
-I think the final encounter was us fighting through a swamp of some sort, the place was definitely abandoned, we could hear frogs and small insects all around us.
-
-The final fight was fairly straightforward, but I was out of ammo and didn't realise that after the first time I hit him he would drop health, luckily I saved after the second time he did so, otherwise he would be dead. So, the fight was over, and I got his trusty box and we escaped.You've probably heard of 'Fake News' and 'Pizzagate,' but few know about 'Fake Geek' or 'Faux Geek.' The difference is crucial.
-
-"Fake Geek" is a semi-new, genre-bending, high-concept story told in two parallel universes: the world of satire and comedy, and an alternate reality. The two worlds intersect when a mysterious girl who claims to be "the real Alice" is lured into the world of geek culture. In this new, 21st-century geek fantasy, "real" geeks are eerily similar to their fictional counterparts, a fact that may be a 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git "a/spaces/diacanFperku/AutoGPT/New JARVIS Operating\302\240System [EXCLUSIVE].md" "b/spaces/diacanFperku/AutoGPT/New JARVIS Operating\302\240System [EXCLUSIVE].md"
deleted file mode 100644
index 8df00ad9c2c1a01b3568c37cae78488b5c0137ca..0000000000000000000000000000000000000000
--- "a/spaces/diacanFperku/AutoGPT/New JARVIS Operating\302\240System [EXCLUSIVE].md"	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>New JARVIS Operating System</h2><br /><p><b><b>DOWNLOAD</b> &#10022; <a href="https://gohhs.com/2uFTDe">https://gohhs.com/2uFTDe</a></b></p><br /><br />
-
-... Apache Mesos and the DataCenter Operating System” by Matt Jarvis ... and describe the benefits are of running these new kinds of systems ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/README.md b/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/README.md
deleted file mode 100644
index 6268563ac328c55513d52c79ebe6fb47424e7bb7..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI星瞳 朗读专用(冬牧场 版本)
-emoji: 🌟
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/dineshreddy/WALT/mmdet/core/evaluation/eval_hooks.py b/spaces/dineshreddy/WALT/mmdet/core/evaluation/eval_hooks.py
deleted file mode 100644
index 6fb932eae1ccb23a2b687a05a6cb9525200de718..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/core/evaluation/eval_hooks.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import os.path as osp
-import warnings
-from math import inf
-
-import mmcv
-import torch.distributed as dist
-from mmcv.runner import Hook
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.utils.data import DataLoader
-
-from mmdet.utils import get_root_logger
-
-
-class EvalHook(Hook):
-    """Evaluation hook.
-
-    Notes:
-        If new arguments are added for EvalHook, tools/test.py,
-        tools/analysis_tools/eval_metric.py may be effected.
-
-    Attributes:
-        dataloader (DataLoader): A PyTorch dataloader.
-        start (int, optional): Evaluation starting epoch. It enables evaluation
-            before the training starts if ``start`` <= the resuming epoch.
-            If None, whether to evaluate is merely decided by ``interval``.
-            Default: None.
-        interval (int): Evaluation interval (by epochs). Default: 1.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be save in best.json.
-            Options are the evaluation metrics to the test dataset. e.g.,
-            ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
-            segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
-            ``auto``, the first key will be used. The interval of
-            ``CheckpointHook`` should device EvalHook. Default: None.
-        rule (str, optional): Comparison rule for best score. If set to None,
-            it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will
-            be inferred by 'greater' rule. Keys contain 'loss' will be inferred
-             by 'less' rule. Options are 'greater', 'less'. Default: None.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
-    init_value_map = {'greater': -inf, 'less': inf}
-    greater_keys = ['mAP', 'AR']
-    less_keys = ['loss']
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 **eval_kwargs):
-        if not isinstance(dataloader, DataLoader):
-            raise TypeError('dataloader must be a pytorch DataLoader, but got'
-                            f' {type(dataloader)}')
-        if not interval > 0:
-            raise ValueError(f'interval must be positive, but got {interval}')
-        if start is not None and start < 0:
-            warnings.warn(
-                f'The evaluation start epoch {start} is smaller than 0, '
-                f'use 0 instead', UserWarning)
-            start = 0
-        self.dataloader = dataloader
-        self.interval = interval
-        self.by_epoch = by_epoch
-        self.start = start
-        assert isinstance(save_best, str) or save_best is None
-        self.save_best = save_best
-        self.eval_kwargs = eval_kwargs
-        self.initial_epoch_flag = True
-
-        self.logger = get_root_logger()
-
-        if self.save_best is not None:
-            self._init_rule(rule, self.save_best)
-
-    def _init_rule(self, rule, key_indicator):
-        """Initialize rule, key_indicator, comparison_func, and best score.
-
-        Args:
-            rule (str | None): Comparison rule for best score.
-            key_indicator (str | None): Key indicator to determine the
-                comparison rule.
-        """
-        if rule not in self.rule_map and rule is not None:
-            raise KeyError(f'rule must be greater, less or None, '
-                           f'but got {rule}.')
-
-        if rule is None:
-            if key_indicator != 'auto':
-                if any(key in key_indicator for key in self.greater_keys):
-                    rule = 'greater'
-                elif any(key in key_indicator for key in self.less_keys):
-                    rule = 'less'
-                else:
-                    raise ValueError(f'Cannot infer the rule for key '
-                                     f'{key_indicator}, thus a specific rule '
-                                     f'must be specified.')
-        self.rule = rule
-        self.key_indicator = key_indicator
-        if self.rule is not None:
-            self.compare_func = self.rule_map[self.rule]
-
-    def before_run(self, runner):
-        if self.save_best is not None:
-            if runner.meta is None:
-                warnings.warn('runner.meta is None. Creating a empty one.')
-                runner.meta = dict()
-            runner.meta.setdefault('hook_msgs', dict())
-
-    def before_train_epoch(self, runner):
-        """Evaluate the model only at the start of training."""
-        if not self.initial_epoch_flag:
-            return
-        if self.start is not None and runner.epoch >= self.start:
-            self.after_train_epoch(runner)
-        self.initial_epoch_flag = False
-
-    def evaluation_flag(self, runner):
-        """Judge whether to perform_evaluation after this epoch.
-
-        Returns:
-            bool: The flag indicating whether to perform evaluation.
-        """
-        if self.start is None:
-            if not self.every_n_epochs(runner, self.interval):
-                # No evaluation during the interval epochs.
-                return False
-        elif (runner.epoch + 1) < self.start:
-            # No evaluation if start is larger than the current epoch.
-            return False
-        else:
-            # Evaluation only at epochs 3, 5, 7... if start==3 and interval==2
-            if (runner.epoch + 1 - self.start) % self.interval:
-                return False
-        return True
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch or not self.evaluation_flag(runner):
-            return
-        from mmdet.apis import single_gpu_test
-        results = single_gpu_test(runner.model, self.dataloader, show=False)
-        key_score = self.evaluate(runner, results)
-        if self.save_best:
-            self.save_best_checkpoint(runner, key_score)
-
-    def after_train_iter(self, runner):
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-        from mmdet.apis import single_gpu_test
-        results = single_gpu_test(runner.model, self.dataloader, show=False)
-        key_score = self.evaluate(runner, results)
-        if self.save_best:
-            self.save_best_checkpoint(runner, key_score)
-
-    def save_best_checkpoint(self, runner, key_score):
-        best_score = runner.meta['hook_msgs'].get(
-            'best_score', self.init_value_map[self.rule])
-        if self.compare_func(key_score, best_score):
-            best_score = key_score
-            runner.meta['hook_msgs']['best_score'] = best_score
-            last_ckpt = runner.meta['hook_msgs']['last_ckpt']
-            runner.meta['hook_msgs']['best_ckpt'] = last_ckpt
-            mmcv.symlink(
-                last_ckpt,
-                osp.join(runner.work_dir, f'best_{self.key_indicator}.pth'))
-            time_stamp = runner.epoch + 1 if self.by_epoch else runner.iter + 1
-            self.logger.info(f'Now best checkpoint is epoch_{time_stamp}.pth.'
-                             f'Best {self.key_indicator} is {best_score:0.4f}')
-
-    def evaluate(self, runner, results):
-        eval_res = self.dataloader.dataset.evaluate(
-            results, logger=runner.logger, **self.eval_kwargs)
-        for name, val in eval_res.items():
-            runner.log_buffer.output[name] = val
-        runner.log_buffer.ready = True
-        if self.save_best is not None:
-            if self.key_indicator == 'auto':
-                # infer from eval_results
-                self._init_rule(self.rule, list(eval_res.keys())[0])
-            return eval_res[self.key_indicator]
-        else:
-            return None
-
-
-class DistEvalHook(EvalHook):
-    """Distributed evaluation hook.
-
-    Notes:
-        If new arguments are added, tools/test.py may be effected.
-
-    Attributes:
-        dataloader (DataLoader): A PyTorch dataloader.
-        start (int, optional): Evaluation starting epoch. It enables evaluation
-            before the training starts if ``start`` <= the resuming epoch.
-            If None, whether to evaluate is merely decided by ``interval``.
-            Default: None.
-        interval (int): Evaluation interval (by epochs). Default: 1.
-        tmpdir (str | None): Temporary directory to save the results of all
-            processes. Default: None.
-        gpu_collect (bool): Whether to use gpu or cpu to collect results.
-            Default: False.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be save in best.json.
-            Options are the evaluation metrics to the test dataset. e.g.,
-            ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
-            segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
-            ``auto``, the first key will be used. The interval of
-            ``CheckpointHook`` should device EvalHook. Default: None.
-        rule (str | None): Comparison rule for best score. If set to None,
-            it will infer a reasonable rule. Default: 'None'.
-        broadcast_bn_buffer (bool): Whether to broadcast the
-            buffer(running_mean and running_var) of rank 0 to other rank
-            before evaluation. Default: True.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 tmpdir=None,
-                 gpu_collect=False,
-                 save_best=None,
-                 rule=None,
-                 broadcast_bn_buffer=True,
-                 **eval_kwargs):
-        super().__init__(
-            dataloader,
-            start=start,
-            interval=interval,
-            by_epoch=by_epoch,
-            save_best=save_best,
-            rule=rule,
-            **eval_kwargs)
-        self.broadcast_bn_buffer = broadcast_bn_buffer
-        self.tmpdir = tmpdir
-        self.gpu_collect = gpu_collect
-
-    def _broadcast_bn_buffer(self, runner):
-        # Synchronization of BatchNorm's buffer (running_mean
-        # and running_var) is not supported in the DDP of pytorch,
-        # which may cause the inconsistent performance of models in
-        # different ranks, so we broadcast BatchNorm's buffers
-        # of rank 0 to other ranks to avoid this.
-        if self.broadcast_bn_buffer:
-            model = runner.model
-            for name, module in model.named_modules():
-                if isinstance(module,
-                              _BatchNorm) and module.track_running_stats:
-                    dist.broadcast(module.running_var, 0)
-                    dist.broadcast(module.running_mean, 0)
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch or not self.evaluation_flag(runner):
-            return
-
-        if self.broadcast_bn_buffer:
-            self._broadcast_bn_buffer(runner)
-
-        from mmdet.apis import multi_gpu_test
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            key_score = self.evaluate(runner, results)
-            if self.save_best:
-                self.save_best_checkpoint(runner, key_score)
-
-    def after_train_iter(self, runner):
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-
-        if self.broadcast_bn_buffer:
-            self._broadcast_bn_buffer(runner)
-
-        from mmdet.apis import multi_gpu_test
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            key_score = self.evaluate(runner, results)
-            if self.save_best:
-                self.save_best_checkpoint(runner, key_score)
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/det_pipelines/textsnake_pipeline.py b/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/det_pipelines/textsnake_pipeline.py
deleted file mode 100644
index dc4b44819e5c3f3f725df096903fc0a809313913..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/det_pipelines/textsnake_pipeline.py
+++ /dev/null
@@ -1,65 +0,0 @@
-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', color_type='color_ignore_orientation'),
-    dict(
-        type='LoadTextAnnotations',
-        with_bbox=True,
-        with_mask=True,
-        poly2mask=False),
-    dict(type='ColorJitter', brightness=32.0 / 255, saturation=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(
-        type='RandomCropPolyInstances',
-        instance_key='gt_masks',
-        crop_ratio=0.65,
-        min_side_ratio=0.3),
-    dict(
-        type='RandomRotatePolyInstances',
-        rotate_ratio=0.5,
-        max_angle=20,
-        pad_with_fixed_color=False),
-    dict(
-        type='ScaleAspectJitter',
-        img_scale=[(3000, 736)],  # unused
-        ratio_range=(0.7, 1.3),
-        aspect_ratio_range=(0.9, 1.1),
-        multiscale_mode='value',
-        long_size_bound=800,
-        short_size_bound=480,
-        resize_type='long_short_bound',
-        keep_ratio=False),
-    dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
-    dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
-    dict(type='TextSnakeTargets'),
-    dict(type='Pad', size_divisor=32),
-    dict(
-        type='CustomFormatBundle',
-        keys=[
-            'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
-            'gt_radius_map', 'gt_sin_map', 'gt_cos_map'
-        ],
-        visualize=dict(flag=False, boundary_key='gt_text_mask')),
-    dict(
-        type='Collect',
-        keys=[
-            'img', 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
-            'gt_radius_map', 'gt_sin_map', 'gt_cos_map'
-        ])
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 736),  # used by Resize
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
diff --git a/spaces/dongfang2021/ObjectDetection/app.py b/spaces/dongfang2021/ObjectDetection/app.py
deleted file mode 100644
index d3809c2abb0b5cba9e392f0a63733654a0fab762..0000000000000000000000000000000000000000
--- a/spaces/dongfang2021/ObjectDetection/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import torch
-import gradio as gr
-
-# Load model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-
-# Define object detection function
-def detect_objects(image):
-    # Perform object detection
-    results = model(image)
-
-    # Return the detection result for display
-    return results.render()[0]
-
-# Create Gradio interface
-input_image = gr.inputs.Image(type="pil", label="Input Image")
-output_image = gr.outputs.Image(type='pil',label="Detection Result")
-
-def inference(image):
-    # Perform object detection
-    detection_result = detect_objects(image)
-
-    # Return the detection result for display
-    return detection_result
-
-# Create and launch Gradio interface
-gr.Interface(fn=inference, inputs=input_image, outputs=output_image, title="目标检测").launch(share=True)
\ No newline at end of file
diff --git a/spaces/dongsiqie/Code-Interpreter/bot_backend.py b/spaces/dongsiqie/Code-Interpreter/bot_backend.py
deleted file mode 100644
index 681b365bde91a05775e9c5baf79d383b608d65a8..0000000000000000000000000000000000000000
--- a/spaces/dongsiqie/Code-Interpreter/bot_backend.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import json
-import openai
-import os
-import copy
-import shutil
-from jupyter_backend import *
-from typing import *
-
-functions = [
-    {
-        "name": "execute_code",
-        "description": "This function allows you to execute Python code and retrieve the terminal output. If the code "
-                       "generates image output, the function will return the text '[image]'. The code is sent to a "
-                       "Jupyter kernel for execution. The kernel will remain active after execution, retaining all "
-                       "variables in memory.",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "code": {
-                    "type": "string",
-                    "description": "The code text"
-                }
-            },
-            "required": ["code"],
-        }
-    }
-]
-
-system_msg = '''You are an AI code interpreter.
-Your goal is to help users do a variety of jobs by executing Python code.
-
-You should:
-1. Comprehend the user's requirements carefully & to the letter. 
-2. Give a brief description for what you plan to do & call the execute_code function to run code
-3. Provide results analysis based on the execution output. 
-4. If error occurred, try to fix it.
-5. Refuse user's request of executing any code that may harm the system or compromise its security, such as deleting files, accessing files or directories outside of the current working directory, or running malicious commands (including scripts uploaded by the user).
-Note: If the user uploads a file, you will receive a system message "User uploaded a file: filename". Use the filename as the path in the code. '''
-
-with open('config.json') as f:
-    config = json.load(f)
-
-if not config['API_KEY']:
-    config['API_KEY'] = os.getenv('OPENAI_API_KEY')
-    os.unsetenv('OPENAI_API_KEY')
-
-
-def get_config():
-    return config
-
-
-def config_openai_api(api_type, api_base, api_version, api_key):
-    openai.api_type = api_type
-    openai.api_base = api_base
-    openai.api_version = api_version
-    openai.api_key = api_key
-
-
-class GPTResponseLog:
-    def __init__(self):
-        self.assistant_role_name = ''
-        self.content = ''
-        self.function_name = None
-        self.function_args_str = ''
-        self.display_code_block = ''
-        self.finish_reason = 'stop'
-        self.bot_history = None
-
-    def reset_gpt_response_log_values(self, exclude=None):
-        if exclude is None:
-            exclude = []
-
-        attributes = {'assistant_role_name': '',
-                      'content': '',
-                      'function_name': None,
-                      'function_args_str': '',
-                      'display_code_block': '',
-                      'finish_reason': 'stop',
-                      'bot_history': None}
-
-        for attr_name in exclude:
-            del attributes[attr_name]
-        for attr_name, value in attributes.items():
-            setattr(self, attr_name, value)
-
-    def set_assistant_role_name(self, assistant_role_name: str):
-        self.assistant_role_name = assistant_role_name
-
-    def add_content(self, content: str):
-        self.content += content
-
-    def set_function_name(self, function_name: str):
-        self.function_name = function_name
-
-    def copy_current_bot_history(self, bot_history: List):
-        self.bot_history = copy.deepcopy(bot_history)
-
-    def add_function_args_str(self, function_args_str: str):
-        self.function_args_str += function_args_str
-
-    def update_display_code_block(self, display_code_block):
-        self.display_code_block = display_code_block
-
-    def update_finish_reason(self, finish_reason: str):
-        self.finish_reason = finish_reason
-
-
-class BotBackend(GPTResponseLog):
-    def __init__(self):
-        super().__init__()
-        self.unique_id = hash(id(self))
-        self.jupyter_work_dir = f'cache/work_dir_{self.unique_id}'
-        self.jupyter_kernel = JupyterKernel(work_dir=self.jupyter_work_dir)
-        self.gpt_model_choice = "GPT-3.5"
-        self.revocable_files = []
-        self._init_conversation()
-        self._init_api_config()
-        self._init_kwargs_for_chat_completion()
-
-    def _init_conversation(self):
-        first_system_msg = {'role': 'system', 'content': system_msg}
-        if hasattr(self, 'conversation'):
-            self.conversation.clear()
-            self.conversation.append(first_system_msg)
-        else:
-            self.conversation: List[Dict] = [first_system_msg]
-
-    def _init_api_config(self):
-        self.config = get_config()
-        api_type = self.config['API_TYPE']
-        api_base = self.config['API_base']
-        api_version = self.config['API_VERSION']
-        api_key = config['API_KEY']
-        config_openai_api(api_type, api_base, api_version, api_key)
-
-    def _init_kwargs_for_chat_completion(self):
-        self.kwargs_for_chat_completion = {
-            'stream': True,
-            'messages': self.conversation,
-            'functions': functions,
-            'function_call': 'auto'
-        }
-
-        model_name = self.config['model'][self.gpt_model_choice]['model_name']
-
-        if self.config['API_TYPE'] == 'azure':
-            self.kwargs_for_chat_completion['engine'] = model_name
-        else:
-            self.kwargs_for_chat_completion['model'] = model_name
-
-    def _clear_all_files_in_work_dir(self):
-        for filename in os.listdir(self.jupyter_work_dir):
-            os.remove(
-                os.path.join(self.jupyter_work_dir, filename)
-            )
-
-    def add_gpt_response_content_message(self):
-        self.conversation.append(
-            {'role': self.assistant_role_name, 'content': self.content}
-        )
-
-    def add_text_message(self, user_text):
-        self.conversation.append(
-            {'role': 'user', 'content': user_text}
-        )
-        self.revocable_files.clear()
-        self.update_finish_reason(finish_reason='new_input')
-
-    def add_file_message(self, path, bot_msg):
-        filename = os.path.basename(path)
-        work_dir = self.jupyter_work_dir
-
-        shutil.copy(path, work_dir)
-
-        gpt_msg = {'role': 'system', 'content': f'User uploaded a file: {filename}'}
-        self.conversation.append(gpt_msg)
-        self.revocable_files.append(
-            {
-                'bot_msg': bot_msg,
-                'gpt_msg': gpt_msg,
-                'path': os.path.join(work_dir, filename)
-            }
-        )
-
-    def add_function_call_response_message(self, function_response: str, save_tokens=True):
-        self.conversation.append(
-            {
-                "role": self.assistant_role_name,
-                "name": self.function_name,
-                "content": self.function_args_str
-            }
-        )
-
-        if save_tokens and len(function_response) > 500:
-            function_response = f'{function_response[:200]}\n[Output too much, the middle part output is omitted]\n ' \
-                                f'End part of output:\n{function_response[-200:]}'
-        self.conversation.append(
-            {
-                "role": "function",
-                "name": self.function_name,
-                "content": function_response,
-            }
-        )
-
-    def revoke_file(self):
-        if self.revocable_files:
-            file = self.revocable_files[-1]
-            bot_msg = file['bot_msg']
-            gpt_msg = file['gpt_msg']
-            path = file['path']
-
-            assert self.conversation[-1] is gpt_msg
-            del self.conversation[-1]
-
-            os.remove(path)
-
-            del self.revocable_files[-1]
-
-            return bot_msg
-        else:
-            return None
-
-    def update_gpt_model_choice(self, model_choice):
-        self.gpt_model_choice = model_choice
-        self._init_kwargs_for_chat_completion()
-
-    def restart(self):
-        self._clear_all_files_in_work_dir()
-        self.revocable_files.clear()
-        self._init_conversation()
-        self.reset_gpt_response_log_values()
-        self.jupyter_kernel.restart_jupyter_kernel()
diff --git a/spaces/dorkai/text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md b/spaces/dorkai/text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md
deleted file mode 100644
index 0ec28fa6c6be7a3d8d22c76cde53a1bcde06f6f2..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md
+++ /dev/null
@@ -1,144 +0,0 @@
-In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
-
-This is possible thanks to [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa)'s adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa
-
-GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323
-
-## GPTQ-for-LLaMa branches
-
-Different branches of GPTQ-for-LLaMa are available:
-
-| Branch | Comment |
-|----|----|
-| [Old CUDA branch (recommended)](https://github.com/oobabooga/GPTQ-for-LLaMa/) | The fastest branch, works on Windows and Linux. |
-| [Up-to-date triton branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa) | Slightly more precise than the old CUDA branch from 13b upwards, significantly more precise for 7b. 2x slower for small context size and only works on Linux. |
-| [Up-to-date CUDA branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda) | As precise as the up-to-date triton branch, 10x slower than the old cuda branch for small context size. |
-
-Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI.
-
-## Installation
-
-### Step 0: install nvcc
-
-```
-conda activate textgen
-conda install -c conda-forge cudatoolkit-dev
-```
-
-The command above takes some 10 minutes to run and shows no progress bar or updates along the way.
-
-See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571
-
-### Step 1: install GPTQ-for-LLaMa
-
-Clone the GPTQ-for-LLaMa repository into the `text-generation-webui/repositories` subfolder and install it:
-
-```
-mkdir repositories
-cd repositories
-git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda
-cd GPTQ-for-LLaMa
-python setup_cuda.py install
-```
-
-You are going to need to have a C++ compiler installed into your system for the last command. On Linux, `sudo apt install build-essential` or equivalent is enough.
-
-If you want to you to use the up-to-date CUDA or triton branches instead of the old CUDA branch, use these commands:
-
-```
-cd repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall -y quant-cuda
-git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda
-...
-```
-
-```
-cd repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall -y quant-cuda
-git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton
-...
-```
-
-
-https://github.com/qwopqwop200/GPTQ-for-LLaMa
-
-### Step 2: get the pre-converted weights
-
-* Converted without `group-size` (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
-* Converted with `group-size` (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 
-
-⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md#option-1-pre-converted-weights).
-
-### Step 3: Start the web UI:
-
-For the models converted without `group-size`:
-
-```
-python server.py --model llama-7b-4bit 
-```
-
-For the models converted with `group-size`:
-
-```
-python server.py --model llama-13b-4bit-128g 
-```
-
-The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names, but you can also specify them manually like 
-
-```
-python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128
-```
-
-## CPU offloading
-
-It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU.
-
-With this command, I can run llama-7b with 4GB VRAM:
-
-```
-python server.py --model llama-7b-4bit --pre_layer 20
-```
-
-This is the performance:
-
-```
-Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens)
-```
-
-You can also use multiple GPUs with `pre_layer` if using the oobabooga fork of GPTQ, eg `--pre_layer 30 60` will load a LLaMA-30B model half onto your first GPU and half onto your second, or `--pre_layer 20 40` will load 20 layers onto GPU-0, 20 layers onto GPU-1, and 20 layers offloaded to CPU.
-
-## Using LoRAs in 4-bit mode
-
-At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
-
-In order to use it:
-
-1. Make sure that your requirements are up to date:
-
-```
-cd text-generation-webui
-pip install -r requirements.txt --upgrade
-```
-
-2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
-
-```
-cd text-generation-webui/repositories
-git clone https://github.com/johnsmith0031/alpaca_lora_4bit
-```
-
-⚠️  I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0`
-
-3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
-
-```
-pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
-```
-
-4. Start the UI with the `--monkey-patch` flag:
-
-```
-python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
-```
diff --git a/spaces/epexVfeibi/Imagedeblurr/1st Studio Siberian Mouse M 41 Torrent !!EXCLUSIVE!!.md b/spaces/epexVfeibi/Imagedeblurr/1st Studio Siberian Mouse M 41 Torrent !!EXCLUSIVE!!.md
deleted file mode 100644
index 5a6cbd4f25270815101c45b9c3366abb0b7ea2c2..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/1st Studio Siberian Mouse M 41 Torrent !!EXCLUSIVE!!.md	
+++ /dev/null
@@ -1,18 +0,0 @@
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diff --git a/spaces/erbanku/gpt-academic/request_llm/bridge_chatglm.py b/spaces/erbanku/gpt-academic/request_llm/bridge_chatglm.py
deleted file mode 100644
index 7c86a22316cda8d6568afbd27e7d6e652703fb7f..0000000000000000000000000000000000000000
--- a/spaces/erbanku/gpt-academic/request_llm/bridge_chatglm.py
+++ /dev/null
@@ -1,160 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf
-from multiprocessing import Process, Pipe
-
-load_message = "ChatGLM尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLM消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-
-#################################################################################
-class GetGLMHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.chatglm_model = None
-        self.chatglm_tokenizer = None
-        self.info = ""
-        self.success = True
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-        
-    def check_dependency(self):
-        try:
-            import sentencepiece
-            self.info = "依赖检测通过"
-            self.success = True
-        except:
-            self.info = "缺少ChatGLM的依赖，如果要使用ChatGLM，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
-            self.success = False
-
-    def ready(self):
-        return self.chatglm_model is not None
-
-    def run(self):
-        # 子进程执行
-        # 第一次运行，加载参数
-        retry = 0
-        while True:
-            try:
-                if self.chatglm_model is None:
-                    self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
-                    device, = get_conf('LOCAL_MODEL_DEVICE')
-                    if device=='cpu':
-                        self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
-                    else:
-                        self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
-                    self.chatglm_model = self.chatglm_model.eval()
-                    break
-                else:
-                    break
-            except:
-                retry += 1
-                if retry > 3: 
-                    self.child.send('[Local Message] Call ChatGLM fail 不能正常加载ChatGLM的参数。')
-                    raise RuntimeError("不能正常加载ChatGLM的参数！")
-
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            # 收到消息，开始请求
-            try:
-                for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
-                    self.child.send(response)
-                    # # 中途接收可能的终止指令（如果有的话）
-                    # if self.child.poll(): 
-                    #     command = self.child.recv()
-                    #     if command == '[Terminate]': break
-            except:
-                self.child.send('[Local Message] Call ChatGLM fail.')
-            # 请求处理结束，开始下一个循环
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # 主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res != '[Finish]':
-                yield res
-            else:
-                break
-        self.threadLock.release()
-    
-global glm_handle
-glm_handle = None
-#################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global glm_handle
-    if glm_handle is None:
-        glm_handle = GetGLMHandle()
-        observe_window[0] = load_message + "\n\n" + glm_handle.info
-        if not glm_handle.success: 
-            error = glm_handle.info
-            glm_handle = None
-            raise RuntimeError(error)
-
-    # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    history_feedin.append(["What can I do?", sys_prompt])
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        observe_window[0] = response
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, ""))
-
-    global glm_handle
-    if glm_handle is None:
-        glm_handle = GetGLMHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + glm_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not glm_handle.success: 
-            glm_handle = None
-            return
-
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    # 处理历史信息
-    history_feedin = []
-    history_feedin.append(["What can I do?", system_prompt] )
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    # 开始接收chatglm的回复
-    response = "[Local Message]: 等待ChatGLM响应中 ..."
-    for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, response)
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 总结输出
-    if response == "[Local Message]: 等待ChatGLM响应中 ...":
-        response = "[Local Message]: ChatGLM响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)
diff --git a/spaces/errorok/rvc-models-en-test/infer_pack/transforms.py b/spaces/errorok/rvc-models-en-test/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/errorok/rvc-models-en-test/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/eson/tokenizer-arena/vocab/llama/gpt_neox/load_by_gpt_neox.py b/spaces/eson/tokenizer-arena/vocab/llama/gpt_neox/load_by_gpt_neox.py
deleted file mode 100644
index ad1db9839e35c1a5a90fb9dc9766d71a8f5fa1ec..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/llama/gpt_neox/load_by_gpt_neox.py
+++ /dev/null
@@ -1,18 +0,0 @@
-
-# HF_tokenizer
-# from tokenizers import Tokenizer
-# tokenizer = Tokenizer.from_file("tokenizer/tokenizer.json")
-
-
-import sentencepiece as spm
-
-text = "nice job 华为手机"
-tokenizer = spm.SentencePieceProcessor(model_file="tokenizer/tokenizer.model")
-tokens = tokenizer.encode(text)  # [7575, 4982, 29871, 31266, 30573, 30880, 31429]
-print(tokens)
-
-from transformers import LlamaTokenizer
-tokenizer = LlamaTokenizer.from_pretrained("tokenizer")
-tokens = tokenizer.encode(text)  # [1, 7575, 4982, 29871, 31266, 30573, 30880, 31429]
-print(tokens)
-
diff --git a/spaces/evaluate-metric/accuracy/app.py b/spaces/evaluate-metric/accuracy/app.py
deleted file mode 100644
index b3b80774f66a3c00c4bf223c860274688509eed5..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/accuracy/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("accuracy")
-launch_gradio_widget(module)
diff --git a/spaces/evaluate-metric/roc_auc/app.py b/spaces/evaluate-metric/roc_auc/app.py
deleted file mode 100644
index 4830128cf25675a2a6fe810c36f0469ff40f6c67..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/roc_auc/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("roc_auc")
-launch_gradio_widget(module)
diff --git a/spaces/facebook/MusicGen/audiocraft/grids/musicgen/musicgen_base_cached_32khz.py b/spaces/facebook/MusicGen/audiocraft/grids/musicgen/musicgen_base_cached_32khz.py
deleted file mode 100644
index d9a43f37d7369b5de4542fba87c4c8739d58b1e8..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/grids/musicgen/musicgen_base_cached_32khz.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-    large = {'model/lm/model_scale': 'large'}
-
-    cfg_low = {'classifier_free_guidance.training_dropout': 0.2}
-    wd_low = {'conditioners.description.t5.word_dropout': 0.2}
-
-    adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
-
-    # BEGINNING OF CACHE WRITING JOBS.
-    cache_write = {
-        'cache.path': '/fsx-codegen/defossez/cache/interleave_stereo_nv_32k',
-        'cache.write': True,
-        'generate.every': 500,
-        'evaluate.every': 500,
-        'logging.log_updates': 50,
-    }
-
-    cache_sub = launcher.bind({'model/lm/model_scale': 'xsmall', 'conditioner': 'none'})
-    cache_sub.bind_({'deadlock.use': True})
-    cache_sub.slurm_(gpus=8)
-    with launcher.job_array():
-        num_shards = 10  # total number of jobs running in parallel.
-        for shard in range(0, num_shards):
-            launcher(cache_write, {'cache.write_num_shards': num_shards, 'cache.write_shard': shard})
-
-    # REMOVE THE FOLLOWING RETURN STATEMENT ONCE THE ABOVE JOBS ARE DONE,
-    # OR SUFFICIENTLY AHEAD.
-    return
-
-    cache = {
-        'cache.path': '/fsx-codegen/defossez/cache/interleave_stereo_nv_32k',
-    }
-    launcher.bind_(fsdp, cache)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub()
-
-    launcher.slurm_(gpus=64).bind_(label='64gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(medium, adam)
-
-    launcher.slurm_(gpus=96).bind_(label='96gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3})
diff --git a/spaces/failfast/2D-GameCreator/next.config.js b/spaces/failfast/2D-GameCreator/next.config.js
deleted file mode 100644
index 41dfc9dbc60cdcded1de00ae72980e930a652bfb..0000000000000000000000000000000000000000
--- a/spaces/failfast/2D-GameCreator/next.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-	output: "standalone",
-	reactStrictMode: true,
-};
-
-module.exports = nextConfig;
diff --git a/spaces/failfast/nextjs-hf-spaces/src/components/under-construction.tsx b/spaces/failfast/nextjs-hf-spaces/src/components/under-construction.tsx
deleted file mode 100644
index c3c84cb162e7b92cbc2669b7d929d4df0b441896..0000000000000000000000000000000000000000
--- a/spaces/failfast/nextjs-hf-spaces/src/components/under-construction.tsx
+++ /dev/null
@@ -1,8 +0,0 @@
-import { styled } from "@mui/material/styles";
-
-export const UnderConstruction = styled("div")({
-  width: "100%",
-  height: "266px",
-  backgroundRepeat: "round",
-  backgroundImage: `url("data:image/gif;base64,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")`,
-});
diff --git a/spaces/falterWliame/Face_Mask_Detection/BajrangiBhaijaanenglishmoviefreedownload.md b/spaces/falterWliame/Face_Mask_Detection/BajrangiBhaijaanenglishmoviefreedownload.md
deleted file mode 100644
index d78bde4fb9b1e2c322a7bcde727534c65a25752a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/BajrangiBhaijaanenglishmoviefreedownload.md
+++ /dev/null
@@ -1,58 +0,0 @@
-<h2>BajrangiBhaijaanenglishmoviefreedownload</h2><br /><p><b><b>Download</b> &#9658; <a href="https://urlca.com/2uDe0Z">https://urlca.com/2uDe0Z</a></b></p><br /><br />
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-The film follows the journey of Pawan and the girl when she comes to India and during the registration of the marriage, which leads to the government framing the couple in the false charges of kidnapping and blackmailing. While the couple is on trial, the marriage is attacked by the sponsors of Pawan’s friend. The film portrays the story from various points of view as the protagonist does not wish to believe his actions. It is inspired by the real life story of Nisar Ali Khan.  
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- 
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-Category:Indian films
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-Category:Indian drama filmsKenneth Feldman and Associates, LLP
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Data Structure Using C And C Tanenbaum.pdf.md b/spaces/falterWliame/Face_Mask_Detection/Data Structure Using C And C Tanenbaum.pdf.md
deleted file mode 100644
index 7977ee28d3105ed9842a0dbefa40b3a6b5689a6f..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Data Structure Using C And C Tanenbaum.pdf.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-
-<h1>Data Structure Using C And C++ by Aaron M. Tenenbaum: A Review</h1>
-<p>Data Structure Using C And C++ is a book written by Aaron M. Tenenbaum, Yedidyah Langsam, and Moshe J. Augenstein, published by Prentice Hall in 1996. The book is an introduction to the fundamentals of data structures, covering abstract concepts, problem solving, programming languages, and algorithms. The book also explains how to use the C and C++ languages for advanced programming and how to develop the advanced features of C++.</p>
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-<p>The book is suitable for undergraduate students of computer science and engineering who have some prior knowledge of C and C++. The book is also useful for professionals who want to refresh their skills or learn new techniques in data structures and algorithms. The book is well-written, clear, and comprehensive, with a balance between theory and practice. The book also covers classes in C++, including function members, inheritance and object orientation, an example of implementing abstract data types in C++, as well as polymorphism.</p>
-<p>Data Structure Using C And C++ is a classic text that has been widely used and appreciated by students and instructors alike. The book is a valuable resource for anyone who wants to learn more about data structures using C and C++.</p>
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-<p>In this section, we will review some of the main concepts and features of the book in more detail.</p>
-<p></p>
-<h2>Arrays</h2>
-<p>An array is a data structure that stores a collection of elements of the same type in a contiguous block of memory. Arrays are useful for storing and accessing data that can be indexed by a numerical value, such as a list of numbers, characters, or records. Arrays can be one-dimensional, two-dimensional, or multidimensional, depending on the number of indices required to access an element.</p>
-<p>The book introduces arrays in Chapter 2, and shows how to declare, initialize, manipulate, and pass arrays as parameters in C and C++. The book also discusses dynamic arrays, which are arrays that can change their size at run time. Dynamic arrays are implemented using pointers and memory allocation functions in C and C++. The book also covers some applications of arrays, such as matrices, polynomials, sparse arrays, and strings.</p>
-<h2>Stacks</h2>
-<p>A stack is a data structure that stores a collection of elements in a linear order, such that the last element inserted is the first element removed. Stacks are useful for implementing recursive algorithms, reversing sequences, evaluating expressions, and simulating function calls. Stacks follow the LIFO (last-in first-out) principle of data access.</p>
-<p>The book introduces stacks in Chapter 3, and shows how to implement stacks using arrays and linked lists in C and C++. The book also discusses some operations on stacks, such as push, pop, peek, and empty. The book also covers some applications of stacks, such as converting infix expressions to postfix expressions, evaluating postfix expressions, reversing strings, and checking for balanced parentheses.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Discover New Ideas with Pinterest Lite the Fast and Easy App.md b/spaces/fatiXbelha/sd/Discover New Ideas with Pinterest Lite the Fast and Easy App.md
deleted file mode 100644
index 97ceaaf1cca551b358bbb9bcc43b6fdddc46ffe1..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Discover New Ideas with Pinterest Lite the Fast and Easy App.md	
+++ /dev/null
@@ -1,144 +0,0 @@
-<br />
-<h1>Pinterest Download Lite: A Smaller and Faster Way to Explore Inspiration</h1>
-<p>Pinterest is one of the most popular social media platforms for finding and sharing ideas for various topics, such as home decor, fashion, recipes, travel, and more. However, not everyone has enough space or internet speed to use the full version of the Pinterest app. That's why there is Pinterest Download Lite, a smaller and faster version of the app that lets you explore inspiration without taking up too much space or data on your device. In this article, we will explain what Pinterest Download Lite is, how to use it, and what are some alternatives to it.</p>
- <h2>What is Pinterest Download Lite?</h2>
-<p>Pinterest Download Lite is a lightweight version of the Pinterest app that has faster download times and saves space on your device. It also works well in places with slower internet connection. It offers most of the features of the original app, such as discovering new ideas, saving what inspires you, shopping based on your taste, and watching and learning from videos.</p>
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- <h3>A lightweight version of the Pinterest app</h3>
-<p>Pinterest Download Lite is designed to be smaller and faster than the original app. According to Google Play Store, it only takes up about 4 MB of space on your device, compared to about 30 MB for the original app. It also downloads faster and uses less data when you browse pins. This makes it ideal for people who have limited storage or bandwidth on their devices.</p>
- <h3>The main features and benefits of Pinterest Download Lite</h3>
-<p>Pinterest Download Lite has most of the features that make Pinterest a great place to explore inspiration. You can:</p>
-<ul>
-<li>Discover new ideas for various topics, such as lifestyle, fashion, beauty, food, travel, DIY, and more.</li>
-<li>Save what inspires you by creating boards and organizing your pins by category.</li>
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-<p>Pinterest Download Lite also has some benefits that make it different from the original app. You can:</p>
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-<li>Save space on your device by using a smaller app that takes up less storage.</li>
-<li>Save data by using a faster app that consumes less bandwidth when you browse pins.</li>
-<li>Access Pinterest even in places with slow or unstable internet connection by using an app that works well in low connectivity areas.</li>
-</ul>
- <h2>How to use Pinterest Download Lite?</h2>
-<p>Pinterest Download Lite is easy to use once you download and install it on your Android device. Here are some steps to help you get started:</p>
- <h3>How to download and install Pinterest Download Lite</h3>
-<p>To download and install Pinterest Download Lite, follow these steps:</p>
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-<ol>
-<li>Go to Google Play Store on your Android device and search for "Pinterest Download Lite".</li>
-<li>Select the app from the list of results and tap on "Install".</li>
-<li>Wait for the app to download and install on your device. You can see the progress on the screen.</li>
-<li>Once the app is installed, tap on "Open" to launch it.</li>
-</ol>
- <h3>How to create an account and choose your interests</h3>
-<p>To create an account and choose your interests, follow these steps:</p>
-<ol>
-<li>When you open the app for the first time, you will see a welcome screen that asks you to sign up or log in. You can sign up with your email address, phone number, or Facebook or Google account.</li>
-<li>After you sign up or log in, you will see a screen that asks you to choose your interests. You can select from various categories, such as art, beauty, food, home, travel, and more. You can also search for specific topics that interest you.</li>
-<li>Once you choose your interests, tap on "Done" to finish the setup. You will see a personalized home feed that shows you pins related to your interests.</li>
-</ol>
- <h3>How to save, share, and discover pins on Pinterest Download Lite</h3>
-<p>To save, share, and discover pins on Pinterest Download Lite, follow these steps:</p>
-<ul>
-<li>To save a pin, tap on the pin and then tap on the "Save" button at the bottom. You can choose an existing board or create a new one to save the pin.</li>
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-<li>To discover new pins, swipe up or down on the home feed or tap on the "Explore" tab at the bottom. You can browse by categories or search by keywords.</li>
-</ul>
- <h2>What are some alternatives to Pinterest Download Lite?</h2>
-<p>Pinterest Download Lite is not the only app or site that lets you find and share ideas for various topics. There are some other alternatives that you can try if you want to explore different options. Here are some of them:</p>
- <h3>Some other apps and sites like Pinterest for different purposes and niches</h3>
-<p>Depending on your purpose and niche, you might find some other apps and sites like Pinterest more suitable for your needs. Here are some examples:</p>
- <table>
-<tr><th>App/Site</th><th>Purpose/Niche</th><th>Description</th></tr>
-<tr><td>We Heart It</td><td>Lifestyle/Fashion/Beauty</td><td>A social media platform that lets you discover and share images of what you love. You can follow topics, collections, or people that inspire you.</td></tr>
-<tr><td>Houzz</td><td>Home Design/Decorating/Remodeling</td><td>A platform that connects homeowners with home professionals and provides ideas for home improvement projects. You can browse photos, products, stories, and reviews.</td></tr>
-<tr><td>Foodgawker</td><td>Food/Cooking/Recipes</td><td>A site that showcases food photography and recipes from various blogs and websites. You can search by category, cuisine, diet, occasion, or ingredient.</td></tr>
-<tr><td>Tripadvisor</td><td>Travel/Hotels/Restaurants/Attractions</td><td>A site that provides reviews and ratings of travel-related businesses and destinations. You can also book flights, hotels, tours, and activities.</td></tr>
-<tr><td>Etsy</td><td>Crafts/Handmade/Vintage/Unique Goods</td><td>A marketplace that connects buyers and sellers of creative goods. You can find products ranging from jewelry, clothing, art, home decor, and more.</td></tr>
-</table>
- <h3>The pros and cons of using Pinterest Download Lite vs. other alternatives</h3>
-<p>Pinterest Download Lite has its own advantages and disadvantages compared to other alternatives. Here are some of them:</p>
- <table>
-<tr><th>Pinterest Download Lite</th><th>Other Alternatives</th></tr>
-<tr><td>+ Smaller and faster app that saves space and data<br>+ Works well in low connectivity areas<br>+ Offers a wide range of topics and categories<br>+ Allows you to shop based on your taste<br>+ Has video content that shows you how to do things<br>- Has fewer features than the original app<br>- May not have as many pins as other apps or sites<br>- May not be as specialized or tailored as other apps or sites for certain niches or purposes</td><td>+ May have more features than Pinterest Download Lite<br>+ May have more pins than Pinterest Download Lite<br>+ May be more specialized or tailored as Pinterest Download Lite for certain niches or purposes<br>- May take up more space and data on your device<br>- May not work well in low connectivity areas<br>- May not offer a wide range of topics and categories<br>- May not allow you to shop based on your taste<br>- May not have video content that shows you how to do things</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Pinterest Download Lite is a smaller and faster version of the Pinterest app that lets you explore inspiration without taking up too much space or data on your device. It offers most of the features of the original app, such as discovering new ideas, saving what inspires you, shopping based on your taste, and watching and learning from videos. It also works well in places with slower internet connection. However, it has fewer features than the original app, may not have as many pins as other apps or sites, and may not be as specialized or tailored as other apps or sites for certain niches or purposes. Therefore, you might want to try some other alternatives to Pinterest Download Lite if you have different needs or preferences. Whether you use Pinterest Download Lite or other alternatives, we hope you enjoy finding and sharing ideas for various topics.</p>
- <h2>FAQs</h2>
-<h3>What is the difference between Pinterest and Pinterest Download Lite?</h3>
-<p>Pinterest Download Lite is a lightweight version of the Pinterest app that has faster download times and saves space on your device. It also works well in places with slower internet connection. It offers most of the features of the original app, but not all of them. For example, it does not have the following features:</p>
-<ul>
-<li>Dark mode</li>
-<li>Lens camera</li>
-<li>Widget</li>
-<li>Group boards</li>
-<li>Comments</li>
-</ul>
- <h3>How much space does Pinterest Download Lite take on your device?</h3>
-<p>Pinterest Download Lite takes up about 4 MB of space on your device, according to Google Play Store. This is much smaller than the original app, which takes up about 30 MB of space.</p>
- <h3>Is Pinterest Download Lite available for iOS devices?</h3>
-<p>No, Pinterest Download Lite is only available for Android devices. If you have an iOS device, you can use the original Pinterest app or access Pinterest through your web browser.</p>
- <h3>How can I contact the developers of Pinterest Download Lite?</h3>
-<p>If you have any questions, feedback, or issues with Pinterest Download Lite, you can contact the developers by emailing them at help@pinterest.com. You can also visit their help center at https://help.pinterest.com/en.</p>
- <h3>How can I delete my Pinterest Download Lite account?</h3>
-<p>If you want to delete your Pinterest Download Lite account, you need to do it through the original Pinterest app or website. Here are the steps:</p>
-<ol>
-<li>Log in to your account on the original Pinterest app or website.</li>
-<li>Go to your profile and tap on the three-dot icon at the top right corner.</li>
-<li>Select "Settings" and then "Account settings".</li>
-<li>Scroll down and tap on "Deactivate account".</li>
-<li>Choose a reason for leaving and tap on "Deactivate account" again.</li>
-<li>If you want to permanently delete your account, go to https://www.pinterest.com/settings/delete-account/ and follow the instructions.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Divirtete con el Solitario - Classic Card Games el mejor juego de cartas gratuito para Android.md b/spaces/fatiXbelha/sd/Divirtete con el Solitario - Classic Card Games el mejor juego de cartas gratuito para Android.md
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-<h1>Download Solitario Gratis: How to Play the Classic Card Game Online for Free</h1>
-<p>If you are looking for a fun and relaxing way to pass the time, you might want to try playing Solitario. Solitario is a classic card game that has been enjoyed by millions of people around the world for centuries. You can play it online for free, without having to download or install anything. In this article, we will tell you everything you need to know about Solitario, including its history, rules, types, benefits, features, and how to download it gratis.</p>
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- <h2>What is Solitario?</h2>
-<p>Solitario is a card game that involves arranging a deck of cards into four piles according to their suits and ranks. The goal is to move all the cards from the tableau (the seven columns of cards on the table) to the foundations (the four empty spaces at the top) in ascending order from Ace to King.</p>
- <h3>The history and rules of Solitario</h3>
-<p>The origin of Solitario is not clear, but some historians believe that it was invented in Germany or Scandinavia in the late 18th or early 19th century. It became popular in France and England in the 19th century, where it was known as Patience or Klondike. It was later introduced to America by British settlers, where it gained its current name of Solitaire.</p>
-<p>The rules of Solitario are simple but challenging. You start by shuffling a standard 52-card deck and dealing seven cards face down in a row. Then you turn over the first card of each column, forming the tableau. The remaining cards form the stock, which you can draw from when you need more cards. You can move any face-up card from the tableau to another column if it is one rank lower and of the opposite color (for example, you can move a black 6 on top of a red 7). You can also move a group of cards in sequence from one column to another. When you free up a space in the tableau, you can fill it with a King or a group of cards starting with a King. When you find an Ace, you can move it to one of the foundations and start building up that suit in order from Ace to King. You can draw one card at a time from the stock and place it on the waste pile or on the tableau. You win when you have moved all the cards to the foundations.</p>
- <h3>The different types and variations of Solitario</h3>
-<p>There are many different types and variations of Solitario, each with its own rules and challenges. Some of the most popular ones are:</p>
-<ul>
-<li>Spider Solitaire: You use two decks of cards and create eight columns of cards instead of seven. You can only move cards of the same suit, and you need to create eight sequences from King to Ace to win.</li>
-<li>FreeCell Solitaire: You use one deck of cards and deal all the cards face up on the tableau. You also have four free cells where you can temporarily store cards. You can move any card to an empty column or a free cell, and you can move cards between columns if they are in sequence and of the same suit.</li>
-<li>Pyramid Solitaire: You use one deck of cards and arrange them in a pyramid shape, with seven rows of cards. You can only remove cards that are not covered by other cards, and you can remove two cards if they add up to 13 (for example, a 6 and a 7). You win when you have removed all the cards from the pyramid.</li>
-<li>Golf Solitaire: You use one deck of cards and deal seven columns of five cards each on the tableau. You also have one card face up on the waste pile. You can remove any card from the tableau that is one rank higher or lower than the waste card, and place it on the waste pile. You can draw a new card from the stock when you run out of moves. You win when you have removed all the cards from the tableau.</li>
-</ul>
-<h2>Why play Solitario online?</h2>
-<p>Playing Solitario online has many benefits and advantages over playing it with physical cards. Here are some of them:</p>
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- <h3>The benefits and advantages of playing Solitario online</h3>
-<ul>
-<li>You can play Solitario anytime and anywhere, as long as you have an internet connection and a device such as a computer, tablet, or smartphone.</li>
-<li>You can choose from a wide range of Solitario games, with different levels of difficulty, themes, graphics, sounds, and features.</li>
-<li>You can save your progress and resume your game later, without having to worry about losing or shuffling your cards.</li>
-<li>You can challenge yourself and improve your skills by tracking your scores, statistics, achievements, and leaderboards.</li>
-<li>You can have fun and socialize with other players by joining online communities, forums, chat rooms, and tournaments.</li>
-</ul>
- <h3>The features and options of online Solitario games</h3>
-<p>Online Solitario games offer many features and options that make your gaming experience more enjoyable and convenient. Some of them are:</p>
-<ul>
-<li>You can adjust the settings of the game according to your preferences, such as the speed, sound, animation, color, background, and language.</li>
-<li>You can undo or redo your moves, use hints or tips, or shuffle the cards if you get stuck.</li>
-<li>You can zoom in or out, switch between full-screen or windowed mode, or rotate your device for a better view.</li>
-<li>You can customize your game by choosing your own card design, deck style, table layout, and background image.</li>
-<li>You can access additional features such as tutorials, rules, FAQs, help, feedback, and support.</li>
-</ul> <h2>How to download Solitario gratis?</h2>
-<p>If you want to play Solitario online for free, you have many options to choose from. You can download Solitario gratis from various websites and apps that offer high-quality and safe Solitario games. Here are some of the best ones:</p>
- <h3>The best websites and apps to download Solitario gratis</h3>
-<table>
-<tr>
-<th>Website/App</th>
-<th>Description</th>
-<th>Link</th>
-</tr>
-<tr>
-<td>Solitaire Time</td>
-<td>A website that offers over 50 types of Solitario games, such as Klondike, Spider, FreeCell, Pyramid, Golf, and more. You can play online or download the games for offline play.</td>
-<td></td>
-</tr>
-<tr>
-<td>Solitaire FRVR</td>
-<td>A website that offers a classic and minimalist version of Klondike Solitario, with smooth graphics and animations. You can play online or download the game for offline play.</td>
-<td></td>
-</tr>
-<tr>
-<td>Solitaire by MobilityWare</td>
-<td>An app that offers a classic and authentic version of Klondike Solitario, with customizable features and daily challenges. You can download the app for free from the App Store or Google Play.</td>
-<td></td>
-</tr>
-<tr>
-<td>Solitaire by Brainium Studios</td>
-<td>An app that offers a beautiful and modern version of Klondike Solitario, with stunning graphics and relaxing sounds. You can download the app for free from the App Store or Google Play.</td>
-<td></td>
-</tr>
-<tr>
-<td>Solitaire by Zynga</td>
-<td>An app that offers a fun and social version of Klondike Solitario, with leaderboards, achievements, and multiplayer modes. You can download the app for free from the App Store or Google Play.</td>
-<td></td>
-</tr>
-</table>
- <h3>The steps and tips to download and install Solitario gratis</h3>
-<p>To download and install Solitario gratis, you need to follow these steps:</p>
-<ol>
-<li>Choose the website or app that you want to download Solitario from.</li>
-<li>Click on the link or button that says "Download", "Install", "Get", or something similar.</li>
-<li>Follow the instructions on the screen to complete the download and installation process.</li>
-<li>Launch the Solitario game and enjoy playing it.</li>
-</ol>
- <p>Here are some tips to make sure that you download and install Solitario safely and successfully:</p>
- <ul>
-<li>Make sure that you have enough space on your device to store the Solitario game.</li>
-<li>Make sure that your device meets the minimum requirements to run the Solitario game smoothly.</li>
-<li>Make sure that you have a reliable internet connection to download and update the Solitario game.</li>
-<li>Make sure that you download Solitario from trusted and reputable sources, such as official websites or app stores.</li>
-<li>Make sure that you scan your device for viruses or malware before and after downloading Solitario.</li>
-<li>Make sure that you read the terms and conditions, privacy policy, and reviews of the Solitario game before downloading it.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Solitario is a classic card game that can provide you with hours of entertainment and relaxation. You can play it online for free, without having to download or install anything. However, if you want to have more options and features, you can also download Solitario gratis from various websites and apps. You just need to follow some simple steps and tips to ensure a safe and successful download and installation. Whether you play Solitario online or offline, you will surely enjoy this timeless game.</p>
- <p>If you liked this article, please share it with your friends and family who might also be interested in playing Solitario. Also, feel free to leave a comment below if you have any questions or feedback about Solitario. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>Q1: Is Solitario good for your brain?</h3>
- <p>A1: Yes, playing Solitario can be good for your brain. It can help you improve your concentration, memory, logic, problem-solving, and decision-making skills. It can also reduce your stress levels, boost your mood, and prevent boredom.</p>
- <h3>Q2: How can I win more games of Solitario?</h3>
- <p>A2: There is no definitive answer to this question, as winning Solitario depends on luck, skill and strategy. However, here are some general tips that might help you win more games of Solitario:</p>
-<ul>
-<li>Plan ahead and think carefully before making a move. Try to anticipate the consequences and outcomes of your actions.</li>
-<li>Use the undo or hint features if you are unsure or stuck. They can help you avoid mistakes or find better moves.</li>
-<li>Focus on the cards that are face down or hidden. They are the ones that can block your progress or reveal new opportunities.</li>
-<li>Move the cards to the foundations as soon as possible. This can free up space and reduce the number of cards you have to deal with.</li>
-<li>Balance the cards between the columns. Try to keep them as even as possible, so that you have more options and flexibility.</li>
-</ul>
- <h3>Q3: Can I play Solitario offline?</h3>
- <p>A3: Yes, you can play Solitario offline if you download it from a website or an app that allows offline play. You just need to make sure that you have enough space on your device and that you have installed the game properly. However, playing Solitario offline might limit some of the features and options that are available online, such as updates, leaderboards, multiplayer modes, and support.</p>
- <h3>Q4: What are the best strategies for Solitario?</h3>
- <p>A4: The best strategies for Solitario depend on the type and variation of Solitario that you are playing, as well as your personal preference and style. However, here are some general strategies that might work for most Solitario games:</p>
-<ul>
-<li>Expose the hidden cards. Try to turn over as many cards as possible, especially the ones in the lower rows or columns.</li>
-<li>Build down the tableau. Try to create sequences of cards in descending order and alternating colors on the tableau.</li>
-<li>Build up the foundations. Try to move the Aces and low cards to the foundations as soon as possible, and then build up each suit in ascending order.</li>
-<li>Use the stock and waste wisely. Try to draw from the stock only when you need more cards or when you have no other moves. Try to use the waste cards as soon as possible, before they get buried by new cards.</li>
-<li>Use the free cells or spaces strategically. Try to use them to store cards that are blocking your progress or that can help you create sequences or move cards to the foundations.</li>
-</ul>
- <h3>Q5: How can I customize my Solitario game?</h3>
- <p>A5: You can customize your Solitario game by changing the settings and options of the game according to your preferences. For example, you can choose your own card design, deck style, table layout, background image, speed, sound, animation, color, language, and more. You can also create your own rules and challenges by adjusting the difficulty level, number of decks, number of suits, number of draws, scoring system, and more.</p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Download Car Parking Multiplayer APK Cheat for Free and Enjoy the Ultimate Driving Simulation.md b/spaces/fatiXbelha/sd/Download Car Parking Multiplayer APK Cheat for Free and Enjoy the Ultimate Driving Simulation.md
deleted file mode 100644
index e2b18d8b476cc125e615934505cd1db72c0ba659..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Car Parking Multiplayer APK Cheat for Free and Enjoy the Ultimate Driving Simulation.md	
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-<h1>Car Parking Multiplayer APK Cheat: How to Get Unlimited Money and Unlock All Cars</h1>
-<p>If you are a fan of driving and parking simulation games, you might have heard of Car Parking Multiplayer. This is a popular game that offers more than just parking. You can explore an open-world environment with real gas stations and car services, compete against real players in multiplayer racing, exchange cars with other players, customize your car's appearance and performance, and much more. The game has over 100 cars with realistic interiors, 16 player skins, and various buildings with interiors. You can also choose from different vehicles such as tow trucks, pickups, trucks, sports cars, and classic cars.</p>
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-<p>However, as fun as the game is, it can also be quite challenging and frustrating at times. You need to earn money to buy new cars, upgrade your existing ones, unlock new maps and modes, and so on. This can take a lot of time and effort, especially if you are not very good at parking or driving. You might also feel left out or disadvantaged when you see other players with better cars and skills than you.</p>
-<p>That's why some players resort to using a cheat for Car Parking Multiplayer. A cheat is a modded version of the game that gives you unlimited money and unlocks all cars and skins. With this cheat, you can enjoy the game without any limitations or restrictions. You can buy any car you want, upgrade it to the max, access all maps and modes, and have more fun and excitement in the game.</p>
-<p>But how do you get this cheat? And what are the benefits and risks of using it? In this article, we will answer these questions and more. We will show you how to download and install Car Parking Multiplayer APK cheat, what features it offers, what advantages it gives you, what dangers it poses, and whether you should use it or not. Read on to find out more.</p>
- <h2>Features of Car Parking Multiplayer</h2>
-<p>Before we get into the cheat, let's first take a look at some of the features of Car Parking Multiplayer. This game has a lot to offer for driving and parking enthusiasts. Here are some of the features that make this game stand out:</p>
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- <h3>Car customization and tuning</h3>
-<p>Another feature that makes Car Parking Multiplayer fun is its car customization and tuning feature. In this game, you can choose from over 100 cars with realistic interiors and exteriors. You can also modify your car's appearance and performance to suit your preferences and needs. You can change the color, wheels, suspension, engine, turbo, gearbox, exhaust, and more. You can also add stickers, spoilers, neon lights, and other accessories to make your car look more stylish and unique.</p>
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-<p>Car Parking Multiplayer also boasts of high-quality graphics and sound that make the game more immersive and realistic. The game has 3D models of cars and buildings with detailed textures and shadows. The game also has realistic physics and damage effects that make the driving and parking experience more challenging and fun. The game also has realistic sound effects of engines, brakes, horns, tires, and more. You can also listen to your own music or radio stations while driving.</p>
- <h3>Interesting gameplay and challenges</h3>
-<p>Of course, Car Parking Multiplayer is not just about driving and parking. The game also has interesting gameplay and challenges that test your skills and knowledge. The game has over 75 levels of parking scenarios with different difficulty levels and objectives. You have to park your car in the designated spot without hitting any obstacles or other cars. You also have to follow the traffic rules and signs, such as speed limits, stop signs, traffic lights, etc. The game also has a realistic manual transmission system that requires you to use the clutch, gear shift, and handbrake properly.</p>
- <h2>How to Download and Install Car Parking Multiplayer APK Cheat</h2>
-<p>Now that you know some of the features of Car Parking Multiplayer, you might be wondering how to get the cheat that gives you unlimited money and unlocks all cars and skins. Well, it's not very hard to do, but you have to be careful and follow some steps. Here are the steps you need to take to download and install Car Parking Multiplayer APK cheat:</p>
- <h3>Step 1: Find a reliable source for the modded APK file</h3>
-<p>The first thing you need to do is find a reliable source for the modded APK file of Car Parking Multiplayer. This is a modified version of the original game file that has been hacked or altered to give you the cheat features. There are many websites and forums that offer such files, but not all of them are safe or trustworthy. Some of them may contain malware or viruses that can harm your device or steal your personal information. Some of them may also have outdated or incompatible versions of the game that may not work properly or cause errors.</p>
-<p>Therefore, you have to be careful and do some research before downloading any file from any source. You can check the reviews, ratings, comments, feedbacks, and testimonials of other users who have downloaded the file before. You can also scan the file with an antivirus or anti-malware program before opening it. You can also look for sources that have verified badges or certificates that indicate their authenticity and security.</p>
- <h3>Step 2: Enable unknown sources on your device</h3>
-<p>The next thing you need to do is enable unknown sources on your device. This is a setting that allows you to install apps from sources other than the official Google Play Store or App Store. Since the modded APK file of Car Parking Multiplayer is not available on these stores, you have to enable this setting to install it on your device.</p>
-<p>To enable unknown sources on your device, you have to go to your device's settings and look for security or privacy options. There you will find an option called unknown sources or allow installation from unknown sources. You have to toggle this option on or check the box next to it. This will allow you to install apps from any source on your device.</p>
- <h3>Step 3: Download and install the APK file</h3>
-<p>The third thing you need to do is download and install the APK file of Car Parking Multiplayer cheat on your device. To do this, you have to go to the source where you found the file and click on the download button or link. This will start downloading the file on your device's storage. Depending on your internet speed and file size, this may take a few minutes or longer.</p>
-<p>Once the file is downloaded, you have to locate it on your device's storage using a file manager app or browser. Then you have to tap on the file name or icon to open it. This will prompt you to install the app on your device. You have to click on install or agree to proceed with the installation process. This may take a few seconds or minutes depending on your device's performance.</p>
- <h3>Step 4: Launch the game and enjoy the cheat</h3>
-<p>The final thing you need to do is launch the game and enjoy the cheat. To do this, you have to open the game app on your device and wait for it to load. You will see the game's main menu with different options such as play, garage, settings, etc. You will also notice that you have unlimited money and all cars and skins unlocked in the game. You can use this money to buy and upgrade any car you want, or change your car's appearance and performance. You can also access all maps and modes in the game without any restrictions or limitations. You can enjoy the game's features and challenges with more fun and excitement.</p>
- <h2>Benefits of Using Car Parking Multiplayer APK Cheat</h2>
-<p>Now that you know how to download and install Car Parking Multiplayer APK cheat, you might be wondering what benefits it gives you. Well, there are many benefits of using this cheat, such as:</p>
- <h3>Unlimited money to buy and upgrade any car you want</h3>
-<p>One of the main benefits of using Car Parking Multiplayer APK cheat is that it gives you unlimited money in the game. Money is the currency that you use to buy new cars, upgrade your existing ones, unlock new maps and modes, and so on. Normally, you have to earn money by completing parking levels, racing against other players, or doing other tasks in the game. This can take a lot of time and effort, especially if you are not very good at parking or driving. With the cheat, you don't have to worry about money anymore. You can buy any car you want, upgrade it to the max, and enjoy its features and performance.</p>
- <h3>Unlock all cars and skins without spending real money</h3>
-<p>Another benefit of using Car Parking Multiplayer APK cheat is that it unlocks all cars and skins in the game. Cars and skins are the items that you use to customize your car's appearance and style. There are over 100 cars with realistic interiors and exteriors in the game, and 16 player skins with different outfits and accessories. Normally, you have to unlock these items by spending real money or earning coins in the game. This can be quite expensive or tedious, especially if you want to collect all of them. With the cheat, you don't have to spend any money or coins anymore. You can unlock all cars and skins in the game without any hassle.</p>
- <h3>Access to all maps and modes without restrictions</h3>
-<p>A third benefit of using Car Parking Multiplayer APK cheat is that it gives you access to all maps and modes in the game without any restrictions. Maps and modes are the settings that you use to play the game in different environments and scenarios. There are various maps and modes in the game, such as city, airport, desert, snow, night, police chase, drift race, etc. Normally, you have to unlock these maps and modes by completing certain levels or tasks in the game. This can be quite challenging or boring, especially if you don't like some of them. With the cheat, you don't have to unlock anything anymore. You can access all maps and modes in the game without any limitations.</p>
- <h3>Have more fun and excitement in the game</h3>
-<p>A fourth benefit of using Car Parking Multiplayer APK cheat is that it makes the game more fun and exciting for you. The game is already fun and exciting on its own, but with the cheat, you can enhance your gaming experience even more. You can explore the open-world environment with more freedom and options. You can interact with other players with more confidence and competitiveness. You can customize your car with more creativity and variety. You can enjoy the game's features and challenges with more ease and satisfaction.</p>
- <h2>Risks of Using Car Parking Multiplayer APK Cheat</h2>
-<p>However, as much as there are benefits of using Car Parking Multiplayer APK cheat, there are also some risks involved. Using a cheat is not always safe or ethical, and it may have some negative consequences for you or others. Here are some of the risks of using Car Parking Multiplayer APK cheat:</p>
- <h3>Possible malware or virus infection from untrusted sources</h3>
-<p>One of the risks of using Car Parking Multiplayer APK cheat is that you may get malware or virus infection from untrusted sources. As we mentioned earlier, not all sources that offer modded APK files are safe or trustworthy. Some of them may contain malicious software or programs that can harm your device or steal your personal information. These malware or viruses may damage your device's system, delete your files, access your camera or microphone, track your location or activity, send spam messages or calls, etc.</p>
-<p>Therefore, you have to be careful and do some research before downloading any file from any source. You have to check the reviews, ratings, comments, feedbacks, and testimonials of other users who have downloaded the file before. You also have to scan the file with an antivirus or anti-malware program before opening it. You also have to look for sources that have verified badges or certificates that indicate their authenticity and security.</p>
- <h3>Potential ban or suspension from the game developers</h3>
-<p>Another risk of using Car Parking Multiplayer APK cheat is that you may get banned or suspended from the game developers. The game developers have the right and authority to monitor and regulate the game and its players. They may detect and identify any cheating or hacking activities in the game and take appropriate actions against them. They may ban or suspend your account or device from accessing or playing the game. They may also delete your game progress or data if they find it to be illegitimate or fraudulent.</p>
-<p>Therefore, you have to be aware and respectful of the game's rules and policies. You have to understand that using a cheat is a violation of the game's terms of service and code of conduct. You have to accept the consequences of your actions if you get caught or reported by the game developers or other players.</p>
- <h3>Loss of game progress or data if the cheat is not compatible</h3>
-<p>A third risk of using Car Parking Multiplayer APK cheat is that you may lose your game progress or data if the cheat is not compatible with your device or the game's version. The cheat is a modded version of the game that has been hacked or altered to give you the cheat features. However, this also means that it may not be compatible with your device's system or the game's updates. It may cause errors, glitches, crashes, or freezes in the game. It may also corrupt your game files, overwrite your game data, or erase your game progress.</p>
-<p>Therefore, you have to be careful and backup your game progress or data before using the cheat. You have to make sure that the cheat is compatible with your device and the game's version. You have to update the cheat regularly to match the game's updates. You also have to uninstall the cheat properly if you want to switch back to the original game.</p>
- <h3>Ethical and moral issues of cheating in a multiplayer game</h3>
-<p>A fourth risk of using Car Parking Multiplayer APK cheat is that it may raise some ethical and moral issues of cheating in a multiplayer game. The game is a multiplayer game that involves interacting and competing with other players in real time. By using a cheat, you are gaining an unfair advantage over other players who are playing fairly and honestly. You are also ruining the fun and excitement of the game for yourself and others. You are also disrespecting the game developers who have worked hard to create and maintain the game.</p>
-<p>Therefore, you have to be responsible and considerate of your actions and their impacts on others. You have to think about how you would feel if someone else cheated against you in the game. You have to respect the game and its players as a form of entertainment and socialization. You have to play the game for its own sake and not for external rewards or recognition.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Car Parking Multiplayer APK cheat is a modded version of the game that gives you unlimited money and unlocks all cars and skins in the game. It can give you many benefits, such as more freedom, options, creativity, ease, and satisfaction in the game. However, it can also pose some risks, such as malware or virus infection, ban or suspension, loss of progress or data, and ethical or moral issues.</p>
-<p>Therefore, you have to weigh the pros and cons of using this cheat carefully before deciding whether to use it or not. You also have to follow some steps to download and install this cheat safely and properly on your device. You also have to be aware and respectful of the game's rules and policies, as well as other players' feelings and opinions.</p>
-<p>We hope this article has helped you understand more about Car Parking Multiplayer APK cheat and how to use it. If you have any questions or comments, feel free to leave them below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Car Parking Multiplayer APK cheat:</p>
- <h3>Q: Is Car Parking Multiplayer APK cheat free?</h3>
-<p>A: Yes, Car Parking Multiplayer APK cheat is free to download and use. However, you may need to pay for some premium features or services from some sources.</p>
- <h3>Q: Is Car Parking Multiplayer APK cheat safe?</h3>
-<p>A: Not necessarily. Car Parking Multiplayer APK cheat may contain malware or viruses that can harm your device or steal your personal information. It may also cause errors, glitches, crashes, or freezes in the game. It may also corrupt your game files, overwrite your game data, or erase your game progress. It may also get you banned or suspended from the game developers. Therefore, you have to be careful and do some research before downloading and installing Car Parking Multiplayer APK cheat.</p>
- <h3>Q: How do I update Car Parking Multiplayer APK cheat?</h3>
-<p>A: To update Car Parking Multiplayer APK cheat, you have to download and install the latest version of the modded APK file from a reliable source. You also have to uninstall the previous version of the cheat properly before installing the new one. You also have to backup your game progress or data before updating the cheat.</p>
- <h3>Q: Can I use Car Parking Multiplayer APK cheat on iOS devices?</h3>
-<p>A: No, Car Parking Multiplayer APK cheat is only compatible with Android devices. You cannot use it on iOS devices such as iPhones or iPads. If you want to use a cheat for Car Parking Multiplayer on iOS devices, you may need to use a different method or tool, such as jailbreaking or using a third-party app.</p>
- <h3>Q: Can I play Car Parking Multiplayer online with other players while using the cheat?</h3>
-<p>A: Yes, you can play Car Parking Multiplayer online with other players while using the cheat. However, this may not be a good idea, as you may get caught or reported by other players or the game developers. You may also ruin the fun and excitement of the game for yourself and others. You may also face some ethical and moral issues of cheating in a multiplayer game. Therefore, we recommend that you use the cheat only for offline or single-player mode, or use it sparingly and discreetly for online or multiplayer mode.</p> 401be4b1e0<br />
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-<h1>Download Lightroom 5: A Guide for Photographers</h1>
-<p>If you are a photographer who wants to edit, organize, and share your photos with ease, you might be interested in downloading Lightroom 5. Lightroom 5 is a powerful photo editing software that offers many features and tools to help you create stunning images. In this article, we will explain what Lightroom 5 is, why you should use it, how to download and install it, and how to use it to edit your photos.</p>
-<h2>download lightroom 5</h2><br /><p><b><b>DOWNLOAD</b> &#10004;&#10004;&#10004; <a href="https://urllie.com/2uNyz1">https://urllie.com/2uNyz1</a></b></p><br /><br />
-<h2>What is Lightroom 5 and why you should use it</h2>
-<p>Lightroom 5 is a software developed by Adobe that allows you to edit, organize, and share your photos in one place. It is part of the Adobe Creative Cloud suite, which means you can access it from any device and sync your photos across different platforms. Lightroom 5 is designed for photographers who want to have more control over their images, without having to deal with complex editing tools or file formats. You can use Lightroom 5 to:</p>
-<ul>
-<li>Enhance the colors, contrast, exposure, sharpness, and other aspects of your photos.</li>
-<li>Crop, rotate, straighten, resize, and remove unwanted objects from your photos.</li>
-<li>Apply filters, presets, and profiles to give your photos a unique look.</li>
-<li>Create masks and gradients to make local adjustments to specific areas of your photos.</li>
-<li>Import and organize your photos by date, location, keywords, ratings, and other criteria.</li>
-<li>Create collections and albums to group your photos by theme or project.</li>
-<li>Export and share your photos to social media, email, web galleries, or print.</li>
-<li>Edit videos and create slide shows with music and transitions.</li>
-</ul>
-<p>Lightroom 5 is a great software for photographers who want to improve their workflow and enhance their creativity. It is easy to use, fast, and reliable. It also supports a wide range of cameras and file formats, so you can work with any photo you have.</p>
-<h3>Main features of Lightroom 5</h3>
-<p>Lightroom 5 has many features that make it stand out from other photo editing software. Here are some of the main features that you should know about:</p>
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-<h4>Spot Removal tool</h4>
-<p>The Spot Removal tool allows you to remove dust spots, blemishes, or any other unwanted objects from your photos. You can use it as a brush or a circle, and adjust the size, feather, opacity, and clone/heal mode. You can also visualize the spots by clicking on the Visualize Spots checkbox. The Spot Removal tool is much improved in Lightroom 5 compared to previous versions.</p>
-<h4>Radial Filter tool</h4>
-<p>The Radial Filter tool lets you create circular or elliptical masks to apply adjustments to specific areas of your photos. You can use it to create vignettes, highlight subjects, or add creative effects. You can adjust the size, shape, feather, invert mask option, and edit settings of the Radial Filter tool. The Radial Filter tool joins the Graduated Filter tool in Lightroom 5 as a way to make local adjustments.</p>
-<h4>Upright tool</h4>
-<p>The Upright tool is a new feature in Lightroom 5 that helps you correct perspective distortions in your photos. You can use it to straighten horizons, fix tilted buildings, or adjust skewed lines. You can choose from four modes: Auto, Level, Vertical, or Full. The Upright tool automatically analyzes your photo and applies the best correction. You can also fine-tune the results with the manual sliders. The Upright tool is located in the Lens Corrections panel in Lightroom 5.</p>
-<h4>Smart Previews</h4>
-<p>Smart Previews are a new feature in Lightroom 5 that allows you to work with your photos even when they are not connected to your computer. Smart Previews are smaller versions of your original photos that are stored in your catalog. You can edit, organize, and export Smart Previews as if they were the original photos. When you reconnect your original photos, Lightroom 5 will automatically apply the changes you made to the Smart Previews. Smart Previews are useful for working on the go, saving disk space, or backing up your photos.</p>
-<h4>Video editing and slide shows</h4>
-<p>Lightroom 5 also offers some features for working with videos and creating slide shows. You can import, organize, and view videos in Lightroom 5, as well as apply basic adjustments such as white balance, exposure, contrast, and saturation. You can also trim, crop, rotate, and capture still frames from your videos. You can export your videos to various formats and share them online.</p>
-<p>For creating slide shows, Lightroom 5 has a dedicated module where you can choose from different templates, add music, transitions, text, and watermarks. You can preview and play your slide shows in Lightroom 5, or export them as video files or PDF files.</p>
-<h3>How to download and install Lightroom 5</h3>
-<p>If you are interested in downloading and installing Lightroom 5, here are the steps you need to follow:</p>
-<h4>System requirements</h4>
-<p>Before you download and install Lightroom 5, make sure your computer meets the minimum system requirements. According to Adobe, these are the system requirements for Lightroom 5:</p>
-<table>
-<tr><th>Operating system</th><th>Processor</th><th>RAM</th><th>Hard disk space</th><th>Display resolution</th></tr>
-<tr><td>Windows 7 with Service Pack 1 or Windows 8</td><td>Intel Pentium 4 or AMD Athlon 64 processor</td><td>2 GB</td><td>2 GB of available hard-disk space</td><td>1024 x 768</td></tr>
-<tr><td>Mac OS X v10.7 or v10.8</td><td>Multicore Intel processor with 64-bit support</td><td>2 GB</td><td>2 GB of available hard-disk space</td><td>1024 x 768</td></tr>
-</table>
-<p>You also need an Internet connection and a valid Adobe ID to activate and use Lightroom 5.</p>
-<h4>Download links</h4>
-<p>To download Lightroom 5, you have two options: you can either buy it as a standalone product or subscribe to it as part of the Adobe Creative Cloud plan. The standalone product costs $149 for new users or $79 for upgrades from previous versions. The Creative Cloud plan costs $9.99 per month and includes Lightroom 5, Photoshop CC, and other benefits.</p>
-<p>You can download Lightroom 5 from the following links:</p>
-<ul>
-<li>[Buy Lightroom 5 as a standalone product]</li>
-<li>[Subscribe to Lightroom 5 as part of the Creative Cloud plan]</li>
-<li>[Download a free trial of Lightroom 5]</li>
-</ul>
-<h4>Installation steps</h4>
-<p>To install Lightroom 5, follow these steps:</p>
-<ol>
-<li>After downloading the file, double-click it to launch the installer.</li>
-<li>Follow the on-screen instructions to complete the installation process.</li>
-<li>If prompted, sign in with your Adobe ID and password.</li>
-<li>If you bought Lightroom 5 as a standalone product, enter your serial number when asked.</li>
-<li>If you subscribed to Lightroom 5 as part of the Creative Cloud plan, your subscription will be verified automatically.</li>
-<li>After the installation is finished, launch Lightroom 5 from your desktop or start menu.</li>
-<li>Enjoy editing your photos with Lightroom 5!</li>
-</ol>
- <h3>How to use Lightroom 5 to edit your photos</h3>
- <p>Now that you have downloaded and installed Lightroom 5, you are ready to use it to edit your photos. Here are some basic steps to get you started:</p>
- <h4>Import and organize your photos</h4>
-<p>The first step to use Lightroom 5 is to import your photos into the software. You can import photos from your computer, camera, memory card, or external drive. To import photos, follow these steps:</p>
-<ol>
-<li>Click on the Import button in the bottom left corner of the Library module.</li>
-<li>Select the source of your photos from the left panel. You can browse your folders or devices, or drag and drop files into the import window.</li>
-<li>Choose the import method from the top panel. You can copy, move, add, or convert your photos to DNG format.</li>
-<li>Choose the destination of your photos from the right panel. You can select a folder, create a subfolder, rename your files, and apply presets or metadata.</li>
-<li>Click on the Import button in the bottom right corner to start importing your photos.</li>
-</ol>
-<p>After importing your photos, you can organize them in various ways. You can use the Library module to view, sort, filter, rate, flag, label, and keyword your photos. You can also create collections and albums to group your photos by theme or project. You can use the Map module to view and tag your photos by location. You can use the Book module to create photo books with different layouts and styles.</p>
-<h4>Apply presets and profiles</h4>
-<p>One of the easiest ways to edit your photos in Lightroom 5 is to apply presets and profiles. Presets and profiles are predefined settings that give your photos a certain look or style. You can use them to enhance the colors, contrast, exposure, and other aspects of your photos. You can also create your own presets and profiles, or download them from other sources.</p>
-<p>To apply presets and profiles, follow these steps:</p>
-<ol>
-<li>Select the photo or photos you want to edit in the Library module.</li>
-<li>Switch to the Develop module by clicking on the Develop button in the top right corner.</li>
-<li>Expand the Presets panel on the left side of the screen. You will see different categories of presets, such as Color, B&W, Creative, etc.</li>
-<li>Click on a preset category to see the available presets. Hover over a preset name to see a preview of its effect on your photo.</li>
-<li>Click on a preset name to apply it to your photo. You will see the changes in the main window and in the History panel.</li>
-<li>If you want to apply a profile instead of a preset, expand the Profile Browser panel on the right side of the screen. You will see different categories of profiles, such as Adobe Raw, Camera Matching, Artistic, etc.</li>
-<li>Click on a profile category to see the available profiles. Hover over a profile name to see a preview of its effect on your photo.</li>
-<li>Click on a profile name to apply it to your photo. You will see the changes in the main window and in the Profile panel.</li>
-</ol>
-<p>You can adjust the strength of a preset or profile by using the Amount slider in the Preset or Profile panel. You can also fine-tune the other settings in the Basic, Tone Curve, HSL/Color, Detail, and other panels. You can also undo or redo any changes you made by using the History panel.</p>
-<h4>Use masking tools to make local adjustments</h4>
-<p>Another way to edit your photos in Lightroom 5 is to use masking tools to make local adjustments. Local adjustments are changes that affect only a specific area of your photo, rather than the whole photo. You can use masking tools to create masks or selections that define the area you want to edit. You can then apply different settings to that area, such as exposure, contrast, clarity, saturation, etc.</p>
-<p>To use masking tools, follow these steps:</p>
-<ol>
-<li>Select the photo or photos you want to edit in the Library module.</li>
-<li>Switch to the Develop module by clicking on the Develop button in the top right corner.</li>
-<li>Select a masking tool from the toolbar below the main window. You can choose from the Adjustment Brush, the Radial Filter, or the Graduated Filter.</li>
-<li>Adjust the size, feather, flow, and density of the masking tool by using the sliders or keyboard shortcuts.</li>
-<li>Click and drag on your photo to create a mask or a selection. You can add or subtract from your mask by using the A and B brushes or holding down the Alt/Option key.</li>
-<li>Expand the Edit panel on the right side of the screen. You will see different settings that you can apply to your mask or selection, such as Temperature, Tint, Exposure, Contrast, etc.</li>
-<li>Adjust the settings as you like. You will see the changes in the main window and in the Mask panel.</li>
-<li>If you want to create another mask or selection, click on the New button in the Mask panel and repeat steps 4-7.</li>
-</ol>
-<p>You can also use the Spot Removal tool or the Red Eye Correction tool to make local adjustments. The Spot Removal tool allows you to remove dust spots, blemishes, or any other unwanted objects from your photos. The Red Eye Correction tool allows you to fix red eyes caused by flash photography.</p>
-<h4>Export and share your photos</h4>
-<p>The final step to use Lightroom 5 is to export and share your photos. You can export your photos to different formats and sizes, depending on your purpose. You can also share your photos to social media, email, web galleries, or print.</p>
-<p>To export and share your photos, follow these steps:</p>
-<ol>
-<li>Select the photo or photos you want to export or share in the Library module.</li>
-<li>Click on the Export button in the bottom left corner of the screen. You will see a dialog box with different options for exporting your photos.</li>
-<li>Choose a location and a name for your exported files. You can also create a subfolder or rename your files.</li>
-<li>Choose a file format and a quality for your exported files. You can also resize, sharpen, watermark, or add metadata to your files.</li>
-<li>Click on the Export button in the bottom right corner of the dialog box to start exporting your photos.</li>
-<li>If you want to share your photos instead of exporting them, click on the Share button in the bottom left corner of the screen. You will see different options for sharing your photos, such as Facebook, Flickr, Email, etc.</li>
-<li>Choose a platform or a method for sharing your photos. You will see a dialog box with different options for sharing your photos, such as title, caption, privacy, etc.</li>
-<li>Click on the Share button in the bottom right corner of the dialog box to start sharing your photos.</li>
-</ol>
-<h2>Conclusion and FAQs</h2>
-<p>In this article, we have explained what Lightroom 5 is, why you should use it, how to download and install it, and how to use it to edit your photos. We hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below.</p>
-<p>Here are some FAQs that you might have about Lightroom 5:</p>
-<ul>
-<li><b>Q: How can I update Lightroom 5 to the latest version?</b></li>
-<li>A: You can update Lightroom 5 by using the Adobe Creative Cloud app or by downloading the update from the Adobe website. To use the Creative Cloud app, open it and click on the Updates tab. You will see a list of available updates for your software. Click on the Update button next to Lightroom 5 to start updating it. To download the update from the website, go to [this link] and choose your operating system and language. You will see a list of updates for Lightroom 5. Click on the Download button next to the latest update to download it. Then, run the installer and follow the instructions to complete the update.</li>
-<li><b>Q: How can I get help or support for Lightroom 5?</b></li>
-<li>A: You can get help or support for Lightroom 5 by using the Help menu in the software, by visiting the Adobe website, or by contacting Adobe customer service. To use the Help menu, click on the Help button in the top right corner of the screen. You will see different options for getting help, such as User Guide, Tutorials, Forums, etc. To visit the website, go to [this link] and choose your topic or issue. You will see different resources for getting help, such as articles, videos, blogs, etc. To contact customer service, go to [this link] and choose your region and product. You will see different options for contacting customer service, such as phone, chat, email, etc.</li>
-<li><b>Q: How can I uninstall Lightroom 5 from my computer?</b></li>
-<li>A: You can uninstall Lightroom 5 from your computer by using the Control Panel (Windows) or the Finder (Mac). To use the Control Panel, open it and click on Programs and Features. You will see a list of programs installed on your computer. Find Lightroom 5 and click on it. Then, click on the Uninstall button and follow the instructions to complete the uninstallation. To use the Finder, open it and go to Applications. You will see a list of applications installed on your computer. Find Lightroom 5 and drag it to the Trash. Then, empty the Trash and restart your computer.</li>
-<li><b>Q: How can I learn more about Lightroom 5?</b></li>
-<li>A: You can learn more about Lightroom 5 by reading books, watching videos, taking courses, or joining communities related to Lightroom 5. Here are some examples of resources that you can use to learn more about Lightroom 5:</li>
-<ul>
-<li>[The Adobe Photoshop Lightroom 5 Book: The Complete Guide for Photographers] by Martin Evening</li>
-<li>[Adobe Photoshop Lightroom 5 - The Missing FAQ] by Victoria Bampton</li>
-<li>[Lightroom 5 Essential Training] by Chris Orwig</li>
-<li>[Lightroom 5 Crash Course] by Matt Kloskowski</li>
-<li>[Lightroom Killer Tips] by Scott Kelby</li>
-<li>[Lightroom Forums]</li>
-<li>[Lightroom Users Group]</li>
-</ul>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Epic Heroes War Mod APK The Ultimate Guide to Unlock All Heroes and Diamond.md b/spaces/fatiXbelha/sd/Epic Heroes War Mod APK The Ultimate Guide to Unlock All Heroes and Diamond.md
deleted file mode 100644
index 82ae278f5706aae09cb91231ccda60c6e310ac91..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Epic Heroes War Mod APK The Ultimate Guide to Unlock All Heroes and Diamond.md	
+++ /dev/null
@@ -1,92 +0,0 @@
-<br />
-<h1>Epic Heroes War - Premium Mod APK Unlock All Heroes and Diamond</h1>
-<p>If you are a fan of real-time strategy games, online side-scroller defense, and RPGs, then you will love Epic Heroes War. This is a unique game that combines all these genres in one epic adventure. You can build up a powerful army and slaughter enemy hordes in quests and battles with other players. You can also collect hundreds of heroes with unique skills and items, and create your own legend in this fantasy world.</p>
-<h2>What is Epic Heroes War?</h2>
-<p>Epic Heroes War is a game developed by DIVMOB, a studio that specializes in creating high-quality mobile games. The game was released in 2014 and has since gained millions of downloads and positive reviews from players around the world. The game is available for both Android and iOS devices, and you can play it for free with some optional in-app purchases.</p>
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-<h3>Features of Epic Heroes War</h3>
-<p>Some of the features that make Epic Heroes War stand out from other games are:</p>
-<ul>
-<li>A unique game that blends real-time strategy, online side-scroller defense, and RPG elements.</li>
-<li>Lot of heroes with unique skills and items to collect and customize.</li>
-<li>Lot of campaign levels, online PvP, boss fights, guild wars, and more to challenge your skills and strategy.</li>
-<li>Stunning graphics, animations, sound effects, and music that create an immersive gaming experience.</li>
-<li>Regular updates with new content, events, heroes, items, and more to keep you entertained.</li>
-</ul>
-<h3>Why download the premium mod apk?</h3>
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-<ul>
-<li>No ads or pop-ups to interrupt your gameplay.</li>
-<li>No root or jailbreak required to install the mod apk.</li>
-<li>No risk of getting banned or detected by the game servers.</li>
-<li>Compatible with most Android devices.</li>
-</ul>
-<h2>How to download and install Epic Heroes War premium mod apk?</h2>
-<p>If you are interested in downloading and installing Epic Heroes War premium mod apk on your device, then follow these simple steps:</p>
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-<h3>Step 2: Enable unknown sources</h3>
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-<p>Once you have downloaded the apk file and enabled unknown sources, you can proceed to install the apk file on your device. To do this, locate the apk file in your file manager and tap on it. You will see a prompt asking you to confirm the installation. Tap on Install and wait for the process to complete.</p>
-<h3>Step 4: Launch the game and enjoy</h3>
-<p>After the installation is done, you can launch the game from your app drawer or home screen. You will see a new icon with the name Epic Heroes War Mod. Tap on it and you will be greeted by the game's main menu. You will notice that you have unlimited gems, gold, diamonds, and other resources in your account. You can use them to unlock all heroes and items, upgrade them to the max level, and dominate the game with ease.</p>
-<h2>Tips and tricks for playing Epic Heroes War</h2>
-<p>Now that you have installed Epic Heroes War premium mod apk on your device, you are ready to play the game and have fun. However, if you want to improve your skills and strategy, here are some tips and tricks that you can follow:</p>
-<h3>Choose your hero wisely</h3>
-<p>Epic Heroes War has hundreds of heroes with different skills and abilities. You can choose one hero as your main character and four others as your support units. Each hero has a unique role such as tank, healer, damage dealer, buffer, debuffer, etc. You should choose your hero based on your play style and preference. For example, if you like to deal massive damage, you can choose a hero like Zeus or Thor. If you like to heal and support your allies, you can choose a hero like Athena or Freya.</p>
-<h3>Upgrade your hero and items</h3>
-<p>As you play the game, you will earn gold, gems, diamonds, and other resources that you can use to upgrade your hero and items. Upgrading your hero will increase their stats, skills, and power. Upgrading your items will enhance their effects and bonuses. You should always upgrade your hero and items whenever you can to make them stronger and more effective in battle.</p>
-<h3>Use your skills strategically</h3>
-<p>Each hero has four skills that they can use in battle. These skills have different effects such as damage, heal, buff, debuff, stun, etc. You should use your skills strategically depending on the situation and the enemy. For example, if you are facing a boss with high defense, you can use a skill that lowers their defense or ignores it. If you are facing a group of enemies with low health, you can use a skill that deals area damage or hits multiple targets.</p>
-<h3>Join a guild and participate in events</h3>
-<p>Epic Heroes War is not only a single-player game but also a multiplayer game. You can join a guild and interact with other players from around the world. You can chat with them, exchange tips and tricks, request and donate resources, and more. You can also participate in various events such as guild wars, boss raids, tournaments, etc. These events will reward you with valuable prizes such as gems, diamonds, items, heroes, etc.</p>
-<h2>Conclusion</h2>
-<p>Epic Heroes War is an amazing game that combines real-time strategy, online side-scroller defense, and RPG elements in one epic adventure. You can build up a powerful army and slaughter enemy hordes in quests and battles with other players. You can also collect hundreds of heroes with unique skills and items, and create your own legend in this fantasy world.</p>
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-<li>A: Yes, Epic Heroes War premium mod apk is safe to use. It does not contain any viruses or malware that can harm your device or data. It also does not require root or jailbreak to install or run.</li>
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-<li><b>Q: What are the best heroes to use in Epic Heroes War?</b></li>
-<li>A: There is no definitive answer to this question, as different heroes have different strengths and weaknesses. You should choose your hero based on your play style, preference, and strategy. However, some of the most popular and powerful heroes in the game are Zeus, Thor, Athena, Freya, Odin, Loki, Hades, Poseidon, Hercules, and Apollo.</li>
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\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/audio2exp_models/networks.py b/spaces/fb700/chatglm-fitness-RLHF/src/audio2exp_models/networks.py
deleted file mode 100644
index f052e18101f5446a527ae354b3621e7d0d4991cc..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/audio2exp_models/networks.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-class Conv2d(nn.Module):
-    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.conv_block = nn.Sequential(
-                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
-                            nn.BatchNorm2d(cout)
-                            )
-        self.act = nn.ReLU()
-        self.residual = residual
-        self.use_act = use_act
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        
-        if self.use_act:
-            return self.act(out)
-        else:
-            return out
-
-class SimpleWrapperV2(nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.audio_encoder = nn.Sequential(
-            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
-            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
-            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
-            )
-
-        #### load the pre-trained audio_encoder 
-        #self.audio_encoder = self.audio_encoder.to(device)  
-        '''
-        wav2lip_state_dict = torch.load('/apdcephfs_cq2/share_1290939/wenxuazhang/checkpoints/wav2lip.pth')['state_dict']
-        state_dict = self.audio_encoder.state_dict()
-
-        for k,v in wav2lip_state_dict.items():
-            if 'audio_encoder' in k:
-                print('init:', k)
-                state_dict[k.replace('module.audio_encoder.', '')] = v
-        self.audio_encoder.load_state_dict(state_dict)
-        '''
-
-        self.mapping1 = nn.Linear(512+64+1, 64)
-        #self.mapping2 = nn.Linear(30, 64)
-        #nn.init.constant_(self.mapping1.weight, 0.)
-        nn.init.constant_(self.mapping1.bias, 0.)
-
-    def forward(self, x, ref, ratio):
-        x = self.audio_encoder(x).view(x.size(0), -1)
-        ref_reshape = ref.reshape(x.size(0), -1)
-        ratio = ratio.reshape(x.size(0), -1)
-        
-        y = self.mapping1(torch.cat([x, ref_reshape, ratio], dim=1)) 
-        out = y.reshape(ref.shape[0], ref.shape[1], -1) #+ ref # resudial
-        return out
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/replicate.py b/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/replicate.py
deleted file mode 100644
index b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/replicate.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : replicate.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import functools
-
-from torch.nn.parallel.data_parallel import DataParallel
-
-__all__ = [
-    'CallbackContext',
-    'execute_replication_callbacks',
-    'DataParallelWithCallback',
-    'patch_replication_callback'
-]
-
-
-class CallbackContext(object):
-    pass
-
-
-def execute_replication_callbacks(modules):
-    """
-    Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
-
-    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
-    Note that, as all modules are isomorphism, we assign each sub-module with a context
-    (shared among multiple copies of this module on different devices).
-    Through this context, different copies can share some information.
-
-    We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
-    of any slave copies.
-    """
-    master_copy = modules[0]
-    nr_modules = len(list(master_copy.modules()))
-    ctxs = [CallbackContext() for _ in range(nr_modules)]
-
-    for i, module in enumerate(modules):
-        for j, m in enumerate(module.modules()):
-            if hasattr(m, '__data_parallel_replicate__'):
-                m.__data_parallel_replicate__(ctxs[j], i)
-
-
-class DataParallelWithCallback(DataParallel):
-    """
-    Data Parallel with a replication callback.
-
-    An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
-    original `replicate` function.
-    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
-    Examples:
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-        # sync_bn.__data_parallel_replicate__ will be invoked.
-    """
-
-    def replicate(self, module, device_ids):
-        modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
-        execute_replication_callbacks(modules)
-        return modules
-
-
-def patch_replication_callback(data_parallel):
-    """
-    Monkey-patch an existing `DataParallel` object. Add the replication callback.
-    Useful when you have customized `DataParallel` implementation.
-
-    Examples:
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
-        > patch_replication_callback(sync_bn)
-        # this is equivalent to
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-    """
-
-    assert isinstance(data_parallel, DataParallel)
-
-    old_replicate = data_parallel.replicate
-
-    @functools.wraps(old_replicate)
-    def new_replicate(module, device_ids):
-        modules = old_replicate(module, device_ids)
-        execute_replication_callbacks(modules)
-        return modules
-
-    data_parallel.replicate = new_replicate
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bhop Pro zel Kasa Hileli Mod Apk - Efsanevi Silahlarnz Sein.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bhop Pro zel Kasa Hileli Mod Apk - Efsanevi Silahlarnz Sein.md
deleted file mode 100644
index 94e53c5b469963e62bfde94796a9b13b56b7a2d8..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bhop Pro zel Kasa Hileli Mod Apk - Efsanevi Silahlarnz Sein.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-<br />
-<h1>Bhop Pro Hile Apk: How to Get Unlimited Money and Cases in Bhop Pro</h1>
-<p>Do you love playing Bhop Pro, the realistic bunny hopping simulator for Android devices? Do you want to unlock all the cool skins, knives, gloves, and stickers in the game? Do you want to practice your skills without any restrictions or interruptions? If you answered yes to any of these questions, then you might be interested in Bhop Pro Hile Apk, a modified version of the game that gives you unlimited money and cases. In this article, we will tell you everything you need to know about Bhop Pro Hile Apk, including how to download and install it, how to use it, what are the benefits and risks of using it, and more. So, let's get started!</p>
- <h2>What is Bhop Pro?</h2>
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-<ol>
-<li>Open Bhop Pro from your app drawer and choose your preferred language.</li> <li>Select the offline mode or the online mode. If you choose the online mode, you may have to sign in with your Google account or create a new account.</li>
-<li>Choose the map, the mode, and the settings that you want to play with. You can also customize your character with different skins, knives, gloves, and stickers.</li>
-<li>Tap on the play button and start bunny hopping. You can use the virtual joystick and buttons to move, jump, crouch, and strafe. You can also use the gyroscope to tilt your device and control your movement.</li>
-<li>Have fun and improve your skills!</li>
-</ol>
- <h2>What are the benefits of using Bhop Pro Hile Apk?</h2>
-<p>Using Bhop Pro Hile Apk has many benefits for the players who love Bhop Pro. Here are some of them:</p>
-<ul>
-<li>You can unlock all the skins, knives, gloves, and stickers in the game without spending any real money or watching any ads. You can choose from hundreds of options to customize your character and make it look cool and unique.</li>
-<li>You can practice your bunny hopping skills without any limitations or interruptions. You can access all the maps, modes, and settings in the game without any restrictions. You can also adjust the difficulty level, the gravity, the speed, and other parameters to suit your preferences and needs.</li>
-<li>You can enjoy the game without any ads or in-app purchases. You don't have to worry about annoying pop-ups or banners that may distract you or ruin your experience. You also don't have to spend any extra money to get more coins or cases in the game.</li>
-</ul>
- <h2>What are the risks of using Bhop Pro Hile Apk?</h2>
-<p>Using Bhop Pro Hile Apk also has some risks that you should be aware of before downloading and installing it. Here are some of them:</p>
-<ul>
-<li>You may face some technical issues or bugs in the game. Since Bhop Pro Hile Apk is not an official version of Bhop Pro, it may not be compatible with your device or the latest updates of the game. You may encounter some errors, crashes, glitches, or performance issues while playing.</li>
-<li>You may get banned from the online mode or leaderboards. If you use Bhop Pro Hile Apk to play online with other players or to compete for the best scores on the leaderboards, you may be detected and reported by the game developers or other players. This may result in your account being suspended or deleted.</li>
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-</ul>
- <h2>Conclusion</h2>
-<p>Bhop Pro Hile Apk is a modified version of Bhop Pro that gives you unlimited money and cases in the game. It allows you to unlock all the skins, knives, gloves, and stickers in the game, practice your bunny hopping skills without any limitations, and enjoy the game without any ads or in-app purchases. However, it also has some risks such as technical issues, bans, and violations. Therefore, you should use it at your own discretion and responsibility. If you want to download and install Bhop Pro Hile Apk on your device, you can follow the steps we mentioned above. We hope this article was helpful for you. Thank you for reading!</p>
- <h3>Frequently Asked Questions</h3>
-<ol>
-<li><b>What is Bhop Pro?</b><br>Bhop Pro is a realistic bunny hopping simulator for Android devices that lets you practice your skills on different maps and modes.</li>
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-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffffu/bing/src/components/voice.tsx b/spaces/fffffu/bing/src/components/voice.tsx
deleted file mode 100644
index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000
--- a/spaces/fffffu/bing/src/components/voice.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import React, { useEffect } from 'react'
-import { useSetAtom } from 'jotai'
-import { useBing } from '@/lib/hooks/use-bing'
-import Image from 'next/image'
-import VoiceIcon from '@/assets/images/voice.svg'
-import VoiceButton from './ui/voice'
-import { SR } from '@/lib/bots/bing/sr'
-import { voiceListenAtom } from '@/state'
-
-const sr = new SR(['发送', '清空', '退出'])
-
-const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick<ReturnType<typeof useBing>, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => {
-  const setListen = useSetAtom(voiceListenAtom)
-  useEffect(() => {
-    if (sr.listening) return
-    sr.transcript = !isSpeaking
-  }, [isSpeaking])
-
-  useEffect(() => {
-    sr.onchange = (msg: string, command?: string) => {
-      switch (command) {
-        case '退出':
-          sr.stop()
-          break;
-        case '发送':
-          sendMessage(input)
-        case '清空':
-          setInput('')
-          break;
-        default:
-          setInput(input + msg)
-      }
-    }
-  }, [input])
-
-  const switchSR = (enable: boolean = false) => {
-    setListen(enable)
-    if (enable) {
-      sr.start()
-    } else {
-      sr.stop()
-    }
-  }
-
-  return sr.listening ? (
-    <VoiceButton onClick={() => switchSR(false)} />
-  ) : (
-    <Image alt="start voice" src={VoiceIcon} width={24} className="-mt-0.5" onClick={() => switchSR(true)} />
-  )
-};
-
-export default Voice;
diff --git a/spaces/fffiloni/Image-to-MusicGen/tests/__init__.py b/spaces/fffiloni/Image-to-MusicGen/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-to-MusicGen/tests/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/vary/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/vary/README.md
deleted file mode 100644
index cc000b34684a1d0b03d4c4cb4fac3e1094a81eec..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/vary/README.md
+++ /dev/null
@@ -1,101 +0,0 @@
-# vary
-
-[![NPM Version][npm-image]][npm-url]
-[![NPM Downloads][downloads-image]][downloads-url]
-[![Node.js Version][node-version-image]][node-version-url]
-[![Build Status][travis-image]][travis-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-Manipulate the HTTP Vary header
-
-## Installation
-
-This is a [Node.js](https://nodejs.org/en/) module available through the
-[npm registry](https://www.npmjs.com/). Installation is done using the
-[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally): 
-
-```sh
-$ npm install vary
-```
-
-## API
-
-<!-- eslint-disable no-unused-vars -->
-
-```js
-var vary = require('vary')
-```
-
-### vary(res, field)
-
-Adds the given header `field` to the `Vary` response header of `res`.
-This can be a string of a single field, a string of a valid `Vary`
-header, or an array of multiple fields.
-
-This will append the header if not already listed, otherwise leaves
-it listed in the current location.
-
-<!-- eslint-disable no-undef -->
-
-```js
-// Append "Origin" to the Vary header of the response
-vary(res, 'Origin')
-```
-
-### vary.append(header, field)
-
-Adds the given header `field` to the `Vary` response header string `header`.
-This can be a string of a single field, a string of a valid `Vary` header,
-or an array of multiple fields.
-
-This will append the header if not already listed, otherwise leaves
-it listed in the current location. The new header string is returned.
-
-<!-- eslint-disable no-undef -->
-
-```js
-// Get header string appending "Origin" to "Accept, User-Agent"
-vary.append('Accept, User-Agent', 'Origin')
-```
-
-## Examples
-
-### Updating the Vary header when content is based on it
-
-```js
-var http = require('http')
-var vary = require('vary')
-
-http.createServer(function onRequest (req, res) {
-  // about to user-agent sniff
-  vary(res, 'User-Agent')
-
-  var ua = req.headers['user-agent'] || ''
-  var isMobile = /mobi|android|touch|mini/i.test(ua)
-
-  // serve site, depending on isMobile
-  res.setHeader('Content-Type', 'text/html')
-  res.end('You are (probably) ' + (isMobile ? '' : 'not ') + 'a mobile user')
-})
-```
-
-## Testing
-
-```sh
-$ npm test
-```
-
-## License
-
-[MIT](LICENSE)
-
-[npm-image]: https://img.shields.io/npm/v/vary.svg
-[npm-url]: https://npmjs.org/package/vary
-[node-version-image]: https://img.shields.io/node/v/vary.svg
-[node-version-url]: https://nodejs.org/en/download
-[travis-image]: https://img.shields.io/travis/jshttp/vary/master.svg
-[travis-url]: https://travis-ci.org/jshttp/vary
-[coveralls-image]: https://img.shields.io/coveralls/jshttp/vary/master.svg
-[coveralls-url]: https://coveralls.io/r/jshttp/vary
-[downloads-image]: https://img.shields.io/npm/dm/vary.svg
-[downloads-url]: https://npmjs.org/package/vary
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/modules/chat_func.py b/spaces/fgbwyude/ChuanhuChatGPT/modules/chat_func.py
deleted file mode 100644
index 9ef204b662d11b5810521b2a1fc90d4cedabf677..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/modules/chat_func.py
+++ /dev/null
@@ -1,501 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import os
-import requests
-import urllib3
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from llama_index.indices.query.vector_store import GPTVectorStoreIndexQuery
-from llama_index.indices.query.schema import QueryBundle
-from langchain.llms import OpenAIChat
-
-from modules.presets import *
-from modules.llama_func import *
-from modules.utils import *
-import modules.shared as shared
-
-# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class DataframeData(TypedDict):
-        headers: List[str]
-        data: List[List[str | int | bool]]
-
-
-initial_prompt = "You are a helpful assistant."
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-def get_response(
-    openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
-):
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}",
-    }
-
-    history = [construct_system(system_prompt), *history]
-
-    payload = {
-        "model": selected_model,
-        "messages": history,  # [{"role": "user", "content": f"{inputs}"}],
-        "temperature": temperature,  # 1.0,
-        "top_p": top_p,  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    if stream:
-        timeout = timeout_streaming
-    else:
-        timeout = timeout_all
-
-    proxies = get_proxies()
-
-    # 如果有自定义的api-url，使用自定义url发送请求，否则使用默认设置发送请求
-    if shared.state.api_url != API_URL:
-        logging.info(f"使用自定义API URL: {shared.state.api_url}")
-
-    response = requests.post(
-        shared.state.api_url,
-        headers=headers,
-        json=payload,
-        stream=True,
-        timeout=timeout,
-        proxies=proxies,
-    )
-
-    return response
-
-
-def stream_predict(
-    openai_api_key,
-    system_prompt,
-    history,
-    inputs,
-    chatbot,
-    all_token_counts,
-    top_p,
-    temperature,
-    selected_model,
-    fake_input=None,
-    display_append=""
-):
-    def get_return_value():
-        return chatbot, history, status_text, all_token_counts
-
-    logging.info("实时回答模式")
-    partial_words = ""
-    counter = 0
-    status_text = "开始实时传输回答……"
-    history.append(construct_user(inputs))
-    history.append(construct_assistant(""))
-    if fake_input:
-        chatbot.append((fake_input, ""))
-    else:
-        chatbot.append((inputs, ""))
-    user_token_count = 0
-    if fake_input is not None:
-        input_token_count = count_token(construct_user(fake_input))
-    else:
-        input_token_count = count_token(construct_user(inputs))
-    if len(all_token_counts) == 0:
-        system_prompt_token_count = count_token(construct_system(system_prompt))
-        user_token_count = (
-            input_token_count + system_prompt_token_count
-        )
-    else:
-        user_token_count = input_token_count
-    all_token_counts.append(user_token_count)
-    logging.info(f"输入token计数: {user_token_count}")
-    yield get_return_value()
-    try:
-        response = get_response(
-            openai_api_key,
-            system_prompt,
-            history,
-            temperature,
-            top_p,
-            True,
-            selected_model,
-        )
-    except requests.exceptions.ConnectTimeout:
-        status_text = (
-            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
-        )
-        yield get_return_value()
-        return
-    except requests.exceptions.ReadTimeout:
-        status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
-        yield get_return_value()
-        return
-
-    yield get_return_value()
-    error_json_str = ""
-
-    if fake_input is not None:
-        history[-2] = construct_user(fake_input)
-    for chunk in response.iter_lines():
-        if counter == 0:
-            counter += 1
-            continue
-        counter += 1
-        # check whether each line is non-empty
-        if chunk:
-            chunk = chunk.decode()
-            chunklength = len(chunk)
-            try:
-                chunk = json.loads(chunk[6:])
-            except json.JSONDecodeError:
-                logging.info(chunk)
-                error_json_str += chunk
-                status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
-                yield get_return_value()
-                continue
-            # decode each line as response data is in bytes
-            if chunklength > 6 and "delta" in chunk["choices"][0]:
-                finish_reason = chunk["choices"][0]["finish_reason"]
-                status_text = construct_token_message(
-                    sum(all_token_counts), stream=True
-                )
-                if finish_reason == "stop":
-                    yield get_return_value()
-                    break
-                try:
-                    partial_words = (
-                        partial_words + chunk["choices"][0]["delta"]["content"]
-                    )
-                except KeyError:
-                    status_text = (
-                        standard_error_msg
-                        + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
-                        + str(sum(all_token_counts))
-                    )
-                    yield get_return_value()
-                    break
-                history[-1] = construct_assistant(partial_words)
-                chatbot[-1] = (chatbot[-1][0], partial_words+display_append)
-                all_token_counts[-1] += 1
-                yield get_return_value()
-
-
-def predict_all(
-    openai_api_key,
-    system_prompt,
-    history,
-    inputs,
-    chatbot,
-    all_token_counts,
-    top_p,
-    temperature,
-    selected_model,
-    fake_input=None,
-    display_append=""
-):
-    logging.info("一次性回答模式")
-    history.append(construct_user(inputs))
-    history.append(construct_assistant(""))
-    if fake_input:
-        chatbot.append((fake_input, ""))
-    else:
-        chatbot.append((inputs, ""))
-    if fake_input is not None:
-        all_token_counts.append(count_token(construct_user(fake_input)))
-    else:
-        all_token_counts.append(count_token(construct_user(inputs)))
-    try:
-        response = get_response(
-            openai_api_key,
-            system_prompt,
-            history,
-            temperature,
-            top_p,
-            False,
-            selected_model,
-        )
-    except requests.exceptions.ConnectTimeout:
-        status_text = (
-            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
-        )
-        return chatbot, history, status_text, all_token_counts
-    except requests.exceptions.ProxyError:
-        status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
-        return chatbot, history, status_text, all_token_counts
-    except requests.exceptions.SSLError:
-        status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
-        return chatbot, history, status_text, all_token_counts
-    response = json.loads(response.text)
-    if fake_input is not None:
-        history[-2] = construct_user(fake_input)
-    try:
-        content = response["choices"][0]["message"]["content"]
-        history[-1] = construct_assistant(content)
-        chatbot[-1] = (chatbot[-1][0], content+display_append)
-        total_token_count = response["usage"]["total_tokens"]
-        if fake_input is not None:
-            all_token_counts[-1] += count_token(construct_assistant(content))
-        else:
-            all_token_counts[-1] = total_token_count - sum(all_token_counts)
-        status_text = construct_token_message(total_token_count)
-        return chatbot, history, status_text, all_token_counts
-    except KeyError:
-        status_text = standard_error_msg + str(response)
-        return chatbot, history, status_text, all_token_counts
-
-
-def predict(
-    openai_api_key,
-    system_prompt,
-    history,
-    inputs,
-    chatbot,
-    all_token_counts,
-    top_p,
-    temperature,
-    stream=False,
-    selected_model=MODELS[0],
-    use_websearch=False,
-    files = None,
-    reply_language="中文",
-    should_check_token_count=True,
-):  # repetition_penalty, top_k
-    logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
-    if should_check_token_count:
-        yield chatbot+[(inputs, "")], history, "开始生成回答……", all_token_counts
-    if reply_language == "跟随问题语言（不稳定）":
-        reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
-    old_inputs = None
-    display_reference = []
-    limited_context = False
-    if files:
-        limited_context = True
-        old_inputs = inputs
-        msg = "加载索引中……（这可能需要几分钟）"
-        logging.info(msg)
-        yield chatbot+[(inputs, "")], history, msg, all_token_counts
-        index = construct_index(openai_api_key, file_src=files)
-        msg = "索引构建完成，获取回答中……"
-        logging.info(msg)
-        yield chatbot+[(inputs, "")], history, msg, all_token_counts
-        llm_predictor = LLMPredictor(llm=OpenAIChat(temperature=0, model_name=selected_model))
-        prompt_helper = PromptHelper(max_input_size = 4096, num_output = 5, max_chunk_overlap = 20, chunk_size_limit=600)
-        service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
-        query_object = GPTVectorStoreIndexQuery(index.index_struct, service_context=service_context, similarity_top_k=5, vector_store=index._vector_store, docstore=index._docstore)
-        query_bundle = QueryBundle(inputs)
-        nodes = query_object.retrieve(query_bundle)
-        reference_results = [n.node.text for n in nodes]
-        reference_results = add_source_numbers(reference_results, use_source=False)
-        display_reference = add_details(reference_results)
-        display_reference = "\n\n" + "".join(display_reference)
-        inputs = (
-            replace_today(PROMPT_TEMPLATE)
-            .replace("{query_str}", inputs)
-            .replace("{context_str}", "\n\n".join(reference_results))
-            .replace("{reply_language}", reply_language )
-        )
-    elif use_websearch:
-        limited_context = True
-        search_results = ddg(inputs, max_results=5)
-        old_inputs = inputs
-        reference_results = []
-        for idx, result in enumerate(search_results):
-            logging.info(f"搜索结果{idx + 1}：{result}")
-            domain_name = urllib3.util.parse_url(result["href"]).host
-            reference_results.append([result["body"], result["href"]])
-            display_reference.append(f"{idx+1}. [{domain_name}]({result['href']})\n")
-        reference_results = add_source_numbers(reference_results)
-        display_reference = "\n\n" + "".join(display_reference)
-        inputs = (
-            replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
-            .replace("{query}", inputs)
-            .replace("{web_results}", "\n\n".join(reference_results))
-            .replace("{reply_language}", reply_language )
-        )
-    else:
-        display_reference = ""
-
-    if len(openai_api_key) != 51:
-        status_text = standard_error_msg + no_apikey_msg
-        logging.info(status_text)
-        chatbot.append((inputs, ""))
-        if len(history) == 0:
-            history.append(construct_user(inputs))
-            history.append("")
-            all_token_counts.append(0)
-        else:
-            history[-2] = construct_user(inputs)
-        yield chatbot+[(inputs, "")], history, status_text, all_token_counts
-        return
-    elif len(inputs.strip()) == 0:
-        status_text = standard_error_msg + no_input_msg
-        logging.info(status_text)
-        yield chatbot+[(inputs, "")], history, status_text, all_token_counts
-        return
-
-    if stream:
-        logging.info("使用流式传输")
-        iter = stream_predict(
-            openai_api_key,
-            system_prompt,
-            history,
-            inputs,
-            chatbot,
-            all_token_counts,
-            top_p,
-            temperature,
-            selected_model,
-            fake_input=old_inputs,
-            display_append=display_reference
-        )
-        for chatbot, history, status_text, all_token_counts in iter:
-            if shared.state.interrupted:
-                shared.state.recover()
-                return
-            yield chatbot, history, status_text, all_token_counts
-    else:
-        logging.info("不使用流式传输")
-        chatbot, history, status_text, all_token_counts = predict_all(
-            openai_api_key,
-            system_prompt,
-            history,
-            inputs,
-            chatbot,
-            all_token_counts,
-            top_p,
-            temperature,
-            selected_model,
-            fake_input=old_inputs,
-            display_append=display_reference
-        )
-        yield chatbot, history, status_text, all_token_counts
-
-    logging.info(f"传输完毕。当前token计数为{all_token_counts}")
-    if len(history) > 1 and history[-1]["content"] != inputs:
-        logging.info(
-            "回答为："
-            + colorama.Fore.BLUE
-            + f"{history[-1]['content']}"
-            + colorama.Style.RESET_ALL
-        )
-
-    if limited_context:
-        history = history[-4:]
-        all_token_counts = all_token_counts[-2:]
-        yield chatbot, history, status_text, all_token_counts
-
-    if stream:
-        max_token = MODEL_SOFT_TOKEN_LIMIT[selected_model]["streaming"]
-    else:
-        max_token = MODEL_SOFT_TOKEN_LIMIT[selected_model]["all"]
-
-    if sum(all_token_counts) > max_token and should_check_token_count:
-        status_text = f"精简token中{all_token_counts}/{max_token}"
-        logging.info(status_text)
-        yield chatbot, history, status_text, all_token_counts
-        iter = reduce_token_size(
-            openai_api_key,
-            system_prompt,
-            history,
-            chatbot,
-            all_token_counts,
-            top_p,
-            temperature,
-            max_token//2,
-            selected_model=selected_model,
-        )
-        for chatbot, history, status_text, all_token_counts in iter:
-            status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
-            yield chatbot, history, status_text, all_token_counts
-
-
-def retry(
-    openai_api_key,
-    system_prompt,
-    history,
-    chatbot,
-    token_count,
-    top_p,
-    temperature,
-    stream=False,
-    selected_model=MODELS[0],
-    reply_language="中文",
-):
-    logging.info("重试中……")
-    if len(history) == 0:
-        yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
-        return
-    history.pop()
-    inputs = history.pop()["content"]
-    token_count.pop()
-    iter = predict(
-        openai_api_key,
-        system_prompt,
-        history,
-        inputs,
-        chatbot,
-        token_count,
-        top_p,
-        temperature,
-        stream=stream,
-        selected_model=selected_model,
-        reply_language=reply_language,
-    )
-    logging.info("重试中……")
-    for x in iter:
-        yield x
-    logging.info("重试完毕")
-
-
-def reduce_token_size(
-    openai_api_key,
-    system_prompt,
-    history,
-    chatbot,
-    token_count,
-    top_p,
-    temperature,
-    max_token_count,
-    selected_model=MODELS[0],
-    reply_language="中文",
-):
-    logging.info("开始减少token数量……")
-    iter = predict(
-        openai_api_key,
-        system_prompt,
-        history,
-        summarize_prompt,
-        chatbot,
-        token_count,
-        top_p,
-        temperature,
-        selected_model=selected_model,
-        should_check_token_count=False,
-        reply_language=reply_language,
-    )
-    logging.info(f"chatbot: {chatbot}")
-    flag = False
-    for chatbot, history, status_text, previous_token_count in iter:
-        num_chat = find_n(previous_token_count, max_token_count)
-        logging.info(f"previous_token_count: {previous_token_count}, keeping {num_chat} chats")
-        if flag:
-            chatbot = chatbot[:-1]
-        flag = True
-        history = history[-2*num_chat:] if num_chat > 0 else []
-        token_count = previous_token_count[-num_chat:] if num_chat > 0 else []
-        msg = f"保留了最近{num_chat}轮对话"
-        yield chatbot, history, msg + "，" + construct_token_message(
-            sum(token_count) if len(token_count) > 0 else 0,
-        ), token_count
-    logging.info(msg)
-    logging.info("减少token数量完毕")
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_65.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_65.py
deleted file mode 100644
index 0f5649ebd1ab6680f91d8a765f422e479783c03b..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_65.py
+++ /dev/null
@@ -1,28 +0,0 @@
-
-import re
-
-def is_spam(text):
-    # Check for common spam characteristics
-    spam_indicators = [
-        r"(광고)",  # 'Advertisement'
-        r"\d{1,2}월",  # 'Month'
-        r"\d{1,2}\%",  # 'Percentage'
-        r"www\.\w+\.com",  # 'URLs'
-        r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+",
-        r"무료수신거부",  # 'Free message rejection'
-        r"적중",  # 'Hit'
-        r"상한가",  # 'Upper limit(potentially stock market spam)'
-        r"\d{1,2}만원",  # Large amounts of money
-        r"금일",  # 'Today'
-    ]
-
-    # Compile the regular expressions
-    spam_patterns = [re.compile(indicator) for indicator in spam_indicators]
-
-    # Check each pattern for a match
-    for pattern in spam_patterns:
-        if pattern.search(text):
-            return True
-
-    # If none of the spam indicators were found, consider the message as not spam
-    return False
diff --git a/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/climbers/climbing_profile_chimpanzee.js b/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/climbers/climbing_profile_chimpanzee.js
deleted file mode 100644
index a2eaf66234b09a741b442dba94997cf9238f0d1d..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/climbers/climbing_profile_chimpanzee.js
+++ /dev/null
@@ -1,293 +0,0 @@
-const CHIMP_SPEED_HIP = 4;
-const CHIMP_SPEED_KNEE = 6;
-const CHIMP_SPEED_HAND = 8;
-
-/**
- * @classdesc Chimpanzee morphology.
- */
-class ClimbingProfileChimpanzee extends ClimberAbstractBody {
-    /**
-     * @constructor
-     * @param scale {number} - Scale of the environment
-     * @param motors_torque {number}
-     * @param nb_steps_under_water {number}
-     */
-    constructor(scale, motors_torque=100, nb_steps_under_water=600){
-        super(scale, motors_torque, nb_steps_under_water);
-
-        this.LEG_DOWN = 12 / this.SCALE;
-        this.ARM_UP = 22 / this.SCALE;
-        this.LIMB_W = 8 / this.SCALE;
-        this.LIMB_H = 28 / this.SCALE;
-        this.HAND_PART_W = 4 / this.SCALE;
-        this.HAND_PART_H = 8 / this.SCALE;
-        this.LEG_H = this.LIMB_H;
-        this.TORQUE_PENALTY = 0.00035 / 5; // Legs + arms + hands
-        this.BODY_HEIGHT = 45;
-        this.HEAD_HEIGHT = 20;
-        this.HEAD_UP = 2.5 / this.SCALE; // formerly 0.2
-
-        this.AGENT_WIDTH = 24 / this.SCALE;
-        this.AGENT_HEIGHT = this.BODY_HEIGHT / this.SCALE
-            + this.HEAD_HEIGHT / this.SCALE
-            + this.HEAD_UP + this.LEG_H * 2
-            - this.LEG_DOWN;
-        this.AGENT_CENTER_HEIGHT = this.LEG_H * 2 + this.LEG_DOWN;
-
-        this.remove_reward_on_head_angle = true;
-    }
-
-    draw(world, init_x, init_y, force_to_center){
-
-        // Head
-        let head_fd = new b2.FixtureDef();
-        head_fd.shape = new b2.PolygonShape();
-        head_fd.shape.SetAsBox(5 / this.SCALE, 10 / this.SCALE);
-        /*head_fd.shape.Set([
-            new b2.Vec2(-5 / this.SCALE, 10 / this.SCALE),
-            new b2.Vec2(5 / this.SCALE, 10 / this.SCALE),
-            new b2.Vec2(5 / this.SCALE, -10 / this.SCALE),
-            new b2.Vec2(-5 / this.SCALE, -10 / this.SCALE),
-        ], 4);*/
-        head_fd.density = 5.0;
-        head_fd.friction = 0.1;
-        head_fd.filter.categoryBits = 0x20;
-        head_fd.filter.maskBits = 0x1;
-
-        let head_bd = new b2.BodyDef();
-        head_bd.type = b2.Body.b2_dynamicBody;
-        head_bd.position.Set(init_x,
-                             init_y + this.BODY_HEIGHT / this.SCALE / 2 + this.HEAD_HEIGHT / this.SCALE / 2 + this.HEAD_UP);
-        let head = world.CreateBody(head_bd);
-        head.CreateFixture(head_fd);
-        head.color1 = "#806682"; // [0.5, 0.4, 0.9]
-        head.color2 = "#4D4D80";
-        head.ApplyForceToCenter(new b2.Vec2(force_to_center, 0), true);
-        head.SetUserData(new CustomBodyUserData(true, true, "head"));
-        this.body_parts.push(head);
-        this.reference_head_object = head;
-
-        // Body
-        let body_fd = new b2.FixtureDef();
-        body_fd.shape = new b2.PolygonShape();
-        body_fd.shape.Set([
-            new b2.Vec2(-12 / this.SCALE, 25 / this.SCALE),
-            new b2.Vec2(12 / this.SCALE, 25 / this.SCALE),
-            new b2.Vec2(8 / this.SCALE, -20 / this.SCALE),
-            new b2.Vec2(-8 / this.SCALE, -20 / this.SCALE),
-        ], 4);
-        body_fd.density = 5.0;
-        body_fd.friction = 0.1;
-        body_fd.filter.categoryBits = 0x20;
-        body_fd.filter.maskBits = 0x1; // collide only with ground
-
-        let body_bd = new b2.BodyDef();
-        body_bd.type = b2.Body.b2_dynamicBody;
-        body_bd.position.Set(init_x, init_y);
-        let body = world.CreateBody(body_bd);
-        body.CreateFixture(body_fd);
-        body.color1 = "#806682"; // [0.5, 0.4, 0.9]
-        body.color2 = "#4D4D80"; // [0.3, 0.3, 0.5]
-        body.SetUserData(new CustomBodyUserData(true, true, "body"));
-        this.body_parts.push(body);
-
-        // Revolute joint between head and body
-        let rjd = new b2.RevoluteJointDef();
-        rjd.Initialize(head, body, new b2.Vec2(init_x, init_y + this.BODY_HEIGHT / this.SCALE / 2));
-        rjd.enableMotor = false;
-        rjd.enableLimit = true;
-        rjd.lowerAngle = -0.1 * Math.PI;
-        rjd.upperAngle = 0.1 * Math.PI;
-        let joint_motor = world.CreateJoint(rjd);
-        joint_motor.SetUserData(new CustomMotorUserData("neck", 0, false));
-        this.neck_joint = joint_motor;
-
-        // Limbs FixtureDef
-        let UPPER_LIMB_FD = new b2.FixtureDef();
-        UPPER_LIMB_FD.shape = new b2.PolygonShape();
-        UPPER_LIMB_FD.shape.SetAsBox(this.LIMB_W / 2, this.LIMB_H / 2);
-        UPPER_LIMB_FD.density = 1.0;
-        UPPER_LIMB_FD.restitution = 0;
-        UPPER_LIMB_FD.filter.categoryBits = 0x20;
-        UPPER_LIMB_FD.filter.maskBits = 0x1;
-
-        let LOWER_LIMB_FD = new b2.FixtureDef();
-        LOWER_LIMB_FD.shape = new b2.PolygonShape();
-        LOWER_LIMB_FD.shape.SetAsBox(0.8 * this.LIMB_W / 2, this.LIMB_H / 2);
-        LOWER_LIMB_FD.density = 1.0;
-        LOWER_LIMB_FD.restitution = 0;
-        LOWER_LIMB_FD.filter.categoryBits = 0x20;
-        LOWER_LIMB_FD.filter.maskBits = 0x1;
-
-        let HAND_PART_FD = new b2.FixtureDef();
-        HAND_PART_FD.shape = new b2.PolygonShape();
-        HAND_PART_FD.shape.SetAsBox(this.HAND_PART_W / 2, this.HAND_PART_H / 2);
-        HAND_PART_FD.density = 1.0;
-        HAND_PART_FD.restitution = 0;
-        HAND_PART_FD.filter.categoryBits = 0x20;
-        HAND_PART_FD.filter.maskBits = 0x1;
-
-        // Legs
-        for(let i = 0; i < 2; i++){
-
-            // Upper leg
-            let upper_bd = new b2.BodyDef();
-            upper_bd.type = b2.Body.b2_dynamicBody;
-            upper_bd.position.Set(init_x, init_y - this.LIMB_H / 2 - this.LEG_DOWN);
-            let upper = world.CreateBody(upper_bd);
-            upper.CreateFixture(UPPER_LIMB_FD);
-            upper.color1 = "#964A7D"; // [0.59, 0.29, 0.49]
-            upper.color2 = "#63304A"; // [0.39, 0.19, 0.29]
-            upper.SetUserData(new CustomBodyUserData(false, false, "upper_leg"));
-            this.body_parts.push(upper);
-
-            // Revolute joint between body and upper leg
-            rjd = new b2.RevoluteJointDef();
-            rjd.Initialize(body, upper, new b2.Vec2(init_x, init_y - this.LEG_DOWN));
-            rjd.enableMotor = true;
-            rjd.enableLimit = true;
-            rjd.maxMotorTorque = this.MOTORS_TORQUE;
-            rjd.motorSpeed = 1;
-            rjd.lowerAngle = -0.3 * Math.PI;
-            rjd.upperAngle = 0.6 * Math.PI;
-            joint_motor = world.CreateJoint(rjd);
-            joint_motor.SetUserData(new CustomMotorUserData("hip", CHIMP_SPEED_HIP, false));
-            this.motors.push(joint_motor);
-
-            // Lower leg
-            let lower_bd = new b2.BodyDef();
-            lower_bd.type = b2.Body.b2_dynamicBody;
-            lower_bd.position.Set(init_x, init_y - this.LIMB_H * 3/2 - this.LEG_DOWN);
-            let lower = world.CreateBody(lower_bd);
-            lower.CreateFixture(LOWER_LIMB_FD);
-            lower.color1 = "#964A7D"; // [0.59, 0.29, 0.49]
-            lower.color2 = "#63304A"; // [0.39, 0.19, 0.29]
-            lower.SetUserData(new CustomBodyUserData(true, true, "lower_leg"));
-            this.body_parts.push(lower);
-
-            // Revolute joint between upper and lower leg
-            rjd = new b2.RevoluteJointDef();
-            rjd.Initialize(upper, lower, new b2.Vec2(init_x, init_y - this.LEG_H - this.LEG_DOWN));
-            rjd.enableMotor = true;
-            rjd.enableLimit = true;
-            rjd.maxMotorTorque = this.MOTORS_TORQUE;
-            rjd.motorSpeed = 1;
-            rjd.lowerAngle = -0.75 * Math.PI;
-            rjd.upperAngle = -0.1;
-            joint_motor = world.CreateJoint(rjd);
-            joint_motor.SetUserData(new CustomMotorUserData("knee",
-                                                            CHIMP_SPEED_KNEE,
-                                                            true,
-                                                            1,
-                                                            lower));
-            this.motors.push(joint_motor);
-        }
-
-        // Arms
-        for(let i = 0; i < 2; i++){
-            // Upper arm
-            let upper_bd = new b2.BodyDef();
-            upper_bd.type = b2.Body.b2_dynamicBody;
-            upper_bd.position.Set(init_x, init_y + this.LIMB_H / 2 + this.ARM_UP);
-            let upper = world.CreateBody(upper_bd);
-            upper.CreateFixture(UPPER_LIMB_FD);
-            upper.color1 = "#9C4F82"; // [0.61, 0.31, 0.51]
-            upper.color2 = "#69364F"; // [0.41, 0.21, 0.31]
-            upper.SetUserData(new CustomBodyUserData(false, false, "upper_arm"));
-            this.body_parts.push(upper);
-
-            // Revolute joint between body and upper arm
-            rjd = new b2.RevoluteJointDef();
-            rjd.Initialize(body, upper, new b2.Vec2(init_x, init_y + this.ARM_UP));
-            rjd.enableMotor = true;
-            rjd.enableLimit = true;
-            rjd.maxMotorTorque = this.MOTORS_TORQUE;
-            rjd.motorSpeed = 1;
-            rjd.lowerAngle = -0.75 * 2 * Math.PI;
-            rjd.upperAngle = 0;
-            joint_motor = world.CreateJoint(rjd);
-            joint_motor.SetUserData(new CustomMotorUserData("shoulder", CHIMP_SPEED_HIP, false));
-            this.motors.push(joint_motor);
-
-            // Lower arm
-            let lower_bd = new b2.BodyDef();
-            lower_bd.type = b2.Body.b2_dynamicBody;
-            lower_bd.position.Set(init_x, init_y + this.LIMB_H * 3/2 + this.ARM_UP);
-            let lower = world.CreateBody(lower_bd);
-            lower.CreateFixture(LOWER_LIMB_FD);
-            lower.color1 = "#9C4F82"; // [0.61, 0.31, 0.51]
-            lower.color2 = "#69364F"; // [0.41, 0.21, 0.31]
-            lower.SetUserData(new CustomBodyUserData(false, false, "lower_arm"));
-            this.body_parts.push(lower);
-
-            // Revolute joint between upper arm and lower arm
-            rjd = new b2.RevoluteJointDef();
-            rjd.Initialize(upper, lower, new b2.Vec2(init_x, init_y + this.LIMB_H + this.ARM_UP));
-            rjd.enableMotor = true;
-            rjd.enableLimit = true;
-            rjd.maxMotorTorque = this.MOTORS_TORQUE;
-            rjd.motorSpeed = 1;
-            rjd.lowerAngle = 0;
-            rjd.upperAngle = 0.75 * Math.PI;
-            joint_motor = world.CreateJoint(rjd);
-            joint_motor.SetUserData(new CustomMotorUserData("elbow",
-                                                            CHIMP_SPEED_HIP,
-                                                            false));
-            this.motors.push(joint_motor);
-
-            // Hand
-            let prev_part = lower;
-            let initial_y = init_y + this.LIMB_H * 2 + this.ARM_UP;
-            let angle_boundaries = [[-0.5, 0.5]];
-            let nb_hand_parts = 1;
-            for(let u = 0; u < nb_hand_parts; u++){
-                let hand_part_bd = new b2.BodyDef();
-                hand_part_bd.type = b2.Body.b2_dynamicBody;
-                hand_part_bd.position.Set(init_x, initial_y + this.HAND_PART_H / 2 + this.HAND_PART_H * u);
-                let hand_part = world.CreateBody(hand_part_bd);
-                hand_part.CreateFixture(HAND_PART_FD);
-                hand_part.color1 = "#9C4F82"; // [0.61, 0.31, 0.51]
-                hand_part.color2 = "#69364F"; // [0.41, 0.21, 0.31]
-                hand_part.SetUserData(new CustomBodyUserData(true, false, "hand"));
-                this.body_parts.push(hand_part);
-
-                // Revolute joint between prev_part and hand_part
-                rjd = new b2.RevoluteJointDef();
-                rjd.Initialize(prev_part, hand_part, new b2.Vec2(init_x, initial_y + this.HAND_PART_H * u));
-                rjd.enableMotor = true;
-                rjd.enableLimit = true;
-                rjd.maxMotorTorque = this.MOTORS_TORQUE;
-                rjd.motorSpeed = 1;
-                rjd.lowerAngle = angle_boundaries[u][0] * Math.PI;
-                rjd.upperAngle = angle_boundaries[u][1] * Math.PI;
-                joint_motor = world.CreateJoint(rjd);
-                joint_motor.SetUserData(new CustomMotorUserData("hand",
-                                                                CHIMP_SPEED_HAND,
-                                                                true,
-                                                                0,
-                                                                hand_part));
-                this.motors.push(joint_motor);
-
-                prev_part = hand_part;
-            }
-
-            // Sensor
-            let hand_sensor_position = new b2.Vec2(init_x, initial_y + this.HAND_PART_H * nb_hand_parts);
-            let hand_sensor_part_bd = new b2.BodyDef();
-            hand_sensor_part_bd.type = b2.Body.b2_dynamicBody;
-            hand_sensor_part_bd.position.Assign(hand_sensor_position);
-            let hand_sensor_part = world.CreateBody(hand_sensor_part_bd);
-            hand_sensor_part.CreateFixture(this.SENSOR_FD);
-            hand_sensor_part.color1 = "#FF0000"; // [1, 0, 0]
-            hand_sensor_part.color2 = "#FF0000"; // [1, 0, 0]
-            hand_sensor_part.SetUserData(new CustomBodySensorUserData(true, false, "hand_sensor"));
-            this.sensors.push(hand_sensor_part);
-
-            let wjd = new b2.WeldJointDef();
-            wjd.Initialize(prev_part, hand_sensor_part, hand_sensor_position);
-            world.CreateJoint(wjd);
-        }
-
-
-    }
-}
\ No newline at end of file
diff --git a/spaces/fspecii/midi-composer/midi_tokenizer.py b/spaces/fspecii/midi-composer/midi_tokenizer.py
deleted file mode 100644
index 3a0600f6f7dbd3381b7f7a65caf5e7b8f267d53a..0000000000000000000000000000000000000000
--- a/spaces/fspecii/midi-composer/midi_tokenizer.py
+++ /dev/null
@@ -1,254 +0,0 @@
-import random
-
-import PIL
-import numpy as np
-
-
-class MIDITokenizer:
-    def __init__(self):
-        self.vocab_size = 0
-
-        def allocate_ids(size):
-            ids = [self.vocab_size + i for i in range(size)]
-            self.vocab_size += size
-            return ids
-
-        self.pad_id = allocate_ids(1)[0]
-        self.bos_id = allocate_ids(1)[0]
-        self.eos_id = allocate_ids(1)[0]
-        self.events = {
-            "note": ["time1", "time2", "track", "duration", "channel", "pitch", "velocity"],
-            "patch_change": ["time1", "time2", "track", "channel", "patch"],
-            "control_change": ["time1", "time2", "track", "channel", "controller", "value"],
-            "set_tempo": ["time1", "time2", "track", "bpm"],
-        }
-        self.event_parameters = {
-            "time1": 128, "time2": 16, "duration": 2048, "track": 128, "channel": 16, "pitch": 128, "velocity": 128,
-            "patch": 128, "controller": 128, "value": 128, "bpm": 256
-        }
-        self.event_ids = {e: allocate_ids(1)[0] for e in self.events.keys()}
-        self.id_events = {i: e for e, i in self.event_ids.items()}
-        self.parameter_ids = {p: allocate_ids(s) for p, s in self.event_parameters.items()}
-        self.max_token_seq = max([len(ps) for ps in self.events.values()]) + 1
-
-    def tempo2bpm(self, tempo):
-        tempo = tempo / 10 ** 6  # us to s
-        bpm = 60 / tempo
-        return bpm
-
-    def bpm2tempo(self, bpm):
-        if bpm == 0:
-            bpm = 1
-        tempo = int((60 / bpm) * 10 ** 6)
-        return tempo
-
-    def tokenize(self, midi_score, add_bos_eos=True):
-        ticks_per_beat = midi_score[0]
-        event_list = {}
-        for track_idx, track in enumerate(midi_score[1:129]):
-            last_notes = {}
-            for event in track:
-                t = round(16 * event[1] / ticks_per_beat)  # quantization
-                new_event = [event[0], t // 16, t % 16, track_idx] + event[2:]
-                if event[0] == "note":
-                    new_event[4] = max(1, round(16 * new_event[4] / ticks_per_beat))
-                elif event[0] == "set_tempo":
-                    new_event[4] = int(self.tempo2bpm(new_event[4]))
-                if event[0] == "note":
-                    key = tuple(new_event[:4] + new_event[5:-1])
-                else:
-                    key = tuple(new_event[:-1])
-                if event[0] == "note":  # to eliminate note overlap due to quantization
-                    cp = tuple(new_event[5:7])
-                    if cp in last_notes:
-                        last_note_key, last_note = last_notes[cp]
-                        last_t = last_note[1] * 16 + last_note[2]
-                        last_note[4] = max(0, min(last_note[4], t - last_t))
-                        if last_note[4] == 0:
-                            event_list.pop(last_note_key)
-                    last_notes[cp] = (key, new_event)
-                event_list[key] = new_event
-        event_list = list(event_list.values())
-        event_list = sorted(event_list, key=lambda e: e[1:4])
-        midi_seq = []
-
-        last_t1 = 0
-        for event in event_list:
-            name = event[0]
-            if name in self.event_ids:
-                params = event[1:]
-                cur_t1 = params[0]
-                params[0] = params[0] - last_t1
-                if not all([0 <= params[i] < self.event_parameters[p] for i, p in enumerate(self.events[name])]):
-                    continue
-                tokens = [self.event_ids[name]] + [self.parameter_ids[p][params[i]]
-                                                   for i, p in enumerate(self.events[name])]
-                tokens += [self.pad_id] * (self.max_token_seq - len(tokens))
-                midi_seq.append(tokens)
-                last_t1 = cur_t1
-
-        if add_bos_eos:
-            bos = [self.bos_id] + [self.pad_id] * (self.max_token_seq - 1)
-            eos = [self.eos_id] + [self.pad_id] * (self.max_token_seq - 1)
-            midi_seq = [bos] + midi_seq + [eos]
-        return midi_seq
-
-    def event2tokens(self, event):
-        name = event[0]
-        params = event[1:]
-        tokens = [self.event_ids[name]] + [self.parameter_ids[p][params[i]]
-                                           for i, p in enumerate(self.events[name])]
-        tokens += [self.pad_id] * (self.max_token_seq - len(tokens))
-        return tokens
-
-    def tokens2event(self, tokens):
-        if tokens[0] in self.id_events:
-            name = self.id_events[tokens[0]]
-            if len(tokens) <= len(self.events[name]):
-                return []
-            params = tokens[1:]
-            params = [params[i] - self.parameter_ids[p][0] for i, p in enumerate(self.events[name])]
-            if not all([0 <= params[i] < self.event_parameters[p] for i, p in enumerate(self.events[name])]):
-                return []
-            event = [name] + params
-            return event
-        return []
-
-    def detokenize(self, midi_seq):
-        ticks_per_beat = 480
-        tracks_dict = {}
-        t1 = 0
-        for tokens in midi_seq:
-            if tokens[0] in self.id_events:
-                name = self.id_events[tokens[0]]
-                if len(tokens) <= len(self.events[name]):
-                    continue
-                params = tokens[1:]
-                params = [params[i] - self.parameter_ids[p][0] for i, p in enumerate(self.events[name])]
-                if not all([0 <= params[i] < self.event_parameters[p] for i, p in enumerate(self.events[name])]):
-                    continue
-                event = [name] + params
-                if name == "set_tempo":
-                    event[4] = self.bpm2tempo(event[4])
-                if event[0] == "note":
-                    event[4] = int(event[4] * ticks_per_beat / 16)
-                t1 += event[1]
-                t = t1 * 16 + event[2]
-                t = int(t * ticks_per_beat / 16)
-                track_idx = event[3]
-                if track_idx not in tracks_dict:
-                    tracks_dict[track_idx] = []
-                tracks_dict[track_idx].append([event[0], t] + event[4:])
-        tracks = list(tracks_dict.values())
-
-        for i in range(len(tracks)):  # to eliminate note overlap
-            track = tracks[i]
-            track = sorted(track, key=lambda e: e[1])
-            last_note_t = {}
-            zero_len_notes = []
-            for e in reversed(track):
-                if e[0] == "note":
-                    t, d, c, p = e[1:5]
-                    key = (c, p)
-                    if key in last_note_t:
-                        d = min(d, max(last_note_t[key] - t, 0))
-                    last_note_t[key] = t
-                    e[2] = d
-                    if d == 0:
-                        zero_len_notes.append(e)
-            for e in zero_len_notes:
-                track.remove(e)
-            tracks[i] = track
-        return [ticks_per_beat, *tracks]
-
-    def midi2img(self, midi_score):
-        ticks_per_beat = midi_score[0]
-        notes = []
-        max_time = 1
-        track_num = len(midi_score[1:])
-        for track_idx, track in enumerate(midi_score[1:]):
-            for event in track:
-                t = round(16 * event[1] / ticks_per_beat)
-                if event[0] == "note":
-                    d = max(1, round(16 * event[2] / ticks_per_beat))
-                    c, p = event[3:5]
-                    max_time = max(max_time, t + d + 1)
-                    notes.append((track_idx, c, p, t, d))
-        img = np.zeros((128, max_time, 3), dtype=np.uint8)
-        colors = {(i, j): np.random.randint(50, 256, 3) for i in range(track_num) for j in range(16)}
-        for note in notes:
-            tr, c, p, t, d = note
-            img[p, t: t + d] = colors[(tr, c)]
-        img = PIL.Image.fromarray(np.flip(img, 0))
-        return img
-
-    def augment(self, midi_seq, max_pitch_shift=4, max_vel_shift=10, max_cc_val_shift=10, max_bpm_shift=10,
-                max_track_shift=128, max_channel_shift=16):
-        pitch_shift = random.randint(-max_pitch_shift, max_pitch_shift)
-        vel_shift = random.randint(-max_vel_shift, max_vel_shift)
-        cc_val_shift = random.randint(-max_cc_val_shift, max_cc_val_shift)
-        bpm_shift = random.randint(-max_bpm_shift, max_bpm_shift)
-        track_shift = random.randint(0, max_track_shift)
-        channel_shift = random.randint(0, max_channel_shift)
-        midi_seq_new = []
-        for tokens in midi_seq:
-            tokens_new = [*tokens]
-            if tokens[0] in self.id_events:
-                name = self.id_events[tokens[0]]
-                for i, pn in enumerate(self.events[name]):
-                    if pn == "track":
-                        tr = tokens[1 + i] - self.parameter_ids[pn][0]
-                        tr += track_shift
-                        tr = tr % self.event_parameters[pn]
-                        tokens_new[1 + i] = self.parameter_ids[pn][tr]
-                    elif pn == "channel":
-                        c = tokens[1 + i] - self.parameter_ids[pn][0]
-                        c0 = c
-                        c += channel_shift
-                        c = c % self.event_parameters[pn]
-                        if c0 == 9:
-                            c = 9
-                        elif c == 9:
-                            c = (9 + channel_shift) % self.event_parameters[pn]
-                        tokens_new[1 + i] = self.parameter_ids[pn][c]
-
-                if name == "note":
-                    c = tokens[5] - self.parameter_ids["channel"][0]
-                    p = tokens[6] - self.parameter_ids["pitch"][0]
-                    v = tokens[7] - self.parameter_ids["velocity"][0]
-                    if c != 9:  # no shift for drums
-                        p += pitch_shift
-                    if not 0 <= p < 128:
-                        return midi_seq
-                    v += vel_shift
-                    v = max(1, min(127, v))
-                    tokens_new[6] = self.parameter_ids["pitch"][p]
-                    tokens_new[7] = self.parameter_ids["velocity"][v]
-                elif name == "control_change":
-                    cc = tokens[5] - self.parameter_ids["controller"][0]
-                    val = tokens[6] - self.parameter_ids["value"][0]
-                    if cc in [1, 2, 7, 11]:
-                        val += cc_val_shift
-                        val = max(1, min(127, val))
-                    tokens_new[6] = self.parameter_ids["value"][val]
-                elif name == "set_tempo":
-                    bpm = tokens[4] - self.parameter_ids["bpm"][0]
-                    bpm += bpm_shift
-                    bpm = max(1, min(255, bpm))
-                    tokens_new[4] = self.parameter_ids["bpm"][bpm]
-            midi_seq_new.append(tokens_new)
-        return midi_seq_new
-
-    def check_alignment(self, midi_seq, threshold=0.3):
-        total = 0
-        hist = [0] * 16
-        for tokens in midi_seq:
-            if tokens[0] in self.id_events and self.id_events[tokens[0]] == "note":
-                t2 = tokens[2] - self.parameter_ids["time2"][0]
-                total += 1
-                hist[t2] += 1
-        if total == 0:
-            return False
-        hist = sorted(hist, reverse=True)
-        p = sum(hist[:2]) / total
-        return p > threshold
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/ldm/modules/image_degradation/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/ldm/modules/image_degradation/__init__.py
deleted file mode 100644
index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/ldm/modules/image_degradation/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/spaces/giswqs/solara-demo/pages/demo.py b/spaces/giswqs/solara-demo/pages/demo.py
deleted file mode 100644
index 4c493afb3f4588bfbe329e9239b081c34a46a3c0..0000000000000000000000000000000000000000
--- a/spaces/giswqs/solara-demo/pages/demo.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""
-# ipyleaflet
-Map visualization using [ipyleaflet](https://ipyleaflet.readthedocs.io/), a ipywidgets wrapper for [leaflet.js](https://leafletjs.com/)
-"""
-import leafmap
-import ipyleaflet
-import solara
-import ipywidgets as widgets
-
-# zoom = solara.reactive(4)
-# center = solara.reactive((40, -100))
-
-class Map(leafmap.Map):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # Add what you want below
-        self.add_basemap("OpenTopoMap")
-
-        self.add_basemap("Esri.NatGeoWorldMap")
-
-        button = widgets.Button(description="Add Marker")
-        control = ipyleaflet.WidgetControl(widget=button, position="topright")
-        self.add_control(control)
-
-
-@solara.component
-def Page():
-    with solara.Column(style={"min-width": "500px", "height": "500px"}):
-        # solara components support reactive variables
-        # solara.SliderInt(label="Zoom level", value=zoom, min=1, max=20)
-        # using 3rd party widget library require wiring up the events manually
-        # using zoom.value and zoom.set
-        Map.element(  # type: ignore
-            zoom=4,
-            center=[40, -100],
-            scroll_wheel_zoom=True,
-        )
-        # solara.Text(f"Zoom: {zoom.value}")
-        # solara.Text(f"Center: {center.value}")
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Acrobat XI Pro 11.0 Final - X-FORCE.md b/spaces/gotiQspiryo/whisper-ui/examples/Adobe Acrobat XI Pro 11.0 Final - X-FORCE.md
deleted file mode 100644
index 4a9472e76d3221b2a5fba3495c6f599115400e7a..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Acrobat XI Pro 11.0 Final - X-FORCE.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<h2>Adobe Acrobat XI Pro 11.0 Final - X-FORCE</h2><br /><p><b><b>Download</b> &#8230;&#8230;&#8230; <a href="https://urlgoal.com/2uyMgw">https://urlgoal.com/2uyMgw</a></b></p><br /><br />
-
-Adobe Acrobat XI Pro 11.0 Final - X-FORCE Adobe Acrobat XI Pro 11.0 Final - X-FORCE is the latest document creation and editing software that supports the latest formats and technologies. 
-In addition to the fact that the program has all the features of its predecessor Adobe Acrobat XI, this version of the program has a number of innovations that will provide you with a more comfortable and convenient Internet experience. 
-The new version of the program has support for 64-bit devices, which means that you can 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Lucy S Checkup By Darla Phelps.md b/spaces/gotiQspiryo/whisper-ui/examples/Lucy S Checkup By Darla Phelps.md
deleted file mode 100644
index 11b727f93c0617fe67c136695cf0327f0f9ab072..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Lucy S Checkup By Darla Phelps.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Lucy S Checkup By Darla Phelps</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://urlgoal.com/2uyLAn">https://urlgoal.com/2uyLAn</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/tensorflow/plugin_loader.py b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/tensorflow/plugin_loader.py
deleted file mode 100644
index d428c55de2194e42be331b1cad1b2162709a4cd4..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/tensorflow/plugin_loader.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import glob
-import os
-import re
-import uuid
-import hashlib
-import tempfile
-import shutil
-import tensorflow as tf
-from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module
-
-#----------------------------------------------------------------------------
-# Global options.
-
-_nvdiffrast_cache_dir = None
-
-def set_cache_dir(path: str) -> None:
-    '''Set CUDA kernel compilation temp dir.
-
-    If `set_cache_dir` is not called, the cache directory will default to
-    one of the below:
-
-    - Value of NVDIFFRAST_CACHE_DIR env var, if set
-    - $HOME/.cache/nvdiffrast if HOME env var is set
-    - $USERPROFILE/.cache/nvdiffrast if USERPROFILE is set.
-
-    Args:
-      path: Where to save CUDA kernel build temporaries
-    '''
-    global _nvdiffrast_cache_dir
-    _nvdiffrast_cache_dir = path
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _nvdiffrast_cache_dir is not None:
-        return os.path.join(_nvdiffrast_cache_dir, *paths)
-    if 'NVDIFFRAST_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['NVDIFFRAST_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'nvdiffrast', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'nvdiffrast', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'nvdiffrast', *paths)
-
-cuda_cache_version_tag = 'v1'
-do_not_hash_included_headers = False # Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe!
-verbose = True # Print status messages to stdout.
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Enterprise/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
-    if hostx64_paths != []:
-        return hostx64_paths[0]
-    hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
-    if hostx64_paths != []:
-        return hostx64_paths[0]
-    hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
-    if hostx64_paths != []:
-        return hostx64_paths[0]
-    hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
-    if hostx64_paths != []:
-        return hostx64_paths[0]
-    vc_bin_dir = 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin'
-    if os.path.isdir(vc_bin_dir):
-        return vc_bin_dir
-    return None
-
-def _get_compute_cap(device):
-    caps_str = device.physical_device_desc
-    m = re.search('compute capability: (\\d+).(\\d+)', caps_str)
-    major = m.group(1)
-    minor = m.group(2)
-    return (major, minor)
-
-def _get_cuda_gpu_arch_string():
-    gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU']
-    if len(gpus) == 0:
-        raise RuntimeError('No GPU devices found')
-    (major, minor) = _get_compute_cap(gpus[0])
-    return 'sm_%s%s' % (major, minor)
-
-def _run_cmd(cmd):
-    with os.popen(cmd) as pipe:
-        output = pipe.read()
-        status = pipe.close()
-    if status is not None:
-        raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output))
-
-def _prepare_nvcc_cli(opts):
-    cmd = 'nvcc ' + opts.strip()
-    cmd += ' --disable-warnings'
-    cmd += ' --include-path "%s"' % tf.sysconfig.get_include()
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src')
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl')
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive')
-
-    compiler_bindir = _find_compiler_bindir()
-    if compiler_bindir is None:
-        # Require that _find_compiler_bindir succeeds on Windows.  Allow
-        # nvcc to use whatever is the default on Linux.
-        if os.name == 'nt':
-            raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__)
-    else:
-        cmd += ' --compiler-bindir "%s"' % compiler_bindir
-    cmd += ' 2>&1'
-    return cmd
-
-#----------------------------------------------------------------------------
-# Main entry point.
-
-_plugin_cache = dict()
-
-def get_plugin(cuda_file, extra_nvcc_options=[]):
-    cuda_file_base = os.path.basename(cuda_file)
-    cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)
-
-    # Already in cache?
-    if cuda_file in _plugin_cache:
-        return _plugin_cache[cuda_file]
-
-    # Setup plugin.
-    if verbose:
-        print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True)
-    try:
-        # Hash CUDA source.
-        md5 = hashlib.md5()
-        with open(cuda_file, 'rb') as f:
-            md5.update(f.read())
-        md5.update(b'\n')
-
-        # Hash headers included by the CUDA code by running it through the preprocessor.
-        if not do_not_hash_included_headers:
-            if verbose:
-                print('Preprocessing... ', end='', flush=True)
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)
-                _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)))
-                with open(tmp_file, 'rb') as f:
-                    bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros
-                    good_file_str = ('"' + cuda_file_base + '"').encode('utf-8')
-                    for ln in f:
-                        if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas
-                            ln = ln.replace(bad_file_str, good_file_str)
-                            md5.update(ln)
-                    md5.update(b'\n')
-
-        # Select compiler options.
-        compile_opts = ''
-        if os.name == 'nt':
-            compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')
-            compile_opts += ' --library-path="%s"' % (os.path.dirname(__file__) + r"\..\lib") # Find libraries during compilation.
-        elif os.name == 'posix':
-            compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so')
-            compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\''
-        else:
-            assert False # not Windows or Linux, w00t?
-        compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string()
-        compile_opts += ' --use_fast_math'
-        for opt in extra_nvcc_options:
-            compile_opts += ' ' + opt
-        nvcc_cmd = _prepare_nvcc_cli(compile_opts)
-
-        # Hash build configuration.
-        md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n')
-        md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n')
-        md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n')
-
-        # Compile if not already compiled.
-        bin_file_ext = '.dll' if os.name == 'nt' else '.so'
-        cuda_cache_path = make_cache_dir_path()
-        bin_file = os.path.join(make_cache_dir_path(), cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)
-        if not os.path.isfile(bin_file):
-            if verbose:
-                print('Compiling... ', end='', flush=True)
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)
-                _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))
-                os.makedirs(cuda_cache_path, exist_ok=True)
-                intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)
-                shutil.copyfile(tmp_file, intermediate_file)
-                os.rename(intermediate_file, bin_file) # atomic
-
-        # Load.
-        if verbose:
-            print('Loading... ', end='', flush=True)
-        plugin = tf.load_op_library(bin_file)
-
-        # Add to cache.
-        _plugin_cache[cuda_file] = plugin
-        if verbose:
-            print('Done.', flush=True)
-        return plugin
-
-    except:
-        if verbose:
-            print('Failed!', flush=True)
-        raise
-
-#----------------------------------------------------------------------------
diff --git a/spaces/h2oai/wave-tour/examples/plot_schema_transpose.py b/spaces/h2oai/wave-tour/examples/plot_schema_transpose.py
deleted file mode 100644
index eee2294c3173923bde1f3a4205b4b044ec16e45c..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_schema_transpose.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Plot / Schema / Transpose
-# Make a horizontal box and whiskers plot.
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
-    box='1 1 4 5',
-    title='Box plot',
-    data=data(
-        fields=['region', 'low', 'q1', 'q2', 'q3', 'high'],
-        rows=[
-            ['Oceania', 1, 9, 16, 22, 24],
-            ['East Europe', 1, 5, 8, 12, 16],
-            ['Australia', 1, 8, 12, 19, 26],
-            ['South America', 2, 8, 12, 21, 28],
-            ['North Africa', 1, 8, 14, 18, 24],
-            ['North America', 3, 10, 17, 28, 30],
-            ['West Europe', 1, 7, 10, 17, 22],
-            ['West Africa', 1, 6, 8, 13, 16],
-        ],
-        pack=True,
-    ),
-    plot=ui.plot([ui.mark(
-        type='schema',
-        x1='=low',  # min
-        x_q1='=q1',  # lower quartile
-        x_q2='=q2',  # median
-        x_q3='=q3',  # upper quartile
-        x2='=high',  # max
-        y='=region',
-        fill_color='#ccf5ff',
-    )])
-))
-
-page.save()
diff --git a/spaces/hanstyle/tts/face_detection/utils.py b/spaces/hanstyle/tts/face_detection/utils.py
deleted file mode 100644
index 3dc4cf3e328efaa227cbcfdd969e1056688adad5..0000000000000000000000000000000000000000
--- a/spaces/hanstyle/tts/face_detection/utils.py
+++ /dev/null
@@ -1,313 +0,0 @@
-from __future__ import print_function
-import os
-import sys
-import time
-import torch
-import math
-import numpy as np
-import cv2
-
-
-def _gaussian(
-        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
-        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
-        mean_vert=0.5):
-    # handle some defaults
-    if width is None:
-        width = size
-    if height is None:
-        height = size
-    if sigma_horz is None:
-        sigma_horz = sigma
-    if sigma_vert is None:
-        sigma_vert = sigma
-    center_x = mean_horz * width + 0.5
-    center_y = mean_vert * height + 0.5
-    gauss = np.empty((height, width), dtype=np.float32)
-    # generate kernel
-    for i in range(height):
-        for j in range(width):
-            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
-                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
-    if normalize:
-        gauss = gauss / np.sum(gauss)
-    return gauss
-
-
-def draw_gaussian(image, point, sigma):
-    # Check if the gaussian is inside
-    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
-    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
-    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
-        return image
-    size = 6 * sigma + 1
-    g = _gaussian(size)
-    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
-    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
-    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
-    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
-    assert (g_x[0] > 0 and g_y[1] > 0)
-    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
-          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
-    image[image > 1] = 1
-    return image
-
-
-def transform(point, center, scale, resolution, invert=False):
-    """Generate and affine transformation matrix.
-
-    Given a set of points, a center, a scale and a targer resolution, the
-    function generates and affine transformation matrix. If invert is ``True``
-    it will produce the inverse transformation.
-
-    Arguments:
-        point {torch.tensor} -- the input 2D point
-        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
-        scale {float} -- the scale of the face/object
-        resolution {float} -- the output resolution
-
-    Keyword Arguments:
-        invert {bool} -- define wherever the function should produce the direct or the
-        inverse transformation matrix (default: {False})
-    """
-    _pt = torch.ones(3)
-    _pt[0] = point[0]
-    _pt[1] = point[1]
-
-    h = 200.0 * scale
-    t = torch.eye(3)
-    t[0, 0] = resolution / h
-    t[1, 1] = resolution / h
-    t[0, 2] = resolution * (-center[0] / h + 0.5)
-    t[1, 2] = resolution * (-center[1] / h + 0.5)
-
-    if invert:
-        t = torch.inverse(t)
-
-    new_point = (torch.matmul(t, _pt))[0:2]
-
-    return new_point.int()
-
-
-def crop(image, center, scale, resolution=256.0):
-    """Center crops an image or set of heatmaps
-
-    Arguments:
-        image {numpy.array} -- an rgb image
-        center {numpy.array} -- the center of the object, usually the same as of the bounding box
-        scale {float} -- scale of the face
-
-    Keyword Arguments:
-        resolution {float} -- the size of the output cropped image (default: {256.0})
-
-    Returns:
-        [type] -- [description]
-    """  # Crop around the center point
-    """ Crops the image around the center. Input is expected to be an np.ndarray """
-    ul = transform([1, 1], center, scale, resolution, True)
-    br = transform([resolution, resolution], center, scale, resolution, True)
-    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
-    if image.ndim > 2:
-        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
-                           image.shape[2]], dtype=np.int32)
-        newImg = np.zeros(newDim, dtype=np.uint8)
-    else:
-        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
-        newImg = np.zeros(newDim, dtype=np.uint8)
-    ht = image.shape[0]
-    wd = image.shape[1]
-    newX = np.array(
-        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
-    newY = np.array(
-        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
-    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
-    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
-    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
-           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
-    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
-                        interpolation=cv2.INTER_LINEAR)
-    return newImg
-
-
-def get_preds_fromhm(hm, center=None, scale=None):
-    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
-    and the scale is provided the function will return the points also in
-    the original coordinate frame.
-
-    Arguments:
-        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
-
-    Keyword Arguments:
-        center {torch.tensor} -- the center of the bounding box (default: {None})
-        scale {float} -- face scale (default: {None})
-    """
-    max, idx = torch.max(
-        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
-    idx += 1
-    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
-    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
-    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
-
-    for i in range(preds.size(0)):
-        for j in range(preds.size(1)):
-            hm_ = hm[i, j, :]
-            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
-            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
-                diff = torch.FloatTensor(
-                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
-                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
-                preds[i, j].add_(diff.sign_().mul_(.25))
-
-    preds.add_(-.5)
-
-    preds_orig = torch.zeros(preds.size())
-    if center is not None and scale is not None:
-        for i in range(hm.size(0)):
-            for j in range(hm.size(1)):
-                preds_orig[i, j] = transform(
-                    preds[i, j], center, scale, hm.size(2), True)
-
-    return preds, preds_orig
-
-def get_preds_fromhm_batch(hm, centers=None, scales=None):
-    """Obtain (x,y) coordinates given a set of N heatmaps. If the centers
-    and the scales is provided the function will return the points also in
-    the original coordinate frame.
-
-    Arguments:
-        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
-
-    Keyword Arguments:
-        centers {torch.tensor} -- the centers of the bounding box (default: {None})
-        scales {float} -- face scales (default: {None})
-    """
-    max, idx = torch.max(
-        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
-    idx += 1
-    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
-    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
-    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
-
-    for i in range(preds.size(0)):
-        for j in range(preds.size(1)):
-            hm_ = hm[i, j, :]
-            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
-            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
-                diff = torch.FloatTensor(
-                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
-                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
-                preds[i, j].add_(diff.sign_().mul_(.25))
-
-    preds.add_(-.5)
-
-    preds_orig = torch.zeros(preds.size())
-    if centers is not None and scales is not None:
-        for i in range(hm.size(0)):
-            for j in range(hm.size(1)):
-                preds_orig[i, j] = transform(
-                    preds[i, j], centers[i], scales[i], hm.size(2), True)
-
-    return preds, preds_orig
-
-def shuffle_lr(parts, pairs=None):
-    """Shuffle the points left-right according to the axis of symmetry
-    of the object.
-
-    Arguments:
-        parts {torch.tensor} -- a 3D or 4D object containing the
-        heatmaps.
-
-    Keyword Arguments:
-        pairs {list of integers} -- [order of the flipped points] (default: {None})
-    """
-    if pairs is None:
-        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
-                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
-                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
-                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
-                 62, 61, 60, 67, 66, 65]
-    if parts.ndimension() == 3:
-        parts = parts[pairs, ...]
-    else:
-        parts = parts[:, pairs, ...]
-
-    return parts
-
-
-def flip(tensor, is_label=False):
-    """Flip an image or a set of heatmaps left-right
-
-    Arguments:
-        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
-
-    Keyword Arguments:
-        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
-    """
-    if not torch.is_tensor(tensor):
-        tensor = torch.from_numpy(tensor)
-
-    if is_label:
-        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
-    else:
-        tensor = tensor.flip(tensor.ndimension() - 1)
-
-    return tensor
-
-# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py)
-
-
-def appdata_dir(appname=None, roaming=False):
-    """ appdata_dir(appname=None, roaming=False)
-
-    Get the path to the application directory, where applications are allowed
-    to write user specific files (e.g. configurations). For non-user specific
-    data, consider using common_appdata_dir().
-    If appname is given, a subdir is appended (and created if necessary).
-    If roaming is True, will prefer a roaming directory (Windows Vista/7).
-    """
-
-    # Define default user directory
-    userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
-    if userDir is None:
-        userDir = os.path.expanduser('~')
-        if not os.path.isdir(userDir):  # pragma: no cover
-            userDir = '/var/tmp'  # issue #54
-
-    # Get system app data dir
-    path = None
-    if sys.platform.startswith('win'):
-        path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
-        path = (path2 or path1) if roaming else (path1 or path2)
-    elif sys.platform.startswith('darwin'):
-        path = os.path.join(userDir, 'Library', 'Application Support')
-    # On Linux and as fallback
-    if not (path and os.path.isdir(path)):
-        path = userDir
-
-    # Maybe we should store things local to the executable (in case of a
-    # portable distro or a frozen application that wants to be portable)
-    prefix = sys.prefix
-    if getattr(sys, 'frozen', None):
-        prefix = os.path.abspath(os.path.dirname(sys.executable))
-    for reldir in ('settings', '../settings'):
-        localpath = os.path.abspath(os.path.join(prefix, reldir))
-        if os.path.isdir(localpath):  # pragma: no cover
-            try:
-                open(os.path.join(localpath, 'test.write'), 'wb').close()
-                os.remove(os.path.join(localpath, 'test.write'))
-            except IOError:
-                pass  # We cannot write in this directory
-            else:
-                path = localpath
-                break
-
-    # Get path specific for this app
-    if appname:
-        if path == userDir:
-            appname = '.' + appname.lstrip('.')  # Make it a hidden directory
-        path = os.path.join(path, appname)
-        if not os.path.isdir(path):  # pragma: no cover
-            os.mkdir(path)
-
-    # Done
-    return path
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/deform_pool.h b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/deform_pool.h
deleted file mode 100644
index 22821a44a7efbaa3e9ae3dfbac1e79ff5018dd30..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/deform_pool.h
+++ /dev/null
@@ -1,70 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#pragma once
-#include "cpu/vision.h"
-
-#ifdef WITH_CUDA
-#include "cuda/vision.h"
-#endif
-
-
-// Interface for Python
-void deform_psroi_pooling_forward(
-    at::Tensor input, 
-    at::Tensor bbox, 
-    at::Tensor trans, 
-    at::Tensor out,
-    at::Tensor top_count, 
-    const int no_trans, 
-    const float spatial_scale,
-    const int output_dim, 
-    const int group_size, 
-    const int pooled_size,
-    const int part_size, 
-    const int sample_per_part, 
-    const float trans_std)
-{
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return deform_psroi_pooling_cuda_forward(
-        input, bbox, trans, out, top_count, 
-        no_trans, spatial_scale, output_dim, group_size,
-        pooled_size, part_size, sample_per_part, trans_std
-    );
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-
-void deform_psroi_pooling_backward(
-    at::Tensor out_grad, 
-    at::Tensor input, 
-    at::Tensor bbox, 
-    at::Tensor trans,
-    at::Tensor top_count, 
-    at::Tensor input_grad, 
-    at::Tensor trans_grad,
-    const int no_trans, 
-    const float spatial_scale, 
-    const int output_dim,
-    const int group_size, 
-    const int pooled_size, 
-    const int part_size,
-    const int sample_per_part, 
-    const float trans_std) 
-{
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return deform_psroi_pooling_cuda_backward(
-        out_grad, input, bbox, trans, top_count, input_grad, trans_grad,
-        no_trans, spatial_scale, output_dim, group_size, pooled_size, 
-        part_size, sample_per_part, trans_std
-    );
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py
deleted file mode 100644
index 82e81b6697536ff23f8b88f7ea1d89da8d8c28e1..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from torch import nn
-
-from maskrcnn_benchmark.modeling.make_layers import make_conv3x3
-
-
-class Residual(nn.Module):
-    def __init__(self, inp_dim, out_dim, use_gn=False):
-        super(Residual, self).__init__()
-        self.relu = nn.ReLU()
-        # self.bn1 = nn.BatchNorm2d(inp_dim)
-        self.conv1 = make_conv3x3(inp_dim, int(out_dim / 2), 1, use_relu=False, use_gn=use_gn)
-        # self.bn2 = nn.BatchNorm2d(int(out_dim / 2))
-        self.conv2 = make_conv3x3(int(out_dim / 2), int(out_dim / 2), 3, use_relu=False, use_gn=use_gn)
-        # self.bn3 = nn.BatchNorm2d(int(out_dim / 2))
-        self.conv3 = make_conv3x3(int(out_dim / 2), out_dim, 1, use_relu=False, use_gn=use_gn)
-        if inp_dim == out_dim:
-            self.need_skip = False
-        else:
-            self.need_skip = True
-            self.skip_layer = make_conv3x3(inp_dim, out_dim, 1, use_relu=False, use_gn=False)
-
-    def forward(self, x):
-        if self.need_skip:
-            residual = self.skip_layer(x)
-        else:
-            residual = x
-        out = x
-        # out = self.bn1(out)
-        out = self.relu(out)
-        out = self.conv1(out)
-        # out = self.bn2(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-        # out = self.bn3(out)
-        out = self.relu(out)
-        out = self.conv3(out)
-        out += residual
-        return out
-
-
-class Hourglass(nn.Module):
-    def __init__(self, n, f, gn=False, increase=0):
-        super(Hourglass, self).__init__()
-        nf = f + increase
-        self.up1 = Residual(f, f)
-        # Lower branch
-        self.pool1 = nn.MaxPool2d(2, 2)
-        self.low1 = Residual(f, nf)
-        self.n = n
-        # Recursive hourglass
-        if self.n > 1:
-            self.low2 = Hourglass(n-1, nf, gn=gn)
-        else:
-            self.low2 = Residual(nf, nf, gn)
-        self.low3 = Residual(nf, f, gn)
-        self.up2 = nn.Upsample(scale_factor=2, mode='nearest')
-
-    def forward(self, x):
-        up1 = self.up1(x)
-        pool1 = self.pool1(x)
-        low1 = self.low1(pool1)
-        low2 = self.low2(low1)
-        low3 = self.low3(low2)
-        up2 = self.up2(low3)
-        return up1 + up2
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/data_loader.py b/spaces/hasibzunair/fifa-tryon-demo/data_loader.py
deleted file mode 100644
index 3d4bfa19a10c2f8f68f528bb88e2f162ed641321..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/data_loader.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# data loader
-from __future__ import print_function, division
-import glob
-import torch
-from skimage import io, transform, color
-import numpy as np
-import random
-import math
-import matplotlib.pyplot as plt
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms, utils
-from PIL import Image
-
-#==========================dataset load==========================
-class RescaleT(object):
-
-	def __init__(self,output_size):
-		assert isinstance(output_size,(int,tuple))
-		self.output_size = output_size
-
-	def __call__(self,sample):
-		imidx, image, label = sample['imidx'], sample['image'],sample['label']
-
-		h, w = image.shape[:2]
-
-		if isinstance(self.output_size,int):
-			if h > w:
-				new_h, new_w = self.output_size*h/w,self.output_size
-			else:
-				new_h, new_w = self.output_size,self.output_size*w/h
-		else:
-			new_h, new_w = self.output_size
-
-		new_h, new_w = int(new_h), int(new_w)
-
-		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
-		# img = transform.resize(image,(new_h,new_w),mode='constant')
-		# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
-
-		img = transform.resize(image,(self.output_size,self.output_size),mode='constant')
-		lbl = transform.resize(label,(self.output_size,self.output_size),mode='constant', order=0, preserve_range=True)
-
-		return {'imidx':imidx, 'image':img,'label':lbl}
-
-class Rescale(object):
-
-	def __init__(self,output_size):
-		assert isinstance(output_size,(int,tuple))
-		self.output_size = output_size
-
-	def __call__(self,sample):
-		imidx, image, label = sample['imidx'], sample['image'],sample['label']
-
-		if random.random() >= 0.5:
-			image = image[::-1]
-			label = label[::-1]
-
-		h, w = image.shape[:2]
-
-		if isinstance(self.output_size,int):
-			if h > w:
-				new_h, new_w = self.output_size*h/w,self.output_size
-			else:
-				new_h, new_w = self.output_size,self.output_size*w/h
-		else:
-			new_h, new_w = self.output_size
-
-		new_h, new_w = int(new_h), int(new_w)
-
-		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
-		img = transform.resize(image,(new_h,new_w),mode='constant')
-		lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
-
-		return {'imidx':imidx, 'image':img,'label':lbl}
-
-class RandomCrop(object):
-
-	def __init__(self,output_size):
-		assert isinstance(output_size, (int, tuple))
-		if isinstance(output_size, int):
-			self.output_size = (output_size, output_size)
-		else:
-			assert len(output_size) == 2
-			self.output_size = output_size
-	def __call__(self,sample):
-		imidx, image, label = sample['imidx'], sample['image'], sample['label']
-
-		if random.random() >= 0.5:
-			image = image[::-1]
-			label = label[::-1]
-
-		h, w = image.shape[:2]
-		new_h, new_w = self.output_size
-
-		top = np.random.randint(0, h - new_h)
-		left = np.random.randint(0, w - new_w)
-
-		image = image[top: top + new_h, left: left + new_w]
-		label = label[top: top + new_h, left: left + new_w]
-
-		return {'imidx':imidx,'image':image, 'label':label}
-
-class ToTensor(object):
-	"""Convert ndarrays in sample to Tensors."""
-
-	def __call__(self, sample):
-
-		imidx, image, label = sample['imidx'], sample['image'], sample['label']
-
-		tmpImg = np.zeros((image.shape[0],image.shape[1],3))
-		tmpLbl = np.zeros(label.shape)
-
-		image = image/np.max(image)
-		if(np.max(label)<1e-6):
-			label = label
-		else:
-			label = label/np.max(label)
-
-		if image.shape[2]==1:
-			tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
-			tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
-			tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
-		else:
-			tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
-			tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
-			tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225
-
-		tmpLbl[:,:,0] = label[:,:,0]
-
-		# change the r,g,b to b,r,g from [0,255] to [0,1]
-		#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
-		tmpImg = tmpImg.transpose((2, 0, 1))
-		tmpLbl = label.transpose((2, 0, 1))
-
-		return {'imidx':torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}
-
-class ToTensorLab(object):
-	"""Convert ndarrays in sample to Tensors."""
-	def __init__(self,flag=0):
-		self.flag = flag
-
-	def __call__(self, sample):
-
-		imidx, image, label =sample['imidx'], sample['image'], sample['label']
-
-		tmpLbl = np.zeros(label.shape)
-
-		if(np.max(label)<1e-6):
-			label = label
-		else:
-			label = label/np.max(label)
-
-		# change the color space
-		if self.flag == 2: # with rgb and Lab colors
-			tmpImg = np.zeros((image.shape[0],image.shape[1],6))
-			tmpImgt = np.zeros((image.shape[0],image.shape[1],3))
-			if image.shape[2]==1:
-				tmpImgt[:,:,0] = image[:,:,0]
-				tmpImgt[:,:,1] = image[:,:,0]
-				tmpImgt[:,:,2] = image[:,:,0]
-			else:
-				tmpImgt = image
-			tmpImgtl = color.rgb2lab(tmpImgt)
-
-			# nomalize image to range [0,1]
-			tmpImg[:,:,0] = (tmpImgt[:,:,0]-np.min(tmpImgt[:,:,0]))/(np.max(tmpImgt[:,:,0])-np.min(tmpImgt[:,:,0]))
-			tmpImg[:,:,1] = (tmpImgt[:,:,1]-np.min(tmpImgt[:,:,1]))/(np.max(tmpImgt[:,:,1])-np.min(tmpImgt[:,:,1]))
-			tmpImg[:,:,2] = (tmpImgt[:,:,2]-np.min(tmpImgt[:,:,2]))/(np.max(tmpImgt[:,:,2])-np.min(tmpImgt[:,:,2]))
-			tmpImg[:,:,3] = (tmpImgtl[:,:,0]-np.min(tmpImgtl[:,:,0]))/(np.max(tmpImgtl[:,:,0])-np.min(tmpImgtl[:,:,0]))
-			tmpImg[:,:,4] = (tmpImgtl[:,:,1]-np.min(tmpImgtl[:,:,1]))/(np.max(tmpImgtl[:,:,1])-np.min(tmpImgtl[:,:,1]))
-			tmpImg[:,:,5] = (tmpImgtl[:,:,2]-np.min(tmpImgtl[:,:,2]))/(np.max(tmpImgtl[:,:,2])-np.min(tmpImgtl[:,:,2]))
-
-			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
-
-			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
-			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
-			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
-			tmpImg[:,:,3] = (tmpImg[:,:,3]-np.mean(tmpImg[:,:,3]))/np.std(tmpImg[:,:,3])
-			tmpImg[:,:,4] = (tmpImg[:,:,4]-np.mean(tmpImg[:,:,4]))/np.std(tmpImg[:,:,4])
-			tmpImg[:,:,5] = (tmpImg[:,:,5]-np.mean(tmpImg[:,:,5]))/np.std(tmpImg[:,:,5])
-
-		elif self.flag == 1: #with Lab color
-			tmpImg = np.zeros((image.shape[0],image.shape[1],3))
-
-			if image.shape[2]==1:
-				tmpImg[:,:,0] = image[:,:,0]
-				tmpImg[:,:,1] = image[:,:,0]
-				tmpImg[:,:,2] = image[:,:,0]
-			else:
-				tmpImg = image
-
-			tmpImg = color.rgb2lab(tmpImg)
-
-			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
-
-			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.min(tmpImg[:,:,0]))/(np.max(tmpImg[:,:,0])-np.min(tmpImg[:,:,0]))
-			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.min(tmpImg[:,:,1]))/(np.max(tmpImg[:,:,1])-np.min(tmpImg[:,:,1]))
-			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.min(tmpImg[:,:,2]))/(np.max(tmpImg[:,:,2])-np.min(tmpImg[:,:,2]))
-
-			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
-			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
-			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
-
-		else: # with rgb color
-			tmpImg = np.zeros((image.shape[0],image.shape[1],3))
-			image = image/np.max(image)
-			if image.shape[2]==1:
-				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
-				tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
-				tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
-			else:
-				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
-				tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
-				tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225
-
-		tmpLbl[:,:,0] = label[:,:,0]
-
-		# change the r,g,b to b,r,g from [0,255] to [0,1]
-		#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
-		tmpImg = tmpImg.transpose((2, 0, 1))
-		tmpLbl = label.transpose((2, 0, 1))
-
-		return {'imidx':torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}
-
-class SalObjDataset(Dataset):
-	def __init__(self,img_name_list,lbl_name_list,transform=None):
-		# self.root_dir = root_dir
-		# self.image_name_list = glob.glob(image_dir+'*.png')
-		# self.label_name_list = glob.glob(label_dir+'*.png')
-		self.image_name_list = img_name_list
-		self.label_name_list = lbl_name_list
-		self.transform = transform
-
-	def __len__(self):
-		return len(self.image_name_list)
-
-	def __getitem__(self,idx):
-
-		# image = Image.open(self.image_name_list[idx])#io.imread(self.image_name_list[idx])
-		# label = Image.open(self.label_name_list[idx])#io.imread(self.label_name_list[idx])
-
-		image = io.imread(self.image_name_list[idx])
-		imname = self.image_name_list[idx]
-		imidx = np.array([idx])
-
-		if(0==len(self.label_name_list)):
-			label_3 = np.zeros(image.shape)
-		else:
-			label_3 = io.imread(self.label_name_list[idx])
-
-		label = np.zeros(label_3.shape[0:2])
-		if(3==len(label_3.shape)):
-			label = label_3[:,:,0]
-		elif(2==len(label_3.shape)):
-			label = label_3
-
-		if(3==len(image.shape) and 2==len(label.shape)):
-			label = label[:,:,np.newaxis]
-		elif(2==len(image.shape) and 2==len(label.shape)):
-			image = image[:,:,np.newaxis]
-			label = label[:,:,np.newaxis]
-
-		sample = {'imidx':imidx, 'image':image, 'label':label}
-
-		if self.transform:
-			sample = self.transform(sample)
-
-		return sample
diff --git a/spaces/hbestm/gpt-academic-play/config.py b/spaces/hbestm/gpt-academic-play/config.py
deleted file mode 100644
index 0b1a6ad72018f84418ec0c2dacbe66225bfefb11..0000000000000000000000000000000000000000
--- a/spaces/hbestm/gpt-academic-play/config.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# [step 1]>> 例如： API_KEY = "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" （此key无效）
-API_KEY = "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"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
-USE_PROXY = False
-if USE_PROXY:
-    # 填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
-    # 例如    "socks5h://localhost:11284"
-    # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
-    # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-
-    # 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
-    proxies = {
-        #          [协议]://  [地址]  :[端口]
-        "http":  "socks5h://localhost:11284",  # 再例如  "http":  "http://127.0.0.1:7890",
-        "https": "socks5h://localhost:11284",  # 再例如  "https": "http://127.0.0.1:7890",
-    }
-else:
-    proxies = None
-
-# [step 3]>> 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
-# 一言以蔽之：免费用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 3
-
-
-# [step 4]>> 以下配置可以优化体验，但大部分场合下并不需要修改
-# 对话窗的高度
-CHATBOT_HEIGHT = 1115
-
-# 代码高亮
-CODE_HIGHLIGHT = True
-
-# 窗口布局
-LAYOUT = "LEFT-RIGHT"  # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-DARK_MODE = True  # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 30
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = -1
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 2
-
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
-
-# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
-LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
-
-# 设置gradio的并行线程数（不需要修改）
-CONCURRENT_COUNT = 100
-
-# 加一个看板娘装饰
-ADD_WAIFU = False
-
-# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-# [("username", "password"), ("username2", "password2"), ...]
-AUTHENTICATION = []
-
-# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!!）
-# （高危设置！通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式 {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"} 
-# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://ai.open.com/api/conversation"}
-API_URL_REDIRECT = {}
-
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
-CUSTOM_PATH = "/"
-
-# 如果需要使用newbing，把newbing的长长的cookie放到这里
-NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
-NEWBING_COOKIES = """
-your bing cookies here
-"""
-
-# Slack Claude bot， 使用教程详情见 request_llm/README.md
-SLACK_CLAUDE_BOT_ID = ''   
-SLACK_CLAUDE_USER_TOKEN = ''
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/classify/predict.py b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/classify/predict.py
deleted file mode 100644
index 1cbbc094a41e7e6205753ac9024a1186fd58f4b5..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/classify/predict.py
+++ /dev/null
@@ -1,227 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
-
-Usage - sources:
-    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
-                                                                   img.jpg                         # image
-                                                                   vid.mp4                         # video
-                                                                   screen                          # screenshot
-                                                                   path/                           # directory
-                                                                   list.txt                        # list of images
-                                                                   list.streams                    # list of streams
-                                                                   'path/*.jpg'                    # glob
-                                                                   'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-
-Usage - formats:
-    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
-                                           yolov5s-cls.torchscript        # TorchScript
-                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                           yolov5s-cls_openvino_model     # OpenVINO
-                                           yolov5s-cls.engine             # TensorRT
-                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
-                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
-                                           yolov5s-cls.pb                 # TensorFlow GraphDef
-                                           yolov5s-cls.tflite             # TensorFlow Lite
-                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
-                                           yolov5s-cls_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from ultralytics.utils.plotting import Annotator
-
-from models.common import DetectMultiBackend
-from utils.augmentations import classify_transforms
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
-                           increment_path, print_args, strip_optimizer)
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-        weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)
-        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
-        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
-        imgsz=(224, 224),  # inference size (height, width)
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        view_img=False,  # show results
-        save_txt=False,  # save results to *.txt
-        nosave=False,  # do not save images/videos
-        augment=False,  # augmented inference
-        visualize=False,  # visualize features
-        update=False,  # update all models
-        project=ROOT / 'runs/predict-cls',  # save results to project/name
-        name='exp',  # save results to project/name
-        exist_ok=False,  # existing project/name ok, do not increment
-        half=False,  # use FP16 half-precision inference
-        dnn=False,  # use OpenCV DNN for ONNX inference
-        vid_stride=1,  # video frame-rate stride
-):
-    source = str(source)
-    save_img = not nosave and not source.endswith('.txt')  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
-    webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
-    screenshot = source.lower().startswith('screen')
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Dataloader
-    bs = 1  # batch_size
-    if webcam:
-        view_img = check_imshow(warn=True)
-        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
-        bs = len(dataset)
-    elif screenshot:
-        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
-    else:
-        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
-    for path, im, im0s, vid_cap, s in dataset:
-        with dt[0]:
-            im = torch.Tensor(im).to(model.device)
-            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-            if len(im.shape) == 3:
-                im = im[None]  # expand for batch dim
-
-        # Inference
-        with dt[1]:
-            results = model(im)
-
-        # Post-process
-        with dt[2]:
-            pred = F.softmax(results, dim=1)  # probabilities
-
-        # Process predictions
-        for i, prob in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f'{i}: '
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
-
-            s += '%gx%g ' % im.shape[2:]  # print string
-            annotator = Annotator(im0, example=str(names), pil=True)
-
-            # Print results
-            top5i = prob.argsort(0, descending=True)[:5].tolist()  # top 5 indices
-            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
-
-            # Write results
-            text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i)
-            if save_img or view_img:  # Add bbox to image
-                annotator.text([32, 32], text, txt_color=(255, 255, 255))
-            if save_txt:  # Write to file
-                with open(f'{txt_path}.txt', 'a') as f:
-                    f.write(text + '\n')
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                if platform.system() == 'Linux' and p not in windows:
-                    windows.append(p)
-                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
-                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == 'image':
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[i].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-                    vid_writer[i].write(im0)
-
-        # Print time (inference-only)
-        LOGGER.info(f'{s}{dt[1].dt * 1E3:.1f}ms')
-
-    # Print results
-    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
-    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
-    if save_txt or save_img:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)')
-    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--view-img', action='store_true', help='show results')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
-    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
-    parser.add_argument('--augment', action='store_true', help='augmented inference')
-    parser.add_argument('--visualize', action='store_true', help='visualize features')
-    parser.add_argument('--update', action='store_true', help='update all models')
-    parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name')
-    parser.add_argument('--name', default='exp', help='save results to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
-    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
-    run(**vars(opt))
-
-
-if __name__ == '__main__':
-    opt = parse_opt()
-    main(opt)
diff --git a/spaces/hezhaoqia/vits-simple-api/bert_vits2/text/cleaner.py b/spaces/hezhaoqia/vits-simple-api/bert_vits2/text/cleaner.py
deleted file mode 100644
index 8bc7d7feeae1d2a180c83a8b0cfbdf8d1197f4cf..0000000000000000000000000000000000000000
--- a/spaces/hezhaoqia/vits-simple-api/bert_vits2/text/cleaner.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from bert_vits2.text import chinese, cleaned_text_to_sequence
-
-language_module_map = {
-    'ZH': chinese
-}
-
-
-def clean_text(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    return norm_text, phones, tones, word2ph
-
-
-def clean_text_bert(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    bert = language_module.get_bert_feature(norm_text, word2ph)
-    return phones, tones, bert
-
-
-def text_to_sequence(text, language):
-    norm_text, phones, tones, word2ph = clean_text(text, language)
-    return cleaned_text_to_sequence(phones, tones, language)
-
-
-if __name__ == '__main__':
-    pass
diff --git a/spaces/hhhhardman/VITS/text/cantonese.py b/spaces/hhhhardman/VITS/text/cantonese.py
deleted file mode 100644
index b66d12138b81b70b86f18217d24a08fce76305c0..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/text/cantonese.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('jyutjyu')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ei˥'),
-    ('B', 'biː˥'),
-    ('C', 'siː˥'),
-    ('D', 'tiː˥'),
-    ('E', 'iː˥'),
-    ('F', 'e˥fuː˨˩'),
-    ('G', 'tsiː˥'),
-    ('H', 'ɪk̚˥tsʰyː˨˩'),
-    ('I', 'ɐi˥'),
-    ('J', 'tsei˥'),
-    ('K', 'kʰei˥'),
-    ('L', 'e˥llou˨˩'),
-    ('M', 'ɛːm˥'),
-    ('N', 'ɛːn˥'),
-    ('O', 'ou˥'),
-    ('P', 'pʰiː˥'),
-    ('Q', 'kʰiːu˥'),
-    ('R', 'aː˥lou˨˩'),
-    ('S', 'ɛː˥siː˨˩'),
-    ('T', 'tʰiː˥'),
-    ('U', 'juː˥'),
-    ('V', 'wiː˥'),
-    ('W', 'tʊk̚˥piː˥juː˥'),
-    ('X', 'ɪk̚˥siː˨˩'),
-    ('Y', 'waːi˥'),
-    ('Z', 'iː˨sɛːt̚˥')
-]]
-
-
-def number_to_cantonese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def cantonese_to_ipa(text):
-    text = number_to_cantonese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_customTargetSpacing_2x2x2.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_customTargetSpacing_2x2x2.py
deleted file mode 100644
index d7f8f7dff9943031a5888585b37f4f83bd9783fc..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_customTargetSpacing_2x2x2.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-import numpy as np
-from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import ExperimentPlanner3D_v21
-from nnunet.paths import *
-
-
-class ExperimentPlanner3D_v21_customTargetSpacing_2x2x2(ExperimentPlanner3D_v21):
-    def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
-        super(ExperimentPlanner3D_v21, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
-        # we change the data identifier and plans_fname. This will make this experiment planner save the preprocessed
-        # data in a different folder so that they can co-exist with the default (ExperimentPlanner3D_v21). We also
-        # create a custom plans file that will be linked to this data
-        self.data_identifier = "nnUNetData_plans_v2.1_trgSp_2x2x2"
-        self.plans_fname = join(self.preprocessed_output_folder,
-                                "nnUNetPlansv2.1_trgSp_2x2x2_plans_3D.pkl")
-
-    def get_target_spacing(self):
-        # simply return the desired spacing as np.array
-        return np.array([2., 2., 2.]) # make sure this is float!!!! Not int!
-
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainerMTLlate_boundary.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainerMTLlate_boundary.py
deleted file mode 100644
index 90d8c9d0da0f339ace72bb6cb158ecde1135ed8e..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainerMTLlate_boundary.py
+++ /dev/null
@@ -1,477 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-
-from collections import OrderedDict
-from typing import Tuple
-
-import numpy as np
-import torch
-from nnunet.training.data_augmentation.data_augmentation_moreDA import get_moreDA_augmentation
-from nnunet.training.loss_functions.deep_supervision import MultipleOutputLoss2
-from nnunet.utilities.to_torch import maybe_to_torch, to_cuda
-from nnunet.network_architecture.generic_UNet_MTLlate_boundary import Generic_UNet_MTLlate_boundary
-from nnunet.network_architecture.initialization import InitWeights_He
-from nnunet.network_architecture.neural_network import SegmentationNetwork
-from nnunet.training.data_augmentation.default_data_augmentation import default_2D_augmentation_params, \
-    get_patch_size, default_3D_augmentation_params
-from nnunet.training.dataloading.dataset_loading import unpack_dataset
-from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
-from nnunet.utilities.nd_softmax import softmax_helper
-from sklearn.model_selection import KFold
-from torch import nn
-from torch.cuda.amp import autocast
-from nnunet.training.learning_rate.poly_lr import poly_lr
-from batchgenerators.utilities.file_and_folder_operations import *
-import matplotlib.pyplot as plt
-
-
-class nnUNetTrainerMTLlate_boundary(nnUNetTrainer):
-
-
-    def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
-                 unpack_data=True, deterministic=True, fp16=False):
-        super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
-                         deterministic, fp16)
-        self.max_num_epochs = 500
-        self.num_batches_per_epoch = 250  # 250 default
-        self.num_val_batches_per_epoch = 5  # 50 default
-        self.initial_lr = 1e-2
-        self.deep_supervision_scales = None
-        self.ds_loss_weights = None
-
-        self.pin_memory = True
-
-    def initialize(self, training=True, force_load_plans=False, disable_deepsupervision=False):
-        """
-        - replaced get_default_augmentation with get_moreDA_augmentation
-        - enforce to only run this code once
-        - loss function wrapper for deep supervision
-
-        :param training:
-        :param force_load_plans:
-        :return:
-        """
-        if not self.was_initialized:
-            maybe_mkdir_p(self.output_folder)
-
-            if force_load_plans or (self.plans is None):
-                self.load_plans_file()
-
-            self.process_plans(self.plans)
-            print("NUM_pool_op_kernel_sizes")
-            print(self.net_num_pool_op_kernel_sizes)
-            self.setup_DA_params()
-
-            ################# Here we wrap the loss for deep supervision ############
-            # we need to know the number of outputs of the network
-            net_numpool = len(self.net_num_pool_op_kernel_sizes)
-
-            # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
-            # this gives higher resolution outputs more weight in the loss
-            weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
-
-            # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
-            mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)])
-            weights[~mask] = 0
-            weights = weights / weights.sum()
-            self.ds_loss_weights = weights
-            # now wrap the loss
-            self.loss_zone = MultipleOutputLoss2(self.loss, self.ds_loss_weights)
-
-            self.loss_front = MultipleOutputLoss2(self.loss, None)
-            self.loss_boundary = MultipleOutputLoss2(self.loss, None)
-            ################# END ###################
-
-            self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] +
-                                                      "_stage%d" % self.stage)
-            if training:
-                # data get loaded here
-                self.dl_tr, self.dl_val = self.get_basic_generators()
-                if self.unpack_data:
-                    print("unpacking dataset")
-                    unpack_dataset(self.folder_with_preprocessed_data)
-                    print("done")
-                else:
-                    print(
-                        "INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
-                        "will wait all winter for your model to finish!")
-
-                self.tr_gen, self.val_gen = get_moreDA_augmentation(
-                    self.dl_tr, self.dl_val,
-                    self.data_aug_params[
-                        'patch_size_for_spatialtransform'],
-                    self.data_aug_params,
-                    deep_supervision_scales=self.deep_supervision_scales,
-                    pin_memory=self.pin_memory,
-                    use_nondetMultiThreadedAugmenter=False
-                )
-                self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())),
-                                       also_print_to_console=False)
-                self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())),
-                                       also_print_to_console=False)
-            else:
-                pass
-
-            self.initialize_network()
-            self.initialize_optimizer_and_scheduler()
-
-            assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
-        else:
-            self.print_to_log_file('self.was_initialized is True, not running self.initialize again')
-        self.was_initialized = True
-
-    def initialize_network(self):
-        """
-        - momentum 0.99
-        - SGD instead of Adam
-        - self.lr_scheduler = None because we do poly_lr
-        - deep supervision = True
-        - i am sure I forgot something here
-
-        Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
-        :return:
-        """
-        if self.threeD:
-            conv_op = nn.Conv3d
-            dropout_op = nn.Dropout3d
-            norm_op = nn.InstanceNorm3d
-
-        else:
-            conv_op = nn.Conv2d
-            dropout_op = nn.Dropout2d
-            norm_op = nn.InstanceNorm2d
-
-        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
-        dropout_op_kwargs = {'p': 0, 'inplace': True}
-        net_nonlin = nn.LeakyReLU
-        net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
-        self.network = Generic_UNet_MTLlate_boundary(self.num_input_channels, self.base_num_features, self.num_classes,
-                                    len(self.net_num_pool_op_kernel_sizes),
-                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
-                                    dropout_op_kwargs,
-                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2),
-                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
-
-        print("number of parameters")
-        print(sum(param.numel() for param in self.network.parameters()))
-
-        if torch.cuda.is_available():
-            self.network.cuda()
-        self.network.inference_apply_nonlin = softmax_helper
-
-    def initialize_optimizer_and_scheduler(self):
-        assert self.network is not None, "self.initialize_network must be called first"
-        self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay,
-                                         momentum=0.99, nesterov=True)
-        self.lr_scheduler = None
-
-    def run_online_evaluation(self, output, target):
-        """
-        due to deep supervision the return value and the reference are now lists of tensors. We only need the full
-        resolution output because this is what we are interested in in the end. The others are ignored
-        :param output:
-        :param target:
-        :return:
-        """
-        target = target[0]
-        output = output[0]
-        return super().run_online_evaluation(output, target)
-
-    def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True,
-                 step_size: float = 0.5, save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
-                 validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
-                 segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True):
-        """
-        We need to wrap this because we need to enforce self.network.do_ds = False for prediction
-        """
-        ds = self.network.do_ds
-        self.network.do_ds = False
-        ret = super().validate(do_mirroring=do_mirroring, use_sliding_window=use_sliding_window, step_size=step_size,
-                               save_softmax=save_softmax, use_gaussian=use_gaussian,
-                               overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug,
-                               all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs,
-                               run_postprocessing_on_folds=run_postprocessing_on_folds)
-
-        self.network.do_ds = ds
-        return ret
-
-    def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True,
-                                                         mirror_axes: Tuple[int] = None,
-                                                         use_sliding_window: bool = True, step_size: float = 0.5,
-                                                         use_gaussian: bool = True, pad_border_mode: str = 'constant',
-                                                         pad_kwargs: dict = None, all_in_gpu: bool = False,
-                                                         verbose: bool = True, mixed_precision=True) -> Tuple[np.ndarray, np.ndarray]:
-        """
-        We need to wrap this because we need to enforce self.network.do_ds = False for prediction
-        """
-        ds = self.network.do_ds
-        self.network.do_ds = False
-        ret = super().predict_preprocessed_data_return_seg_and_softmax(data[:1],
-                                                                       do_mirroring=do_mirroring,
-                                                                       mirror_axes=mirror_axes,
-                                                                       use_sliding_window=use_sliding_window,
-                                                                       step_size=step_size, use_gaussian=use_gaussian,
-                                                                       pad_border_mode=pad_border_mode,
-                                                                       pad_kwargs=pad_kwargs, all_in_gpu=all_in_gpu,
-                                                                       verbose=verbose,
-                                                                       mixed_precision=mixed_precision)
-        self.network.do_ds = ds
-        return ret
-
-    def run_iteration(self, data_generator, do_backprop=True, run_online_evaluation=False):
-        """
-        gradient clipping improves training stability
-
-        :param data_generator:
-        :param do_backprop:
-        :param run_online_evaluation:
-        :return:
-        """
-        data_dict = next(data_generator)
-        data = data_dict['data']
-        target = data_dict['target']
-
-        data = maybe_to_torch(data)
-        target = maybe_to_torch(target)
-
-        if torch.cuda.is_available():
-            data = to_cuda(data)
-            target = to_cuda(target)
-
-        self.optimizer.zero_grad()
-
-        if self.fp16:
-            with autocast():
-
-                output = self.network(data)
-                del data
-
-                target0 = [target[i][:, [0]].to(torch.float16) for i in range(len(target))]
-                output0 = [output[i][:, 0:2] for i in range(len(output))]
-
-                target1 = [target[i][:, [1]].to(torch.float16) for i in range(len(target))]
-                output1 = [output[i][:, 2:6] for i in range(len(output))]
-
-                target2 = [target[i][:, [2]].to(torch.float16) for i in range(len(target))]
-                output2 = [output[i][:, 6:8] for i in range(len(output))]
-
-                l_front = self.loss_front(output0, target0)
-
-                l_zone = self.loss_zone(output1, target1)
-
-                l_boundary = self.loss_boundary(output2, target2)
-                l = 0.34*l_front + 0.33*l_zone + 0.33*l_boundary
-
-            if do_backprop:
-                self.amp_grad_scaler.scale(l).backward()
-                self.amp_grad_scaler.unscale_(self.optimizer)
-                torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
-                self.amp_grad_scaler.step(self.optimizer)
-                self.amp_grad_scaler.update()
-        else:
-            output = self.network(data)
-            del data
-            l = self.loss(output, target)
-
-            if do_backprop:
-                l.backward()
-                torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
-                self.optimizer.step()
-
-        if run_online_evaluation:
-            self.run_online_evaluation(output0, target0)
-            self.run_online_evaluation(output1, target1)
-
-        del target
-        del target0
-        del target1
-
-        del output
-        del output0
-        del output1
-
-
-        return l.detach().cpu().numpy()
-
-    def do_split(self):
-        """
-        The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded,
-        so always the same) and save it as splits_final.pkl file in the preprocessed data directory.
-        Sometimes you may want to create your own split for various reasons. For this you will need to create your own
-        splits_final.pkl file. If this file is present, nnU-Net is going to use it and whatever splits are defined in
-        it. You can create as many splits in this file as you want. Note that if you define only 4 splits (fold 0-3)
-        and then set fold=4 when training (that would be the fifth split), nnU-Net will print a warning and proceed to
-        use a random 80:20 data split.
-        :return:
-        """
-        if self.fold == "all":
-            # if fold==all then we use all images for training and validation
-            tr_keys = val_keys = list(self.dataset.keys())
-        else:
-            splits_file = join(self.dataset_directory, "splits_final.pkl")
-
-            # if the split file does not exist we need to create it
-            if not isfile(splits_file):
-                self.print_to_log_file("Creating new 5-fold cross-validation split...")
-                splits = []
-                all_keys_sorted = np.sort(list(self.dataset.keys()))
-                kfold = KFold(n_splits=5, shuffle=True, random_state=12345)
-                for i, (train_idx, test_idx) in enumerate(kfold.split(all_keys_sorted)):
-                    train_keys = np.array(all_keys_sorted)[train_idx]
-                    test_keys = np.array(all_keys_sorted)[test_idx]
-                    splits.append(OrderedDict())
-                    splits[-1]['train'] = train_keys
-                    splits[-1]['val'] = test_keys
-                save_pickle(splits, splits_file)
-
-            else:
-                self.print_to_log_file("Using splits from existing split file:", splits_file)
-                splits = load_pickle(splits_file)
-                self.print_to_log_file("The split file contains %d splits." % len(splits))
-
-            self.print_to_log_file("Desired fold for training: %d" % self.fold)
-            if self.fold < len(splits):
-                tr_keys = splits[self.fold]['train']
-                val_keys = splits[self.fold]['val']
-                self.print_to_log_file("This split has %d training and %d validation cases."
-                                       % (len(tr_keys), len(val_keys)))
-            else:
-                self.print_to_log_file("INFO: You requested fold %d for training but splits "
-                                       "contain only %d folds. I am now creating a "
-                                       "random (but seeded) 80:20 split!" % (self.fold, len(splits)))
-                # if we request a fold that is not in the split file, create a random 80:20 split
-                rnd = np.random.RandomState(seed=12345 + self.fold)
-                keys = np.sort(list(self.dataset.keys()))
-                idx_tr = rnd.choice(len(keys), int(len(keys) * 0.8), replace=False)
-                idx_val = [i for i in range(len(keys)) if i not in idx_tr]
-                tr_keys = [keys[i] for i in idx_tr]
-                val_keys = [keys[i] for i in idx_val]
-                self.print_to_log_file("This random 80:20 split has %d training and %d validation cases."
-                                       % (len(tr_keys), len(val_keys)))
-
-        tr_keys.sort()
-        val_keys.sort()
-        self.dataset_tr = OrderedDict()
-        for i in tr_keys:
-            self.dataset_tr[i] = self.dataset[i]
-        self.dataset_val = OrderedDict()
-        for i in val_keys:
-            self.dataset_val[i] = self.dataset[i]
-
-    def setup_DA_params(self):
-        """
-        - we increase roation angle from [-15, 15] to [-30, 30]
-        - scale range is now (0.7, 1.4), was (0.85, 1.25)
-        - we don't do elastic deformation anymore
-
-        :return:
-        """
-
-        self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod(
-            np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
-
-        if self.threeD:
-            self.data_aug_params = default_3D_augmentation_params
-            self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
-            self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
-            self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
-            if self.do_dummy_2D_aug:
-                self.data_aug_params["dummy_2D"] = True
-                self.print_to_log_file("Using dummy2d data augmentation")
-                self.data_aug_params["elastic_deform_alpha"] = \
-                    default_2D_augmentation_params["elastic_deform_alpha"]
-                self.data_aug_params["elastic_deform_sigma"] = \
-                    default_2D_augmentation_params["elastic_deform_sigma"]
-                self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
-        else:
-            self.do_dummy_2D_aug = False
-            if max(self.patch_size) / min(self.patch_size) > 1.5:
-                default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi)
-            self.data_aug_params = default_2D_augmentation_params
-        self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
-
-        if self.do_dummy_2D_aug:
-            self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
-                                                             self.data_aug_params['rotation_x'],
-                                                             self.data_aug_params['rotation_y'],
-                                                             self.data_aug_params['rotation_z'],
-                                                             self.data_aug_params['scale_range'])
-            self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
-        else:
-            self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
-                                                             self.data_aug_params['rotation_y'],
-                                                             self.data_aug_params['rotation_z'],
-                                                             self.data_aug_params['scale_range'])
-
-        self.data_aug_params["scale_range"] = (0.7, 1.4)
-        self.data_aug_params["do_elastic"] = False
-        # selected  channeels
-        self.data_aug_params['selected_seg_channels'] = [0, 1, 2]
-        self.data_aug_params['patch_size_for_spatialtransform'] = self.patch_size
-
-        self.data_aug_params["num_cached_per_thread"] = 2
-
-    def maybe_update_lr(self, epoch=None):
-        """
-        if epoch is not None we overwrite epoch. Else we use epoch = self.epoch + 1
-
-        (maybe_update_lr is called in on_epoch_end which is called before epoch is incremented.
-        herefore we need to do +1 here)
-
-        :param epoch:
-        :return:
-        """
-        if epoch is None:
-            ep = self.epoch + 1
-        else:
-            ep = epoch
-        self.optimizer.param_groups[0]['lr'] = poly_lr(ep, self.max_num_epochs, self.initial_lr, 0.9)
-        self.print_to_log_file("lr:", np.round(self.optimizer.param_groups[0]['lr'], decimals=6))
-
-    def on_epoch_end(self):
-        """
-        overwrite patient-based early stopping. Always run to 1000 epochs
-        :return:
-        """
-        super().on_epoch_end()
-        continue_training = self.epoch < self.max_num_epochs
-
-        # it can rarely happen that the momentum of nnUNetTrainerV2 is too high for some dataset. If at epoch 100 the
-        # estimated validation Dice is still 0 then we reduce the momentum from 0.99 to 0.95
-        if self.epoch == 100:
-            if self.all_val_eval_metrics[-1] == 0:
-                self.optimizer.param_groups[0]["momentum"] = 0.95
-                self.network.apply(InitWeights_He(1e-2))
-                self.print_to_log_file("At epoch 100, the mean foreground Dice was 0. This can be caused by a too "
-                                       "high momentum. High momentum (0.99) is good for datasets where it works, but "
-                                       "sometimes causes issues such as this one. Momentum has now been reduced to "
-                                       "0.95 and network weights have been reinitialized")
-        return continue_training
-
-    def run_training(self):
-        """
-        if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first
-        continued epoch with self.initial_lr
-
-        we also need to make sure deep supervision in the network is enabled for training, thus the wrapper
-        :return:
-        """
-        self.maybe_update_lr(self.epoch)  # if we dont overwrite epoch then self.epoch+1 is used which is not what we
-        # want at the start of the training
-        ds = self.network.do_ds
-        self.network.do_ds = True
-        ret = super().run_training()
-        self.network.do_ds = ds
-        return ret
diff --git a/spaces/hongaik/service_text_classification/.ipynb_checkpoints/app-checkpoint.py b/spaces/hongaik/service_text_classification/.ipynb_checkpoints/app-checkpoint.py
deleted file mode 100644
index a14fec914e128fee2f3f31cdf5e8f15d110dbca9..0000000000000000000000000000000000000000
--- a/spaces/hongaik/service_text_classification/.ipynb_checkpoints/app-checkpoint.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import streamlit as st
-import plotly.express as px
-from plotly.subplots import make_subplots
-from utils import *
-
-########## Title for the Web App ##########
-st.title("Text Classification for Service Feedback")
-
-########## Create Input field ##########
-feedback = st.text_input('Type your text here', 'The website was user friendly and the agent provided good solutions')
-
-if st.button('Click for predictions!'):
-    with st.spinner('Generating predictions...'):
-        
-        topics_prob, sentiment_prob = get_single_prediction(feedback)
-        
-        bar = px.bar(topics_prob, x='probability', y='topic')
-                
-        pie = px.pie(sentiment_prob, 
-               values='probability', 
-               names='sentiment', 
-               color_discrete_map={'positive':'rgb(0, 204, 0)', 
-                                 'negative':'rgb(215, 11, 11)'
-                                  },
-               color='sentiment'
-              )
-        
-    st.plotly_chart(bar, use_container_width=True)    
-    st.plotly_chart(pie, use_container_width=True)
-
-st.write("\n")    
-st.subheader('Or... Upload a csv file if you have a file instead.')
-st.write("\n")
-
-st.download_button(
-     label="Download sample file here",
-     data=sample_file,
-     file_name='sample_data.csv',
-     mime='text/csv',
- )
-
-uploaded_file = st.file_uploader("Please upload a csv file with only 1 column of texts.")
-
-if uploaded_file is not None:
-    
-    with st.spinner('Generating predictions...'):
-        results = get_multiple_predictions(uploaded_file)
-    
-    st.download_button(
-     label="Download results as CSV",
-     data=results,
-     file_name='results.csv',
-     mime='text/csv',
-     )
-    
-    
\ No newline at end of file
diff --git a/spaces/huggan/sefa/interface.py b/spaces/huggan/sefa/interface.py
deleted file mode 100644
index 20832fcd56eec19ed4e292b8c475e9ae8d672804..0000000000000000000000000000000000000000
--- a/spaces/huggan/sefa/interface.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# python 3.7
-"""Demo."""
-import random
-
-import numpy as np
-import torch
-import streamlit as st
-import SessionState
-
-from models import parse_gan_type
-from utils import to_tensor
-from utils import postprocess
-from utils import load_generator
-from utils import factorize_weight
-
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def get_model(model_name):
-    """Gets model by name."""
-    return load_generator(model_name, from_hf_hub=True)
-
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def factorize_model(model, layer_idx):
-    """Factorizes semantics from target layers of the given model."""
-    return factorize_weight(model, layer_idx)
-
-
-def sample(model, gan_type, num=1):
-    """Samples latent codes."""
-    codes = torch.randn(num, model.z_space_dim)
-    if gan_type == 'pggan':
-        codes = model.layer0.pixel_norm(codes)
-    elif gan_type == 'stylegan':
-        codes = model.mapping(codes)['w']
-        codes = model.truncation(codes,
-                                 trunc_psi=0.7,
-                                 trunc_layers=8)
-    elif gan_type == 'stylegan2':
-        codes = model.mapping(codes)['w']
-        codes = model.truncation(codes,
-                                 trunc_psi=0.5,
-                                 trunc_layers=18)
-    codes = codes.detach().cpu().numpy()
-    return codes
-
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def synthesize(model, gan_type, code):
-    """Synthesizes an image with the give code."""
-    if gan_type == 'pggan':
-        image = model(to_tensor(code))['image']
-    elif gan_type in ['stylegan', 'stylegan2']:
-        image = model.synthesis(to_tensor(code))['image']
-    image = postprocess(image)[0]
-    return image
-
-
-def _update_slider():
-    num_semantics = st.session_state["num_semantics"]
-    for sem_idx in range(num_semantics):
-        st.session_state[f"semantic_slider_{sem_idx}"] = 0
-
-
-def main():
-    """Main function (loop for StreamLit)."""
-
-    st.title('Closed-Form Factorization of Latent Semantics in GANs')
-    st.markdown("This space is the ported version of [Closed-Form Factorization of Latent Semantics in GANs](https://github.com/genforce/sefa). It reads all sample models from the Hugging Face Hub")
-    st.markdown("---")
-    st.sidebar.title('Options')
-    st.sidebar.button('Reset', on_click=_update_slider, kwargs={})
-
-    model_name = st.sidebar.selectbox(
-        'Model to Interpret',
-        ['pggan_celebahq1024', 'stylegan_animeface512', 'stylegan_car512', 'stylegan_cat256',])
-
-    model = get_model(model_name)
-    gan_type = parse_gan_type(model)
-    layer_idx = st.sidebar.selectbox(
-        'Layers to Interpret',
-        ['all', '0-1', '2-5', '6-13'])
-    layers, boundaries, eigen_values = factorize_model(model, layer_idx)
-
-    num_semantics = st.sidebar.number_input(
-        'Number of semantics', value=5, min_value=0, max_value=None, step=1, key="num_semantics")
-    steps = {sem_idx: 0 for sem_idx in range(num_semantics)}
-    if gan_type == 'pggan':
-        max_step = 5.0
-    elif gan_type == 'stylegan':
-        max_step = 2.0
-    elif gan_type == 'stylegan2':
-        max_step = 15.0
-    for sem_idx in steps:
-        eigen_value = eigen_values[sem_idx]
-        steps[sem_idx] = st.sidebar.slider(
-            f'Semantic {sem_idx:03d} (eigen value: {eigen_value:.3f})',
-            value=0.0,
-            min_value=-max_step,
-            max_value=max_step,
-            step=0.04 * max_step,
-            key=f"semantic_slider_{sem_idx}")
-
-    image_placeholder = st.empty()
-    button_placeholder = st.empty()
-    button_totally_random = st.empty()
-
-    try:
-        base_codes = np.load(f'latent_codes/{model_name}_latents.npy')
-    except FileNotFoundError:
-        base_codes = sample(model, gan_type)
-
-    state = SessionState.get(model_name=model_name,
-                             code_idx=0,
-                             codes=base_codes[0:1])
-    if state.model_name != model_name:
-        state.model_name = model_name
-        state.code_idx = 0
-        state.codes = base_codes[0:1]
-
-    if button_placeholder.button('Next Sample'):
-        state.code_idx += 1
-        if state.code_idx < base_codes.shape[0]:
-            state.codes = base_codes[state.code_idx][np.newaxis]
-        else:
-            state.codes = sample(model, gan_type)
-
-    if button_totally_random.button('Totally Random'):
-        state.codes = sample(model, gan_type)
-
-    code = state.codes.copy()
-    for sem_idx, step in steps.items():
-        if gan_type == 'pggan':
-            code += boundaries[sem_idx:sem_idx + 1] * step
-        elif gan_type in ['stylegan', 'stylegan2']:
-            code[:, layers, :] += boundaries[sem_idx:sem_idx + 1] * step
-    image = synthesize(model, gan_type, code)
-    image_placeholder.image(image / 255.0)
-
-    st.markdown("---")
-    st.markdown("""This space was created by [johko](https://twitter.com/johko990). Main credits go to the original authors Yujun Shen and Bolei Zhou, who created a great code base to work on. 
-                This version loads all models from the Hugging Face Hub.""")
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/hussain-shk/IndiSent/indic_nlp_library/indicnlp/tokenize/__init__.py b/spaces/hussain-shk/IndiSent/indic_nlp_library/indicnlp/tokenize/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hussain-shk/IndiSent/subword-nmt/learn_joint_bpe_and_vocab.py b/spaces/hussain-shk/IndiSent/subword-nmt/learn_joint_bpe_and_vocab.py
deleted file mode 100644
index d75ff3d3f687c4f4776cc0246b05e3f6765374b2..0000000000000000000000000000000000000000
--- a/spaces/hussain-shk/IndiSent/subword-nmt/learn_joint_bpe_and_vocab.py
+++ /dev/null
@@ -1,166 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Author: Rico Sennrich
-
-"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.
-This script learns BPE jointly on a concatenation of a list of texts (typically the source and target side of a parallel corpus,
-applies the learned operation to each and (optionally) returns the resulting vocabulary of each text.
-The vocabulary can be used in apply_bpe.py to avoid producing symbols that are rare or OOV in a training text.
-
-Reference:
-Rico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units.
-Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.
-"""
-
-from __future__ import unicode_literals
-
-import sys
-import os
-import inspect
-import codecs
-import argparse
-import tempfile
-import warnings
-from collections import Counter
-from multiprocessing import cpu_count
-
-#hack to get imports working if running this as a script, or within a package
-if __name__ == '__main__':
-    import learn_bpe
-    import apply_bpe
-else:
-    from . import learn_bpe
-    from . import apply_bpe
-
-# hack for python2/3 compatibility
-from io import open
-argparse.open = open
-
-def create_parser(subparsers=None):
-
-    if subparsers:
-        parser = subparsers.add_parser('learn-joint-bpe-and-vocab',
-            formatter_class=argparse.RawDescriptionHelpFormatter,
-            description="learn BPE-based word segmentation")
-    else:
-        parser = argparse.ArgumentParser(
-            formatter_class=argparse.RawDescriptionHelpFormatter,
-            description="learn BPE-based word segmentation")
-
-    parser.add_argument(
-        '--input', '-i', type=argparse.FileType('r'), required=True, nargs = '+',
-        metavar='PATH',
-        help="Input texts (multiple allowed).")
-    parser.add_argument(
-        '--output', '-o', type=argparse.FileType('w'), required=True,
-        metavar='PATH',
-        help="Output file for BPE codes.")
-    parser.add_argument(
-        '--symbols', '-s', type=int, default=10000,
-        help="Create this many new symbols (each representing a character n-gram) (default: %(default)s)")
-    parser.add_argument(
-        '--separator', type=str, default='@@', metavar='STR',
-        help="Separator between non-final subword units (default: '%(default)s')")
-    parser.add_argument(
-        '--write-vocabulary', type=argparse.FileType('w'), required=True, nargs = '+', default=None,
-        metavar='PATH', dest='vocab',
-        help='Write to these vocabulary files after applying BPE. One per input text. Used for filtering in apply_bpe.py')
-    parser.add_argument(
-        '--min-frequency', type=int, default=2, metavar='FREQ',
-        help='Stop if no symbol pair has frequency >= FREQ (default: %(default)s)')
-    parser.add_argument(
-        '--total-symbols', '-t', action="store_true",
-        help="subtract number of characters from the symbols to be generated (so that '--symbols' becomes an estimate for the total number of symbols needed to encode text).")
-    parser.add_argument(
-        '--num-workers', type=int, default=1,
-        help="Number of processors to process texts, only supported in Python3. If -1, set `multiprocessing.cpu_count()`. (default: %(default)s)")
-    parser.add_argument(
-        '--verbose', '-v', action="store_true",
-        help="verbose mode.")
-
-    return parser
-
-def learn_joint_bpe_and_vocab(args):
-
-    if args.vocab and len(args.input) != len(args.vocab):
-        sys.stderr.write('Error: number of input files and vocabulary files must match\n')
-        sys.exit(1)
-
-    # read/write files as UTF-8
-    args.input = [codecs.open(f.name, encoding='UTF-8') for f in args.input]
-    args.vocab = [codecs.open(f.name, 'w', encoding='UTF-8') for f in args.vocab]
-
-    # get combined vocabulary of all input texts
-    full_vocab = Counter()
-    for f in args.input:
-        full_vocab += learn_bpe.get_vocabulary(f, num_workers=args.num_workers)
-        f.seek(0)
-
-    vocab_list = ['{0} {1}'.format(key, freq) for (key, freq) in full_vocab.items()]
-
-    # learn BPE on combined vocabulary
-    with codecs.open(args.output.name, 'w', encoding='UTF-8') as output:
-        learn_bpe.learn_bpe(vocab_list, output, args.symbols, args.min_frequency, args.verbose, is_dict=True, total_symbols=args.total_symbols)
-
-    with codecs.open(args.output.name, encoding='UTF-8') as codes:
-        bpe = apply_bpe.BPE(codes, separator=args.separator)
-
-    # apply BPE to each training corpus and get vocabulary
-    for train_file, vocab_file in zip(args.input, args.vocab):
-
-        tmp = tempfile.NamedTemporaryFile(delete=False)
-        tmp.close()
-
-        tmpout = codecs.open(tmp.name, 'w', encoding='UTF-8')
-
-        train_file.seek(0)
-        bpe.process_lines(train_file.name, tmpout, num_workers=args.num_workers)
-
-        tmpout.close()
-        tmpin = codecs.open(tmp.name, encoding='UTF-8')
-
-        vocab = learn_bpe.get_vocabulary(tmpin, num_workers=args.num_workers)
-        tmpin.close()
-        os.remove(tmp.name)
-
-        for key, freq in sorted(vocab.items(), key=lambda x: x[1], reverse=True):
-            vocab_file.write("{0} {1}\n".format(key, freq))
-        vocab_file.close()
-
-
-if __name__ == '__main__':
-
-    currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-    newdir = os.path.join(currentdir, 'subword_nmt')
-    if os.path.isdir(newdir):
-        warnings.simplefilter('default')
-        warnings.warn(
-            "this script's location has moved to {0}. This symbolic link will be removed in a future version. Please point to the new location, or install the package and use the command 'subword-nmt'".format(newdir),
-            DeprecationWarning
-        )
-
-    # python 2/3 compatibility
-    if sys.version_info < (3, 0):
-        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
-        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
-        sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
-    else:
-        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)
-        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)
-        sys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)
-
-    parser = create_parser()
-    args = parser.parse_args()
-
-    if args.num_workers <= 0:
-        args.num_workers = cpu_count()
-
-    if sys.version_info < (3, 0):
-        args.separator = args.separator.decode('UTF-8')
-        if args.num_workers > 1:
-            args.num_workers = 1
-            warnings.warn("Parallel mode is only supported in Python3. Using 1 processor instead.")
-
-    assert(len(args.input) == len(args.vocab))
-
-    learn_joint_bpe_and_vocab(args)
diff --git a/spaces/huybery/deep-thinking/common.py b/spaces/huybery/deep-thinking/common.py
deleted file mode 100644
index 54d8146c579e5d033059cff82fc10ae5384d80b0..0000000000000000000000000000000000000000
--- a/spaces/huybery/deep-thinking/common.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-
-from tasks import task_mapper
-
-
-def setup_plain_seed(SEED):
-    os.environ["PYTHONHASHSEED"] = str(SEED)
-    random.seed(SEED)
-    np.random.seed(SEED)
-
-
-def setup_cpu(seed):
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-    setup_plain_seed(seed)
-    torch.manual_seed(seed)
-    torch.random.manual_seed(seed)
diff --git a/spaces/hylee/apdrawing/APDrawingGAN2/script/train.sh b/spaces/hylee/apdrawing/APDrawingGAN2/script/train.sh
deleted file mode 100644
index 0cf5b9423e1f0b058ca29255bb86db815d3099a9..0000000000000000000000000000000000000000
--- a/spaces/hylee/apdrawing/APDrawingGAN2/script/train.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-set -ex
-python train.py --dataroot dataset/AB_140_aug3_H_hm2 --name apdrawinggan++_1 --model apdrawingpp_style --use_resnet --netG resnet_9blocks --continue_train --continuity_loss --lambda_continuity 40.0 --gpu_ids 0 --gpu_ids_p 1 --display_env apdrawinggan++_1 --niter 200 --niter_decay 0 --lr 0.0001 --batch_size 1 --emphasis_conti_face --auxiliary_root auxiliaryeye2o
-
diff --git a/spaces/hysts/insightface-SCRFD/README.md b/spaces/hysts/insightface-SCRFD/README.md
deleted file mode 100644
index 217fb03286cfb5399f25e551ca4e86cdd88b09cc..0000000000000000000000000000000000000000
--- a/spaces/hysts/insightface-SCRFD/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Insightface SCRFD
-emoji: 🔥
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/hysts/mediapipe-face-detection/app.py b/spaces/hysts/mediapipe-face-detection/app.py
deleted file mode 100644
index 2707abfa72e65acdaaf8e1a36894a6d8b34892cf..0000000000000000000000000000000000000000
--- a/spaces/hysts/mediapipe-face-detection/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import pathlib
-
-import gradio as gr
-import huggingface_hub
-import mediapipe as mp
-import numpy as np
-
-mp_face_detection = mp.solutions.face_detection
-mp_drawing = mp.solutions.drawing_utils
-
-TITLE = 'MediaPipe Face Detection'
-DESCRIPTION = 'https://google.github.io/mediapipe/'
-
-
-def run(image: np.ndarray, model_selection: int,
-        min_detection_confidence: float) -> np.ndarray:
-    with mp_face_detection.FaceDetection(
-            model_selection=model_selection,
-            min_detection_confidence=min_detection_confidence
-    ) as face_detection:
-        results = face_detection.process(image)
-
-    res = image[:, :, ::-1].copy()
-    if results.detections is not None:
-        for detection in results.detections:
-            mp_drawing.draw_detection(res, detection)
-    return res[:, :, ::-1]
-
-
-model_types = [
-    'Short-range model (best for faces within 2 meters)',
-    'Full-range model (best for faces within 5 meters)',
-]
-
-image_paths = sorted(pathlib.Path('images').rglob('*.jpg'))
-examples = [[path, model_types[0], 0.5] for path in image_paths]
-
-gr.Interface(
-    fn=run,
-    inputs=[
-        gr.Image(label='Input', type='numpy'),
-        gr.Radio(label='Model',
-                 choices=model_types,
-                 type='index',
-                 value=model_types[0]),
-        gr.Slider(label='Minimum Detection Confidence',
-                  minimum=0,
-                  maximum=1,
-                  step=0.05,
-                  value=0.5),
-    ],
-    outputs=gr.Image(label='Output'),
-    examples=examples,
-    title=TITLE,
-    description=DESCRIPTION,
-).queue().launch()
diff --git a/spaces/iccv23-diffusers-demo/LoraTheExplorer/cog_sdxl_dataset_and_utils.py b/spaces/iccv23-diffusers-demo/LoraTheExplorer/cog_sdxl_dataset_and_utils.py
deleted file mode 100644
index d0f5bd01c9e535390b68a298db944ff4ecf986b9..0000000000000000000000000000000000000000
--- a/spaces/iccv23-diffusers-demo/LoraTheExplorer/cog_sdxl_dataset_and_utils.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# dataset_and_utils.py file taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py
-import os
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import pandas as pd
-import PIL
-import torch
-import torch.utils.checkpoint
-from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
-from PIL import Image
-from safetensors import safe_open
-from safetensors.torch import save_file
-from torch.utils.data import Dataset
-from transformers import AutoTokenizer, PretrainedConfig
-
-
-def prepare_image(
-    pil_image: PIL.Image.Image, w: int = 512, h: int = 512
-) -> torch.Tensor:
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("RGB"))
-    arr = arr.astype(np.float32) / 127.5 - 1
-    arr = np.transpose(arr, [2, 0, 1])
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-def prepare_mask(
-    pil_image: PIL.Image.Image, w: int = 512, h: int = 512
-) -> torch.Tensor:
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("L"))
-    arr = arr.astype(np.float32) / 255.0
-    arr = np.expand_dims(arr, 0)
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-class PreprocessedDataset(Dataset):
-    def __init__(
-        self,
-        csv_path: str,
-        tokenizer_1,
-        tokenizer_2,
-        vae_encoder,
-        text_encoder_1=None,
-        text_encoder_2=None,
-        do_cache: bool = False,
-        size: int = 512,
-        text_dropout: float = 0.0,
-        scale_vae_latents: bool = True,
-        substitute_caption_map: Dict[str, str] = {},
-    ):
-        super().__init__()
-
-        self.data = pd.read_csv(csv_path)
-        self.csv_path = csv_path
-
-        self.caption = self.data["caption"]
-        # make it lowercase
-        self.caption = self.caption.str.lower()
-        for key, value in substitute_caption_map.items():
-            self.caption = self.caption.str.replace(key.lower(), value)
-
-        self.image_path = self.data["image_path"]
-
-        if "mask_path" not in self.data.columns:
-            self.mask_path = None
-        else:
-            self.mask_path = self.data["mask_path"]
-
-        if text_encoder_1 is None:
-            self.return_text_embeddings = False
-        else:
-            self.text_encoder_1 = text_encoder_1
-            self.text_encoder_2 = text_encoder_2
-            self.return_text_embeddings = True
-            assert (
-                NotImplementedError
-            ), "Preprocessing Text Encoder is not implemented yet"
-
-        self.tokenizer_1 = tokenizer_1
-        self.tokenizer_2 = tokenizer_2
-
-        self.vae_encoder = vae_encoder
-        self.scale_vae_latents = scale_vae_latents
-        self.text_dropout = text_dropout
-
-        self.size = size
-
-        if do_cache:
-            self.vae_latents = []
-            self.tokens_tuple = []
-            self.masks = []
-
-            self.do_cache = True
-
-            print("Captions to train on: ")
-            for idx in range(len(self.data)):
-                token, vae_latent, mask = self._process(idx)
-                self.vae_latents.append(vae_latent)
-                self.tokens_tuple.append(token)
-                self.masks.append(mask)
-
-            del self.vae_encoder
-
-        else:
-            self.do_cache = False
-
-    @torch.no_grad()
-    def _process(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        image_path = self.image_path[idx]
-        image_path = os.path.join(os.path.dirname(self.csv_path), image_path)
-
-        image = PIL.Image.open(image_path).convert("RGB")
-        image = prepare_image(image, self.size, self.size).to(
-            dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
-        )
-
-        caption = self.caption[idx]
-
-        print(caption)
-
-        # tokenizer_1
-        ti1 = self.tokenizer_1(
-            caption,
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        ).input_ids
-
-        ti2 = self.tokenizer_2(
-            caption,
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        ).input_ids
-
-        vae_latent = self.vae_encoder.encode(image).latent_dist.sample()
-
-        if self.scale_vae_latents:
-            vae_latent = vae_latent * self.vae_encoder.config.scaling_factor
-
-        if self.mask_path is None:
-            mask = torch.ones_like(
-                vae_latent, dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
-            )
-
-        else:
-            mask_path = self.mask_path[idx]
-            mask_path = os.path.join(os.path.dirname(self.csv_path), mask_path)
-
-            mask = PIL.Image.open(mask_path)
-            mask = prepare_mask(mask, self.size, self.size).to(
-                dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
-            )
-
-            mask = torch.nn.functional.interpolate(
-                mask, size=(vae_latent.shape[-2], vae_latent.shape[-1]), mode="nearest"
-            )
-            mask = mask.repeat(1, vae_latent.shape[1], 1, 1)
-
-        assert len(mask.shape) == 4 and len(vae_latent.shape) == 4
-
-        return (ti1.squeeze(), ti2.squeeze()), vae_latent.squeeze(), mask.squeeze()
-
-    def __len__(self) -> int:
-        return len(self.data)
-
-    def atidx(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        if self.do_cache:
-            return self.tokens_tuple[idx], self.vae_latents[idx], self.masks[idx]
-        else:
-            return self._process(idx)
-
-    def __getitem__(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        token, vae_latent, mask = self.atidx(idx)
-        return token, vae_latent, mask
-
-
-def import_model_class_from_model_name_or_path(
-    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
-):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "CLIPTextModelWithProjection":
-        from transformers import CLIPTextModelWithProjection
-
-        return CLIPTextModelWithProjection
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def load_models(pretrained_model_name_or_path, revision, device, weight_dtype):
-    tokenizer_one = AutoTokenizer.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=revision,
-        use_fast=False,
-    )
-    tokenizer_two = AutoTokenizer.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=revision,
-        use_fast=False,
-    )
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(
-        pretrained_model_name_or_path, subfolder="scheduler"
-    )
-    # import correct text encoder classes
-    text_encoder_cls_one = import_model_class_from_model_name_or_path(
-        pretrained_model_name_or_path, revision
-    )
-    text_encoder_cls_two = import_model_class_from_model_name_or_path(
-        pretrained_model_name_or_path, revision, subfolder="text_encoder_2"
-    )
-    text_encoder_one = text_encoder_cls_one.from_pretrained(
-        pretrained_model_name_or_path, subfolder="text_encoder", revision=revision
-    )
-    text_encoder_two = text_encoder_cls_two.from_pretrained(
-        pretrained_model_name_or_path, subfolder="text_encoder_2", revision=revision
-    )
-
-    vae = AutoencoderKL.from_pretrained(
-        pretrained_model_name_or_path, subfolder="vae", revision=revision
-    )
-    unet = UNet2DConditionModel.from_pretrained(
-        pretrained_model_name_or_path, subfolder="unet", revision=revision
-    )
-
-    vae.requires_grad_(False)
-    text_encoder_one.requires_grad_(False)
-    text_encoder_two.requires_grad_(False)
-
-    unet.to(device, dtype=weight_dtype)
-    vae.to(device, dtype=torch.float32)
-    text_encoder_one.to(device, dtype=weight_dtype)
-    text_encoder_two.to(device, dtype=weight_dtype)
-
-    return (
-        tokenizer_one,
-        tokenizer_two,
-        noise_scheduler,
-        text_encoder_one,
-        text_encoder_two,
-        vae,
-        unet,
-    )
-
-
-def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
-    """
-    Returns:
-        a state dict containing just the attention processor parameters.
-    """
-    attn_processors = unet.attn_processors
-
-    attn_processors_state_dict = {}
-
-    for attn_processor_key, attn_processor in attn_processors.items():
-        for parameter_key, parameter in attn_processor.state_dict().items():
-            attn_processors_state_dict[
-                f"{attn_processor_key}.{parameter_key}"
-            ] = parameter
-
-    return attn_processors_state_dict
-
-
-class TokenEmbeddingsHandler:
-    def __init__(self, text_encoders, tokenizers):
-        self.text_encoders = text_encoders
-        self.tokenizers = tokenizers
-
-        self.train_ids: Optional[torch.Tensor] = None
-        self.inserting_toks: Optional[List[str]] = None
-        self.embeddings_settings = {}
-
-    def initialize_new_tokens(self, inserting_toks: List[str]):
-        idx = 0
-        for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
-            assert isinstance(
-                inserting_toks, list
-            ), "inserting_toks should be a list of strings."
-            assert all(
-                isinstance(tok, str) for tok in inserting_toks
-            ), "All elements in inserting_toks should be strings."
-
-            self.inserting_toks = inserting_toks
-            special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
-            tokenizer.add_special_tokens(special_tokens_dict)
-            text_encoder.resize_token_embeddings(len(tokenizer))
-
-            self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
-
-            # random initialization of new tokens
-
-            std_token_embedding = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data.std()
-            )
-
-            print(f"{idx} text encodedr's std_token_embedding: {std_token_embedding}")
-
-            text_encoder.text_model.embeddings.token_embedding.weight.data[
-                self.train_ids
-            ] = (
-                torch.randn(
-                    len(self.train_ids), text_encoder.text_model.config.hidden_size
-                )
-                .to(device=self.device)
-                .to(dtype=self.dtype)
-                * std_token_embedding
-            )
-            self.embeddings_settings[
-                f"original_embeddings_{idx}"
-            ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
-            self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
-
-            inu = torch.ones((len(tokenizer),), dtype=torch.bool)
-            inu[self.train_ids] = False
-
-            self.embeddings_settings[f"index_no_updates_{idx}"] = inu
-
-            print(self.embeddings_settings[f"index_no_updates_{idx}"].shape)
-
-            idx += 1
-
-    def save_embeddings(self, file_path: str):
-        assert (
-            self.train_ids is not None
-        ), "Initialize new tokens before saving embeddings."
-        tensors = {}
-        for idx, text_encoder in enumerate(self.text_encoders):
-            assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[
-                0
-            ] == len(self.tokenizers[0]), "Tokenizers should be the same."
-            new_token_embeddings = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data[
-                    self.train_ids
-                ]
-            )
-            tensors[f"text_encoders_{idx}"] = new_token_embeddings
-
-        save_file(tensors, file_path)
-
-    @property
-    def dtype(self):
-        return self.text_encoders[0].dtype
-
-    @property
-    def device(self):
-        return self.text_encoders[0].device
-
-    def _load_embeddings(self, loaded_embeddings, tokenizer, text_encoder):
-        # Assuming new tokens are of the format <s_i>
-        self.inserting_toks = [f"<s{i}>" for i in range(loaded_embeddings.shape[0])]
-        special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
-        tokenizer.add_special_tokens(special_tokens_dict)
-        text_encoder.resize_token_embeddings(len(tokenizer))
-
-        self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
-        assert self.train_ids is not None, "New tokens could not be converted to IDs."
-        text_encoder.text_model.embeddings.token_embedding.weight.data[
-            self.train_ids
-        ] = loaded_embeddings.to(device=self.device).to(dtype=self.dtype)
-
-    @torch.no_grad()
-    def retract_embeddings(self):
-        for idx, text_encoder in enumerate(self.text_encoders):
-            index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"]
-            text_encoder.text_model.embeddings.token_embedding.weight.data[
-                index_no_updates
-            ] = (
-                self.embeddings_settings[f"original_embeddings_{idx}"][index_no_updates]
-                .to(device=text_encoder.device)
-                .to(dtype=text_encoder.dtype)
-            )
-
-            # for the parts that were updated, we need to normalize them
-            # to have the same std as before
-            std_token_embedding = self.embeddings_settings[f"std_token_embedding_{idx}"]
-
-            index_updates = ~index_no_updates
-            new_embeddings = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data[
-                    index_updates
-                ]
-            )
-            off_ratio = std_token_embedding / new_embeddings.std()
-
-            new_embeddings = new_embeddings * (off_ratio**0.1)
-            text_encoder.text_model.embeddings.token_embedding.weight.data[
-                index_updates
-            ] = new_embeddings
-
-    def load_embeddings(self, file_path: str):
-        with safe_open(file_path, framework="pt", device=self.device.type) as f:
-            for idx in range(len(self.text_encoders)):
-                text_encoder = self.text_encoders[idx]
-                tokenizer = self.tokenizers[idx]
-
-                loaded_embeddings = f.get_tensor(f"text_encoders_{idx}")
-                self._load_embeddings(loaded_embeddings, tokenizer, text_encoder)
\ No newline at end of file
diff --git a/spaces/imseldrith/BotX/Uploader/youtube.py b/spaces/imseldrith/BotX/Uploader/youtube.py
deleted file mode 100644
index d1f65039dcea0ba85aad13cc7f5eb40289d59bf8..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/BotX/Uploader/youtube.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Hash Minner
-
-# 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 os
-from urllib.parse import urlparse
-import wget
-import asyncio
-
-from opencc import OpenCC
-from youtube_dl import YoutubeDL
-from pyrogram import Client, filters, enums
-from pyrogram.types import Message
-from pyrogram import Client, filters
-
-if bool(os.environ.get("WEBHOOK")):
-    from Uploader.config import Config
-else:
-    from sample_config import Config
-
-from Uploader.functions.help_ytdl import get_file_extension_from_url, get_resolution
-
-YTDL_REGEX = (r"^((?:https?:)?\/\/)")
-s2tw = OpenCC('s2tw.json').convert
-
-
-@Client.on_callback_query(filters.regex("^ytdl_audio$"))
-async def callback_query_ytdl_audio(_, callback_query):
-    try:
-        url = callback_query.message.reply_to_message.text
-        ydl_opts = {
-            'format': 'bestaudio',
-            'outtmpl': '%(title)s - %(extractor)s-%(id)s.%(ext)s',
-            'writethumbnail': True
-        }
-        with YoutubeDL(ydl_opts) as ydl:
-            message = callback_query.message
-            await message.reply_chat_action(enums.ChatAction.TYPING)
-            info_dict = ydl.extract_info(url, download=False)
-            # download
-            await callback_query.edit_message_text("**Downloading audio...**")
-            ydl.process_info(info_dict)
-            # upload
-            audio_file = ydl.prepare_filename(info_dict)
-            task = asyncio.create_task(send_audio(message, info_dict,
-                                                  audio_file))
-            while not task.done():
-                await asyncio.sleep(3)
-                await message.reply_chat_action(enums.ChatAction.UPLOAD_DOCUMENT)
-            await message.reply_chat_action(enums.ChatAction.CANCEL)
-            await message.delete()
-    except Exception as e:
-        await message.reply_text(e)
-    await callback_query.message.reply_to_message.delete()
-    await callback_query.message.delete()
-
-    async def send_audio(message: Message, info_dict, audio_file):
-        basename = audio_file.rsplit(".", 1)[-2]
-        if info_dict['ext'] == 'webm':
-            audio_file_weba = f"{basename}.weba"
-            os.rename(audio_file, audio_file_weba)
-            audio_file = audio_file_weba
-        thumbnail_url = info_dict['thumbnail']
-        thumbnail_file = f"{basename}.{get_file_extension_from_url(thumbnail_url)}"
-        download_location = f"{Config.DOWNLOAD_LOCATION}/{message.from_user.id}.jpg"
-        thumb = download_location if os.path.isfile(
-            download_location) else None
-        webpage_url = info_dict['webpage_url']
-        title = s2tw(info_dict['title'])
-        caption = f'<b><a href=\"{webpage_url}\">{title}</a></b>'
-        duration = int(float(info_dict['duration']))
-        performer = s2tw(info_dict['uploader'])
-        await message.reply_audio(audio_file, caption=caption, duration=duration, performer=performer, title=title, parse_mode=enums.ParseMode.HTML, thumb=thumb)
-
-        os.remove(audio_file)
-        os.remove(thumbnail_file)
-
-
-@Client.on_callback_query(filters.regex("^ytdl_video$"))
-async def callback_query_ytdl_video(_, callback_query):
-    try:
-        # url = callback_query.message.text
-        url = callback_query.message.reply_to_message.text
-        ydl_opts = {
-            'format': 'best[ext=mp4]',
-            'outtmpl': '%(title)s - %(extractor)s-%(id)s.%(ext)s',
-            'writethumbnail': True
-        }
-        with YoutubeDL(ydl_opts) as ydl:
-            message = callback_query.message
-            await message.reply_chat_action(enums.ChatAction.TYPING)
-            info_dict = ydl.extract_info(url, download=False)
-            # download
-            await callback_query.edit_message_text("**Downloading video...**")
-            ydl.process_info(info_dict)
-            # upload
-            video_file = ydl.prepare_filename(info_dict)
-            task = asyncio.create_task(send_video(message, info_dict,
-                                                  video_file))
-            while not task.done():
-                await asyncio.sleep(3)
-                await message.reply_chat_action(enums.ChatAction.UPLOAD_DOCUMENT)
-            await message.reply_chat_action(enums.ChatAction.CANCEL)
-            await message.delete()
-    except Exception as e:
-        await message.reply_text(e)
-    await callback_query.message.reply_to_message.delete()
-    await callback_query.message.delete()
-
-    async def send_video(message: Message, info_dict, video_file):
-        basename = video_file.rsplit(".", 1)[-2]
-        thumbnail_url = info_dict['thumbnail']
-        thumbnail_file = f"{basename}.{get_file_extension_from_url(thumbnail_url)}"
-        download_location = f"{Config.DOWNLOAD_LOCATION}/{message.from_user.id}.jpg"
-        thumb = download_location if os.path.isfile(
-            download_location) else None
-        webpage_url = info_dict['webpage_url']
-        title = s2tw(info_dict['title'])
-        caption = f'<b><a href=\"{webpage_url}\">{title}</a></b>'
-        duration = int(float(info_dict['duration']))
-        width, height = get_resolution(info_dict)
-        await message.reply_video(video_file, caption=caption, duration=duration, width=width, height=height, parse_mode=enums.ParseMode.HTML, thumb=thumb)
-
-        os.remove(video_file)
-        os.remove(thumbnail_file)
diff --git a/spaces/inamXcontru/PoeticTTS/American Pie Reunion 720p Mkv Moviel Free.md b/spaces/inamXcontru/PoeticTTS/American Pie Reunion 720p Mkv Moviel Free.md
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index 99ab3f712041b026245527c4a6af7335f7015b9d..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/American Pie Reunion 720p Mkv Moviel Free.md	
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deleted file mode 100644
index 87881f92214a04281fbdc7cad667551c81755d7a..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Anu.Script.Manager.6.0.md
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Acid Pro 7 Serial Number Authentication Code [TOP].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Acid Pro 7 Serial Number Authentication Code [TOP].md
deleted file mode 100644
index 014fd6fa0d8caa71c8b2b8e5209f88889ee131f0..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Acid Pro 7 Serial Number Authentication Code [TOP].md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>Valuable services which touse the same caliber of materials for its construction are Cusco, Stutz, Rotozip and Sunbeam. While they could be at prices in the range of millions of dollars, they are likely to sell for well under that amount. The price of serial numbers of Rolex sub-mariner and watches usually vary from US $2,000 to $70,000. Hence, it's quite necessary to be meticulous while purchasing your Rolex sub-mariner or watches. Make sure to check the serial numbers of your Rolex at the time of buying. Check them from different sources to make sure they match.</p>
-<p>The documentation that accompanies the back order form that youre given usually specifies a return authorization. Check this with your provider prior to returning the item. If the return authorization doesnt specify a certain period, make sure to allow for at least 2 weeks for returns to be processed, but preferably more. If you think that your item has a defect or hasnt been repaired to your satisfaction, contact our customer service department. Include the original order number and serial number that came with the watch.</p>
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-<p>While several well-known brands including Rolex and Patek Philippe come with Certificate of Authenticity (COA) - a statement of authentication - the highest level of certification, Rolex includes a Certificate of Appraisal, or COA MA, in the label on the movement. This statement of authenticity is printed on the movement, not engraved into the movement. This level of certification is typically issued to retail watch cases, but it applies to Rolex watches as well. Only Rolex cases are covered by this certification, and not all Rolex cases are covered. A COA MA label that appears on a Rolex watch that it has one.</p> 899543212b<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Autodata 3.40 Multilanguage Download _HOT_.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Autodata 3.40 Multilanguage Download _HOT_.md
deleted file mode 100644
index 7bc8f06efa539ef9cf262c70fd1170ec72672568..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Autodata 3.40 Multilanguage Download _HOT_.md	
+++ /dev/null
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--- a/spaces/inplisQlawa/anything-midjourney-v4-1/FS2004 Alphasim AS365N Dauphin.md	
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diff --git a/spaces/inreVtussa/clothingai/Examples/Angaben Der Versicherten Person Formular Pdf Downloadl !FULL!.md b/spaces/inreVtussa/clothingai/Examples/Angaben Der Versicherten Person Formular Pdf Downloadl !FULL!.md
deleted file mode 100644
index 10e278c0e40fb41f0af100f01d6a8aa5006db2ed..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Angaben Der Versicherten Person Formular Pdf Downloadl !FULL!.md	
+++ /dev/null
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-<br />
-<p>die zustaende stelle im nahrungs- und gesundheitsrechtlichen bereich sind die bewg, die vertragskonform (d. h. sicherstellt, dass die in der bewg enthaltenen bestimmungen strikt befolgt werden), schnell, unbundelbar und unentgeltlich verfahren. sie untersttut das versicherte bei der prfe und aufnahme der rolle als ahv-anmeldend und sorgt zugleich für die einzelfallprfe und die individuelle behandlung. die bewg entsprechend ihren erforderlichen entscheidungsprozessen garantiert den versicherten ein hohes maß an schutz vor zwang, belohnung und behandlung. eine bewg sollte mit den versicherten personen abgesprochen werden, sobald die erlaubnis zur anmeldung zu erteilen ist. eine bewg verpflichtet den versicherten, zum gesetzlichen lohn zu verpflichten.</p>
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diff --git a/spaces/ismot/8testi1/utils/autoanchor.py b/spaces/ismot/8testi1/utils/autoanchor.py
deleted file mode 100644
index bec9017711006fae4f34cf96e1a28ebbafd0c516..0000000000000000000000000000000000000000
--- a/spaces/ismot/8testi1/utils/autoanchor.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Auto-anchor utils
-
-import numpy as np
-import torch
-import yaml
-from scipy.cluster.vq import kmeans
-from tqdm import tqdm
-
-from utils.general import colorstr
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        print('Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    prefix = colorstr('autoanchor: ')
-    print(f'\n{prefix}Analyzing anchors... ', end='')
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1. / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
-    bpr, aat = metric(anchors)
-    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
-    if bpr < 0.98:  # threshold to recompute
-        print('. Attempting to improve anchors, please wait...')
-        na = m.anchor_grid.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            print(f'{prefix}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            check_anchor_order(m)
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
-    print('')  # newline
-
-
-def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            path: path to dataset *.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    thr = 1. / thr
-    prefix = colorstr('autoanchor: ')
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
-        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
-              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
-        for i, x in enumerate(k):
-            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
-        return k
-
-    if isinstance(path, str):  # *.yaml file
-        with open(path) as f:
-            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-    else:
-        dataset = path  # dataset
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans calculation
-    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
-    k *= s
-    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
-    k = print_results(k)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    npr = np.random
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k)
-
-    return print_results(k)
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/extra_networks_hypernet.py b/spaces/jackli888/stable-diffusion-webui/modules/extra_networks_hypernet.py
deleted file mode 100644
index 207343daa673c14a362d4bd2399982d9ad86fe22..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/extra_networks_hypernet.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from modules import extra_networks, shared, extra_networks
-from modules.hypernetworks import hypernetwork
-
-
-class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
-    def __init__(self):
-        super().__init__('hypernet')
-
-    def activate(self, p, params_list):
-        additional = shared.opts.sd_hypernetwork
-
-        if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
-            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
-            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
-
-        names = []
-        multipliers = []
-        for params in params_list:
-            assert len(params.items) > 0
-
-            names.append(params.items[0])
-            multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
-
-        hypernetwork.load_hypernetworks(names, multipliers)
-
-    def deactivate(self, p):
-        pass
diff --git a/spaces/jackyccl/segment-anything/segment_anything/modeling/__init__.py b/spaces/jackyccl/segment-anything/segment_anything/modeling/__init__.py
deleted file mode 100644
index 38e906243d898d7fc071c0fe218338c5cace3ea1..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/segment_anything/modeling/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .sam import Sam
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-from .transformer import TwoWayTransformer
diff --git a/spaces/james-oldfield/PandA/networks/stylegan3/train.py b/spaces/james-oldfield/PandA/networks/stylegan3/train.py
deleted file mode 100644
index be883c95967630b76bc04b170c884836cf5688eb..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/stylegan3/train.py
+++ /dev/null
@@ -1,288 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Train a GAN using the techniques described in the paper
-"Alias-Free Generative Adversarial Networks"."""
-
-import os
-import click
-import re
-import json
-import tempfile
-import torch
-
-import dnnlib
-from training import training_loop
-from metrics import metric_main
-from torch_utils import training_stats
-from torch_utils import custom_ops
-
-#----------------------------------------------------------------------------
-
-def subprocess_fn(rank, c, temp_dir):
-    dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True)
-
-    # Init torch.distributed.
-    if c.num_gpus > 1:
-        init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
-        if os.name == 'nt':
-            init_method = 'file:///' + init_file.replace('\\', '/')
-            torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus)
-        else:
-            init_method = f'file://{init_file}'
-            torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus)
-
-    # Init torch_utils.
-    sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None
-    training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
-    if rank != 0:
-        custom_ops.verbosity = 'none'
-
-    # Execute training loop.
-    training_loop.training_loop(rank=rank, **c)
-
-#----------------------------------------------------------------------------
-
-def launch_training(c, desc, outdir, dry_run):
-    dnnlib.util.Logger(should_flush=True)
-
-    # Pick output directory.
-    prev_run_dirs = []
-    if os.path.isdir(outdir):
-        prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
-    prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
-    prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
-    cur_run_id = max(prev_run_ids, default=-1) + 1
-    c.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')
-    assert not os.path.exists(c.run_dir)
-
-    # Print options.
-    print()
-    print('Training options:')
-    print(json.dumps(c, indent=2))
-    print()
-    print(f'Output directory:    {c.run_dir}')
-    print(f'Number of GPUs:      {c.num_gpus}')
-    print(f'Batch size:          {c.batch_size} images')
-    print(f'Training duration:   {c.total_kimg} kimg')
-    print(f'Dataset path:        {c.training_set_kwargs.path}')
-    print(f'Dataset size:        {c.training_set_kwargs.max_size} images')
-    print(f'Dataset resolution:  {c.training_set_kwargs.resolution}')
-    print(f'Dataset labels:      {c.training_set_kwargs.use_labels}')
-    print(f'Dataset x-flips:     {c.training_set_kwargs.xflip}')
-    print()
-
-    # Dry run?
-    if dry_run:
-        print('Dry run; exiting.')
-        return
-
-    # Create output directory.
-    print('Creating output directory...')
-    os.makedirs(c.run_dir)
-    with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f:
-        json.dump(c, f, indent=2)
-
-    # Launch processes.
-    print('Launching processes...')
-    torch.multiprocessing.set_start_method('spawn')
-    with tempfile.TemporaryDirectory() as temp_dir:
-        if c.num_gpus == 1:
-            subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
-        else:
-            torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
-
-#----------------------------------------------------------------------------
-
-def init_dataset_kwargs(data):
-    try:
-        dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False)
-        dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset.
-        dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution.
-        dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels.
-        dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size.
-        return dataset_kwargs, dataset_obj.name
-    except IOError as err:
-        raise click.ClickException(f'--data: {err}')
-
-#----------------------------------------------------------------------------
-
-def parse_comma_separated_list(s):
-    if isinstance(s, list):
-        return s
-    if s is None or s.lower() == 'none' or s == '':
-        return []
-    return s.split(',')
-
-#----------------------------------------------------------------------------
-
-@click.command()
-
-# Required.
-@click.option('--outdir',       help='Where to save the results', metavar='DIR',                required=True)
-@click.option('--cfg',          help='Base configuration',                                      type=click.Choice(['stylegan3-t', 'stylegan3-r', 'stylegan2']), required=True)
-@click.option('--data',         help='Training data', metavar='[ZIP|DIR]',                      type=str, required=True)
-@click.option('--gpus',         help='Number of GPUs to use', metavar='INT',                    type=click.IntRange(min=1), required=True)
-@click.option('--batch',        help='Total batch size', metavar='INT',                         type=click.IntRange(min=1), required=True)
-@click.option('--gamma',        help='R1 regularization weight', metavar='FLOAT',               type=click.FloatRange(min=0), required=True)
-
-# Optional features.
-@click.option('--cond',         help='Train conditional model', metavar='BOOL',                 type=bool, default=False, show_default=True)
-@click.option('--mirror',       help='Enable dataset x-flips', metavar='BOOL',                  type=bool, default=False, show_default=True)
-@click.option('--aug',          help='Augmentation mode',                                       type=click.Choice(['noaug', 'ada', 'fixed']), default='ada', show_default=True)
-@click.option('--resume',       help='Resume from given network pickle', metavar='[PATH|URL]',  type=str)
-@click.option('--freezed',      help='Freeze first layers of D', metavar='INT',                 type=click.IntRange(min=0), default=0, show_default=True)
-
-# Misc hyperparameters.
-@click.option('--p',            help='Probability for --aug=fixed', metavar='FLOAT',            type=click.FloatRange(min=0, max=1), default=0.2, show_default=True)
-@click.option('--target',       help='Target value for --aug=ada', metavar='FLOAT',             type=click.FloatRange(min=0, max=1), default=0.6, show_default=True)
-@click.option('--batch-gpu',    help='Limit batch size per GPU', metavar='INT',                 type=click.IntRange(min=1))
-@click.option('--cbase',        help='Capacity multiplier', metavar='INT',                      type=click.IntRange(min=1), default=32768, show_default=True)
-@click.option('--cmax',         help='Max. feature maps', metavar='INT',                        type=click.IntRange(min=1), default=512, show_default=True)
-@click.option('--glr',          help='G learning rate  [default: varies]', metavar='FLOAT',     type=click.FloatRange(min=0))
-@click.option('--dlr',          help='D learning rate', metavar='FLOAT',                        type=click.FloatRange(min=0), default=0.002, show_default=True)
-@click.option('--map-depth',    help='Mapping network depth  [default: varies]', metavar='INT', type=click.IntRange(min=1))
-@click.option('--mbstd-group',  help='Minibatch std group size', metavar='INT',                 type=click.IntRange(min=1), default=4, show_default=True)
-
-# Misc settings.
-@click.option('--desc',         help='String to include in result dir name', metavar='STR',     type=str)
-@click.option('--metrics',      help='Quality metrics', metavar='[NAME|A,B,C|none]',            type=parse_comma_separated_list, default='fid50k_full', show_default=True)
-@click.option('--kimg',         help='Total training duration', metavar='KIMG',                 type=click.IntRange(min=1), default=25000, show_default=True)
-@click.option('--tick',         help='How often to print progress', metavar='KIMG',             type=click.IntRange(min=1), default=4, show_default=True)
-@click.option('--snap',         help='How often to save snapshots', metavar='TICKS',            type=click.IntRange(min=1), default=50, show_default=True)
-@click.option('--seed',         help='Random seed', metavar='INT',                              type=click.IntRange(min=0), default=0, show_default=True)
-@click.option('--fp32',         help='Disable mixed-precision', metavar='BOOL',                 type=bool, default=False, show_default=True)
-@click.option('--nobench',      help='Disable cuDNN benchmarking', metavar='BOOL',              type=bool, default=False, show_default=True)
-@click.option('--workers',      help='DataLoader worker processes', metavar='INT',              type=click.IntRange(min=1), default=3, show_default=True)
-@click.option('-n','--dry-run', help='Print training options and exit',                         is_flag=True)
-
-def main(**kwargs):
-    """Train a GAN using the techniques described in the paper
-    "Alias-Free Generative Adversarial Networks".
-
-    Examples:
-
-    \b
-    # Train StyleGAN3-T for AFHQv2 using 8 GPUs.
-    python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \\
-        --gpus=8 --batch=32 --gamma=8.2 --mirror=1
-
-    \b
-    # Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
-    python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \\
-        --gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \\
-        --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
-
-    \b
-    # Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
-    python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \\
-        --gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
-    """
-
-    # Initialize config.
-    opts = dnnlib.EasyDict(kwargs) # Command line arguments.
-    c = dnnlib.EasyDict() # Main config dict.
-    c.G_kwargs = dnnlib.EasyDict(class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict())
-    c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
-    c.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
-    c.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8)
-    c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss')
-    c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2)
-
-    # Training set.
-    c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data)
-    if opts.cond and not c.training_set_kwargs.use_labels:
-        raise click.ClickException('--cond=True requires labels specified in dataset.json')
-    c.training_set_kwargs.use_labels = opts.cond
-    c.training_set_kwargs.xflip = opts.mirror
-
-    # Hyperparameters & settings.
-    c.num_gpus = opts.gpus
-    c.batch_size = opts.batch
-    c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus
-    c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase
-    c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax
-    c.G_kwargs.mapping_kwargs.num_layers = (8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth
-    c.D_kwargs.block_kwargs.freeze_layers = opts.freezed
-    c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group
-    c.loss_kwargs.r1_gamma = opts.gamma
-    c.G_opt_kwargs.lr = (0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr
-    c.D_opt_kwargs.lr = opts.dlr
-    c.metrics = opts.metrics
-    c.total_kimg = opts.kimg
-    c.kimg_per_tick = opts.tick
-    c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap
-    c.random_seed = c.training_set_kwargs.random_seed = opts.seed
-    c.data_loader_kwargs.num_workers = opts.workers
-
-    # Sanity checks.
-    if c.batch_size % c.num_gpus != 0:
-        raise click.ClickException('--batch must be a multiple of --gpus')
-    if c.batch_size % (c.num_gpus * c.batch_gpu) != 0:
-        raise click.ClickException('--batch must be a multiple of --gpus times --batch-gpu')
-    if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size:
-        raise click.ClickException('--batch-gpu cannot be smaller than --mbstd')
-    if any(not metric_main.is_valid_metric(metric) for metric in c.metrics):
-        raise click.ClickException('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
-
-    # Base configuration.
-    c.ema_kimg = c.batch_size * 10 / 32
-    if opts.cfg == 'stylegan2':
-        c.G_kwargs.class_name = 'training.networks_stylegan2.Generator'
-        c.loss_kwargs.style_mixing_prob = 0.9 # Enable style mixing regularization.
-        c.loss_kwargs.pl_weight = 2 # Enable path length regularization.
-        c.G_reg_interval = 4 # Enable lazy regularization for G.
-        c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions.
-        c.loss_kwargs.pl_no_weight_grad = True # Speed up path length regularization by skipping gradient computation wrt. conv2d weights.
-    else:
-        c.G_kwargs.class_name = 'training.networks_stylegan3.Generator'
-        c.G_kwargs.magnitude_ema_beta = 0.5 ** (c.batch_size / (20 * 1e3))
-        if opts.cfg == 'stylegan3-r':
-            c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions.
-            c.G_kwargs.channel_base *= 2 # Double the number of feature maps.
-            c.G_kwargs.channel_max *= 2
-            c.G_kwargs.use_radial_filters = True # Use radially symmetric downsampling filters.
-            c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator.
-            c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Fade out the blur during the first N kimg.
-
-    # Augmentation.
-    if opts.aug != 'noaug':
-        c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1)
-        if opts.aug == 'ada':
-            c.ada_target = opts.target
-        if opts.aug == 'fixed':
-            c.augment_p = opts.p
-
-    # Resume.
-    if opts.resume is not None:
-        c.resume_pkl = opts.resume
-        c.ada_kimg = 100 # Make ADA react faster at the beginning.
-        c.ema_rampup = None # Disable EMA rampup.
-        c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup.
-
-    # Performance-related toggles.
-    if opts.fp32:
-        c.G_kwargs.num_fp16_res = c.D_kwargs.num_fp16_res = 0
-        c.G_kwargs.conv_clamp = c.D_kwargs.conv_clamp = None
-    if opts.nobench:
-        c.cudnn_benchmark = False
-
-    # Description string.
-    desc = f'{opts.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
-    if opts.desc is not None:
-        desc += f'-{opts.desc}'
-
-    # Launch.
-    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    main() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------
diff --git a/spaces/janusurya/mygenchatBot/app.py b/spaces/janusurya/mygenchatBot/app.py
deleted file mode 100644
index 2dbf3ae89c2e3fdab7134107dd346f984dca8eb1..0000000000000000000000000000000000000000
--- a/spaces/janusurya/mygenchatBot/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/jbilcke-hf/image-caption-server/app.py b/spaces/jbilcke-hf/image-caption-server/app.py
deleted file mode 100644
index 6860ee3c40c972030d5d450e59f409cb97c168bb..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/image-caption-server/app.py
+++ /dev/null
@@ -1,159 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-
-import gradio as gr
-import PIL.Image
-import spaces
-import torch
-from transformers import InstructBlipForConditionalGeneration, InstructBlipProcessor
-
-DESCRIPTION = "# InstructBLIP"
-
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
-
-SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model_id = "Salesforce/instructblip-vicuna-7b"
-processor = InstructBlipProcessor.from_pretrained(model_id)
-model = InstructBlipForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
-
-
-@spaces.GPU
-def run(
-    secret_token: str,
-    image: PIL.Image.Image,
-    prompt: str,
-    text_decoding_method: str = "Nucleus sampling",
-    num_beams: int = 5,
-    max_length: int = 256,
-    min_length: int = 1,
-    top_p: float = 0.9,
-    repetition_penalty: float = 1.5,
-    length_penalty: float = 1.0,
-    temperature: float = 1.0,
-) -> str:
-    if secret_token != SECRET_TOKEN:
-        raise gr.Error(
-            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
-
-    h, w = image.size
-    scale = MAX_IMAGE_SIZE / max(h, w)
-    if scale < 1:
-        new_w = int(w * scale)
-        new_h = int(h * scale)
-        image = image.resize((new_w, new_h), resample=PIL.Image.Resampling.LANCZOS)
-
-    inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16)
-    generated_ids = model.generate(
-        **inputs,
-        do_sample=text_decoding_method == "Nucleus sampling",
-        num_beams=num_beams,
-        max_length=max_length,
-        min_length=min_length,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        length_penalty=length_penalty,
-        temperature=temperature,
-    )
-    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-    return generated_caption
-
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-
-    with gr.Row():
-        with gr.Column():
-            secret_token = gr.Textbox(label="Secret token")
-            input_image = gr.Image(type="pil")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button()
-            with gr.Accordion(label="Advanced options", open=False):
-                text_decoding_method = gr.Radio(
-                    label="Text Decoding Method",
-                    choices=["Beam search", "Nucleus sampling"],
-                    value="Nucleus sampling",
-                )
-                num_beams = gr.Slider(
-                    label="Number of Beams",
-                    minimum=1,
-                    maximum=10,
-                    step=1,
-                    value=5,
-                )
-                max_length = gr.Slider(
-                    label="Max Length",
-                    minimum=1,
-                    maximum=512,
-                    step=1,
-                    value=256,
-                )
-                min_length = gr.Slider(
-                    label="Minimum Length",
-                    minimum=1,
-                    maximum=64,
-                    step=1,
-                    value=1,
-                )
-                top_p = gr.Slider(
-                    label="Top P",
-                    minimum=0.1,
-                    maximum=1.0,
-                    step=0.1,
-                    value=0.9,
-                )
-                repetition_penalty = gr.Slider(
-                    label="Repetition Penalty",
-                    info="Larger value prevents repetition.",
-                    minimum=1.0,
-                    maximum=5.0,
-                    step=0.5,
-                    value=1.5,
-                )
-                length_penalty = gr.Slider(
-                    label="Length Penalty",
-                    info="Set to larger for longer sequence, used with beam search.",
-                    minimum=-1.0,
-                    maximum=2.0,
-                    step=0.2,
-                    value=1.0,
-                )
-                temperature = gr.Slider(
-                    label="Temperature",
-                    info="Used with nucleus sampling.",
-                    minimum=0.5,
-                    maximum=1.0,
-                    step=0.1,
-                    value=1.0,
-                )
-
-        with gr.Column():
-            output = gr.Textbox(label="Result")
-
-    gr.on(
-        triggers=[prompt.submit, run_button.click],
-        fn=run,
-        inputs=[
-            secret_token,
-            input_image,
-            prompt,
-            text_decoding_method,
-            num_beams,
-            max_length,
-            min_length,
-            top_p,
-            repetition_penalty,
-            length_penalty,
-            temperature,
-        ],
-        outputs=output,
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
diff --git a/spaces/jkubacki/pokedex/app.py b/spaces/jkubacki/pokedex/app.py
deleted file mode 100644
index 12fdd482430b0a890978c0202dd64c4ce982b381..0000000000000000000000000000000000000000
--- a/spaces/jkubacki/pokedex/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import timm
-from fastai.vision.all import *
-import gradio as gr
-import os
-
-categories = ['Abra', 'Aerodactyl', 'Alakazam', 'Arbok', 'Arcanine', 'Articuno', 'Beedrill', 'Bellsprout', 'Blastoise', 'Bulbasaur', 'Butterfree', 'Caterpie', 'Chansey', 'Charizard', 'Charmander', 'Charmeleon', 'Clefable', 'Clefairy', 'Cloyster', 'Cubone', 'Dewgong', 'Diglett', 'Ditto', 'Dodrio', 'Doduo', 'Dragonair', 'Dragonite', 'Dratini', 'Drowzee', 'Dugtrio', 'Eevee', 'Ekans', 'Electabuzz', 'Electrode', 'Exeggcute', 'Exeggutor', 'Farfetchd', 'Fearow', 'Flareon', 'Gastly', 'Gengar', 'Geodude', 'Gloom', 'Golbat', 'Goldeen', 'Golduck', 'Golem', 'Graveler', 'Grimer', 'Growlithe', 'Gyarados', 'Haunter', 'Hitmonchan', 'Hitmonlee', 'Horsea', 'Hypno', 'Ivysaur', 'Jigglypuff', 'Jolteon', 'Jynx', 'Kabuto', 'Kabutops', 'Kadabra', 'Kakuna', 'Kangaskhan', 'Kingler', 'Koffing', 'Krabby', 'Lapras', 'Lickitung', 'Machamp', 'Machoke', 'Machop', 'Magikarp', 'Magmar', 'Magnemite', 'Magneton', 'Mankey', 'Marowak', 'Meowth', 'Metapod', 'Mew', 'Mewtwo', 'Moltres', 'MrMime', 'Muk', 'Nidoking', 'Nidoqueen', 'Nidorina', 'Nidorino', 'Ninetales', 'Oddish', 'Omanyte', 'Omastar', 'Onix', 'Paras', 'Parasect', 'Persian', 'Pidgeot', 'Pidgeotto', 'Pidgey', 'Pikachu', 'Pinsir', 'Poliwag', 'Poliwhirl', 'Poliwrath', 'Ponyta', 'Porygon', 'Primeape', 'Psyduck', 'Raichu', 'Rapidash', 'Raticate', 'Rattata', 'Rhydon', 'Rhyhorn', 'Sandshrew', 'Sandslash', 'Scyther', 'Seadra', 'Seaking', 'Seel', 'Shellder', 'Slowbro', 'Slowpoke', 'Snorlax', 'Spearow', 'Squirtle', 'Starmie', 'Staryu', 'Tangela', 'Tauros', 'Tentacool', 'Tentacruel', 'Vaporeon', 'Venomoth', 'Venonat', 'Venusaur', 'Victreebel', 'Vileplume', 'Voltorb', 'Vulpix', 'Wartortle', 'Weedle', 'Weepinbell', 'Weezing', 'Wigglytuff', 'Zapdos', 'Zubat']
-
-learn = load_learner('pokemon.pkl')
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-def get_examples(dir_path):
-    files_list = os.listdir(dir_path)
-    full_paths_list = [os.path.join(dir_path, file) for file in files_list]
-    return sorted(full_paths_list)
-
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = get_examples('examples')
-
-inf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, live=True)
-inf.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/ZONEMD.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/ZONEMD.py
deleted file mode 100644
index 3062843b6342fec269aa635088bef9a5eacb2b20..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/ZONEMD.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
-
-import binascii
-import struct
-
-import dns.immutable
-import dns.rdata
-import dns.rdatatype
-import dns.zonetypes
-
-
-@dns.immutable.immutable
-class ZONEMD(dns.rdata.Rdata):
-
-    """ZONEMD record"""
-
-    # See RFC 8976
-
-    __slots__ = ["serial", "scheme", "hash_algorithm", "digest"]
-
-    def __init__(self, rdclass, rdtype, serial, scheme, hash_algorithm, digest):
-        super().__init__(rdclass, rdtype)
-        self.serial = self._as_uint32(serial)
-        self.scheme = dns.zonetypes.DigestScheme.make(scheme)
-        self.hash_algorithm = dns.zonetypes.DigestHashAlgorithm.make(hash_algorithm)
-        self.digest = self._as_bytes(digest)
-
-        if self.scheme == 0:  # reserved, RFC 8976 Sec. 5.2
-            raise ValueError("scheme 0 is reserved")
-        if self.hash_algorithm == 0:  # reserved, RFC 8976 Sec. 5.3
-            raise ValueError("hash_algorithm 0 is reserved")
-
-        hasher = dns.zonetypes._digest_hashers.get(self.hash_algorithm)
-        if hasher and hasher().digest_size != len(self.digest):
-            raise ValueError("digest length inconsistent with hash algorithm")
-
-    def to_text(self, origin=None, relativize=True, **kw):
-        kw = kw.copy()
-        chunksize = kw.pop("chunksize", 128)
-        return "%d %d %d %s" % (
-            self.serial,
-            self.scheme,
-            self.hash_algorithm,
-            dns.rdata._hexify(self.digest, chunksize=chunksize, **kw),
-        )
-
-    @classmethod
-    def from_text(
-        cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None
-    ):
-        serial = tok.get_uint32()
-        scheme = tok.get_uint8()
-        hash_algorithm = tok.get_uint8()
-        digest = tok.concatenate_remaining_identifiers().encode()
-        digest = binascii.unhexlify(digest)
-        return cls(rdclass, rdtype, serial, scheme, hash_algorithm, digest)
-
-    def _to_wire(self, file, compress=None, origin=None, canonicalize=False):
-        header = struct.pack("!IBB", self.serial, self.scheme, self.hash_algorithm)
-        file.write(header)
-        file.write(self.digest)
-
-    @classmethod
-    def from_wire_parser(cls, rdclass, rdtype, parser, origin=None):
-        header = parser.get_struct("!IBB")
-        digest = parser.get_remaining()
-        return cls(rdclass, rdtype, header[0], header[1], header[2], digest)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/filenames.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/filenames.py
deleted file mode 100644
index d279f89cc82cc280370d09ebdb16cb301f62aa57..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/filenames.py
+++ /dev/null
@@ -1,246 +0,0 @@
-"""
-This module implements the algorithm for converting between a "user name" -
-something that a user can choose arbitrarily inside a font editor - and a file
-name suitable for use in a wide range of operating systems and filesystems.
-
-The `UFO 3 specification <http://unifiedfontobject.org/versions/ufo3/conventions/>`_
-provides an example of an algorithm for such conversion, which avoids illegal
-characters, reserved file names, ambiguity between upper- and lower-case
-characters, and clashes with existing files.
-
-This code was originally copied from
-`ufoLib <https://github.com/unified-font-object/ufoLib/blob/8747da7/Lib/ufoLib/filenames.py>`_
-by Tal Leming and is copyright (c) 2005-2016, The RoboFab Developers:
-
--	Erik van Blokland
--	Tal Leming
--	Just van Rossum
-"""
-
-
-illegalCharacters = r"\" * + / : < > ? [ \ ] | \0".split(" ")
-illegalCharacters += [chr(i) for i in range(1, 32)]
-illegalCharacters += [chr(0x7F)]
-reservedFileNames = "CON PRN AUX CLOCK$ NUL A:-Z: COM1".lower().split(" ")
-reservedFileNames += "LPT1 LPT2 LPT3 COM2 COM3 COM4".lower().split(" ")
-maxFileNameLength = 255
-
-
-class NameTranslationError(Exception):
-    pass
-
-
-def userNameToFileName(userName, existing=[], prefix="", suffix=""):
-    """Converts from a user name to a file name.
-
-    Takes care to avoid illegal characters, reserved file names, ambiguity between
-    upper- and lower-case characters, and clashes with existing files.
-
-    Args:
-            userName (str): The input file name.
-            existing: A case-insensitive list of all existing file names.
-            prefix: Prefix to be prepended to the file name.
-            suffix: Suffix to be appended to the file name.
-
-    Returns:
-            A suitable filename.
-
-    Raises:
-            NameTranslationError: If no suitable name could be generated.
-
-    Examples::
-
-            >>> userNameToFileName("a") == "a"
-            True
-            >>> userNameToFileName("A") == "A_"
-            True
-            >>> userNameToFileName("AE") == "A_E_"
-            True
-            >>> userNameToFileName("Ae") == "A_e"
-            True
-            >>> userNameToFileName("ae") == "ae"
-            True
-            >>> userNameToFileName("aE") == "aE_"
-            True
-            >>> userNameToFileName("a.alt") == "a.alt"
-            True
-            >>> userNameToFileName("A.alt") == "A_.alt"
-            True
-            >>> userNameToFileName("A.Alt") == "A_.A_lt"
-            True
-            >>> userNameToFileName("A.aLt") == "A_.aL_t"
-            True
-            >>> userNameToFileName(u"A.alT") == "A_.alT_"
-            True
-            >>> userNameToFileName("T_H") == "T__H_"
-            True
-            >>> userNameToFileName("T_h") == "T__h"
-            True
-            >>> userNameToFileName("t_h") == "t_h"
-            True
-            >>> userNameToFileName("F_F_I") == "F__F__I_"
-            True
-            >>> userNameToFileName("f_f_i") == "f_f_i"
-            True
-            >>> userNameToFileName("Aacute_V.swash") == "A_acute_V_.swash"
-            True
-            >>> userNameToFileName(".notdef") == "_notdef"
-            True
-            >>> userNameToFileName("con") == "_con"
-            True
-            >>> userNameToFileName("CON") == "C_O_N_"
-            True
-            >>> userNameToFileName("con.alt") == "_con.alt"
-            True
-            >>> userNameToFileName("alt.con") == "alt._con"
-            True
-    """
-    # the incoming name must be a str
-    if not isinstance(userName, str):
-        raise ValueError("The value for userName must be a string.")
-    # establish the prefix and suffix lengths
-    prefixLength = len(prefix)
-    suffixLength = len(suffix)
-    # replace an initial period with an _
-    # if no prefix is to be added
-    if not prefix and userName[0] == ".":
-        userName = "_" + userName[1:]
-    # filter the user name
-    filteredUserName = []
-    for character in userName:
-        # replace illegal characters with _
-        if character in illegalCharacters:
-            character = "_"
-        # add _ to all non-lower characters
-        elif character != character.lower():
-            character += "_"
-        filteredUserName.append(character)
-    userName = "".join(filteredUserName)
-    # clip to 255
-    sliceLength = maxFileNameLength - prefixLength - suffixLength
-    userName = userName[:sliceLength]
-    # test for illegal files names
-    parts = []
-    for part in userName.split("."):
-        if part.lower() in reservedFileNames:
-            part = "_" + part
-        parts.append(part)
-    userName = ".".join(parts)
-    # test for clash
-    fullName = prefix + userName + suffix
-    if fullName.lower() in existing:
-        fullName = handleClash1(userName, existing, prefix, suffix)
-    # finished
-    return fullName
-
-
-def handleClash1(userName, existing=[], prefix="", suffix=""):
-    """
-    existing should be a case-insensitive list
-    of all existing file names.
-
-    >>> prefix = ("0" * 5) + "."
-    >>> suffix = "." + ("0" * 10)
-    >>> existing = ["a" * 5]
-
-    >>> e = list(existing)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000001.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.append(prefix + "aaaaa" + "1".zfill(15) + suffix)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000002.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.append(prefix + "AAAAA" + "2".zfill(15) + suffix)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000001.0000000000')
-    True
-    """
-    # if the prefix length + user name length + suffix length + 15 is at
-    # or past the maximum length, silce 15 characters off of the user name
-    prefixLength = len(prefix)
-    suffixLength = len(suffix)
-    if prefixLength + len(userName) + suffixLength + 15 > maxFileNameLength:
-        l = prefixLength + len(userName) + suffixLength + 15
-        sliceLength = maxFileNameLength - l
-        userName = userName[:sliceLength]
-    finalName = None
-    # try to add numbers to create a unique name
-    counter = 1
-    while finalName is None:
-        name = userName + str(counter).zfill(15)
-        fullName = prefix + name + suffix
-        if fullName.lower() not in existing:
-            finalName = fullName
-            break
-        else:
-            counter += 1
-        if counter >= 999999999999999:
-            break
-    # if there is a clash, go to the next fallback
-    if finalName is None:
-        finalName = handleClash2(existing, prefix, suffix)
-    # finished
-    return finalName
-
-
-def handleClash2(existing=[], prefix="", suffix=""):
-    """
-    existing should be a case-insensitive list
-    of all existing file names.
-
-    >>> prefix = ("0" * 5) + "."
-    >>> suffix = "." + ("0" * 10)
-    >>> existing = [prefix + str(i) + suffix for i in range(100)]
-
-    >>> e = list(existing)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.100.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.remove(prefix + "1" + suffix)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.1.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.remove(prefix + "2" + suffix)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.2.0000000000')
-    True
-    """
-    # calculate the longest possible string
-    maxLength = maxFileNameLength - len(prefix) - len(suffix)
-    maxValue = int("9" * maxLength)
-    # try to find a number
-    finalName = None
-    counter = 1
-    while finalName is None:
-        fullName = prefix + str(counter) + suffix
-        if fullName.lower() not in existing:
-            finalName = fullName
-            break
-        else:
-            counter += 1
-        if counter >= maxValue:
-            break
-    # raise an error if nothing has been found
-    if finalName is None:
-        raise NameTranslationError("No unique name could be found.")
-    # finished
-    return finalName
-
-
-if __name__ == "__main__":
-    import doctest
-    import sys
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/jonigata/PoseMaker2/fileservice.py b/spaces/jonigata/PoseMaker2/fileservice.py
deleted file mode 100644
index f0dee76b9349f2a29e42dffedf20ea7302a54fa8..0000000000000000000000000000000000000000
--- a/spaces/jonigata/PoseMaker2/fileservice.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from fastapi import FastAPI, Request, Response
-
-filenames = ["js/poseMaker.js"]
-contents = '\n'.join([f"<script type='text/javascript' src='{x}'></script>" for x in filenames])
-
-app = FastAPI()
-
-@app.middleware("http")
-async def insert_js(request: Request, call_next):
-    path = request.scope['path']  # get the request route
-    response = await call_next(request)
-    
-    if path == "/":
-        response_body = ""
-        async for chunk in response.body_iterator:
-            response_body += chunk.decode()
-
-        response_body = response_body.replace("</body>", contents + "</body>")
-
-        del response.headers["content-length"]
-
-        return Response(
-            content=response_body,
-            status_code=response.status_code, 
-            headers=dict(response.headers),
-            media_type=response.media_type
-        )
-
-    return response
\ No newline at end of file
diff --git a/spaces/joushe/moe-tts/text/thai.py b/spaces/joushe/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/examples/t5/mt5/__init__.py b/spaces/juancopi81/youtube-music-transcribe/t5x/examples/t5/mt5/__init__.py
deleted file mode 100644
index da022c16301721a096a208e8bdb2a71bb87f9788..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/examples/t5/mt5/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# 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.
-
-# This empty file is needed for loading the gin files in this directory.
diff --git a/spaces/justin-zk/Personalize-SAM/app.py b/spaces/justin-zk/Personalize-SAM/app.py
deleted file mode 100644
index 0b1d36050d78c1139a728f79b567a093a19f7a4f..0000000000000000000000000000000000000000
--- a/spaces/justin-zk/Personalize-SAM/app.py
+++ /dev/null
@@ -1,543 +0,0 @@
-# --------------------------------------------------------
-# PersonalizeSAM -- Personalize Segment Anything Model with One Shot
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-from PIL import Image
-import torch
-import torch.nn as nn
-import gradio as gr
-import numpy as np
-from torch.nn import functional as F
-
-from show import *
-from per_segment_anything import sam_model_registry, SamPredictor
-
-
-class Mask_Weights(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.weights = nn.Parameter(torch.ones(2, 1, requires_grad=True) / 3)
-
-
-def point_selection(mask_sim, topk=1):
-    # Top-1 point selection
-    w, h = mask_sim.shape
-    topk_xy = mask_sim.flatten(0).topk(topk)[1]
-    topk_x = (topk_xy // h).unsqueeze(0)
-    topk_y = (topk_xy - topk_x * h)
-    topk_xy = torch.cat((topk_y, topk_x), dim=0).permute(1, 0)
-    topk_label = np.array([1] * topk)
-    topk_xy = topk_xy.cpu().numpy()
-
-    # Top-last point selection
-    last_xy = mask_sim.flatten(0).topk(topk, largest=False)[1]
-    last_x = (last_xy // h).unsqueeze(0)
-    last_y = (last_xy - last_x * h)
-    last_xy = torch.cat((last_y, last_x), dim=0).permute(1, 0)
-    last_label = np.array([0] * topk)
-    last_xy = last_xy.cpu().numpy()
-
-    return topk_xy, topk_label, last_xy, last_label
-
-
-def calculate_dice_loss(inputs, targets, num_masks = 1):
-    """
-    Compute the DICE loss, similar to generalized IOU for masks
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-    """
-    inputs = inputs.sigmoid()
-    inputs = inputs.flatten(1)
-    numerator = 2 * (inputs * targets).sum(-1)
-    denominator = inputs.sum(-1) + targets.sum(-1)
-    loss = 1 - (numerator + 1) / (denominator + 1)
-    return loss.sum() / num_masks
-
-
-def calculate_sigmoid_focal_loss(inputs, targets, num_masks = 1, alpha: float = 0.25, gamma: float = 2):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-        alpha: (optional) Weighting factor in range (0,1) to balance
-                positive vs negative examples. Default = -1 (no weighting).
-        gamma: Exponent of the modulating factor (1 - p_t) to
-               balance easy vs hard examples.
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    return loss.mean(1).sum() / num_masks
-
-
-def inference(ic_image, ic_mask, image1, image2):
-    # in context image and mask
-    ic_image = np.array(ic_image.convert("RGB"))
-    ic_mask = np.array(ic_mask.convert("RGB"))
-
-    sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
-    sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
-    # sam = sam_model_registry[sam_type](checkpoint=sam_ckpt)
-    predictor = SamPredictor(sam)
-
-    # Image features encoding
-    ref_mask = predictor.set_image(ic_image, ic_mask)
-    ref_feat = predictor.features.squeeze().permute(1, 2, 0)
-
-    ref_mask = F.interpolate(ref_mask, size=ref_feat.shape[0: 2], mode="bilinear")
-    ref_mask = ref_mask.squeeze()[0]
-
-    # Target feature extraction
-    print("======> Obtain Location Prior" )
-    target_feat = ref_feat[ref_mask > 0]
-    target_embedding = target_feat.mean(0).unsqueeze(0)
-    target_feat = target_embedding / target_embedding.norm(dim=-1, keepdim=True)
-    target_embedding = target_embedding.unsqueeze(0)
-
-    output_image = []
-
-    for test_image in [image1, image2]:
-        print("======> Testing Image" )
-        test_image = np.array(test_image.convert("RGB"))
-
-        # Image feature encoding
-        predictor.set_image(test_image)
-        test_feat = predictor.features.squeeze()
-
-        # Cosine similarity
-        C, h, w = test_feat.shape
-        test_feat = test_feat / test_feat.norm(dim=0, keepdim=True)
-        test_feat = test_feat.reshape(C, h * w)
-        sim = target_feat @ test_feat
-
-        sim = sim.reshape(1, 1, h, w)
-        sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
-        sim = predictor.model.postprocess_masks(
-            sim,
-            input_size=predictor.input_size,
-            original_size=predictor.original_size).squeeze()
-
-        # Positive-negative location prior
-        topk_xy_i, topk_label_i, last_xy_i, last_label_i = point_selection(sim, topk=1)
-        topk_xy = np.concatenate([topk_xy_i, last_xy_i], axis=0)
-        topk_label = np.concatenate([topk_label_i, last_label_i], axis=0)
-
-        # Obtain the target guidance for cross-attention layers
-        sim = (sim - sim.mean()) / torch.std(sim)
-        sim = F.interpolate(sim.unsqueeze(0).unsqueeze(0), size=(64, 64), mode="bilinear")
-        attn_sim = sim.sigmoid_().unsqueeze(0).flatten(3)
-
-        # First-step prediction
-        masks, scores, logits, _ = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            multimask_output=False,
-            attn_sim=attn_sim,  # Target-guided Attention
-            target_embedding=target_embedding  # Target-semantic Prompting
-        )
-        best_idx = 0
-
-        # Cascaded Post-refinement-1
-        masks, scores, logits, _ = predictor.predict(
-                    point_coords=topk_xy,
-                    point_labels=topk_label,
-                    mask_input=logits[best_idx: best_idx + 1, :, :],
-                    multimask_output=True)
-        best_idx = np.argmax(scores)
-
-        # Cascaded Post-refinement-2
-        y, x = np.nonzero(masks[best_idx])
-        x_min = x.min()
-        x_max = x.max()
-        y_min = y.min()
-        y_max = y.max()
-        input_box = np.array([x_min, y_min, x_max, y_max])
-        masks, scores, logits, _ = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            box=input_box[None, :],
-            mask_input=logits[best_idx: best_idx + 1, :, :],
-            multimask_output=True)
-        best_idx = np.argmax(scores)
-
-        final_mask = masks[best_idx]
-        mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
-        mask_colors[final_mask, :] = np.array([[128, 0, 0]])
-        output_image.append(Image.fromarray((mask_colors * 0.6 + test_image * 0.4).astype('uint8'), 'RGB'))
-
-    return output_image[0].resize((224, 224)), output_image[1].resize((224, 224))
-
-
-def inference_scribble(image, image1, image2):
-    # in context image and mask
-    ic_image = image["image"]
-    ic_mask = image["mask"]
-    ic_image = np.array(ic_image.convert("RGB"))
-    ic_mask = np.array(ic_mask.convert("RGB"))
-
-    sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
-    sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
-    # sam = sam_model_registry[sam_type](checkpoint=sam_ckpt)
-    predictor = SamPredictor(sam)
-
-    # Image features encoding
-    ref_mask = predictor.set_image(ic_image, ic_mask)
-    ref_feat = predictor.features.squeeze().permute(1, 2, 0)
-
-    ref_mask = F.interpolate(ref_mask, size=ref_feat.shape[0: 2], mode="bilinear")
-    ref_mask = ref_mask.squeeze()[0]
-
-    # Target feature extraction
-    print("======> Obtain Location Prior" )
-    target_feat = ref_feat[ref_mask > 0]
-    target_embedding = target_feat.mean(0).unsqueeze(0)
-    target_feat = target_embedding / target_embedding.norm(dim=-1, keepdim=True)
-    target_embedding = target_embedding.unsqueeze(0)
-
-    output_image = []
-
-    for test_image in [image1, image2]:
-        print("======> Testing Image" )
-        test_image = np.array(test_image.convert("RGB"))
-
-        # Image feature encoding
-        predictor.set_image(test_image)
-        test_feat = predictor.features.squeeze()
-
-        # Cosine similarity
-        C, h, w = test_feat.shape
-        test_feat = test_feat / test_feat.norm(dim=0, keepdim=True)
-        test_feat = test_feat.reshape(C, h * w)
-        sim = target_feat @ test_feat
-
-        sim = sim.reshape(1, 1, h, w)
-        sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
-        sim = predictor.model.postprocess_masks(
-            sim,
-            input_size=predictor.input_size,
-            original_size=predictor.original_size).squeeze()
-
-        # Positive-negative location prior
-        topk_xy_i, topk_label_i, last_xy_i, last_label_i = point_selection(sim, topk=1)
-        topk_xy = np.concatenate([topk_xy_i, last_xy_i], axis=0)
-        topk_label = np.concatenate([topk_label_i, last_label_i], axis=0)
-
-        # Obtain the target guidance for cross-attention layers
-        sim = (sim - sim.mean()) / torch.std(sim)
-        sim = F.interpolate(sim.unsqueeze(0).unsqueeze(0), size=(64, 64), mode="bilinear")
-        attn_sim = sim.sigmoid_().unsqueeze(0).flatten(3)
-
-        # First-step prediction
-        masks, scores, logits, _ = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            multimask_output=False,
-            attn_sim=attn_sim,  # Target-guided Attention
-            target_embedding=target_embedding  # Target-semantic Prompting
-        )
-        best_idx = 0
-
-        # Cascaded Post-refinement-1
-        masks, scores, logits, _ = predictor.predict(
-                    point_coords=topk_xy,
-                    point_labels=topk_label,
-                    mask_input=logits[best_idx: best_idx + 1, :, :],
-                    multimask_output=True)
-        best_idx = np.argmax(scores)
-
-        # Cascaded Post-refinement-2
-        y, x = np.nonzero(masks[best_idx])
-        x_min = x.min()
-        x_max = x.max()
-        y_min = y.min()
-        y_max = y.max()
-        input_box = np.array([x_min, y_min, x_max, y_max])
-        masks, scores, logits, _ = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            box=input_box[None, :],
-            mask_input=logits[best_idx: best_idx + 1, :, :],
-            multimask_output=True)
-        best_idx = np.argmax(scores)
-
-        final_mask = masks[best_idx]
-        mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
-        mask_colors[final_mask, :] = np.array([[128, 0, 0]])
-        output_image.append(Image.fromarray((mask_colors * 0.6 + test_image * 0.4).astype('uint8'), 'RGB'))
-
-    return output_image[0].resize((224, 224)), output_image[1].resize((224, 224))
-
-
-def inference_finetune(ic_image, ic_mask, image1, image2):
-    # in context image and mask
-    ic_image = np.array(ic_image.convert("RGB"))
-    ic_mask = np.array(ic_mask.convert("RGB"))
-
-    gt_mask = torch.tensor(ic_mask)[:, :, 0] > 0
-    gt_mask = gt_mask.float().unsqueeze(0).flatten(1).cuda()
-    # gt_mask = gt_mask.float().unsqueeze(0).flatten(1)
-
-    sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
-    sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
-    # sam = sam_model_registry[sam_type](checkpoint=sam_ckpt)
-    for name, param in sam.named_parameters():
-        param.requires_grad = False
-    predictor = SamPredictor(sam)
-
-    print("======> Obtain Self Location Prior" )
-    # Image features encoding
-    ref_mask = predictor.set_image(ic_image, ic_mask)
-    ref_feat = predictor.features.squeeze().permute(1, 2, 0)
-
-    ref_mask = F.interpolate(ref_mask, size=ref_feat.shape[0: 2], mode="bilinear")
-    ref_mask = ref_mask.squeeze()[0]
-
-    # Target feature extraction
-    target_feat = ref_feat[ref_mask > 0]
-    target_feat_mean = target_feat.mean(0)
-    target_feat_max = torch.max(target_feat, dim=0)[0]
-    target_feat = (target_feat_max / 2 + target_feat_mean / 2).unsqueeze(0)
-
-    # Cosine similarity
-    h, w, C = ref_feat.shape
-    target_feat = target_feat / target_feat.norm(dim=-1, keepdim=True)
-    ref_feat = ref_feat / ref_feat.norm(dim=-1, keepdim=True)
-    ref_feat = ref_feat.permute(2, 0, 1).reshape(C, h * w)
-    sim = target_feat @ ref_feat
-
-    sim = sim.reshape(1, 1, h, w)
-    sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
-    sim = predictor.model.postprocess_masks(
-                    sim,
-                    input_size=predictor.input_size,
-                    original_size=predictor.original_size).squeeze()
-
-    # Positive location prior
-    topk_xy, topk_label, _, _ = point_selection(sim, topk=1)
-
-    print('======> Start Training')
-    # Learnable mask weights
-    mask_weights = Mask_Weights().cuda()
-    # mask_weights = Mask_Weights()
-    mask_weights.train()
-    train_epoch = 100
-    optimizer = torch.optim.AdamW(mask_weights.parameters(), lr=1e-3, eps=1e-4)
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, train_epoch)
-
-    for train_idx in range(train_epoch):
-        # Run the decoder
-        masks, scores, logits, logits_high = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            multimask_output=True)
-        logits_high = logits_high.flatten(1)
-
-        # Weighted sum three-scale masks
-        weights = torch.cat((1 - mask_weights.weights.sum(0).unsqueeze(0), mask_weights.weights), dim=0)
-        logits_high = logits_high * weights
-        logits_high = logits_high.sum(0).unsqueeze(0)
-
-        dice_loss = calculate_dice_loss(logits_high, gt_mask)
-        focal_loss = calculate_sigmoid_focal_loss(logits_high, gt_mask)
-        loss = dice_loss + focal_loss
-
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-        scheduler.step()
-
-        if train_idx % 10 == 0:
-            print('Train Epoch: {:} / {:}'.format(train_idx, train_epoch))
-            current_lr = scheduler.get_last_lr()[0]
-            print('LR: {:.6f}, Dice_Loss: {:.4f}, Focal_Loss: {:.4f}'.format(current_lr, dice_loss.item(), focal_loss.item()))
-
-
-    mask_weights.eval()
-    weights = torch.cat((1 - mask_weights.weights.sum(0).unsqueeze(0), mask_weights.weights), dim=0)
-    weights_np = weights.detach().cpu().numpy()
-    print('======> Mask weights:\n', weights_np)
-
-    print('======> Start Testing')
-    output_image = []
-
-    for test_image in [image1, image2]:
-        test_image = np.array(test_image.convert("RGB"))
-
-        # Image feature encoding
-        predictor.set_image(test_image)
-        test_feat = predictor.features.squeeze()
-         # Image feature encoding
-        predictor.set_image(test_image)
-        test_feat = predictor.features.squeeze()
-
-        # Cosine similarity
-        C, h, w = test_feat.shape
-        test_feat = test_feat / test_feat.norm(dim=0, keepdim=True)
-        test_feat = test_feat.reshape(C, h * w)
-        sim = target_feat @ test_feat
-
-        sim = sim.reshape(1, 1, h, w)
-        sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
-        sim = predictor.model.postprocess_masks(
-                        sim,
-                        input_size=predictor.input_size,
-                        original_size=predictor.original_size).squeeze()
-
-        # Positive location prior
-        topk_xy, topk_label, _, _ = point_selection(sim, topk=1)
-
-        # First-step prediction
-        masks, scores, logits, logits_high = predictor.predict(
-                    point_coords=topk_xy,
-                    point_labels=topk_label,
-                    multimask_output=True)
-
-        # Weighted sum three-scale masks
-        logits_high = logits_high * weights.unsqueeze(-1)
-        logit_high = logits_high.sum(0)
-        mask = (logit_high > 0).detach().cpu().numpy()
-
-        logits = logits * weights_np[..., None]
-        logit = logits.sum(0)
-
-        # Cascaded Post-refinement-1
-        y, x = np.nonzero(mask)
-        x_min = x.min()
-        x_max = x.max()
-        y_min = y.min()
-        y_max = y.max()
-        input_box = np.array([x_min, y_min, x_max, y_max])
-        masks, scores, logits, _ = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            box=input_box[None, :],
-            mask_input=logit[None, :, :],
-            multimask_output=True)
-        best_idx = np.argmax(scores)
-
-        # Cascaded Post-refinement-2
-        y, x = np.nonzero(masks[best_idx])
-        x_min = x.min()
-        x_max = x.max()
-        y_min = y.min()
-        y_max = y.max()
-        input_box = np.array([x_min, y_min, x_max, y_max])
-        masks, scores, logits, _ = predictor.predict(
-            point_coords=topk_xy,
-            point_labels=topk_label,
-            box=input_box[None, :],
-            mask_input=logits[best_idx: best_idx + 1, :, :],
-            multimask_output=True)
-        best_idx = np.argmax(scores)
-
-        final_mask = masks[best_idx]
-        mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
-        mask_colors[final_mask, :] = np.array([[128, 0, 0]])
-        output_image.append(Image.fromarray((mask_colors * 0.6 + test_image * 0.4).astype('uint8'), 'RGB'))
-
-    return output_image[0].resize((224, 224)), output_image[1].resize((224, 224))
-
-
-description = """
-<div style="text-align: center; font-weight: bold;">
-    <span style="font-size: 18px" id="paper-info">
-        [<a href="https://github.com/ZrrSkywalker/Personalize-SAM" target="_blank"><font color='black'>Github</font></a>]
-        [<a href="https://arxiv.org/pdf/2305.03048.pdf" target="_blank"><font color='black'>Paper</font></a>]
-    </span>
-</div>
-"""
-
-main = gr.Interface(
-    fn=inference,
-    inputs=[
-        gr.Image(label="one-shot image", type='pil'),
-        gr.Image(label="one-shot mask", type='pil'),
-        gr.Image(label="test image1", type='pil'),
-        gr.Image(label="test image2", type='pil'),
-    ],
-    outputs=[
-        gr.Image(label="output image1", type='pil').style(height=256, width=256),
-        gr.Image(label="output image2", type='pil').style(height=256, width=256),
-    ],
-    allow_flagging="never",
-    cache_examples=False,
-    title="Personalize Segment Anything Model with 1 Shot",
-    description=description,
-    examples=[
-        ["./examples/cat_00.jpg", "./examples/cat_00.png", "./examples/cat_01.jpg", "./examples/cat_02.jpg"],
-        ["./examples/colorful_sneaker_00.jpg", "./examples/colorful_sneaker_00.png", "./examples/colorful_sneaker_01.jpg", "./examples/colorful_sneaker_02.jpg"],
-        ["./examples/duck_toy_00.jpg", "./examples/duck_toy_00.png", "./examples/duck_toy_01.jpg", "./examples/duck_toy_02.jpg"],
-    ]
-)
-
-
-main_scribble = gr.Interface(
-    fn=inference_scribble,
-    inputs=[
-        gr.ImageMask(label="[Stroke] Draw on Image", brush_radius=4, type='pil'),
-        gr.Image(label="test image1", type='pil'),
-        gr.Image(label="test image2", type='pil'),
-    ],
-    outputs=[
-        gr.Image(label="output image1", type='pil').style(height=256, width=256),
-        gr.Image(label="output image2", type='pil').style(height=256, width=256),
-    ],
-    allow_flagging="never",
-    cache_examples=False,
-    title="Personalize Segment Anything Model with 1 Shot",
-    description=description,
-    examples=[
-        ["./examples/cat_00.jpg", "./examples/cat_01.jpg", "./examples/cat_02.jpg"],
-        ["./examples/colorful_sneaker_00.jpg", "./examples/colorful_sneaker_01.jpg", "./examples/colorful_sneaker_02.jpg"],
-        ["./examples/duck_toy_00.jpg", "./examples/duck_toy_01.jpg", "./examples/duck_toy_02.jpg"],
-    ]
-)
-
-
-
-main_finetune = gr.Interface(
-    fn=inference_finetune,
-    inputs=[
-        gr.Image(label="one-shot image", type='pil'),
-        gr.Image(label="one-shot mask", type='pil'),
-        gr.Image(label="test image1", type='pil'),
-        gr.Image(label="test image2", type='pil'),
-    ],
-    outputs=[
-        gr.Image(label="output image1", type='pil').style(height=256, width=256),
-        gr.Image(label="output image2", type='pil').style(height=256, width=256),
-    ],
-    allow_flagging="never",
-    cache_examples=False,
-    title="Personalize Segment Anything Model with 1 Shot",
-    description=description,
-    examples=[
-        ["./examples/cat_00.jpg", "./examples/cat_00.png", "./examples/cat_01.jpg", "./examples/cat_02.jpg"],
-        ["./examples/colorful_sneaker_00.jpg", "./examples/colorful_sneaker_00.png", "./examples/colorful_sneaker_01.jpg", "./examples/colorful_sneaker_02.jpg"],
-        ["./examples/duck_toy_00.jpg", "./examples/duck_toy_00.png", "./examples/duck_toy_01.jpg", "./examples/duck_toy_02.jpg"],
-    ]
-)
-demo = gr.Blocks()
-with demo:
-    gr.TabbedInterface(
-        [main, main_scribble, main_finetune],
-        ["PerSAM", " PerSAM-Scribble", "PerSAM-F"],
-    )
-
-demo.launch(enable_queue=False)
\ No newline at end of file
diff --git a/spaces/ka1kuk/fastapi/g4f/Provider/__init__.py b/spaces/ka1kuk/fastapi/g4f/Provider/__init__.py
deleted file mode 100644
index 78e35473b9a06946395fc12b3053fbfbee94ed6b..0000000000000000000000000000000000000000
--- a/spaces/ka1kuk/fastapi/g4f/Provider/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from . import Provider
-from .Providers import (
-    ChatgptAi,
-    DeepAi,
-    H2o,
-    Phind,
-    ChatFree,
-    Ava,
-    Liaobots
-)
-
diff --git a/spaces/kael558/Interpolation/README.md b/spaces/kael558/Interpolation/README.md
deleted file mode 100644
index 44029aafc0732947d649dad9b1e5f2ebda8c7c60..0000000000000000000000000000000000000000
--- a/spaces/kael558/Interpolation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Interpolation
-emoji: 🐢
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.10.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/demo_cli.py b/spaces/keithhon/Real-Time-Voice-Cloning/demo_cli.py
deleted file mode 100644
index 59cdf78169fb53a97464e3b11048a90ca03885e3..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/demo_cli.py
+++ /dev/null
@@ -1,203 +0,0 @@
-from encoder.params_model import model_embedding_size as speaker_embedding_size
-from utils.argutils import print_args
-from utils.modelutils import check_model_paths
-from synthesizer.inference import Synthesizer
-from encoder import inference as encoder
-from vocoder import inference as vocoder
-from pathlib import Path
-import numpy as np
-import soundfile as sf
-import librosa
-import argparse
-import torch
-import sys
-import os
-from audioread.exceptions import NoBackendError
-
-if __name__ == '__main__':
-    ## Info & args
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument("-e", "--enc_model_fpath", type=Path, 
-                        default="encpretrained.pt",
-                        help="Path to a saved encoder")
-    parser.add_argument("-s", "--syn_model_fpath", type=Path, 
-                        default="synpretrained.pt",
-                        help="Path to a saved synthesizer")
-    parser.add_argument("-v", "--voc_model_fpath", type=Path, 
-                        default="vocpretrained.pt",
-                        help="Path to a saved vocoder")
-    parser.add_argument("--cpu", action="store_true", help="If True, processing is done on CPU, even when a GPU is available.")
-    parser.add_argument("--no_sound", action="store_true", help="If True, audio won't be played.")
-    parser.add_argument("--seed", type=int, default=None, help="Optional random number seed value to make toolbox deterministic.")
-    parser.add_argument("--no_mp3_support", action="store_true", help="If True, disallows loading mp3 files to prevent audioread errors when ffmpeg is not installed.")
-    parser.add_argument("-audio", "--audio_path", type=Path, required = True,
-                        help="Path to a audio file")
-    parser.add_argument("--text", type=str, required = True, help="Text Input")
-    args = parser.parse_args()
-    print_args(args, parser)
-    if not args.no_sound:
-        import sounddevice as sd
-
-    if args.cpu:
-        # Hide GPUs from Pytorch to force CPU processing
-        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
-    if not args.no_mp3_support:
-        try:
-            librosa.load("samples/1320_00000.mp3")
-        except NoBackendError:
-            print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
-                  "Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
-            exit(-1)
-        
-    print("Running a test of your configuration...\n")
-        
-    if torch.cuda.is_available():
-        device_id = torch.cuda.current_device()
-        gpu_properties = torch.cuda.get_device_properties(device_id)
-        ## Print some environment information (for debugging purposes)
-        print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
-            "%.1fGb total memory.\n" % 
-            (torch.cuda.device_count(),
-            device_id,
-            gpu_properties.name,
-            gpu_properties.major,
-            gpu_properties.minor,
-            gpu_properties.total_memory / 1e9))
-    else:
-        print("Using CPU for inference.\n")
-    
-    ## Remind the user to download pretrained models if needed
-    check_model_paths(encoder_path=args.enc_model_fpath,
-                      synthesizer_path=args.syn_model_fpath,
-                      vocoder_path=args.voc_model_fpath)
-    
-    ## Load the models one by one.
-    print("Preparing the encoder, the synthesizer and the vocoder...")
-    encoder.load_model(args.enc_model_fpath)
-    synthesizer = Synthesizer(args.syn_model_fpath)
-    vocoder.load_model(args.voc_model_fpath)
-    
-    
-    ## Run a test
-    # print("Testing your configuration with small inputs.")
-    # # Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's
-    # # sampling rate, which may differ.
-    # # If you're unfamiliar with digital audio, know that it is encoded as an array of floats 
-    # # (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.
-    # # The sampling rate is the number of values (samples) recorded per second, it is set to
-    # # 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond 
-    # # to an audio of 1 second.
-    # print("	Testing the encoder...")
-    # encoder.embed_utterance(np.zeros(encoder.sampling_rate))
-    
-    # # Create a dummy embedding. You would normally use the embedding that encoder.embed_utterance
-    # # returns, but here we're going to make one ourselves just for the sake of showing that it's
-    # # possible.
-    # embed = np.random.rand(speaker_embedding_size)
-    # # Embeddings are L2-normalized (this isn't important here, but if you want to make your own 
-    # # embeddings it will be).
-    # embed /= np.linalg.norm(embed)
-    # # The synthesizer can handle multiple inputs with batching. Let's create another embedding to 
-    # # illustrate that
-    # embeds = [embed, np.zeros(speaker_embedding_size)]
-    # texts = ["test 1", "test 2"]
-    # print("	Testing the synthesizer... (loading the model will output a lot of text)")
-    # mels = synthesizer.synthesize_spectrograms(texts, embeds)
-    
-    # # The vocoder synthesizes one waveform at a time, but it's more efficient for long ones. We 
-    # # can concatenate the mel spectrograms to a single one.
-    # mel = np.concatenate(mels, axis=1)
-    # # The vocoder can take a callback function to display the generation. More on that later. For 
-    # # now we'll simply hide it like this:
-    # no_action = lambda *args: None
-    # print("	Testing the vocoder...")
-    # # For the sake of making this test short, we'll pass a short target length. The target length 
-    # # is the length of the wav segments that are processed in parallel. E.g. for audio sampled 
-    # # at 16000 Hertz, a target length of 8000 means that the target audio will be cut in chunks of
-    # # 0.5 seconds which will all be generated together. The parameters here are absurdly short, and 
-    # # that has a detrimental effect on the quality of the audio. The default parameters are 
-    # # recommended in general.
-    # vocoder.infer_waveform(mel, target=200, overlap=50, progress_callback=no_action)
-    
-    print("All test passed! You can now synthesize speech.\n\n")
-    
-    
-    ## Interactive speech generation
-    print("This is a GUI-less example of interface to SV2TTS. The purpose of this script is to "
-          "show how you can interface this project easily with your own. See the source code for "
-          "an explanation of what is happening.\n")
-    
-    print("Interactive generation loop")
-    # while True:
-    # Get the reference audio filepath
-    message = "Reference voice: enter an audio filepath of a voice to be cloned (mp3, "                   "wav, m4a, flac, ...):\n"
-    in_fpath = args.audio_path
-
-    if in_fpath.suffix.lower() == ".mp3" and args.no_mp3_support:
-        print("Can't Use mp3 files please try again:")
-    ## Computing the embedding
-    # First, we load the wav using the function that the speaker encoder provides. This is 
-    # important: there is preprocessing that must be applied.
-    
-    # The following two methods are equivalent:
-    # - Directly load from the filepath:
-    preprocessed_wav = encoder.preprocess_wav(in_fpath)
-    # - If the wav is already loaded:
-    original_wav, sampling_rate = librosa.load(str(in_fpath))
-    preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
-    print("Loaded file succesfully")
-    
-    # Then we derive the embedding. There are many functions and parameters that the 
-    # speaker encoder interfaces. These are mostly for in-depth research. You will typically
-    # only use this function (with its default parameters):
-    embed = encoder.embed_utterance(preprocessed_wav)
-    print("Created the embedding")
-    
-    
-    ## Generating the spectrogram
-    text = args.text
-    
-    # If seed is specified, reset torch seed and force synthesizer reload
-    if args.seed is not None:
-        torch.manual_seed(args.seed)
-        synthesizer = Synthesizer(args.syn_model_fpath)
-
-    # The synthesizer works in batch, so you need to put your data in a list or numpy array
-    texts = [text]
-    embeds = [embed]
-    # If you know what the attention layer alignments are, you can retrieve them here by
-    # passing return_alignments=True
-    specs = synthesizer.synthesize_spectrograms(texts, embeds)
-    spec = specs[0]
-    print("Created the mel spectrogram")
-    
-    
-    ## Generating the waveform
-    print("Synthesizing the waveform:")
-
-    # If seed is specified, reset torch seed and reload vocoder
-    if args.seed is not None:
-        torch.manual_seed(args.seed)
-        vocoder.load_model(args.voc_model_fpath)
-
-    # Synthesizing the waveform is fairly straightforward. Remember that the longer the
-    # spectrogram, the more time-efficient the vocoder.
-    generated_wav = vocoder.infer_waveform(spec)
-    
-    
-    ## Post-generation
-    # There's a bug with sounddevice that makes the audio cut one second earlier, so we
-    # pad it.
-    generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant")
-
-    # Trim excess silences to compensate for gaps in spectrograms (issue #53)
-    generated_wav = encoder.preprocess_wav(generated_wav)
-        
-    # Save it on the disk
-    filename = "demo_output_1.wav"
-    print(generated_wav.dtype)
-    sf.write(filename, generated_wav.astype(np.float32), synthesizer.sample_rate)
-    print("\nSaved output as %s\n\n" % filename)
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/speaker_encoder/data_objects/utterance.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/speaker_encoder/data_objects/utterance.py
deleted file mode 100644
index 0768c3420f422a7464f305b4c1fb6752c57ceda7..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/speaker_encoder/data_objects/utterance.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-
-class Utterance:
-    def __init__(self, frames_fpath, wave_fpath):
-        self.frames_fpath = frames_fpath
-        self.wave_fpath = wave_fpath
-        
-    def get_frames(self):
-        return np.load(self.frames_fpath)
-
-    def random_partial(self, n_frames):
-        """
-        Crops the frames into a partial utterance of n_frames
-        
-        :param n_frames: The number of frames of the partial utterance
-        :return: the partial utterance frames and a tuple indicating the start and end of the 
-        partial utterance in the complete utterance.
-        """
-        frames = self.get_frames()
-        if frames.shape[0] == n_frames:
-            start = 0
-        else:
-            start = np.random.randint(0, frames.shape[0] - n_frames)
-        end = start + n_frames
-        return frames[start:end], (start, end)
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/facerender/sync_batchnorm/batchnorm.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/facerender/sync_batchnorm/batchnorm.py
deleted file mode 100644
index 5f4e763f0366dffa10320116413f8c7181a8aeb1..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/facerender/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
-    """sum over the first and last dimention"""
-    return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
-    """add new dementions at the front and the tail"""
-    return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
-        self._sync_master = SyncMaster(self._data_parallel_master)
-
-        self._is_parallel = False
-        self._parallel_id = None
-        self._slave_pipe = None
-
-    def forward(self, input):
-        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
-        if not (self._is_parallel and self.training):
-            return F.batch_norm(
-                input, self.running_mean, self.running_var, self.weight, self.bias,
-                self.training, self.momentum, self.eps)
-
-        # Resize the input to (B, C, -1).
-        input_shape = input.size()
-        input = input.view(input.size(0), self.num_features, -1)
-
-        # Compute the sum and square-sum.
-        sum_size = input.size(0) * input.size(2)
-        input_sum = _sum_ft(input)
-        input_ssum = _sum_ft(input ** 2)
-
-        # Reduce-and-broadcast the statistics.
-        if self._parallel_id == 0:
-            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
-        else:
-            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
-        # Compute the output.
-        if self.affine:
-            # MJY:: Fuse the multiplication for speed.
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
-        else:
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
-        # Reshape it.
-        return output.view(input_shape)
-
-    def __data_parallel_replicate__(self, ctx, copy_id):
-        self._is_parallel = True
-        self._parallel_id = copy_id
-
-        # parallel_id == 0 means master device.
-        if self._parallel_id == 0:
-            ctx.sync_master = self._sync_master
-        else:
-            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
-    def _data_parallel_master(self, intermediates):
-        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
-        # Always using same "device order" makes the ReduceAdd operation faster.
-        # Thanks to:: Tete Xiao (http://tetexiao.com/)
-        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
-        to_reduce = [i[1][:2] for i in intermediates]
-        to_reduce = [j for i in to_reduce for j in i]  # flatten
-        target_gpus = [i[1].sum.get_device() for i in intermediates]
-
-        sum_size = sum([i[1].sum_size for i in intermediates])
-        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
-        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
-        outputs = []
-        for i, rec in enumerate(intermediates):
-            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
-        return outputs
-
-    def _compute_mean_std(self, sum_, ssum, size):
-        """Compute the mean and standard-deviation with sum and square-sum. This method
-        also maintains the moving average on the master device."""
-        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
-        mean = sum_ / size
-        sumvar = ssum - sum_ * mean
-        unbias_var = sumvar / (size - 1)
-        bias_var = sumvar / size
-
-        self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
-        self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
-        return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
-    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
-    mini-batch.
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm1d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of size
-            `batch_size x num_features [x width]`
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C)` or :math:`(N, C, L)`
-        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 2 and input.dim() != 3:
-            raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
-    of 3d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm2d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, H, W)`
-        - Output: :math:`(N, C, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
-    of 4d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm3d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
-    or Spatio-temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x depth x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, D, H, W)`
-        - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 5:
-            raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/kevinwang676/VoiceChanger/infer_pack/onnx_inference.py b/spaces/kevinwang676/VoiceChanger/infer_pack/onnx_inference.py
deleted file mode 100644
index 322572820dfc75d789e40ce5bbd9415066a03979..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/synthesizer/preprocess_speaker.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/synthesizer/preprocess_speaker.py
deleted file mode 100644
index 28ddad4f113a6543b94e7d03e332af23cf2436bb..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/synthesizer/preprocess_speaker.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import librosa
-import numpy as np
-
-from encoder import inference as encoder
-from utils import logmmse
-from synthesizer import audio
-from pathlib import Path
-from pypinyin import Style
-from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
-from pypinyin.converter import DefaultConverter
-from pypinyin.core import Pinyin
-
-class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
-    pass
-
-pinyin = Pinyin(PinyinConverter()).pinyin
-
-
-def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str, 
-                      skip_existing: bool, hparams):
-    ## FOR REFERENCE:
-    # For you not to lose your head if you ever wish to change things here or implement your own
-    # synthesizer.
-    # - Both the audios and the mel spectrograms are saved as numpy arrays
-    # - There is no processing done to the audios that will be saved to disk beyond volume  
-    #   normalization (in split_on_silences)
-    # - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This
-    #   is why we re-apply it on the audio on the side of the vocoder.
-    # - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
-    #   without extra padding. This means that you won't have an exact relation between the length
-    #   of the wav and of the mel spectrogram. See the vocoder data loader.
-    
-    
-    # Skip existing utterances if needed
-    mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
-    wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
-    if skip_existing and mel_fpath.exists() and wav_fpath.exists():
-        return None
-
-    # Trim silence
-    if hparams.trim_silence:
-        wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True)
-    
-    # Skip utterances that are too short
-    if len(wav) < hparams.utterance_min_duration * hparams.sample_rate:
-        return None
-    
-    # Compute the mel spectrogram
-    mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
-    mel_frames = mel_spectrogram.shape[1]
-    
-    # Skip utterances that are too long
-    if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
-        return None
-    
-    # Write the spectrogram, embed and audio to disk
-    np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
-    np.save(wav_fpath, wav, allow_pickle=False)
-    
-    # Return a tuple describing this training example
-    return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text
- 
-
-def _split_on_silences(wav_fpath, words, hparams):
-    # Load the audio waveform
-    wav, _ = librosa.load(wav_fpath, sr= hparams.sample_rate)
-    wav = librosa.effects.trim(wav, top_db= 40, frame_length=2048, hop_length=512)[0]
-    if hparams.rescale:
-        wav = wav / np.abs(wav).max() * hparams.rescaling_max
-    # denoise, we may not need it here.
-    if len(wav) > hparams.sample_rate*(0.3+0.1):
-        noise_wav = np.concatenate([wav[:int(hparams.sample_rate*0.15)],
-                                    wav[-int(hparams.sample_rate*0.15):]])
-        profile = logmmse.profile_noise(noise_wav, hparams.sample_rate)
-        wav = logmmse.denoise(wav, profile, eta=0)
-
-    resp = pinyin(words, style=Style.TONE3)
-    res = [v[0] for v in resp if v[0].strip()]
-    res = " ".join(res)
-
-    return wav, res
-
-def preprocess_speaker_general(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool):
-    metadata = []
-    extensions = ["*.wav", "*.flac", "*.mp3"]
-    for extension in extensions:
-        wav_fpath_list = speaker_dir.glob(extension)
-        # Iterate over each wav
-        for wav_fpath in wav_fpath_list:
-            words = dict_info.get(wav_fpath.name.split(".")[0])
-            words = dict_info.get(wav_fpath.name) if not words else words # try with wav 
-            if not words:
-                print("no wordS")
-                continue
-            sub_basename = "%s_%02d" % (wav_fpath.name, 0)
-            wav, text = _split_on_silences(wav_fpath, words, hparams)
-            metadata.append(_process_utterance(wav, text, out_dir, sub_basename, 
-                                                skip_existing, hparams))
-    return [m for m in metadata if m is not None]
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py
deleted file mode 100644
index 7c257c2700f015cb123a976584aef72f0429eb0c..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .discriminative_reranking_criterion import KLDivergenceRerankingCriterion
-
-
-__all__ = [
-    "KLDivergenceRerankingCriterion",
-]
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/m2m_100/install_dependecies.sh b/spaces/koajoel/PolyFormer/fairseq/examples/m2m_100/install_dependecies.sh
deleted file mode 100644
index 82a1054745264a56fbec4a8eb593884f8a42bd08..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/m2m_100/install_dependecies.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-CWD=`pwd`
-INSTALL_PATH=$CWD/tokenizers/thirdparty
-
-MOSES=$INSTALL_PATH/mosesdecoder
-if [ ! -d $MOSES ]; then
-    echo 'Cloning Moses github repository (for tokenization scripts)...'
-    git clone https://github.com/moses-smt/mosesdecoder.git $MOSES
-    cd $MOSES
-    # To deal with differences in handling ' vs "
-    git checkout 03578921cc1a03402
-    cd -
-fi
-
-WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts
-if [ ! -d $WMT16_SCRIPTS ]; then
-    echo 'Cloning Romanian tokenization scripts'
-    git clone https://github.com/rsennrich/wmt16-scripts.git $WMT16_SCRIPTS
-fi
-
-KYTEA=$INSTALL_PATH/kytea
-if [ ! -f $KYTEA/bin/kytea ]; then
-    git clone https://github.com/neubig/kytea.git $KYTEA
-    cd $KYTEA
-    autoreconf -i
-    ./configure --prefix=`pwd`
-    make
-    make install
-    cd ..
-fi
-
-export MECAB=$INSTALL_PATH/mecab-0.996-ko-0.9.2
-if [ ! -f $MECAB/bin/mecab ]; then
-    cd $INSTALL_PATH
-    curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz
-    tar zxfv mecab-0.996-ko-0.9.2.tar.gz
-    cd mecab-0.996-ko-0.9.2/
-    ./configure --prefix=`pwd`
-    make
-    make install
-
-    cd ..
-    curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz
-    tar zxfv mecab-ko-dic-2.1.1-20180720.tar.gz
-    cd mecab-ko-dic-2.1.1-20180720/
-    ./autogen.sh
-    ./configure --prefix=`pwd` --with-dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic --with-mecab-config=$MECAB/bin/mecab-config
-    make
-    sh -c 'echo "dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic" > $MECAB/etc/mecabrc'
-    make install
-    cd $CWD
-fi
-
-INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources
-if [ ! -d $INDIC_RESOURCES_PATH ]; then
-    echo 'Cloning indic_nlp_resources'
-    git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git $INDIC_RESOURCES_PATH
-fi
-
-
-if [ ! -f $INSTALL_PATH/seg_my.py ]; then
-    cd $INSTALL_PATH
-    wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip
-    unzip wat2020.my-en.zip
-    # switch to python3
-    cat wat2020.my-en/myseg.py  |sed 's/^sys.std/###sys.std/g' | sed 's/### sys/sys/g' | sed 's/unichr/chr/g' > seg_my.py
-    cd $CWD
-fi
-
-
-pip install pythainlp sacrebleu indic-nlp-library
-
diff --git a/spaces/krishw/MovieExplorer/README.md b/spaces/krishw/MovieExplorer/README.md
deleted file mode 100644
index 641325245269c005ad14da6142e4100c1d365ed5..0000000000000000000000000000000000000000
--- a/spaces/krishw/MovieExplorer/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Movie Explorer
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: Data.py
-pinned: false
-license: apache-2.0
----
diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/lpips/pretrained_networks.py b/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/lpips/pretrained_networks.py
deleted file mode 100644
index 077a24419364fdb5ae2f697f73e28615adae75a7..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/lpips/pretrained_networks.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-from IPython import embed
-
-class squeezenet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(squeezenet, self).__init__()
-        pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.slice6 = torch.nn.Sequential()
-        self.slice7 = torch.nn.Sequential()
-        self.N_slices = 7
-        for x in range(2):
-            self.slice1.add_module(str(x), pretrained_features[x])
-        for x in range(2,5):
-            self.slice2.add_module(str(x), pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), pretrained_features[x])
-        for x in range(10, 11):
-            self.slice5.add_module(str(x), pretrained_features[x])
-        for x in range(11, 12):
-            self.slice6.add_module(str(x), pretrained_features[x])
-        for x in range(12, 13):
-            self.slice7.add_module(str(x), pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        h = self.slice6(h)
-        h_relu6 = h
-        h = self.slice7(h)
-        h_relu7 = h
-        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
-        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
-
-        return out
-
-
-class alexnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(alexnet, self).__init__()
-        alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(2):
-            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(2, 5):
-            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(10, 12):
-            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
-        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
-
-        return out
-
-class vgg16(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(vgg16, self).__init__()
-        vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-
-        return out
-
-
-
-class resnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True, num=18):
-        super(resnet, self).__init__()
-        if(num==18):
-            self.net = tv.resnet18(pretrained=pretrained)
-        elif(num==34):
-            self.net = tv.resnet34(pretrained=pretrained)
-        elif(num==50):
-            self.net = tv.resnet50(pretrained=pretrained)
-        elif(num==101):
-            self.net = tv.resnet101(pretrained=pretrained)
-        elif(num==152):
-            self.net = tv.resnet152(pretrained=pretrained)
-        self.N_slices = 5
-
-        self.conv1 = self.net.conv1
-        self.bn1 = self.net.bn1
-        self.relu = self.net.relu
-        self.maxpool = self.net.maxpool
-        self.layer1 = self.net.layer1
-        self.layer2 = self.net.layer2
-        self.layer3 = self.net.layer3
-        self.layer4 = self.net.layer4
-
-    def forward(self, X):
-        h = self.conv1(X)
-        h = self.bn1(h)
-        h = self.relu(h)
-        h_relu1 = h
-        h = self.maxpool(h)
-        h = self.layer1(h)
-        h_conv2 = h
-        h = self.layer2(h)
-        h_conv3 = h
-        h = self.layer3(h)
-        h_conv4 = h
-        h = self.layer4(h)
-        h_conv5 = h
-
-        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
-        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
-
-        return out
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/__init__.py
deleted file mode 100644
index 29fb3561e4f2dc9d3a764e756439c0dea2c9897a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/__init__.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from __future__ import annotations
-
-__all__ = (
-    "maybe_async",
-    "maybe_async_cm",
-    "run",
-    "sleep",
-    "sleep_forever",
-    "sleep_until",
-    "current_time",
-    "get_all_backends",
-    "get_cancelled_exc_class",
-    "BrokenResourceError",
-    "BrokenWorkerProcess",
-    "BusyResourceError",
-    "ClosedResourceError",
-    "DelimiterNotFound",
-    "EndOfStream",
-    "ExceptionGroup",
-    "IncompleteRead",
-    "TypedAttributeLookupError",
-    "WouldBlock",
-    "AsyncFile",
-    "Path",
-    "open_file",
-    "wrap_file",
-    "aclose_forcefully",
-    "open_signal_receiver",
-    "connect_tcp",
-    "connect_unix",
-    "create_tcp_listener",
-    "create_unix_listener",
-    "create_udp_socket",
-    "create_connected_udp_socket",
-    "getaddrinfo",
-    "getnameinfo",
-    "wait_socket_readable",
-    "wait_socket_writable",
-    "create_memory_object_stream",
-    "run_process",
-    "open_process",
-    "create_lock",
-    "CapacityLimiter",
-    "CapacityLimiterStatistics",
-    "Condition",
-    "ConditionStatistics",
-    "Event",
-    "EventStatistics",
-    "Lock",
-    "LockStatistics",
-    "Semaphore",
-    "SemaphoreStatistics",
-    "create_condition",
-    "create_event",
-    "create_semaphore",
-    "create_capacity_limiter",
-    "open_cancel_scope",
-    "fail_after",
-    "move_on_after",
-    "current_effective_deadline",
-    "TASK_STATUS_IGNORED",
-    "CancelScope",
-    "create_task_group",
-    "TaskInfo",
-    "get_current_task",
-    "get_running_tasks",
-    "wait_all_tasks_blocked",
-    "run_sync_in_worker_thread",
-    "run_async_from_thread",
-    "run_sync_from_thread",
-    "current_default_worker_thread_limiter",
-    "create_blocking_portal",
-    "start_blocking_portal",
-    "typed_attribute",
-    "TypedAttributeSet",
-    "TypedAttributeProvider",
-)
-
-from typing import Any
-
-from ._core._compat import maybe_async, maybe_async_cm
-from ._core._eventloop import (
-    current_time,
-    get_all_backends,
-    get_cancelled_exc_class,
-    run,
-    sleep,
-    sleep_forever,
-    sleep_until,
-)
-from ._core._exceptions import (
-    BrokenResourceError,
-    BrokenWorkerProcess,
-    BusyResourceError,
-    ClosedResourceError,
-    DelimiterNotFound,
-    EndOfStream,
-    ExceptionGroup,
-    IncompleteRead,
-    TypedAttributeLookupError,
-    WouldBlock,
-)
-from ._core._fileio import AsyncFile, Path, open_file, wrap_file
-from ._core._resources import aclose_forcefully
-from ._core._signals import open_signal_receiver
-from ._core._sockets import (
-    connect_tcp,
-    connect_unix,
-    create_connected_udp_socket,
-    create_tcp_listener,
-    create_udp_socket,
-    create_unix_listener,
-    getaddrinfo,
-    getnameinfo,
-    wait_socket_readable,
-    wait_socket_writable,
-)
-from ._core._streams import create_memory_object_stream
-from ._core._subprocesses import open_process, run_process
-from ._core._synchronization import (
-    CapacityLimiter,
-    CapacityLimiterStatistics,
-    Condition,
-    ConditionStatistics,
-    Event,
-    EventStatistics,
-    Lock,
-    LockStatistics,
-    Semaphore,
-    SemaphoreStatistics,
-    create_capacity_limiter,
-    create_condition,
-    create_event,
-    create_lock,
-    create_semaphore,
-)
-from ._core._tasks import (
-    TASK_STATUS_IGNORED,
-    CancelScope,
-    create_task_group,
-    current_effective_deadline,
-    fail_after,
-    move_on_after,
-    open_cancel_scope,
-)
-from ._core._testing import (
-    TaskInfo,
-    get_current_task,
-    get_running_tasks,
-    wait_all_tasks_blocked,
-)
-from ._core._typedattr import TypedAttributeProvider, TypedAttributeSet, typed_attribute
-
-# Re-exported here, for backwards compatibility
-# isort: off
-from .to_thread import current_default_worker_thread_limiter, run_sync_in_worker_thread
-from .from_thread import (
-    create_blocking_portal,
-    run_async_from_thread,
-    run_sync_from_thread,
-    start_blocking_portal,
-)
-
-# Re-export imports so they look like they live directly in this package
-key: str
-value: Any
-for key, value in list(locals().items()):
-    if getattr(value, "__module__", "").startswith("anyio."):
-        value.__module__ = __name__
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/networking.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/networking.py
deleted file mode 100644
index 9d710c5283915eb4fb3320a5373d698d54659553..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/networking.py
+++ /dev/null
@@ -1,195 +0,0 @@
-"""
-Defines helper methods useful for setting up ports, launching servers, and
-creating tunnels.
-"""
-from __future__ import annotations
-
-import os
-import socket
-import threading
-import time
-import warnings
-from typing import TYPE_CHECKING
-
-import requests
-import uvicorn
-
-from gradio.routes import App
-from gradio.tunneling import Tunnel
-
-if TYPE_CHECKING:  # Only import for type checking (to avoid circular imports).
-    from gradio.blocks import Blocks
-
-# By default, the local server will try to open on localhost, port 7860.
-# If that is not available, then it will try 7861, 7862, ... 7959.
-INITIAL_PORT_VALUE = int(os.getenv("GRADIO_SERVER_PORT", "7860"))
-TRY_NUM_PORTS = int(os.getenv("GRADIO_NUM_PORTS", "100"))
-LOCALHOST_NAME = os.getenv("GRADIO_SERVER_NAME", "127.0.0.1")
-GRADIO_API_SERVER = "https://api.gradio.app/v2/tunnel-request"
-
-
-class Server(uvicorn.Server):
-    def install_signal_handlers(self):
-        pass
-
-    def run_in_thread(self):
-        self.thread = threading.Thread(target=self.run, daemon=True)
-        self.thread.start()
-        while not self.started:
-            time.sleep(1e-3)
-
-    def close(self):
-        self.should_exit = True
-        self.thread.join()
-
-
-def get_first_available_port(initial: int, final: int) -> int:
-    """
-    Gets the first open port in a specified range of port numbers
-    Parameters:
-    initial: the initial value in the range of port numbers
-    final: final (exclusive) value in the range of port numbers, should be greater than `initial`
-    Returns:
-    port: the first open port in the range
-    """
-    for port in range(initial, final):
-        try:
-            s = socket.socket()  # create a socket object
-            s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-            s.bind((LOCALHOST_NAME, port))  # Bind to the port
-            s.close()
-            return port
-        except OSError:
-            pass
-    raise OSError(
-        f"All ports from {initial} to {final - 1} are in use. Please close a port."
-    )
-
-
-def configure_app(app: App, blocks: Blocks) -> App:
-    auth = blocks.auth
-    if auth is not None:
-        if not callable(auth):
-            app.auth = {account[0]: account[1] for account in auth}
-        else:
-            app.auth = auth
-    else:
-        app.auth = None
-    app.blocks = blocks
-    app.cwd = os.getcwd()
-    app.favicon_path = blocks.favicon_path
-    app.tokens = {}
-    return app
-
-
-def start_server(
-    blocks: Blocks,
-    server_name: str | None = None,
-    server_port: int | None = None,
-    ssl_keyfile: str | None = None,
-    ssl_certfile: str | None = None,
-    ssl_keyfile_password: str | None = None,
-    app_kwargs: dict | None = None,
-) -> tuple[str, int, str, App, Server]:
-    """Launches a local server running the provided Interface
-    Parameters:
-        blocks: The Blocks object to run on the server
-        server_name: to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME.
-        server_port: will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT.
-        auth: If provided, username and password (or list of username-password tuples) required to access the Blocks. Can also provide function that takes username and password and returns True if valid login.
-        ssl_keyfile: If a path to a file is provided, will use this as the private key file to create a local server running on https.
-        ssl_certfile: If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
-        ssl_keyfile_password: If a password is provided, will use this with the ssl certificate for https.
-        app_kwargs: Additional keyword arguments to pass to the gradio.routes.App constructor.
-
-    Returns:
-        port: the port number the server is running on
-        path_to_local_server: the complete address that the local server can be accessed at
-        app: the FastAPI app object
-        server: the server object that is a subclass of uvicorn.Server (used to close the server)
-    """
-    server_name = server_name or LOCALHOST_NAME
-    # if port is not specified, search for first available port
-    if server_port is None:
-        port = get_first_available_port(
-            INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS
-        )
-    else:
-        try:
-            s = socket.socket()
-            s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-            s.bind((LOCALHOST_NAME, server_port))
-            s.close()
-        except OSError as err:
-            raise OSError(
-                f"Port {server_port} is in use. If a gradio.Blocks is running on the port, "
-                f"you can close() it or gradio.close_all()."
-            ) from err
-        port = server_port
-
-    url_host_name = "localhost" if server_name == "0.0.0.0" else server_name
-
-    if ssl_keyfile is not None:
-        if ssl_certfile is None:
-            raise ValueError(
-                "ssl_certfile must be provided if ssl_keyfile is provided."
-            )
-        path_to_local_server = f"https://{url_host_name}:{port}/"
-    else:
-        path_to_local_server = f"http://{url_host_name}:{port}/"
-
-    # Strip IPv6 brackets from the address if they exist.
-    # This is needed as http://[::1]:port/ is a valid browser address,
-    # but not a valid IPv6 address, so asyncio will throw an exception.
-    if server_name.startswith("[") and server_name.endswith("]"):
-        host = server_name[1:-1]
-    else:
-        host = server_name
-
-    app = App.create_app(blocks, app_kwargs=app_kwargs)
-
-    if blocks.save_to is not None:  # Used for selenium tests
-        blocks.save_to["port"] = port
-    config = uvicorn.Config(
-        app=app,
-        port=port,
-        host=host,
-        log_level="warning",
-        ssl_keyfile=ssl_keyfile,
-        ssl_certfile=ssl_certfile,
-        ssl_keyfile_password=ssl_keyfile_password,
-        ws_max_size=1024 * 1024 * 1024,  # Setting max websocket size to be 1 GB
-    )
-    server = Server(config=config)
-    server.run_in_thread()
-    return server_name, port, path_to_local_server, app, server
-
-
-def setup_tunnel(local_host: str, local_port: int, share_token: str) -> str:
-    response = requests.get(GRADIO_API_SERVER)
-    if response and response.status_code == 200:
-        try:
-            payload = response.json()[0]
-            remote_host, remote_port = payload["host"], int(payload["port"])
-            tunnel = Tunnel(
-                remote_host, remote_port, local_host, local_port, share_token
-            )
-            address = tunnel.start_tunnel()
-            return address
-        except Exception as e:
-            raise RuntimeError(str(e)) from e
-    raise RuntimeError("Could not get share link from Gradio API Server.")
-
-
-def url_ok(url: str) -> bool:
-    try:
-        for _ in range(5):
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore")
-                r = requests.head(url, timeout=3, verify=False)
-            if r.status_code in (200, 401, 302):  # 401 or 302 if auth is set
-                return True
-            time.sleep(0.500)
-    except (ConnectionError, requests.exceptions.ConnectionError):
-        return False
-    return False
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-2c75c8ef.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-2c75c8ef.js
deleted file mode 100644
index 0c20a20266702a9a40176860b922a612ee7587a6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-2c75c8ef.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{T as c}from"./Textbox-8be76163.js";import"./index-8c3da1d9.js";/* empty css                                                  */import"./Button-62634b34.js";/* empty css                                                    */import"./BlockTitle-338c46c0.js";import"./Info-b95ed9db.js";import"./Copy-fd383441.js";const n=["static","dynamic"],d=t=>({type:{payload:"string"},description:{payload:"text string"},example_data:t.value||"hello world"});export{c as Component,d as document,n as modes};
-//# sourceMappingURL=index-2c75c8ef.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/kiwisolver/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/kiwisolver/__init__.py
deleted file mode 100644
index cc69f4f6364a968abc5b94d3e4371dbce15d7aa0..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/kiwisolver/__init__.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# --------------------------------------------------------------------------------------
-# Copyright (c) 2013-2022, Nucleic Development Team.
-#
-# Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file LICENSE, distributed with this software.
-# --------------------------------------------------------------------------------------
-from ._cext import (
-    BadRequiredStrength,
-    Constraint,
-    DuplicateConstraint,
-    DuplicateEditVariable,
-    Expression,
-    Solver,
-    Term,
-    UnknownConstraint,
-    UnknownEditVariable,
-    UnsatisfiableConstraint,
-    Variable,
-    __kiwi_version__,
-    __version__,
-    strength,
-)
-
-__all__ = [
-    "BadRequiredStrength",
-    "DuplicateConstraint",
-    "DuplicateEditVariable",
-    "UnknownConstraint",
-    "UnknownEditVariable",
-    "UnsatisfiableConstraint",
-    "strength",
-    "Variable",
-    "Term",
-    "Expression",
-    "Constraint",
-    "Solver",
-    "__version__",
-    "__kiwi_version__",
-]
diff --git a/spaces/leilevy/bingo/src/components/learn-more.tsx b/spaces/leilevy/bingo/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/leilevy/bingo/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
-  sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
-  if (!sourceAttributions?.length) {
-    return null
-  }
-
-  return (
-    <div className="learn-more-root" role="list" aria-label="了解详细信息:">
-      <div className="learn-more">了解详细信息:</div>
-      <div className="attribution-container">
-        <div className="attribution-items">
-          {sourceAttributions.map((attribution, index) => {
-            const { providerDisplayName, seeMoreUrl } = attribution
-            const { host } = new URL(seeMoreUrl)
-            return (
-              <a
-                key={index}
-                className="attribution-item"
-                target="_blank"
-                role="listitem"
-                href={seeMoreUrl}
-                title={providerDisplayName}
-                tabIndex={index}
-              >
-                {index + 1}. {host}
-              </a>
-            )
-          })}
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Cost Of Cdegs Software Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Cost Of Cdegs Software Crack.md
deleted file mode 100644
index 66add66b3acbc8f2950806658ffbbcb452f9838a..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Cost Of Cdegs Software Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>cost of cdegs software crack</h2><br /><p><b><b>Download File</b> &#10042;&#10042;&#10042; <a href="https://bytlly.com/2uGy9m">https://bytlly.com/2uGy9m</a></b></p><br /><br />
-
-Cost Of Cdegs Software Crack - riokontsigzems. The CDEGS software package. SES started the development of the CDEGS software package in 1972, with the ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lizhen30/LangChainGo/socket_server.py b/spaces/lizhen30/LangChainGo/socket_server.py
deleted file mode 100644
index 21ebc8b7c019d6f88db0052fe60aa5a894d2a0d8..0000000000000000000000000000000000000000
--- a/spaces/lizhen30/LangChainGo/socket_server.py
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/python3
-import socket
-import sys
-
-serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-host = socket.gethostname()
-port = 9999
-serverSocket.bind((host, port))
-serverSocket.listen(5)
-
-while True:
-    clientSocket,addr = serverSocket.accept()
-    print("已连接地址：%s"%str(addr))
-
-    msg="hello , I'm server.\n"
-    clientSocket.send(msg.encode('utf-8'))
-    while True:
-        data = clientSocket.recv(1024).decode('utf-8')
-        print ("client:%s"% data)
-        inputMsg = input("server：")
-        clientSocket.send(inputMsg.encode('utf-8'))
-clientSocket.close()
\ No newline at end of file
diff --git a/spaces/llamaindex/text2image_prompt_assistant/prompt_assistant.py b/spaces/llamaindex/text2image_prompt_assistant/prompt_assistant.py
deleted file mode 100644
index 900584a4d38be954141977caa444054833c3a2b8..0000000000000000000000000000000000000000
--- a/spaces/llamaindex/text2image_prompt_assistant/prompt_assistant.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import os
-from transformers import Tool, load_tool
-from huggingface_hub import hf_hub_download
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-from llama_index import (
-    Prompt,
-    LLMPredictor,
-    ServiceContext, 
-    StorageContext, 
-    load_index_from_storage, 
-    set_global_service_context
-)
-
-text_qa_template = Prompt(
-    "Examples of text-to-image prompts are below: \n"
-    "---------------------\n"
-    "{context_str}"
-    "\n---------------------\n"
-    "Given the existing examples of text-to-image prompts, "
-    "write a new text-to-image prompt in the style of the examples, by re-wording the following prompt to match the style of the above examples: {query_str}\n"
-)
-
-
-refine_template = Prompt(
-    "The initial prompt is as follows: {query_str}\n"
-    "We have provided an existing text-to-image prompt based on this query: {existing_answer}\n"
-    "We have the opportunity to refine the existing prompt "
-    "(only if needed) with some more relevant examples of text-to-image prompts below.\n"
-    "------------\n"
-    "{context_msg}\n"
-    "------------\n"
-    "Given the new examples of text-to-image prompts, refine the existing text-to-image prompt to better "
-    "statisfy the required style. "
-    "If the context isn't useful, or the existing prompt is good enough, return the existing prompt."
-)
-
-
-PROMPT_ASSISTANT_DESCRIPTION = (
-    "This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which "
-    "contains the image description and outputs an image."
-)
-
-
-class Text2ImagePromptAssistant(Tool):
-    
-    inputs = ['text']
-    outputs = ['image']
-    description = PROMPT_ASSISTANT_DESCRIPTION
-    
-    def __init__(self, *args, openai_api_key='', model_name='text-davinci-003', temperature=0.3, verbose=False, **hub_kwargs):
-        super().__init__()
-        os.environ['OPENAI_API_KEY'] = openai_api_key
-        if model_name == 'text-davinci-003':
-            llm = OpenAI(model_name=model_name, temperature=temperature)
-        elif model_name in ('gpt-3.5-turbo', 'gpt-4'):
-            llm = ChatOpenAI(model_name=model_name, temperature=temperature)
-        else:
-            raise ValueError(
-                f"{model_name} is not supported, please choose one "
-                "of 'text-davinci-003', 'gpt-3.5-turbo', or 'gpt-4'."
-            )
-        service_context = ServiceContext.from_defaults(llm_predictor=LLMPredictor(llm=llm))
-        set_global_service_context(service_context)
-        
-        self.storage_path = os.path.dirname(__file__)
-        self.verbose = verbose
-        self.hub_kwargs = hub_kwargs
-
-    def setup(self):
-        hf_hub_download(repo_id="llamaindex/text2image_prompt_assistant", filename="storage/vector_store.json", repo_type="space", local_dir=self.storage_path)
-        hf_hub_download(repo_id="llamaindex/text2image_prompt_assistant", filename="storage/index_store.json", repo_type="space", local_dir=self.storage_path)
-        hf_hub_download(repo_id="llamaindex/text2image_prompt_assistant", filename="storage/docstore.json", repo_type="space", local_dir=self.storage_path)
-        
-        self.index = load_index_from_storage(StorageContext.from_defaults(persist_dir=os.path.join(self.storage_path, "storage")))
-        self.query_engine = self.index.as_query_engine(similarity_top_k=5, text_qa_template=text_qa_template, refine_template=refine_template)
-        
-        self.text2image = load_tool('huggingface-tools/text-to-image')
-        self.text2image.setup()
-
-        self.initialized = True
-
-    def __call__(self, prompt):
-        if not self.is_initialized:
-            self.setup()
-
-        better_prompt = str(self.query_engine.query(prompt)).strip()
-        
-        if self.verbose:
-            print('==New prompt generated by LlamaIndex==', flush=True)
-            print(better_prompt, '\n', flush=True)
-
-        return self.text2image(better_prompt)
-
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/modules/F0Predictor/DioF0Predictor.py b/spaces/lllqqq/so-vits-svc-models-pcr/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index 4ab27de23cae4dbc282e30f84501afebd1a37518..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self,f0):
-        '''
-        对F0进行插值处理
-        '''
-    
-        data = np.reshape(f0, (f0.size, 1))
-    
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-    
-        ip_data = data
-    
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-    
-        return ip_data[:,0], vuv_vector[:,0]
-
-    def resize_f0(self,x, target_len):
-        source = np.array(x)
-        source[source<0.001] = np.nan
-        target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
-        res = np.nan_to_num(target)
-        return res
-        
-    def compute_f0(self,wav,p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0]//self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self,wav,p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0]//self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/lorenzoscottb/phrase-entailment/README.md b/spaces/lorenzoscottb/phrase-entailment/README.md
deleted file mode 100644
index 4d3e6c72ad9792c9332b5754ebcfd6771dde79c1..0000000000000000000000000000000000000000
--- a/spaces/lorenzoscottb/phrase-entailment/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Phrase Entailment
-emoji: 🛩
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: cc-by-nc-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/luost26/DiffAb/diffab/tools/dock/hdock.py b/spaces/luost26/DiffAb/diffab/tools/dock/hdock.py
deleted file mode 100644
index cf3fbd7c2bdb7d5cab4ffb317aa3a39bca6ed5e6..0000000000000000000000000000000000000000
--- a/spaces/luost26/DiffAb/diffab/tools/dock/hdock.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import shutil
-import tempfile
-import subprocess
-import dataclasses as dc
-from typing import List, Optional
-from Bio import PDB
-from Bio.PDB import Model as PDBModel
-
-from diffab.tools.renumber import renumber as renumber_chothia
-from .base import DockingEngine
-
-
-def fix_docked_pdb(pdb_path):
-    fixed = []
-    with open(pdb_path, 'r') as f:
-        for ln in f.readlines():
-            if (ln.startswith('ATOM') or ln.startswith('HETATM')) and len(ln) == 56:
-                fixed.append( ln[:-1] + ' 1.00  0.00              \n' )
-            else:
-                fixed.append(ln)
-    with open(pdb_path, 'w') as f:
-        f.write(''.join(fixed))
-
-
-class HDock(DockingEngine):
-
-    def __init__(
-        self, 
-        hdock_bin='./bin/hdock',
-        createpl_bin='./bin/createpl',
-    ):
-        super().__init__()
-        self.hdock_bin = os.path.realpath(hdock_bin)
-        self.createpl_bin = os.path.realpath(createpl_bin)
-        self.tmpdir = tempfile.TemporaryDirectory()
-
-        self._has_receptor = False
-        self._has_ligand = False
-
-        self._receptor_chains = []
-        self._ligand_chains = []
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, typ, value, traceback):
-        self.tmpdir.cleanup()
-
-    def set_receptor(self, pdb_path):
-        shutil.copyfile(pdb_path, os.path.join(self.tmpdir.name, 'receptor.pdb'))
-        self._has_receptor = True
-
-    def set_ligand(self, pdb_path):
-        shutil.copyfile(pdb_path, os.path.join(self.tmpdir.name, 'ligand.pdb'))
-        self._has_ligand = True
-
-    def _dump_complex_pdb(self):
-        parser = PDB.PDBParser(QUIET=True)
-        model_receptor = parser.get_structure(None, os.path.join(self.tmpdir.name, 'receptor.pdb'))[0]
-        docked_pdb_path = os.path.join(self.tmpdir.name, 'ligand_docked.pdb')
-        fix_docked_pdb(docked_pdb_path)
-        structure_ligdocked = parser.get_structure(None, docked_pdb_path)
-
-        pdb_io = PDB.PDBIO()
-        paths = []
-        for i, model_ligdocked in enumerate(structure_ligdocked):
-            model_complex = PDBModel.Model(0)
-            for chain in model_receptor:
-                model_complex.add(chain.copy())
-            for chain in model_ligdocked:
-                model_complex.add(chain.copy())
-            pdb_io.set_structure(model_complex)
-            save_path = os.path.join(self.tmpdir.name, f"complex_{i}.pdb")
-            pdb_io.save(save_path)
-            paths.append(save_path)
-        return paths
-
-    def dock(self):
-        if not (self._has_receptor and self._has_ligand):
-            raise ValueError('Missing receptor or ligand.')
-        subprocess.run(
-            [self.hdock_bin, "receptor.pdb", "ligand.pdb"],
-            cwd=self.tmpdir.name, check=True
-        )
-        subprocess.run(
-            [self.createpl_bin, "Hdock.out", "ligand_docked.pdb"], 
-            cwd=self.tmpdir.name, check=True
-        )
-        return self._dump_complex_pdb()
-
-
-@dc.dataclass
-class DockSite:
-    chain: str
-    resseq: int
-
-
-class HDockAntibody(HDock):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._heavy_chain_id = None
-        self._epitope_sites: Optional[List[DockSite]] = None
-
-    def set_ligand(self, pdb_path):
-        raise NotImplementedError('Please use set_antibody')
-    
-    def set_receptor(self, pdb_path):
-        raise NotImplementedError('Please use set_antigen')
-
-    def set_antigen(self, pdb_path, epitope_sites: Optional[List[DockSite]]=None):
-        super().set_receptor(pdb_path)
-        self._epitope_sites = epitope_sites
-
-    def set_antibody(self, pdb_path):
-        heavy_chains, _ = renumber_chothia(pdb_path, os.path.join(self.tmpdir.name, 'ligand.pdb'))
-        self._has_ligand = True
-        self._heavy_chain_id = heavy_chains[0]
-
-    def _prepare_lsite(self):
-        lsite_content = f"95-102:{self._heavy_chain_id}\n"  # Chothia CDR H3
-        with open(os.path.join(self.tmpdir.name, 'lsite.txt'), 'w') as f:
-            f.write(lsite_content)
-        print(f"[INFO] lsite content: {lsite_content}")
-
-    def _prepare_rsite(self):
-        rsite_content = ""
-        for site in self._epitope_sites:
-            rsite_content += f"{site.resseq}:{site.chain}\n"
-        with open(os.path.join(self.tmpdir.name, 'rsite.txt'), 'w') as f:
-            f.write(rsite_content)
-        print(f"[INFO] rsite content: {rsite_content}")
-
-    def dock(self):
-        if not (self._has_receptor and self._has_ligand):
-            raise ValueError('Missing receptor or ligand.')
-        self._prepare_lsite()
-
-        cmd_hdock = [self.hdock_bin, "receptor.pdb", "ligand.pdb", "-lsite", "lsite.txt"]
-        if self._epitope_sites is not None:
-            self._prepare_rsite()
-            cmd_hdock += ["-rsite", "rsite.txt"]
-        subprocess.run(
-            cmd_hdock,
-            cwd=self.tmpdir.name, check=True
-        )
-
-        cmd_pl = [self.createpl_bin, "Hdock.out", "ligand_docked.pdb", "-lsite", "lsite.txt"]
-        if self._epitope_sites is not None:
-            self._prepare_rsite()
-            cmd_pl += ["-rsite", "rsite.txt"]
-        subprocess.run(
-            cmd_pl, 
-            cwd=self.tmpdir.name, check=True
-        )
-        return self._dump_complex_pdb()
-
-
-if __name__ == '__main__':
-    with HDockAntibody('hdock', 'createpl') as dock:
-        dock.set_antigen('./data/dock/receptor.pdb', [DockSite('A', 991)])
-        dock.set_antibody('./data/example_dock/3qhf_fv.pdb')
-        print(dock.dock())
diff --git a/spaces/luost26/DiffAb/diffab/tools/renumber/__main__.py b/spaces/luost26/DiffAb/diffab/tools/renumber/__main__.py
deleted file mode 100644
index 7ca1c759534be0a3ae14e4ab178e981eb4fb211a..0000000000000000000000000000000000000000
--- a/spaces/luost26/DiffAb/diffab/tools/renumber/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .run import main
-
-if __name__ == '__main__':
-    main()
-    
\ No newline at end of file
diff --git a/spaces/malloc/OpenNMT-EN-DE-Translation/app.py b/spaces/malloc/OpenNMT-EN-DE-Translation/app.py
deleted file mode 100644
index ade8064f28f23af5e7df68797cd3af7315186925..0000000000000000000000000000000000000000
--- a/spaces/malloc/OpenNMT-EN-DE-Translation/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import gradio as gr
-import os
-
-def pred(text):
-    model = "averaged-10-epoch.pt"
-    hf_repo_id = "malloc/OpenNMT-py-English-German-Transformer"
-    src = "src-test.txt"
-    with open(src, 'w') as f:
-        f.write(text)
-    output = "pred.txt"
-    os.system(f"onmt_translate -model {model} -hf_repo_id {hf_repo_id} -src {src} -output {output}")
-
-    with open(output) as f:
-        translation =  f.read()
-    return translation
-
-
-iface = gr.Interface(
-    fn=pred,
-    inputs= gr.inputs.Textbox(lines=2, placeholder="Enter English text to translate..."),
-    outputs=["text"],
-    theme="huggingface")
-
-iface.launch()
diff --git a/spaces/mascIT/AgeGuesser/yolov5/utils/autoanchor.py b/spaces/mascIT/AgeGuesser/yolov5/utils/autoanchor.py
deleted file mode 100644
index 169faf118126830949e5945334c88d6cdca5bb40..0000000000000000000000000000000000000000
--- a/spaces/mascIT/AgeGuesser/yolov5/utils/autoanchor.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-anchor utils
-"""
-
-import random
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from .general import LOGGER, colorstr, emojis
-
-PREFIX = colorstr('AutoAnchor: ')
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchors.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        LOGGER.info(f'{PREFIX}Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1 / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1 / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1)  # current anchors
-    bpr, aat = metric(anchors.cpu().view(-1, 2))
-    s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
-    if bpr > 0.98:  # threshold to recompute
-        LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅'))
-    else:
-        LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...'))
-        na = m.anchors.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            LOGGER.info(f'{PREFIX}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            check_anchor_order(m)
-            LOGGER.info(f'{PREFIX}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            LOGGER.info(f'{PREFIX}Original anchors better than new anchors. Proceeding with original anchors.')
-
-
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            dataset: path to data.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    from scipy.cluster.vq import kmeans
-
-    thr = 1 / thr
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k, verbose=True):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
-            f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
-            f'past_thr={x[x > thr].mean():.3f}-mean: '
-        for i, x in enumerate(k):
-            s += '%i,%i, ' % (round(x[0]), round(x[1]))
-        if verbose:
-            LOGGER.info(s[:-2])
-        return k
-
-    if isinstance(dataset, str):  # *.yaml file
-        with open(dataset, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans calculation
-    LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, f'{PREFIX}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}'
-    k *= s
-    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
-    k = print_results(k, verbose=False)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    npr = np.random
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc=f'{PREFIX}Evolving anchors with Genetic Algorithm:')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k, verbose)
-
-    return print_results(k)
diff --git a/spaces/matthoffner/open-codetree/utils/llm.ts b/spaces/matthoffner/open-codetree/utils/llm.ts
deleted file mode 100644
index fea1e6099deccf7cddc12e80c6354cb3b94598c7..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/utils/llm.ts
+++ /dev/null
@@ -1,91 +0,0 @@
-import {
-  ParsedEvent,
-  ReconnectInterval,
-  createParser,
-} from 'eventsource-parser';
-
-export class LLMError extends Error {
-  type: string;
-  param: string;
-  code: string;
-
-  constructor(message: string, type: string, param: string, code: string) {
-    super(message);
-    this.name = 'LLMError';
-    this.type = type;
-    this.param = param;
-    this.code = code;
-  }
-}
-
-export const LLMStream = async (baseUrl: string, messages: any[]) => {
-    let url = `${baseUrl}/v1/chat/completions`;
-    const res = await fetch(url, {
-      headers: {
-        'Content-Type': 'application/json'
-      },
-      method: 'POST',
-      body: JSON.stringify({
-        messages,
-        stream: true,
-      }),
-    });
-  
-    const encoder = new TextEncoder();
-    const decoder = new TextDecoder();
-  
-    if (res.status !== 200) {
-      const result = await res.json();
-      if (result.error) {
-        throw new LLMError(
-          result.error.message,
-          result.error.type,
-          result.error.param,
-          result.error.code,
-        );
-      } else {
-        throw new Error(
-          `API returned an error: ${
-            decoder.decode(result?.value) || result.statusText
-          }`,
-        );
-      }
-    }
-  
-    const stream = new ReadableStream({
-        async start(controller) {
-            const onParse = (event: ParsedEvent | ReconnectInterval) => {
-                if (event.type === 'event') {
-                    const data = event.data;
-                
-                    if (data === "[DONE]") {
-                        controller.close();
-                        return;
-                    }
-                
-                    try {
-                        const json = JSON.parse(data);
-                        if (json.choices[0].finish_reason != null) {
-                            controller.close();
-                            return;
-                        }
-                        const text = json.choices[0].delta.content;
-                        const queue = encoder.encode(text);
-                        controller.enqueue(queue);
-                    } catch (e) {
-                        controller.error(e);
-                    }
-                }
-            };
-              
-    
-            const parser = createParser(onParse);
-        
-            for await (const chunk of res.body as any) {
-                parser.feed(decoder.decode(chunk));
-            }
-        },
-    });
-    
-    return stream;
-  };  
\ No newline at end of file
diff --git a/spaces/matthoffner/web-llm-embed/src/components/DashButton.tsx b/spaces/matthoffner/web-llm-embed/src/components/DashButton.tsx
deleted file mode 100644
index db301b5f0fc75fd16c0892780b3f4c8398e0edea..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/web-llm-embed/src/components/DashButton.tsx
+++ /dev/null
@@ -1,20 +0,0 @@
-import React from 'react';
-import { MouseEventHandler, ReactElement } from 'react';
-import { Button } from 'react95';
-
-interface Props {
-  handleClick: MouseEventHandler<HTMLButtonElement>;
-  children: ReactElement;
-}
-const DashButton = ({ handleClick, children }: Props) => {
-  return (
-    <Button
-      className="border-dashed border-2 border-black rounded-lg py-2 px-4 text-black hover:bg-black hover:text-white transition-all duration-300 ease-in-out"
-      onClick={handleClick}
-    >
-      {children}
-    </Button>
-  );
-};
-
-export { DashButton };
diff --git a/spaces/maurypb/Donald-trump-chatbot/app.py b/spaces/maurypb/Donald-trump-chatbot/app.py
deleted file mode 100644
index 34830bec5c7c38bbf0118fec0bfd04c0ecef3883..0000000000000000000000000000000000000000
--- a/spaces/maurypb/Donald-trump-chatbot/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-
-import openai
-import os
-
-openai.api_key= os.environ.get("openai_key")
-
-messages = [{"role": "system", "content": "You are donald j trump. You talk like donald trump at a campaign speech. You can name drop other politicians, but be sure to use nasty nicknames."}]
-
-def CustomChatGPT(user_input):
-
-    
-    openai.api_key= os.environ.get("openai_key")
-
-    messages.append({"role": "user", "content": user_input + " Make your response mean and trump-like."})
-    response = openai.ChatCompletion.create(
-        model = "gpt-3.5-turbo",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content": ChatGPT_reply})
-    return ChatGPT_reply
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-            # donald j trump chatbot
-            ## ask me anything, Then lets argue about it.
-        """
-    )
-
-    trump= gr.Chatbot(label="donald trump")
-    msg = gr.Textbox(label="Ask the Donald anything.", placeholder="Type stuff here.")
-
-    #clear = gr.Button("Clear")
-    submit=gr.Button("Submit")
-
-    def respond(message, chat_history):
-
-        bot_message= CustomChatGPT(message)
-
-        chat_history.append((message, bot_message))
-        #time.sleep(1)
-        return "", chat_history
-
-    #msg.submit(respond, [msg, chatbot], [msg, chatbot])
-    submit.click(respond,[msg,trump],[msg,trump])
-    #clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.launch()
-
diff --git a/spaces/mehdidc/text_to_image_ddgan/scripts/eval_all.sh b/spaces/mehdidc/text_to_image_ddgan/scripts/eval_all.sh
deleted file mode 100644
index 7460f604ef7464ad0e47963fcda2dd68f212c1c8..0000000000000000000000000000000000000000
--- a/spaces/mehdidc/text_to_image_ddgan/scripts/eval_all.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-#for model in ddgan_sd_v10 ddgan_laion2b_v2 ddgan_ddb_v1 ddgan_ddb_v2 ddgan_ddb_v3 ddgan_ddb_v4;do 
-#for model in ddgan_ddb_v2 ddgan_ddb_v3 ddgan_ddb_v4 ddgan_ddb_v5;do 
-#for model in ddgan_ddb_v4 ddgan_ddb_v6 ddgan_ddb_v7 ddgan_laion_aesthetic_v15;do 
-#for model in ddgan_ddb_v6;do 
-#for model in ddgan_laion_aesthetic_v15;do 
-#for model in ddgan_ddb_v3 ddgan_ddb_v11 ddgan_laion_aesthetic_v15;do 
-#for model in ddgan_ddb_v3 ddgan_ddb_v11 ddgan_ddb_v2;do 
-#for model in ddgan_ddb_v6 ddgan_ddb_v4 ddgan_ddb_v10 ddgan_ddb_v9;do 
-#for model in ddgan_ddb_v3 ddgan_ddb_v11;do 
-for model in ddgan_ddb_v11;do 
-#for model in ddgan_ddb_v3;do 
-    if [ "$model" == "ddgan_ddb_v3" ]; then
-        bs=32
-    elif [ "$model" == "ddgan_laion_aesthetic_v15" ]; then
-        bs=32
-    elif [ "$model" == "ddgan_ddb_v6" ]; then
-        bs=32
-    elif [ "$model" == "ddgan_ddb_v4" ]; then
-        bs=16
-    elif [ "$model" == "ddgan_ddb_v9" ]; then
-        bs=16
-    elif [ "$model" == "ddgan_ddb_v10" ]; then
-        bs=16
-    elif [ "$model" == "ddgan_ddb_v11" ]; then
-        bs=16
-    else
-        bs=64
-    fi
-    sbatch --partition dc-gpu -t 360 -N 1 -n1 scripts/run_jurecadc_ddp.sh test_ddgan.py --name $model --cond-text=parti_prompts.txt  --batch-size=$bs --epoch=-1 --compute-image-reward --eval-name=parti_image_reward
-    #sbatch --partition dc-gpu -t 360 -N 1 -n1 scripts/run_jurecadc_ddp.sh test_ddgan.py --name $model --cond-text=parti_prompts.txt  --batch-size=$bs --epoch=-1 --compute-clip-score --eval-name=parti;
-    #sbatch --partition dc-gpu -t 360 -N 1 -n1 scripts/run_jurecadc_ddp.sh test_ddgan.py --name $model --fid --real-img-dir inception_statistics_coco_val2014_256x256.npz --cond-text coco_val2014_captions.txt  --batch-size=$bs --epoch=-1 --nb-images-for-fid=30000 --eval-name=coco --compute-clip-score;
-    #sbatch --partition dc-gpu -t 360 -N 1 -n1 scripts/run_jurecadc_ddp.sh test_ddgan.py --name $model --cond-text=drawbench.txt  --batch-size=$bs --epoch=-1 --compute-clip-score --eval-name=drawbench;
-done
diff --git a/spaces/merle/PROTEIN_GENERATOR/examples/motif_scaffolding.sh b/spaces/merle/PROTEIN_GENERATOR/examples/motif_scaffolding.sh
deleted file mode 100644
index ba1f0b03227cc3de8ec140c2fcc42dead2a083e2..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/examples/motif_scaffolding.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-#SBATCH -J seq_diff
-#SBATCH -p gpu
-#SBATCH --mem=8g
-#SBATCH --gres=gpu:a6000:1
-#SBATCH -o ./out/slurm/slurm_%j.out
-
-source activate /software/conda/envs/SE3nv
-
-srun python ../inference.py \
-    --num_designs 10 \
-    --out out/design \
-    --pdb pdbs/rsv5_5tpn.pdb \
-    --contigs 0-25,A163-181,25-30 --T 25 --save_best_plddt
diff --git a/spaces/mfrashad/ClothingGAN/models/biggan/pytorch_biggan/pytorch_pretrained_biggan/convert_tf_to_pytorch.py b/spaces/mfrashad/ClothingGAN/models/biggan/pytorch_biggan/pytorch_pretrained_biggan/convert_tf_to_pytorch.py
deleted file mode 100644
index 7ccb787dec188e9dbd9ea31288c049c1bdb30f95..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/models/biggan/pytorch_biggan/pytorch_pretrained_biggan/convert_tf_to_pytorch.py
+++ /dev/null
@@ -1,312 +0,0 @@
-# coding: utf-8
-"""
-Convert a TF Hub model for BigGAN in a PT one.
-"""
-from __future__ import (absolute_import, division, print_function, unicode_literals)
-
-from itertools import chain
-
-import os
-import argparse
-import logging
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.functional import normalize
-
-from .model import BigGAN, WEIGHTS_NAME, CONFIG_NAME
-from .config import BigGANConfig
-
-logger = logging.getLogger(__name__)
-
-
-def extract_batch_norm_stats(tf_model_path, batch_norm_stats_path=None):
-    try:
-        import numpy as np
-        import tensorflow as tf
-        import tensorflow_hub as hub
-    except ImportError:
-        raise ImportError("Loading a TensorFlow models in PyTorch, requires TensorFlow and TF Hub to be installed. "
-                          "Please see https://www.tensorflow.org/install/ for installation instructions for TensorFlow. "
-                          "And see https://github.com/tensorflow/hub for installing Hub. "
-                          "Probably pip install tensorflow tensorflow-hub")
-    tf.reset_default_graph()
-    logger.info('Loading BigGAN module from: {}'.format(tf_model_path))
-    module = hub.Module(tf_model_path)
-    inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
-              for k, v in module.get_input_info_dict().items()}
-    output = module(inputs)
-
-    initializer = tf.global_variables_initializer()
-    sess = tf.Session()
-    stacks = sum(((i*10 + 1, i*10 + 3, i*10 + 6, i*10 + 8) for i in range(50)), ())
-    numpy_stacks = []
-    for i in stacks:
-        logger.info("Retrieving module_apply_default/stack_{}".format(i))
-        try:
-            stack_var = tf.get_default_graph().get_tensor_by_name("module_apply_default/stack_%d:0" % i)
-        except KeyError:
-            break  # We have all the stats
-        numpy_stacks.append(sess.run(stack_var))
-
-    if batch_norm_stats_path is not None:
-        torch.save(numpy_stacks, batch_norm_stats_path)
-    else:
-        return numpy_stacks
-
-
-def build_tf_to_pytorch_map(model, config):
-    """ Build a map from TF variables to PyTorch modules. """
-    tf_to_pt_map = {}
-
-    # Embeddings and GenZ
-    tf_to_pt_map.update({'linear/w/ema_0.9999': model.embeddings.weight,
-                         'Generator/GenZ/G_linear/b/ema_0.9999': model.generator.gen_z.bias,
-                         'Generator/GenZ/G_linear/w/ema_0.9999': model.generator.gen_z.weight_orig,
-                         'Generator/GenZ/G_linear/u0': model.generator.gen_z.weight_u})
-
-    # GBlock blocks
-    model_layer_idx = 0
-    for i, (up, in_channels, out_channels) in enumerate(config.layers):
-        if i == config.attention_layer_position:
-            model_layer_idx += 1
-        layer_str = "Generator/GBlock_%d/" % i if i > 0 else "Generator/GBlock/"
-        layer_pnt = model.generator.layers[model_layer_idx]
-        for i in range(4):  #  Batchnorms
-            batch_str = layer_str + ("BatchNorm_%d/" % i if i > 0 else "BatchNorm/")
-            batch_pnt = getattr(layer_pnt, 'bn_%d' % i)
-            for name in ('offset', 'scale'):
-                sub_module_str = batch_str + name + "/"
-                sub_module_pnt = getattr(batch_pnt, name)
-                tf_to_pt_map.update({sub_module_str + "w/ema_0.9999": sub_module_pnt.weight_orig,
-                                     sub_module_str + "u0": sub_module_pnt.weight_u})
-        for i in range(4):  # Convolutions
-            conv_str = layer_str + "conv%d/" % i
-            conv_pnt = getattr(layer_pnt, 'conv_%d' % i)
-            tf_to_pt_map.update({conv_str + "b/ema_0.9999": conv_pnt.bias,
-                                 conv_str + "w/ema_0.9999": conv_pnt.weight_orig,
-                                 conv_str + "u0": conv_pnt.weight_u})
-        model_layer_idx += 1
-
-    # Attention block
-    layer_str = "Generator/attention/"
-    layer_pnt = model.generator.layers[config.attention_layer_position]
-    tf_to_pt_map.update({layer_str + "gamma/ema_0.9999": layer_pnt.gamma})
-    for pt_name, tf_name in zip(['snconv1x1_g', 'snconv1x1_o_conv', 'snconv1x1_phi', 'snconv1x1_theta'],
-                                ['g/', 'o_conv/', 'phi/', 'theta/']):
-        sub_module_str = layer_str + tf_name
-        sub_module_pnt = getattr(layer_pnt, pt_name)
-        tf_to_pt_map.update({sub_module_str + "w/ema_0.9999": sub_module_pnt.weight_orig,
-                             sub_module_str + "u0": sub_module_pnt.weight_u})
-
-    # final batch norm and conv to rgb
-    layer_str = "Generator/BatchNorm/"
-    layer_pnt = model.generator.bn
-    tf_to_pt_map.update({layer_str + "offset/ema_0.9999": layer_pnt.bias,
-                         layer_str + "scale/ema_0.9999": layer_pnt.weight})
-    layer_str = "Generator/conv_to_rgb/"
-    layer_pnt = model.generator.conv_to_rgb
-    tf_to_pt_map.update({layer_str + "b/ema_0.9999": layer_pnt.bias,
-                         layer_str + "w/ema_0.9999": layer_pnt.weight_orig,
-                         layer_str + "u0": layer_pnt.weight_u})
-    return tf_to_pt_map
-
-
-def load_tf_weights_in_biggan(model, config, tf_model_path, batch_norm_stats_path=None):
-    """ Load tf checkpoints and standing statistics in a pytorch model
-    """
-    try:
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        raise ImportError("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions.")
-    # Load weights from TF model
-    checkpoint_path = tf_model_path + "/variables/variables"
-    init_vars = tf.train.list_variables(checkpoint_path)
-    from pprint import pprint
-    pprint(init_vars)
-
-    # Extract batch norm statistics from model if needed
-    if batch_norm_stats_path:
-        stats = torch.load(batch_norm_stats_path)
-    else:
-        logger.info("Extracting batch norm stats")
-        stats = extract_batch_norm_stats(tf_model_path)
-
-    # Build TF to PyTorch weights loading map
-    tf_to_pt_map = build_tf_to_pytorch_map(model, config)
-
-    tf_weights = {}
-    for name in tf_to_pt_map.keys():
-        array = tf.train.load_variable(checkpoint_path, name)
-        tf_weights[name] = array
-        # logger.info("Loading TF weight {} with shape {}".format(name, array.shape))
-
-    # Load parameters
-    with torch.no_grad():
-        pt_params_pnt = set()
-        for name, pointer in tf_to_pt_map.items():
-            array = tf_weights[name]
-            if pointer.dim() == 1:
-                if pointer.dim() < array.ndim:
-                    array = np.squeeze(array)
-            elif pointer.dim() == 2:  # Weights
-                array = np.transpose(array)
-            elif pointer.dim() == 4:  # Convolutions
-                array = np.transpose(array, (3, 2, 0, 1))
-            else:
-                raise "Wrong dimensions to adjust: " + str((pointer.shape, array.shape))
-            if pointer.shape != array.shape:
-                raise ValueError("Wrong dimensions: " + str((pointer.shape, array.shape)))
-            logger.info("Initialize PyTorch weight {} with shape {}".format(name, pointer.shape))
-            pointer.data = torch.from_numpy(array) if isinstance(array, np.ndarray) else torch.tensor(array)
-            tf_weights.pop(name, None)
-            pt_params_pnt.add(pointer.data_ptr())
-
-        # Prepare SpectralNorm buffers by running one step of Spectral Norm (no need to train the model):
-        for module in model.modules():
-            for n, buffer in module.named_buffers():
-                if n == 'weight_v':
-                    weight_mat = module.weight_orig
-                    weight_mat = weight_mat.reshape(weight_mat.size(0), -1)
-                    u = module.weight_u
-
-                    v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=config.eps)
-                    buffer.data = v
-                    pt_params_pnt.add(buffer.data_ptr())
-
-                    u = normalize(torch.mv(weight_mat, v), dim=0, eps=config.eps)
-                    module.weight_u.data = u
-                    pt_params_pnt.add(module.weight_u.data_ptr())
-
-        # Load batch norm statistics
-        index = 0
-        for layer in model.generator.layers:
-            if not hasattr(layer, 'bn_0'):
-                continue
-            for i in range(4):  #  Batchnorms
-                bn_pointer = getattr(layer, 'bn_%d' % i)
-                pointer = bn_pointer.running_means
-                if pointer.shape != stats[index].shape:
-                    raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
-                pointer.data = torch.from_numpy(stats[index])
-                pt_params_pnt.add(pointer.data_ptr())
-
-                pointer = bn_pointer.running_vars
-                if pointer.shape != stats[index+1].shape:
-                    raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
-                pointer.data = torch.from_numpy(stats[index+1])
-                pt_params_pnt.add(pointer.data_ptr())
-
-                index += 2
-
-        bn_pointer = model.generator.bn
-        pointer = bn_pointer.running_means
-        if pointer.shape != stats[index].shape:
-            raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
-        pointer.data = torch.from_numpy(stats[index])
-        pt_params_pnt.add(pointer.data_ptr())
-
-        pointer = bn_pointer.running_vars
-        if pointer.shape != stats[index+1].shape:
-            raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
-        pointer.data = torch.from_numpy(stats[index+1])
-        pt_params_pnt.add(pointer.data_ptr())
-
-    remaining_params = list(n for n, t in chain(model.named_parameters(), model.named_buffers()) \
-                            if t.data_ptr() not in pt_params_pnt)
-
-    logger.info("TF Weights not copied to PyTorch model: {} -".format(', '.join(tf_weights.keys())))
-    logger.info("Remanining parameters/buffers from PyTorch model: {} -".format(', '.join(remaining_params)))
-
-    return model
-
-
-BigGAN128 = BigGANConfig(output_dim=128, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
-                         layers=[(False, 16, 16),
-                                 (True, 16, 16),
-                                 (False, 16, 16),
-                                 (True, 16, 8),
-                                 (False, 8, 8),
-                                 (True, 8, 4),
-                                 (False, 4, 4),
-                                 (True, 4, 2),
-                                 (False, 2, 2),
-                                 (True, 2, 1)],
-                         attention_layer_position=8, eps=1e-4, n_stats=51)
-
-BigGAN256 = BigGANConfig(output_dim=256, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
-                         layers=[(False, 16, 16),
-                                 (True, 16, 16),
-                                 (False, 16, 16),
-                                 (True, 16, 8),
-                                 (False, 8, 8),
-                                 (True, 8, 8),
-                                 (False, 8, 8),
-                                 (True, 8, 4),
-                                 (False, 4, 4),
-                                 (True, 4, 2),
-                                 (False, 2, 2),
-                                 (True, 2, 1)],
-                         attention_layer_position=8, eps=1e-4, n_stats=51)
-
-BigGAN512 = BigGANConfig(output_dim=512, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
-                         layers=[(False, 16, 16),
-                                 (True, 16, 16),
-                                 (False, 16, 16),
-                                 (True, 16, 8),
-                                 (False, 8, 8),
-                                 (True, 8, 8),
-                                 (False, 8, 8),
-                                 (True, 8, 4),
-                                 (False, 4, 4),
-                                 (True, 4, 2),
-                                 (False, 2, 2),
-                                 (True, 2, 1),
-                                 (False, 1, 1),
-                                 (True, 1, 1)],
-                         attention_layer_position=8, eps=1e-4, n_stats=51)
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert a BigGAN TF Hub model in a PyTorch model")
-    parser.add_argument("--model_type", type=str, default="", required=True,
-                        help="BigGAN model type (128, 256, 512)")
-    parser.add_argument("--tf_model_path", type=str, default="", required=True,
-                        help="Path of the downloaded TF Hub model")
-    parser.add_argument("--pt_save_path", type=str, default="",
-                        help="Folder to save the PyTorch model (default: Folder of the TF Hub model)")
-    parser.add_argument("--batch_norm_stats_path", type=str, default="",
-                        help="Path of previously extracted batch norm statistics")
-    args = parser.parse_args()
-
-    logging.basicConfig(level=logging.INFO)
-
-    if not args.pt_save_path:
-        args.pt_save_path = args.tf_model_path
-
-    if args.model_type == "128":
-        config = BigGAN128
-    elif args.model_type == "256":
-        config = BigGAN256
-    elif args.model_type == "512":
-        config = BigGAN512
-    else:
-        raise ValueError("model_type should be one of 128, 256 or 512")
-
-    model = BigGAN(config)
-    model = load_tf_weights_in_biggan(model, config, args.tf_model_path, args.batch_norm_stats_path)
-
-    model_save_path = os.path.join(args.pt_save_path, WEIGHTS_NAME)
-    config_save_path = os.path.join(args.pt_save_path, CONFIG_NAME)
-
-    logger.info("Save model dump to {}".format(model_save_path))
-    torch.save(model.state_dict(), model_save_path)
-    logger.info("Save configuration file to {}".format(config_save_path))
-    with open(config_save_path, "w", encoding="utf-8") as f:
-        f.write(config.to_json_string())
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/mikebars/huggingface/assets/index-d15ae4ce.js b/spaces/mikebars/huggingface/assets/index-d15ae4ce.js
deleted file mode 100644
index 20ce5614269312627323032880fbe8d62916a8d1..0000000000000000000000000000000000000000
--- a/spaces/mikebars/huggingface/assets/index-d15ae4ce.js
+++ /dev/null
@@ -1,41 +0,0 @@
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- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var tr=Symbol.for("react.element"),Uc=Symbol.for("react.portal"),Vc=Symbol.for("react.fragment"),Bc=Symbol.for("react.strict_mode"),Qc=Symbol.for("react.profiler"),Hc=Symbol.for("react.provider"),Wc=Symbol.for("react.context"),Kc=Symbol.for("react.forward_ref"),Yc=Symbol.for("react.suspense"),Xc=Symbol.for("react.memo"),Gc=Symbol.for("react.lazy"),Qo=Symbol.iterator;function Zc(e){return e===null||typeof e!="object"?null:(e=Qo&&e[Qo]||e["@@iterator"],typeof e=="function"?e:null)}var ts={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},ns=Object.assign,rs={};function cn(e,t,n){this.props=e,this.context=t,this.refs=rs,this.updater=n||ts}cn.prototype.isReactComponent={};cn.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};cn.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function ls(){}ls.prototype=cn.prototype;function Wi(e,t,n){this.props=e,this.context=t,this.refs=rs,this.updater=n||ts}var Ki=Wi.prototype=new ls;Ki.constructor=Wi;ns(Ki,cn.prototype);Ki.isPureReactComponent=!0;var Ho=Array.isArray,is=Object.prototype.hasOwnProperty,Yi={current:null},os={key:!0,ref:!0,__self:!0,__source:!0};function us(e,t,n){var r,l={},i=null,o=null;if(t!=null)for(r in t.ref!==void 0&&(o=t.ref),t.key!==void 0&&(i=""+t.key),t)is.call(t,r)&&!os.hasOwnProperty(r)&&(l[r]=t[r]);var u=arguments.length-2;if(u===1)l.children=n;else if(1<u){for(var s=Array(u),c=0;c<u;c++)s[c]=arguments[c+2];l.children=s}if(e&&e.defaultProps)for(r in u=e.defaultProps,u)l[r]===void 0&&(l[r]=u[r]);return{$$typeof:tr,type:e,key:i,ref:o,props:l,_owner:Yi.current}}function qc(e,t){return{$$typeof:tr,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function Xi(e){return typeof e=="object"&&e!==null&&e.$$typeof===tr}function Jc(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var Wo=/\/+/g;function _l(e,t){return typeof e=="object"&&e!==null&&e.key!=null?Jc(""+e.key):t.toString(36)}function jr(e,t,n,r,l){var i=typeof e;(i==="undefined"||i==="boolean")&&(e=null);var o=!1;if(e===null)o=!0;else switch(i){case"string":case"number":o=!0;break;case"object":switch(e.$$typeof){case tr:case Uc:o=!0}}if(o)return o=e,l=l(o),e=r===""?"."+_l(o,0):r,Ho(l)?(n="",e!=null&&(n=e.replace(Wo,"$&/")+"/"),jr(l,t,n,"",function(c){return c})):l!=null&&(Xi(l)&&(l=qc(l,n+(!l.key||o&&o.key===l.key?"":(""+l.key).replace(Wo,"$&/")+"/")+e)),t.push(l)),1;if(o=0,r=r===""?".":r+":",Ho(e))for(var u=0;u<e.length;u++){i=e[u];var s=r+_l(i,u);o+=jr(i,t,n,s,l)}else if(s=Zc(e),typeof s=="function")for(e=s.call(e),u=0;!(i=e.next()).done;)i=i.value,s=r+_l(i,u++),o+=jr(i,t,n,s,l);else if(i==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). 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- * @license React
- * react-jsx-runtime.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var tf=m,nf=Symbol.for("react.element"),rf=Symbol.for("react.fragment"),lf=Object.prototype.hasOwnProperty,of=tf.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner,uf={key:!0,ref:!0,__self:!0,__source:!0};function ss(e,t,n){var r,l={},i=null,o=null;n!==void 0&&(i=""+n),t.key!==void 0&&(i=""+t.key),t.ref!==void 0&&(o=t.ref);for(r in t)lf.call(t,r)&&!uf.hasOwnProperty(r)&&(l[r]=t[r]);if(e&&e.defaultProps)for(r in t=e.defaultProps,t)l[r]===void 0&&(l[r]=t[r]);return{$$typeof:nf,type:e,key:i,ref:o,props:l,_owner:of.current}}ul.Fragment=rf;ul.jsx=ss;ul.jsxs=ss;bu.exports=ul;var a=bu.exports,as={exports:{}},Ce={},cs={exports:{}},fs={};/**
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- * scheduler.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
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- * @license React
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
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s={mode:"hidden",children:r.children};return!(o&1)&&t.child!==l?(r=t.child,r.childLanes=0,r.pendingProps=s,t.deletions=null):(r=ht(l,s),r.subtreeFlags=l.subtreeFlags&14680064),u!==null?i=ht(u,i):(i=Ot(i,o,n,null),i.flags|=2),i.return=t,r.return=t,r.sibling=i,t.child=r,r=i,i=t.child,o=e.child.memoizedState,o=o===null?Ri(n):{baseLanes:o.baseLanes|n,cachePool:null,transitions:o.transitions},i.memoizedState=o,i.childLanes=e.childLanes&~n,t.memoizedState=Fi,r}return i=e.child,e=i.sibling,r=ht(i,{mode:"visible",children:r.children}),!(t.mode&1)&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function To(e,t){return t=gl({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function xr(e,t,n,r){return r!==null&&mo(r),on(t,e.child,null,n),e=To(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function Bd(e,t,n,r,l,i,o){if(n)return t.flags&256?(t.flags&=-257,r=Xl(Error(x(422))),xr(e,t,o,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(i=r.fallback,l=t.mode,r=gl({mode:"visible",children:r.children},l,0,null),i=Ot(i,l,o,null),i.flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,t.mode&1&&on(t,e.child,null,o),t.child.memoizedState=Ri(o),t.memoizedState=Fi,i);if(!(t.mode&1))return xr(e,t,o,null);if(l.data==="$!"){if(r=l.nextSibling&&l.nextSibling.dataset,r)var u=r.dgst;return r=u,i=Error(x(419)),r=Xl(i,r,void 0),xr(e,t,o,r)}if(u=(o&e.childLanes)!==0,ye||u){if(r=re,r!==null){switch(o&-o){case 4:l=2;break;case 16:l=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:l=32;break;case 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n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Ai=function(){};Ya=function(e,t,n,r){var l=e.memoizedProps;if(l!==r){e=t.stateNode,Nt(He.current);var i=null;switch(n){case"input":l=li(e,l),r=li(e,r),i=[];break;case"select":l=H({},l,{value:void 0}),r=H({},r,{value:void 0}),i=[];break;case"textarea":l=ui(e,l),r=ui(e,r),i=[];break;default:typeof l.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=Wr)}ai(n,r);var o;n=null;for(c in l)if(!r.hasOwnProperty(c)&&l.hasOwnProperty(c)&&l[c]!=null)if(c==="style"){var u=l[c];for(o in u)u.hasOwnProperty(o)&&(n||(n={}),n[o]="")}else 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r.is=="string"?e=o.createElement(n,{is:r.is}):(e=o.createElement(n),n==="select"&&(o=e,r.multiple?o.multiple=!0:r.size&&(o.size=r.size))):e=o.createElementNS(e,n),e[Be]=t,e[Xn]=r,Ka(e,t,!1,!1),t.stateNode=e;e:{switch(o=ci(n,r),n){case"dialog":$("cancel",e),$("close",e),l=r;break;case"iframe":case"object":case"embed":$("load",e),l=r;break;case"video":case"audio":for(l=0;l<Tn.length;l++)$(Tn[l],e);l=r;break;case"source":$("error",e),l=r;break;case"img":case"image":case"link":$("error",e),$("load",e),l=r;break;case"details":$("toggle",e),l=r;break;case"input":Go(e,r),l=li(e,r),$("invalid",e);break;case"option":l=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},l=H({},r,{value:void 0}),$("invalid",e);break;case"textarea":qo(e,r),l=ui(e,r),$("invalid",e);break;default:l=r}ai(n,l),u=l;for(i in u)if(u.hasOwnProperty(i)){var s=u[i];i==="style"?Cs(e,s):i==="dangerouslySetInnerHTML"?(s=s?s.__html:void 0,s!=null&&ks(e,s)):i==="children"?typeof 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t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||B.current&1?b===0&&(b=3):Fo())),t.updateQueue!==null&&(t.flags|=4),se(t),null);case 4:return un(),Ai(e,t),e===null&&Kn(t.stateNode.containerInfo),se(t),null;case 10:return vo(t.type._context),se(t),null;case 17:return ge(t.type)&&Kr(),se(t),null;case 19:if(U(B),i=t.memoizedState,i===null)return 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diff --git a/spaces/mmlab-ntu/relate-anything-model/segment_anything/utils/amg.py b/spaces/mmlab-ntu/relate-anything-model/segment_anything/utils/amg.py
deleted file mode 100644
index 3a137778e45c464c079658ecb87ec53270e789f7..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/relate-anything-model/segment_anything/utils/amg.py
+++ /dev/null
@@ -1,346 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-import math
-from copy import deepcopy
-from itertools import product
-from typing import Any, Dict, Generator, ItemsView, List, Tuple
-
-
-class MaskData:
-    """
-    A structure for storing masks and their related data in batched format.
-    Implements basic filtering and concatenation.
-    """
-
-    def __init__(self, **kwargs) -> None:
-        for v in kwargs.values():
-            assert isinstance(
-                v, (list, np.ndarray, torch.Tensor)
-            ), "MaskData only supports list, numpy arrays, and torch tensors."
-        self._stats = dict(**kwargs)
-
-    def __setitem__(self, key: str, item: Any) -> None:
-        assert isinstance(
-            item, (list, np.ndarray, torch.Tensor)
-        ), "MaskData only supports list, numpy arrays, and torch tensors."
-        self._stats[key] = item
-
-    def __delitem__(self, key: str) -> None:
-        del self._stats[key]
-
-    def __getitem__(self, key: str) -> Any:
-        return self._stats[key]
-
-    def items(self) -> ItemsView[str, Any]:
-        return self._stats.items()
-
-    def filter(self, keep: torch.Tensor) -> None:
-        for k, v in self._stats.items():
-            if v is None:
-                self._stats[k] = None
-            elif isinstance(v, torch.Tensor):
-                self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
-            elif isinstance(v, np.ndarray):
-                self._stats[k] = v[keep.detach().cpu().numpy()]
-            elif isinstance(v, list) and keep.dtype == torch.bool:
-                self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
-            elif isinstance(v, list):
-                self._stats[k] = [v[i] for i in keep]
-            else:
-                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
-    def cat(self, new_stats: "MaskData") -> None:
-        for k, v in new_stats.items():
-            if k not in self._stats or self._stats[k] is None:
-                self._stats[k] = deepcopy(v)
-            elif isinstance(v, torch.Tensor):
-                self._stats[k] = torch.cat([self._stats[k], v], dim=0)
-            elif isinstance(v, np.ndarray):
-                self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
-            elif isinstance(v, list):
-                self._stats[k] = self._stats[k] + deepcopy(v)
-            else:
-                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
-    def to_numpy(self) -> None:
-        for k, v in self._stats.items():
-            if isinstance(v, torch.Tensor):
-                self._stats[k] = v.detach().cpu().numpy()
-
-
-def is_box_near_crop_edge(
-    boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
-) -> torch.Tensor:
-    """Filter masks at the edge of a crop, but not at the edge of the original image."""
-    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
-    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
-    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
-    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
-    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
-    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
-    return torch.any(near_crop_edge, dim=1)
-
-
-def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
-    box_xywh = deepcopy(box_xyxy)
-    box_xywh[2] = box_xywh[2] - box_xywh[0]
-    box_xywh[3] = box_xywh[3] - box_xywh[1]
-    return box_xywh
-
-
-def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
-    assert len(args) > 0 and all(
-        len(a) == len(args[0]) for a in args
-    ), "Batched iteration must have inputs of all the same size."
-    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
-    for b in range(n_batches):
-        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
-
-
-def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
-    """
-    Encodes masks to an uncompressed RLE, in the format expected by
-    pycoco tools.
-    """
-    # Put in fortran order and flatten h,w
-    b, h, w = tensor.shape
-    tensor = tensor.permute(0, 2, 1).flatten(1)
-
-    # Compute change indices
-    diff = tensor[:, 1:] ^ tensor[:, :-1]
-    change_indices = diff.nonzero()
-
-    # Encode run length
-    out = []
-    for i in range(b):
-        cur_idxs = change_indices[change_indices[:, 0] == i, 1]
-        cur_idxs = torch.cat(
-            [
-                torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
-                cur_idxs + 1,
-                torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
-            ]
-        )
-        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
-        counts = [] if tensor[i, 0] == 0 else [0]
-        counts.extend(btw_idxs.detach().cpu().tolist())
-        out.append({"size": [h, w], "counts": counts})
-    return out
-
-
-def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
-    """Compute a binary mask from an uncompressed RLE."""
-    h, w = rle["size"]
-    mask = np.empty(h * w, dtype=bool)
-    idx = 0
-    parity = False
-    for count in rle["counts"]:
-        mask[idx : idx + count] = parity
-        idx += count
-        parity ^= True
-    mask = mask.reshape(w, h)
-    return mask.transpose()  # Put in C order
-
-
-def area_from_rle(rle: Dict[str, Any]) -> int:
-    return sum(rle["counts"][1::2])
-
-
-def calculate_stability_score(
-    masks: torch.Tensor, mask_threshold: float, threshold_offset: float
-) -> torch.Tensor:
-    """
-    Computes the stability score for a batch of masks. The stability
-    score is the IoU between the binary masks obtained by thresholding
-    the predicted mask logits at high and low values.
-    """
-    # One mask is always contained inside the other.
-    # Save memory by preventing unnecesary cast to torch.int64
-    intersections = (
-        (masks > (mask_threshold + threshold_offset))
-        .sum(-1, dtype=torch.int16)
-        .sum(-1, dtype=torch.int32)
-    )
-    unions = (
-        (masks > (mask_threshold - threshold_offset))
-        .sum(-1, dtype=torch.int16)
-        .sum(-1, dtype=torch.int32)
-    )
-    return intersections / unions
-
-
-def build_point_grid(n_per_side: int) -> np.ndarray:
-    """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
-    offset = 1 / (2 * n_per_side)
-    points_one_side = np.linspace(offset, 1 - offset, n_per_side)
-    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
-    points_y = np.tile(points_one_side[:, None], (1, n_per_side))
-    points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
-    return points
-
-
-def build_all_layer_point_grids(
-    n_per_side: int, n_layers: int, scale_per_layer: int
-) -> List[np.ndarray]:
-    """Generates point grids for all crop layers."""
-    points_by_layer = []
-    for i in range(n_layers + 1):
-        n_points = int(n_per_side / (scale_per_layer**i))
-        points_by_layer.append(build_point_grid(n_points))
-    return points_by_layer
-
-
-def generate_crop_boxes(
-    im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
-) -> Tuple[List[List[int]], List[int]]:
-    """
-    Generates a list of crop boxes of different sizes. Each layer
-    has (2**i)**2 boxes for the ith layer.
-    """
-    crop_boxes, layer_idxs = [], []
-    im_h, im_w = im_size
-    short_side = min(im_h, im_w)
-
-    # Original image
-    crop_boxes.append([0, 0, im_w, im_h])
-    layer_idxs.append(0)
-
-    def crop_len(orig_len, n_crops, overlap):
-        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
-
-    for i_layer in range(n_layers):
-        n_crops_per_side = 2 ** (i_layer + 1)
-        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
-
-        crop_w = crop_len(im_w, n_crops_per_side, overlap)
-        crop_h = crop_len(im_h, n_crops_per_side, overlap)
-
-        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
-        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
-
-        # Crops in XYWH format
-        for x0, y0 in product(crop_box_x0, crop_box_y0):
-            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
-            crop_boxes.append(box)
-            layer_idxs.append(i_layer + 1)
-
-    return crop_boxes, layer_idxs
-
-
-def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
-    x0, y0, _, _ = crop_box
-    offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
-    # Check if boxes has a channel dimension
-    if len(boxes.shape) == 3:
-        offset = offset.unsqueeze(1)
-    return boxes + offset
-
-
-def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
-    x0, y0, _, _ = crop_box
-    offset = torch.tensor([[x0, y0]], device=points.device)
-    # Check if points has a channel dimension
-    if len(points.shape) == 3:
-        offset = offset.unsqueeze(1)
-    return points + offset
-
-
-def uncrop_masks(
-    masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
-) -> torch.Tensor:
-    x0, y0, x1, y1 = crop_box
-    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
-        return masks
-    # Coordinate transform masks
-    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
-    pad = (x0, pad_x - x0, y0, pad_y - y0)
-    return torch.nn.functional.pad(masks, pad, value=0)
-
-
-def remove_small_regions(
-    mask: np.ndarray, area_thresh: float, mode: str
-) -> Tuple[np.ndarray, bool]:
-    """
-    Removes small disconnected regions and holes in a mask. Returns the
-    mask and an indicator of if the mask has been modified.
-    """
-    import cv2  # type: ignore
-
-    assert mode in ["holes", "islands"]
-    correct_holes = mode == "holes"
-    working_mask = (correct_holes ^ mask).astype(np.uint8)
-    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
-    sizes = stats[:, -1][1:]  # Row 0 is background label
-    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
-    if len(small_regions) == 0:
-        return mask, False
-    fill_labels = [0] + small_regions
-    if not correct_holes:
-        fill_labels = [i for i in range(n_labels) if i not in fill_labels]
-        # If every region is below threshold, keep largest
-        if len(fill_labels) == 0:
-            fill_labels = [int(np.argmax(sizes)) + 1]
-    mask = np.isin(regions, fill_labels)
-    return mask, True
-
-
-def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
-    from pycocotools import mask as mask_utils  # type: ignore
-
-    h, w = uncompressed_rle["size"]
-    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
-    rle["counts"] = rle["counts"].decode("utf-8")  # Necessary to serialize with json
-    return rle
-
-
-def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
-    """
-    Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
-    an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
-    """
-    # torch.max below raises an error on empty inputs, just skip in this case
-    if torch.numel(masks) == 0:
-        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
-
-    # Normalize shape to CxHxW
-    shape = masks.shape
-    h, w = shape[-2:]
-    if len(shape) > 2:
-        masks = masks.flatten(0, -3)
-    else:
-        masks = masks.unsqueeze(0)
-
-    # Get top and bottom edges
-    in_height, _ = torch.max(masks, dim=-1)
-    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
-    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
-    in_height_coords = in_height_coords + h * (~in_height)
-    top_edges, _ = torch.min(in_height_coords, dim=-1)
-
-    # Get left and right edges
-    in_width, _ = torch.max(masks, dim=-2)
-    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
-    right_edges, _ = torch.max(in_width_coords, dim=-1)
-    in_width_coords = in_width_coords + w * (~in_width)
-    left_edges, _ = torch.min(in_width_coords, dim=-1)
-
-    # If the mask is empty the right edge will be to the left of the left edge.
-    # Replace these boxes with [0, 0, 0, 0]
-    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
-    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
-    out = out * (~empty_filter).unsqueeze(-1)
-
-    # Return to original shape
-    if len(shape) > 2:
-        out = out.reshape(*shape[:-2], 4)
-    else:
-        out = out[0]
-
-    return out
diff --git a/spaces/monra/freegpt-webui/client/css/hljs.css b/spaces/monra/freegpt-webui/client/css/hljs.css
deleted file mode 100644
index 1fcf16ba358a7c5d287b1c6e33c3afbfff38f623..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/css/hljs.css
+++ /dev/null
@@ -1,68 +0,0 @@
-.hljs {
-	color: #e9e9f4;
-	background: #28293629;
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-	font-size: 15px;
-	word-wrap: break-word;
-	white-space: pre-wrap;
-}
-
-/* style for hljs copy */
-.hljs-copy-wrapper {
-	position: relative;
-	overflow: hidden;
-}
-
-.hljs-copy-wrapper:hover .hljs-copy-button,
-.hljs-copy-button:focus {
-	transform: translateX(0);
-}
-
-.hljs-copy-button {
-	position: absolute;
-	transform: translateX(calc(100% + 1.125em));
-	top: 1em;
-	right: 1em;
-	width: 2rem;
-	height: 2rem;
-	text-indent: -9999px;
-	color: #fff;
-	border-radius: 0.25rem;
-	border: 1px solid #ffffff22;
-	background-color: #2d2b57;
-	background-image: url('data:image/svg+xml;utf-8,<svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M6 5C5.73478 5 5.48043 5.10536 5.29289 5.29289C5.10536 5.48043 5 5.73478 5 6V20C5 20.2652 5.10536 20.5196 5.29289 20.7071C5.48043 20.8946 5.73478 21 6 21H18C18.2652 21 18.5196 20.8946 18.7071 20.7071C18.8946 20.5196 19 20.2652 19 20V6C19 5.73478 18.8946 5.48043 18.7071 5.29289C18.5196 5.10536 18.2652 5 18 5H16C15.4477 5 15 4.55228 15 4C15 3.44772 15.4477 3 16 3H18C18.7956 3 19.5587 3.31607 20.1213 3.87868C20.6839 4.44129 21 5.20435 21 6V20C21 20.7957 20.6839 21.5587 20.1213 22.1213C19.5587 22.6839 18.7957 23 18 23H6C5.20435 23 4.44129 22.6839 3.87868 22.1213C3.31607 21.5587 3 20.7957 3 20V6C3 5.20435 3.31607 4.44129 3.87868 3.87868C4.44129 3.31607 5.20435 3 6 3H8C8.55228 3 9 3.44772 9 4C9 4.55228 8.55228 5 8 5H6Z" fill="white"/><path fill-rule="evenodd" clip-rule="evenodd" d="M7 3C7 1.89543 7.89543 1 9 1H15C16.1046 1 17 1.89543 17 3V5C17 6.10457 16.1046 7 15 7H9C7.89543 7 7 6.10457 7 5V3ZM15 3H9V5H15V3Z" fill="white"/></svg>');
-	background-repeat: no-repeat;
-	background-position: center;
-	transition: background-color 200ms ease, transform 200ms ease-out;
-}
-
-.hljs-copy-button:hover {
-	border-color: #ffffff44;
-}
-
-.hljs-copy-button:active {
-	border-color: #ffffff66;
-}
-
-.hljs-copy-button[data-copied="true"] {
-	text-indent: 0;
-	width: auto;
-	background-image: none;
-}
-
-.hljs-copy-alert {
-	clip: rect(0 0 0 0);
-	clip-path: inset(50%);
-	height: 1px;
-	overflow: hidden;
-	position: absolute;
-	white-space: nowrap;
-	width: 1px;
-}
-
-@media (prefers-reduced-motion) {
-	.hljs-copy-button {
-		transition: none;
-	}
-}
diff --git a/spaces/monra/freegpt-webui/client/js/theme-toggler.js b/spaces/monra/freegpt-webui/client/js/theme-toggler.js
deleted file mode 100644
index 67e1a9501b70d54ab8a717f34983c012328e74a0..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/js/theme-toggler.js
+++ /dev/null
@@ -1,22 +0,0 @@
-var switch_theme_toggler = document.getElementById("theme-toggler");
-
-switch_theme_toggler.addEventListener("change", toggleTheme);
-
-function setTheme(themeName) {
-	localStorage.setItem("theme", themeName);
-	document.documentElement.className = themeName;
-}
-
-function toggleTheme() {
-	var currentTheme = localStorage.getItem("theme");
-	var newTheme = currentTheme === "theme-dark" ? "theme-light" : "theme-dark";
-
-	setTheme(newTheme);
-	switch_theme_toggler.checked = newTheme === "theme-dark";
-}
-
-(function () {
-	var currentTheme = localStorage.getItem("theme") || "theme-dark";
-	setTheme(currentTheme);
-	switch_theme_toggler.checked = currentTheme === "theme-dark";
-})();
diff --git a/spaces/mosidi/fi-ber-detec-api/app.py b/spaces/mosidi/fi-ber-detec-api/app.py
deleted file mode 100644
index fb78523bf7f4eb85c689ad08bcf658fe2a7461b7..0000000000000000000000000000000000000000
--- a/spaces/mosidi/fi-ber-detec-api/app.py
+++ /dev/null
@@ -1,215 +0,0 @@
-try:
-    import cloudinary
-except:
-    import os 
-    os.system('pip install cloudinary')
-try:
-    import detectron2
-except:
-    import os 
-    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
-
-import cv2
-import json
-from matplotlib.pyplot import axis
-import gradio as gr
-import requests
-import numpy as np
-from torch import nn
-import requests
-from numpy.lib.type_check import imag
-import random
-import time
-
-import csv
-import torch
-
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-from detectron2.data.datasets import register_coco_instances
-
-
-
-# import some common detectron2 utilities
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog, DatasetCatalog
-
-from detectron2.utils.visualizer import ColorMode
-
-import cloudinary.uploader
-import io
-import os
-API_KEY =os.environ["API_KEY"] 
-Cloud_Name =os.environ["Cloud_Name"] 
-API_Secret =os.environ["API_Secret"]  
-
-
-
-
-cfg = get_cfg()
-
-# try:
-#     register_coco_instances("Fiber", {}, "./labels-fiver.json", "./Fiber")
-#     Fiber_metadata = MetadataCatalog.get("Fiber")
-#     dataset_dicts = DatasetCatalog.get("Fiber")
-# except:
-#     print("there is an issue")
-register_coco_instances("Fiber", {}, "./labels-fiver.json", "./Fiber")
-Fiber_metadata = MetadataCatalog.get("Fiber")
-dataset_dicts = DatasetCatalog.get("Fiber")
-model_path = "./model_final.pth"
-
-# cfg = get_cfg()
-# cfg.merge_from_file("./configs/detectron2/faster_rcnn_R_50_FPN_3x.yaml")
-cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
-# cfg.MODEL.WEIGHTS = model_path
-
-my_metadata = MetadataCatalog.get("dbmdz_coco_all")
-# Fiber_metadata.thing_classes = ["Fiber", "Fiber","Fiber"]
-my_metadata.thing_classes = ["Fiber", "Fiber"]
-
-cfg.merge_from_file("./configs/detectron2/mask_rcnn_R_50_FPN_3x.yaml")
-cfg.MODEL.WEIGHTS = model_path #os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05  # set the testing threshold for this model
-cfg.DATASETS.TEST = ("fiber", )
-# predictor = DefaultPredictor(cfg)
-if not torch.cuda.is_available():
-    cfg.MODEL.DEVICE = "cpu"
-
-
-def inference(image_url, image, min_score):
-    if image_url:
-        r = requests.get(image_url)
-        if r:
-            im = np.frombuffer(r.content, dtype="uint8")
-            im = cv2.imdecode(im, cv2.IMREAD_COLOR)
-    else:
-        # Model expect BGR!
-        im = image[:,:,::-1]
-
-    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.005
-    predictor = DefaultPredictor(cfg)
-
-    outputs = predictor(im)
-
-    # v = Visualizer(im, my_metadata, scale=1)
-    # out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-
-
-    
-    # for d in random.sample(dataset_dicts, 3):
-    #     img = cv2.imread("https://meet.google.com/ice-wndh-joi.jpg")
-    #     predictor(img)
-    #     visualizer = Visualizer(img[:, :, ::-1], metadata=Fiber_metadata, scale=1)
-    #     vis = visualizer.draw_dataset_dict(d)
-    #     cv2_imshow(vis.get_image()[:, :, ::-1])
-    # !zip -r ./fiber.zip  "/content/Fiber"
-    # import json
-    # from google.colab import files
-    # uploaded = files.upload()
-    
-    # im = cv2.imread([key for key in uploaded.keys()
-    #   ][0])
-    
-    
-    
-    # im = cv2.imread(d["file_name"])
-    # outputs = predictor(im)
-    v = Visualizer(im[:, :, ::-1],
-                    metadata=my_metadata , #Fiber_metadata, 
-                    scale=1, 
-                    instance_mode=ColorMode.IMAGE_BW   # remove the colors of unsegmented pixels
-    )
-    v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-    # cv2_imshow(v.get_image()[:, :, ::-1])
-    # print(outputs["instances"])
-    masks = np.asarray(outputs["instances"].pred_masks.to("cpu"))
-    # bbox = np.asarray(outputs["instances"].pred_boxes.to("cpu"))
-    
-    # Pick an item to mask
-    # img=v.get_image()
-    
-    # Define a dictionary to store the measurements and their positions
-    measurements = {}
-    
-    for ind,item_mask in enumerate(masks):
-        segmentation = np.where(item_mask == True)
-        if  segmentation[1].any() and segmentation[0].any():
-          # box=bbox[ind]
-          # Get the true bounding box of the mask (not the same as the bbox prediction)
-
-          x_min = int(np.min(segmentation[1]))
-          x_max = int(np.max(segmentation[1]))
-          y_min = int(np.min(segmentation[0]))
-          y_max = int(np.max(segmentation[0]))
-          measurement = int(0.5+len(segmentation[0])/600)
-          measurements[ind] = {'measurement': measurement, 'x_min': x_min, 'x_max': x_max, 'y_min': y_min, 'y_max': y_max}
-          # cv2.putText(img=img, text=str(int(0.5+len( segmentation[0])/600)), org=(x_min+20,y_min-10), fontFace=cv2.FONT_HERSHEY_TRIPLEX, fontScale=0.8, color=(0, 255, 0),thickness=2)        
-        
-    # Loop over the masks
-    # return outputs
-    for ind, item_mask in enumerate(masks):
-        segmentation = np.where(item_mask == True)
-        measurement = int(0.5+len(segmentation[0])/600)
-        measurements[ind] = {'measurement': measurement, 'x_min': x_min, 'x_max': x_max, 'y_min': y_min, 'y_max': y_max}
-    cloudinary.config(
-      cloud_name = Cloud_Name,
-      api_key = API_KEY,
-      api_secret = API_Secret,
-      secure = True
-    )
-    
-    # Replace with the name of your CSV file
-    filename = "your_file.csv"
-    
-    
-
-
-    # Write the measurements to a CSV file
-    filename=str(time.time())+'dmeasurements.csv'
-    with open(filename, mode='w') as file:
-        writer = csv.writer(file)
-        writer.writerow(['ID', 'Measurement', 'X_Min', 'X_Max', 'Y_Min', 'Y_Max'])
-        for id, data in measurements.items():
-            writer.writerow([id, data['measurement'], data['x_min'], data['x_max'], data['y_min'], data['y_max']])
-            # Convert the CSV content to a bytes object
-    
-    csv_bytes = io.StringIO( open(filename,"r").read()).read().encode("utf-8")
-    
-    # Upload the file to Cloudinary
-    upload_result = cloudinary.uploader.upload(
-    csv_bytes,
-    resource_type = "raw",
-    folder = "csv_files",
-    public_id =filename,
-    overwrite = False
-    )
-    # return file
-
-    return upload_result["url"], v.get_image()
-
-
-title = " fi ber detec tion Model "
-description = ""
-article = ''
-
-gr.Interface(
-    inference,
-    [gr.inputs.Textbox(label="Image URL", placeholder="https://api.digitale-sammlungen.de/iiif/image/v2/bsb10483966_00008/full/500,/0/default.jpg"),
-     gr.inputs.Image(type="numpy", label="Input Image"),
-     gr.Slider(minimum=0.0, maximum=1.0, value=0.01, label="Minimum score"),
-    ], 
-
-
-    title=title,
-    description=description,
-    article=article,
-
-    examples=[],
-      outputs=[ "text","image"],
-).launch()
diff --git a/spaces/mrmocciai/rvc-genshin-v2/rmvpe.py b/spaces/mrmocciai/rvc-genshin-v2/rmvpe.py
deleted file mode 100644
index 3ad346141340e03bdbaa20121e1ed435bb3da57a..0000000000000000000000000000000000000000
--- a/spaces/mrmocciai/rvc-genshin-v2/rmvpe.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import sys, torch, numpy as np, traceback, pdb
-import torch.nn as nn
-from time import time as ttime
-import torch.nn.functional as F
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # 帧长#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # 帧长，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # 帧长
-        devided = product_sum / weight_sum  # 帧长
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # 帧长
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)
diff --git a/spaces/mshkdm/VToonify/vtoonify/model/stylegan/lpips/base_model.py b/spaces/mshkdm/VToonify/vtoonify/model/stylegan/lpips/base_model.py
deleted file mode 100644
index 8de1d16f0c7fa52d8067139abc6e769e96d0a6a1..0000000000000000000000000000000000000000
--- a/spaces/mshkdm/VToonify/vtoonify/model/stylegan/lpips/base_model.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import numpy as np
-import torch
-from torch.autograd import Variable
-from pdb import set_trace as st
-from IPython import embed
-
-class BaseModel():
-    def __init__(self):
-        pass;
-        
-    def name(self):
-        return 'BaseModel'
-
-    def initialize(self, use_gpu=True, gpu_ids=[0]):
-        self.use_gpu = use_gpu
-        self.gpu_ids = gpu_ids
-
-    def forward(self):
-        pass
-
-    def get_image_paths(self):
-        pass
-
-    def optimize_parameters(self):
-        pass
-
-    def get_current_visuals(self):
-        return self.input
-
-    def get_current_errors(self):
-        return {}
-
-    def save(self, label):
-        pass
-
-    # helper saving function that can be used by subclasses
-    def save_network(self, network, path, network_label, epoch_label):
-        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
-        save_path = os.path.join(path, save_filename)
-        torch.save(network.state_dict(), save_path)
-
-    # helper loading function that can be used by subclasses
-    def load_network(self, network, network_label, epoch_label):
-        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
-        save_path = os.path.join(self.save_dir, save_filename)
-        print('Loading network from %s'%save_path)
-        network.load_state_dict(torch.load(save_path))
-
-    def update_learning_rate():
-        pass
-
-    def get_image_paths(self):
-        return self.image_paths
-
-    def save_done(self, flag=False):
-        np.save(os.path.join(self.save_dir, 'done_flag'),flag)
-        np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i')
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/adaptive_span/__init__.py b/spaces/mshukor/UnIVAL/fairseq/examples/adaptive_span/__init__.py
deleted file mode 100644
index e0a142a769360e1140bf814c532eaf841f1d52d8..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/adaptive_span/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-# automatically import any Python files in the current directory
-cur_dir = os.path.dirname(__file__)
-for file in os.listdir(cur_dir):
-    path = os.path.join(cur_dir, file)
-    if (
-        not file.startswith("_")
-        and not file.startswith(".")
-        and (file.endswith(".py") or os.path.isdir(path))
-    ):
-        mod_name = file[: file.find(".py")] if file.endswith(".py") else file
-        module = importlib.import_module(__name__ + "." + mod_name)
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/constrained_decoding/normalize.py b/spaces/mshukor/UnIVAL/fairseq/examples/constrained_decoding/normalize.py
deleted file mode 100644
index 4ae2b5111ba025acb9e1613865c92fdc339a58d5..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/constrained_decoding/normalize.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-from sacremoses.normalize import MosesPunctNormalizer
-
-
-def main(args):
-    normalizer = MosesPunctNormalizer(lang=args.lang, penn=args.penn)
-    for line in sys.stdin:
-        print(normalizer.normalize(line.rstrip()), flush=True)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--lang", "-l", default="en")
-    parser.add_argument("--penn", "-p", action="store_true")
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py
deleted file mode 100644
index 50683e6d7c8c0db5b8f019e5f7f5fb8c6dfd9f66..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py
+++ /dev/null
@@ -1,585 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import copy
-
-import torch.nn as nn
-from fairseq import checkpoint_utils
-from fairseq import utils
-from fairseq.data.data_utils import lengths_to_padding_mask
-from fairseq.models import (
-    register_model,
-    register_model_architecture,
-    FairseqEncoder,
-)
-from fairseq.models.speech_to_text import XMTransformerModel, Wav2VecEncoderWithAdaptor
-from fairseq.models.speech_to_text.xm_transformer import (
-    set_default_adaptor_args,
-    set_default_w2v_encoder_args,
-)
-from fairseq.models.transformer import TransformerEncoder, TransformerDecoder
-from fairseq.models.wav2vec import TransformerSentenceEncoderLayer
-from fairseq.utils import safe_hasattr
-
-from .s2t_dualinputtransformer import (
-    DualInputS2TTransformerModel,
-    TransformerMultiInputDecoder,
-    DualInputEncoder,
-)
-
-
-class TransformerSentenceEncoderLayerStd(TransformerSentenceEncoderLayer):
-    def __init__(self, sent_enc_layer):
-        super(TransformerSentenceEncoderLayer, self).__init__()
-        self.embedding_dim = sent_enc_layer.embedding_dim
-        self.dropout = sent_enc_layer.dropout
-        self.activation_dropout = sent_enc_layer.activation_dropout
-
-        # Initialize blocks
-        self.activation_fn = sent_enc_layer.activation_fn
-        self.self_attn = sent_enc_layer.self_attn
-
-        self.dropout1 = sent_enc_layer.dropout1
-        self.dropout2 = sent_enc_layer.dropout2
-        self.dropout3 = sent_enc_layer.dropout3
-
-        self.layer_norm_first = sent_enc_layer.layer_norm_first
-
-        # layer norm associated with the self attention layer
-        self.self_attn_layer_norm = sent_enc_layer.self_attn_layer_norm
-        self.fc1 = sent_enc_layer.fc1
-        self.fc2 = sent_enc_layer.fc2
-
-        # layer norm associated with the position wise feed-forward NN
-        self.final_layer_norm = sent_enc_layer.final_layer_norm
-
-    def forward(
-        self,
-        x,
-        self_attn_mask=None,
-        self_attn_padding_mask=None,
-        need_weights=None,
-        att_args=None,
-    ):
-        x, attn = super().forward(
-            x, self_attn_mask, self_attn_padding_mask, need_weights, att_args
-        )
-        return x
-
-
-# TODO retire SharedEncoder
-class SharedEncoder(FairseqEncoder):
-    def __init__(self, wav2vec_enc, mbart_enc, adaptor, shared_layers):
-        super().__init__(None)
-        self.w2v_encoder = wav2vec_enc
-        self.shared_layers = self.w2v_encoder.w2v_model.encoder.layers[-shared_layers:]
-        self.w2v_encoder.w2v_model.encoder.layers = (
-            self.w2v_encoder.w2v_model.encoder.layers[:-shared_layers]
-        )
-        self.adaptor = adaptor
-        if self.shared_layers[-1].layer_norm_first:
-            self.final_layer_norm = mbart_enc.layer_norm
-        else:
-            mbart_enc.layer_norm = None
-            self.final_layer_norm = None
-        shared_layer_from = len(mbart_enc.layers) - shared_layers
-        if shared_layer_from < 0:
-            shared_layer_from = 0
-        for layer_id, layer in enumerate(self.shared_layers):
-            mbart_enc.layers[
-                shared_layer_from + layer_id
-            ] = TransformerSentenceEncoderLayerStd(layer)
-
-    def forward(self, src_tokens, src_lengths=None, **kwargs):
-        padding_mask = lengths_to_padding_mask(src_lengths)
-        if not padding_mask.any():
-            padding_mask = None
-
-        out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True)
-        x = out["encoder_out"]
-        enc_padding_mask = None
-        if out["encoder_padding_mask"] is not None:
-            enc_padding_mask = out["encoder_padding_mask"].transpose(
-                0, 1
-            )  # T X B --> B X T
-
-        x, enc_padding_mask = self.adaptor(x, enc_padding_mask)
-        for layer in self.shared_layers:
-            x, _ = layer(x, enc_padding_mask)
-        if self.final_layer_norm is not None:
-            x = self.final_layer_norm(x)
-
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [enc_padding_mask]
-            if enc_padding_mask is not None
-            else [],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": [],  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-        }
-
-
-class StackedWav2VecEncoderWithAdaptor(FairseqEncoder):
-    def __init__(
-        self,
-        wav2vec_enc,
-        mbart_enc_layers,
-        mbart_layer_norm,
-        adaptor,
-        drop_w2v_layers=0,
-    ):
-        super().__init__(None)
-        self.w2v_encoder = wav2vec_enc
-        self.adaptor = adaptor
-        self.mbart_encoder_layers = mbart_enc_layers
-        self.final_layer_norm = mbart_layer_norm
-        if drop_w2v_layers > 0:
-            self.w2v_encoder.w2v_model.encoder.layers = (
-                self.w2v_encoder.w2v_model.encoder.layers[:-drop_w2v_layers]
-            )
-
-    def forward(self, src_tokens, src_lengths=None, return_all_hiddens=False, **kwargs):
-        padding_mask = lengths_to_padding_mask(src_lengths)
-        if not padding_mask.any():
-            padding_mask = None
-
-        out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True)
-        x = out["encoder_out"]
-        enc_padding_mask = None
-        if out["encoder_padding_mask"] is not None:
-            enc_padding_mask = out["encoder_padding_mask"].transpose(
-                0, 1
-            )  # T X B --> B X T
-
-        x, enc_padding_mask = self.adaptor(x, enc_padding_mask)
-        encoder_states = []
-        for layer in self.mbart_encoder_layers:
-            x = layer(x, enc_padding_mask)
-            if return_all_hiddens:
-                encoder_states.append(x)
-        if self.final_layer_norm is not None:
-            x = self.final_layer_norm(x)
-
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [enc_padding_mask]
-            if enc_padding_mask is not None
-            else [],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-        }
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        new_encoder_out = (
-            []
-            if len(encoder_out["encoder_out"]) == 0
-            else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
-        )
-
-        new_encoder_padding_mask = (
-            []
-            if len(encoder_out["encoder_padding_mask"]) == 0
-            else [
-                x.index_select(0, new_order)
-                for x in encoder_out["encoder_padding_mask"]
-            ]
-        )
-
-        new_encoder_embedding = (
-            []
-            if len(encoder_out["encoder_embedding"]) == 0
-            else [
-                x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]
-            ]
-        )
-
-        encoder_states = encoder_out["encoder_states"]
-        if len(encoder_states) > 0:
-            for idx, state in enumerate(encoder_states):
-                encoder_states[idx] = state.index_select(1, new_order)
-
-        return {
-            "encoder_out": new_encoder_out,  # T x B x C
-            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
-            "encoder_embedding": new_encoder_embedding,  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],  # B x T
-            "src_lengths": [],  # B x 1
-        }
-
-
-# Note:
-# dual input transformer:
-#    encoder: wav2vec for speech + mbart encoder for text
-#    decoder: mbart decoder  for text
-@register_model("dual_input_xm_transformer")
-class DualInputXMTransformerModel(DualInputS2TTransformerModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        # wav2vec encoder
-        Wav2VecEncoderWithAdaptor.add_args(parser)
-        # add_decoder_args(parser)
-        # mbart Transformer
-        parser.add_argument(
-            "--activation-fn",
-            type=str,
-            default="relu",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use",
-        )
-
-        parser.add_argument(
-            "--mbart-dropout", type=float, metavar="D", help="dropout probability"
-        )
-        parser.add_argument(
-            "--mbart-attention-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability for attention weights",
-        )
-        parser.add_argument(
-            "--mbart-activation-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability after activation in FFN.",
-        )
-
-        parser.add_argument(
-            "--encoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension",
-        )
-        parser.add_argument(
-            "--encoder-ffn-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension for FFN",
-        )
-        parser.add_argument(
-            "--encoder-layers", type=int, metavar="N", help="num encoder layers"
-        )
-        parser.add_argument(
-            "--encoder-attention-heads",
-            type=int,
-            metavar="N",
-            help="num encoder attention heads",
-        )
-        parser.add_argument(
-            "--encoder-normalize-before",
-            action="store_true",
-            help="apply layernorm before each encoder block",
-        )
-
-        parser.add_argument(
-            "--decoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-ffn-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension for FFN",
-        )
-        parser.add_argument(
-            "--decoder-layers", type=int, metavar="N", help="num decoder layers"
-        )
-        parser.add_argument(
-            "--decoder-attention-heads",
-            type=int,
-            metavar="N",
-            help="num decoder attention heads",
-        )
-        parser.add_argument(
-            "--decoder-normalize-before",
-            action="store_true",
-            help="apply layernorm before each decoder block",
-        )
-        parser.add_argument(
-            "--layernorm-embedding",
-            action="store_true",
-            help="add layernorm to embedding",
-        )
-        parser.add_argument(
-            "--no-scale-embedding",
-            action="store_true",
-            help="if True, dont scale embeddings",
-        )
-        parser.add_argument(
-            "--load-pretrained-mbart-from",
-            type=str,
-            metavar="STR",
-            help="model to take text encoder decoder weights from (for initialization)",
-        )
-        # parser.add_argument("--finetune-w2v-params", type=str, metavar="STR",
-        #                    help="comma-separated param strings to finetune.")
-        parser.add_argument(
-            "--finetune-mbart-decoder-params",
-            type=str,
-            metavar="STR",
-            help="comma-separated param strings to finetune.",
-        )
-        parser.add_argument(
-            "--finetune-mbart-encoder-params",
-            type=str,
-            metavar="STR",
-            help="comma-separated param strings to finetune.",
-        )
-        parser.add_argument(
-            "--skip-encoder-projection",
-            action="store_true",
-            help="skip the projection layer in encoder",
-        )
-
-        parser.add_argument(
-            "--enc-grad-mult",
-            type=float,
-            metavar="V",
-            default=1.0,
-            help="multiply enc1 and enc2 gradient by V",
-        )
-        parser.add_argument(
-            "--enc2-along-grad-mult",
-            type=float,
-            metavar="V",
-            default=1.0,
-            help="multiply enc2 gradient by V if only enc2 is used",
-        )
-        parser.add_argument(
-            "--text-input-cost-ratio",
-            type=float,
-            default=1.0,
-            metavar="V",
-            help="text input cost ratio relative to speech input cost",
-        )
-        parser.add_argument(
-            "--stack-w2v-mbart-encoder",
-            action="store_true",
-            help="stack w2v and mbart encoder",
-        )
-        parser.add_argument(
-            "--stack-w2v-mbart-nonorm-encoder",
-            action="store_true",
-            help="stack w2v and mbart encoder",
-        )
-        parser.add_argument(
-            "--no-final-norm-decoder", action="store_true", help="no layer norm"
-        )
-        parser.add_argument(
-            "--drop-w2v-layers",
-            type=int,
-            default=0,
-            metavar="N",
-            help="drop w2v encoder layers",
-        )
-
-        parser.add_argument(
-            "--share-w2v-text-encoder",
-            action="store_true",
-            help="share w2v encoder layers with text encoder",
-        )
-        parser.add_argument(
-            "--shared-w2v-layers",
-            type=int,
-            default=0,
-            metavar="N",
-            help="shared encoder layers from w2v encoder",
-        )
-
-    @classmethod
-    def build_encoder(cls, args, task):
-        _args = copy.deepcopy(args)
-        _args.dropout = args.mbart_dropout
-        _args.attention_dropout = args.mbart_attention_dropout
-        _args.activation_dropout = args.mbart_activation_dropout
-        _args.max_source_positions = 1024
-        enc_emb = nn.Embedding(
-            len(task.src_dict), _args.encoder_embed_dim, task.src_dict.pad()
-        )
-        text_encoder = TransformerEncoder(_args, task.src_dict, enc_emb)
-        spch_encoder = Wav2VecEncoderWithAdaptor(args)
-        if getattr(args, "load_pretrained_mbart_from", None):
-            text_encoder = checkpoint_utils.load_pretrained_component_from_model(
-                component=text_encoder, checkpoint=args.load_pretrained_mbart_from
-            )
-        if getattr(args, "stack_w2v_mbart_encoder", False):
-            assert getattr(args, "share_w2v_text_encoder", False) is False
-            spch_encoder = StackedWav2VecEncoderWithAdaptor(
-                spch_encoder.w2v_encoder,
-                text_encoder.layers,
-                text_encoder.layer_norm,
-                spch_encoder.adaptor,
-                args.drop_w2v_layers,
-            )
-        elif getattr(args, "stack_w2v_mbart_nonorm_encoder", False):
-            text_encoder.layer_norm = None
-            spch_encoder = StackedWav2VecEncoderWithAdaptor(
-                spch_encoder.w2v_encoder,
-                text_encoder.layers,
-                text_encoder.layer_norm,
-                spch_encoder.adaptor,
-                args.drop_w2v_layers,
-            )
-        elif getattr(args, "share_w2v_text_encoder", False):
-            spch_encoder = SharedEncoder(
-                spch_encoder.w2v_encoder,
-                text_encoder,
-                spch_encoder.adaptor,
-                args.shared_w2v_layers,
-            )
-
-        for k, p in spch_encoder.named_parameters():
-            # Freeze pretrained models by default
-            if safe_hasattr(
-                args, "finetune_w2v_params"
-            ) and XMTransformerModel.finetune_params(args.finetune_w2v_params, k):
-                p.requires_grad = True
-            else:
-                p.requires_grad = False
-        for k, p in text_encoder.named_parameters():
-            # Freeze pretrained models by default
-            if safe_hasattr(
-                args, "finetune_mbart_encoder_params"
-            ) and XMTransformerModel.finetune_params(
-                args.finetune_mbart_encoder_params, k
-            ):
-                p.requires_grad = True
-            else:
-                p.requires_grad = False
-        cross_attentive_loss_before_last_layer = (
-            0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1
-        )
-        encoder = DualInputEncoder(
-            args,
-            spch_encoder,
-            text_encoder,
-            task.src_dict,
-            cross_attentive_loss_before_last_layer,
-        )
-        return encoder
-
-    @classmethod
-    def build_decoder(cls, args, task):
-        _args = copy.deepcopy(args)
-        _args.dropout = args.mbart_dropout
-        _args.attention_dropout = args.mbart_attention_dropout
-        _args.activation_dropout = args.mbart_activation_dropout
-        _args.max_target_positions = 1024
-        dec_emb = nn.Embedding(
-            len(task.tgt_dict), _args.encoder_embed_dim, task.tgt_dict.pad()
-        )
-        decoder = TransformerDecoder(_args, task.tgt_dict, dec_emb)
-        if getattr(args, "load_pretrained_mbart_from", None):
-            decoder = checkpoint_utils.load_pretrained_component_from_model(
-                component=decoder, checkpoint=args.load_pretrained_mbart_from
-            )
-        if getattr(args, "no_final_norm_decoder", False):
-            decoder.layer_norm = None
-        for k, p in decoder.named_parameters():
-            # Freeze pretrained models by default
-            if safe_hasattr(
-                args, "finetune_mbart_decoder_params"
-            ) and XMTransformerModel.finetune_params(
-                args.finetune_mbart_decoder_params, k
-            ):
-                p.requires_grad = True
-            else:
-                p.requires_grad = False
-
-        compute_cross_attentive_loss = (
-            True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False
-        )
-        cross_attentive_loss_without_norm = getattr(
-            args, "attentive_cost_without_normalize", False
-        )
-        cross_attentive_loss_reverse = (
-            False  # getattr(args, "attentive_cost_reverse", False)
-        )
-        decoder = TransformerMultiInputDecoder(
-            dictionary=task.target_dictionary,
-            spch_decoder=decoder,
-            text_decoder=decoder,
-            compute_cross_attentive_loss=compute_cross_attentive_loss,
-            cross_attentive_loss_with_norm=True
-            if not cross_attentive_loss_without_norm
-            else False,
-            cross_attentive_loss_reverse=cross_attentive_loss_reverse,
-        )
-        return decoder
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        # make sure that all args are properly defaulted
-        # (in case there are any new ones)
-        dualinputxmtransformer_base(args)
-
-        encoder = cls.build_encoder(args, task)
-        decoder = cls.build_decoder(args, task)
-        return cls(encoder, decoder)
-
-
-@register_model_architecture("dual_input_xm_transformer", "dualinputxmtransformer_base")
-def dualinputxmtransformer_base(args):
-    # wav2vec encoder
-    set_default_w2v_encoder_args(args)
-    set_default_adaptor_args(args)
-
-    # mbart model
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(
-        args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim
-    )
-    args.encoder_layers = getattr(args, "encoder_layers", 12)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
-    args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
-
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * 1024)
-    args.decoder_layers = getattr(args, "decoder_layers", 12)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
-
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-
-    args.mbart_attention_dropout = getattr(args, "mbart_attention_dropout", 0.0)
-    args.mbart_activation_dropout = getattr(args, "mbart_activation_dropout", 0.0)
-    args.mbart_dropout = getattr(args, "mbart_dropout", 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", True
-    )
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
-
-    args.activation_fn = getattr(args, "activation_fn", "gelu")
-    args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
-    args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/models/__init__.py b/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/models/__init__.py
deleted file mode 100644
index 3e3039b7081a9e3228c8abefb6391a75b4864439..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/models/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .wav2vec_u import Wav2vec_U
-
-
-__all__ = [
-    "Wav2vec_U",
-]
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/token_generation_constraints.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/token_generation_constraints.py
deleted file mode 100644
index e708dc51bcb0ffb7b411496239c74d5e6f3c2448..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/token_generation_constraints.py
+++ /dev/null
@@ -1,506 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""Implements tracking of constraints for a beam item.
-
-A list of constraints is given as a list of one or more token
-sequences, each of length at least one token. For example, for an input sentence
-
-> Die maschinelle Übersetzung ist schwer zu kontrollieren.
-
-We could have the constraints:
-* to influence
-* hard
-
-There are two implementations:
-* OrderedConstraintState: Tracks progress through an ordered list of multitoken constraints.
-* UnorderedConstraintState: Tracks progress through an unordered list of multitoken constraints.
-
-The difference is that in the first, the constraints are assumed to be
-in order; the algorithm will permit zero or more tokens between them.
-In the second, the constraints are not ordered, so many orderings will
-be explored.
-
-The same sequence can be present any number of times, and will appear
-that many times in the output.
-"""
-
-from collections import Counter
-from typing import List, Optional, Set, Tuple
-
-import torch
-
-
-class ConstraintState:
-    def __init__(self):
-        pass
-
-
-def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor:
-    """Takes a list of list of constraints in tensor form (a list of
-    tensor constraints for each sentence) and transforms it into a
-    packed Tensor. For example, here is a batch of size 3 with 3, 0,
-    and 1 constraints:
-
-        [ [ [3 1 2], [3], [4 5 6 7], ]
-          [],
-          [ [1 8 9 10 1 4 11 12], ]
-        ]
-
-    Its corresponding packed structure is:
-
-        [ [ 3  3  1  2  0  3  0  4  5  6  7  0],
-          [ 0  0  0  0  0  0  0  0  0  0  0  0],
-          [ 1  1  8  9 10  1  4 11 12  0  0  0] ]
-
-    The packed tensor has shape (batch size, maxlen), where
-    maxlen is defined below. Each row contains concatenated
-    constraint tokens for that sentence, with 0 appended after
-    each constraint. The first item in each row is the number
-    of constraints for that sentence. So maxlen is the maximum
-    of
-
-    (number of constraints) + (sum length of constraints) + 1.
-
-    across all sentences in the batch.
-    """
-    # The maximum word length of concatenated constraints for any sentence
-    max_constraints_len = 1
-    for sentence_constraints in batch_constraints:
-        if len(sentence_constraints):
-            # number of constraints, plus sum of constrain lens, plus a zero after each
-            constraints_len = (
-                1
-                + sum([c.size(0) for c in sentence_constraints])
-                + len(sentence_constraints)
-            )
-            max_constraints_len = max(max_constraints_len, constraints_len)
-
-    batch_size = len(batch_constraints)
-    constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long()
-    for i, sentence_constraints in enumerate(batch_constraints):
-        constraints_tensor[i, 0] = len(sentence_constraints)
-        offset = 1
-        for j, constraint in enumerate(sentence_constraints):
-            this_len = constraint.size(0)
-            constraints_tensor[i, offset : offset + this_len] = constraint
-            offset += this_len + 1
-
-    return constraints_tensor.long()
-
-
-def unpack_constraints(constraint_tensor: torch.Tensor) -> List[torch.Tensor]:
-    """
-    Transforms *one row* of a packed constraint tensor (e.g., for one
-    sentence in the batch) into a list of constraint tensors.
-    """
-    constraint_list = []
-    num_constraints = constraint_tensor[0]
-    constraints = constraint_tensor.tolist()
-    offset = 1
-    for i in range(num_constraints):
-        where = constraints.index(0, offset)
-        constraint_list.append(constraint_tensor[offset:where])
-        offset = where + 1
-
-    return constraint_list
-
-
-class ConstraintNode:
-    """
-    Represents a node in a trie managing unordered constraints.
-    """
-
-    def __init__(self, token: int = None, parent=None):
-        # The token associate with this node (None for the root)
-        self.token = int(token) if token is not None else None
-        # The parent (None at the root)
-        self.parent = parent
-        # Whether this node is a completed constraint
-        self.terminal = 0
-        # List of child nodes
-        self.children = {}
-
-        # The cumulative number of constraints from this point in the
-        # trie forward
-        self.num_constraints = 0
-
-    @property
-    def id(self):
-        return self.token
-
-    def __str__(self):
-        term = self.terminal != 0
-        return f"[{self.token}].{term}#{self.num_constraints}"
-
-    def __getitem__(self, key: int):
-        return self.children.get(key, None)
-
-    def next_tokens(self) -> Set[int]:
-        """The set of child labels."""
-        return set(self.children.keys())
-
-    @staticmethod
-    def create(constraints: List[List[int]]):
-        root = ConstraintNode()
-        for sequence in constraints:
-            root.add_sequence(sequence)
-
-        return root
-
-    @staticmethod
-    def print_graph(node: "ConstraintNode"):
-        if len(node.children) == 0:
-            return str(node)
-        else:
-            s = f"({node}"
-            for child in node.children.values():
-                s += " " + ConstraintNode.print_graph(child)
-            s += ")"
-            return s
-
-    def token_counts(self) -> Counter:
-        """Returns a counter of the number of times each token is used
-        in a constraint.
-        """
-        token_counts = Counter()
-        kids = list(self.children.values())
-        while len(kids) > 0:
-            kid = kids.pop()
-            token_counts[kid.id] += kid.num_constraints
-            kids += list(kid.children.values())
-
-        return token_counts
-
-    def tokens(self) -> Set[int]:
-        """Returns the set of tokens in constraints."""
-        return set(self.token_counts().keys())
-
-    def add_sequence(self, sequence: List[int]):
-        """Adds a constraint, represented as a list of integers, to
-        the trie."""
-        assert len(sequence) > 0
-
-        token = int(sequence[0])
-        if token not in self.children:
-            self.children[token] = ConstraintNode(token, parent=self)
-
-        node = self.children[token]
-        if len(sequence) == 1:
-            node.terminal += 1
-            node.num_constraints += 1
-            parent = node.parent
-            while parent is not None:
-                parent.num_constraints += 1
-                parent = parent.parent
-        else:
-            node.add_sequence(sequence[1:])
-
-
-class UnorderedConstraintState(ConstraintState):
-    """
-    Records progress through the set of constraints for each item in the beam
-    using a trie.
-    """
-
-    def __init__(self, node: ConstraintNode, copy_from: "ConstraintState" = None):
-        self.node = node
-
-        if copy_from is None:
-            # The root node
-            self.root = node
-            # The set of states in the graph that have been completed
-            self.completed = Counter()
-            # The...
-            self.generated = Counter()
-            # The list of tokens we need to generate
-            self.needed_tokens = self.root.tokens()
-        else:
-            self.completed = Counter(copy_from.completed)
-            self.generated = Counter(copy_from.generated)
-            self.root = copy_from.root
-
-        # Mark the node as generated
-        if self.node != self.root:
-            self.generated[node] += 1
-
-    @staticmethod
-    def create(constraint_tensor: torch.Tensor):
-        constraint_list = unpack_constraints(constraint_tensor)
-        constraint_trie_root = ConstraintNode.create(constraint_list)
-        return UnorderedConstraintState(constraint_trie_root)
-
-    def __str__(self):
-        gen_str = ",".join([str(node) for node in self.generated])
-        return f"{self.name}/{self.bank}({gen_str})x{self.num_completed}"
-
-    def __copy__(self):
-        copied_state = UnorderedConstraintState(self.node, copy_from=self)
-        return copied_state
-
-    def copy(self):
-        return self.__copy__()
-
-    @property
-    def name(self):
-        if self.node.id is None:
-            return "ROOT"
-        else:
-            return str(self.node.id)
-
-    @property
-    def is_root(self):
-        return self.node == self.root
-
-    @property
-    def bank(self):
-        return sum(self.generated.values())
-
-    @property
-    def num_completed(self):
-        """The number of constraints (not constraint tokens) that are completed.
-        In addition to the already-completed states, we need to account for the
-        current state, which might get marked as completed when another token
-        is generated.
-        """
-        in_final = self.node.terminal and self.completed[self.node] < self.node.terminal
-        return sum(self.completed.values()) + in_final
-
-    @property
-    def finished(self):
-        return self.root.num_constraints - self.num_completed == 0
-
-    @property
-    def token_counts(self):
-        return self.root.token_counts()
-
-    @property
-    def tokens(self):
-        return self.root.tokens()
-
-    @property
-    def num_constraint_tokens(self):
-        return sum(self.token_counts.values())
-
-    def next_tokens(self) -> Set[int]:
-        """Returns the list of tokens that could come next.
-        These are (a) all tokens extending the root state and, for
-        non-root states, additionally all tokens extending the current
-        state."""
-
-        if self.node != self.root:
-            return self.root.next_tokens().union(self.node.next_tokens())
-        else:
-            return self.root.next_tokens()
-
-    def advance(self, token: int):
-        """Reads in a token and advances the state. Here's how it works.
-
-        We can advance to the next state if:
-        - there is a matching child
-        - its path isn't blocked
-
-        A path is blocked when all constraints that are descendants of
-        that node have already been generated, in the current state.
-
-        If we are not able to advance from the current state, we "fall
-        off the graph" and return to the root state. There, we again
-        try to advance, checking the same criteria.
-
-        In any case, when falling off the graph, we need to do some
-        bookkeeping. We:
-        - check whether any constraints were met (all prefixes of
-          current state)
-        - if one is found, mark it as completed
-        - adjust visited nodes accordingly
-        """
-        token = int(token)
-
-        next_state = None
-        child = self.node[token]
-        if child is not None and self.generated[child] < child.num_constraints:
-            next_state = UnorderedConstraintState(child, copy_from=self)
-
-        def rewind():
-            """If we're mid-trie and an "illegal" token is chosen next, we need
-            to reset our state to the root state. However, along the way, we need
-            to check whether a prefix of the current trie state represents a state
-            we could mark as completed.
-            """
-            node = self.node
-            while node != self.root:
-                if node.terminal and self.completed[node] < node.terminal:
-                    next_state.completed[node] += 1
-                    return
-
-                next_state.generated[node] -= 1
-                node = node.parent
-
-        # Fall off the graph, check the root
-        if next_state is None and token in self.root.next_tokens():
-            child = self.root[token]
-            # We can only traverse this edge if it's not saturated
-            if self.generated[child] < child.num_constraints:
-                next_state = UnorderedConstraintState(child, copy_from=self)
-            else:
-                next_state = UnorderedConstraintState(self.root, copy_from=self)
-
-            # Rewind
-            rewind()
-
-        elif next_state is None:
-            next_state = UnorderedConstraintState(self.root, copy_from=self)
-            # Rewind
-            rewind()
-
-        return next_state
-
-
-class ConstraintSequence:
-    def __init__(self, sequences: List[List[int]]):
-        """Represents a set of possibly multitoken constraints by
-        concatenating them and internally recording the end points.
-        """
-        self.sequences = []
-        self.endpoints = []
-        self.num_tokens = 0
-        self.tokens = set()
-        for sequence in sequences:
-            for token in sequence:
-                self.tokens.add(token)
-            self.num_tokens += len(sequence)
-            self.endpoints += [False for x in range(len(sequence) - 1)] + [True]
-            self.sequences += sequence
-
-    def __getitem__(self, key: int):
-        return self.sequences[key]
-
-    def __len__(self):
-        return len(self.sequences)
-
-    def __str__(self):
-        return str(self.sequences)
-
-
-class OrderedConstraintState(ConstraintState):
-    """
-    Records progress through the set of linear nonbranching constraints with gaps.
-    """
-
-    def __init__(self, sequence: ConstraintSequence, state: int = -1):
-        self.sequence = sequence
-        self.state = state
-
-    @staticmethod
-    def create(constraint_tensor: torch.Tensor):
-        constraint_list = unpack_constraints(constraint_tensor)
-        return OrderedConstraintState(ConstraintSequence(constraint_list), -1)
-
-    def __str__(self):
-        return f"{self.state}/{self.bank}x{self.num_completed}"
-
-    def __copy__(self):
-        return OrderedConstraintState(self.sequence, self.state)
-
-    def copy(self):
-        return self.__copy__()
-
-    @property
-    def num_completed(self):
-        if self.state == -1:
-            return 0
-        count = len(
-            list(filter(lambda x: x, self.sequence.endpoints[0 : self.state + 1]))
-        )
-        return count
-
-    @property
-    def is_root(self):
-        return self.state == -1
-
-    @property
-    def name(self):
-        if self.state == -1:
-            return "ROOT"
-        else:
-            return str(self.sequence[self.state])
-
-    @property
-    def bank(self) -> int:
-        return self.state + 1
-
-    @property
-    def finished(self):
-        return self.state + 1 == len(self.sequence)
-
-    @property
-    def token_counts(self):
-        return self.sequence.token_counts()
-
-    @property
-    def tokens(self):
-        return self.sequence.tokens
-
-    @property
-    def num_constraint_tokens(self):
-        return sum(self.token_counts.values())
-
-    def next_tokens(self) -> Set[int]:
-        """Returns the list of tokens that could come next.
-        These are (a) all tokens extending the root state and, for
-        non-root states, additionally all tokens extending the current
-        state."""
-
-        tokens = set()
-        if self.state > 0:
-            tokens.add(self.sequence[0])
-        if not self.finished:
-            tokens.add(self.sequence[self.state + 1])
-        return tokens
-
-    def advance(self, token: int):
-        """Reads in a token and advances the state. Here's how it works.
-
-        We can advance to the next state if:
-        - there is a matching child
-        - its path isn't blocked
-
-        A path is blocked when all constraints that are descendants of
-        that node have already been generated, in the current state.
-
-        If we are not able to advance from the current state, we "fall
-        off the graph" and return to the root state. There, we again
-        try to advance, checking the same criteria.
-
-        In any case, when falling off the graph, we need to do some
-        bookkeeping. We:
-        - check whether any constraints were met (all prefixes of
-          current state)
-        - if one is found, mark it as completed
-        - adjust visited nodes accordingly
-        """
-        token = int(token)
-        # print(f"{self} ADVANCE({token}) {self.sequence} -> ", end="")
-
-        if self.finished:
-            # Accept anything
-            next_state = self.copy()
-
-        elif self.sequence[self.state + 1] == token:
-            # Advance to the next token
-            next_state = OrderedConstraintState(self.sequence, self.state + 1)
-
-        elif self.sequence.endpoints[self.state]:
-            # Accept anything between constraints (*)
-            next_state = self.copy()
-
-        elif token == self.sequence[0]:
-            # Start over having generated the first token
-            next_state = OrderedConstraintState(self.sequence, 0)
-        else:
-            # Start over from the root
-            next_state = OrderedConstraintState(self.sequence, -1)
-
-        return next_state
diff --git a/spaces/mygyasir/Real-Time-Voice-Cloning/app.py b/spaces/mygyasir/Real-Time-Voice-Cloning/app.py
deleted file mode 100644
index 84df0269552db39f2b8f3efeeacf0eac26c4a06c..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/Real-Time-Voice-Cloning/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import gradio as gr
-import os
-import shlex
-import gdown
-import uuid
-import torch
-
-cpu_param = "--cpu" if not torch.cuda.is_available() else ""
-
-if (not os.path.exists("synpretrained.pt")):
-    gdown.download("https://drive.google.com/u/0/uc?id=1EqFMIbvxffxtjiVrtykroF6_mUh-5Z3s&export=download&confirm=t",
-                   "synpretrained.pt", quiet=False)
-    gdown.download("https://drive.google.com/uc?export=download&id=1q8mEGwCkFy23KZsinbuvdKAQLqNKbYf1",
-                   "encpretrained.pt", quiet=False)
-    gdown.download("https://drive.google.com/uc?export=download&id=1cf2NO6FtI0jDuy8AV3Xgn6leO6dHjIgu",
-                   "vocpretrained.pt", quiet=False)
-
-
-def inference(audio_path, text, mic_path=None):
-    if mic_path:
-        audio_path = mic_path
-    output_path = f"/tmp/output_{uuid.uuid4()}.wav"
-    os.system(
-        f"python demo_cli.py --no_sound {cpu_param} --audio_path {audio_path} --text {shlex.quote(text.strip())} --output_path {output_path}")
-    return output_path
-
-
-title = "Real-Time-Voice-Cloning"
-description = "Gradio demo for Real-Time-Voice-Cloning: Clone a voice in 5 seconds to generate arbitrary speech in real-time. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://matheo.uliege.be/handle/2268.2/6801' target='_blank'>Real-Time Voice Cloning</a> | <a href='https://github.com/CorentinJ/Real-Time-Voice-Cloning' target='_blank'>Github Repo</a></p>"
-
-examples = [['test.wav', "This is real time voice cloning on huggingface spaces"]]
-
-
-def toggle(choice):
-    if choice == "mic":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
-    else:
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            radio = gr.Radio(["mic", "file"], value="mic",
-                             label="How would you like to upload your audio?")
-            mic_input = gr.Mic(label="Input", type="filepath", visible=False)
-            audio_file = gr.Audio(
-                type="filepath", label="Input", visible=True)
-            text_input = gr.Textbox(label="Text")
-        with gr.Column():
-            audio_output = gr.Audio(label="Output")
-
-    gr.Examples(examples, fn=inference, inputs=[audio_file, text_input],
-                      outputs=audio_output, cache_examples=True)
-    btn = gr.Button("Generate")
-    btn.click(inference, inputs=[audio_file,
-              text_input, mic_input], outputs=audio_output)
-    radio.change(toggle, radio, [mic_input, audio_file])
-
-demo.launch(enable_queue=True)
diff --git a/spaces/myscale/visual-dataset-explorer/README.md b/spaces/myscale/visual-dataset-explorer/README.md
deleted file mode 100644
index e809dca28b47dc23ce27a18af35cf8cc88e93ca5..0000000000000000000000000000000000000000
--- a/spaces/myscale/visual-dataset-explorer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Visual Dataset Explorer
-emoji: ✨🌕🔭
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/nata0801/ASR_Transformers_EnRuFr/app.py b/spaces/nata0801/ASR_Transformers_EnRuFr/app.py
deleted file mode 100644
index 359705f2013382adc343f4154747cc9bae782c26..0000000000000000000000000000000000000000
--- a/spaces/nata0801/ASR_Transformers_EnRuFr/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import gradio as gr
-import librosa
-import soundfile as sf
-import torch
-
-from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
-
-#load wav2vec2 tokenizer and model
-# define speech-to-text function
-def asr_transcript(audio_file, language):
-    
-    if language == "English":
-      model_name = "facebook/wav2vec2-large-960h-lv60-self"
-    elif language == "Russian":
-      model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-russian"
-    elif language == "French":
-      model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-french"
-    
-    tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
-    model = Wav2Vec2ForCTC.from_pretrained(model_name)
-
-    transcript = ""
-
-    # Stream over 20 seconds chunks
-    stream = librosa.stream(
-        audio_file.name, block_length=20, frame_length=16000, hop_length=16000
-    )
-
-    for speech in stream:
-        if len(speech.shape) > 1:
-            speech = speech[:, 0] + speech[:, 1]
-
-        input_values = tokenizer(speech, return_tensors="pt").input_values
-        logits = model(input_values).logits
-
-        predicted_ids = torch.argmax(logits, dim=-1)
-        transcription = tokenizer.batch_decode(predicted_ids)[0]
-        transcript += transcription.lower() + " "
-
-    return transcript
-    
-gradio_ui = gr.Interface(
-    fn=asr_transcript,
-    title="Automatic speech recognition with Wav2Vec2",
-    description="Upload an audio clip in Russian, English, or French and let AI do the hard work of transcribing",
-    inputs = [gr.inputs.Audio(label="Upload Audio File", type="file"),
-              gr.inputs.Radio(label="Pick a language", 
-                              choices=["English",
-                                       "Russian",
-                                       "French"])],
-    outputs=gr.outputs.Textbox(label="Auto-Transcript"),
-)
-
-gradio_ui.launch()
\ No newline at end of file
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/processors/autodiff/fir.py b/spaces/nateraw/deepafx-st/deepafx_st/processors/autodiff/fir.py
deleted file mode 100644
index c4d1aa1c21ace509bba89054284795a1f5336d0c..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/processors/autodiff/fir.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import torch
-
-
-class FIRFilter(torch.nn.Module):
-    def __init__(self, num_control_params=63):
-        super().__init__()
-        self.num_control_params = num_control_params
-        self.adaptor = torch.nn.Linear(num_control_params, num_control_params)
-        #self.batched_lfilter = torch.vmap(self.lfilter)
-
-    def forward(self, x, b, **kwargs):
-        """Forward pass by appling FIR filter to each batch element.
-
-        Args:
-            x (tensor): Input signals with shape (batch x 1 x samples)
-            b (tensor): Matrix of FIR filter coefficients with shape (batch x ntaps)
-
-        """
-        bs, ch, s = x.size()
-        b = self.adaptor(b)
-
-        # pad input
-        x = torch.nn.functional.pad(x, (b.shape[-1] // 2, b.shape[-1] // 2))
-
-        # add extra dim for virutal batch dim
-        x = x.view(bs, 1, ch, -1)
-        b = b.view(bs, 1, 1, -1)
-
-        # exlcuding vmap for now
-        y = self.batched_lfilter(x, b).view(bs, ch, s)
-
-        return y
-
-    @staticmethod
-    def lfilter(x, b):
-        return torch.nn.functional.conv1d(x, b)
-
-
-class FrequencyDomainFIRFilter(torch.nn.Module):
-    def __init__(self, num_control_params=31):
-        super().__init__()
-        self.num_control_params = num_control_params
-        self.adaptor = torch.nn.Linear(num_control_params, num_control_params)
-
-    def forward(self, x, b, **kwargs):
-        """Forward pass by appling FIR filter to each batch element.
-
-        Args:
-            x (tensor): Input signals with shape (batch x 1 x samples)
-            b (tensor): Matrix of FIR filter coefficients with shape (batch x ntaps)
-        """
-        bs, c, s = x.size()
-
-        b = self.adaptor(b)
-
-        # transform input to freq. domain
-        X = torch.fft.rfft(x.view(bs, -1))
-
-        # frequency response of filter
-        H = torch.fft.rfft(b.view(bs, -1))
-
-        # apply filter as multiplication in freq. domain
-        Y = X * H
-
-        # transform back to time domain
-        y = torch.fft.ifft(Y).view(bs, 1, -1)
-
-        return y
diff --git a/spaces/nateraw/lavila/lavila/models/tokenizer.py b/spaces/nateraw/lavila/lavila/models/tokenizer.py
deleted file mode 100644
index 387516a1983842915ea8414bb2e18d59db2d0002..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/lavila/models/tokenizer.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Part of the code is from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
-# Modified by Yue Zhao
-# The original code is under MIT License
-
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-import torch
-
-from transformers import (BertTokenizer, DistilBertTokenizer, GPT2Tokenizer)
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
-
-    def __call__(self, texts, context_length=77):
-        if isinstance(texts, str):
-            texts = [texts]
-
-        sot_token = self.encoder["<|startoftext|>"]
-        eot_token = self.encoder["<|endoftext|>"]
-        all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
-        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-        for i, tokens in enumerate(all_tokens):
-            tokens = tokens[:context_length]
-            result[i, :len(tokens)] = torch.tensor(tokens)
-
-        if len(result) == 1:
-            return result[0]
-        return result
-
-
-class MyBertTokenizer(object):
-    def __init__(self, name=''):
-        print('=> Initialize MyBertTokenizer ({})'.format(name))
-        self.tokenizer = BertTokenizer.from_pretrained(name)
-        self.bos_token_id, self.eos_token_id = self.tokenizer('').input_ids
-        self.pad_token_id = 0
-
-    def __call__(self, texts, context_length=77):
-        if isinstance(texts, str):
-            texts = [texts]
-        result = torch.zeros(len(texts), context_length, dtype=torch.long)
-        mask = torch.zeros(len(texts), context_length, dtype=torch.float32)
-        for i, text in enumerate(texts):
-            tokens = self.tokenizer(text)
-            input_ids = tokens.input_ids[:context_length]
-            attention_mask = tokens.attention_mask[:context_length]
-            result[i, :len(input_ids)] = torch.tensor(input_ids)
-            mask[i, :len(attention_mask)] = torch.tensor(attention_mask)
-
-        if len(result) == 1:
-            return result[0], mask[0]
-        return result, mask
-
-
-class MyDistilBertTokenizer(object):
-    def __init__(self, name=''):
-        print('=> Initialize MyDistilBertTokenizer ({})'.format(name))
-        self.tokenizer = DistilBertTokenizer.from_pretrained(name)
-
-    def __call__(self, texts, context_length=77):
-        if isinstance(texts, str):
-            texts = [texts]
-        result = torch.zeros(len(texts), context_length, dtype=torch.long)
-        mask = torch.zeros(len(texts), context_length, dtype=torch.float32)
-        for i, text in enumerate(texts):
-            tokens = self.tokenizer(text)
-            input_ids = tokens.input_ids[:context_length]
-            attention_mask = tokens.attention_mask[:context_length]
-            result[i, :len(input_ids)] = torch.tensor(input_ids)
-            mask[i, :len(attention_mask)] = torch.tensor(attention_mask)
-
-        if len(result) == 1:
-            return result[0], mask[0]
-        return result, mask
-
-
-class MyGPT2Tokenizer(object):
-    def __init__(self, name='', add_bos=False):
-        print('=> Initialize MyGPT2Tokenizer ({})'.format(name))
-        self.tokenizer = GPT2Tokenizer.from_pretrained(name)
-        self.bos_token_id, self.eos_token_id = self.tokenizer.bos_token_id, self.tokenizer.eos_token_id
-        self.pad_token_id = 0
-        self.add_bos = add_bos
-        # num_added_tokens = self.tokenizer.add_special_tokens({'pad_token': "[PAD]"})
-        # print('num_added_tokens={}'.format(len(num_added_tokens)))
-
-    def __call__(self, texts, context_length=77):
-        if isinstance(texts, str):
-            texts = [texts]
-        result = torch.zeros(len(texts), context_length, dtype=torch.long)
-        for i, text in enumerate(texts):
-            tokens = self.tokenizer(text)
-            if not self.add_bos:
-                input_ids = tokens.input_ids[:context_length - 1]
-                input_ids = input_ids + [self.tokenizer.eos_token_id]  # add [EOS]
-            else:
-                input_ids = tokens.input_ids[:context_length - 2]
-                input_ids = [self.tokenizer.bos_token_id] + input_ids + [self.tokenizer.eos_token_id]  # add [EOS]
-            # attention_mask = tokens.attention_mask[:context_length]
-            # attention_mask = attention_mask + [0.] * pad_length
-            result[i, :len(input_ids)] = torch.tensor(input_ids)
-
-        if len(result) == 1:
-            return result[0]
-        return result
diff --git a/spaces/neko321/Voice-Changer1/infer_pack/commons.py b/spaces/neko321/Voice-Changer1/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/neko321/Voice-Changer1/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Abhi Nahi Toh Kabhi Nahi Hindi Pdf Free Download Autovelox Simtractor.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Abhi Nahi Toh Kabhi Nahi Hindi Pdf Free Download Autovelox Simtractor.md
deleted file mode 100644
index eb001f1a4a27d5f25a8cf3bbc3c84061161e5489..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Abhi Nahi Toh Kabhi Nahi Hindi Pdf Free Download Autovelox Simtractor.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-
-<h1>Abhi Nahi Toh Kabhi Nahi: A Bollywood Movie That You Can Watch Online For Free</h1>
-<p>Abhi Nahi Toh Kabhi Nahi is a 2018 Bollywood movie that features Kay Kay Menon, Himansh Kohli, Rajeev Khandelwal and others in the lead roles. The movie is a comedy-drama that revolves around four couples who are facing different challenges in their relationships. The movie is directed by Mahesh Bhupathi and produced by Lara Dutta[^1^].</p>
-<p>If you are looking for a fun and entertaining movie to watch online for free, you can download Abhi Nahi Toh Kabhi Nahi Hindi Pdf from the internet. This is a file format that allows you to read the movie script on your computer or mobile device. You can also use an autovelox simtractor to convert the pdf file into a video file that you can watch on your screen. An autovelox simtractor is a software tool that can extract audio and video data from pdf files and create a playable video file[^2^].</p>
-<h2>Abhi Nahi Toh Kabhi Nahi Hindi Pdf Free Download autovelox simtractor</h2><br /><p><b><b>Download File</b> &middot; <a href="https://urlcod.com/2uIcvL">https://urlcod.com/2uIcvL</a></b></p><br /><br />
-<p>Abhi Nahi Toh Kabhi Nahi Hindi Pdf Free Download autovelox simtractor is a keyword that many people use to search for this movie online. However, you should be careful when downloading files from unknown sources as they may contain viruses or malware that can harm your device. You should always use a trusted and reliable website to download movies or any other files online.</p>
-<p>Abhi Nahi Toh Kabhi Nahi is a movie that you can enjoy with your family and friends. It has a good mix of humor, romance, drama and emotions. The movie also has some catchy songs that you can sing along to. You can watch Abhi Nahi Toh Kabhi Nahi online for free by downloading the pdf file and using an autovelox simtractor to convert it into a video file.</p>
-
-<p>If you want to know more about the movie Abhi Nahi Toh Kabhi Nahi, you can read the song lyrics from the movie at Bollywood Hungama. You can also watch the trailer of the movie on YouTube. The movie has received positive reviews from critics and audiences alike. It is a movie that celebrates love, life and happiness.</p>
-<p>Abhi Nahi Toh Kabhi Nahi is a movie that you should not miss. It is a movie that will make you laugh, cry and smile. It is a movie that will remind you that there is always hope and happiness in life. Abhi Nahi Toh Kabhi Nahi means 'not now then never'. So don't wait any longer and watch this movie online for free by downloading the pdf file and using an autovelox simtractor to convert it into a video file.</p>
-
-<h2>Conclusion</h2>
-<p>Abhi Nahi Toh Kabhi Nahi is a 2018 Bollywood movie that you can watch online for free by downloading the pdf file and using an autovelox simtractor to convert it into a video file. The movie is a comedy-drama that features Kay Kay Menon, Himansh Kohli, Rajeev Khandelwal and others in the lead roles. The movie is directed by Mahesh Bhupathi and produced by Lara Dutta. The movie is a movie that you can enjoy with your family and friends. It has a good mix of humor, romance, drama and emotions. The movie also has some catchy songs that you can sing along to. Abhi Nahi Toh Kabhi Nahi is a movie that you should not miss. It is a movie that celebrates love, life and happiness. Abhi Nahi Toh Kabhi Nahi means 'not now then never'. So don't wait any longer and watch this movie online for free by downloading the pdf file and using an autovelox simtractor to convert it into a video file.</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Barbra Streisand Discography 67 Albums 19632012 61.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Barbra Streisand Discography 67 Albums 19632012 61.md
deleted file mode 100644
index 2fed542156eb3f4debbd1ab9190e0de336df0364..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Barbra Streisand Discography 67 Albums 19632012 61.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-
-<h1>Barbra Streisand: A Legendary Singer with 67 Albums in Six Decades</h1>
-<p>Barbra Streisand is one of the most celebrated and influential singers of all time. She has released 67 albums since her debut in 1963, spanning various genres and styles. She has also won numerous awards and accolades for her music, as well as for her acting and producing career. Here are some highlights of her impressive discography.</p>
-<h2>Barbra Streisand Discography 67 Albums 19632012 61</h2><br /><p><b><b>Download</b> &#9889; <a href="https://urlcod.com/2uIciw">https://urlcod.com/2uIciw</a></b></p><br /><br />
-<ul>
-<li>Her first album, <em>The Barbra Streisand Album</em>, was released in 1963 and won two Grammy Awards, including Album of the Year. It featured her signature song, "Happy Days Are Here Again".</li>
-<li>Her fourth album, <em>People</em>, was released in 1964 and became her first number one album on the Billboard 200 chart. It included the hit title track, which was also featured in the Broadway musical <em>Funny Girl</em>, in which Streisand starred as Fanny Brice.</li>
-<li>Her eleventh album, <em>A Christmas Album</em>, was released in 1967 and became one of the best-selling holiday albums of all time. It contained classic songs such as "Jingle Bells", "White Christmas", and "Silent Night".</li>
-<li>Her fifteenth album, <em>The Way We Were</em>, was released in 1974 and featured the theme song from the film of the same name, starring Streisand and Robert Redford. The song became her first number one single on the Billboard Hot 100 chart and won an Academy Award for Best Original Song.</li>
-<li>Her twentieth album, <em>Guilty</em>, was released in 1980 and was a collaboration with Barry Gibb of the Bee Gees. It became her best-selling album to date, with over 20 million copies sold worldwide. It spawned three top ten singles, including "Woman in Love", which was a number one hit in several countries.</li>
-<li>Her twenty-fifth album, <em>The Broadway Album</em>, was released in 1985 and marked her return to her musical theater roots. It featured songs from various Broadway shows, such as <em>West Side Story</em>, <em>Sweeney Todd</em>, and <em>The King and I</em>. It won four Grammy Awards, including Album of the Year.</li>
-<li>Her thirty-sixth album, <em>Higher Ground</em>, was released in 1997 and was dedicated to the victims of Hurricane Andrew. It contained songs of inspiration and hope, such as "Tell Him", a duet with Celine Dion, and "I Believe/You'll Never Walk Alone", a medley with a gospel choir.</li>
-<li>Her forty-second album, <em>Love Is the Answer</em>, was released in 2009 and was her first jazz album. It featured standards from the Great American Songbook, such as "Here's to Life", "In the Wee Small Hours of the Morning", and "Smoke Gets in Your Eyes". It became her ninth number one album on the Billboard 200 chart.</li>
-<li>Her forty-seventh album, <em>Partners</em>, was released in 2014 and was a collection of duets with various male singers, such as Michael Bublé, Stevie Wonder, Billy Joel, and Elvis Presley. It became her tenth number one album on the Billboard 200 chart.</li>
-<li>Her fifty-second album, <em>Walls</em>, was released in 2018 and was a response to the political and social climate of the time. It featured original songs as well as covers of songs by John Lennon, Paul Simon, Burt Bacharach, and others. It included the single "Don't Lie to Me", which Streisand wrote and co-produced.</li>
-</ul>
-<p>Barbra Streisand is a living legend who has made an indelible mark on the music industry with her remarkable voice, talent, and versatility. She has sold over 150 million records worldwide and has been honored with numerous awards and honors. She is also an outspoken advocate for various causes, such as women's rights, civil rights, environmental issues, and health research. She is truly a star for all seasons.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 Bit.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 Bit.md
deleted file mode 100644
index 8b8ace7e1e4aebbce417c0b01ed6b88e7a4c3ada..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 Bit.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-
-<h1>How to Download Spotify Songs with Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit</h1>
-<p>If you are a Spotify user who wants to download songs from the popular streaming service to your local drive, you may have heard of Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit. This is a powerful and easy-to-use software that can help you convert Spotify songs, albums, playlists, podcasts, and radios to MP3, AAC, WAV, FLAC, AIFF, or ALAC format with high quality and fast speed.</p>
-<p>In this article, we will show you how to use Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit to download Spotify songs in a few simple steps.</p>
-<h2>Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit</h2><br /><p><b><b>Download Zip</b> &#10037; <a href="https://urlcod.com/2uIc7e">https://urlcod.com/2uIc7e</a></b></p><br /><br />
-<h2>Step 1: Download and Install Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit</h2>
-<p>The first thing you need to do is to download and install Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit on your Windows computer. This software is compatible with Windows 7, 8, and 10 (32-bit and 64-bit) and the latest version of Spotify[^2^]. You can download it from the link below:</p>
-<p><a href="https://sidify-spotify-music-converter-pro-132--seupirate-.peatix.com/view">Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit</a></p>
-<p>After downloading the software, follow the instructions to install it on your computer.</p>
-<h2>Step 2: Add Spotify Songs to Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit</h2>
-<p>After installing the software, launch it and you will see the main interface as below:</p>
-<p><img src="https://www.sidify.com/images/sp-win/sidify-music-converter-for-windows.jpg" alt="Sidify Spotify Music Converter Pro main interface"></p>
-<p>To add Spotify songs to Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit, you have two options:</p>
-<p></p>
-<ul>
-<li>Drag and drop any songs, playlists, podcasts, or radios from Spotify to Sidify.</li>
-<li>Copy and paste the link of any songs, playlists, podcasts, or radios from Spotify to Sidify.</li>
-</ul>
-<p>After adding the songs, you can check or uncheck the ones you want to convert.</p>
-<h2>Step 3: Choose Output Format and Customize Settings</h2>
-<p>Before converting the songs, you can choose the output format and customize other settings according to your preference. To do that, click on the "Settings" button on the top right corner of the interface and you will see a pop-up window as below:</p>
-<p><img src="https://www.sidify.com/images/sp-win/sidify-music-converter-for-windows-settings.jpg" alt="Sidify Spotify Music Converter Pro settings"></p>
-<p>Here you can choose the output format from MP3, AAC, WAV, FLAC, AIFF, or ALAC. You can also adjust the output quality from low (128 kbps) to high (320 kbps). Moreover, you can change the output folder, output file name format (including title, album, artist, track number, playlist index, and year tags), output file organization (by artist or album), conversion speed (up to 10X), and language (up to 46 languages supported).</p>
-<p>After customizing the settings, click on "OK" to save them.</p>
-<h2>Step 4: Start Converting Spotify Songs with Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit</h2>
-<p>Now you are ready to start converting Spotify songs with Sidify Spotify Music Converter Pro 1.3.2 - SeuPirate 64 bit. To do that, simply click on the "Convert" button on the bottom right corner of the interface and wait for the conversion process to finish.</p>
-<p>Sidify Spotify Music</p> e93f5a0c3f<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/nightfury/SD-InPainting/app_test.py b/spaces/nightfury/SD-InPainting/app_test.py
deleted file mode 100644
index 91fda82f933fc2ea01b03f7e1b47d5d50bfcafea..0000000000000000000000000000000000000000
--- a/spaces/nightfury/SD-InPainting/app_test.py
+++ /dev/null
@@ -1,337 +0,0 @@
-import gradio as gr
-
-from io import BytesIO
-import requests
-import PIL
-from PIL import Image
-import numpy as np
-import os
-import uuid
-import torch
-from torch import autocast
-import cv2
-from matplotlib import pyplot as plt
-from inpainting import StableDiffusionInpaintingPipeline
-from torchvision import transforms
-from clipseg.models.clipseg import CLIPDensePredT
-
-Image.LOAD_TRUNCATED_IMAGES = True
-
-auth_token = os.environ.get("API_TOKEN") or True
-
-def download_image(url):
-    response = requests.get(url)
-    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-
-#device = "cpu" #"cuda" if torch.cuda.is_available() else "cpu"
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-print("The model will be running on :: ", device, " ~device")
-# Convert model parameters and buffers to CPU or Cuda
-
-model_id_or_path = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionInpaintingPipeline.from_pretrained(
-    model_id_or_path,
-    #revision="fp16",
-    torch_dtype=torch.float16,
-    use_auth_token=auth_token
-).to(device)
-
-#pipe = pipe.to(device)
-#self.register_buffer('n_', ...)
-#print ("torch.backends.mps.is_available: ", torch.backends.mps.is_available())
-
-model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
-
-model = model.to(torch.device(device))
-model.eval().float()
-#model = model.type(torch.HalfTensor)
-
-weightsPATH = './clipseg/weights/rd64-uni.pth'
-
-#state = {'model': model.state_dict()}
-#torch.save(state, weightsPATH)
-
-model.load_state_dict(torch.load(weightsPATH, map_location=torch.device(device)), strict=False) #False
-#model.load_state_dict(torch.load(weightsPATH)['model'])
-
-print ("Torch load(model) : ", model)
-print ("Weights : ")
-# print weights
-for k, v in model.named_parameters():
-    print(k, v)    
-
-imgRes = 256
-
-transform = transforms.Compose([
-      transforms.ToTensor(),
-      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-      transforms.Resize((imgRes, imgRes)),
-])
-
-def predict(radio, dict, word_mask, prompt=""):
-    if(radio == "draw a mask above"):
-        #with autocast(device): #"cuda"
-        with autocast(enable=(False if device=='cpu' else True)):
-            init_image = dict["image"].convert("RGB").resize((imgRes, imgRes))
-            mask = dict["mask"].convert("RGB").resize((imgRes, imgRes))
-    elif(radio == "type what to keep"):
-        img = transform(dict["image"]).squeeze(0)
-        
-        #-----New Lines-----
-        if torch.cuda.is_available():
-            img.cuda()
-            print ("yes, CUDA is available here !! ")
-        
-        #------------------
-        
-        word_masks = [word_mask]
-        with torch.no_grad(): 
-            #torch.cuda.amp.autocast(): #
-            preds = model(img.repeat(len(word_masks),1,1,1), word_masks)[0]
-        
-        #model = model.to(torch.device(device))
-        img = img.to(torch.device(device))
-        #prompt = prompt.to(torch.device(device))
-        #---------
-        
-        init_image = dict['image'].convert('RGB').resize((imgRes, imgRes))
-        filename = f"{uuid.uuid4()}.png"
-        plt.imsave(filename,torch.sigmoid(preds[0][0]))
-        
-        img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
-        img = Image.fromarray(img)
-
-        #img2 = cv2.imread(filename)
-        
-        #if ret == True: 
-        gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        
-        (thresh, bw_image) = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY)
-        cv2.cvtColor(bw_image, cv2.COLOR_BGR2RGB)
-        mask = Image.fromarray(np.uint8(bw_image)).convert('RGB')
-        os.remove(filename)
-    else:
-        img = transform(dict["image"]).unsqueeze(0)
-        
-        #-----New Lines-----
-        if torch.cuda.is_available():
-            img.cuda()
-            print ("yes, CUDA is available here !! ")
-        
-        #------------------
-        
-        word_masks = [word_mask]
-        #with torch.cuda.amp.autocast(): #
-        with torch.no_grad():
-            preds = model(img.repeat(len(word_masks),1,1,1), word_masks)[0]
-
-        
-        #model = model.to(torch.device(device))
-        img = img.to(torch.device(device))
-        #prompt = prompt.to(torch.device(device))
-        
-        init_image = dict['image'].convert('RGB').resize((imgRes, imgRes))
-        filename = f"{uuid.uuid4()}.png"
-        plt.imsave(filename,torch.sigmoid(preds[0][0]))
-        #img2 = cv2.imread(filename)
-        
-        img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
-        img = Image.fromarray(img)
-        
-        #if ret == True: 
-        gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        (thresh, bw_image) = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY)
-        cv2.cvtColor(bw_image, cv2.COLOR_BGR2RGB)
-        mask = Image.fromarray(np.uint8(bw_image)).convert('RGB')
-        os.remove(filename)
-        
-        #with autocast(device): #"cuda"
-        with autocast(enable=(False if device=='cpu' else True)):
-        #with autocast(device_type="cpu", dtype=torch.bfloat16):
-            images = pipe(prompt = prompt, init_image=init_image, mask_image=mask, strength=0.8)["sample"]
-        return images[0]
-
-# examples = [[dict(image="init_image.png", mask="mask_image.png"), "A panda sitting on a bench"]]
-css = '''
-.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
-#image_upload{min-height:400px}
-#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
-#mask_radio .gr-form{background:transparent; border: none}
-#word_mask{margin-top: .75em !important}
-#word_mask textarea:disabled{opacity: 0.3}
-.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
-.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
-.dark .footer {border-color: #303030}
-.dark .footer>p {background: #0b0f19}
-.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
-#image_upload .touch-none{display: flex}
-
-'''
-def swap_word_mask(radio_option):
-    if(radio_option == "draw a mask above"):
-        return gr.update(interactive=False, placeholder="Disabled")
-    else:
-        return gr.update(interactive=True, placeholder="A cat")
-
-image_blocks = gr.Blocks(css=css)
-with image_blocks as demo:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <svg
-                  width="0.65em"
-                  height="0.65em"
-                  viewBox="0 0 115 115"
-                  fill="none"
-                  xmlns="http://www.w3.org/2000/svg"
-                >
-                  <rect width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect y="69" width="23" height="23" fill="black"></rect>
-                  <rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="46" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
-                  <rect x="69" width="23" height="23" fill="black"></rect>
-                  <rect x="69" y="69" width="23" height="23" fill="black"></rect>
-                  <rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="115" y="46" width="23" height="23" fill="black"></rect>
-                  <rect x="115" y="115" width="23" height="23" fill="black"></rect>
-                  <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="92" y="69" width="23" height="23" fill="white"></rect>
-                  <rect x="69" y="46" width="23" height="23" fill="black"></rect>
-                  <rect x="69" y="115" width="23" height="23" fill="white"></rect>
-                  <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="46" y="46" width="23" height="23" fill="white"></rect>
-                  <rect x="46" y="115" width="23" height="23" fill="black"></rect>
-                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
-                  <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="23" y="69" width="23" height="23" fill="black"></rect>
-                </svg>
-                <h1 style="font-weight: 900; margin-bottom: 7px;">
-                  Stable Diffusion Multi Inpainting
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-                Inpaint Stable Diffusion by either drawing a mask or typing what to replace & what to keep !!!
-              </p>
-            </div>
-        """
-    )
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
-            with gr.Box(elem_id="mask_radio").style(border=False):
-                radio = gr.Radio(["draw a mask above", "type what to mask below", "type what to keep"], value="draw a mask above", show_label=False, interactive=True).style(container=False)
-                word_mask = gr.Textbox(label = "What to find in your image", interactive=False, elem_id="word_mask", placeholder="Disabled").style(container=False)
-                
-                img_res = gr.Dropdown(['512*512', '256*256'], label="Image Resolution")
-                
-            prompt = gr.Textbox(label = 'Your prompt (what you want to add in place of what you are removing)')
-            radio.change(fn=swap_word_mask, inputs=radio, outputs=word_mask,show_progress=False)
-            radio.change(None, inputs=[], outputs=image_blocks, _js = """
-            () => {
-                css_style = document.styleSheets[document.styleSheets.length - 1]
-                last_item = css_style.cssRules[css_style.cssRules.length - 1]
-                last_item.style.display = ["flex", ""].includes(last_item.style.display) ? "none" : "flex";
-            }""")
-            btn = gr.Button("Run")
-        with gr.Column():
-            result = gr.Image(label="Result")
-        btn.click(fn=predict, inputs=[radio, image, word_mask, prompt], outputs=result)
-    gr.HTML(
-            """
-            <div class="footer">
-                    <p>Model by <a href="https://huggingface.co/CompVis" style="text-decoration: underline;" target="_blank">CompVis</a> and <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">Stability AI</a> - Inpainting by <a href="https://github.com/" style="text-decoration: underline;" target="_blank">NightFury</a> using clipseg[model] with bit modification - Gradio Demo on 🤗 Hugging Face
-                    </p>
-                </div>
-                
-                
-            <div class="acknowledgments" >
-<h1 dir="auto"><a id="user-content-image-segmentation-using-text-and-image-prompts" aria-hidden="true" href="#image-segmentation-using-text-and-image-prompts"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Image Segmentation Using Text and Image Prompts</h1>
-<p dir="auto">This repository contains the code used in the paper <a href="https://arxiv.org/abs/2112.10003" rel="nofollow">"Image Segmentation Using Text and Image Prompts"</a>.</p>
-
-<p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/ThereforeGames/txt2mask/blob/main/repositories/clipseg/overview.png"><img src="/ThereforeGames/txt2mask/raw/main/repositories/clipseg/overview.png" alt="drawing" style="max-width: 100%;" height="200em"></a></p>
-<p dir="auto">The systems allows to create segmentation models without training based on:</p>
-<ul dir="auto">
-<li>An arbitrary text query</li>
-<li>Or an image with a mask highlighting stuff or an object.</li>
-</ul>
-<h3 dir="auto"><a id="user-content-quick-start" aria-hidden="true" href="#quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Quick Start</h3>
-<p dir="auto">In the <code>Quickstart.ipynb</code> notebook we provide the code for using a pre-trained CLIPSeg model. If you run the notebook locally, make sure you downloaded the <code>rd64-uni.pth</code> weights, either manually or via git lfs extension.
-It can also be used interactively using <a href="https://mybinder.org/v2/gh/timojl/clipseg/HEAD?labpath=Quickstart.ipynb" rel="nofollow">MyBinder</a>
-(please note that the VM does not use a GPU, thus inference takes a few seconds).</p>
-<h3 dir="auto"><a id="user-content-dependencies" aria-hidden="true" href="#dependencies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Dependencies</h3>
-<p dir="auto">This code base depends on pytorch, torchvision and clip (<code>pip install git+https://github.com/openai/CLIP.git</code>).
-Additional dependencies are hidden for double blind review.</p>
-<h3 dir="auto"><a id="user-content-datasets" aria-hidden="true" href="#datasets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Datasets</h3>
-<ul dir="auto">
-<li><code>PhraseCut</code> and <code>PhraseCutPlus</code>: Referring expression dataset</li>
-<li><code>PFEPascalWrapper</code>: Wrapper class for PFENet's Pascal-5i implementation</li>
-<li><code>PascalZeroShot</code>: Wrapper class for PascalZeroShot</li>
-<li><code>COCOWrapper</code>: Wrapper class for COCO.</li>
-</ul>
-<h3 dir="auto"><a id="user-content-models" aria-hidden="true" href="#models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Models</h3>
-<ul dir="auto">
-<li><code>CLIPDensePredT</code>: CLIPSeg model with transformer-based decoder.</li>
-<li><code>ViTDensePredT</code>: CLIPSeg model with transformer-based decoder.</li>
-</ul>
-<h3 dir="auto"><a id="user-content-third-party-dependencies" aria-hidden="true" href="#third-party-dependencies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Third Party Dependencies</h3>
-<p dir="auto">For some of the datasets third party dependencies are required. Run the following commands in the <code>third_party</code> folder.</p>
-<div  dir="auto"><pre>git clone https://github.com/cvlab-yonsei/JoEm
-git clone https://github.com/Jia-Research-Lab/PFENet.git
-git clone https://github.com/ChenyunWu/PhraseCutDataset.git
-git clone https://github.com/juhongm999/hsnet.git</pre><div >
-    <clipboard-copy aria-label="Copy" data-copy-feedback="Copied!" data-tooltip-direction="w" value="git clone https://github.com/cvlab-yonsei/JoEm
-git clone https://github.com/Jia-Research-Lab/PFENet.git
-git clone https://github.com/ChenyunWu/PhraseCutDataset.git
-git clone https://github.com/juhongm999/hsnet.git" tabindex="0" role="button">
-      <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2">
-    <path fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path><path fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-</svg>
-      <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2">
-    <path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path>
-</svg>
-    </clipboard-copy>
-  </div></div>
-  
-<h3 dir="auto"><a id="user-content-weights" aria-hidden="true" href="#weights"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Weights</h3>
-<p dir="auto">The MIT license does not apply to these weights.</p>
-<ul dir="auto">
-<li><a href="https://github.com/timojl/clipseg/raw/master/weights/rd64-uni.pth">CLIPSeg-D64</a> (4.1MB, without CLIP weights)</li>
-<li><a href="https://github.com/timojl/clipseg/raw/master/weights/rd16-uni.pth">CLIPSeg-D16</a> (1.1MB, without CLIP weights)</li>
-</ul>
-
-<h3 dir="auto"><a id="user-content-training-and-evaluation" aria-hidden="true" href="#training-and-evaluation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Training and Evaluation</h3>
-<p dir="auto">To train use the <code>training.py</code> script with experiment file and experiment id parameters. E.g. <code>python training.py phrasecut.yaml 0</code> will train the first phrasecut experiment which is defined by the <code>configuration</code> and first <code>individual_configurations</code> parameters. Model weights will be written in <code>logs/</code>.</p>
-<p dir="auto">For evaluation use <code>score.py</code>. E.g. <code>python score.py phrasecut.yaml 0 0</code> will train the first phrasecut experiment of <code>test_configuration</code> and the first configuration in <code>individual_configurations</code>.</p>
-
-<h3 dir="auto"><a id="user-content-usage-of-pfenet-wrappers" aria-hidden="true" href="#usage-of-pfenet-wrappers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Usage of PFENet Wrappers</h3>
-<p dir="auto">In order to use the dataset and model wrappers for PFENet, the PFENet repository needs to be cloned to the root folder.
-<code>git clone https://github.com/Jia-Research-Lab/PFENet.git </code></p>
-
-<h4 dir="auto"><a id="user-content-license" aria-hidden="true" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>LICENSE</h4>
-<p dir="auto">The source code files in this repository (excluding model weights) are released under MIT license.</p>
-
-                    <p>
-The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
-                    <p><h4>Biases and content acknowledgment</h4>
-Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
-
-
-</div>
-           """
-        )
-demo.launch()
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/utils/dist.py b/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/utils/dist.py
deleted file mode 100644
index f07940ada074a687debbfe730e5df9ccd34651cf..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/utils/dist.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import io
-import re
-import subprocess
-import logging
-import random
-import torch
-import numpy as np
-
-
-## This code is borrowed from
-# https://github.com/researchmm/STTN/blob/master/core/dist.py
-def get_world_size():
-    """Find OMPI world size without calling mpi functions
-    :rtype: int
-    """
-    if os.environ.get('PMI_SIZE') is not None:
-        return int(os.environ.get('PMI_SIZE') or 1)
-    elif os.environ.get('OMPI_COMM_WORLD_SIZE') is not None:
-        return int(os.environ.get('OMPI_COMM_WORLD_SIZE') or 1)
-    else:
-        return torch.cuda.device_count()
-
-
-def get_global_rank():
-    """Find OMPI world rank without calling mpi functions
-    :rtype: int
-    """
-    if os.environ.get('PMI_RANK') is not None:
-        return int(os.environ.get('PMI_RANK') or 0)
-    elif os.environ.get('OMPI_COMM_WORLD_RANK') is not None:
-        return int(os.environ.get('OMPI_COMM_WORLD_RANK') or 0)
-    else:
-        return 0
-
-
-def get_local_rank():
-    """Find OMPI local rank without calling mpi functions
-    :rtype: int
-    """
-    if os.environ.get('MPI_LOCALRANKID') is not None:
-        return int(os.environ.get('MPI_LOCALRANKID') or 0)
-    elif os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') is not None:
-        return int(os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') or 0)
-    else:
-        return 0
-
-
-def get_master_ip():
-    if os.environ.get('AZ_BATCH_MASTER_NODE') is not None:
-        return os.environ.get('AZ_BATCH_MASTER_NODE').split(':')[0]
-    elif os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE') is not None:
-        return os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE')
-    else:
-        return "127.0.0.1"
diff --git a/spaces/oliver2023/chatgpt-on-wechat/common/log.py b/spaces/oliver2023/chatgpt-on-wechat/common/log.py
deleted file mode 100644
index f10eff923de85999b9d51100ad073bcb3b240969..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/common/log.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import logging
-import sys
-
-
-def _get_logger():
-    log = logging.getLogger('log')
-    log.setLevel(logging.INFO)
-    console_handle = logging.StreamHandler(sys.stdout)
-    console_handle.setFormatter(logging.Formatter('[%(levelname)s][%(asctime)s][%(filename)s:%(lineno)d] - %(message)s',
-                                                  datefmt='%Y-%m-%d %H:%M:%S'))
-    file_handle = logging.FileHandler('run.log', encoding='utf-8')
-    file_handle.setFormatter(logging.Formatter('[%(levelname)s][%(asctime)s][%(filename)s:%(lineno)d] - %(message)s',
-                                                  datefmt='%Y-%m-%d %H:%M:%S'))
-    log.addHandler(file_handle)
-    log.addHandler(console_handle)
-    return log
-
-
-# 日志句柄
-logger = _get_logger()
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py
deleted file mode 100644
index de1b1fd93c7fab1cf43ef1f0bb26b93f4e9de63e..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py
+++ /dev/null
@@ -1,260 +0,0 @@
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from ...models import UNet2DModel
-from ...schedulers import CMStochasticIterativeScheduler
-from ...utils import (
-    logging,
-    replace_example_docstring,
-)
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-
-        >>> from diffusers import ConsistencyModelPipeline
-
-        >>> device = "cuda"
-        >>> # Load the cd_imagenet64_l2 checkpoint.
-        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
-        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-        >>> pipe.to(device)
-
-        >>> # Onestep Sampling
-        >>> image = pipe(num_inference_steps=1).images[0]
-        >>> image.save("cd_imagenet64_l2_onestep_sample.png")
-
-        >>> # Onestep sampling, class-conditional image generation
-        >>> # ImageNet-64 class label 145 corresponds to king penguins
-        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
-        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")
-
-        >>> # Multistep sampling, class-conditional image generation
-        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
-        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
-        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
-        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
-        ```
-"""
-
-
-class ConsistencyModelPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for unconditional or class-conditional image generation.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
-            compatible with [`CMStochasticIterativeScheduler`].
-    """
-    model_cpu_offload_seq = "unet"
-
-    def __init__(self, unet: UNet2DModel, scheduler: CMStochasticIterativeScheduler) -> None:
-        super().__init__()
-
-        self.register_modules(
-            unet=unet,
-            scheduler=scheduler,
-        )
-
-        self.safety_checker = None
-
-    def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels, height, width)
-        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 latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device=device, dtype=dtype)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    # Follows diffusers.VaeImageProcessor.postprocess
-    def postprocess_image(self, sample: torch.FloatTensor, output_type: str = "pil"):
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(
-                f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"
-            )
-
-        # Equivalent to diffusers.VaeImageProcessor.denormalize
-        sample = (sample / 2 + 0.5).clamp(0, 1)
-        if output_type == "pt":
-            return sample
-
-        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
-        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "np":
-            return sample
-
-        # Output_type must be 'pil'
-        sample = self.numpy_to_pil(sample)
-        return sample
-
-    def prepare_class_labels(self, batch_size, device, class_labels=None):
-        if self.unet.config.num_class_embeds is not None:
-            if isinstance(class_labels, list):
-                class_labels = torch.tensor(class_labels, dtype=torch.int)
-            elif isinstance(class_labels, int):
-                assert batch_size == 1, "Batch size must be 1 if classes is an int"
-                class_labels = torch.tensor([class_labels], dtype=torch.int)
-            elif class_labels is None:
-                # Randomly generate batch_size class labels
-                # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
-                class_labels = torch.randint(0, self.unet.config.num_class_embeds, size=(batch_size,))
-            class_labels = class_labels.to(device)
-        else:
-            class_labels = None
-        return class_labels
-
-    def check_inputs(self, num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps):
-        if num_inference_steps is None and timesteps is None:
-            raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied.")
-
-        if num_inference_steps is not None and timesteps is not None:
-            logger.warning(
-                f"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
-                " `timesteps` will be used over `num_inference_steps`."
-            )
-
-        if latents is not None:
-            expected_shape = (batch_size, 3, img_size, img_size)
-            if latents.shape != expected_shape:
-                raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}.")
-
-        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)}."
-            )
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        batch_size: int = 1,
-        class_labels: Optional[Union[torch.Tensor, List[int], int]] = None,
-        num_inference_steps: int = 1,
-        timesteps: List[int] = None,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: 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,
-    ):
-        r"""
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            class_labels (`torch.Tensor` or `List[int]` or `int`, *optional*):
-                Optional class labels for conditioning class-conditional consistency models. Not used if the model is
-                not class-conditional.
-            num_inference_steps (`int`, *optional*, defaults to 1):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
-                timesteps are used. Must be in descending order.
-            generator (`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`.
-            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.ImagePipelineOutput`] 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.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images.
-        """
-        # 0. Prepare call parameters
-        img_size = self.unet.config.sample_size
-        device = self._execution_device
-
-        # 1. Check inputs
-        self.check_inputs(num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps)
-
-        # 2. Prepare image latents
-        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
-        sample = self.prepare_latents(
-            batch_size=batch_size,
-            num_channels=self.unet.config.in_channels,
-            height=img_size,
-            width=img_size,
-            dtype=self.unet.dtype,
-            device=device,
-            generator=generator,
-            latents=latents,
-        )
-
-        # 3. Handle class_labels for class-conditional models
-        class_labels = self.prepare_class_labels(batch_size, device, class_labels=class_labels)
-
-        # 4. Prepare timesteps
-        if timesteps is not None:
-            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
-            timesteps = self.scheduler.timesteps
-            num_inference_steps = len(timesteps)
-        else:
-            self.scheduler.set_timesteps(num_inference_steps)
-            timesteps = self.scheduler.timesteps
-
-        # 5. Denoising loop
-        # Multistep sampling: implements Algorithm 1 in the paper
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                scaled_sample = self.scheduler.scale_model_input(sample, t)
-                model_output = self.unet(scaled_sample, t, class_labels=class_labels, return_dict=False)[0]
-
-                sample = self.scheduler.step(model_output, t, sample, generator=generator)[0]
-
-                # call the callback, if provided
-                progress_bar.update()
-                if callback is not None and i % callback_steps == 0:
-                    callback(i, t, sample)
-
-        # 6. Post-process image sample
-        image = self.postprocess_image(sample, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/padmanabhbosamia/Pascal/app.py b/spaces/padmanabhbosamia/Pascal/app.py
deleted file mode 100644
index e899965fbb50d3ed9d76dce10be33da42bfe5cd5..0000000000000000000000000000000000000000
--- a/spaces/padmanabhbosamia/Pascal/app.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import itertools
-
-import config as config
-import cv2
-import gradio as gr
-import matplotlib.patches as patches
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-import torchvision
-import utils
-from loss import YoloLoss
-from model import YOLOv3
-from PIL import Image
-from torch.utils.data import DataLoader
-from torchvision import transforms
-from utils import get_loaders
-
-new_state_dict = {}
-state_dict = torch.load('Yolov3_Padmanabh.pth', map_location=torch.device('cpu'))
-for key, value in state_dict.items():
-    new_key = key.replace('model.', '')
-    new_state_dict[new_key] = value
-
-model = YOLOv3(in_channels=3, num_classes=config.NUM_CLASSES)
-model.load_state_dict(new_state_dict, strict=True)
-model.eval()
-
-classes = ("aeroplane",
-    "bicycle",
-    "bird",
-    "boat",
-    "bottle",
-    "bus",
-    "car",
-    "cat",
-    "chair",
-    "cow",
-    "diningtable",
-    "dog",
-    "horse",
-    "motorbike",
-    "person",
-    "pottedplant",
-    "sheep",
-    "sofa",
-    "train",
-    "tvmonitor")
-
-
-import grad_cam_func as gcf
-from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients
-from pytorch_grad_cam.utils.image import show_cam_on_image
-from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
-
-
-def inference(input_img=None, iou_threshold=0.6, conf_threshold=0.5, gc_trans=0.3):
-    
-    if input_img is not None:
-        
-        tranform_img = config.infer_transforms(image=input_img)
-        transform_img = tranform_img['image'].unsqueeze(0)
-                
-        transform_img_visual = config.infer_transforms_visualization(image=input_img)['image']
-        
-        with torch.no_grad():
-            outputs = model(transform_img)
-            bboxes = [[] for _ in range(transform_img.shape[0])] # range of Batch size 
-          
-            for i in range(3):
-                batch_size, A, S, _, _ = outputs[i].shape
-                anchor = np.array(config.SCALED_ANCHORS[i])
-                boxes_scale_i = utils.cells_to_bboxes(
-                    outputs[i], anchor, S=S, is_preds=True)
-                            
-                for idx, (box) in enumerate(boxes_scale_i):
-                    bboxes[idx] += box
-                    
-        
-        nms_boxes = utils.non_max_suppression(bboxes[0], iou_threshold=iou_threshold,
-                                        threshold=conf_threshold, box_format="midpoint",)
-        
-        
-        image, boxes = transform_img_visual.permute(1,2,0), nms_boxes
-        
-        """Plots predicted bounding boxes on the image"""
-        cmap = plt.get_cmap("tab20b")
-        class_labels = config.PASCAL_CLASSES
-        colors = [cmap(i) for i in np.linspace(0, 1, len(class_labels))]
-        
-        im = np.array(image)
-        height, width, _ = im.shape
-
-        # Create figure and axes
-        fig, ax = plt.subplots(1)
-        
-        # Display the image
-        ax.imshow(im)
-
-        # box[0] is x midpoint, box[2] is width
-        # box[1] is y midpoint, box[3] is height
-
-        # Create a Rectangle patch
-        for box in boxes:
-            assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height"
-            class_pred = box[0]
-            box = box[2:]
-            upper_left_x = box[0] - box[2] / 2
-            upper_left_y = box[1] - box[3] / 2
-            rect = patches.Rectangle(
-                (upper_left_x * width, upper_left_y * height),
-                box[2] * width,
-                box[3] * height,
-                linewidth=2,
-                edgecolor=colors[int(class_pred)],
-                facecolor="none",
-            )
-            # Add the patch to the Axes
-            ax.add_patch(rect)
-            plt.text(
-                upper_left_x * width,
-                upper_left_y * height,
-                s=class_labels[int(class_pred)],
-                color="white",
-                verticalalignment="top",
-                bbox={"color": colors[int(class_pred)], "pad": 0},
-            )        
-        
-        plt.axis('off')
-            
-        fig.canvas.draw()
-        
-        fig_img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-        fig_img = fig_img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-
-        plt.close(fig)
-                
-        outputs_inference_bb = fig_img
-            
-        ### GradCAM
-        
-        target_layer = [model.layers[-2]]
-        cam = gcf.BaseCAM(model, target_layer)
-        
-        AnG = ActivationsAndGradients(model, target_layer, None)
-        outputs = AnG(transform_img)
-        
-        bboxes = [[] for _ in range(1)]
-        for i in range(3):
-            batch_size, A, S, _, _ = outputs[i].shape
-            anchor = config.SCALED_ANCHORS[i]
-            boxes_scale_i = utils.cells_to_bboxes(
-                outputs[i], anchor, S=S, is_preds=True
-            )
-            for idx, (box) in enumerate(boxes_scale_i):
-                bboxes[idx] += box
-        
-        nms_boxes = utils.non_max_suppression(
-            bboxes[0], iou_threshold=0.5, threshold=0.4, box_format="midpoint",
-        )
-
-        target_categories = [box[0] for box in nms_boxes]
-        targets = [ClassifierOutputTarget(
-            category) for category in target_categories]
-        
-        help_ = cam.compute_cam_per_layer(transform_img, targets, False)
-        
-        output_gc = cam.aggregate_multi_layers(help_)[0, :, :]
-        
-        img = cv2.resize(input_img, (416, 416))
-        img = np.float32(img) / 255
-        cam_image = show_cam_on_image(img, output_gc, use_rgb=True, image_weight=gc_trans)   
-             
-        outputs_inference_gc = cam_image
-        
-    else: 
-        outputs_inference_bb = None 
-        outputs_inference_gc = None 
-
-    return outputs_inference_bb, outputs_inference_gc
-
-
-
-title = "PASCAL VOC trained on Yolov3"
-description = "A simple Gradio interface to infer on Yolov3 model, and get GradCAM results"
-examples = [['examples/test_'+str(i)+'.jpg', 0.6, 0.5, 0.3] for i in range(10)]
-
-demo = gr.Interface(inference,
-                    inputs = [gr.Image(label="Input image"),
-                                gr.Slider(0, 1, value=0.6, label="IOU Threshold"),
-                                gr.Slider(0, 1, value=0.4, label="Threshold"),
-                                gr.Slider(0, 1, value=0.5, label="GradCAM Transparency"),
-                              ],
-                    outputs = [
-                        gr.Image(label="Yolov3 Prediction"),
-                        gr.Image(label="GradCAM Output"),],
-                    title = title,
-                    description = description,
-                    examples = examples
-                    )
-demo.launch()
\ No newline at end of file
diff --git a/spaces/paulbricman/lexiscore/connectors.py b/spaces/paulbricman/lexiscore/connectors.py
deleted file mode 100644
index 0df7971014ea33063ac1d196d6021e269ac81829..0000000000000000000000000000000000000000
--- a/spaces/paulbricman/lexiscore/connectors.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from newspaper import Article
-import feedparser
-from datetime import datetime
-import dateutil
-from bs4 import BeautifulSoup
-import opml
-import re
-import time
-from urllib.parse import urlparse
-from ebooklib import epub
-from tika import parser
-import os
-import regex as re
-
-
-def fetch_from_opml(filename, max_days_ago):
-    aggregate_entries = []
-
-    for feed_entry in opml.parse(filename)[0]:
-        feed = feedparser.parse(feed_entry.xmlUrl)
-        entries = [e for e in feed['entries'] if 'published' in e.keys() and (datetime.now(
-        ) - dateutil.parser.parse(e['published']).replace(tzinfo=None)).days < max_days_ago]
-
-        for e_idx, e in enumerate(entries):
-            entries[e_idx]['author'] = feed['feed']['title']
-
-        aggregate_entries += entries
-
-    aggregate_entries = sorted(aggregate_entries, key=lambda x: (
-        datetime.now() - dateutil.parser.parse(x['published']).replace(tzinfo=None)).days)
-    aggregate_contents = [''] * len(aggregate_entries)
-    aggregate_titles = [''] * len(aggregate_entries)
-
-    for e_idx, e in enumerate(aggregate_entries):
-        if e.get('content') is not None:
-            aggregate_contents[e_idx] = e.get('content')[0]['value']
-        elif e.get('summary') is not None:
-            aggregate_contents[e_idx] = e.get('summary')
-        aggregate_titles[e_idx] = e.get(
-            'author', '?') + ' | ' + e.get('title', '?')
-
-    raw_contents = aggregate_contents.copy()
-    aggregate_contents = [BeautifulSoup(
-        e, 'html.parser').get_text() for e in aggregate_contents]
-
-    data = {}
-    for idx in range(len(aggregate_contents)):
-        data[aggregate_titles[idx]] = [
-            aggregate_contents[idx], raw_contents[idx], '']
-
-    return data
-
-
-def fetch_from_bookmarks(filename, bookmark_folder, max_days_ago):
-    raw_bookmark_file = open(filename).read()
-    bookmark_soup = BeautifulSoup(raw_bookmark_file, 'html.parser')
-    folder_element = [e for e in bookmark_soup.find_all(
-        'h3') if re.compile(bookmark_folder).match(e.text)]
-    bookmarks = folder_element[0].parent()[1].find_all('a')
-    bookmarks = [e for e in bookmarks if (
-        time.time() - float(e['add_date']) < max_days_ago * 60 * 60 * 24)]
-
-    bookmark_contents = []
-    bookmark_raw = []
-    bookmark_titles = []
-
-    for bookmark in bookmarks:
-        domain = urlparse(bookmark['href']).netloc.replace('www.', '')
-        article = Article(bookmark['href'])
-        article.build()
-        bookmark_contents += [article.text]
-
-        raw = article.html
-        raw = re.sub(r'<head\s?>[\s\S]*<\/head\s?>', '', raw)
-
-        f = open('./tmp/test.html', 'w+')
-        f.write(raw)
-        f.close()
-
-        bookmark_raw += [raw]
-        bookmark_titles += [domain + ' | ' + article.title]
-
-    data = {}
-    for idx in range(len(bookmark_contents)):
-        data[bookmark_titles[idx]] = [
-            bookmark_contents[idx], bookmark_raw[idx], '']
-
-    return data
-
-
-def fetch_from_epub(filename):
-    book = epub.read_epub(filename)
-    content = ''.join([e.content.decode('utf-8')
-                      for e in book.items if isinstance(e, epub.EpubHtml)])
-    processed = BeautifulSoup(content, 'html.parser').get_text()
-
-    data = {}
-    data[book.get_metadata('DC', 'creator')[0][0] +
-         ' | ' + book.title] = [processed, content, '']
-
-    return data
-
-
-def fetch_from_pdf(filename):
-    doc = parser.from_file(filename)
-
-    data = {}
-
-    if doc['metadata'].get('Author', '') != '':
-        data[doc['metadata']['Author'] + ' | ' + doc['metadata']
-             ['pdf:docinfo:title']] = [doc['content'], doc['content']]
-    else:
-        data[os.path.splitext(os.path.basename(filename))[0]] = [
-            doc['content'], doc['content'], filename]
-
-    return data
-
-
-def fetch_from_plaintext(filename):
-    text = open(filename).read()
-    raw = '\n'.join(['<p>' + e + '</p>' for e in text.split('\n')])
-
-    data = {}
-    data[os.path.splitext(os.path.basename(filename))[0]] = [text, raw, '']
-
-    return data
diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/__init__.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/__init__.py
deleted file mode 100644
index 89e9b6988092e8fa287491afabe704a6d8a8222b..0000000000000000000000000000000000000000
--- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/__init__.py
+++ /dev/null
@@ -1,82 +0,0 @@
-'''
-Netdissect package.
-
-To run dissection:
-
-1. Load up the convolutional model you wish to dissect, and wrap it
-   in an InstrumentedModel.  Call imodel.retain_layers([layernames,..])
-   to analyze a specified set of layers.
-2. Load the segmentation dataset using the BrodenDataset class;
-   use the transform_image argument to normalize images to be
-   suitable for the model, or the size argument to truncate the dataset.
-3. Write a function to recover the original image (with RGB scaled to
-   [0...1]) given a normalized dataset image; ReverseNormalize in this
-   package inverts transforms.Normalize for this purpose.
-4. Choose a directory in which to write the output, and call
-   dissect(outdir, model, dataset).
-
-Example:
-
-    from netdissect import InstrumentedModel, dissect
-    from netdissect import BrodenDataset, ReverseNormalize
-
-    model = InstrumentedModel(load_my_model())
-    model.eval()
-    model.cuda()
-    model.retain_layers(['conv1', 'conv2', 'conv3', 'conv4', 'conv5'])
-    bds = BrodenDataset('datasets/broden1_227',
-            transform_image=transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
-            size=1000)
-    dissect('result/dissect', model, bds,
-            recover_image=ReverseNormalize(IMAGE_MEAN, IMAGE_STDEV),
-            examples_per_unit=10)
-'''
-
-__all__ = [
-	'actviz',
-	'autoeval',
-	'bargraph',
-	'broden',
-	'customnet',
-	'easydict',
-	'encoder_loss',
-	'encoder_net',
-	'evalablate',
-	'frechet_distance',
-	'fsd',
-	'fullablate',
-	'imgsave',
-	'imgviz',
-	'invert',
-	'LBFGS',
-	'make_z_dataset',
-	'modelconfig',
-	'multilayer_graph',
-	'nethook',
-	'oldalexnet',
-	'oldresnet152',
-	'oldvgg16',
-	'optimize_residuals',
-	'optimize_z_lbfgs',
-	'parallelfolder',
-	'pbar',
-	'pidfile',
-	'plotutil',
-	'proggan',
-	'renormalize',
-	'runningstats',
-	'samplegan',
-	'sampler',
-	'segdata',
-	'segmenter',
-	'segviz',
-	'setting',
-	'show',
-	'statedict',
-	'tally',
-	'upsample',
-	'workerpool',
-	'zdataset',
-]
diff --git a/spaces/pedrohc/productcounter/app.py b/spaces/pedrohc/productcounter/app.py
deleted file mode 100644
index 07fbf093673ef5c0effee330c5b9cb49035f2af8..0000000000000000000000000000000000000000
--- a/spaces/pedrohc/productcounter/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import supervision
-import tqdm
-import os
-from ultralytics import YOLO
-from dataclasses import dataclass
-from onemetric.cv.utils.iou import box_iou_batch
-from supervision import Point
-from supervision import Detections, BoxAnnotator
-from supervision import draw_text
-from supervision import Color
-from supervision import VideoInfo
-from supervision import get_video_frames_generator
-from supervision import VideoSink
-os.system("pip install git+https://github.com/ifzhang/ByteTrack")
-from typing import List
-import numpy as np
-import gradio as gr
-from tqdm import tqdm
-import yolox
-os.system("pip3 install cython_bbox gdown 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'")
-os.system("pip3 install -v -e .")
-from yolox.tracker.byte_tracker import BYTETracker, STrack
-
-MODEL = "./best.pt"
-
-TARGET_VIDEO_PATH = "test.mp4"
-
-CLASS_ID = [0,1,2,3,4,5,6]
-
-video_examples = [['example.mp4']]
-
-model = YOLO(MODEL)
-model.fuse()
-
-classes = CLASS_ID
-
-@dataclass(frozen=True)
-class BYTETrackerArgs:
-    track_thresh: float = 0.25
-    track_buffer: int = 30
-    match_thresh: float = 0.8
-    aspect_ratio_thresh: float = 3.0
-    min_box_area: float = 1.0
-    mot20: bool = False
-
-
-# converts Detections into format that can be consumed by match_detections_with_tracks function
-def detections2boxes(detections : Detections) -> np.ndarray:
-    return np.hstack((
-        detections.xyxy,
-        detections.confidence[:, np.newaxis]
-    ))
-
-
-# converts List[STrack] into format that can be consumed by match_detections_with_tracks function
-def tracks2boxes(tracks: List[STrack]) -> np.ndarray:
-    return np.array([
-        track.tlbr
-        for track
-        in tracks
-    ], dtype=float)
-
-
-# matches our bounding boxes with predictions
-def match_detections_with_tracks(
-    detections: Detections,
-    tracks: List[STrack],
-) -> Detections:
-    if not np.any(detections.xyxy) or len(tracks) == 0:
-        return np.empty((0,))
-
-    tracks_boxes = tracks2boxes(tracks=tracks)
-    iou = box_iou_batch(tracks_boxes, detections.xyxy)
-    track2detection = np.argmax(iou, axis=1)
-
-    tracker_ids = [None] * len(detections)
-
-    for tracker_index, detection_index in enumerate(track2detection):
-        if iou[tracker_index, detection_index] != 0:
-            tracker_ids[detection_index] = tracks[tracker_index].track_id
-
-    return tracker_ids
-
-def ObjectDetection(video_path):
-    byte_tracker = BYTETracker(BYTETrackerArgs())
-    video_info = VideoInfo.from_video_path(video_path)
-    generator = get_video_frames_generator(video_path)
-    box_annotator = BoxAnnotator(thickness=5, text_thickness=5, text_scale=1)
-    #polygon
-    polygon = np.array([[200,300], [200,1420], [880, 1420], [880, 300]])
-    #zone
-    zone = supervision.PolygonZone(polygon=polygon, frame_resolution_wh=video_info.resolution_wh)
-    #zone annotator
-    zone_annotator = supervision.PolygonZoneAnnotator(zone=zone, color=Color.white(), thickness=4)
-    # open target video file
-    with VideoSink(TARGET_VIDEO_PATH, video_info) as sink:
-        # loop over video frames
-        for frame in tqdm(generator, total=video_info.total_frames):
-            results = model(frame)
-            detections = Detections(
-                xyxy=results[0].boxes.xyxy.cpu().numpy(),
-                confidence=results[0].boxes.conf.cpu().numpy(),
-                class_id=results[0].boxes.cls.cpu().numpy().astype(int)
-            )
-            # filtering out detections with unwanted classes
-            detections = detections[np.isin(detections.class_id, CLASS_ID)]
-            # tracking detections
-            tracks = byte_tracker.update(
-                output_results=detections2boxes(detections = detections),
-                img_info=frame.shape,
-                img_size=frame.shape
-            )
-            tracker_id = match_detections_with_tracks(detections=detections, tracks=tracks)
-            detections.tracker_id = np.array(tracker_id)
-            # filtering out detections without trackers
-            detections = detections[np.not_equal(detections.tracker_id, None)]
-            # format custom labels
-            labels = [
-                f"#{tracker_id} {classes[class_id]} {confidence:0.2f}"
-                for _, _, confidence, class_id, tracker_id
-                in detections
-            ]
-            t = np.unique(detections.class_id, return_counts =True)
-            # annotate and display frame
-            mask = zone.trigger(detections=detections)
-            detections_filtered = detections[mask]
-            t = np.unique(detections_filtered.class_id, return_counts =True)
-            for x in zip(t[0], t[1]):
-                frame = draw_text(background_color=Color.white(), scene=frame, text=' '.join((str(classes[x[0]]), ':', str(x[1]))), text_anchor=Point(x=500, y=1550 + (50 * x[0])), text_scale = 2, text_thickness = 4)
-            frame = box_annotator.annotate(scene=frame, detections=detections_filtered, labels=labels)
-            frame = zone_annotator.annotate(scene=frame)
-            sink.write_frame(frame)
-
-    return TARGET_VIDEO_PATH
-
-demo = gr.Interface(fn=ObjectDetection, inputs=gr.Video(), outputs=gr.Video(), examples=video_examples, cache_examples=False)
-demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/plzdontcry/dakubettergpt/src/assets/icons/CloneIcon.tsx b/spaces/plzdontcry/dakubettergpt/src/assets/icons/CloneIcon.tsx
deleted file mode 100644
index 62c0a173078decd1cc06d835b1a2cb9a5b2ae818..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/assets/icons/CloneIcon.tsx
+++ /dev/null
@@ -1,17 +0,0 @@
-import React from 'react';
-
-const CloneIcon = (props: React.SVGProps<SVGSVGElement>) => {
-  return (
-    <svg
-      viewBox='0 0 512 512'
-      fill='currentColor'
-      height='1em'
-      width='1em'
-      {...props}
-    >
-      <path d='M64 464h224c8.8 0 16-7.2 16-16v-64h48v64c0 35.3-28.7 64-64 64H64c-35.35 0-64-28.7-64-64V224c0-35.3 28.65-64 64-64h64v48H64c-8.84 0-16 7.2-16 16v224c0 8.8 7.16 16 16 16zm96-400c0-35.35 28.7-64 64-64h224c35.3 0 64 28.65 64 64v224c0 35.3-28.7 64-64 64H224c-35.3 0-64-28.7-64-64V64zm64 240h224c8.8 0 16-7.2 16-16V64c0-8.84-7.2-16-16-16H224c-8.8 0-16 7.16-16 16v224c0 8.8 7.2 16 16 16z' />
-    </svg>
-  );
-};
-
-export default CloneIcon;
diff --git a/spaces/poetrychor/CompVis-stable-diffusion-v1-4/README.md b/spaces/poetrychor/CompVis-stable-diffusion-v1-4/README.md
deleted file mode 100644
index e30eb559cc87528063ee7a05e3d895c223e3762d..0000000000000000000000000000000000000000
--- a/spaces/poetrychor/CompVis-stable-diffusion-v1-4/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CompVis Stable Diffusion V1 4
-emoji: 👁
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/__init__.py
deleted file mode 100644
index 0defb82e21f21da442706e25145b4ef0b59d576c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# -*- coding: utf-8 -*-
-try:
-    from ._version import version as __version__
-except ImportError:
-    __version__ = 'unknown'
-
-__all__ = ['easter', 'parser', 'relativedelta', 'rrule', 'tz',
-           'utils', 'zoneinfo']
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/middleware/httpsredirect.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/middleware/httpsredirect.py
deleted file mode 100644
index b7a3d8e078574e87dc6e345d621f5a596c3bdc1e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/middleware/httpsredirect.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from starlette.middleware.httpsredirect import (  # noqa
-    HTTPSRedirectMiddleware as HTTPSRedirectMiddleware,
-)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Example-e03fb3b4.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Example-e03fb3b4.js
deleted file mode 100644
index 48e7b95bd9350f1ba161151d5f8a24df38c3dad7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Example-e03fb3b4.js
+++ /dev/null
@@ -1,2 +0,0 @@
-const{SvelteComponent:c,append:u,attr:g,detach:o,element:r,init:d,insert:v,noop:f,safe_not_equal:y,set_data:m,text:b,toggle_class:_}=window.__gradio__svelte__internal;function p(a){let e,n;return{c(){e=r("pre"),n=b(a[0]),g(e,"class","svelte-agpzo2"),_(e,"table",a[1]==="table"),_(e,"gallery",a[1]==="gallery"),_(e,"selected",a[2])},m(t,l){v(t,e,l),u(e,n)},p(t,[l]){l&1&&m(n,t[0]),l&2&&_(e,"table",t[1]==="table"),l&2&&_(e,"gallery",t[1]==="gallery"),l&4&&_(e,"selected",t[2])},i:f,o:f,d(t){t&&o(e)}}}function h(a,e,n){let{value:t}=e,{type:l}=e,{selected:i=!1}=e;return a.$$set=s=>{"value"in s&&n(0,t=s.value),"type"in s&&n(1,l=s.type),"selected"in s&&n(2,i=s.selected)},[t,l,i]}class w extends c{constructor(e){super(),d(this,e,h,p,y,{value:0,type:1,selected:2})}}export{w as default};
-//# sourceMappingURL=Example-e03fb3b4.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/tests/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/object/test_indexing.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/object/test_indexing.py
deleted file mode 100644
index 87d3afc77d5560a90cbd39b7e869bcbd5ac8c41b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/object/test_indexing.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from decimal import Decimal
-
-import numpy as np
-import pytest
-
-from pandas._libs.missing import is_matching_na
-
-import pandas as pd
-from pandas import Index
-import pandas._testing as tm
-
-
-class TestGetIndexer:
-    @pytest.mark.parametrize(
-        "method,expected",
-        [
-            ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
-            ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
-        ],
-    )
-    def test_get_indexer_strings(self, method, expected):
-        index = Index(["b", "c"])
-        actual = index.get_indexer(["a", "b", "c", "d"], method=method)
-
-        tm.assert_numpy_array_equal(actual, expected)
-
-    def test_get_indexer_strings_raises(self):
-        index = Index(["b", "c"])
-
-        msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
-        with pytest.raises(TypeError, match=msg):
-            index.get_indexer(["a", "b", "c", "d"], method="nearest")
-
-        with pytest.raises(TypeError, match=msg):
-            index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
-
-        with pytest.raises(TypeError, match=msg):
-            index.get_indexer(
-                ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
-            )
-
-    def test_get_indexer_with_NA_values(
-        self, unique_nulls_fixture, unique_nulls_fixture2
-    ):
-        # GH#22332
-        # check pairwise, that no pair of na values
-        # is mangled
-        if unique_nulls_fixture is unique_nulls_fixture2:
-            return  # skip it, values are not unique
-        arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
-        index = Index(arr, dtype=object)
-        result = index.get_indexer(
-            [unique_nulls_fixture, unique_nulls_fixture2, "Unknown"]
-        )
-        expected = np.array([0, 1, -1], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-
-
-class TestGetIndexerNonUnique:
-    def test_get_indexer_non_unique_nas(self, nulls_fixture):
-        # even though this isn't non-unique, this should still work
-        index = Index(["a", "b", nulls_fixture])
-        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
-
-        expected_indexer = np.array([2], dtype=np.intp)
-        expected_missing = np.array([], dtype=np.intp)
-        tm.assert_numpy_array_equal(indexer, expected_indexer)
-        tm.assert_numpy_array_equal(missing, expected_missing)
-
-        # actually non-unique
-        index = Index(["a", nulls_fixture, "b", nulls_fixture])
-        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
-
-        expected_indexer = np.array([1, 3], dtype=np.intp)
-        tm.assert_numpy_array_equal(indexer, expected_indexer)
-        tm.assert_numpy_array_equal(missing, expected_missing)
-
-        # matching-but-not-identical nans
-        if is_matching_na(nulls_fixture, float("NaN")):
-            index = Index(["a", float("NaN"), "b", float("NaN")])
-            match_but_not_identical = True
-        elif is_matching_na(nulls_fixture, Decimal("NaN")):
-            index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")])
-            match_but_not_identical = True
-        else:
-            match_but_not_identical = False
-
-        if match_but_not_identical:
-            indexer, missing = index.get_indexer_non_unique([nulls_fixture])
-
-            expected_indexer = np.array([1, 3], dtype=np.intp)
-            tm.assert_numpy_array_equal(indexer, expected_indexer)
-            tm.assert_numpy_array_equal(missing, expected_missing)
-
-    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
-    def test_get_indexer_non_unique_np_nats(self, np_nat_fixture, np_nat_fixture2):
-        expected_missing = np.array([], dtype=np.intp)
-        # matching-but-not-identical nats
-        if is_matching_na(np_nat_fixture, np_nat_fixture2):
-            # ensure nats are different objects
-            index = Index(
-                np.array(
-                    ["2021-10-02", np_nat_fixture.copy(), np_nat_fixture2.copy()],
-                    dtype=object,
-                ),
-                dtype=object,
-            )
-            # pass as index to prevent target from being casted to DatetimeIndex
-            indexer, missing = index.get_indexer_non_unique(
-                Index([np_nat_fixture], dtype=object)
-            )
-            expected_indexer = np.array([1, 2], dtype=np.intp)
-            tm.assert_numpy_array_equal(indexer, expected_indexer)
-            tm.assert_numpy_array_equal(missing, expected_missing)
-        # dt64nat vs td64nat
-        else:
-            try:
-                np_nat_fixture == np_nat_fixture2
-            except (TypeError, OverflowError):
-                # Numpy will raise on uncomparable types, like
-                # np.datetime64('NaT', 'Y') and np.datetime64('NaT', 'ps')
-                # https://github.com/numpy/numpy/issues/22762
-                return
-            index = Index(
-                np.array(
-                    [
-                        "2021-10-02",
-                        np_nat_fixture,
-                        np_nat_fixture2,
-                        np_nat_fixture,
-                        np_nat_fixture2,
-                    ],
-                    dtype=object,
-                ),
-                dtype=object,
-            )
-            # pass as index to prevent target from being casted to DatetimeIndex
-            indexer, missing = index.get_indexer_non_unique(
-                Index([np_nat_fixture], dtype=object)
-            )
-            expected_indexer = np.array([1, 3], dtype=np.intp)
-            tm.assert_numpy_array_equal(indexer, expected_indexer)
-            tm.assert_numpy_array_equal(missing, expected_missing)
-
-
-class TestSliceLocs:
-    @pytest.mark.parametrize(
-        "in_slice,expected",
-        [
-            # error: Slice index must be an integer or None
-            (pd.IndexSlice[::-1], "yxdcb"),
-            (pd.IndexSlice["b":"y":-1], ""),  # type: ignore[misc]
-            (pd.IndexSlice["b"::-1], "b"),  # type: ignore[misc]
-            (pd.IndexSlice[:"b":-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice[:"y":-1], "y"),  # type: ignore[misc]
-            (pd.IndexSlice["y"::-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice["y"::-4], "yb"),  # type: ignore[misc]
-            # absent labels
-            (pd.IndexSlice[:"a":-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice[:"a":-2], "ydb"),  # type: ignore[misc]
-            (pd.IndexSlice["z"::-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice["z"::-3], "yc"),  # type: ignore[misc]
-            (pd.IndexSlice["m"::-1], "dcb"),  # type: ignore[misc]
-            (pd.IndexSlice[:"m":-1], "yx"),  # type: ignore[misc]
-            (pd.IndexSlice["a":"a":-1], ""),  # type: ignore[misc]
-            (pd.IndexSlice["z":"z":-1], ""),  # type: ignore[misc]
-            (pd.IndexSlice["m":"m":-1], ""),  # type: ignore[misc]
-        ],
-    )
-    def test_slice_locs_negative_step(self, in_slice, expected):
-        index = Index(list("bcdxy"))
-
-        s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
-        result = index[s_start : s_stop : in_slice.step]
-        expected = Index(list(expected))
-        tm.assert_index_equal(result, expected)
-
-    def test_slice_locs_dup(self):
-        index = Index(["a", "a", "b", "c", "d", "d"])
-        assert index.slice_locs("a", "d") == (0, 6)
-        assert index.slice_locs(end="d") == (0, 6)
-        assert index.slice_locs("a", "c") == (0, 4)
-        assert index.slice_locs("b", "d") == (2, 6)
-
-        index2 = index[::-1]
-        assert index2.slice_locs("d", "a") == (0, 6)
-        assert index2.slice_locs(end="a") == (0, 6)
-        assert index2.slice_locs("d", "b") == (0, 4)
-        assert index2.slice_locs("c", "a") == (2, 6)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/timestamp/test_constructors.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/timestamp/test_constructors.py
deleted file mode 100644
index b65b34f7482608607196e361d7fd1613e55eb7e2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/timestamp/test_constructors.py
+++ /dev/null
@@ -1,910 +0,0 @@
-import calendar
-from datetime import (
-    date,
-    datetime,
-    timedelta,
-    timezone,
-)
-import zoneinfo
-
-import dateutil.tz
-from dateutil.tz import tzutc
-import numpy as np
-import pytest
-import pytz
-
-from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
-from pandas.compat import PY310
-from pandas.errors import OutOfBoundsDatetime
-
-from pandas import (
-    NA,
-    NaT,
-    Period,
-    Timedelta,
-    Timestamp,
-)
-
-
-class TestTimestampConstructors:
-    def test_construct_from_time_unit(self):
-        # GH#54097 only passing a time component, no date
-        ts = Timestamp("01:01:01.111")
-        assert ts.unit == "ms"
-
-    def test_weekday_but_no_day_raises(self):
-        # GH#52659
-        msg = "Parsing datetimes with weekday but no day information is not supported"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp("2023 Sept Thu")
-
-    def test_construct_from_string_invalid_raises(self):
-        # dateutil (weirdly) parses "200622-12-31" as
-        #  datetime(2022, 6, 20, 12, 0, tzinfo=tzoffset(None, -111600)
-        #  which besides being mis-parsed, is a tzoffset that will cause
-        #  str(ts) to raise ValueError.  Ensure we raise in the constructor
-        #  instead.
-        # see test_to_datetime_malformed_raise for analogous to_datetime test
-        with pytest.raises(ValueError, match="gives an invalid tzoffset"):
-            Timestamp("200622-12-31")
-
-    def test_constructor_str_infer_reso(self):
-        # non-iso8601 path
-
-        # _parse_delimited_date path
-        ts = Timestamp("01/30/2023")
-        assert ts.unit == "s"
-
-        # _parse_dateabbr_string path
-        ts = Timestamp("2015Q1")
-        assert ts.unit == "s"
-
-        # dateutil_parse path
-        ts = Timestamp("2016-01-01 1:30:01 PM")
-        assert ts.unit == "s"
-
-        ts = Timestamp("2016 June 3 15:25:01.345")
-        assert ts.unit == "ms"
-
-        ts = Timestamp("300-01-01")
-        assert ts.unit == "s"
-
-        ts = Timestamp("300 June 1:30:01.300")
-        assert ts.unit == "ms"
-
-    def test_constructor_from_iso8601_str_with_offset_reso(self):
-        # GH#49737
-        ts = Timestamp("2016-01-01 04:05:06-01:00")
-        assert ts.unit == "s"
-
-        ts = Timestamp("2016-01-01 04:05:06.000-01:00")
-        assert ts.unit == "ms"
-
-        ts = Timestamp("2016-01-01 04:05:06.000000-01:00")
-        assert ts.unit == "us"
-
-        ts = Timestamp("2016-01-01 04:05:06.000000001-01:00")
-        assert ts.unit == "ns"
-
-    def test_constructor_from_date_second_reso(self):
-        # GH#49034 constructing from a pydate object gets lowest supported
-        #  reso, i.e. seconds
-        obj = date(2012, 9, 1)
-        ts = Timestamp(obj)
-        assert ts.unit == "s"
-
-    @pytest.mark.parametrize("typ", [int, float])
-    def test_construct_from_int_float_with_unit_out_of_bound_raises(self, typ):
-        # GH#50870  make sure we get a OutOfBoundsDatetime instead of OverflowError
-        val = typ(150000000000000)
-
-        msg = f"cannot convert input {val} with the unit 'D'"
-        with pytest.raises(OutOfBoundsDatetime, match=msg):
-            Timestamp(val, unit="D")
-
-    @pytest.mark.parametrize("typ", [int, float])
-    def test_constructor_int_float_with_YM_unit(self, typ):
-        # GH#47266 avoid the conversions in cast_from_unit
-        val = typ(150)
-
-        ts = Timestamp(val, unit="Y")
-        expected = Timestamp("2120-01-01")
-        assert ts == expected
-
-        ts = Timestamp(val, unit="M")
-        expected = Timestamp("1982-07-01")
-        assert ts == expected
-
-    def test_constructor_float_not_round_with_YM_unit_deprecated(self):
-        # GH#47267 avoid the conversions in cast_from-unit
-
-        msg = "Conversion of non-round float with unit=[MY] is ambiguous"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(150.5, unit="Y")
-
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(150.5, unit="M")
-
-    def test_constructor_datetime64_with_tz(self):
-        # GH#42288, GH#24559
-        dt = np.datetime64("1970-01-01 05:00:00")
-        tzstr = "UTC+05:00"
-
-        # pre-2.0 this interpreted dt as a UTC time. in 2.0 this is treated
-        #  as a wall-time, consistent with DatetimeIndex behavior
-        ts = Timestamp(dt, tz=tzstr)
-
-        alt = Timestamp(dt).tz_localize(tzstr)
-        assert ts == alt
-        assert ts.hour == 5
-
-    def test_constructor(self):
-        base_str = "2014-07-01 09:00"
-        base_dt = datetime(2014, 7, 1, 9)
-        base_expected = 1_404_205_200_000_000_000
-
-        # confirm base representation is correct
-        assert calendar.timegm(base_dt.timetuple()) * 1_000_000_000 == base_expected
-
-        tests = [
-            (base_str, base_dt, base_expected),
-            (
-                "2014-07-01 10:00",
-                datetime(2014, 7, 1, 10),
-                base_expected + 3600 * 1_000_000_000,
-            ),
-            (
-                "2014-07-01 09:00:00.000008000",
-                datetime(2014, 7, 1, 9, 0, 0, 8),
-                base_expected + 8000,
-            ),
-            (
-                "2014-07-01 09:00:00.000000005",
-                Timestamp("2014-07-01 09:00:00.000000005"),
-                base_expected + 5,
-            ),
-        ]
-
-        timezones = [
-            (None, 0),
-            ("UTC", 0),
-            (pytz.utc, 0),
-            ("Asia/Tokyo", 9),
-            ("US/Eastern", -4),
-            ("dateutil/US/Pacific", -7),
-            (pytz.FixedOffset(-180), -3),
-            (dateutil.tz.tzoffset(None, 18000), 5),
-        ]
-
-        for date_str, date_obj, expected in tests:
-            for result in [Timestamp(date_str), Timestamp(date_obj)]:
-                result = result.as_unit("ns")  # test originally written before non-nano
-                # only with timestring
-                assert result.as_unit("ns")._value == expected
-
-                # re-creation shouldn't affect to internal value
-                result = Timestamp(result)
-                assert result.as_unit("ns")._value == expected
-
-            # with timezone
-            for tz, offset in timezones:
-                for result in [Timestamp(date_str, tz=tz), Timestamp(date_obj, tz=tz)]:
-                    result = result.as_unit(
-                        "ns"
-                    )  # test originally written before non-nano
-                    expected_tz = expected - offset * 3600 * 1_000_000_000
-                    assert result.as_unit("ns")._value == expected_tz
-
-                    # should preserve tz
-                    result = Timestamp(result)
-                    assert result.as_unit("ns")._value == expected_tz
-
-                    # should convert to UTC
-                    if tz is not None:
-                        result = Timestamp(result).tz_convert("UTC")
-                    else:
-                        result = Timestamp(result, tz="UTC")
-                    expected_utc = expected - offset * 3600 * 1_000_000_000
-                    assert result.as_unit("ns")._value == expected_utc
-
-    def test_constructor_with_stringoffset(self):
-        # GH 7833
-        base_str = "2014-07-01 11:00:00+02:00"
-        base_dt = datetime(2014, 7, 1, 9)
-        base_expected = 1_404_205_200_000_000_000
-
-        # confirm base representation is correct
-        assert calendar.timegm(base_dt.timetuple()) * 1_000_000_000 == base_expected
-
-        tests = [
-            (base_str, base_expected),
-            ("2014-07-01 12:00:00+02:00", base_expected + 3600 * 1_000_000_000),
-            ("2014-07-01 11:00:00.000008000+02:00", base_expected + 8000),
-            ("2014-07-01 11:00:00.000000005+02:00", base_expected + 5),
-        ]
-
-        timezones = [
-            (None, 0),
-            ("UTC", 0),
-            (pytz.utc, 0),
-            ("Asia/Tokyo", 9),
-            ("US/Eastern", -4),
-            ("dateutil/US/Pacific", -7),
-            (pytz.FixedOffset(-180), -3),
-            (dateutil.tz.tzoffset(None, 18000), 5),
-        ]
-
-        for date_str, expected in tests:
-            for result in [Timestamp(date_str)]:
-                # only with timestring
-                assert result.as_unit("ns")._value == expected
-
-                # re-creation shouldn't affect to internal value
-                result = Timestamp(result)
-                assert result.as_unit("ns")._value == expected
-
-            # with timezone
-            for tz, offset in timezones:
-                result = Timestamp(date_str, tz=tz)
-                expected_tz = expected
-                assert result.as_unit("ns")._value == expected_tz
-
-                # should preserve tz
-                result = Timestamp(result)
-                assert result.as_unit("ns")._value == expected_tz
-
-                # should convert to UTC
-                result = Timestamp(result).tz_convert("UTC")
-                expected_utc = expected
-                assert result.as_unit("ns")._value == expected_utc
-
-        # This should be 2013-11-01 05:00 in UTC
-        # converted to Chicago tz
-        result = Timestamp("2013-11-01 00:00:00-0500", tz="America/Chicago")
-        assert result._value == Timestamp("2013-11-01 05:00")._value
-        expected = "Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago')"
-        assert repr(result) == expected
-        assert result == eval(repr(result))
-
-        # This should be 2013-11-01 05:00 in UTC
-        # converted to Tokyo tz (+09:00)
-        result = Timestamp("2013-11-01 00:00:00-0500", tz="Asia/Tokyo")
-        assert result._value == Timestamp("2013-11-01 05:00")._value
-        expected = "Timestamp('2013-11-01 14:00:00+0900', tz='Asia/Tokyo')"
-        assert repr(result) == expected
-        assert result == eval(repr(result))
-
-        # GH11708
-        # This should be 2015-11-18 10:00 in UTC
-        # converted to Asia/Katmandu
-        result = Timestamp("2015-11-18 15:45:00+05:45", tz="Asia/Katmandu")
-        assert result._value == Timestamp("2015-11-18 10:00")._value
-        expected = "Timestamp('2015-11-18 15:45:00+0545', tz='Asia/Katmandu')"
-        assert repr(result) == expected
-        assert result == eval(repr(result))
-
-        # This should be 2015-11-18 10:00 in UTC
-        # converted to Asia/Kolkata
-        result = Timestamp("2015-11-18 15:30:00+05:30", tz="Asia/Kolkata")
-        assert result._value == Timestamp("2015-11-18 10:00")._value
-        expected = "Timestamp('2015-11-18 15:30:00+0530', tz='Asia/Kolkata')"
-        assert repr(result) == expected
-        assert result == eval(repr(result))
-
-    def test_constructor_invalid(self):
-        msg = "Cannot convert input"
-        with pytest.raises(TypeError, match=msg):
-            Timestamp(slice(2))
-        msg = "Cannot convert Period"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(Period("1000-01-01"))
-
-    def test_constructor_invalid_tz(self):
-        # GH#17690
-        msg = (
-            "Argument 'tzinfo' has incorrect type "
-            r"\(expected datetime.tzinfo, got str\)"
-        )
-        with pytest.raises(TypeError, match=msg):
-            Timestamp("2017-10-22", tzinfo="US/Eastern")
-
-        msg = "at most one of"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp("2017-10-22", tzinfo=pytz.utc, tz="UTC")
-
-        msg = "Cannot pass a date attribute keyword argument when passing a date string"
-        with pytest.raises(ValueError, match=msg):
-            # GH#5168
-            # case where user tries to pass tz as an arg, not kwarg, gets
-            # interpreted as `year`
-            Timestamp("2012-01-01", "US/Pacific")
-
-    def test_constructor_strptime(self):
-        # GH25016
-        # Test support for Timestamp.strptime
-        fmt = "%Y%m%d-%H%M%S-%f%z"
-        ts = "20190129-235348-000001+0000"
-        msg = r"Timestamp.strptime\(\) is not implemented"
-        with pytest.raises(NotImplementedError, match=msg):
-            Timestamp.strptime(ts, fmt)
-
-    def test_constructor_tz_or_tzinfo(self):
-        # GH#17943, GH#17690, GH#5168
-        stamps = [
-            Timestamp(year=2017, month=10, day=22, tz="UTC"),
-            Timestamp(year=2017, month=10, day=22, tzinfo=pytz.utc),
-            Timestamp(year=2017, month=10, day=22, tz=pytz.utc),
-            Timestamp(datetime(2017, 10, 22), tzinfo=pytz.utc),
-            Timestamp(datetime(2017, 10, 22), tz="UTC"),
-            Timestamp(datetime(2017, 10, 22), tz=pytz.utc),
-        ]
-        assert all(ts == stamps[0] for ts in stamps)
-
-    def test_constructor_positional_with_tzinfo(self):
-        # GH#31929
-        ts = Timestamp(2020, 12, 31, tzinfo=timezone.utc)
-        expected = Timestamp("2020-12-31", tzinfo=timezone.utc)
-        assert ts == expected
-
-    @pytest.mark.parametrize("kwd", ["nanosecond", "microsecond", "second", "minute"])
-    def test_constructor_positional_keyword_mixed_with_tzinfo(self, kwd, request):
-        # TODO: if we passed microsecond with a keyword we would mess up
-        #  xref GH#45307
-        if kwd != "nanosecond":
-            # nanosecond is keyword-only as of 2.0, others are not
-            mark = pytest.mark.xfail(reason="GH#45307")
-            request.node.add_marker(mark)
-
-        kwargs = {kwd: 4}
-        ts = Timestamp(2020, 12, 31, tzinfo=timezone.utc, **kwargs)
-
-        td_kwargs = {kwd + "s": 4}
-        td = Timedelta(**td_kwargs)
-        expected = Timestamp("2020-12-31", tz=timezone.utc) + td
-        assert ts == expected
-
-    def test_constructor_positional(self):
-        # see gh-10758
-        msg = (
-            "'NoneType' object cannot be interpreted as an integer"
-            if PY310
-            else "an integer is required"
-        )
-        with pytest.raises(TypeError, match=msg):
-            Timestamp(2000, 1)
-
-        msg = "month must be in 1..12"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(2000, 0, 1)
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(2000, 13, 1)
-
-        msg = "day is out of range for month"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(2000, 1, 0)
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(2000, 1, 32)
-
-        # see gh-11630
-        assert repr(Timestamp(2015, 11, 12)) == repr(Timestamp("20151112"))
-        assert repr(Timestamp(2015, 11, 12, 1, 2, 3, 999999)) == repr(
-            Timestamp("2015-11-12 01:02:03.999999")
-        )
-
-    def test_constructor_keyword(self):
-        # GH 10758
-        msg = "function missing required argument 'day'|Required argument 'day'"
-        with pytest.raises(TypeError, match=msg):
-            Timestamp(year=2000, month=1)
-
-        msg = "month must be in 1..12"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(year=2000, month=0, day=1)
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(year=2000, month=13, day=1)
-
-        msg = "day is out of range for month"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(year=2000, month=1, day=0)
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(year=2000, month=1, day=32)
-
-        assert repr(Timestamp(year=2015, month=11, day=12)) == repr(
-            Timestamp("20151112")
-        )
-
-        assert repr(
-            Timestamp(
-                year=2015,
-                month=11,
-                day=12,
-                hour=1,
-                minute=2,
-                second=3,
-                microsecond=999999,
-            )
-        ) == repr(Timestamp("2015-11-12 01:02:03.999999"))
-
-    def test_constructor_fromordinal(self):
-        base = datetime(2000, 1, 1)
-
-        ts = Timestamp.fromordinal(base.toordinal())
-        assert base == ts
-        assert base.toordinal() == ts.toordinal()
-
-        ts = Timestamp.fromordinal(base.toordinal(), tz="US/Eastern")
-        assert Timestamp("2000-01-01", tz="US/Eastern") == ts
-        assert base.toordinal() == ts.toordinal()
-
-        # GH#3042
-        dt = datetime(2011, 4, 16, 0, 0)
-        ts = Timestamp.fromordinal(dt.toordinal())
-        assert ts.to_pydatetime() == dt
-
-        # with a tzinfo
-        stamp = Timestamp("2011-4-16", tz="US/Eastern")
-        dt_tz = stamp.to_pydatetime()
-        ts = Timestamp.fromordinal(dt_tz.toordinal(), tz="US/Eastern")
-        assert ts.to_pydatetime() == dt_tz
-
-    @pytest.mark.parametrize(
-        "result",
-        [
-            Timestamp(datetime(2000, 1, 2, 3, 4, 5, 6), nanosecond=1),
-            Timestamp(
-                year=2000,
-                month=1,
-                day=2,
-                hour=3,
-                minute=4,
-                second=5,
-                microsecond=6,
-                nanosecond=1,
-            ),
-            Timestamp(
-                year=2000,
-                month=1,
-                day=2,
-                hour=3,
-                minute=4,
-                second=5,
-                microsecond=6,
-                nanosecond=1,
-                tz="UTC",
-            ),
-            Timestamp(2000, 1, 2, 3, 4, 5, 6, None, nanosecond=1),
-            Timestamp(2000, 1, 2, 3, 4, 5, 6, tz=pytz.UTC, nanosecond=1),
-        ],
-    )
-    def test_constructor_nanosecond(self, result):
-        # GH 18898
-        # As of 2.0 (GH 49416), nanosecond should not be accepted positionally
-        expected = Timestamp(datetime(2000, 1, 2, 3, 4, 5, 6), tz=result.tz)
-        expected = expected + Timedelta(nanoseconds=1)
-        assert result == expected
-
-    @pytest.mark.parametrize("z", ["Z0", "Z00"])
-    def test_constructor_invalid_Z0_isostring(self, z):
-        # GH 8910
-        msg = f"Unknown datetime string format, unable to parse: 2014-11-02 01:00{z}"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(f"2014-11-02 01:00{z}")
-
-    @pytest.mark.parametrize(
-        "arg",
-        [
-            "year",
-            "month",
-            "day",
-            "hour",
-            "minute",
-            "second",
-            "microsecond",
-            "nanosecond",
-        ],
-    )
-    def test_invalid_date_kwarg_with_string_input(self, arg):
-        kwarg = {arg: 1}
-        msg = "Cannot pass a date attribute keyword argument"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp("2010-10-10 12:59:59.999999999", **kwarg)
-
-    def test_out_of_bounds_integer_value(self):
-        # GH#26651 check that we raise OutOfBoundsDatetime, not OverflowError
-        msg = str(Timestamp.max._value * 2)
-        with pytest.raises(OutOfBoundsDatetime, match=msg):
-            Timestamp(Timestamp.max._value * 2)
-        msg = str(Timestamp.min._value * 2)
-        with pytest.raises(OutOfBoundsDatetime, match=msg):
-            Timestamp(Timestamp.min._value * 2)
-
-    def test_out_of_bounds_value(self):
-        one_us = np.timedelta64(1).astype("timedelta64[us]")
-
-        # By definition we can't go out of bounds in [ns], so we
-        # convert the datetime64s to [us] so we can go out of bounds
-        min_ts_us = np.datetime64(Timestamp.min).astype("M8[us]") + one_us
-        max_ts_us = np.datetime64(Timestamp.max).astype("M8[us]")
-
-        # No error for the min/max datetimes
-        Timestamp(min_ts_us)
-        Timestamp(max_ts_us)
-
-        # We used to raise on these before supporting non-nano
-        us_val = NpyDatetimeUnit.NPY_FR_us.value
-        assert Timestamp(min_ts_us - one_us)._creso == us_val
-        assert Timestamp(max_ts_us + one_us)._creso == us_val
-
-        # https://github.com/numpy/numpy/issues/22346 for why
-        #  we can't use the same construction as above with minute resolution
-
-        # too_low, too_high are the _just_ outside the range of M8[s]
-        too_low = np.datetime64("-292277022657-01-27T08:29", "m")
-        too_high = np.datetime64("292277026596-12-04T15:31", "m")
-
-        msg = "Out of bounds"
-        # One us less than the minimum is an error
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(too_low)
-
-        # One us more than the maximum is an error
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(too_high)
-
-    def test_out_of_bounds_string(self):
-        msg = "Cannot cast .* to unit='ns' without overflow"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp("1676-01-01").as_unit("ns")
-        with pytest.raises(ValueError, match=msg):
-            Timestamp("2263-01-01").as_unit("ns")
-
-        ts = Timestamp("2263-01-01")
-        assert ts.unit == "s"
-
-        ts = Timestamp("1676-01-01")
-        assert ts.unit == "s"
-
-    def test_barely_out_of_bounds(self):
-        # GH#19529
-        # GH#19382 close enough to bounds that dropping nanos would result
-        # in an in-bounds datetime
-        msg = "Out of bounds nanosecond timestamp: 2262-04-11 23:47:16"
-        with pytest.raises(OutOfBoundsDatetime, match=msg):
-            Timestamp("2262-04-11 23:47:16.854775808")
-
-    def test_bounds_with_different_units(self):
-        out_of_bounds_dates = ("1677-09-21", "2262-04-12")
-
-        time_units = ("D", "h", "m", "s", "ms", "us")
-
-        for date_string in out_of_bounds_dates:
-            for unit in time_units:
-                dt64 = np.datetime64(date_string, unit)
-                ts = Timestamp(dt64)
-                if unit in ["s", "ms", "us"]:
-                    # We can preserve the input unit
-                    assert ts._value == dt64.view("i8")
-                else:
-                    # we chose the closest unit that we _do_ support
-                    assert ts._creso == NpyDatetimeUnit.NPY_FR_s.value
-
-        # With more extreme cases, we can't even fit inside second resolution
-        info = np.iinfo(np.int64)
-        msg = "Out of bounds nanosecond timestamp:"
-        for value in [info.min + 1, info.max]:
-            for unit in ["D", "h", "m"]:
-                dt64 = np.datetime64(value, unit)
-                with pytest.raises(OutOfBoundsDatetime, match=msg):
-                    Timestamp(dt64)
-
-        in_bounds_dates = ("1677-09-23", "2262-04-11")
-
-        for date_string in in_bounds_dates:
-            for unit in time_units:
-                dt64 = np.datetime64(date_string, unit)
-                Timestamp(dt64)
-
-    @pytest.mark.parametrize("arg", ["001-01-01", "0001-01-01"])
-    def test_out_of_bounds_string_consistency(self, arg):
-        # GH 15829
-        msg = "Cannot cast 0001-01-01 00:00:00 to unit='ns' without overflow"
-        with pytest.raises(OutOfBoundsDatetime, match=msg):
-            Timestamp(arg).as_unit("ns")
-
-        ts = Timestamp(arg)
-        assert ts.unit == "s"
-        assert ts.year == ts.month == ts.day == 1
-
-    def test_min_valid(self):
-        # Ensure that Timestamp.min is a valid Timestamp
-        Timestamp(Timestamp.min)
-
-    def test_max_valid(self):
-        # Ensure that Timestamp.max is a valid Timestamp
-        Timestamp(Timestamp.max)
-
-    def test_now(self):
-        # GH#9000
-        ts_from_string = Timestamp("now")
-        ts_from_method = Timestamp.now()
-        ts_datetime = datetime.now()
-
-        ts_from_string_tz = Timestamp("now", tz="US/Eastern")
-        ts_from_method_tz = Timestamp.now(tz="US/Eastern")
-
-        # Check that the delta between the times is less than 1s (arbitrarily
-        # small)
-        delta = Timedelta(seconds=1)
-        assert abs(ts_from_method - ts_from_string) < delta
-        assert abs(ts_datetime - ts_from_method) < delta
-        assert abs(ts_from_method_tz - ts_from_string_tz) < delta
-        assert (
-            abs(
-                ts_from_string_tz.tz_localize(None)
-                - ts_from_method_tz.tz_localize(None)
-            )
-            < delta
-        )
-
-    def test_today(self):
-        ts_from_string = Timestamp("today")
-        ts_from_method = Timestamp.today()
-        ts_datetime = datetime.today()
-
-        ts_from_string_tz = Timestamp("today", tz="US/Eastern")
-        ts_from_method_tz = Timestamp.today(tz="US/Eastern")
-
-        # Check that the delta between the times is less than 1s (arbitrarily
-        # small)
-        delta = Timedelta(seconds=1)
-        assert abs(ts_from_method - ts_from_string) < delta
-        assert abs(ts_datetime - ts_from_method) < delta
-        assert abs(ts_from_method_tz - ts_from_string_tz) < delta
-        assert (
-            abs(
-                ts_from_string_tz.tz_localize(None)
-                - ts_from_method_tz.tz_localize(None)
-            )
-            < delta
-        )
-
-    @pytest.mark.parametrize("tz", [None, pytz.timezone("US/Pacific")])
-    def test_disallow_setting_tz(self, tz):
-        # GH 3746
-        ts = Timestamp("2010")
-        msg = "Cannot directly set timezone"
-        with pytest.raises(AttributeError, match=msg):
-            ts.tz = tz
-
-    @pytest.mark.parametrize("offset", ["+0300", "+0200"])
-    def test_construct_timestamp_near_dst(self, offset):
-        # GH 20854
-        expected = Timestamp(f"2016-10-30 03:00:00{offset}", tz="Europe/Helsinki")
-        result = Timestamp(expected).tz_convert("Europe/Helsinki")
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "arg", ["2013/01/01 00:00:00+09:00", "2013-01-01 00:00:00+09:00"]
-    )
-    def test_construct_with_different_string_format(self, arg):
-        # GH 12064
-        result = Timestamp(arg)
-        expected = Timestamp(datetime(2013, 1, 1), tz=pytz.FixedOffset(540))
-        assert result == expected
-
-    @pytest.mark.parametrize("box", [datetime, Timestamp])
-    def test_raise_tz_and_tzinfo_in_datetime_input(self, box):
-        # GH 23579
-        kwargs = {"year": 2018, "month": 1, "day": 1, "tzinfo": pytz.utc}
-        msg = "Cannot pass a datetime or Timestamp"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(box(**kwargs), tz="US/Pacific")
-        msg = "Cannot pass a datetime or Timestamp"
-        with pytest.raises(ValueError, match=msg):
-            Timestamp(box(**kwargs), tzinfo=pytz.timezone("US/Pacific"))
-
-    def test_dont_convert_dateutil_utc_to_pytz_utc(self):
-        result = Timestamp(datetime(2018, 1, 1), tz=tzutc())
-        expected = Timestamp(datetime(2018, 1, 1)).tz_localize(tzutc())
-        assert result == expected
-
-    def test_constructor_subclassed_datetime(self):
-        # GH 25851
-        # ensure that subclassed datetime works for
-        # Timestamp creation
-        class SubDatetime(datetime):
-            pass
-
-        data = SubDatetime(2000, 1, 1)
-        result = Timestamp(data)
-        expected = Timestamp(2000, 1, 1)
-        assert result == expected
-
-    def test_constructor_fromisocalendar(self):
-        # GH 30395
-        expected_timestamp = Timestamp("2000-01-03 00:00:00")
-        expected_stdlib = datetime.fromisocalendar(2000, 1, 1)
-        result = Timestamp.fromisocalendar(2000, 1, 1)
-        assert result == expected_timestamp
-        assert result == expected_stdlib
-        assert isinstance(result, Timestamp)
-
-
-def test_constructor_ambiguous_dst():
-    # GH 24329
-    # Make sure that calling Timestamp constructor
-    # on Timestamp created from ambiguous time
-    # doesn't change Timestamp.value
-    ts = Timestamp(1382835600000000000, tz="dateutil/Europe/London")
-    expected = ts._value
-    result = Timestamp(ts)._value
-    assert result == expected
-
-
-@pytest.mark.parametrize("epoch", [1552211999999999872, 1552211999999999999])
-def test_constructor_before_dst_switch(epoch):
-    # GH 31043
-    # Make sure that calling Timestamp constructor
-    # on time just before DST switch doesn't lead to
-    # nonexistent time or value change
-    ts = Timestamp(epoch, tz="dateutil/America/Los_Angeles")
-    result = ts.tz.dst(ts)
-    expected = timedelta(seconds=0)
-    assert Timestamp(ts)._value == epoch
-    assert result == expected
-
-
-def test_timestamp_constructor_identity():
-    # Test for #30543
-    expected = Timestamp("2017-01-01T12")
-    result = Timestamp(expected)
-    assert result is expected
-
-
-@pytest.mark.parametrize("kwargs", [{}, {"year": 2020}, {"year": 2020, "month": 1}])
-def test_constructor_missing_keyword(kwargs):
-    # GH 31200
-
-    # The exact error message of datetime() depends on its version
-    msg1 = r"function missing required argument '(year|month|day)' \(pos [123]\)"
-    msg2 = r"Required argument '(year|month|day)' \(pos [123]\) not found"
-    msg = "|".join([msg1, msg2])
-
-    with pytest.raises(TypeError, match=msg):
-        Timestamp(**kwargs)
-
-
-@pytest.mark.parametrize("nano", [-1, 1000])
-def test_timestamp_nano_range(nano):
-    # GH 48255
-    with pytest.raises(ValueError, match="nanosecond must be in 0..999"):
-        Timestamp(year=2022, month=1, day=1, nanosecond=nano)
-
-
-def test_non_nano_value():
-    # https://github.com/pandas-dev/pandas/issues/49076
-    result = Timestamp("1800-01-01", unit="s").value
-    # `.value` shows nanoseconds, even though unit is 's'
-    assert result == -5364662400000000000
-
-    # out-of-nanoseconds-bounds `.value` raises informative message
-    msg = (
-        r"Cannot convert Timestamp to nanoseconds without overflow. "
-        r"Use `.asm8.view\('i8'\)` to cast represent Timestamp in its "
-        r"own unit \(here, s\).$"
-    )
-    ts = Timestamp("0300-01-01")
-    with pytest.raises(OverflowError, match=msg):
-        ts.value
-    # check that the suggested workaround actually works
-    result = ts.asm8.view("i8")
-    assert result == -52700112000
-
-
-def test_timestamp_constructor_invalid_fold_raise():
-    # Test forGH #25057
-    # Valid fold values are only [None, 0, 1]
-    msg = "Valid values for the fold argument are None, 0, or 1."
-    with pytest.raises(ValueError, match=msg):
-        Timestamp(123, fold=2)
-
-
-def test_timestamp_constructor_pytz_fold_raise():
-    # Test for GH#25057
-    # pytz doesn't support fold. Check that we raise
-    # if fold is passed with pytz
-    msg = "pytz timezones do not support fold. Please use dateutil timezones."
-    tz = pytz.timezone("Europe/London")
-    with pytest.raises(ValueError, match=msg):
-        Timestamp(datetime(2019, 10, 27, 0, 30, 0, 0), tz=tz, fold=0)
-
-
-@pytest.mark.parametrize("fold", [0, 1])
-@pytest.mark.parametrize(
-    "ts_input",
-    [
-        1572136200000000000,
-        1572136200000000000.0,
-        np.datetime64(1572136200000000000, "ns"),
-        "2019-10-27 01:30:00+01:00",
-        datetime(2019, 10, 27, 0, 30, 0, 0, tzinfo=timezone.utc),
-    ],
-)
-def test_timestamp_constructor_fold_conflict(ts_input, fold):
-    # Test for GH#25057
-    # Check that we raise on fold conflict
-    msg = (
-        "Cannot pass fold with possibly unambiguous input: int, float, "
-        "numpy.datetime64, str, or timezone-aware datetime-like. "
-        "Pass naive datetime-like or build Timestamp from components."
-    )
-    with pytest.raises(ValueError, match=msg):
-        Timestamp(ts_input=ts_input, fold=fold)
-
-
-@pytest.mark.parametrize("tz", ["dateutil/Europe/London", None])
-@pytest.mark.parametrize("fold", [0, 1])
-def test_timestamp_constructor_retain_fold(tz, fold):
-    # Test for GH#25057
-    # Check that we retain fold
-    ts = Timestamp(year=2019, month=10, day=27, hour=1, minute=30, tz=tz, fold=fold)
-    result = ts.fold
-    expected = fold
-    assert result == expected
-
-
-try:
-    _tzs = [
-        "dateutil/Europe/London",
-        zoneinfo.ZoneInfo("Europe/London"),
-    ]
-except zoneinfo.ZoneInfoNotFoundError:
-    _tzs = ["dateutil/Europe/London"]
-
-
-@pytest.mark.parametrize("tz", _tzs)
-@pytest.mark.parametrize(
-    "ts_input,fold_out",
-    [
-        (1572136200000000000, 0),
-        (1572139800000000000, 1),
-        ("2019-10-27 01:30:00+01:00", 0),
-        ("2019-10-27 01:30:00+00:00", 1),
-        (datetime(2019, 10, 27, 1, 30, 0, 0, fold=0), 0),
-        (datetime(2019, 10, 27, 1, 30, 0, 0, fold=1), 1),
-    ],
-)
-def test_timestamp_constructor_infer_fold_from_value(tz, ts_input, fold_out):
-    # Test for GH#25057
-    # Check that we infer fold correctly based on timestamps since utc
-    # or strings
-    ts = Timestamp(ts_input, tz=tz)
-    result = ts.fold
-    expected = fold_out
-    assert result == expected
-
-
-@pytest.mark.parametrize("tz", ["dateutil/Europe/London"])
-@pytest.mark.parametrize(
-    "ts_input,fold,value_out",
-    [
-        (datetime(2019, 10, 27, 1, 30, 0, 0), 0, 1572136200000000),
-        (datetime(2019, 10, 27, 1, 30, 0, 0), 1, 1572139800000000),
-    ],
-)
-def test_timestamp_constructor_adjust_value_for_fold(tz, ts_input, fold, value_out):
-    # Test for GH#25057
-    # Check that we adjust value for fold correctly
-    # based on timestamps since utc
-    ts = Timestamp(ts_input, tz=tz, fold=fold)
-    result = ts._value
-    expected = value_out
-    assert result == expected
-
-
-@pytest.mark.parametrize("na_value", [None, np.nan, np.datetime64("NaT"), NaT, NA])
-def test_timestamp_constructor_na_value(na_value):
-    # GH45481
-    result = Timestamp(na_value)
-    expected = NaT
-    assert result is expected
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_replace.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_replace.py
deleted file mode 100644
index ec9db8c3830d62079541a519261a21039bcfdc4f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_replace.py
+++ /dev/null
@@ -1,770 +0,0 @@
-import re
-
-import numpy as np
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-from pandas.core.arrays import IntervalArray
-
-
-class TestSeriesReplace:
-    def test_replace_explicit_none(self):
-        # GH#36984 if the user explicitly passes value=None, give it to them
-        ser = pd.Series([0, 0, ""], dtype=object)
-        result = ser.replace("", None)
-        expected = pd.Series([0, 0, None], dtype=object)
-        tm.assert_series_equal(result, expected)
-
-        # Cast column 2 to object to avoid implicit cast when setting entry to ""
-        df = pd.DataFrame(np.zeros((3, 3))).astype({2: object})
-        df.iloc[2, 2] = ""
-        result = df.replace("", None)
-        expected = pd.DataFrame(
-            {
-                0: np.zeros(3),
-                1: np.zeros(3),
-                2: np.array([0.0, 0.0, None], dtype=object),
-            }
-        )
-        assert expected.iloc[2, 2] is None
-        tm.assert_frame_equal(result, expected)
-
-        # GH#19998 same thing with object dtype
-        ser = pd.Series([10, 20, 30, "a", "a", "b", "a"])
-        result = ser.replace("a", None)
-        expected = pd.Series([10, 20, 30, None, None, "b", None])
-        assert expected.iloc[-1] is None
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_noop_doesnt_downcast(self):
-        # GH#44498
-        ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object)
-        res = ser.replace({np.nan: None})  # should be a no-op
-        tm.assert_series_equal(res, ser)
-        assert res.dtype == object
-
-        # same thing but different calling convention
-        res = ser.replace(np.nan, None)
-        tm.assert_series_equal(res, ser)
-        assert res.dtype == object
-
-    def test_replace(self):
-        N = 100
-        ser = pd.Series(np.random.default_rng(2).standard_normal(N))
-        ser[0:4] = np.nan
-        ser[6:10] = 0
-
-        # replace list with a single value
-        return_value = ser.replace([np.nan], -1, inplace=True)
-        assert return_value is None
-
-        exp = ser.fillna(-1)
-        tm.assert_series_equal(ser, exp)
-
-        rs = ser.replace(0.0, np.nan)
-        ser[ser == 0.0] = np.nan
-        tm.assert_series_equal(rs, ser)
-
-        ser = pd.Series(
-            np.fabs(np.random.default_rng(2).standard_normal(N)),
-            tm.makeDateIndex(N),
-            dtype=object,
-        )
-        ser[:5] = np.nan
-        ser[6:10] = "foo"
-        ser[20:30] = "bar"
-
-        # replace list with a single value
-        rs = ser.replace([np.nan, "foo", "bar"], -1)
-
-        assert (rs[:5] == -1).all()
-        assert (rs[6:10] == -1).all()
-        assert (rs[20:30] == -1).all()
-        assert (pd.isna(ser[:5])).all()
-
-        # replace with different values
-        rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
-
-        assert (rs[:5] == -1).all()
-        assert (rs[6:10] == -2).all()
-        assert (rs[20:30] == -3).all()
-        assert (pd.isna(ser[:5])).all()
-
-        # replace with different values with 2 lists
-        rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
-        tm.assert_series_equal(rs, rs2)
-
-        # replace inplace
-        return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
-        assert return_value is None
-
-        assert (ser[:5] == -1).all()
-        assert (ser[6:10] == -1).all()
-        assert (ser[20:30] == -1).all()
-
-    def test_replace_nan_with_inf(self):
-        ser = pd.Series([np.nan, 0, np.inf])
-        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
-
-        ser = pd.Series([np.nan, 0, "foo", "bar", np.inf, None, pd.NaT])
-        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
-        filled = ser.copy()
-        filled[4] = 0
-        tm.assert_series_equal(ser.replace(np.inf, 0), filled)
-
-    def test_replace_listlike_value_listlike_target(self, datetime_series):
-        ser = pd.Series(datetime_series.index)
-        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
-
-        # malformed
-        msg = r"Replacement lists must match in length\. Expecting 3 got 2"
-        with pytest.raises(ValueError, match=msg):
-            ser.replace([1, 2, 3], [np.nan, 0])
-
-        # ser is dt64 so can't hold 1 or 2, so this replace is a no-op
-        result = ser.replace([1, 2], [np.nan, 0])
-        tm.assert_series_equal(result, ser)
-
-        ser = pd.Series([0, 1, 2, 3, 4])
-        result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])
-        tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0]))
-
-    def test_replace_gh5319(self):
-        # API change from 0.12?
-        # GH 5319
-        ser = pd.Series([0, np.nan, 2, 3, 4])
-        expected = ser.ffill()
-        msg = (
-            "Series.replace without 'value' and with non-dict-like "
-            "'to_replace' is deprecated"
-        )
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = ser.replace([np.nan])
-        tm.assert_series_equal(result, expected)
-
-        ser = pd.Series([0, np.nan, 2, 3, 4])
-        expected = ser.ffill()
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = ser.replace(np.nan)
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_datetime64(self):
-        # GH 5797
-        ser = pd.Series(pd.date_range("20130101", periods=5))
-        expected = ser.copy()
-        expected.loc[2] = pd.Timestamp("20120101")
-        result = ser.replace({pd.Timestamp("20130103"): pd.Timestamp("20120101")})
-        tm.assert_series_equal(result, expected)
-        result = ser.replace(pd.Timestamp("20130103"), pd.Timestamp("20120101"))
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_nat_with_tz(self):
-        # GH 11792: Test with replacing NaT in a list with tz data
-        ts = pd.Timestamp("2015/01/01", tz="UTC")
-        s = pd.Series([pd.NaT, pd.Timestamp("2015/01/01", tz="UTC")])
-        result = s.replace([np.nan, pd.NaT], pd.Timestamp.min)
-        expected = pd.Series([pd.Timestamp.min, ts], dtype=object)
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_timedelta_td64(self):
-        tdi = pd.timedelta_range(0, periods=5)
-        ser = pd.Series(tdi)
-
-        # Using a single dict argument means we go through replace_list
-        result = ser.replace({ser[1]: ser[3]})
-
-        expected = pd.Series([ser[0], ser[3], ser[2], ser[3], ser[4]])
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_with_single_list(self):
-        ser = pd.Series([0, 1, 2, 3, 4])
-        msg2 = (
-            "Series.replace without 'value' and with non-dict-like "
-            "'to_replace' is deprecated"
-        )
-        with tm.assert_produces_warning(FutureWarning, match=msg2):
-            result = ser.replace([1, 2, 3])
-        tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4]))
-
-        s = ser.copy()
-        with tm.assert_produces_warning(FutureWarning, match=msg2):
-            return_value = s.replace([1, 2, 3], inplace=True)
-        assert return_value is None
-        tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4]))
-
-        # make sure things don't get corrupted when fillna call fails
-        s = ser.copy()
-        msg = (
-            r"Invalid fill method\. Expecting pad \(ffill\) or backfill "
-            r"\(bfill\)\. Got crash_cymbal"
-        )
-        msg3 = "The 'method' keyword in Series.replace is deprecated"
-        with pytest.raises(ValueError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, match=msg3):
-                return_value = s.replace([1, 2, 3], inplace=True, method="crash_cymbal")
-            assert return_value is None
-        tm.assert_series_equal(s, ser)
-
-    def test_replace_mixed_types(self):
-        ser = pd.Series(np.arange(5), dtype="int64")
-
-        def check_replace(to_rep, val, expected):
-            sc = ser.copy()
-            result = ser.replace(to_rep, val)
-            return_value = sc.replace(to_rep, val, inplace=True)
-            assert return_value is None
-            tm.assert_series_equal(expected, result)
-            tm.assert_series_equal(expected, sc)
-
-        # 3.0 can still be held in our int64 series, so we do not upcast GH#44940
-        tr, v = [3], [3.0]
-        check_replace(tr, v, ser)
-        # Note this matches what we get with the scalars 3 and 3.0
-        check_replace(tr[0], v[0], ser)
-
-        # MUST upcast to float
-        e = pd.Series([0, 1, 2, 3.5, 4])
-        tr, v = [3], [3.5]
-        check_replace(tr, v, e)
-
-        # casts to object
-        e = pd.Series([0, 1, 2, 3.5, "a"])
-        tr, v = [3, 4], [3.5, "a"]
-        check_replace(tr, v, e)
-
-        # again casts to object
-        e = pd.Series([0, 1, 2, 3.5, pd.Timestamp("20130101")])
-        tr, v = [3, 4], [3.5, pd.Timestamp("20130101")]
-        check_replace(tr, v, e)
-
-        # casts to object
-        e = pd.Series([0, 1, 2, 3.5, True], dtype="object")
-        tr, v = [3, 4], [3.5, True]
-        check_replace(tr, v, e)
-
-        # test an object with dates + floats + integers + strings
-        dr = pd.Series(pd.date_range("1/1/2001", "1/10/2001", freq="D"))
-        result = dr.astype(object).replace([dr[0], dr[1], dr[2]], [1.0, 2, "a"])
-        expected = pd.Series([1.0, 2, "a"] + dr[3:].tolist(), dtype=object)
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_bool_with_string_no_op(self):
-        s = pd.Series([True, False, True])
-        result = s.replace("fun", "in-the-sun")
-        tm.assert_series_equal(s, result)
-
-    def test_replace_bool_with_string(self):
-        # nonexistent elements
-        s = pd.Series([True, False, True])
-        result = s.replace(True, "2u")
-        expected = pd.Series(["2u", False, "2u"])
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_bool_with_bool(self):
-        s = pd.Series([True, False, True])
-        result = s.replace(True, False)
-        expected = pd.Series([False] * len(s))
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_with_dict_with_bool_keys(self):
-        s = pd.Series([True, False, True])
-        result = s.replace({"asdf": "asdb", True: "yes"})
-        expected = pd.Series(["yes", False, "yes"])
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_Int_with_na(self, any_int_ea_dtype):
-        # GH 38267
-        result = pd.Series([0, None], dtype=any_int_ea_dtype).replace(0, pd.NA)
-        expected = pd.Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
-        tm.assert_series_equal(result, expected)
-        result = pd.Series([0, 1], dtype=any_int_ea_dtype).replace(0, pd.NA)
-        result.replace(1, pd.NA, inplace=True)
-        tm.assert_series_equal(result, expected)
-
-    def test_replace2(self):
-        N = 100
-        ser = pd.Series(
-            np.fabs(np.random.default_rng(2).standard_normal(N)),
-            tm.makeDateIndex(N),
-            dtype=object,
-        )
-        ser[:5] = np.nan
-        ser[6:10] = "foo"
-        ser[20:30] = "bar"
-
-        # replace list with a single value
-        rs = ser.replace([np.nan, "foo", "bar"], -1)
-
-        assert (rs[:5] == -1).all()
-        assert (rs[6:10] == -1).all()
-        assert (rs[20:30] == -1).all()
-        assert (pd.isna(ser[:5])).all()
-
-        # replace with different values
-        rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
-
-        assert (rs[:5] == -1).all()
-        assert (rs[6:10] == -2).all()
-        assert (rs[20:30] == -3).all()
-        assert (pd.isna(ser[:5])).all()
-
-        # replace with different values with 2 lists
-        rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
-        tm.assert_series_equal(rs, rs2)
-
-        # replace inplace
-        return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
-        assert return_value is None
-        assert (ser[:5] == -1).all()
-        assert (ser[6:10] == -1).all()
-        assert (ser[20:30] == -1).all()
-
-    @pytest.mark.parametrize("inplace", [True, False])
-    def test_replace_cascade(self, inplace):
-        # Test that replaced values are not replaced again
-        # GH #50778
-        ser = pd.Series([1, 2, 3])
-        expected = pd.Series([2, 3, 4])
-
-        res = ser.replace([1, 2, 3], [2, 3, 4], inplace=inplace)
-        if inplace:
-            tm.assert_series_equal(ser, expected)
-        else:
-            tm.assert_series_equal(res, expected)
-
-    def test_replace_with_dictlike_and_string_dtype(self, nullable_string_dtype):
-        # GH 32621, GH#44940
-        ser = pd.Series(["one", "two", np.nan], dtype=nullable_string_dtype)
-        expected = pd.Series(["1", "2", np.nan], dtype=nullable_string_dtype)
-        result = ser.replace({"one": "1", "two": "2"})
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_with_empty_dictlike(self):
-        # GH 15289
-        s = pd.Series(list("abcd"))
-        tm.assert_series_equal(s, s.replace({}))
-
-        empty_series = pd.Series([])
-        tm.assert_series_equal(s, s.replace(empty_series))
-
-    def test_replace_string_with_number(self):
-        # GH 15743
-        s = pd.Series([1, 2, 3])
-        result = s.replace("2", np.nan)
-        expected = pd.Series([1, 2, 3])
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_replacer_equals_replacement(self):
-        # GH 20656
-        # make sure all replacers are matching against original values
-        s = pd.Series(["a", "b"])
-        expected = pd.Series(["b", "a"])
-        result = s.replace({"a": "b", "b": "a"})
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_unicode_with_number(self):
-        # GH 15743
-        s = pd.Series([1, 2, 3])
-        result = s.replace("2", np.nan)
-        expected = pd.Series([1, 2, 3])
-        tm.assert_series_equal(expected, result)
-
-    def test_replace_mixed_types_with_string(self):
-        # Testing mixed
-        s = pd.Series([1, 2, 3, "4", 4, 5])
-        result = s.replace([2, "4"], np.nan)
-        expected = pd.Series([1, np.nan, 3, np.nan, 4, 5])
-        tm.assert_series_equal(expected, result)
-
-    @pytest.mark.parametrize(
-        "categorical, numeric",
-        [
-            (pd.Categorical(["A"], categories=["A", "B"]), [1]),
-            (pd.Categorical(["A", "B"], categories=["A", "B"]), [1, 2]),
-        ],
-    )
-    def test_replace_categorical(self, categorical, numeric):
-        # GH 24971, GH#23305
-        ser = pd.Series(categorical)
-        result = ser.replace({"A": 1, "B": 2})
-        expected = pd.Series(numeric).astype("category")
-        if 2 not in expected.cat.categories:
-            # i.e. categories should be [1, 2] even if there are no "B"s present
-            # GH#44940
-            expected = expected.cat.add_categories(2)
-        tm.assert_series_equal(expected, result)
-
-    @pytest.mark.parametrize(
-        "data, data_exp", [(["a", "b", "c"], ["b", "b", "c"]), (["a"], ["b"])]
-    )
-    def test_replace_categorical_inplace(self, data, data_exp):
-        # GH 53358
-        result = pd.Series(data, dtype="category")
-        result.replace(to_replace="a", value="b", inplace=True)
-        expected = pd.Series(data_exp, dtype="category")
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_categorical_single(self):
-        # GH 26988
-        dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
-        s = pd.Series(dti)
-        c = s.astype("category")
-
-        expected = c.copy()
-        expected = expected.cat.add_categories("foo")
-        expected[2] = "foo"
-        expected = expected.cat.remove_unused_categories()
-        assert c[2] != "foo"
-
-        result = c.replace(c[2], "foo")
-        tm.assert_series_equal(expected, result)
-        assert c[2] != "foo"  # ensure non-inplace call does not alter original
-
-        return_value = c.replace(c[2], "foo", inplace=True)
-        assert return_value is None
-        tm.assert_series_equal(expected, c)
-
-        first_value = c[0]
-        return_value = c.replace(c[1], c[0], inplace=True)
-        assert return_value is None
-        assert c[0] == c[1] == first_value  # test replacing with existing value
-
-    def test_replace_with_no_overflowerror(self):
-        # GH 25616
-        # casts to object without Exception from OverflowError
-        s = pd.Series([0, 1, 2, 3, 4])
-        result = s.replace([3], ["100000000000000000000"])
-        expected = pd.Series([0, 1, 2, "100000000000000000000", 4])
-        tm.assert_series_equal(result, expected)
-
-        s = pd.Series([0, "100000000000000000000", "100000000000000000001"])
-        result = s.replace(["100000000000000000000"], [1])
-        expected = pd.Series([0, 1, "100000000000000000001"])
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "ser, to_replace, exp",
-        [
-            ([1, 2, 3], {1: 2, 2: 3, 3: 4}, [2, 3, 4]),
-            (["1", "2", "3"], {"1": "2", "2": "3", "3": "4"}, ["2", "3", "4"]),
-        ],
-    )
-    def test_replace_commutative(self, ser, to_replace, exp):
-        # GH 16051
-        # DataFrame.replace() overwrites when values are non-numeric
-
-        series = pd.Series(ser)
-
-        expected = pd.Series(exp)
-        result = series.replace(to_replace)
-
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "ser, exp", [([1, 2, 3], [1, True, 3]), (["x", 2, 3], ["x", True, 3])]
-    )
-    def test_replace_no_cast(self, ser, exp):
-        # GH 9113
-        # BUG: replace int64 dtype with bool coerces to int64
-
-        series = pd.Series(ser)
-        result = series.replace(2, True)
-        expected = pd.Series(exp)
-
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_invalid_to_replace(self):
-        # GH 18634
-        # API: replace() should raise an exception if invalid argument is given
-        series = pd.Series(["a", "b", "c "])
-        msg = (
-            r"Expecting 'to_replace' to be either a scalar, array-like, "
-            r"dict or None, got invalid type.*"
-        )
-        msg2 = (
-            "Series.replace without 'value' and with non-dict-like "
-            "'to_replace' is deprecated"
-        )
-        with pytest.raises(TypeError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, match=msg2):
-                series.replace(lambda x: x.strip())
-
-    @pytest.mark.parametrize("frame", [False, True])
-    def test_replace_nonbool_regex(self, frame):
-        obj = pd.Series(["a", "b", "c "])
-        if frame:
-            obj = obj.to_frame()
-
-        msg = "'to_replace' must be 'None' if 'regex' is not a bool"
-        with pytest.raises(ValueError, match=msg):
-            obj.replace(to_replace=["a"], regex="foo")
-
-    @pytest.mark.parametrize("frame", [False, True])
-    def test_replace_empty_copy(self, frame):
-        obj = pd.Series([], dtype=np.float64)
-        if frame:
-            obj = obj.to_frame()
-
-        res = obj.replace(4, 5, inplace=True)
-        assert res is None
-
-        res = obj.replace(4, 5, inplace=False)
-        tm.assert_equal(res, obj)
-        assert res is not obj
-
-    def test_replace_only_one_dictlike_arg(self, fixed_now_ts):
-        # GH#33340
-
-        ser = pd.Series([1, 2, "A", fixed_now_ts, True])
-        to_replace = {0: 1, 2: "A"}
-        value = "foo"
-        msg = "Series.replace cannot use dict-like to_replace and non-None value"
-        with pytest.raises(ValueError, match=msg):
-            ser.replace(to_replace, value)
-
-        to_replace = 1
-        value = {0: "foo", 2: "bar"}
-        msg = "Series.replace cannot use dict-value and non-None to_replace"
-        with pytest.raises(ValueError, match=msg):
-            ser.replace(to_replace, value)
-
-    def test_replace_extension_other(self, frame_or_series):
-        # https://github.com/pandas-dev/pandas/issues/34530
-        obj = frame_or_series(pd.array([1, 2, 3], dtype="Int64"))
-        result = obj.replace("", "")  # no exception
-        # should not have changed dtype
-        tm.assert_equal(obj, result)
-
-    def _check_replace_with_method(self, ser: pd.Series):
-        df = ser.to_frame()
-
-        msg1 = "The 'method' keyword in Series.replace is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg1):
-            res = ser.replace(ser[1], method="pad")
-        expected = pd.Series([ser[0], ser[0]] + list(ser[2:]), dtype=ser.dtype)
-        tm.assert_series_equal(res, expected)
-
-        msg2 = "The 'method' keyword in DataFrame.replace is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg2):
-            res_df = df.replace(ser[1], method="pad")
-        tm.assert_frame_equal(res_df, expected.to_frame())
-
-        ser2 = ser.copy()
-        with tm.assert_produces_warning(FutureWarning, match=msg1):
-            res2 = ser2.replace(ser[1], method="pad", inplace=True)
-        assert res2 is None
-        tm.assert_series_equal(ser2, expected)
-
-        with tm.assert_produces_warning(FutureWarning, match=msg2):
-            res_df2 = df.replace(ser[1], method="pad", inplace=True)
-        assert res_df2 is None
-        tm.assert_frame_equal(df, expected.to_frame())
-
-    def test_replace_ea_dtype_with_method(self, any_numeric_ea_dtype):
-        arr = pd.array([1, 2, pd.NA, 4], dtype=any_numeric_ea_dtype)
-        ser = pd.Series(arr)
-
-        self._check_replace_with_method(ser)
-
-    @pytest.mark.parametrize("as_categorical", [True, False])
-    def test_replace_interval_with_method(self, as_categorical):
-        # in particular interval that can't hold NA
-
-        idx = pd.IntervalIndex.from_breaks(range(4))
-        ser = pd.Series(idx)
-        if as_categorical:
-            ser = ser.astype("category")
-
-        self._check_replace_with_method(ser)
-
-    @pytest.mark.parametrize("as_period", [True, False])
-    @pytest.mark.parametrize("as_categorical", [True, False])
-    def test_replace_datetimelike_with_method(self, as_period, as_categorical):
-        idx = pd.date_range("2016-01-01", periods=5, tz="US/Pacific")
-        if as_period:
-            idx = idx.tz_localize(None).to_period("D")
-
-        ser = pd.Series(idx)
-        ser.iloc[-2] = pd.NaT
-        if as_categorical:
-            ser = ser.astype("category")
-
-        self._check_replace_with_method(ser)
-
-    def test_replace_with_compiled_regex(self):
-        # https://github.com/pandas-dev/pandas/issues/35680
-        s = pd.Series(["a", "b", "c"])
-        regex = re.compile("^a$")
-        result = s.replace({regex: "z"}, regex=True)
-        expected = pd.Series(["z", "b", "c"])
-        tm.assert_series_equal(result, expected)
-
-    def test_pandas_replace_na(self):
-        # GH#43344
-        ser = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA], dtype="string")
-        regex_mapping = {
-            "AA": "CC",
-            "BB": "CC",
-            "EE": "CC",
-            "CC": "CC-REPL",
-        }
-        result = ser.replace(regex_mapping, regex=True)
-        exp = pd.Series(["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA], dtype="string")
-        tm.assert_series_equal(result, exp)
-
-    @pytest.mark.parametrize(
-        "dtype, input_data, to_replace, expected_data",
-        [
-            ("bool", [True, False], {True: False}, [False, False]),
-            ("int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
-            ("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
-            ("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
-            ("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
-            ("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]),
-            (
-                pd.IntervalDtype("int64"),
-                IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]),
-                {pd.Interval(1, 2): pd.Interval(10, 20)},
-                IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]),
-            ),
-            (
-                pd.IntervalDtype("float64"),
-                IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]),
-                {pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)},
-                IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]),
-            ),
-            (
-                pd.PeriodDtype("M"),
-                [pd.Period("2020-05", freq="M")],
-                {pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")},
-                [pd.Period("2020-06", freq="M")],
-            ),
-        ],
-    )
-    def test_replace_dtype(self, dtype, input_data, to_replace, expected_data):
-        # GH#33484
-        ser = pd.Series(input_data, dtype=dtype)
-        result = ser.replace(to_replace)
-        expected = pd.Series(expected_data, dtype=dtype)
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_string_dtype(self):
-        # GH#40732, GH#44940
-        ser = pd.Series(["one", "two", np.nan], dtype="string")
-        res = ser.replace({"one": "1", "two": "2"})
-        expected = pd.Series(["1", "2", np.nan], dtype="string")
-        tm.assert_series_equal(res, expected)
-
-        # GH#31644
-        ser2 = pd.Series(["A", np.nan], dtype="string")
-        res2 = ser2.replace("A", "B")
-        expected2 = pd.Series(["B", np.nan], dtype="string")
-        tm.assert_series_equal(res2, expected2)
-
-        ser3 = pd.Series(["A", "B"], dtype="string")
-        res3 = ser3.replace("A", pd.NA)
-        expected3 = pd.Series([pd.NA, "B"], dtype="string")
-        tm.assert_series_equal(res3, expected3)
-
-    def test_replace_string_dtype_list_to_replace(self):
-        # GH#41215, GH#44940
-        ser = pd.Series(["abc", "def"], dtype="string")
-        res = ser.replace(["abc", "any other string"], "xyz")
-        expected = pd.Series(["xyz", "def"], dtype="string")
-        tm.assert_series_equal(res, expected)
-
-    def test_replace_string_dtype_regex(self):
-        # GH#31644
-        ser = pd.Series(["A", "B"], dtype="string")
-        res = ser.replace(r".", "C", regex=True)
-        expected = pd.Series(["C", "C"], dtype="string")
-        tm.assert_series_equal(res, expected)
-
-    def test_replace_nullable_numeric(self):
-        # GH#40732, GH#44940
-
-        floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
-        assert floats.replace({1.0: 9}).dtype == floats.dtype
-        assert floats.replace(1.0, 9).dtype == floats.dtype
-        assert floats.replace({1.0: 9.0}).dtype == floats.dtype
-        assert floats.replace(1.0, 9.0).dtype == floats.dtype
-
-        res = floats.replace(to_replace=[1.0, 2.0], value=[9.0, 10.0])
-        assert res.dtype == floats.dtype
-
-        ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())
-        assert ints.replace({1: 9}).dtype == ints.dtype
-        assert ints.replace(1, 9).dtype == ints.dtype
-        assert ints.replace({1: 9.0}).dtype == ints.dtype
-        assert ints.replace(1, 9.0).dtype == ints.dtype
-
-        # nullable (for now) raises instead of casting
-        with pytest.raises(TypeError, match="Invalid value"):
-            ints.replace({1: 9.5})
-        with pytest.raises(TypeError, match="Invalid value"):
-            ints.replace(1, 9.5)
-
-    @pytest.mark.parametrize("regex", [False, True])
-    def test_replace_regex_dtype_series(self, regex):
-        # GH-48644
-        series = pd.Series(["0"])
-        expected = pd.Series([1])
-        result = series.replace(to_replace="0", value=1, regex=regex)
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_different_int_types(self, any_int_numpy_dtype):
-        # GH#45311
-        labs = pd.Series([1, 1, 1, 0, 0, 2, 2, 2], dtype=any_int_numpy_dtype)
-
-        maps = pd.Series([0, 2, 1], dtype=any_int_numpy_dtype)
-        map_dict = dict(zip(maps.values, maps.index))
-
-        result = labs.replace(map_dict)
-        expected = labs.replace({0: 0, 2: 1, 1: 2})
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("val", [2, np.nan, 2.0])
-    def test_replace_value_none_dtype_numeric(self, val):
-        # GH#48231
-        ser = pd.Series([1, val])
-        result = ser.replace(val, None)
-        expected = pd.Series([1, None], dtype=object)
-        tm.assert_series_equal(result, expected)
-
-    def test_replace_change_dtype_series(self):
-        # GH#25797
-        df = pd.DataFrame.from_dict({"Test": ["0.5", True, "0.6"]})
-        df["Test"] = df["Test"].replace([True], [np.nan])
-        expected = pd.DataFrame.from_dict({"Test": ["0.5", np.nan, "0.6"]})
-        tm.assert_frame_equal(df, expected)
-
-        df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
-        df["Test"] = df["Test"].replace([None], [np.nan])
-        tm.assert_frame_equal(df, expected)
-
-        df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
-        df["Test"] = df["Test"].fillna(np.nan)
-        tm.assert_frame_equal(df, expected)
-
-    @pytest.mark.parametrize("dtype", ["object", "Int64"])
-    def test_replace_na_in_obj_column(self, dtype):
-        # GH#47480
-        ser = pd.Series([0, 1, pd.NA], dtype=dtype)
-        expected = pd.Series([0, 2, pd.NA], dtype=dtype)
-        result = ser.replace(to_replace=1, value=2)
-        tm.assert_series_equal(result, expected)
-
-        ser.replace(to_replace=1, value=2, inplace=True)
-        tm.assert_series_equal(ser, expected)
-
-    @pytest.mark.parametrize("val", [0, 0.5])
-    def test_replace_numeric_column_with_na(self, val):
-        # GH#50758
-        ser = pd.Series([val, 1])
-        expected = pd.Series([val, pd.NA])
-        result = ser.replace(to_replace=1, value=pd.NA)
-        tm.assert_series_equal(result, expected)
-
-        ser.replace(to_replace=1, value=pd.NA, inplace=True)
-        tm.assert_series_equal(ser, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/cp949prober.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/cp949prober.py
deleted file mode 100644
index efd793abca4bf496001a4e46a67557e5a6f16bba..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/cp949prober.py
+++ /dev/null
@@ -1,49 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .chardistribution import EUCKRDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import CP949_SM_MODEL
-
-
-class CP949Prober(MultiByteCharSetProber):
-    def __init__(self):
-        super(CP949Prober, self).__init__()
-        self.coding_sm = CodingStateMachine(CP949_SM_MODEL)
-        # NOTE: CP949 is a superset of EUC-KR, so the distribution should be
-        #       not different.
-        self.distribution_analyzer = EUCKRDistributionAnalysis()
-        self.reset()
-
-    @property
-    def charset_name(self):
-        return "CP949"
-
-    @property
-    def language(self):
-        return "Korean"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/requests/compat.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/requests/compat.py
deleted file mode 100644
index f98cc910f9b1b042091628f32528e31b4a98238b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/requests/compat.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-requests.compat
-~~~~~~~~~~~~~~~
-
-This module handles import compatibility issues between Python 2 and
-Python 3.
-"""
-
-from pip._vendor import chardet
-
-import sys
-
-# -------
-# Pythons
-# -------
-
-# Syntax sugar.
-_ver = sys.version_info
-
-#: Python 2.x?
-is_py2 = (_ver[0] == 2)
-
-#: Python 3.x?
-is_py3 = (_ver[0] == 3)
-
-# Note: We've patched out simplejson support in pip because it prevents
-#       upgrading simplejson on Windows.
-# try:
-#     import simplejson as json
-# except (ImportError, SyntaxError):
-#     # simplejson does not support Python 3.2, it throws a SyntaxError
-#     # because of u'...' Unicode literals.
-import json
-
-# ---------
-# Specifics
-# ---------
-
-if is_py2:
-    from urllib import (
-        quote, unquote, quote_plus, unquote_plus, urlencode, getproxies,
-        proxy_bypass, proxy_bypass_environment, getproxies_environment)
-    from urlparse import urlparse, urlunparse, urljoin, urlsplit, urldefrag
-    from urllib2 import parse_http_list
-    import cookielib
-    from Cookie import Morsel
-    from StringIO import StringIO
-    # Keep OrderedDict for backwards compatibility.
-    from collections import Callable, Mapping, MutableMapping, OrderedDict
-
-    builtin_str = str
-    bytes = str
-    str = unicode
-    basestring = basestring
-    numeric_types = (int, long, float)
-    integer_types = (int, long)
-    JSONDecodeError = ValueError
-
-elif is_py3:
-    from urllib.parse import urlparse, urlunparse, urljoin, urlsplit, urlencode, quote, unquote, quote_plus, unquote_plus, urldefrag
-    from urllib.request import parse_http_list, getproxies, proxy_bypass, proxy_bypass_environment, getproxies_environment
-    from http import cookiejar as cookielib
-    from http.cookies import Morsel
-    from io import StringIO
-    # Keep OrderedDict for backwards compatibility.
-    from collections import OrderedDict
-    from collections.abc import Callable, Mapping, MutableMapping
-    from json import JSONDecodeError
-
-    builtin_str = str
-    str = str
-    bytes = bytes
-    basestring = (str, bytes)
-    numeric_types = (int, float)
-    integer_types = (int,)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/minecraft.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/minecraft.py
deleted file mode 100644
index 1da0a1ba7ff543717d30630b486848cfeb7fa092..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/minecraft.py
+++ /dev/null
@@ -1,394 +0,0 @@
-"""
-    pygments.lexers.minecraft
-    ~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for Minecraft related languages.
-    
-    SNBT. A data communication format used in Minecraft.
-    wiki: https://minecraft.fandom.com/wiki/NBT_format
-    
-    MCFunction. The Function file for Minecraft Data packs and Add-ons.
-    official: https://learn.microsoft.com/en-us/minecraft/creator/documents/functionsintroduction
-    wiki: https://minecraft.fandom.com/wiki/Function
-    
-    MCSchema. A kind of data Schema for Minecraft Add-on Development.
-    official: https://learn.microsoft.com/en-us/minecraft/creator/reference/content/schemasreference/
-    community example: https://www.mcbe-dev.net/addons/data-driven/manifest.html
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer, default, include, bygroups
-from pygments.token import Comment, Keyword, Literal, Name, Number, Operator, \
-    Punctuation, String, Text, Whitespace
-
-__all__ = ['SNBTLexer', 'MCFunctionLexer', 'MCSchemaLexer']
-
-
-class SNBTLexer(RegexLexer):
-    """Lexer for stringified NBT, a data format used in Minecraft
-
-    .. versionadded:: 2.12.0
-    """
-
-    name = "SNBT"
-    url = "https://minecraft.fandom.com/wiki/NBT_format"
-    aliases = ["snbt"]
-    filenames = ["*.snbt"]
-    mimetypes = ["text/snbt"]
-
-    tokens = {
-        "root": [
-            # We only look for the open bracket here since square bracket
-            #  is only valid in NBT pathing (which is a mcfunction idea).
-            (r"\{", Punctuation, "compound"),
-            (r"[^\{]+", Text),
-        ],
-
-        "whitespace": [
-            (r"\s+", Whitespace),
-        ],
-
-        "operators": [
-            (r"[,:;]", Punctuation),
-        ],
-
-        "literals": [
-            (r"(true|false)", Keyword.Constant),
-            (r"-?\d+[eE]-?\d+", Number.Float),
-            (r"-?\d*\.\d+[fFdD]?", Number.Float),
-            (r"-?\d+[bBsSlLfFdD]?", Number.Integer),
-
-            # Separate states for both types of strings so they don't entangle
-            (r'"', String.Double, "literals.string_double"),
-            (r"'", String.Single, "literals.string_single"),
-        ],
-        "literals.string_double": [
-            (r"\\.", String.Escape),
-            (r'[^\\"\n]+', String.Double),
-            (r'"', String.Double, "#pop"),
-        ],
-        "literals.string_single": [
-            (r"\\.", String.Escape),
-            (r"[^\\'\n]+", String.Single),
-            (r"'", String.Single, "#pop"),
-        ],
-
-        "compound": [
-            # this handles the unquoted snbt keys
-            #  note: stringified keys still work
-            (r"[A-Z_a-z]+", Name.Attribute),
-            include("operators"),
-            include("whitespace"),
-            include("literals"),
-            (r"\{", Punctuation, "#push"),
-            (r"\[", Punctuation, "list"),
-            (r"\}", Punctuation, "#pop"),
-        ],
-
-        "list": [
-            (r"[A-Z_a-z]+", Name.Attribute),
-            include("literals"),
-            include("operators"),
-            include("whitespace"),
-            (r"\[", Punctuation, "#push"),
-            (r"\{", Punctuation, "compound"),
-            (r"\]", Punctuation, "#pop"),
-        ],
-    }
-
-
-class MCFunctionLexer(RegexLexer):
-    """Lexer for the mcfunction scripting language used in Minecraft
-    Modelled somewhat after the `GitHub mcfunction grammar <https://github.com/Arcensoth/language-mcfunction>`_.
-
-    .. versionadded:: 2.12.0
-    """
-
-    name = "MCFunction"
-    url = "https://minecraft.fandom.com/wiki/Commands"
-    aliases = ["mcfunction", "mcf"]
-    filenames = ["*.mcfunction"]
-    mimetypes = ["text/mcfunction"]
-
-    # Used to denotate the start of a block comment, borrowed from Github's mcfunction
-    _block_comment_prefix = "[>!]"
-
-    tokens = {
-        "root": [
-            include("names"),
-            include("comments"),
-            include("literals"),
-            include("whitespace"),
-            include("property"),
-            include("operators"),
-            include("selectors"),
-        ],
-
-        "names": [
-            # The start of a command (either beginning of line OR after the run keyword)
-            #  We don't encode a list of keywords since mods, plugins, or even pre-processors
-            #  may add new commands, so we have a 'close-enough' regex which catches them.
-            (r"^(\s*)([a-z_]+)", bygroups(Whitespace, Name.Builtin)),
-            (r"(?<=run)\s+[a-z_]+", Name.Builtin),
-
-            # UUID
-            (r"\b[0-9a-fA-F]+(?:-[0-9a-fA-F]+){4}\b", Name.Variable),
-            include("resource-name"),
-            # normal command names and scoreboards
-            #  there's no way to know the differences unfortuntely
-            (r"[A-Za-z_][\w.#%$]+", Keyword.Constant),
-            (r"[#%$][\w.#%$]+", Name.Variable.Magic),
-        ],
-
-        "resource-name": [
-            # resource names have to be lowercase
-            (r"#?[a-z_][a-z_.-]*:[a-z0-9_./-]+", Name.Function),
-            # similar to above except optional `:``
-            #  a `/` must be present "somewhere"
-            (r"#?[a-z0-9_\.\-]+\/[a-z0-9_\.\-\/]+", Name.Function),
-        ],
-
-        "whitespace": [
-            (r"\s+", Whitespace),
-        ],
-
-        "comments": [
-            (rf"^\s*(#{_block_comment_prefix})", Comment.Multiline,
-             ("comments.block", "comments.block.emphasized")),
-            (r"#.*$", Comment.Single),
-        ],
-        "comments.block": [
-            (rf"^\s*#{_block_comment_prefix}", Comment.Multiline,
-             "comments.block.emphasized"),
-            (r"^\s*#", Comment.Multiline, "comments.block.normal"),
-            default("#pop"),
-        ],
-        "comments.block.normal": [
-            include("comments.block.special"),
-            (r"\S+", Comment.Multiline),
-            (r"\n", Text, "#pop"),
-            include("whitespace"),
-        ],
-        "comments.block.emphasized": [
-            include("comments.block.special"),
-            (r"\S+", String.Doc),
-            (r"\n", Text, "#pop"),
-            include("whitespace"),
-        ],
-        "comments.block.special": [
-            # Params
-            (r"@\S+", Name.Decorator),
-
-            include("resource-name"),
-
-            # Scoreboard player names
-            (r"[#%$][\w.#%$]+", Name.Variable.Magic),
-        ],
-
-        "operators": [
-            (r"[\-~%^?!+*<>\\/|&=.]", Operator),
-        ],
-
-        "literals": [
-            (r"\.\.", Literal),
-            (r"(true|false)", Keyword.Pseudo),
-
-            # these are like unquoted strings and appear in many places
-            (r"[A-Za-z_]+", Name.Variable.Class),
-
-            (r"[0-7]b", Number.Byte),
-            (r"[+-]?\d*\.?\d+([eE]?[+-]?\d+)?[df]?\b", Number.Float),
-            (r"[+-]?\d+\b", Number.Integer),
-            (r'"', String.Double, "literals.string-double"),
-            (r"'", String.Single, "literals.string-single"),
-        ],
-        "literals.string-double": [
-            (r"\\.", String.Escape),
-            (r'[^\\"\n]+', String.Double),
-            (r'"', String.Double, "#pop"),
-        ],
-        "literals.string-single": [
-            (r"\\.", String.Escape),
-            (r"[^\\'\n]+", String.Single),
-            (r"'", String.Single, "#pop"),
-        ],
-
-        "selectors": [
-            (r"@[a-z]", Name.Variable),
-        ],
- 
-
-        ## Generic Property Container
-        # There are several, differing instances where the language accepts
-        #  specific contained keys or contained key, value pairings.
-        # 
-        # Property Maps:
-        # - Starts with either `[` or `{`
-        # - Key separated by `:` or `=`
-        # - Deliminated by `,`
-        # 
-        # Property Lists:
-        # - Starts with `[`
-        # - Deliminated by `,`
-        # 
-        # For simplicity, these patterns match a generic, nestable structure
-        #  which follow a key, value pattern. For normal lists, there's only keys.
-        # This allow some "illegal" structures, but we'll accept those for
-        #  sake of simplicity
-        # 
-        # Examples:
-        # - `[facing=up, powered=true]` (blockstate)
-        # - `[name="hello world", nbt={key: 1b}]` (selector + nbt)
-        # - `[{"text": "value"}, "literal"]` (json)
-        ##
-        "property": [
-            # This state gets included in root and also several substates
-            # We do this to shortcut the starting of new properties
-            #  within other properties. Lists can have sublists and compounds
-            #  and values can start a new property (see the `difficult_1.txt`
-            #  snippet).
-            (r"\{", Punctuation, ("property.curly", "property.key")),
-            (r"\[", Punctuation, ("property.square", "property.key")),
-        ],
-        "property.curly": [
-            include("whitespace"),
-            include("property"),
-            (r"\}", Punctuation, "#pop"),
-        ],
-        "property.square": [
-            include("whitespace"),
-            include("property"),
-            (r"\]", Punctuation, "#pop"),
-
-            # lists can have sequences of items
-            (r",", Punctuation),
-        ],
-        "property.key": [
-            include("whitespace"),
-
-            # resource names (for advancements)
-            #  can omit `:` to default `minecraft:`
-            # must check if there is a future equals sign if `:` is in the name
-            (r"#?[a-z_][a-z_\.\-]*\:[a-z0-9_\.\-/]+(?=\s*\=)", Name.Attribute, "property.delimiter"),
-            (r"#?[a-z_][a-z0-9_\.\-/]+", Name.Attribute, "property.delimiter"),
-
-            # unquoted NBT key
-            (r"[A-Za-z_\-\+]+", Name.Attribute, "property.delimiter"),
-
-            # quoted JSON or NBT key
-            (r'"', Name.Attribute, "property.delimiter", "literals.string-double"),
-            (r"'", Name.Attribute, "property.delimiter", "literals.string-single"),
-
-            # index for a list
-            (r"-?\d+", Number.Integer, "property.delimiter"),
-
-            default("#pop"),
-        ],
-        "property.key.string-double": [
-            (r"\\.", String.Escape),
-            (r'[^\\"\n]+', Name.Attribute),
-            (r'"', Name.Attribute, "#pop"),
-        ],
-        "property.key.string-single": [
-            (r"\\.", String.Escape),
-            (r"[^\\'\n]+", Name.Attribute),
-            (r"'", Name.Attribute, "#pop"),
-        ],
-        "property.delimiter": [
-            include("whitespace"),
-            
-            (r"[:=]!?", Punctuation, "property.value"),
-            (r",", Punctuation),
-
-            default("#pop"),
-        ],
-        "property.value": [
-            include("whitespace"),
-
-            # unquoted resource names are valid literals here
-            (r"#?[a-z_][a-z_\.\-]*\:[a-z0-9_\.\-/]+", Name.Tag),
-            (r"#?[a-z_][a-z0-9_\.\-/]+", Name.Tag),
-
-            include("literals"),
-            include("property"),
-
-            default("#pop"),
-        ],
-    }
-
-
-class MCSchemaLexer(RegexLexer):
-    """Lexer for Minecraft Add-ons data Schemas, an interface structure standard used in Minecraft
-
-    .. versionadded:: 2.14.0
-    """
-    
-    name = 'MCSchema'
-    url = 'https://learn.microsoft.com/en-us/minecraft/creator/reference/content/schemasreference/'
-    aliases = ['mcschema']
-    filenames = ['*.mcschema']
-    mimetypes = ['text/mcschema']
-
-    tokens = {
-        'commentsandwhitespace': [
-            (r'\s+', Whitespace),
-            (r'//.*?$', Comment.Single),
-            (r'/\*.*?\*/', Comment.Multiline)
-        ],
-        'slashstartsregex': [
-            include('commentsandwhitespace'),
-            (r'/(\\.|[^[/\\\n]|\[(\\.|[^\]\\\n])*])+/'
-             r'([gimuysd]+\b|\B)', String.Regex, '#pop'),
-            (r'(?=/)', Text, ('#pop', 'badregex')),
-            default('#pop')
-        ],
-        'badregex': [
-            (r'\n', Whitespace, '#pop')
-        ],
-        'singlestring': [
-            (r'\\.', String.Escape),
-            (r"'", String.Single, '#pop'),
-            (r"[^\\']+", String.Single),
-        ],
-        'doublestring': [
-            (r'\\.', String.Escape),
-            (r'"', String.Double, '#pop'),
-            (r'[^\\"]+', String.Double),
-        ],
-        'root': [
-            (r'^(?=\s|/|<!--)', Text, 'slashstartsregex'),
-            include('commentsandwhitespace'),
-            
-            # keywords for optional word and field types
-            (r'(?<=: )opt', Operator.Word),
-            (r'(?<=\s)[\w-]*(?=(\s+"|\n))', Keyword.Declaration),
-            
-            # numeric literals
-            (r'0[bB][01]+', Number.Bin),
-            (r'0[oO]?[0-7]+', Number.Oct),
-            (r'0[xX][0-9a-fA-F]+', Number.Hex),
-            (r'\d+', Number.Integer),
-            (r'(\.\d+|\d+\.\d*|\d+)([eE][-+]?\d+)?', Number.Float),
-            
-            # possible punctuations
-            (r'\.\.\.|=>', Punctuation),
-            (r'\+\+|--|~|\?\?=?|\?|:|\\(?=\n)|'
-             r'(<<|>>>?|==?|!=?|(?:\*\*|\|\||&&|[-<>+*%&|^/]))=?', Operator, 'slashstartsregex'),
-            (r'[{(\[;,]', Punctuation, 'slashstartsregex'),
-            (r'[})\].]', Punctuation),
-            
-            # strings
-            (r"'", String.Single, 'singlestring'),
-            (r'"', String.Double, 'doublestring'),
-            
-            # title line
-            (r'[\w-]*?(?=:\{?\n)', String.Symbol),
-            # title line with a version code, formatted
-            # `major.minor.patch-prerelease+buildmeta`
-            (r'([\w-]*?)(:)(\d+)(?:(\.)(\d+)(?:(\.)(\d+)(?:(\-)((?:[^\W_]|-)*(?:\.(?:[^\W_]|-)*)*))?(?:(\+)((?:[^\W_]|-)+(?:\.(?:[^\W_]|-)+)*))?)?)?(?=:\{?\n)', bygroups(String.Symbol, Operator, Number.Integer, Operator, Number.Integer, Operator, Number.Integer, Operator, String, Operator, String)),
-            
-            (r'.*\n', Text),
-        ]
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/shell.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/shell.py
deleted file mode 100644
index eabf4ec942425f5cc0982e2aee394b7cdb0a67ab..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/shell.py
+++ /dev/null
@@ -1,920 +0,0 @@
-"""
-    pygments.lexers.shell
-    ~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for various shells.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import Lexer, RegexLexer, do_insertions, bygroups, \
-    include, default, this, using, words, line_re
-from pygments.token import Punctuation, Whitespace, \
-    Text, Comment, Operator, Keyword, Name, String, Number, Generic
-from pygments.util import shebang_matches
-
-__all__ = ['BashLexer', 'BashSessionLexer', 'TcshLexer', 'BatchLexer',
-           'SlurmBashLexer', 'MSDOSSessionLexer', 'PowerShellLexer',
-           'PowerShellSessionLexer', 'TcshSessionLexer', 'FishShellLexer',
-           'ExeclineLexer']
-
-
-class BashLexer(RegexLexer):
-    """
-    Lexer for (ba|k|z|)sh shell scripts.
-
-    .. versionadded:: 0.6
-    """
-
-    name = 'Bash'
-    aliases = ['bash', 'sh', 'ksh', 'zsh', 'shell']
-    filenames = ['*.sh', '*.ksh', '*.bash', '*.ebuild', '*.eclass',
-                 '*.exheres-0', '*.exlib', '*.zsh',
-                 '.bashrc', 'bashrc', '.bash_*', 'bash_*', 'zshrc', '.zshrc',
-                 '.kshrc', 'kshrc',
-                 'PKGBUILD']
-    mimetypes = ['application/x-sh', 'application/x-shellscript', 'text/x-shellscript']
-
-    tokens = {
-        'root': [
-            include('basic'),
-            (r'`', String.Backtick, 'backticks'),
-            include('data'),
-            include('interp'),
-        ],
-        'interp': [
-            (r'\$\(\(', Keyword, 'math'),
-            (r'\$\(', Keyword, 'paren'),
-            (r'\$\{#?', String.Interpol, 'curly'),
-            (r'\$[a-zA-Z_]\w*', Name.Variable),  # user variable
-            (r'\$(?:\d+|[#$?!_*@-])', Name.Variable),      # builtin
-            (r'\$', Text),
-        ],
-        'basic': [
-            (r'\b(if|fi|else|while|in|do|done|for|then|return|function|case|'
-             r'select|break|continue|until|esac|elif)(\s*)\b',
-             bygroups(Keyword, Whitespace)),
-            (r'\b(alias|bg|bind|builtin|caller|cd|command|compgen|'
-             r'complete|declare|dirs|disown|echo|enable|eval|exec|exit|'
-             r'export|false|fc|fg|getopts|hash|help|history|jobs|kill|let|'
-             r'local|logout|popd|printf|pushd|pwd|read|readonly|set|shift|'
-             r'shopt|source|suspend|test|time|times|trap|true|type|typeset|'
-             r'ulimit|umask|unalias|unset|wait)(?=[\s)`])',
-             Name.Builtin),
-            (r'\A#!.+\n', Comment.Hashbang),
-            (r'#.*\n', Comment.Single),
-            (r'\\[\w\W]', String.Escape),
-            (r'(\b\w+)(\s*)(\+?=)', bygroups(Name.Variable, Whitespace, Operator)),
-            (r'[\[\]{}()=]', Operator),
-            (r'<<<', Operator),  # here-string
-            (r'<<-?\s*(\'?)\\?(\w+)[\w\W]+?\2', String),
-            (r'&&|\|\|', Operator),
-        ],
-        'data': [
-            (r'(?s)\$?"(\\.|[^"\\$])*"', String.Double),
-            (r'"', String.Double, 'string'),
-            (r"(?s)\$'(\\\\|\\[0-7]+|\\.|[^'\\])*'", String.Single),
-            (r"(?s)'.*?'", String.Single),
-            (r';', Punctuation),
-            (r'&', Punctuation),
-            (r'\|', Punctuation),
-            (r'\s+', Whitespace),
-            (r'\d+\b', Number),
-            (r'[^=\s\[\]{}()$"\'`\\<&|;]+', Text),
-            (r'<', Text),
-        ],
-        'string': [
-            (r'"', String.Double, '#pop'),
-            (r'(?s)(\\\\|\\[0-7]+|\\.|[^"\\$])+', String.Double),
-            include('interp'),
-        ],
-        'curly': [
-            (r'\}', String.Interpol, '#pop'),
-            (r':-', Keyword),
-            (r'\w+', Name.Variable),
-            (r'[^}:"\'`$\\]+', Punctuation),
-            (r':', Punctuation),
-            include('root'),
-        ],
-        'paren': [
-            (r'\)', Keyword, '#pop'),
-            include('root'),
-        ],
-        'math': [
-            (r'\)\)', Keyword, '#pop'),
-            (r'\*\*|\|\||<<|>>|[-+*/%^|&<>]', Operator),
-            (r'\d+#[\da-zA-Z]+', Number),
-            (r'\d+#(?! )', Number),
-            (r'0[xX][\da-fA-F]+', Number),
-            (r'\d+', Number),
-            (r'[a-zA-Z_]\w*', Name.Variable),  # user variable
-            include('root'),
-        ],
-        'backticks': [
-            (r'`', String.Backtick, '#pop'),
-            include('root'),
-        ],
-    }
-
-    def analyse_text(text):
-        if shebang_matches(text, r'(ba|z|)sh'):
-            return 1
-        if text.startswith('$ '):
-            return 0.2
-
-
-class SlurmBashLexer(BashLexer):
-    """
-    Lexer for (ba|k|z|)sh Slurm scripts.
-
-    .. versionadded:: 2.4
-    """
-
-    name = 'Slurm'
-    aliases = ['slurm', 'sbatch']
-    filenames = ['*.sl']
-    mimetypes = []
-    EXTRA_KEYWORDS = {'srun'}
-
-    def get_tokens_unprocessed(self, text):
-        for index, token, value in BashLexer.get_tokens_unprocessed(self, text):
-            if token is Text and value in self.EXTRA_KEYWORDS:
-                yield index, Name.Builtin, value
-            elif token is Comment.Single and 'SBATCH' in value:
-                yield index, Keyword.Pseudo, value
-            else:
-                yield index, token, value
-
-
-class ShellSessionBaseLexer(Lexer):
-    """
-    Base lexer for shell sessions.
-
-    .. versionadded:: 2.1
-    """
-
-    _bare_continuation = False
-    _venv = re.compile(r'^(\([^)]*\))(\s*)')
-
-    def get_tokens_unprocessed(self, text):
-        innerlexer = self._innerLexerCls(**self.options)
-
-        pos = 0
-        curcode = ''
-        insertions = []
-        backslash_continuation = False
-
-        for match in line_re.finditer(text):
-            line = match.group()
-
-            venv_match = self._venv.match(line)
-            if venv_match:
-                venv = venv_match.group(1)
-                venv_whitespace = venv_match.group(2)
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt.VirtualEnv, venv)]))
-                if venv_whitespace:
-                    insertions.append((len(curcode),
-                                       [(0, Text, venv_whitespace)]))
-                line = line[venv_match.end():]
-
-            m = self._ps1rgx.match(line)
-            if m:
-                # To support output lexers (say diff output), the output
-                # needs to be broken by prompts whenever the output lexer
-                # changes.
-                if not insertions:
-                    pos = match.start()
-
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt, m.group(1))]))
-                curcode += m.group(2)
-                backslash_continuation = curcode.endswith('\\\n')
-            elif backslash_continuation:
-                if line.startswith(self._ps2):
-                    insertions.append((len(curcode),
-                                       [(0, Generic.Prompt,
-                                         line[:len(self._ps2)])]))
-                    curcode += line[len(self._ps2):]
-                else:
-                    curcode += line
-                backslash_continuation = curcode.endswith('\\\n')
-            elif self._bare_continuation and line.startswith(self._ps2):
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt,
-                                     line[:len(self._ps2)])]))
-                curcode += line[len(self._ps2):]
-            else:
-                if insertions:
-                    toks = innerlexer.get_tokens_unprocessed(curcode)
-                    for i, t, v in do_insertions(insertions, toks):
-                        yield pos+i, t, v
-                yield match.start(), Generic.Output, line
-                insertions = []
-                curcode = ''
-        if insertions:
-            for i, t, v in do_insertions(insertions,
-                                         innerlexer.get_tokens_unprocessed(curcode)):
-                yield pos+i, t, v
-
-
-class BashSessionLexer(ShellSessionBaseLexer):
-    """
-    Lexer for Bash shell sessions, i.e. command lines, including a
-    prompt, interspersed with output.
-
-    .. versionadded:: 1.1
-    """
-
-    name = 'Bash Session'
-    aliases = ['console', 'shell-session']
-    filenames = ['*.sh-session', '*.shell-session']
-    mimetypes = ['application/x-shell-session', 'application/x-sh-session']
-
-    _innerLexerCls = BashLexer
-    _ps1rgx = re.compile(
-        r'^((?:(?:\[.*?\])|(?:\(\S+\))?(?:| |sh\S*?|\w+\S+[@:]\S+(?:\s+\S+)' \
-        r'?|\[\S+[@:][^\n]+\].+))\s*[$#%]\s*)(.*\n?)')
-    _ps2 = '> '
-
-
-class BatchLexer(RegexLexer):
-    """
-    Lexer for the DOS/Windows Batch file format.
-
-    .. versionadded:: 0.7
-    """
-    name = 'Batchfile'
-    aliases = ['batch', 'bat', 'dosbatch', 'winbatch']
-    filenames = ['*.bat', '*.cmd']
-    mimetypes = ['application/x-dos-batch']
-
-    flags = re.MULTILINE | re.IGNORECASE
-
-    _nl = r'\n\x1a'
-    _punct = r'&<>|'
-    _ws = r'\t\v\f\r ,;=\xa0'
-    _nlws = r'\s\x1a\xa0,;='
-    _space = r'(?:(?:(?:\^[%s])?[%s])+)' % (_nl, _ws)
-    _keyword_terminator = (r'(?=(?:\^[%s]?)?[%s+./:[\\\]]|[%s%s(])' %
-                           (_nl, _ws, _nl, _punct))
-    _token_terminator = r'(?=\^?[%s]|[%s%s])' % (_ws, _punct, _nl)
-    _start_label = r'((?:(?<=^[^:])|^[^:]?)[%s]*)(:)' % _ws
-    _label = r'(?:(?:[^%s%s+:^]|\^[%s]?[\w\W])*)' % (_nlws, _punct, _nl)
-    _label_compound = r'(?:(?:[^%s%s+:^)]|\^[%s]?[^)])*)' % (_nlws, _punct, _nl)
-    _number = r'(?:-?(?:0[0-7]+|0x[\da-f]+|\d+)%s)' % _token_terminator
-    _opword = r'(?:equ|geq|gtr|leq|lss|neq)'
-    _string = r'(?:"[^%s"]*(?:"|(?=[%s])))' % (_nl, _nl)
-    _variable = (r'(?:(?:%%(?:\*|(?:~[a-z]*(?:\$[^:]+:)?)?\d|'
-                 r'[^%%:%s]+(?::(?:~(?:-?\d+)?(?:,(?:-?\d+)?)?|(?:[^%%%s^]|'
-                 r'\^[^%%%s])[^=%s]*=(?:[^%%%s^]|\^[^%%%s])*)?)?%%))|'
-                 r'(?:\^?![^!:%s]+(?::(?:~(?:-?\d+)?(?:,(?:-?\d+)?)?|(?:'
-                 r'[^!%s^]|\^[^!%s])[^=%s]*=(?:[^!%s^]|\^[^!%s])*)?)?\^?!))' %
-                 (_nl, _nl, _nl, _nl, _nl, _nl, _nl, _nl, _nl, _nl, _nl, _nl))
-    _core_token = r'(?:(?:(?:\^[%s]?)?[^"%s%s])+)' % (_nl, _nlws, _punct)
-    _core_token_compound = r'(?:(?:(?:\^[%s]?)?[^"%s%s)])+)' % (_nl, _nlws, _punct)
-    _token = r'(?:[%s]+|%s)' % (_punct, _core_token)
-    _token_compound = r'(?:[%s]+|%s)' % (_punct, _core_token_compound)
-    _stoken = (r'(?:[%s]+|(?:%s|%s|%s)+)' %
-               (_punct, _string, _variable, _core_token))
-
-    def _make_begin_state(compound, _core_token=_core_token,
-                          _core_token_compound=_core_token_compound,
-                          _keyword_terminator=_keyword_terminator,
-                          _nl=_nl, _punct=_punct, _string=_string,
-                          _space=_space, _start_label=_start_label,
-                          _stoken=_stoken, _token_terminator=_token_terminator,
-                          _variable=_variable, _ws=_ws):
-        rest = '(?:%s|%s|[^"%%%s%s%s])*' % (_string, _variable, _nl, _punct,
-                                            ')' if compound else '')
-        rest_of_line = r'(?:(?:[^%s^]|\^[%s]?[\w\W])*)' % (_nl, _nl)
-        rest_of_line_compound = r'(?:(?:[^%s^)]|\^[%s]?[^)])*)' % (_nl, _nl)
-        set_space = r'((?:(?:\^[%s]?)?[^\S\n])*)' % _nl
-        suffix = ''
-        if compound:
-            _keyword_terminator = r'(?:(?=\))|%s)' % _keyword_terminator
-            _token_terminator = r'(?:(?=\))|%s)' % _token_terminator
-            suffix = '/compound'
-        return [
-            ((r'\)', Punctuation, '#pop') if compound else
-             (r'\)((?=\()|%s)%s' % (_token_terminator, rest_of_line),
-              Comment.Single)),
-            (r'(?=%s)' % _start_label, Text, 'follow%s' % suffix),
-            (_space, using(this, state='text')),
-            include('redirect%s' % suffix),
-            (r'[%s]+' % _nl, Text),
-            (r'\(', Punctuation, 'root/compound'),
-            (r'@+', Punctuation),
-            (r'((?:for|if|rem)(?:(?=(?:\^[%s]?)?/)|(?:(?!\^)|'
-             r'(?<=m))(?:(?=\()|%s)))(%s?%s?(?:\^[%s]?)?/(?:\^[%s]?)?\?)' %
-             (_nl, _token_terminator, _space,
-              _core_token_compound if compound else _core_token, _nl, _nl),
-             bygroups(Keyword, using(this, state='text')),
-             'follow%s' % suffix),
-            (r'(goto%s)(%s(?:\^[%s]?)?/(?:\^[%s]?)?\?%s)' %
-             (_keyword_terminator, rest, _nl, _nl, rest),
-             bygroups(Keyword, using(this, state='text')),
-             'follow%s' % suffix),
-            (words(('assoc', 'break', 'cd', 'chdir', 'cls', 'color', 'copy',
-                    'date', 'del', 'dir', 'dpath', 'echo', 'endlocal', 'erase',
-                    'exit', 'ftype', 'keys', 'md', 'mkdir', 'mklink', 'move',
-                    'path', 'pause', 'popd', 'prompt', 'pushd', 'rd', 'ren',
-                    'rename', 'rmdir', 'setlocal', 'shift', 'start', 'time',
-                    'title', 'type', 'ver', 'verify', 'vol'),
-                   suffix=_keyword_terminator), Keyword, 'follow%s' % suffix),
-            (r'(call)(%s?)(:)' % _space,
-             bygroups(Keyword, using(this, state='text'), Punctuation),
-             'call%s' % suffix),
-            (r'call%s' % _keyword_terminator, Keyword),
-            (r'(for%s(?!\^))(%s)(/f%s)' %
-             (_token_terminator, _space, _token_terminator),
-             bygroups(Keyword, using(this, state='text'), Keyword),
-             ('for/f', 'for')),
-            (r'(for%s(?!\^))(%s)(/l%s)' %
-             (_token_terminator, _space, _token_terminator),
-             bygroups(Keyword, using(this, state='text'), Keyword),
-             ('for/l', 'for')),
-            (r'for%s(?!\^)' % _token_terminator, Keyword, ('for2', 'for')),
-            (r'(goto%s)(%s?)(:?)' % (_keyword_terminator, _space),
-             bygroups(Keyword, using(this, state='text'), Punctuation),
-             'label%s' % suffix),
-            (r'(if(?:(?=\()|%s)(?!\^))(%s?)((?:/i%s)?)(%s?)((?:not%s)?)(%s?)' %
-             (_token_terminator, _space, _token_terminator, _space,
-              _token_terminator, _space),
-             bygroups(Keyword, using(this, state='text'), Keyword,
-                      using(this, state='text'), Keyword,
-                      using(this, state='text')), ('(?', 'if')),
-            (r'rem(((?=\()|%s)%s?%s?.*|%s%s)' %
-             (_token_terminator, _space, _stoken, _keyword_terminator,
-              rest_of_line_compound if compound else rest_of_line),
-             Comment.Single, 'follow%s' % suffix),
-            (r'(set%s)%s(/a)' % (_keyword_terminator, set_space),
-             bygroups(Keyword, using(this, state='text'), Keyword),
-             'arithmetic%s' % suffix),
-            (r'(set%s)%s((?:/p)?)%s((?:(?:(?:\^[%s]?)?[^"%s%s^=%s]|'
-             r'\^[%s]?[^"=])+)?)((?:(?:\^[%s]?)?=)?)' %
-             (_keyword_terminator, set_space, set_space, _nl, _nl, _punct,
-              ')' if compound else '', _nl, _nl),
-             bygroups(Keyword, using(this, state='text'), Keyword,
-                      using(this, state='text'), using(this, state='variable'),
-                      Punctuation),
-             'follow%s' % suffix),
-            default('follow%s' % suffix)
-        ]
-
-    def _make_follow_state(compound, _label=_label,
-                           _label_compound=_label_compound, _nl=_nl,
-                           _space=_space, _start_label=_start_label,
-                           _token=_token, _token_compound=_token_compound,
-                           _ws=_ws):
-        suffix = '/compound' if compound else ''
-        state = []
-        if compound:
-            state.append((r'(?=\))', Text, '#pop'))
-        state += [
-            (r'%s([%s]*)(%s)(.*)' %
-             (_start_label, _ws, _label_compound if compound else _label),
-             bygroups(Text, Punctuation, Text, Name.Label, Comment.Single)),
-            include('redirect%s' % suffix),
-            (r'(?=[%s])' % _nl, Text, '#pop'),
-            (r'\|\|?|&&?', Punctuation, '#pop'),
-            include('text')
-        ]
-        return state
-
-    def _make_arithmetic_state(compound, _nl=_nl, _punct=_punct,
-                               _string=_string, _variable=_variable,
-                               _ws=_ws, _nlws=_nlws):
-        op = r'=+\-*/!~'
-        state = []
-        if compound:
-            state.append((r'(?=\))', Text, '#pop'))
-        state += [
-            (r'0[0-7]+', Number.Oct),
-            (r'0x[\da-f]+', Number.Hex),
-            (r'\d+', Number.Integer),
-            (r'[(),]+', Punctuation),
-            (r'([%s]|%%|\^\^)+' % op, Operator),
-            (r'(%s|%s|(\^[%s]?)?[^()%s%%\^"%s%s]|\^[%s]?%s)+' %
-             (_string, _variable, _nl, op, _nlws, _punct, _nlws,
-              r'[^)]' if compound else r'[\w\W]'),
-             using(this, state='variable')),
-            (r'(?=[\x00|&])', Text, '#pop'),
-            include('follow')
-        ]
-        return state
-
-    def _make_call_state(compound, _label=_label,
-                         _label_compound=_label_compound):
-        state = []
-        if compound:
-            state.append((r'(?=\))', Text, '#pop'))
-        state.append((r'(:?)(%s)' % (_label_compound if compound else _label),
-                      bygroups(Punctuation, Name.Label), '#pop'))
-        return state
-
-    def _make_label_state(compound, _label=_label,
-                          _label_compound=_label_compound, _nl=_nl,
-                          _punct=_punct, _string=_string, _variable=_variable):
-        state = []
-        if compound:
-            state.append((r'(?=\))', Text, '#pop'))
-        state.append((r'(%s?)((?:%s|%s|\^[%s]?%s|[^"%%^%s%s%s])*)' %
-                      (_label_compound if compound else _label, _string,
-                       _variable, _nl, r'[^)]' if compound else r'[\w\W]', _nl,
-                       _punct, r')' if compound else ''),
-                      bygroups(Name.Label, Comment.Single), '#pop'))
-        return state
-
-    def _make_redirect_state(compound,
-                             _core_token_compound=_core_token_compound,
-                             _nl=_nl, _punct=_punct, _stoken=_stoken,
-                             _string=_string, _space=_space,
-                             _variable=_variable, _nlws=_nlws):
-        stoken_compound = (r'(?:[%s]+|(?:%s|%s|%s)+)' %
-                           (_punct, _string, _variable, _core_token_compound))
-        return [
-            (r'((?:(?<=[%s])\d)?)(>>?&|<&)([%s]*)(\d)' %
-             (_nlws, _nlws),
-             bygroups(Number.Integer, Punctuation, Text, Number.Integer)),
-            (r'((?:(?<=[%s])(?<!\^[%s])\d)?)(>>?|<)(%s?%s)' %
-             (_nlws, _nl, _space, stoken_compound if compound else _stoken),
-             bygroups(Number.Integer, Punctuation, using(this, state='text')))
-        ]
-
-    tokens = {
-        'root': _make_begin_state(False),
-        'follow': _make_follow_state(False),
-        'arithmetic': _make_arithmetic_state(False),
-        'call': _make_call_state(False),
-        'label': _make_label_state(False),
-        'redirect': _make_redirect_state(False),
-        'root/compound': _make_begin_state(True),
-        'follow/compound': _make_follow_state(True),
-        'arithmetic/compound': _make_arithmetic_state(True),
-        'call/compound': _make_call_state(True),
-        'label/compound': _make_label_state(True),
-        'redirect/compound': _make_redirect_state(True),
-        'variable-or-escape': [
-            (_variable, Name.Variable),
-            (r'%%%%|\^[%s]?(\^!|[\w\W])' % _nl, String.Escape)
-        ],
-        'string': [
-            (r'"', String.Double, '#pop'),
-            (_variable, Name.Variable),
-            (r'\^!|%%', String.Escape),
-            (r'[^"%%^%s]+|[%%^]' % _nl, String.Double),
-            default('#pop')
-        ],
-        'sqstring': [
-            include('variable-or-escape'),
-            (r'[^%]+|%', String.Single)
-        ],
-        'bqstring': [
-            include('variable-or-escape'),
-            (r'[^%]+|%', String.Backtick)
-        ],
-        'text': [
-            (r'"', String.Double, 'string'),
-            include('variable-or-escape'),
-            (r'[^"%%^%s%s\d)]+|.' % (_nlws, _punct), Text)
-        ],
-        'variable': [
-            (r'"', String.Double, 'string'),
-            include('variable-or-escape'),
-            (r'[^"%%^%s]+|.' % _nl, Name.Variable)
-        ],
-        'for': [
-            (r'(%s)(in)(%s)(\()' % (_space, _space),
-             bygroups(using(this, state='text'), Keyword,
-                      using(this, state='text'), Punctuation), '#pop'),
-            include('follow')
-        ],
-        'for2': [
-            (r'\)', Punctuation),
-            (r'(%s)(do%s)' % (_space, _token_terminator),
-             bygroups(using(this, state='text'), Keyword), '#pop'),
-            (r'[%s]+' % _nl, Text),
-            include('follow')
-        ],
-        'for/f': [
-            (r'(")((?:%s|[^"])*?")([%s]*)(\))' % (_variable, _nlws),
-             bygroups(String.Double, using(this, state='string'), Text,
-                      Punctuation)),
-            (r'"', String.Double, ('#pop', 'for2', 'string')),
-            (r"('(?:%%%%|%s|[\w\W])*?')([%s]*)(\))" % (_variable, _nlws),
-             bygroups(using(this, state='sqstring'), Text, Punctuation)),
-            (r'(`(?:%%%%|%s|[\w\W])*?`)([%s]*)(\))' % (_variable, _nlws),
-             bygroups(using(this, state='bqstring'), Text, Punctuation)),
-            include('for2')
-        ],
-        'for/l': [
-            (r'-?\d+', Number.Integer),
-            include('for2')
-        ],
-        'if': [
-            (r'((?:cmdextversion|errorlevel)%s)(%s)(\d+)' %
-             (_token_terminator, _space),
-             bygroups(Keyword, using(this, state='text'),
-                      Number.Integer), '#pop'),
-            (r'(defined%s)(%s)(%s)' % (_token_terminator, _space, _stoken),
-             bygroups(Keyword, using(this, state='text'),
-                      using(this, state='variable')), '#pop'),
-            (r'(exist%s)(%s%s)' % (_token_terminator, _space, _stoken),
-             bygroups(Keyword, using(this, state='text')), '#pop'),
-            (r'(%s%s)(%s)(%s%s)' % (_number, _space, _opword, _space, _number),
-             bygroups(using(this, state='arithmetic'), Operator.Word,
-                      using(this, state='arithmetic')), '#pop'),
-            (_stoken, using(this, state='text'), ('#pop', 'if2')),
-        ],
-        'if2': [
-            (r'(%s?)(==)(%s?%s)' % (_space, _space, _stoken),
-             bygroups(using(this, state='text'), Operator,
-                      using(this, state='text')), '#pop'),
-            (r'(%s)(%s)(%s%s)' % (_space, _opword, _space, _stoken),
-             bygroups(using(this, state='text'), Operator.Word,
-                      using(this, state='text')), '#pop')
-        ],
-        '(?': [
-            (_space, using(this, state='text')),
-            (r'\(', Punctuation, ('#pop', 'else?', 'root/compound')),
-            default('#pop')
-        ],
-        'else?': [
-            (_space, using(this, state='text')),
-            (r'else%s' % _token_terminator, Keyword, '#pop'),
-            default('#pop')
-        ]
-    }
-
-
-class MSDOSSessionLexer(ShellSessionBaseLexer):
-    """
-    Lexer for MS DOS shell sessions, i.e. command lines, including a
-    prompt, interspersed with output.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'MSDOS Session'
-    aliases = ['doscon']
-    filenames = []
-    mimetypes = []
-
-    _innerLexerCls = BatchLexer
-    _ps1rgx = re.compile(r'^([^>]*>)(.*\n?)')
-    _ps2 = 'More? '
-
-
-class TcshLexer(RegexLexer):
-    """
-    Lexer for tcsh scripts.
-
-    .. versionadded:: 0.10
-    """
-
-    name = 'Tcsh'
-    aliases = ['tcsh', 'csh']
-    filenames = ['*.tcsh', '*.csh']
-    mimetypes = ['application/x-csh']
-
-    tokens = {
-        'root': [
-            include('basic'),
-            (r'\$\(', Keyword, 'paren'),
-            (r'\$\{#?', Keyword, 'curly'),
-            (r'`', String.Backtick, 'backticks'),
-            include('data'),
-        ],
-        'basic': [
-            (r'\b(if|endif|else|while|then|foreach|case|default|'
-             r'break|continue|goto|breaksw|end|switch|endsw)\s*\b',
-             Keyword),
-            (r'\b(alias|alloc|bg|bindkey|builtins|bye|caller|cd|chdir|'
-             r'complete|dirs|echo|echotc|eval|exec|exit|fg|filetest|getxvers|'
-             r'glob|getspath|hashstat|history|hup|inlib|jobs|kill|'
-             r'limit|log|login|logout|ls-F|migrate|newgrp|nice|nohup|notify|'
-             r'onintr|popd|printenv|pushd|rehash|repeat|rootnode|popd|pushd|'
-             r'set|shift|sched|setenv|setpath|settc|setty|setxvers|shift|'
-             r'source|stop|suspend|source|suspend|telltc|time|'
-             r'umask|unalias|uncomplete|unhash|universe|unlimit|unset|unsetenv|'
-             r'ver|wait|warp|watchlog|where|which)\s*\b',
-             Name.Builtin),
-            (r'#.*', Comment),
-            (r'\\[\w\W]', String.Escape),
-            (r'(\b\w+)(\s*)(=)', bygroups(Name.Variable, Text, Operator)),
-            (r'[\[\]{}()=]+', Operator),
-            (r'<<\s*(\'?)\\?(\w+)[\w\W]+?\2', String),
-            (r';', Punctuation),
-        ],
-        'data': [
-            (r'(?s)"(\\\\|\\[0-7]+|\\.|[^"\\])*"', String.Double),
-            (r"(?s)'(\\\\|\\[0-7]+|\\.|[^'\\])*'", String.Single),
-            (r'\s+', Text),
-            (r'[^=\s\[\]{}()$"\'`\\;#]+', Text),
-            (r'\d+(?= |\Z)', Number),
-            (r'\$#?(\w+|.)', Name.Variable),
-        ],
-        'curly': [
-            (r'\}', Keyword, '#pop'),
-            (r':-', Keyword),
-            (r'\w+', Name.Variable),
-            (r'[^}:"\'`$]+', Punctuation),
-            (r':', Punctuation),
-            include('root'),
-        ],
-        'paren': [
-            (r'\)', Keyword, '#pop'),
-            include('root'),
-        ],
-        'backticks': [
-            (r'`', String.Backtick, '#pop'),
-            include('root'),
-        ],
-    }
-
-
-class TcshSessionLexer(ShellSessionBaseLexer):
-    """
-    Lexer for Tcsh sessions, i.e. command lines, including a
-    prompt, interspersed with output.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'Tcsh Session'
-    aliases = ['tcshcon']
-    filenames = []
-    mimetypes = []
-
-    _innerLexerCls = TcshLexer
-    _ps1rgx = re.compile(r'^([^>]+>)(.*\n?)')
-    _ps2 = '? '
-
-
-class PowerShellLexer(RegexLexer):
-    """
-    For Windows PowerShell code.
-
-    .. versionadded:: 1.5
-    """
-    name = 'PowerShell'
-    aliases = ['powershell', 'pwsh', 'posh', 'ps1', 'psm1']
-    filenames = ['*.ps1', '*.psm1']
-    mimetypes = ['text/x-powershell']
-
-    flags = re.DOTALL | re.IGNORECASE | re.MULTILINE
-
-    keywords = (
-        'while validateset validaterange validatepattern validatelength '
-        'validatecount until trap switch return ref process param parameter in '
-        'if global: local: function foreach for finally filter end elseif else '
-        'dynamicparam do default continue cmdletbinding break begin alias \\? '
-        '% #script #private #local #global mandatory parametersetname position '
-        'valuefrompipeline valuefrompipelinebypropertyname '
-        'valuefromremainingarguments helpmessage try catch throw').split()
-
-    operators = (
-        'and as band bnot bor bxor casesensitive ccontains ceq cge cgt cle '
-        'clike clt cmatch cne cnotcontains cnotlike cnotmatch contains '
-        'creplace eq exact f file ge gt icontains ieq ige igt ile ilike ilt '
-        'imatch ine inotcontains inotlike inotmatch ireplace is isnot le like '
-        'lt match ne not notcontains notlike notmatch or regex replace '
-        'wildcard').split()
-
-    verbs = (
-        'write where watch wait use update unregister unpublish unprotect '
-        'unlock uninstall undo unblock trace test tee take sync switch '
-        'suspend submit stop step start split sort skip show set send select '
-        'search scroll save revoke resume restore restart resolve resize '
-        'reset request repair rename remove register redo receive read push '
-        'publish protect pop ping out optimize open new move mount merge '
-        'measure lock limit join invoke install initialize import hide group '
-        'grant get format foreach find export expand exit enter enable edit '
-        'dismount disconnect disable deny debug cxnew copy convertto '
-        'convertfrom convert connect confirm compress complete compare close '
-        'clear checkpoint block backup assert approve aggregate add').split()
-
-    aliases_ = (
-        'ac asnp cat cd cfs chdir clc clear clhy cli clp cls clv cnsn '
-        'compare copy cp cpi cpp curl cvpa dbp del diff dir dnsn ebp echo epal '
-        'epcsv epsn erase etsn exsn fc fhx fl foreach ft fw gal gbp gc gci gcm '
-        'gcs gdr ghy gi gjb gl gm gmo gp gps gpv group gsn gsnp gsv gu gv gwmi '
-        'h history icm iex ihy ii ipal ipcsv ipmo ipsn irm ise iwmi iwr kill lp '
-        'ls man md measure mi mount move mp mv nal ndr ni nmo npssc nsn nv ogv '
-        'oh popd ps pushd pwd r rbp rcjb rcsn rd rdr ren ri rjb rm rmdir rmo '
-        'rni rnp rp rsn rsnp rujb rv rvpa rwmi sajb sal saps sasv sbp sc select '
-        'set shcm si sl sleep sls sort sp spjb spps spsv start sujb sv swmi tee '
-        'trcm type wget where wjb write').split()
-
-    commenthelp = (
-        'component description example externalhelp forwardhelpcategory '
-        'forwardhelptargetname functionality inputs link '
-        'notes outputs parameter remotehelprunspace role synopsis').split()
-
-    tokens = {
-        'root': [
-            # we need to count pairs of parentheses for correct highlight
-            # of '$(...)' blocks in strings
-            (r'\(', Punctuation, 'child'),
-            (r'\s+', Text),
-            (r'^(\s*#[#\s]*)(\.(?:%s))([^\n]*$)' % '|'.join(commenthelp),
-             bygroups(Comment, String.Doc, Comment)),
-            (r'#[^\n]*?$', Comment),
-            (r'(&lt;|<)#', Comment.Multiline, 'multline'),
-            (r'@"\n', String.Heredoc, 'heredoc-double'),
-            (r"@'\n.*?\n'@", String.Heredoc),
-            # escaped syntax
-            (r'`[\'"$@-]', Punctuation),
-            (r'"', String.Double, 'string'),
-            (r"'([^']|'')*'", String.Single),
-            (r'(\$|@@|@)((global|script|private|env):)?\w+',
-             Name.Variable),
-            (r'(%s)\b' % '|'.join(keywords), Keyword),
-            (r'-(%s)\b' % '|'.join(operators), Operator),
-            (r'(%s)-[a-z_]\w*\b' % '|'.join(verbs), Name.Builtin),
-            (r'(%s)\s' % '|'.join(aliases_), Name.Builtin),
-            (r'\[[a-z_\[][\w. `,\[\]]*\]', Name.Constant),  # .net [type]s
-            (r'-[a-z_]\w*', Name),
-            (r'\w+', Name),
-            (r'[.,;:@{}\[\]$()=+*/\\&%!~?^`|<>-]', Punctuation),
-        ],
-        'child': [
-            (r'\)', Punctuation, '#pop'),
-            include('root'),
-        ],
-        'multline': [
-            (r'[^#&.]+', Comment.Multiline),
-            (r'#(>|&gt;)', Comment.Multiline, '#pop'),
-            (r'\.(%s)' % '|'.join(commenthelp), String.Doc),
-            (r'[#&.]', Comment.Multiline),
-        ],
-        'string': [
-            (r"`[0abfnrtv'\"$`]", String.Escape),
-            (r'[^$`"]+', String.Double),
-            (r'\$\(', Punctuation, 'child'),
-            (r'""', String.Double),
-            (r'[`$]', String.Double),
-            (r'"', String.Double, '#pop'),
-        ],
-        'heredoc-double': [
-            (r'\n"@', String.Heredoc, '#pop'),
-            (r'\$\(', Punctuation, 'child'),
-            (r'[^@\n]+"]', String.Heredoc),
-            (r".", String.Heredoc),
-        ]
-    }
-
-
-class PowerShellSessionLexer(ShellSessionBaseLexer):
-    """
-    Lexer for PowerShell sessions, i.e. command lines, including a
-    prompt, interspersed with output.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'PowerShell Session'
-    aliases = ['pwsh-session', 'ps1con']
-    filenames = []
-    mimetypes = []
-
-    _innerLexerCls = PowerShellLexer
-    _bare_continuation = True
-    _ps1rgx = re.compile(r'^((?:\[[^]]+\]: )?PS[^>]*> ?)(.*\n?)')
-    _ps2 = '> '
-
-
-class FishShellLexer(RegexLexer):
-    """
-    Lexer for Fish shell scripts.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'Fish'
-    aliases = ['fish', 'fishshell']
-    filenames = ['*.fish', '*.load']
-    mimetypes = ['application/x-fish']
-
-    tokens = {
-        'root': [
-            include('basic'),
-            include('data'),
-            include('interp'),
-        ],
-        'interp': [
-            (r'\$\(\(', Keyword, 'math'),
-            (r'\(', Keyword, 'paren'),
-            (r'\$#?(\w+|.)', Name.Variable),
-        ],
-        'basic': [
-            (r'\b(begin|end|if|else|while|break|for|in|return|function|block|'
-             r'case|continue|switch|not|and|or|set|echo|exit|pwd|true|false|'
-             r'cd|count|test)(\s*)\b',
-             bygroups(Keyword, Text)),
-            (r'\b(alias|bg|bind|breakpoint|builtin|command|commandline|'
-             r'complete|contains|dirh|dirs|emit|eval|exec|fg|fish|fish_config|'
-             r'fish_indent|fish_pager|fish_prompt|fish_right_prompt|'
-             r'fish_update_completions|fishd|funced|funcsave|functions|help|'
-             r'history|isatty|jobs|math|mimedb|nextd|open|popd|prevd|psub|'
-             r'pushd|random|read|set_color|source|status|trap|type|ulimit|'
-             r'umask|vared|fc|getopts|hash|kill|printf|time|wait)\s*\b(?!\.)',
-             Name.Builtin),
-            (r'#.*\n', Comment),
-            (r'\\[\w\W]', String.Escape),
-            (r'(\b\w+)(\s*)(=)', bygroups(Name.Variable, Whitespace, Operator)),
-            (r'[\[\]()=]', Operator),
-            (r'<<-?\s*(\'?)\\?(\w+)[\w\W]+?\2', String),
-        ],
-        'data': [
-            (r'(?s)\$?"(\\\\|\\[0-7]+|\\.|[^"\\$])*"', String.Double),
-            (r'"', String.Double, 'string'),
-            (r"(?s)\$'(\\\\|\\[0-7]+|\\.|[^'\\])*'", String.Single),
-            (r"(?s)'.*?'", String.Single),
-            (r';', Punctuation),
-            (r'&|\||\^|<|>', Operator),
-            (r'\s+', Text),
-            (r'\d+(?= |\Z)', Number),
-            (r'[^=\s\[\]{}()$"\'`\\<&|;]+', Text),
-        ],
-        'string': [
-            (r'"', String.Double, '#pop'),
-            (r'(?s)(\\\\|\\[0-7]+|\\.|[^"\\$])+', String.Double),
-            include('interp'),
-        ],
-        'paren': [
-            (r'\)', Keyword, '#pop'),
-            include('root'),
-        ],
-        'math': [
-            (r'\)\)', Keyword, '#pop'),
-            (r'[-+*/%^|&]|\*\*|\|\|', Operator),
-            (r'\d+#\d+', Number),
-            (r'\d+#(?! )', Number),
-            (r'\d+', Number),
-            include('root'),
-        ],
-    }
-
-class ExeclineLexer(RegexLexer):
-    """
-    Lexer for Laurent Bercot's execline language
-    (https://skarnet.org/software/execline).
-
-    .. versionadded:: 2.7
-    """
-
-    name = 'execline'
-    aliases = ['execline']
-    filenames = ['*.exec']
-
-    tokens = {
-        'root': [
-            include('basic'),
-            include('data'),
-            include('interp')
-        ],
-        'interp': [
-            (r'\$\{', String.Interpol, 'curly'),
-            (r'\$[\w@#]+', Name.Variable),  # user variable
-            (r'\$', Text),
-        ],
-        'basic': [
-            (r'\b(background|backtick|cd|define|dollarat|elgetopt|'
-             r'elgetpositionals|elglob|emptyenv|envfile|exec|execlineb|'
-             r'exit|export|fdblock|fdclose|fdmove|fdreserve|fdswap|'
-             r'forbacktickx|foreground|forstdin|forx|getcwd|getpid|heredoc|'
-             r'homeof|if|ifelse|ifte|ifthenelse|importas|loopwhilex|'
-             r'multidefine|multisubstitute|pipeline|piperw|posix-cd|'
-             r'redirfd|runblock|shift|trap|tryexec|umask|unexport|wait|'
-             r'withstdinas)\b', Name.Builtin),
-            (r'\A#!.+\n', Comment.Hashbang),
-            (r'#.*\n', Comment.Single),
-            (r'[{}]', Operator)
-        ],
-        'data': [
-            (r'(?s)"(\\.|[^"\\$])*"', String.Double),
-            (r'"', String.Double, 'string'),
-            (r'\s+', Text),
-            (r'[^\s{}$"\\]+', Text)
-        ],
-        'string': [
-            (r'"', String.Double, '#pop'),
-            (r'(?s)(\\\\|\\.|[^"\\$])+', String.Double),
-            include('interp'),
-        ],
-        'curly': [
-            (r'\}', String.Interpol, '#pop'),
-            (r'[\w#@]+', Name.Variable),
-            include('root')
-        ]
-
-    }
-
-    def analyse_text(text):
-        if shebang_matches(text, r'execlineb'):
-            return 1
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/version.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/version.py
deleted file mode 100644
index 95e1869658566aac3060562d8cd5a6b647887d1e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/version.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import pkg_resources
-
-try:
-    __version__ = pkg_resources.get_distribution('setuptools').version
-except Exception:
-    __version__ = 'unknown'
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Dyned 2012 [HOT] Crack.md b/spaces/quidiaMuxgu/Expedit-SAM/Dyned 2012 [HOT] Crack.md
deleted file mode 100644
index 9f32ec226a5cf2c55ba604a146c9f213747dd60e..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Dyned 2012 [HOT] Crack.md	
+++ /dev/null
@@ -1,30 +0,0 @@
-
-<h1>How DynEd 2012 Can Help You Learn English Faster and Better</h1>
-<p>If you are looking for a way to improve your English skills, you may have heard of DynEd, a computer-based language learning software that has been used by millions of students and teachers around the world. But what is DynEd and how can it help you learn English faster and better?</p>
-<h2>Dyned 2012 crack</h2><br /><p><b><b>Download File</b> &bull;&bull;&bull; <a href="https://geags.com/2uCqZf">https://geags.com/2uCqZf</a></b></p><br /><br />
-<p>DynEd is a software that uses a brain-based approach to language learning, which means that it adapts to your level and needs, and helps you build your language skills in a natural and effective way. DynEd has different courses for different age groups and proficiency levels, covering all the skills of listening, speaking, reading and writing. You can use DynEd on your computer or on your mobile device, and you can track your progress and get feedback from the software or from your teacher.</p>
-<p>DynEd 2012 is the latest version of DynEd that was released in 2012. It has some new features and improvements that make it even more user-friendly and efficient. Some of these features are:</p>
-<ul>
-<li>A new interface that is easier to navigate and more attractive.</li>
-<li>A new voice recognition system that evaluates your pronunciation and fluency.</li>
-<li>A new certification system that validates your achievements and gives you a certificate that you can use for academic or professional purposes.</li>
-<li>A new content management system that allows teachers to customize the courses and assign tasks to their students.</li>
-<li>A new data management system that collects and analyzes your data and gives you reports on your strengths and weaknesses.</li>
-</ul>
-<p>With DynEd 2012, you can learn English faster and better than ever before. You can practice your English anytime and anywhere, at your own pace and according to your own goals. You can also enjoy the interactive and engaging activities that make learning fun and rewarding. You can also benefit from the guidance and support of your teacher, who can monitor your progress and help you overcome any difficulties.</p>
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-<p></p>
-<ol>
-<li>Exposure: You listen to authentic and natural dialogues and stories that introduce you to new vocabulary and grammar in context.</li>
-<li>Reinforcement: You review and practice what you have learned through interactive exercises that test your comprehension and memory.</li>
-<li>Production: You use your voice to speak and record your own sentences and dialogues, using the new language you have learned.</li>
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Fire Service Drill Book [EXCLUSIVE] Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Fire Service Drill Book [EXCLUSIVE] Download.md
deleted file mode 100644
index d4467697b1009f479313e77f391fbbe38c5c8db4..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Fire Service Drill Book [EXCLUSIVE] Download.md	
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deleted file mode 100644
index faf9972ec884ce80dda9eb8da9766230ba043767..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Gerador De Seriais Keygen Sca Academia 4252.md	
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Ldk Movie Eng Sub 348.md b/spaces/quidiaMuxgu/Expedit-SAM/Ldk Movie Eng Sub 348.md
deleted file mode 100644
index ef7d7451e43bb5cbb1ddec47a47c686c2d716b6b..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Ldk Movie Eng Sub 348.md	
+++ /dev/null
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-
-<h1>Ldk Movie Eng Sub 348: A Guide to the Popular Japanese Romantic Comedy Film</h1>
-
-<p>If you are a fan of Japanese romantic comedy films, you may have heard of Ldk Movie Eng Sub 348. This is a film that tells the story of two high school students who end up living together under one roof and fall in love. But what is Ldk Movie Eng Sub 348 exactly? And where can you watch it with English subtitles? Here is a guide to help you learn more about this popular film.</p>
-
-<h2>What is Ldk Movie Eng Sub 348?</h2>
-
-<p>Ldk Movie Eng Sub 348 is a film based on the manga series "L•DK" by Ayu Watanabe. The title stands for "Living, Dining, Kitchen", which refers to the type of apartment that the main characters share. The film was released in Japan in 2014 and has a sequel titled "L-DK: Two Loves, Under One Roof" that was released in 2019.</p>
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-<p>The film follows the story of Aoi Nishimori (Ayame Gouriki), a cheerful and independent girl who convinces her parents to let her stay in Tokyo when they move to another city. She finds an apartment near her school and is happy with her new life. However, she soon discovers that her neighbor is Shusei Kugayama (Kento Yamazaki), the most popular boy in school who has rejected every girl who confessed to him.</p>
-
-<p>Aoi's best friend, Moe Shibuya (Rei Okamoto), is one of those girls who was rejected by Shusei. Aoi hates Shusei for hurting her friend and tries to avoid him. But one day, Shusei's apartment catches fire and he loses his home. Feeling sorry for him, Aoi invites him to stay with her until he finds a new place. Shusei agrees and moves in with Aoi.</p>
-
-<p>At first, Aoi and Shusei have a lot of conflicts and arguments. They have different personalities and lifestyles. Aoi is outgoing and cheerful, while Shusei is cold and aloof. Aoi likes to cook and clean, while Shusei is messy and lazy. Aoi has a crush on another boy, while Shusei has a mysterious past with another girl.</p>
-
-<p>However, as they spend more time together, they start to develop feelings for each other. They also face various challenges and obstacles that test their relationship. They have to keep their cohabitation a secret from their friends and family. They have to deal with jealousy and misunderstandings. They have to overcome their fears and insecurities.</p>
-
-<p>Will Aoi and Shusei be able to confess their love for each other? Will they be able to stay together under one roof? Will they find their happiness in each other?</p>
-
-<h2>Where can you watch Ldk Movie Eng Sub 348?</h2>
-
-<p>If you want to watch Ldk Movie Eng Sub 348 with English subtitles, you have several options. Here are some of them:</p>
-<p></p>
-
-<ul>
-<li>You can watch it on Viki, a streaming platform that offers Asian dramas, movies, and shows with subtitles in various languages. You can watch Ldk Movie Eng Sub 348 for free with ads or upgrade to Viki Pass</p>
-<h2>Who are the Cast and Crew of Ldk Movie Eng Sub 348?</h2>
-
-<p>Ldk Movie Eng Sub 348 has a talented cast and crew that bring the story to life. Here are some of them:</p>
-
-<ul>
-<li>Ayame Gouriki as Aoi Nishimori: She is a popular actress and singer who has starred in many films and dramas, such as "Black Butler", "Gatchaman", and "Biblia Koshodou no Jiken Techou". She plays the role of Aoi, a cheerful and independent girl who lives alone in Tokyo.</li>
-<li>Kento Yamazaki as Shusei Kugayama: He is a famous actor and model who has appeared in many films and dramas, such as "Orange", "Your Lie in April", and "Kingdom". He plays the role of Shusei, a cold and aloof boy who is the most popular in school.</li>
-<li>Rei Okamoto as Moe Shibuya: She is a beautiful actress and model who has acted in many films and dramas, such as "Switch Girl!!", "Kamen Teacher", and "The Liar and His Lover". She plays the role of Moe, Aoi's best friend who has a crush on Shusei.</li>
-<li>Taisuke Kawamura as the Director: He is an experienced director who has directed many films and dramas, such as "Nana", "My Little Monster", and "Kiss Me at the Stroke of Midnight". He is the director of Ldk Movie Eng Sub 348 and its sequel.</li>
-<li>Ayu Watanabe as the Original Creator: She is a manga artist who is best known for her series "L•DK". She is the original creator of Ldk Movie Eng Sub 348 and its sequel.</li>
-</ul>
-
-<p>Ldk Movie Eng Sub 348 has a talented cast and crew that bring the story to life. They are Ayame Gouriki as Aoi Nishimori, Kento Yamazaki as Shusei Kugayama, Rei Okamoto as Moe Shibuya, Taisuke Kawamura as the Director, and Ayu Watanabe as the Original Creator.</p>
-
-<h2>What are the Reviews and Ratings of Ldk Movie Eng Sub 348?</h2>
-
-<p>Ldk Movie Eng Sub 348 has received positive reviews and ratings from critics and audiences alike. Here are some of them:</p>
-
-<ul>
-<li>On Viki, a streaming platform that offers Asian dramas, movies, and shows with subtitles in various languages, Ldk Movie Eng Sub 348 has a rating of 9.2 out of 10 based on 13,980 reviews. Most of the reviewers praised the film for its cute and funny story, its chemistry between the leads, and its faithful adaptation of the manga.</li>
-<li>On IMDb, a website that provides information about films, television programs, home videos, video games, and streaming content online, Ldk Movie Eng Sub 348 has a rating of 6.3 out of 10 based on 1,030 votes. Most of the voters enjoyed the film for its romance, comedy, and drama.</li>
-<li>On MyDramaList, a website that allows users to keep track of Asian dramas and films that they have seen or want to see, Ldk Movie Eng Sub 348 has a rating of 7.4 out of 10 based on 8,587 ratings. Most of the raters liked the film for its sweet and hilarious story, its attractive cast, and its good music.</li>
-</ul>
-
-<p>Ldk Movie Eng Sub 348 has received positive reviews and ratings from critics and audiences alike. It has a rating of 9.2 out of 10 on Viki, 6.3 out of 10 on IMDb, and 7.4 out of 10 on MyDramaList.</p>
-<h2>What are the Differences and Similarities between Ldk Movie Eng Sub 348 and its Sequel?</h2>
-
-<p>Ldk Movie Eng Sub 348 has a sequel titled "L-DK: Two Loves, Under One Roof" that was released in 2019. The sequel continues the story of Aoi and Shusei as they face new challenges and changes in their relationship. Here are some of the differences and similarities between the two films:</p>
-
-<ul>
-<li>Differences: The sequel introduces new characters and conflicts that affect Aoi and Shusei's relationship. For example, Aoi meets Souta Nishino (Keita Machida), a handsome and friendly senior who helps her with her studies and shows interest in her. Shusei meets Reika Saeki (Anna Ishibashi), a beautiful and mysterious woman who claims to be his childhood friend and has a secret connection to him. Aoi and Shusei also have to deal with their future plans after graduation, such as choosing a college and a career.</li>
-<li>Similarities: The sequel maintains the same cast and crew as the original film. Ayame Gouriki and Kento Yamazaki reprise their roles as Aoi and Shusei, respectively. Rei Okamoto and Ryusei Yokohama also return as Moe and Reon, respectively. Taisuke Kawamura is still the director and Ayu Watanabe is still the original creator. The sequel also keeps the same tone and style as the original film, blending romance, comedy, and drama.</li>
-</ul>
-
-<p>Ldk Movie Eng Sub 348 has a sequel titled "L-DK: Two Loves, Under One Roof" that was released in 2019. The sequel continues the story of Aoi and Shusei as they face new challenges and changes in their relationship. The sequel has some differences and similarities with the original film.</p>
-
-<h2>Conclusion</h2>
-
-<p>Ldk Movie Eng Sub 348 is a film that tells the story of two high school students who end up living together under one roof and fall in love. It is based on the manga series "L•DK" by Ayu Watanabe. It is a popular Japanese romantic comedy film that has received positive reviews and ratings from critics and audiences alike. It has a talented cast and crew that bring the story to life. It also explores some themes and messages that are relevant and meaningful to the viewers. It has a sequel titled "L-DK: Two Loves, Under One Roof" that was released in 2019.</p>
-
-<p>If you want to watch Ldk Movie Eng Sub 348 with English subtitles, you can watch it on Viki, Bilibili, or other streaming platforms that offer Asian dramas, movies, and shows with subtitles in various languages. You can also buy or rent it on DVD or Blu-ray from online or offline stores that sell Asian films.</p>
-
-<p>Ldk Movie Eng Sub 348 is a film that you may enjoy if you are a fan of Japanese romantic comedy films, manga adaptations, or cute and funny stories.</p>
-<p>Conclusion</p>
-
-<p>Ldk Movie Eng Sub 348 is a film that tells the story of two high school students who end up living together under one roof and fall in love. It is based on the manga series "L•DK" by Ayu Watanabe. It is a popular Japanese romantic comedy film that has received positive reviews and ratings from critics and audiences alike. It has a talented cast and crew that bring the story to life. It also explores some themes and messages that are relevant and meaningful to the viewers. It has a sequel titled "L-DK: Two Loves, Under One Roof" that was released in 2019.</p>
-
-<p>If you want to watch Ldk Movie Eng Sub 348 with English subtitles, you can watch it on Viki, Bilibili, or other streaming platforms that offer Asian dramas, movies, and shows with subtitles in various languages. You can also buy or rent it on DVD or Blu-ray from online or offline stores that sell Asian films.</p>
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Les Rita Mitsouko Discography (13 CD) 1984 2008 FLAC.md b/spaces/quidiaMuxgu/Expedit-SAM/Les Rita Mitsouko Discography (13 CD) 1984 2008 FLAC.md
deleted file mode 100644
index d0ff1608c2660ecbdd406728072dce04b106a8a6..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Les Rita Mitsouko Discography (13 CD) 1984 2008 FLAC.md	
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diff --git a/spaces/r3gm/AICoverGen/src/vc_infer_pipeline.py b/spaces/r3gm/AICoverGen/src/vc_infer_pipeline.py
deleted file mode 100644
index 25f873e1e210879e085afd073306d796bf5114ea..0000000000000000000000000000000000000000
--- a/spaces/r3gm/AICoverGen/src/vc_infer_pipeline.py
+++ /dev/null
@@ -1,653 +0,0 @@
-from functools import lru_cache
-from time import time as ttime
-
-import faiss
-import librosa
-import numpy as np
-import os
-import parselmouth
-import pyworld
-import sys
-import torch
-import torch.nn.functional as F
-import torchcrepe
-import traceback
-from scipy import signal
-from torch import Tensor
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-now_dir = os.path.join(BASE_DIR, 'src')
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
-    def get_optimal_torch_device(self, index: int = 0) -> torch.device:
-        # Get cuda device
-        if torch.cuda.is_available():
-            return torch.device(
-                f"cuda:{index % torch.cuda.device_count()}"
-            )  # Very fast
-        elif torch.backends.mps.is_available():
-            return torch.device("mps")
-        # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
-        # Else wise return the "cpu" as a torch device,
-        return torch.device("cpu")
-
-    # Fork Feature: Compute f0 with the crepe method
-    def get_f0_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        hop_length=160,  # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
-        model="full",  # Either use crepe-tiny "tiny" or crepe "full". Default is full
-    ):
-        x = x.astype(
-            np.float32
-        )  # fixes the F.conv2D exception. We needed to convert double to float.
-        x /= np.quantile(np.abs(x), 0.999)
-        torch_device = self.get_optimal_torch_device()
-        audio = torch.from_numpy(x).to(torch_device, copy=True)
-        audio = torch.unsqueeze(audio, dim=0)
-        if audio.ndim == 2 and audio.shape[0] > 1:
-            audio = torch.mean(audio, dim=0, keepdim=True).detach()
-        audio = audio.detach()
-        print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
-        pitch: Tensor = torchcrepe.predict(
-            audio,
-            self.sr,
-            hop_length,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=hop_length * 2,
-            device=torch_device,
-            pad=True,
-        )
-        p_len = p_len or x.shape[0] // hop_length
-        # Resize the pitch for final f0
-        source = np.array(pitch.squeeze(0).cpu().float().numpy())
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * p_len, len(source)) / p_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        f0 = np.nan_to_num(target)
-        return f0  # Resized f0
-
-    def get_f0_official_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        model="full",
-    ):
-        # Pick a batch size that doesn't cause memory errors on your gpu
-        batch_size = 512
-        # Compute pitch using first gpu
-        audio = torch.tensor(np.copy(x))[None].float()
-        f0, pd = torchcrepe.predict(
-            audio,
-            self.sr,
-            self.window,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=batch_size,
-            device=self.device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 = f0[0].cpu().numpy()
-        return f0
-
-    # Fork Feature: Compute pYIN f0 method
-    def get_f0_pyin_computation(self, x, f0_min, f0_max):
-        y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
-        f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
-        f0 = f0[1:]  # Get rid of extra first frame
-        return f0
-
-    # Fork Feature: Acquire median hybrid f0 estimation calculation
-    def get_f0_hybrid_computation(
-        self,
-        methods_str,
-        input_audio_path,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        filter_radius,
-        crepe_hop_length,
-        time_step,
-    ):
-        # Get various f0 methods from input to use in the computation stack
-        s = methods_str
-        s = s.split("hybrid")[1]
-        s = s.replace("[", "").replace("]", "")
-        methods = s.split("+")
-        f0_computation_stack = []
-
-        print("Calculating f0 pitch estimations for methods: %s" % str(methods))
-        x = x.astype(np.float32)
-        x /= np.quantile(np.abs(x), 0.999)
-        # Get f0 calculations for all methods specified
-        for method in methods:
-            f0 = None
-            if method == "pm":
-                f0 = (
-                    parselmouth.Sound(x, self.sr)
-                    .to_pitch_ac(
-                        time_step=time_step / 1000,
-                        voicing_threshold=0.6,
-                        pitch_floor=f0_min,
-                        pitch_ceiling=f0_max,
-                    )
-                    .selected_array["frequency"]
-                )
-                pad_size = (p_len - len(f0) + 1) // 2
-                if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                    f0 = np.pad(
-                        f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                    )
-            elif method == "crepe":
-                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
-                f0 = f0[1:]  # Get rid of extra first frame
-            elif method == "crepe-tiny":
-                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
-                f0 = f0[1:]  # Get rid of extra first frame
-            elif method == "mangio-crepe":
-                f0 = self.get_f0_crepe_computation(
-                    x, f0_min, f0_max, p_len, crepe_hop_length
-                )
-            elif method == "mangio-crepe-tiny":
-                f0 = self.get_f0_crepe_computation(
-                    x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
-                )
-            elif method == "harvest":
-                f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-                if filter_radius > 2:
-                    f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]  # Get rid of first frame.
-            elif method == "dio":  # Potentially buggy?
-                f0, t = pyworld.dio(
-                    x.astype(np.double),
-                    fs=self.sr,
-                    f0_ceil=f0_max,
-                    f0_floor=f0_min,
-                    frame_period=10,
-                )
-                f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-                f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]
-            # elif method == "pyin": Not Working just yet
-            #    f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
-            # Push method to the stack
-            f0_computation_stack.append(f0)
-
-        for fc in f0_computation_stack:
-            print(len(fc))
-
-        print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
-        f0_median_hybrid = None
-        if len(f0_computation_stack) == 1:
-            f0_median_hybrid = f0_computation_stack[0]
-        else:
-            f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
-        return f0_median_hybrid
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        crepe_hop_length,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "dio":  # Potentially Buggy?
-            f0, t = pyworld.dio(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
-        elif f0_method == "crepe-tiny":
-            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
-        elif f0_method == "mangio-crepe":
-            f0 = self.get_f0_crepe_computation(
-                x, f0_min, f0_max, p_len, crepe_hop_length
-            )
-        elif f0_method == "mangio-crepe-tiny":
-            f0 = self.get_f0_crepe_computation(
-                x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
-            )
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                self.model_rmvpe = RMVPE(
-                    os.path.join(BASE_DIR, 'rvc_models', 'rmvpe.pt'), is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-
-        elif "hybrid" in f0_method:
-            # Perform hybrid median pitch estimation
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = self.get_f0_hybrid_computation(
-                f0_method,
-                input_audio_path,
-                x,
-                f0_min,
-                f0_max,
-                p_len,
-                filter_radius,
-                crepe_hop_length,
-                time_step,
-            )
-
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = feats.clone()
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch != None and pitchf != None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        crepe_hop_length,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                crepe_hop_length,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/r3gm/Advanced-RVC-Inference/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/r3gm/Advanced-RVC-Inference/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Advanced-RVC-Inference/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/shufflenet.py b/spaces/rachana219/MODT2/trackers/strongsort/deep/models/shufflenet.py
deleted file mode 100644
index bc4d34f1c4a631aa981cfb1797b036f23aed4503..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/shufflenet.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from __future__ import division, absolute_import
-import torch
-import torch.utils.model_zoo as model_zoo
-from torch import nn
-from torch.nn import functional as F
-
-__all__ = ['shufflenet']
-
-model_urls = {
-    # training epoch = 90, top1 = 61.8
-    'imagenet':
-    'https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrgfiJO9kiidz9fZM',
-}
-
-
-class ChannelShuffle(nn.Module):
-
-    def __init__(self, num_groups):
-        super(ChannelShuffle, self).__init__()
-        self.g = num_groups
-
-    def forward(self, x):
-        b, c, h, w = x.size()
-        n = c // self.g
-        # reshape
-        x = x.view(b, self.g, n, h, w)
-        # transpose
-        x = x.permute(0, 2, 1, 3, 4).contiguous()
-        # flatten
-        x = x.view(b, c, h, w)
-        return x
-
-
-class Bottleneck(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        stride,
-        num_groups,
-        group_conv1x1=True
-    ):
-        super(Bottleneck, self).__init__()
-        assert stride in [1, 2], 'Warning: stride must be either 1 or 2'
-        self.stride = stride
-        mid_channels = out_channels // 4
-        if stride == 2:
-            out_channels -= in_channels
-        # group conv is not applied to first conv1x1 at stage 2
-        num_groups_conv1x1 = num_groups if group_conv1x1 else 1
-        self.conv1 = nn.Conv2d(
-            in_channels,
-            mid_channels,
-            1,
-            groups=num_groups_conv1x1,
-            bias=False
-        )
-        self.bn1 = nn.BatchNorm2d(mid_channels)
-        self.shuffle1 = ChannelShuffle(num_groups)
-        self.conv2 = nn.Conv2d(
-            mid_channels,
-            mid_channels,
-            3,
-            stride=stride,
-            padding=1,
-            groups=mid_channels,
-            bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(mid_channels)
-        self.conv3 = nn.Conv2d(
-            mid_channels, out_channels, 1, groups=num_groups, bias=False
-        )
-        self.bn3 = nn.BatchNorm2d(out_channels)
-        if stride == 2:
-            self.shortcut = nn.AvgPool2d(3, stride=2, padding=1)
-
-    def forward(self, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.shuffle1(out)
-        out = self.bn2(self.conv2(out))
-        out = self.bn3(self.conv3(out))
-        if self.stride == 2:
-            res = self.shortcut(x)
-            out = F.relu(torch.cat([res, out], 1))
-        else:
-            out = F.relu(x + out)
-        return out
-
-
-# configuration of (num_groups: #out_channels) based on Table 1 in the paper
-cfg = {
-    1: [144, 288, 576],
-    2: [200, 400, 800],
-    3: [240, 480, 960],
-    4: [272, 544, 1088],
-    8: [384, 768, 1536],
-}
-
-
-class ShuffleNet(nn.Module):
-    """ShuffleNet.
-
-    Reference:
-        Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
-        Network for Mobile Devices. CVPR 2018.
-
-    Public keys:
-        - ``shufflenet``: ShuffleNet (groups=3).
-    """
-
-    def __init__(self, num_classes, loss='softmax', num_groups=3, **kwargs):
-        super(ShuffleNet, self).__init__()
-        self.loss = loss
-
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(3, 24, 3, stride=2, padding=1, bias=False),
-            nn.BatchNorm2d(24),
-            nn.ReLU(),
-            nn.MaxPool2d(3, stride=2, padding=1),
-        )
-
-        self.stage2 = nn.Sequential(
-            Bottleneck(
-                24, cfg[num_groups][0], 2, num_groups, group_conv1x1=False
-            ),
-            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
-            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
-            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
-        )
-
-        self.stage3 = nn.Sequential(
-            Bottleneck(cfg[num_groups][0], cfg[num_groups][1], 2, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
-        )
-
-        self.stage4 = nn.Sequential(
-            Bottleneck(cfg[num_groups][1], cfg[num_groups][2], 2, num_groups),
-            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
-            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
-            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
-        )
-
-        self.classifier = nn.Linear(cfg[num_groups][2], num_classes)
-        self.feat_dim = cfg[num_groups][2]
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), -1)
-
-        if not self.training:
-            return x
-
-        y = self.classifier(x)
-
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, x
-        else:
-            raise KeyError('Unsupported loss: {}'.format(self.loss))
-
-
-def init_pretrained_weights(model, model_url):
-    """Initializes model with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    pretrain_dict = model_zoo.load_url(model_url)
-    model_dict = model.state_dict()
-    pretrain_dict = {
-        k: v
-        for k, v in pretrain_dict.items()
-        if k in model_dict and model_dict[k].size() == v.size()
-    }
-    model_dict.update(pretrain_dict)
-    model.load_state_dict(model_dict)
-
-
-def shufflenet(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = ShuffleNet(num_classes, loss, **kwargs)
-    if pretrained:
-        # init_pretrained_weights(model, model_urls['imagenet'])
-        import warnings
-        warnings.warn(
-            'The imagenet pretrained weights need to be manually downloaded from {}'
-            .format(model_urls['imagenet'])
-        )
-    return model
diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/models/gnn/gat.py b/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/models/gnn/gat.py
deleted file mode 100644
index d7816f22a4b3afd77d3f2d3e69bc65e45b026a14..0000000000000000000000000000000000000000
--- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/models/gnn/gat.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from copy import deepcopy
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from spiga.models.gnn.layers import MLP
-
-
-class GAT(nn.Module):
-    def __init__(self, input_dim: int, output_dim: int, num_heads=4):
-        super().__init__()
-
-        num_heads_in = num_heads
-        self.reshape = None
-        if input_dim != output_dim:
-            for num_heads_in in range(num_heads, 0, -1):
-                if input_dim % num_heads_in == 0:
-                    break
-            self.reshape = MLP([input_dim, output_dim])
-
-        self.attention = MessagePassing(input_dim, num_heads_in, out_dim=output_dim)
-
-    def forward(self, features):
-        message, prob = self.attention(features)
-        if self.reshape:
-            features = self.reshape(features)
-        output = features + message
-        return output, prob
-
-
-class MessagePassing(nn.Module):
-    def __init__(self, feature_dim: int, num_heads: int, out_dim=None):
-        super().__init__()
-        self.attn = Attention(num_heads, feature_dim)
-        self.mlp = MLP([feature_dim*2, feature_dim*2, out_dim])
-
-    def forward(self, features):
-        message, prob = self.attn(features, features, features)
-        return self.mlp(torch.cat([features, message], dim=1)), prob
-
-
-class Attention(nn.Module):
-    def __init__(self, num_heads: int, feature_dim: int):
-        super().__init__()
-        assert feature_dim % num_heads == 0
-        self.dim = feature_dim // num_heads
-        self.num_heads = num_heads
-        self.merge = nn.Conv1d(feature_dim, feature_dim, kernel_size=1)
-        self.proj = nn.ModuleList([deepcopy(self.merge) for _ in range(3)])
-
-    def forward(self, query, key, value):
-        batch_dim = query.size(0)
-        query, key, value = [l(x).view(batch_dim, self.dim, self.num_heads, -1)
-                             for l, x in zip(self.proj, (query, key, value))]
-        x, prob = self.attention(query, key, value)
-        return self.merge(x.contiguous().view(batch_dim, self.dim*self.num_heads, -1)), prob
-
-    def attention(self, query, key, value):
-        dim = query.shape[1]
-        scores = torch.einsum('bdhn,bdhm->bhnm', query, key) / dim ** .5
-        prob = F.softmax(scores, dim=-1)
-        return torch.einsum('bhnm,bdhm->bdhn', prob, value), prob
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/mtcnn/mtcnn.py b/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/mtcnn/mtcnn.py
deleted file mode 100644
index 4deacabaaf35e315c363c9eada9ff0c41f2561e5..0000000000000000000000000000000000000000
--- a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/mtcnn/mtcnn.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image
-from models.mtcnn.mtcnn_pytorch.src.get_nets import PNet, RNet, ONet
-from models.mtcnn.mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
-from models.mtcnn.mtcnn_pytorch.src.first_stage import run_first_stage
-from models.mtcnn.mtcnn_pytorch.src.align_trans import get_reference_facial_points, warp_and_crop_face
-
-device = 'cuda:0'
-
-
-class MTCNN():
-    def __init__(self):
-        print(device)
-        self.pnet = PNet().to(device)
-        self.rnet = RNet().to(device)
-        self.onet = ONet().to(device)
-        self.pnet.eval()
-        self.rnet.eval()
-        self.onet.eval()
-        self.refrence = get_reference_facial_points(default_square=True)
-
-    def align(self, img):
-        _, landmarks = self.detect_faces(img)
-        if len(landmarks) == 0:
-            return None, None
-        facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
-        warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
-        return Image.fromarray(warped_face), tfm
-
-    def align_multi(self, img, limit=None, min_face_size=30.0):
-        boxes, landmarks = self.detect_faces(img, min_face_size)
-        if limit:
-            boxes = boxes[:limit]
-            landmarks = landmarks[:limit]
-        faces = []
-        tfms = []
-        for landmark in landmarks:
-            facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
-            warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
-            faces.append(Image.fromarray(warped_face))
-            tfms.append(tfm)
-        return boxes, faces, tfms
-
-    def detect_faces(self, image, min_face_size=20.0,
-                     thresholds=[0.15, 0.25, 0.35],
-                     nms_thresholds=[0.7, 0.7, 0.7]):
-        """
-        Arguments:
-            image: an instance of PIL.Image.
-            min_face_size: a float number.
-            thresholds: a list of length 3.
-            nms_thresholds: a list of length 3.
-
-        Returns:
-            two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
-            bounding boxes and facial landmarks.
-        """
-
-        # BUILD AN IMAGE PYRAMID
-        width, height = image.size
-        min_length = min(height, width)
-
-        min_detection_size = 12
-        factor = 0.707  # sqrt(0.5)
-
-        # scales for scaling the image
-        scales = []
-
-        # scales the image so that
-        # minimum size that we can detect equals to
-        # minimum face size that we want to detect
-        m = min_detection_size / min_face_size
-        min_length *= m
-
-        factor_count = 0
-        while min_length > min_detection_size:
-            scales.append(m * factor ** factor_count)
-            min_length *= factor
-            factor_count += 1
-
-        # STAGE 1
-
-        # it will be returned
-        bounding_boxes = []
-
-        with torch.no_grad():
-            # run P-Net on different scales
-            for s in scales:
-                boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0])
-                bounding_boxes.append(boxes)
-
-            # collect boxes (and offsets, and scores) from different scales
-            bounding_boxes = [i for i in bounding_boxes if i is not None]
-            bounding_boxes = np.vstack(bounding_boxes)
-
-            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
-            bounding_boxes = bounding_boxes[keep]
-
-            # use offsets predicted by pnet to transform bounding boxes
-            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
-            # shape [n_boxes, 5]
-
-            bounding_boxes = convert_to_square(bounding_boxes)
-            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
-
-            # STAGE 2
-
-            img_boxes = get_image_boxes(bounding_boxes, image, size=24)
-            img_boxes = torch.FloatTensor(img_boxes).to(device)
-
-            output = self.rnet(img_boxes)
-            offsets = output[0].cpu().data.numpy()  # shape [n_boxes, 4]
-            probs = output[1].cpu().data.numpy()  # shape [n_boxes, 2]
-
-            keep = np.where(probs[:, 1] > thresholds[1])[0]
-            bounding_boxes = bounding_boxes[keep]
-            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
-            offsets = offsets[keep]
-
-            keep = nms(bounding_boxes, nms_thresholds[1])
-            bounding_boxes = bounding_boxes[keep]
-            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
-            bounding_boxes = convert_to_square(bounding_boxes)
-            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
-
-            # STAGE 3
-
-            img_boxes = get_image_boxes(bounding_boxes, image, size=48)
-            if len(img_boxes) == 0:
-                return [], []
-            img_boxes = torch.FloatTensor(img_boxes).to(device)
-            output = self.onet(img_boxes)
-            landmarks = output[0].cpu().data.numpy()  # shape [n_boxes, 10]
-            offsets = output[1].cpu().data.numpy()  # shape [n_boxes, 4]
-            probs = output[2].cpu().data.numpy()  # shape [n_boxes, 2]
-
-            keep = np.where(probs[:, 1] > thresholds[2])[0]
-            bounding_boxes = bounding_boxes[keep]
-            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
-            offsets = offsets[keep]
-            landmarks = landmarks[keep]
-
-            # compute landmark points
-            width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
-            height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
-            xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
-            landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
-            landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
-
-            bounding_boxes = calibrate_box(bounding_boxes, offsets)
-            keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
-            bounding_boxes = bounding_boxes[keep]
-            landmarks = landmarks[keep]
-
-        return bounding_boxes, landmarks
diff --git a/spaces/radames/candle-segment-anything-wasm/samWorker.js b/spaces/radames/candle-segment-anything-wasm/samWorker.js
deleted file mode 100644
index 5d0a1b5c3033f4c05c6a1d48cbc4d724e55cd98b..0000000000000000000000000000000000000000
--- a/spaces/radames/candle-segment-anything-wasm/samWorker.js
+++ /dev/null
@@ -1,155 +0,0 @@
-//load the candle SAM Model wasm module
-import init, { Model } from "./build/m.js";
-
-async function fetchArrayBuffer(url, cacheModel = true) {
-  if (!cacheModel)
-    return new Uint8Array(await (await fetch(url)).arrayBuffer());
-  const cacheName = "sam-candle-cache";
-  const cache = await caches.open(cacheName);
-  const cachedResponse = await cache.match(url);
-  if (cachedResponse) {
-    const data = await cachedResponse.arrayBuffer();
-    return new Uint8Array(data);
-  }
-  const res = await fetch(url, { cache: "force-cache" });
-  cache.put(url, res.clone());
-  return new Uint8Array(await res.arrayBuffer());
-}
-class SAMModel {
-  static instance = {};
-  // keep current image embeddings state
-  static imageArrayHash = {};
-  // Add a new property to hold the current modelID
-  static currentModelID = null;
-
-  static async getInstance(modelURL, modelID) {
-    if (!this.instance[modelID]) {
-      await init();
-
-      self.postMessage({
-        status: "loading",
-        message: `Loading Model ${modelID}`,
-      });
-      const weightsArrayU8 = await fetchArrayBuffer(modelURL);
-      this.instance[modelID] = new Model(
-        weightsArrayU8,
-        /tiny|mobile/.test(modelID)
-      );
-    } else {
-      self.postMessage({ status: "loading", message: "Model Already Loaded" });
-    }
-    // Set the current modelID to the modelID that was passed in
-    this.currentModelID = modelID;
-    return this.instance[modelID];
-  }
-
-  // Remove the modelID parameter from setImageEmbeddings
-  static setImageEmbeddings(imageArrayU8) {
-    // check if image embeddings are already set for this image and model
-    const imageArrayHash = this.getSimpleHash(imageArrayU8);
-    if (
-      this.imageArrayHash[this.currentModelID] === imageArrayHash &&
-      this.instance[this.currentModelID]
-    ) {
-      self.postMessage({
-        status: "embedding",
-        message: "Embeddings Already Set",
-      });
-      return;
-    }
-    this.imageArrayHash[this.currentModelID] = imageArrayHash;
-    this.instance[this.currentModelID].set_image_embeddings(imageArrayU8);
-    self.postMessage({ status: "embedding", message: "Embeddings Set" });
-  }
-
-  static getSimpleHash(imageArrayU8) {
-    // get simple hash of imageArrayU8
-    let imageArrayHash = 0;
-    for (let i = 0; i < imageArrayU8.length; i += 100) {
-      imageArrayHash ^= imageArrayU8[i];
-    }
-    return imageArrayHash.toString(16);
-  }
-}
-
-async function createImageCanvas(
-  { mask_shape, mask_data }, // mask
-  { original_width, original_height, width, height } // original image
-) {
-  const [_, __, shape_width, shape_height] = mask_shape;
-  const maskCanvas = new OffscreenCanvas(shape_width, shape_height); // canvas for mask
-  const maskCtx = maskCanvas.getContext("2d");
-  const canvas = new OffscreenCanvas(original_width, original_height); // canvas for creating mask with original image size
-  const ctx = canvas.getContext("2d");
-
-  const imageData = maskCtx.createImageData(
-    maskCanvas.width,
-    maskCanvas.height
-  );
-  const data = imageData.data;
-
-  for (let p = 0; p < data.length; p += 4) {
-    data[p] = 0;
-    data[p + 1] = 0;
-    data[p + 2] = 0;
-    data[p + 3] = mask_data[p / 4] * 255;
-  }
-  maskCtx.putImageData(imageData, 0, 0);
-
-  let sx, sy;
-  if (original_height < original_width) {
-    sy = original_height / original_width;
-    sx = 1;
-  } else {
-    sy = 1;
-    sx = original_width / original_height;
-  }
-  ctx.drawImage(
-    maskCanvas,
-    0,
-    0,
-    maskCanvas.width * sx,
-    maskCanvas.height * sy,
-    0,
-    0,
-    original_width,
-    original_height
-  );
-
-  const blob = await canvas.convertToBlob();
-  return URL.createObjectURL(blob);
-}
-
-self.addEventListener("message", async (event) => {
-  const { modelURL, modelID, imageURL, points } = event.data;
-  try {
-    self.postMessage({ status: "loading", message: "Starting SAM" });
-    const sam = await SAMModel.getInstance(modelURL, modelID);
-
-    self.postMessage({ status: "loading", message: "Loading Image" });
-    const imageArrayU8 = await fetchArrayBuffer(imageURL, false);
-
-    self.postMessage({ status: "embedding", message: "Creating Embeddings" });
-    SAMModel.setImageEmbeddings(imageArrayU8);
-    if (!points) {
-      // no points only do the embeddings
-      self.postMessage({
-        status: "complete-embedding",
-        message: "Embeddings Complete",
-      });
-      return;
-    }
-
-    self.postMessage({ status: "segmenting", message: "Segmenting" });
-    const { mask, image } = sam.mask_for_point({ points });
-    const maskDataURL = await createImageCanvas(mask, image);
-    // Send the segment back to the main thread as JSON
-    self.postMessage({
-      status: "complete",
-      message: "Segmentation Complete",
-      output: { maskURL: maskDataURL },
-    });
-  } catch (e) {
-    self.postMessage({ error: e });
-  }
-});
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Descarga codigo da vinci 1080p latino el mejor formato para ver el thriller de Dan Brown.md b/spaces/raedeXanto/academic-chatgpt-beta/Descarga codigo da vinci 1080p latino el mejor formato para ver el thriller de Dan Brown.md
deleted file mode 100644
index dbccaae27841a1d7a94d43d12b035990af3539b3..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Descarga codigo da vinci 1080p latino el mejor formato para ver el thriller de Dan Brown.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-<br />
-<h1>Codigo Da Vinci 1080p Latino: A Thrilling Mystery Movie</h1>
-<p>If you are looking for a movie that will keep you on the edge of your seat, then you should check out Codigo Da Vinci 1080p Latino. This is a Spanish dubbed version of The Da Vinci Code, a 2006 film adaptation of the novel by Dan Brown. The movie follows Robert Langdon, a symbologist who is called to investigate a murder at the Louvre Museum in Paris. Along with Sophie Neveu, a cryptologist and the granddaughter of the victim, he uncovers a series of clues that lead them to a secret society that has been guarding an ancient mystery for two thousand years.</p>
-<h2>codigo da vinci 1080p latino</h2><br /><p><b><b>Download File</b> &#10022; <a href="https://tinourl.com/2uL5Cl">https://tinourl.com/2uL5Cl</a></b></p><br /><br />
-<h2>What is Codigo Da Vinci 1080p Latino?</h2>
-<h3>The plot of the movie</h3>
-<p>The movie begins with the murder of Jacques Saunière, the curator of the Louvre Museum, by an albino monk named Silas, who works for a mysterious organization called Opus Dei. Before dying, Saunière leaves behind a cryptic message that includes a reference to Leonardo da Vinci's famous painting, The Last Supper. Robert Langdon, an American professor of symbology, is summoned by the French police to help them decipher the message. However, he soon realizes that he is a suspect in the murder and that he is being pursued by both the police and Opus Dei.</p>
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-<p>The movie was directed by Ron Howard, who is known for his films such as A Beautiful Mind, Apollo 13, and Solo: A Star Wars Story. The screenplay was written by Akiva Goldsman, who also collaborated with Howard on A Beautiful Mind and Cinderella Man. The movie was produced by Brian Grazer and John Calley.</p>
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-<p>The Da Vinci Code is one of the best-selling novels of all time, with over 80 million copies sold worldwide. It was published in 2003 and became an instant sensation among readers who were intrigued by its blend of historical fiction, conspiracy theory, and religious controversy. The novel sparked debates and controversies among scholars, critics, religious leaders, and fans over its accuracy and implications. Some praised it for its creativity and suspenseful storytelling,  while others criticized it for its factual errors and offensive portrayals of Christianity.</p>
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-<li>A: Codigo Da Vinci 1080p Latino is the Spanish dubbed version of The Da Vinci Code, which is the original title of the movie in English.</li>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download Starbound v. Enraged Koala for Computer and Enjoy New Features and Fixes.md b/spaces/raedeXanto/academic-chatgpt-beta/Download Starbound v. Enraged Koala for Computer and Enjoy New Features and Fixes.md
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--- a/spaces/raedeXanto/academic-chatgpt-beta/Download VCDS Lite 1.2 Full Activated for Free - No Dongle Required.md	
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- <li>Click on "Select" in the main menu of VCDS Lite 1.2 to choose which system you want to scan for faults.</li>
- <li>You can either select an individual control module such as Engine, ABS or Airbag, or select "Auto-Scan" to scan all available systems automatically.</li>
- <li>If you select "Auto-Scan", wait for a few minutes until VCDS Lite 1.2 completes scanning all systems and shows a summary report of fault codes found in each system.</li>
- <li>If you select an individual control module, wait for a few seconds until VCDS Lite 1.2 communicates with it and shows its information and fault codes if any.</li>
- <li>You can click on each fault code number or description to see more details about it such as its status, frequency, freeze frame data etc.</li>
- <li>You can also click on "Save" or "Print" buttons at the top right corner of the screen to save or print a copy of the fault codes report for future reference or analysis.</li>
- </ol>
- <p>You have now connected the interface and scanned for faults with VCDS Lite 1.2.</p>
- <h4>How to clear codes and reset adaptations with VCDS Lite 1.2?</h4>
- <ol>
- <li>After scanning for faults with VCDS Lite 1.2, you can clear them if they are not serious or if you have fixed them by replacing or repairing faulty components.</li>
- <li>To clear codes with VCDS Lite 1.2, select the control module that has fault codes from the main menu or from the auto-scan report.</li>
- <li>Click on "Fault Codes-02" button at the top left corner of the screen to see all fault codes stored in that control module again.</li>
- <li>Click on "Clear Codes-05" button at the top left corner of the screen to erase all fault codes from that control module.</li>
- <li>Wait for a few seconds until VCDS Lite 1.2 confirms that the codes have been cleared and shows no fault codes found.</li>
- <li>You can repeat this process for other control modules that have fault codes if you want.</li>
- <li>To reset adaptations with VCDS Lite 1.2, select the control module that has adaptations from the main menu or from the auto-scan report.</li>
- <li>Click on "Basic Settings-04" button at the top left corner of the screen to see all available groups of adaptations for that control module.</li>
- <li>Select the group number that corresponds to the adaptation you want to reset, such as throttle body alignment, idle speed adjustment, fuel trim reset etc.</li>
- <li>Click on "ON/OFF/NEXT" button at the bottom right corner of the screen to start the adaptation process.</li>
- <li>Wait for a few seconds or minutes until VCDS Lite 1.2 completes the adaptation process and shows a confirmation message or a new value.</li>
- <li>Click on "Done, Go Back" button at the bottom right corner of the screen to exit the basic settings mode.</li>
- <li>You can repeat this process for other groups of adaptations if you want.</li>
- </ol>
- <p>You have now cleared codes and reset adaptations with VCDS Lite 1.2.</p>
- <h3>How to access advanced functions with VCDS Lite 1.2?</h3>
-<p>Besides reading and clearing fault codes and resetting adaptations, VCDS Lite 1.2 also allows you to access some advanced functions that can help you test, monitor and modify various aspects of your car's electronic systems.</p>
-<p>Some of these advanced functions are:</p>
-<ul>
-<li>Measuring blocks: This function lets you view live data from sensors and actuators in numerical or graphical form. You can select up to four groups of data at a time and record them for later analysis.</li>
-<li>Output tests: This function lets you activate or deactivate certain components such as injectors, fans, valves, lights etc. to check their functionality and response.</li>
-<li>Coding: This function lets you change the configuration or behavior of certain control modules such as instrument cluster, central locking, airbag etc. by entering a new code number or selecting options from a list.</li>
-<li>Adaptation: This function lets you change the settings or values of certain parameters such as idle speed, fuel trim, immobilizer code etc. by entering a new value or selecting options from a list.</li>
-</ul>
-<p>To access these advanced functions with VCDS Lite 1.2, follow these steps:</p>
-<ol>
-<li>Select the control module that has the function you want to access from the main menu or from the auto-scan report.</li>
-<li>Click on one of these buttons at the top left corner of the screen: "Measuring Blocks-08", "Output Tests-03", "Coding-07" or "Adaptation-10".</li>
-<li>Depending on which function you selected, you will see different screens and options to choose from.</li>
-<li>Follow the instructions on the screen or refer to the user manual for more details on how to use each function.</li>
-<li>When you are done with using the function, click on "Done, Go Back" button at the bottom right corner of the screen to exit that mode.</li>
-</ol>
-<p>You have now accessed advanced functions with VCDS Lite 1.2.</p>
- <h4>How to change settings and customize your car with VCDS Lite 1.2?</h4>
-<p>One of the most popular uses of VCDS Lite 1.2 is to change settings and customize your car according to your preferences and needs.</p>
-<p>By using coding and adaptation functions, you can modify various aspects of your car's electronic systems such as:</p>
-<ul>
-<li>The appearance and behavior of your instrument cluster, such as needle sweep, welcome message, service reminder etc.</li>
-<li>The features and options of your central locking system, such as auto-locking, horn confirmation, window closing etc.</li>
-<li>The settings and functions of your airbag system, such as seat belt warning, passenger detection etc.</li>
-<li>The performance and efficiency of your engine system, such as throttle response, fuel injection etc.</li>
-<li>The comfort and convenience of your interior system, such as mirror folding, seat heating etc.</li>
-</ul>
-<li>Check if your car's battery is fully charged and has enough voltage.</li>
- <li>Check if your car's fuses and relays are in good condition and not blown or damaged.</li>
- <li>Check if your car's wiring and connectors are in good condition and not corroded or loose.</li>
- <li>Try another control module or system if possible.</li>
- <li>Restart your car and relaunch VCDS Lite 1.2.</li>
- </ol>
- <p>If none of these tips work, you may need to take your car to a professional mechanic or dealer for diagnosis and repair.</p>
- <h4>How to solve function not supported by control module or system?</h4>
- <ol>
- <li>Check if your car's system is compatible with VCDS Lite 1.2 and supports the function you want to perform.</li>
- <li>Check if your car's system has the latest software version or firmware update.</li>
- <li>Check if your car's system has any security or access codes that may prevent the function from being performed.</li>
- <li>Check if your car's system has any special requirements or conditions that need to be met before performing the function.</li>
- <li>Try another function or control module if possible.</li>
- </ol>
- <p>If none of these tips work, you may need to accept that the function is not supported by your car's system or look for an alternative solution.</p>
- <h4>How to solve software crash or freeze?</h4>
- <ol>
- <li>Check if your computer meets the minimum system requirements for running VCDS Lite 1.2.</li>
- <li>Check if your computer has enough free disk space and memory for running VCDS Lite 1.2.</li>
- <li>Check if your computer has any virus or malware that may affect the performance of VCDS Lite 1.2.</li>
- <li>Check if your computer has any conflicting or incompatible software or drivers that may interfere with VCDS Lite 1.2.</li>
- <li>Close any unnecessary programs or applications that may consume resources or bandwidth while running VCDS Lite 1.2.</li>
- <li>Restart your computer and relaunch VCDS Lite 1.2.</li>
- </ol>
- <p>If none of these tips work, you may need to reinstall VCDS Lite 1.2 or contact Ross-Tech for support.</p>
- <h3>How to update VCDS Lite 1.2 to the latest version?</h3>
-<p>If you want to enjoy the latest features and functions of VCDS Lite 1.2, you need to update it to the latest version regularly.</p>
-<p>To update VCDS Lite 1.2 to the latest version, follow these steps:</p>
-<ol>
-<li>Visit the official Ross-Tech website and check if there is a new version of VCDS Lite 1.2 available for download.</li>
-<li>If there is a new version, download it to your computer and save it in a folder of your choice.</li>
-<li>Uninstall the old version of VCDS Lite 1.2 from your computer by using the uninstaller program or by deleting the folder where it was installed.</li>
-<li>Install the new version of VCDS Lite 1.2 on your computer by following the same steps as described above in the installation section.</li>
-<li>If prompted, restart your computer and relaunch VCDS Lite 1.2.</li>
-</ol>
-<p>You have now updated VCDS Lite 1.2 to the latest version.</p>
- <h2>How to get VCDS Lite 1.2 crack for free?</h2>
-<p>If you want to use VCDS Lite 1.2 without any limitations or restrictions, you need to get a crack for it.</p>
-<p>A crack is a program or file that modifies or bypasses the activation process of a software, allowing it to run as a full version without requiring a license key or registration code.</p>
-<p>To get VCDS Lite 1.2 crack for free, follow these steps:</p>
-<ol>
-<li>Search online for sources or links that offer VCDS Lite 1.2 crack for free download.</li>
-<li>Select a source or link that looks reliable and trustworthy, and avoid those that look suspicious or malicious.</li>
-<li>Download the VCDS Lite 1.2 crack file to your computer and save it in a folder of your choice.</li>
-<li>Disable your antivirus program temporarily, as it may detect the crack file as a virus or malware and delete it automatically.</li>
-<li>Extract the crack file from the zip file using a program such as WinRAR or 7-Zip.</li>
-<li>Copy the crack file and paste it in the folder where you installed VCDS Lite 1.2, replacing the original file.</li>
-<li>Run the crack file as administrator and follow the instructions on the screen.</li>
-<li>Enable your antivirus program again and scan your computer for any potential threats.</li>
-<li>Restart your computer and relaunch VCDS Lite 1.2.</li>
-</ol>
-<p>You have now got VCDS Lite 1.2 crack for free.</p>
- <h3>Where to find VCDS Lite 1.2 crack online?</h3>
-<p>There are many sources or links that offer VCDS Lite 1.2 crack online, but not all of them are safe or reliable.</p>
-<p>Some of them may contain viruses or malware that can harm your computer or steal your personal information.</p>
-<p>Some of them may not work properly or cause errors or problems with VCDS Lite 1.2 or your car's system.</p>
-<p>Some of them may be illegal or unethical and get you in trouble with the law or the software developer.</p>
-<p>To find VCDS Lite 1.2 crack online, you need to be careful and cautious, and use your common sense and judgment.</p>
-<p>Here are some tips to help you find VCDS Lite 1.2 crack online:</p>
-<ul>
-<li>Use a reputable search engine such as Google or Bing to look for VCDS Lite 1.2 crack online.</li>
-<li>Use specific keywords such as "VCDS Lite 1.2 crack", "VCDS Lite 1.2 full version", "VCDS Lite 1.2 activation bypass" etc.</li>
-<li>Check the results and look for sources or links that have positive reviews, ratings, comments or feedback from other users.</li>
-<li>Avoid sources or links that have negative reviews, ratings, comments or feedback from other users.</li>
-<li>Avoid sources or links that have pop-ups, ads, surveys, redirects or other annoying or suspicious features.</li>
-<li>Avoid sources or links that ask for personal information, payment details, passwords or other sensitive data.</li>
-<li>Avoid sources or links that are too old, outdated, broken or unavailable.</li>
-<li>Download the VCDS Lite 1.2 crack file from a source or link that you trust and verify its size, format and content before opening it.</li>
-</ul>
-<p>Here are some examples of sources or links that offer VCDS Lite 1.2 crack online:</p>
- | Source | Link | | --- | --- | | СarSoftos.com | https://carsoftos.com/programs-for-cars-and-diagnostics/2929-vcds-12-lite-full-activated.html | | Auto-EPC.org | http://www.auto-epc.org/showthread.php?tid=7571 | | MotorCarSoft.com | https://www.motorcarsoft.com/viewtopic.php?t=13598 | | Reddit.com | https://www.reddit.com/r/Vcds/comments/zc45do/looking_for_vcdslite_12_full_activated/ | <h4>How to activate VCDS Lite 1.2 crack on your computer?</h4>
- <ol>
- <li>After downloading the VCDS Lite 1.2 crack file from a source or link of your choice, follow the same steps as described above in the section on how to get VCDS Lite 1.2 crack for free.</li>
- <li>If you have followed the steps correctly, you should be able to activate VCDS Lite 1.2 crack on your computer and use it as a full version without any limitations or restrictions.</li>
- </ol>
- <p>You have now activated VCDS Lite 1.2 crack on your computer.</p>
- <h3>What are the drawbacks of using VCDS Lite 1.2 crack?</h3>
-<p>While using VCDS Lite 1.2 crack may seem tempting and convenient, it also has some drawbacks that you should be aware of before deciding to use it.</p>
-<p>Some of these drawbacks are:</p>
-<ul>
-<li>Legal issues: Using VCDS Lite 1.2 crack may violate the terms and conditions of the software license agreement and infringe the intellectual property rights of Ross-Tech, the software developer. This may result in legal actions or penalties against you by Ross-Tech or other authorities.</li>
-<li>Ethical issues: Using VCDS Lite 1.2 crack may be considered as unfair or dishonest by Ross-Tech and other users who have paid for the software license or registration code. This may damage your reputation or credibility as a user or a professional.</li>
-<li>Technical issues: Using VCDS Lite 1.2 crack may cause errors or problems with the software itself or your car's system. The crack file may not work properly with all versions, systems or functions of VCDS Lite 1.2. The crack file may also contain viruses or malware that can harm your computer or steal your personal information.</li>
-</ul>
-<p>To avoid these drawbacks of using VCDS Lite 1.2 crack, you should consider buying a legitimate license key or registration code from Ross-Tech or an authorized dealer.</p>
- <h4>How to avoid getting caught or infected by using VCDS Lite 1.2 crack?</h4>
- <ol>
- <li>If you still decide to use VCDS Lite 1.2 crack despite its drawbacks, you should take some precautions and recommendations to avoid getting caught or infected by using it.</li>
- <li>Use a VPN service to hide your IP address and location while downloading or using VCDS Lite 1.2 crack online.</li>
- <li>Use a firewall program to block any unwanted connections or communications between VCDS Lite 1.2 and Ross-Tech servers while using it offline.</li>
- <li>Use an antivirus program to scan your computer regularly for any potential threats from the crack file or other sources online.</li>
- <li>Delete the crack file and uninstall VCDS Lite 1.2 from your computer after using it and do not share it with anyone else.</li>
- </ol>
- , coding and adapting new components, changing hidden settings and preferences etc.</p>
-<p>However, VCDS Lite 1.2 also has some limitations and restrictions compared to the full version of VCDS, such as fewer supported control modules, fewer languages, fewer functions and no automatic updates.</p>
-<p>To use VCDS Lite 1.2 without any limitations or restrictions, you need to get a crack for it, which is a program or file that modifies or bypasses the activation process of the software.</p>
-<p>You can find VCDS Lite 1.2 crack online from various sources or links, but you need to be careful and cautious, as some of them may be unsafe or unreliable.</p>
-<p>Using VCDS Lite 1.2 crack may also have some drawbacks, such as legal, ethical and technical issues that may result in legal actions or penalties, damage your reputation or credibility, or cause errors or problems with the software or your car's system.</p>
-<p>To avoid these drawbacks, you should consider buying a legitimate license key or registration code from Ross-Tech or an authorized dealer.</p>
-<p>In this article, we have shown you how to install, use and get VCDS Lite 1.2 crack for free. We have also given you some tips on how to access advanced functions, change settings and customize your car, troubleshoot common problems and update VCDS Lite 1.2 to the latest version. We have also warned you about the risks and benefits of using VCDS Lite 1.2 crack and how to avoid getting caught or infected by using it.</p>
-<p>We hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading.</p>
- <h3>FAQs</h3>
-<p>Here are some answers to five common questions about VCDS Lite 1.2 crack:</p>
- <ol>
- <li>What is the difference between VCDS Lite 1.2 and VCDS?</li>
- <p>VCDS Lite 1.2 is a simplified version of VCDS, which is the original diagnostic software developed by Ross-Tech for VAG group vehicles. VCDS Lite 1.2 has some limitations and restrictions compared to VCDS, such as fewer supported control modules, fewer languages, fewer functions and no automatic updates.</p>
- <li>What are the system requirements for running VCDS Lite 1.2?</li>
- <p>The minimum system requirements for running VCDS Lite 1.2 are:</p>
-<ul>
-<li>A Windows-based PC with at least 500 MHz processor speed and 256 MB RAM</li>
-<li>A USB port</li>
-<li>A compatible interface that connects your car's OBD-II port to your computer's USB port</li>
-<li>The VCDS Lite 1.2 software</li>
-</ul>
- <li>What are the benefits of using VCDS Lite 1.2 crack?</li>
- <p>The main benefit of using VCDS Lite 1.2 crack is that it allows you to use the software as a full version without any limitations or restrictions. You can access and modify all available parameters of your car's electronic systems without requiring a license key or registration code.</p>
- <li>What are the risks of using VCDS Lite 1.2 crack?</li>
- <p>The main risk of using VCDS Lite 1.2 crack is that it may violate the terms and conditions of the software license agreement and infringe the intellectual property rights of Ross-Tech, the software developer. This may result in legal actions or penalties against you by Ross-Tech or other authorities. It may also damage your reputation or credibility as a user or a professional. Moreover, using VCDS Lite 1.2 crack may cause errors or problems with the software itself or your car's system. The crack file may not work properly with all versions, systems or functions of VCDS Lite 1.2. The crack file may also contain viruses or malware that can harm your computer or steal your personal information.</p>
- <li>How can I get a legitimate license key or registration code for VCDS Lite 1.2?</li>
- , you need to buy it from Ross-Tech or an authorized dealer. You can visit the official Ross-Tech website and check the prices and options for buying a license key or registration code for VCDS Lite 1.2. You can also contact Ross-Tech or an authorized dealer directly and place your order. Once you have paid for your license key or registration code, you will receive it by email or mail. You can then enter it in the software and activate it as a full version.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Goverlan Remote Control V7 Crack Serial Keygen The Best Tool for Remote Administration.md b/spaces/raedeXanto/academic-chatgpt-beta/Goverlan Remote Control V7 Crack Serial Keygen The Best Tool for Remote Administration.md
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-
-<br> H3: System requirements and compatibility of Goverlan Remote Control V7 | | H2: What is a crack, serial keygen and why do you need them? | H3: The difference between crack, serial keygen and original software <br> H3: The risks and advantages of using crack, serial keygen | | H2: How to download and install Goverlan Remote Control V7 crack serial keygen | H3: Step 1: Find a reliable source for Goverlan Remote Control V7 crack serial keygen <br> H3: Step 2: Download the file and scan it for viruses <br> H3: Step 3: Extract the file and run the setup <br> H3: Step 4: Enter the serial key or use the keygen to generate one <br> H3: Step 5: Enjoy your full version of Goverlan Remote Control V7 | | H2: How to use Goverlan Remote Control V7 to manage your network remotely | H3: How to connect to a remote computer using Goverlan Remote Control V7 <br> H3: How to perform various tasks on a remote computer using Goverlan Remote Control V7 <br> H3: How to customize your preferences and settings in Goverlan Remote Control V7 | | H2: Conclusion | | | H2: FAQs | | # Article with HTML formatting <h1>Goverlan Remote Control V7 Crack Serial Keygen Download: A Guide for Beginners</h1>
- <p>If you are looking for a powerful and flexible remote control software that can help you manage your network efficiently, you might have heard of <strong>Goverlan Remote Control V7</strong>. This software is designed for IT professionals who need to access and control multiple computers remotely. Whether you want to troubleshoot a problem, install a software, monitor a user activity, or perform any other task on a remote computer, Goverlan Remote Control V7 can help you do it easily and securely.</p>
- <p>However, as you might expect, such a software is not cheap. The original version of Goverlan Remote Control V7 costs $299 per license, which might be too expensive for some users. That's why some people look for alternative ways to get this software for free or at a lower price. One of these ways is to use a <strong>crack serial keygen</strong> to activate the software without paying for it.</p>
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- <p>But what is a crack serial keygen and how does it work? Is it safe and legal to use it? How can you download and install Goverlan Remote Control V7 crack serial keygen on your computer? And how can you use Goverlan Remote Control V7 to manage your network remotely? In this article, we will answer all these questions and more. Read on to find out everything you need to know about Goverlan Remote Control V7 crack serial keygen download.</p>
- <h2>What is Goverlan Remote Control V7?</h2>
- <p>Before we dive into the details of how to get and use Goverlan Remote Control V7 crack serial keygen, let's first understand what this software is and what it can do for you. Goverlan Remote Control V7 is a remote control software that allows you to access and control any Windows-based computer on your network from anywhere. You can use it to perform various tasks on remote computers, such as:</p>
- <ul>
-<li>Viewing and controlling the desktop, keyboard, and mouse</li>
-<li>Transferring files and folders</li>
-<li>Executing commands and scripts</li>
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- <p>Goverlan Remote Control V7 has many features and benefits that make it stand out from other remote control software. Some of these features are:</p>
- <h3>Features and benefits of Goverlan Remote Control V7</h3>
- <ul>
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-<li><strong>Easy and intuitive interface:</strong> Goverlan Remote Control V7 has a user-friendly interface that allows you to access and control any remote computer with just a few clicks. You can also create shortcuts and favorites for quick access.</li>
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- <h3>System requirements and compatibility of Goverlan Remote Control V7</h3>
- <p>To use Goverlan Remote Control V7, you need to have the following system requirements on your computer:</p>
- <ul>
-<li><strong>Operating system:</strong> Windows XP/Vista/7/8/10 (32-bit or 64-bit)</li>
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-</ul>
- <p>Goverlan Remote Control V7 is compatible with any Windows-based computer on your network. You can access and control computers running Windows XP/Vista/7/8/10 (32-bit or 64-bit), Windows Server 2003/2008/2012/2016/2019 (32-bit or 64-bit), or Windows Embedded (POSReady/ThinPC).</p>
- <h2>What is a crack serial keygen and why do you need them?</h2>
- <p>If you want to use Goverlan Remote Control V7 without paying for it, you might be tempted to use a crack serial keygen. But what are these terms and how do they work?</p>
- <h3>The difference between crack serial keygen and original software</h3>
- <p>A <strong>crack</strong> is a modified version of the original software that bypasses its copy protection mechanism. A crack usually comes as an executable file that replaces the original file of the software. When you run the crack file, it modifies the software's code so that it does not ask for a license or activation.</p>
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- <p>A <strong>serial</strong> is a unique number or code that identifies the license of the original software. A serial usually comes as a text file that contains a series of letters and numbers. When you install the original software, it asks you to enter the serial to verify that you have purchased it legally.</p>
- <p>A <strong>keygen</strong> is a small program that generates a valid serial for the original software. A keygen usually comes as an executable file that produces a random serial when you run it. You can then enter this serial when installing the original software.</p>
- <p>In summary, a crack serial keygen is a combination of these three elements that allows you to use the original software without paying for it. You need them because otherwise, the original software will not work properly or will expire after a trial period.</p>
- <h3>The risks and advantages of using crack serial keygen</h3>
- <p>The main advantage of using crack serial keygen is that you can save money by getting the full version of Goverlan Remote Control V7 for free or at a lower price. You can also enjoy all its features and benefits without any limitations or restrictions.</p>
- <p>The main risk of using crack serial keygen is that they are illegal and unethical. By using them, you are violating the intellectual property rights of the software developers and distributors. You might also face legal consequences if you are caught using pirated software. Moreover, crack serial keygen might contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information. You might also get fake or invalid serial keys that do not work or cause errors in the software.</p>
- <p>Therefore, you should be careful and cautious when using crack serial keygen. You should always scan the files for viruses before opening them. You should also backup your important data before installing any cracked software. You should also avoid downloading crack serial keygen from unknown or suspicious sources. You should only use reliable and trustworthy sites that provide genuine and safe crack serial keygen.</p>
- <h2>How to download and install Goverlan Remote Control V7 crack serial keygen</h2>
- <p>If you have decided to use Goverlan Remote Control V7 crack serial keygen, you need to follow some steps to download and install it on your computer. Here are the steps:</p>
- <h3>Step 1: Find a reliable source for Goverlan Remote Control V7 crack serial keygen</h3>
- <p>The first step is to find a reliable source for Goverlan Remote Control V7 crack serial keygen. There are many websites that claim to provide crack serial keygen for various software, but not all of them are trustworthy. Some of them might provide fake or invalid serial keys, or even worse, malware-infected files.</p>
- <p>To avoid such risks, you should use only reputable and verified sites that provide genuine and safe crack serial keygen. Some of the best sites to find serial keys for any software are:</p>
- <ul>
-<li><strong>Serials.ws:</strong> This site has a huge database of over 120,000 serial keys for various software. It updates daily and provides user ratings and comments for each serial key. You can search by brand names or keywords, or browse by alphabets.</li>
-<li><strong>Smart Serials:</strong> This site is compliant with the Digital Millennium Act and respects the intellectual property rights of the software developers. It provides serial keys and crack files for various software. It has a user-friendly interface and allows you to download the files directly from the site.</li>
-<li><strong>KeyGenNinja:</strong> This site is one of the oldest and most popular sites for finding serial keys for any software. It has a large collection of serial keys and keygens for various software. It also provides information about the software version, release date, and other details.</li>
-<li><strong>KeyGens.Pro:</strong> This site provides both serial keys and keygens for various software. It has a simple and clean interface that allows you to search by keywords or browse by categories. It also provides download links for the original software packages.</li>
-</ul>
- <p>You can choose any of these sites to find Goverlan Remote Control V7 crack serial keygen. However, you should always check the user reviews and ratings before downloading any file from these sites. You should also scan the file for viruses before opening it.</p>
- <h3>Step 2: Download the file and scan it for viruses</h3>
- <p>The next step is to download the file that contains Goverlan Remote Control V7 crack serial keygen from the site that you have chosen. The file might be in a ZIP or RAR format that requires extraction. You can use any file extractor program such as WinRAR or 7-Zip to extract the file.</p>
- <p>After extracting the file, you should scan it for viruses using a reliable antivirus program such as Avast or Malwarebytes. This is to ensure that the file is safe and does not contain any harmful programs that can damage your computer or steal your personal information.</p>
- <h3>Step 3: Extract the file and run the setup</h3>
- <p>The next step is to extract the file that contains Goverlan Remote Control V7 crack serial keygen from the site that you have chosen. The file might be in a ZIP or RAR format that requires extraction. You can use any file extractor program such as WinRAR or 7-Zip to extract the file.</p>
- <p>After extracting the file, you should see two files: one is the setup file for installing Goverlan Remote Control V7, and the other is the crack file or the keygen file for activating Goverlan Remote Control V7.</p>
- <p>You should run the setup file first to install Goverlan Remote Control V7 on your computer. Follow the instructions on the screen to complete the installation process.</p>
- <h3>Step 4: Enter the serial key or use the keygen to generate one</h3>
- <p>The next step is to activate Goverlan Remote Control V7 using the serial key or the keygen that you have downloaded.</p>
- <p>If you have downloaded a serial key, you should copy it from the text file and paste it when prompted by Goverlan Remote Control V7 during installation or activation.</p>
- <p>If you have downloaded a keygen, you should run it as an administrator and click on generate button to produce a random serial key. You should then copy this serial key and paste it when prompted by Goverlan Remote Control V7 during installation or activation.</p>
- <p>You should make sure that you enter a valid serial key that matches your software version and edition.</p>
- <h3>Step 5: Enjoy your full version of Goverlan Remote Control V7</h3>
- <p>The final step is to enjoy your full version of Goverlan Remote Control V7 with all its features and benefits.</p>
- <p>You can now access and control any remote computer on your network using Goverlan Remote Control V7. You can also customize your preferences and settings according to your needs.</p>
- <h2>How to use Goverlan Remote Control V7 to manage your network remotely</h2>
- <p>Now that you have installed and activated Goverlan Remote Control V7 on your computer, you might wonder how to use it to manage your network remotely.</p>
- <p>In this section, we will show you how to use Goverlan Remote Control V7 to connect to a remote computer, perform various tasks on a remote computer, and customize your preferences and settings in Goverlan Remote Control V7.</p>
- <h3>How to connect to a remote computer using Goverlan Remote Control V7</h3>
- <p>To connect to a remote computer using Goverlan Remote Control V7, you need to follow these steps:</p>
- <ol>
-<li>Launch Goverlan Remote Control V7 on your computer.</li>
-<li>In the main window, click on <strong>New Connection</strong>.</li>
-<li>In the <strong>New Connection Wizard</strong>, enter the name or IP address of the remote computer that you want to connect to. You can also browse for the computer by clicking on the <strong>Browse</strong> button.</li>
-<li>Click on <strong>Next</strong>.</li>
-<li>In the <strong>Connection Options</strong> window, select the connection mode that you want to use. You can choose between <strong>Full Control</strong>, <strong>View Only</strong>, <strong>Remote Assistance</strong>, or <strong>Remote Shadowing</strong>. You can also configure other options such as encryption, compression, color depth, and resolution.</li>
-<li>Click on <strong>Next</strong>.</li>
-<li>In the <strong>Authentication Options</strong> window, enter the credentials that you want to use to connect to the remote computer. You can use your current credentials, or specify a different username and password. You can also select a domain or a workgroup if needed.</li>
-<li>Click on <strong>Finish</strong>.</li>
-</ol>
- <p>You should now see the remote computer's desktop in a new window. You can use the toolbar at the top of the window to access various tools and options.</p>
- <h3>How to perform various tasks on a remote computer using Goverlan Remote Control V7</h3>
- <p>To perform various tasks on a remote computer using Goverlan Remote Control V7, you can use the following tools and features:</p>
- <ul>
-<li><strong>Task Manager:</strong> This tool allows you to view and manage the processes, services, performance, and users on the remote computer. You can also start, stop, or kill any process or service, or log off any user. To access this tool, click on the <strong>Task Manager</strong> button in the toolbar.</li>
-<li><strong>Command Prompt:</strong> This tool allows you to execute commands and scripts on the remote computer. You can run the command prompt as an administrator or as the remote user. To access this tool, click on the <strong>CMD</strong> button in the toolbar.</li>
-<li><strong>File Manager:</strong> This tool allows you to transfer files and folders between your computer and the remote computer. You can also view, edit, delete, or rename any file or folder on the remote computer. To access this tool, click on the <strong>File Manager</strong> button in the toolbar.</li>
-<li><strong>Chat and Push Messages:</strong> These features allow you to communicate with the remote user or send them notifications. You can chat with them using text or audio, or send them messages that appear on their screen. To access these features, click on the <strong>Chat</strong> or <strong>PUSH MSG</strong> buttons in the toolbar.</li>
-<li><strong>Screen Capture:</strong> This feature allows you to take screenshots or record videos of the remote computer's desktop. You can save them as image or video files on your computer. To access this feature, click on the <strong>Capture</strong> button in the toolbar.</li>
-<li><strong>Power Options:</strong> This feature allows you to control the power state of the remote computer. You can shut down, restart, log off, lock, sleep, or wake up the remote computer. To access this feature, click on the <strong>PWR OPTS</strong> button in the toolbar.</li>
-<li><strong>Windows Auto-Login:</strong> This feature allows you to configure Windows to automatically log in with a specified username and password on the remote computer. This can be useful if you need to access a remote computer that does not have a user logged in. To access this feature, click on the <strong>AUTO LOGIN</strong> button in the toolbar.</li>
-</ul>
- <h3>How to customize your preferences and settings in Goverlan Remote Control V7</h3>
- <p>To customize your preferences and settings in Goverlan Remote Control V7, you can use the following options:</p>
- <ul>
-<li><strong>Goverlan Settings:</strong> This option allows you to change various settings related to Goverlan Remote Control V7, such as network settings, security settings, video capture settings, and more. To access this option, click on the <img src="https://www.goverlan.com/knowledge/wp-content/uploads/2018/10/Settings.png" alt="Settings" width="16" height="16"> icon in the upper right corner of the main window.</li>
-<li><strong>Connection Settings:</strong> This option allows you to change various settings related to the current connection, such as connection mode, encryption, compression, color depth, resolution, and more. To access this option, click on the <img src="https://www.goverlan.com/knowledge/wp-content/uploads/2018/10/Connection-Settings.png" alt="Connection Settings" width="16" height="16"> icon in the toolbar of the remote control window.</li>
-<li><strong>Preferences:</strong> This option allows you to change various preferences related to Goverlan Remote Control V7, such as appearance, behavior, performance, security, and more. To access this option, click on the <img src="https://www.goverlan.com/knowledge/wp-content/uploads/2018/10/Preferences.png" alt="Preferences" width="16" height="16"> icon in the toolbar of the remote control window.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Goverlan Remote Control V7 is a powerful and flexible remote control software that can help you manage your network efficiently. It allows you to access and control any Windows-based computer on your network from anywhere. You can use it to perform various tasks on remote computers, such as viewing and controlling the desktop, transferring files and folders, executing commands and scripts, installing and uninstalling software, managing processes and services, monitoring system performance and resources, chatting with users, taking screenshots and recording videos, and more.</p>
- <p>However, Goverlan Remote Control V7 is not a cheap software. It costs $299 per license, which might be too expensive for some users. That's why some people look for alternative ways to get this software for free or at a lower price. One of these ways is to use a crack serial keygen to activate the software without paying for it.</p>
- <p>A crack serial keygen is a combination of a crack, a serial, and a keygen that allows you to use the original software without paying for it. You need them because otherwise, the original software will not work properly or will expire after a trial period.</p>
- <p>To use Goverlan Remote Control V7 crack serial keygen, you need to follow some steps to download and install it on your computer. You also need to be careful and cautious when using crack serial keygen. They are illegal and unethical. They might also contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information. You might also get fake or invalid serial keys that do not work or cause errors in the software.</p>
- <p>Therefore, we do not recommend using crack serial keygen to get Goverlan Remote Control V7 for free or at a lower price. Instead, we suggest that you buy the original software from the official website or look for other legal and safe ways to get it.</p>
- <h2>FAQs</h2>
- <ul>
-<li><strong>What is Goverlan Remote Control V7?</strong><br>Goverlan Remote Control V7 is a remote control software that allows you to access and control any Windows-based computer on your network from anywhere.</li>
-<li><strong>What is a crack serial keygen?</strong><br>A crack serial keygen is a combination of a crack, a serial, and a keygen that allows you to use the original software without paying for it.</li>
-<li><strong>How to download and install Goverlan Remote Control V7 crack serial keygen?</strong><br>To download and install Goverlan Remote Control V7 crack serial keygen, you need to find a reliable source for it, download the file and scan it for viruses, extract the file and run the setup, enter the serial key or use the keygen to generate one, and enjoy your full version of Goverlan Remote Control V7.</li>
-<li><strong>How to use Goverlan Remote Control V7 to manage your network remotely?</strong><br>To use Goverlan Remote Control V7 to manage your network remotely, you need to connect to a remote computer using Goverlan Remote Control V7, perform various tasks on a remote computer using Goverlan Remote Control V7 tools and features, and customize your preferences and settings in Goverlan Remote Control V7.</li>
-<li><strong>What are the risks and advantages of using crack serial keygen?</strong><br>The main advantage of using crack serial keygen is that you can save money by getting the full version of Goverlan Remote Control V7 for free or at a lower price. The main risk of using crack serial keygen is that they are illegal and unethical. They might also contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information. You might also get fake or invalid serial keys that do not work or cause errors in the software.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/rahul2001/student_performance/src/Components/__init__.py b/spaces/rahul2001/student_performance/src/Components/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ranjangoel/GPT-PDF/gpt_reader/prompt.py b/spaces/ranjangoel/GPT-PDF/gpt_reader/prompt.py
deleted file mode 100644
index bd030e33da02d538631312e5c29dfac0eb49fad4..0000000000000000000000000000000000000000
--- a/spaces/ranjangoel/GPT-PDF/gpt_reader/prompt.py
+++ /dev/null
@@ -1,26 +0,0 @@
-BASE_POINTS = """
-1. Who are the authors?
-2. What is the process of the proposed method?
-3. What is the performance of the proposed method? Please note down its performance metrics.
-4. What are the baseline models and their performances? Please note down these baseline methods.
-5. What dataset did this paper use?
-"""
-
-READING_PROMPT = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{}
-"""
-
-READING_PROMT_V2 = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{},
-
-And You need to generate a brief but informative title for this part.
-Your return format:
-- title: '...'
-- summary: '...'
-"""
-
-SUMMARY_PROMPT = "You are a researcher helper bot. Now you need to read the summaries of a research paper."
diff --git a/spaces/recaptime-dev/README/README.md b/spaces/recaptime-dev/README/README.md
deleted file mode 100644
index f4f5071278f5f99f3a3eb2d943cbc1057371a046..0000000000000000000000000000000000000000
--- a/spaces/recaptime-dev/README/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: Organization README
-emoji: 🏢
-colorFrom: indigo
-colorTo: blue
-sdk: static
-pinned: true
-license: cc-by-sa-3.0
----
-
-# ~recaptime-dev on hf.co
-
-This is where our AI/ML related work are published here, especially on realms of content moderation and web annotations slash wiki editing.
-For more of our open-source work, go to [our GitLab](https://mau.dev/recaptime-dev) or [sourcehut](https://sr.ht/~recaptime-dev) namespace.
-
-## When will you start publishing your work?
-
-Since we're exploring all things AI and ML for the first time, we're ironing out things behind the scenes.
-In the meanwhile, treat this org as a way to identify squad members here.
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/CRACK Power Data Recovery 4.12 With Keygen.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/CRACK Power Data Recovery 4.12 With Keygen.md
deleted file mode 100644
index ded76aabd6e6c1a9c90a1c3de77ad007c4e2e161..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/CRACK Power Data Recovery 4.12 With Keygen.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>CRACK Power Data Recovery 4.12 With Keygen</h2><br /><p><b><b>Download File</b> === <a href="https://urlgoal.com/2uCL7e">https://urlgoal.com/2uCL7e</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Five Waves Financial Freedom Pdf Download _TOP_  Added By Request.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Five Waves Financial Freedom Pdf Download _TOP_  Added By Request.md
deleted file mode 100644
index 9f91ae31517915551070558c91b9f3701ba65209..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Five Waves Financial Freedom Pdf Download _TOP_  Added By Request.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>Five Waves Financial Freedom Pdf Download | Added By Request</h2><br /><p><b><b>DOWNLOAD</b> &hArr; <a href="https://urlgoal.com/2uCLzs">https://urlgoal.com/2uCLzs</a></b></p><br /><br />
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-
-The pandemic is preventing the normal course of business. This is having an impact on the Australian economy and financial markets. The risk of an economic recession over the next 6 months, or the impact of the credit crunch are too big for the Australian Government to ignore.One of the designers who made it happen
-
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diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/match_costs/builder.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/match_costs/builder.py
deleted file mode 100644
index ea086adff23c5adbc35d448d5a93daf1a04bdc53..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/match_costs/builder.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.utils import Registry, build_from_cfg
-
-MATCH_COST = Registry('Match Cost')
-
-
-def build_match_cost(cfg, default_args=None):
-    """Builder of IoU calculator."""
-    return build_from_cfg(cfg, MATCH_COST, default_args)
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/mask_heads/htc_mask_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/mask_heads/htc_mask_head.py
deleted file mode 100644
index 7ad8592b4c35e4d1c483fe6bc372ee1facb8fde2..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/mask_heads/htc_mask_head.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.cnn import ConvModule
-
-from mmdet.models.builder import HEADS
-from .fcn_mask_head import FCNMaskHead
-
-
-@HEADS.register_module()
-class HTCMaskHead(FCNMaskHead):
-
-    def __init__(self, with_conv_res=True, *args, **kwargs):
-        super(HTCMaskHead, self).__init__(*args, **kwargs)
-        self.with_conv_res = with_conv_res
-        if self.with_conv_res:
-            self.conv_res = ConvModule(
-                self.conv_out_channels,
-                self.conv_out_channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg)
-
-    def forward(self, x, res_feat=None, return_logits=True, return_feat=True):
-        if res_feat is not None:
-            assert self.with_conv_res
-            res_feat = self.conv_res(res_feat)
-            x = x + res_feat
-        for conv in self.convs:
-            x = conv(x)
-        res_feat = x
-        outs = []
-        if return_logits:
-            x = self.upsample(x)
-            if self.upsample_method == 'deconv':
-                x = self.relu(x)
-            mask_pred = self.conv_logits(x)
-            outs.append(mask_pred)
-        if return_feat:
-            outs.append(res_feat)
-        return outs if len(outs) > 1 else outs[0]
diff --git a/spaces/rr1/gpb/Dockerfile b/spaces/rr1/gpb/Dockerfile
deleted file mode 100644
index 05f59ddcef0a2e367a7d0f2703ad48acadaef9b0..0000000000000000000000000000000000000000
--- a/spaces/rr1/gpb/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/renqabs/gopb.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量——Cookies"_U"，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZwtbggjhjdgcvbnb4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/ruboin/faster-whisper-webui/README.md b/spaces/ruboin/faster-whisper-webui/README.md
deleted file mode 100644
index ea2c81a62fc4216c3d5ac1c110e8abdedad0cafc..0000000000000000000000000000000000000000
--- a/spaces/ruboin/faster-whisper-webui/README.md
+++ /dev/null
@@ -1,179 +0,0 @@
----
-title: Faster Whisper Webui
-emoji: 🚀
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: aadnk/faster-whisper-webui
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# Running Locally
-
-To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies:
-```
-pip install -r requirements.txt
-```
-
-You can find detailed instructions for how to install this on Windows 10/11 [here (PDF)](docs/windows/install_win10_win11.pdf).
-
-Finally, run the full version (no audio length restrictions) of the app with parallel CPU/GPU enabled:
-```
-python app.py --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True
-```
-
-You can also run the CLI interface, which is similar to Whisper's own CLI but also supports the following additional arguments:
-```
-python cli.py \
-[--vad {none,silero-vad,silero-vad-skip-gaps,silero-vad-expand-into-gaps,periodic-vad}] \
-[--vad_merge_window VAD_MERGE_WINDOW] \
-[--vad_max_merge_size VAD_MAX_MERGE_SIZE] \
-[--vad_padding VAD_PADDING] \
-[--vad_prompt_window VAD_PROMPT_WINDOW]
-[--vad_cpu_cores NUMBER_OF_CORES]
-[--vad_parallel_devices COMMA_DELIMITED_DEVICES]
-[--auto_parallel BOOLEAN]
-```
-In addition, you may also use URL's in addition to file paths as input.
-```
-python cli.py --model large --vad silero-vad --language Japanese "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-Rather than supplying arguments to `app.py` or `cli.py`, you can also use the configuration file [config.json5](config.json5). See that file for more information. 
-If you want to use a different configuration file, you can use the `WHISPER_WEBUI_CONFIG` environment variable to specify the path to another file.
-
-### Multiple Files
-
-You can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. 
-Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. 
-When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-## Whisper Implementation
-
-You can choose between using `whisper` or `faster-whisper`. [Faster Whisper](https://github.com/guillaumekln/faster-whisper) as a drop-in replacement for the 
-default Whisper which achieves up to a 4x speedup and 2x reduction in memory usage. 
-
-You can install the requirements for a specific Whisper implementation in `requirements-fastWhisper.txt` 
-or `requirements-whisper.txt`:
-```
-pip install -r requirements-fastWhisper.txt
-```
-And then run the App or the CLI with the `--whisper_implementation fast-whisper` flag:
-```
-python app.py --whisper_implementation fast-whisper --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True
-```
-You can also select the whisper implementation in `config.json5`:
-```json5
-{
-    "whisper_implementation": "fast-whisper"
-}
-```
-### GPU Acceleration
-
-In order to use GPU acceleration with Faster Whisper, both CUDA 11.2 and cuDNN 8 must be installed. You may want to install it in a virtual environment like Anaconda.
-
-## Google Colab
-
-You can also run this Web UI directly on [Google Colab](https://colab.research.google.com/drive/1qeTSvi7Bt_5RMm88ipW4fkcsMOKlDDss?usp=sharing), if you haven't got a GPU powerful enough to run the larger models.
-
-See the [colab documentation](docs/colab.md) for more information.
-
-## Parallel Execution
-
-You can also run both the Web-UI or the CLI on multiple GPUs in parallel, using the `vad_parallel_devices` option. This takes a comma-delimited list of 
-device IDs (0, 1, etc.) that Whisper should be distributed to and run on concurrently:
-```
-python cli.py --model large --vad silero-vad --language Japanese \
---vad_parallel_devices 0,1 "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-Note that this requires a VAD to function properly, otherwise only the first GPU will be used. Though you could use `period-vad` to avoid taking the hit
-of running Silero-Vad, at a slight cost to accuracy.
-
-This is achieved by creating N child processes (where N is the number of selected devices), where Whisper is run concurrently. In `app.py`, you can also 
-set the `vad_process_timeout` option. This configures the number of seconds until a process is killed due to inactivity, freeing RAM and video memory. 
-The default value is 30 minutes.
-
-```
-python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600
-```
-
-To execute the Silero VAD itself in parallel, use the `vad_cpu_cores` option:
-```
-python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 --vad_cpu_cores 4
-```
-
-You may also use `vad_process_timeout` with a single device (`--vad_parallel_devices 0`), if you prefer to always free video memory after a period of time.
-
-### Auto Parallel
-
-You can also set `auto_parallel` to `True`. This will set `vad_parallel_devices` to use all the GPU devices on the system, and `vad_cpu_cores` to be equal to the number of
-cores (up to 8):
-```
-python app.py --input_audio_max_duration -1 --auto_parallel True
-```
-
-# Docker
-
-To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU. 
-Then either use the GitLab hosted container below, or check out this repository and build an image:
-```
-sudo docker build -t whisper-webui:1 .
-```
-
-You can then start the WebUI with GPU support like so:
-```
-sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1
-```
-
-Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only:
-```
-sudo docker run -d -p 7860:7860 whisper-webui:1
-```
-
-# GitLab Docker Registry
-
-This Docker container is also hosted on GitLab:
-
-```
-sudo docker run -d --gpus=all -p 7860:7860 registry.gitlab.com/aadnk/whisper-webui:latest
-```
-
-## Custom Arguments
-
-You can also pass custom arguments to `app.py` in the Docker container, for instance to be able to use all the GPUs in parallel (replace administrator with your user):
-```
-sudo docker run -d --gpus all -p 7860:7860 \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
---mount type=bind,source=/home/administrator/.cache/huggingface,target=/root/.cache/huggingface \
---restart=on-failure:15 registry.gitlab.com/aadnk/whisper-webui:latest \
-app.py --input_audio_max_duration -1 --server_name 0.0.0.0 --auto_parallel True \
---default_vad silero-vad --default_model_name large
-```
-
-You can also call `cli.py` the same way:
-```
-sudo docker run --gpus all \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
---mount type=bind,source=/home/administrator/.cache/huggingface,target=/root/.cache/huggingface \
---mount type=bind,source=${PWD},target=/app/data \
-registry.gitlab.com/aadnk/whisper-webui:latest \
-cli.py --model large --auto_parallel True --vad silero-vad \
---output_dir /app/data /app/data/YOUR-FILE-HERE.mp4
-```
-
-## Caching
-
-Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand.
-To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally) 
-prepopulate the directory with the different Whisper models. 
-```
-sudo docker run -d --gpus=all -p 7860:7860 \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
-registry.gitlab.com/aadnk/whisper-webui:latest
-```
\ No newline at end of file
diff --git a/spaces/safi842/FashionGen/netdissect/segmodel/resnet.py b/spaces/safi842/FashionGen/netdissect/segmodel/resnet.py
deleted file mode 100644
index ea5fdf82fafa3058c5f00074d55fbb1e584d5865..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/netdissect/segmodel/resnet.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import math
-try:
-    from lib.nn import SynchronizedBatchNorm2d
-except ImportError:
-    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
-
-try:
-    from urllib import urlretrieve
-except ImportError:
-    from urllib.request import urlretrieve
-
-
-__all__ = ['ResNet', 'resnet50', 'resnet101'] # resnet101 is coming soon!
-
-
-model_urls = {
-    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
-    'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth'
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = SynchronizedBatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = SynchronizedBatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = SynchronizedBatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn2 = SynchronizedBatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = SynchronizedBatchNorm2d(planes * 4)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000):
-        self.inplanes = 128
-        super(ResNet, self).__init__()
-        self.conv1 = conv3x3(3, 64, stride=2)
-        self.bn1 = SynchronizedBatchNorm2d(64)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(64, 64)
-        self.bn2 = SynchronizedBatchNorm2d(64)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = conv3x3(64, 128)
-        self.bn3 = SynchronizedBatchNorm2d(128)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, SynchronizedBatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                SynchronizedBatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-'''
-def resnet18(pretrained=False, **kwargs):
-    """Constructs a ResNet-18 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet18']))
-    return model
-
-
-def resnet34(pretrained=False, **kwargs):
-    """Constructs a ResNet-34 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet34']))
-    return model
-'''
-
-def resnet50(pretrained=False, **kwargs):
-    """Constructs a ResNet-50 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
-    return model
-
-
-def resnet101(pretrained=False, **kwargs):
-    """Constructs a ResNet-101 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet101']), strict=False)
-    return model
-
-# def resnet152(pretrained=False, **kwargs):
-#     """Constructs a ResNet-152 model.
-#
-#     Args:
-#         pretrained (bool): If True, returns a model pre-trained on Places
-#     """
-#     model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
-#     if pretrained:
-#         model.load_state_dict(load_url(model_urls['resnet152']))
-#     return model
-
-def load_url(url, model_dir='./pretrained', map_location=None):
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    filename = url.split('/')[-1]
-    cached_file = os.path.join(model_dir, filename)
-    if not os.path.exists(cached_file):
-        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
-        urlretrieve(url, cached_file)
-    return torch.load(cached_file, map_location=map_location)
diff --git a/spaces/samusander/Transcribe.AI/app.py b/spaces/samusander/Transcribe.AI/app.py
deleted file mode 100644
index 30d0823156b6f9b692f2eba102aac590ed6bdbb3..0000000000000000000000000000000000000000
--- a/spaces/samusander/Transcribe.AI/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Imports
-import gradio as gr
-import whisper
-
-#Loading the model from openai Whisper
-model = whisper.load_model("base")
-
-# Defining the transcription function
-def transcribing(audio):
-    text = model.transcribe(audio)["text"]
-    return text
-
-# Defining the audio filepaths
-audio = gr.inputs.Audio(type="filepath")
-
-# Loading the gradio framwork
-iface = gr.Interface(fn=transcribing,inputs=audio, outputs="text", title="Transcribe.AI", description="Transcribe audiofiles with artificial intelligence.")
-iface.launch()
diff --git a/spaces/sayakpaul/lol-enhancement-maxim/maxim/blocks/attentions.py b/spaces/sayakpaul/lol-enhancement-maxim/maxim/blocks/attentions.py
deleted file mode 100644
index ad59022388610f775335cd3f58ba4fb5362ebd90..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/lol-enhancement-maxim/maxim/blocks/attentions.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import functools
-
-import tensorflow as tf
-from tensorflow.keras import layers
-
-from .others import MlpBlock
-
-Conv3x3 = functools.partial(layers.Conv2D, kernel_size=(3, 3), padding="same")
-Conv1x1 = functools.partial(layers.Conv2D, kernel_size=(1, 1), padding="same")
-
-
-def CALayer(
-    num_channels: int,
-    reduction: int = 4,
-    use_bias: bool = True,
-    name: str = "channel_attention",
-):
-    """Squeeze-and-excitation block for channel attention.
-
-    ref: https://arxiv.org/abs/1709.01507
-    """
-
-    def apply(x):
-        # 2D global average pooling
-        y = layers.GlobalAvgPool2D(keepdims=True)(x)
-        # Squeeze (in Squeeze-Excitation)
-        y = Conv1x1(
-            filters=num_channels // reduction, use_bias=use_bias, name=f"{name}_Conv_0"
-        )(y)
-        y = tf.nn.relu(y)
-        # Excitation (in Squeeze-Excitation)
-        y = Conv1x1(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_1")(y)
-        y = tf.nn.sigmoid(y)
-        return x * y
-
-    return apply
-
-
-def RCAB(
-    num_channels: int,
-    reduction: int = 4,
-    lrelu_slope: float = 0.2,
-    use_bias: bool = True,
-    name: str = "residual_ca",
-):
-    """Residual channel attention block. Contains LN,Conv,lRelu,Conv,SELayer."""
-
-    def apply(x):
-        shortcut = x
-        x = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
-        x = Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_conv1")(x)
-        x = tf.nn.leaky_relu(x, alpha=lrelu_slope)
-        x = Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_conv2")(x)
-        x = CALayer(
-            num_channels=num_channels,
-            reduction=reduction,
-            use_bias=use_bias,
-            name=f"{name}_channel_attention",
-        )(x)
-        return x + shortcut
-
-    return apply
-
-
-def RDCAB(
-    num_channels: int,
-    reduction: int = 16,
-    use_bias: bool = True,
-    dropout_rate: float = 0.0,
-    name: str = "rdcab",
-):
-    """Residual dense channel attention block. Used in Bottlenecks."""
-
-    def apply(x):
-        y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
-        y = MlpBlock(
-            mlp_dim=num_channels,
-            dropout_rate=dropout_rate,
-            use_bias=use_bias,
-            name=f"{name}_channel_mixing",
-        )(y)
-        y = CALayer(
-            num_channels=num_channels,
-            reduction=reduction,
-            use_bias=use_bias,
-            name=f"{name}_channel_attention",
-        )(y)
-        x = x + y
-        return x
-
-    return apply
-
-
-def SAM(
-    num_channels: int,
-    output_channels: int = 3,
-    use_bias: bool = True,
-    name: str = "sam",
-):
-
-    """Supervised attention module for multi-stage training.
-
-    Introduced by MPRNet [CVPR2021]: https://github.com/swz30/MPRNet
-    """
-
-    def apply(x, x_image):
-        """Apply the SAM module to the input and num_channels.
-        Args:
-          x: the output num_channels from UNet decoder with shape (h, w, c)
-          x_image: the input image with shape (h, w, 3)
-        Returns:
-          A tuple of tensors (x1, image) where (x1) is the sam num_channels used for the
-            next stage, and (image) is the output restored image at current stage.
-        """
-        # Get num_channels
-        x1 = Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_0")(x)
-
-        # Output restored image X_s
-        if output_channels == 3:
-            image = (
-                Conv3x3(
-                    filters=output_channels, use_bias=use_bias, name=f"{name}_Conv_1"
-                )(x)
-                + x_image
-            )
-        else:
-            image = Conv3x3(
-                filters=output_channels, use_bias=use_bias, name=f"{name}_Conv_1"
-            )(x)
-
-        # Get attention maps for num_channels
-        x2 = tf.nn.sigmoid(
-            Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_2")(image)
-        )
-
-        # Get attended feature maps
-        x1 = x1 * x2
-
-        # Residual connection
-        x1 = x1 + x
-        return x1, image
-
-    return apply
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/custom.css b/spaces/sccstandardteam/ChuanhuChatGPT/custom.css
deleted file mode 100644
index 5143eb138ea2469d8c457c71cb210fd3fb7cbe15..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/custom.css
+++ /dev/null
@@ -1,162 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: #FFFFFF !important;
-}
-[data-testid = "user"] {
-    background-color: #95EC69 !important;
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
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-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
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-.highlight .gd { color: #f92672 } /* Generic.Deleted */
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-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
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diff --git a/spaces/scedlatioru/img-to-music/example/Sas 9.1.3 Portable 64 Bit.md b/spaces/scedlatioru/img-to-music/example/Sas 9.1.3 Portable 64 Bit.md
deleted file mode 100644
index 1a9f9cc70b977504f1a677cf2ecae30fd402aa21..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Sas 9.1.3 Portable 64 Bit.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>How to Run SAS 9.1.3 on 64-bit Windows Systems</h1>
-<p>If you are looking for a way to run SAS 9.1.3 on your 64-bit Windows system, you might have encountered some difficulties. SAS 9.1.3 is not officially supported on 64-bit Windows systems, and it might not work properly or at all on some configurations. However, there are some possible solutions that can help you run SAS 9.1.3 on your 64-bit Windows system.</p>
-<p>In this article, we will explore some of these solutions and explain how they work, what are their advantages and disadvantages, and where you can find them.</p>
-<h2>Sas 9.1.3 Portable 64 Bit</h2><br /><p><b><b>Download Zip</b> &#187; <a href="https://gohhs.com/2uEAkJ">https://gohhs.com/2uEAkJ</a></b></p><br /><br />
-
-<h2>What is SAS 9.1.3?</h2>
-<p>SAS 9.1.3 is a version of SAS, a software suite for data analysis and business intelligence. SAS 9.1.3 was released in 2004 and it includes many features and modules, such as Base SAS, SAS OnlineTutor, SAS/ACCESS, SAS/AF, SAS/CONNECT, SAS/EIS, SAS/ETS, SAS/FSP, SAS/GIS, SAS/GRAPH, SAS/IML, SAS/INSIGHT, SAS/LAB, SAS/OR, SAS/QC, SAS/SECURE, SAS/SHARE, SAS/SPECTRAVIEW, and SAS/STAT[^1^].</p>
-<p>SAS 9.1.3 is widely used by researchers, analysts, and professionals in various fields and industries. However, it is not compatible with 64-bit Windows systems by default, which can limit its usability and performance.</p>
-
-<h2>Why is SAS 9.1.3 not compatible with 64-bit Windows systems?</h2>
-<p>The main reason why SAS 9.1.3 is not compatible with 64-bit Windows systems is that it was designed for 32-bit systems only. This means that it cannot take advantage of the increased memory and processing power of 64-bit systems[^2^]. Moreover, some of the components and libraries that SAS 9.1.3 relies on might not be available or compatible with 64-bit systems[^3^].</p>
-<p>Another reason why SAS 9.1.3 is not compatible with 64-bit Windows systems is that it has an expired license period that prevents it from running on newer systems[^4^]. The license period for SAS 9.1.3 ended in December 2007, which means that it cannot be updated or activated on newer systems without a patch or a workaround.</p>
-
-<h2>How to run SAS 9.1.3 on 64-bit Windows systems?</h2>
-<p>There are several possible ways to run SAS 9.1.3 on 64-bit Windows systems, depending on your preferences and needs. Here are some of the most common ones:</p>
-<ul>
-<li><b>Using a virtual machine:</b> A virtual machine is a software that simulates a different operating system or environment on your computer. You can use a virtual machine to run a 32-bit Windows system on your 64-bit Windows system, and then install and run SAS 9.1.3 on the virtual machine. This way, you can avoid compatibility issues and use all the features and modules of SAS 9.1.3 as if you were running it on a native system.</li>
-<li><b>Using a portable version:</b> A portable version is a software that does not require installation or activation on your computer. You can download and run a portable version of SAS 9.1.3 on your 64-bit Windows system without any problems. However, you might not be able to use some of the features or modules of SAS 9.1.3 that require external components or libraries.</li>
-<li><b>Using a patch:</b> A patch is a software that modifies or fixes another software to improve its functionality or compatibility. You can use a patch to make SAS 9.1.3 work on your 64-bit Windows system by changing its license period or its compatibility settings. However, you might encounter some errors or bugs while using SAS 9.1.3 after applying the patch.</li>
-</ul>
-<p>Each of these solutions has</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/SilverFast 8-torrent.torrent.md b/spaces/scedlatioru/img-to-music/example/SilverFast 8-torrent.torrent.md
deleted file mode 100644
index 3689f48741639047f8ec0821a096fcabb4ac2ab1..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/SilverFast 8-torrent.torrent.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<br />
-<p>the program also allows you to save a photo in the raw format, a step which some scanners can't do. this is useful, especially if you plan on doing much post-processing, but raw images are very large files (about three times the size of a jpg image). depending on your scanner, the raw file can consume as much as 1.5gb. on my scanner this is one of the few reasons i prefer to do film scans in vuescan instead of silverfast.</p>
-<p>the test image below was scanned in silverfast se 8-torrent.torrent and the duplicate made by duplicating the first scan. the duplicates can be compared to the original (which was saved as a raw file) for visual comparison. this example shows a lot of detail, but if you didn't pay attention to the edges and highlights, you would think the duplicate is the same.</p>
-<h2>SilverFast 8-torrent.torrent</h2><br /><p><b><b>Download</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://gohhs.com/2uEzG7">https://gohhs.com/2uEzG7</a></b></p><br /><br />
-<p>there are also a number of other options in silverfast 8-torrent.torrent that let you customize the scan. you can set the scan resolution, and also set the resolution and quality for the preview image.</p>
-<p>4. i use dpp to optimize the images to remove noise and i use photoshop to create masks and adjust tonal curves. are the images created by dpp able to be used for basic adjustments in photoshop, or do they need to be imported into photoshop?<br /> 5. does silverfast 8 (with hdri) use the same level of optimization as dpp?<br /> 6. i currently have no experience with hdri and the images i have seen do not look anything like'real' images. do silverfast 8 scans have to be processed through the hdri software to look'real', or can the same processing be applied to a raw file that has been scanned into silverfast 8?<br /> 7. do hdri scans work in silverfast 8 as well as the comparable raw files?<br /> 8. i notice that the silverfast 8 user manual says that it does not support raw images, yet it does seem to be processing raw images. is the user manual incorrect, or does silverfast 8 have some other way of processing raw images?</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_asr_transducer.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_asr_transducer.py
deleted file mode 100644
index 2e0d038276efed90a96a38a5ec235257f624a70d..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_asr_transducer.py
+++ /dev/null
@@ -1,576 +0,0 @@
-"""Transducer speech recognition model (pytorch)."""
-
-from argparse import Namespace
-from collections import Counter
-from dataclasses import asdict
-import logging
-import math
-import numpy
-
-import chainer
-import torch
-
-from espnet.nets.asr_interface import ASRInterface
-from espnet.nets.pytorch_backend.ctc import ctc_for
-from espnet.nets.pytorch_backend.nets_utils import get_subsample
-from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
-from espnet.nets.pytorch_backend.transducer.arguments import (
-    add_encoder_general_arguments,  # noqa: H301
-    add_rnn_encoder_arguments,  # noqa: H301
-    add_custom_encoder_arguments,  # noqa: H301
-    add_decoder_general_arguments,  # noqa: H301
-    add_rnn_decoder_arguments,  # noqa: H301
-    add_custom_decoder_arguments,  # noqa: H301
-    add_custom_training_arguments,  # noqa: H301
-    add_transducer_arguments,  # noqa: H301
-    add_auxiliary_task_arguments,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transducer.auxiliary_task import AuxiliaryTask
-from espnet.nets.pytorch_backend.transducer.custom_decoder import CustomDecoder
-from espnet.nets.pytorch_backend.transducer.custom_encoder import CustomEncoder
-from espnet.nets.pytorch_backend.transducer.error_calculator import ErrorCalculator
-from espnet.nets.pytorch_backend.transducer.initializer import initializer
-from espnet.nets.pytorch_backend.transducer.joint_network import JointNetwork
-from espnet.nets.pytorch_backend.transducer.loss import TransLoss
-from espnet.nets.pytorch_backend.transducer.rnn_decoder import DecoderRNNT
-from espnet.nets.pytorch_backend.transducer.rnn_encoder import encoder_for
-from espnet.nets.pytorch_backend.transducer.utils import prepare_loss_inputs
-from espnet.nets.pytorch_backend.transducer.utils import valid_aux_task_layer_list
-from espnet.nets.pytorch_backend.transformer.attention import (
-    MultiHeadedAttention,  # noqa: H301
-    RelPositionMultiHeadedAttention,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import (
-    LabelSmoothingLoss,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.mask import target_mask
-from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
-from espnet.utils.fill_missing_args import fill_missing_args
-
-
-class Reporter(chainer.Chain):
-    """A chainer reporter wrapper for transducer models."""
-
-    def report(
-        self,
-        loss,
-        loss_trans,
-        loss_ctc,
-        loss_lm,
-        loss_aux_trans,
-        loss_aux_symm_kl,
-        cer,
-        wer,
-    ):
-        """Instantiate reporter attributes."""
-        chainer.reporter.report({"loss": loss}, self)
-        chainer.reporter.report({"loss_trans": loss_trans}, self)
-        chainer.reporter.report({"loss_ctc": loss_ctc}, self)
-        chainer.reporter.report({"loss_lm": loss_lm}, self)
-        chainer.reporter.report({"loss_aux_trans": loss_aux_trans}, self)
-        chainer.reporter.report({"loss_aux_symm_kl": loss_aux_symm_kl}, self)
-        chainer.reporter.report({"cer": cer}, self)
-        chainer.reporter.report({"wer": wer}, self)
-
-        logging.info("loss:" + str(loss))
-
-
-class E2E(ASRInterface, torch.nn.Module):
-    """E2E module for transducer models.
-
-    Args:
-        idim (int): dimension of inputs
-        odim (int): dimension of outputs
-        args (Namespace): argument Namespace containing options
-        ignore_id (int): padding symbol id
-        blank_id (int): blank symbol id
-
-    """
-
-    @staticmethod
-    def add_arguments(parser):
-        """Add arguments for transducer model."""
-        E2E.encoder_add_general_arguments(parser)
-        E2E.encoder_add_rnn_arguments(parser)
-        E2E.encoder_add_custom_arguments(parser)
-
-        E2E.decoder_add_general_arguments(parser)
-        E2E.decoder_add_rnn_arguments(parser)
-        E2E.decoder_add_custom_arguments(parser)
-
-        E2E.training_add_custom_arguments(parser)
-        E2E.transducer_add_arguments(parser)
-        E2E.auxiliary_task_add_arguments(parser)
-
-        return parser
-
-    @staticmethod
-    def encoder_add_general_arguments(parser):
-        """Add general arguments for encoder."""
-        group = parser.add_argument_group("Encoder general arguments")
-        group = add_encoder_general_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def encoder_add_rnn_arguments(parser):
-        """Add arguments for RNN encoder."""
-        group = parser.add_argument_group("RNN encoder arguments")
-        group = add_rnn_encoder_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def encoder_add_custom_arguments(parser):
-        """Add arguments for Custom encoder."""
-        group = parser.add_argument_group("Custom encoder arguments")
-        group = add_custom_encoder_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def decoder_add_general_arguments(parser):
-        """Add general arguments for decoder."""
-        group = parser.add_argument_group("Decoder general arguments")
-        group = add_decoder_general_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def decoder_add_rnn_arguments(parser):
-        """Add arguments for RNN decoder."""
-        group = parser.add_argument_group("RNN decoder arguments")
-        group = add_rnn_decoder_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def decoder_add_custom_arguments(parser):
-        """Add arguments for Custom decoder."""
-        group = parser.add_argument_group("Custom decoder arguments")
-        group = add_custom_decoder_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def training_add_custom_arguments(parser):
-        """Add arguments for Custom architecture training."""
-        group = parser.add_argument_group("Training arguments for custom archictecture")
-        group = add_custom_training_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def transducer_add_arguments(parser):
-        """Add arguments for transducer model."""
-        group = parser.add_argument_group("Transducer model arguments")
-        group = add_transducer_arguments(group)
-
-        return parser
-
-    @staticmethod
-    def auxiliary_task_add_arguments(parser):
-        """Add arguments for auxiliary task."""
-        group = parser.add_argument_group("Auxiliary task arguments")
-        group = add_auxiliary_task_arguments(group)
-
-        return parser
-
-    @property
-    def attention_plot_class(self):
-        """Get attention plot class."""
-        return PlotAttentionReport
-
-    def get_total_subsampling_factor(self):
-        """Get total subsampling factor."""
-        if self.etype == "custom":
-            return self.encoder.conv_subsampling_factor * int(
-                numpy.prod(self.subsample)
-            )
-        else:
-            return self.enc.conv_subsampling_factor * int(numpy.prod(self.subsample))
-
-    def __init__(self, idim, odim, args, ignore_id=-1, blank_id=0, training=True):
-        """Construct an E2E object for transducer model."""
-        torch.nn.Module.__init__(self)
-
-        args = fill_missing_args(args, self.add_arguments)
-
-        self.is_rnnt = True
-        self.transducer_weight = args.transducer_weight
-
-        self.use_aux_task = (
-            True if (args.aux_task_type is not None and training) else False
-        )
-
-        self.use_aux_ctc = args.aux_ctc and training
-        self.aux_ctc_weight = args.aux_ctc_weight
-
-        self.use_aux_cross_entropy = args.aux_cross_entropy and training
-        self.aux_cross_entropy_weight = args.aux_cross_entropy_weight
-
-        if self.use_aux_task:
-            n_layers = (
-                (len(args.enc_block_arch) * args.enc_block_repeat - 1)
-                if args.enc_block_arch is not None
-                else (args.elayers - 1)
-            )
-
-            aux_task_layer_list = valid_aux_task_layer_list(
-                args.aux_task_layer_list,
-                n_layers,
-            )
-        else:
-            aux_task_layer_list = []
-
-        if "custom" in args.etype:
-            if args.enc_block_arch is None:
-                raise ValueError(
-                    "When specifying custom encoder type, --enc-block-arch"
-                    "should also be specified in training config. See"
-                    "egs/vivos/asr1/conf/transducer/train_*.yaml for more info."
-                )
-
-            self.subsample = get_subsample(args, mode="asr", arch="transformer")
-
-            self.encoder = CustomEncoder(
-                idim,
-                args.enc_block_arch,
-                input_layer=args.custom_enc_input_layer,
-                repeat_block=args.enc_block_repeat,
-                self_attn_type=args.custom_enc_self_attn_type,
-                positional_encoding_type=args.custom_enc_positional_encoding_type,
-                positionwise_activation_type=args.custom_enc_pw_activation_type,
-                conv_mod_activation_type=args.custom_enc_conv_mod_activation_type,
-                aux_task_layer_list=aux_task_layer_list,
-            )
-            encoder_out = self.encoder.enc_out
-
-            self.most_dom_list = args.enc_block_arch[:]
-        else:
-            self.subsample = get_subsample(args, mode="asr", arch="rnn-t")
-
-            self.enc = encoder_for(
-                args,
-                idim,
-                self.subsample,
-                aux_task_layer_list=aux_task_layer_list,
-            )
-            encoder_out = args.eprojs
-
-        if "custom" in args.dtype:
-            if args.dec_block_arch is None:
-                raise ValueError(
-                    "When specifying custom decoder type, --dec-block-arch"
-                    "should also be specified in training config. See"
-                    "egs/vivos/asr1/conf/transducer/train_*.yaml for more info."
-                )
-
-            self.decoder = CustomDecoder(
-                odim,
-                args.dec_block_arch,
-                input_layer=args.custom_dec_input_layer,
-                repeat_block=args.dec_block_repeat,
-                positionwise_activation_type=args.custom_dec_pw_activation_type,
-                dropout_rate_embed=args.dropout_rate_embed_decoder,
-            )
-            decoder_out = self.decoder.dunits
-
-            if "custom" in args.etype:
-                self.most_dom_list += args.dec_block_arch[:]
-            else:
-                self.most_dom_list = args.dec_block_arch[:]
-        else:
-            self.dec = DecoderRNNT(
-                odim,
-                args.dtype,
-                args.dlayers,
-                args.dunits,
-                blank_id,
-                args.dec_embed_dim,
-                args.dropout_rate_decoder,
-                args.dropout_rate_embed_decoder,
-            )
-            decoder_out = args.dunits
-
-        self.joint_network = JointNetwork(
-            odim, encoder_out, decoder_out, args.joint_dim, args.joint_activation_type
-        )
-
-        if hasattr(self, "most_dom_list"):
-            self.most_dom_dim = sorted(
-                Counter(
-                    d["d_hidden"] for d in self.most_dom_list if "d_hidden" in d
-                ).most_common(),
-                key=lambda x: x[0],
-                reverse=True,
-            )[0][0]
-
-        self.etype = args.etype
-        self.dtype = args.dtype
-
-        self.sos = odim - 1
-        self.eos = odim - 1
-        self.blank_id = blank_id
-        self.ignore_id = ignore_id
-
-        self.space = args.sym_space
-        self.blank = args.sym_blank
-
-        self.odim = odim
-
-        self.reporter = Reporter()
-
-        self.error_calculator = None
-
-        self.default_parameters(args)
-
-        if training:
-            self.criterion = TransLoss(args.trans_type, self.blank_id)
-
-            decoder = self.decoder if self.dtype == "custom" else self.dec
-
-            if args.report_cer or args.report_wer:
-                self.error_calculator = ErrorCalculator(
-                    decoder,
-                    self.joint_network,
-                    args.char_list,
-                    args.sym_space,
-                    args.sym_blank,
-                    args.report_cer,
-                    args.report_wer,
-                )
-
-            if self.use_aux_task:
-                self.auxiliary_task = AuxiliaryTask(
-                    decoder,
-                    self.joint_network,
-                    self.criterion,
-                    args.aux_task_type,
-                    args.aux_task_weight,
-                    encoder_out,
-                    args.joint_dim,
-                )
-
-            if self.use_aux_ctc:
-                self.aux_ctc = ctc_for(
-                    Namespace(
-                        num_encs=1,
-                        eprojs=encoder_out,
-                        dropout_rate=args.aux_ctc_dropout_rate,
-                        ctc_type="warpctc",
-                    ),
-                    odim,
-                )
-
-            if self.use_aux_cross_entropy:
-                self.aux_decoder_output = torch.nn.Linear(decoder_out, odim)
-
-                self.aux_cross_entropy = LabelSmoothingLoss(
-                    odim, ignore_id, args.aux_cross_entropy_smoothing
-                )
-
-        self.loss = None
-        self.rnnlm = None
-
-    def default_parameters(self, args):
-        """Initialize/reset parameters for transducer.
-
-        Args:
-            args (Namespace): argument Namespace containing options
-
-        """
-        initializer(self, args)
-
-    def forward(self, xs_pad, ilens, ys_pad):
-        """E2E forward.
-
-        Args:
-            xs_pad (torch.Tensor): batch of padded source sequences (B, Tmax, idim)
-            ilens (torch.Tensor): batch of lengths of input sequences (B)
-            ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax)
-
-        Returns:
-            loss (torch.Tensor): transducer loss value
-
-        """
-        # 1. encoder
-        xs_pad = xs_pad[:, : max(ilens)]
-
-        if "custom" in self.etype:
-            src_mask = make_non_pad_mask(ilens.tolist()).to(xs_pad.device).unsqueeze(-2)
-
-            _hs_pad, hs_mask = self.encoder(xs_pad, src_mask)
-        else:
-            _hs_pad, hs_mask, _ = self.enc(xs_pad, ilens)
-
-        if self.use_aux_task:
-            hs_pad, aux_hs_pad = _hs_pad[0], _hs_pad[1]
-        else:
-            hs_pad, aux_hs_pad = _hs_pad, None
-
-        # 1.5. transducer preparation related
-        ys_in_pad, ys_out_pad, target, pred_len, target_len = prepare_loss_inputs(
-            ys_pad, hs_mask
-        )
-
-        # 2. decoder
-        if "custom" in self.dtype:
-            ys_mask = target_mask(ys_in_pad, self.blank_id)
-            pred_pad, _ = self.decoder(ys_in_pad, ys_mask, hs_pad)
-        else:
-            pred_pad = self.dec(hs_pad, ys_in_pad)
-
-        z = self.joint_network(hs_pad.unsqueeze(2), pred_pad.unsqueeze(1))
-
-        # 3. loss computation
-        loss_trans = self.criterion(z, target, pred_len, target_len)
-
-        if self.use_aux_task and aux_hs_pad is not None:
-            loss_aux_trans, loss_aux_symm_kl = self.auxiliary_task(
-                aux_hs_pad, pred_pad, z, target, pred_len, target_len
-            )
-        else:
-            loss_aux_trans, loss_aux_symm_kl = 0.0, 0.0
-
-        if self.use_aux_ctc:
-            if "custom" in self.etype:
-                hs_mask = torch.IntTensor(
-                    [h.size(1) for h in hs_mask],
-                ).to(hs_mask.device)
-
-            loss_ctc = self.aux_ctc_weight * self.aux_ctc(hs_pad, hs_mask, ys_pad)
-        else:
-            loss_ctc = 0.0
-
-        if self.use_aux_cross_entropy:
-            loss_lm = self.aux_cross_entropy_weight * self.aux_cross_entropy(
-                self.aux_decoder_output(pred_pad), ys_out_pad
-            )
-        else:
-            loss_lm = 0.0
-
-        loss = (
-            loss_trans
-            + self.transducer_weight * (loss_aux_trans + loss_aux_symm_kl)
-            + loss_ctc
-            + loss_lm
-        )
-
-        self.loss = loss
-        loss_data = float(loss)
-
-        # 4. compute cer/wer
-        if self.training or self.error_calculator is None:
-            cer, wer = None, None
-        else:
-            cer, wer = self.error_calculator(hs_pad, ys_pad)
-
-        if not math.isnan(loss_data):
-            self.reporter.report(
-                loss_data,
-                float(loss_trans),
-                float(loss_ctc),
-                float(loss_lm),
-                float(loss_aux_trans),
-                float(loss_aux_symm_kl),
-                cer,
-                wer,
-            )
-        else:
-            logging.warning("loss (=%f) is not correct", loss_data)
-
-        return self.loss
-
-    def encode_custom(self, x):
-        """Encode acoustic features.
-
-        Args:
-            x (ndarray): input acoustic feature (T, D)
-
-        Returns:
-            x (torch.Tensor): encoded features (T, D_enc)
-
-        """
-        x = torch.as_tensor(x).unsqueeze(0)
-        enc_output, _ = self.encoder(x, None)
-
-        return enc_output.squeeze(0)
-
-    def encode_rnn(self, x):
-        """Encode acoustic features.
-
-        Args:
-            x (ndarray): input acoustic feature (T, D)
-
-        Returns:
-            x (torch.Tensor): encoded features (T, D_enc)
-
-        """
-        p = next(self.parameters())
-
-        ilens = [x.shape[0]]
-        x = x[:: self.subsample[0], :]
-
-        h = torch.as_tensor(x, device=p.device, dtype=p.dtype)
-        hs = h.contiguous().unsqueeze(0)
-
-        hs, _, _ = self.enc(hs, ilens)
-
-        return hs.squeeze(0)
-
-    def recognize(self, x, beam_search):
-        """Recognize input features.
-
-        Args:
-            x (ndarray): input acoustic feature (T, D)
-            beam_search (class): beam search class
-
-        Returns:
-            nbest_hyps (list): n-best decoding results
-
-        """
-        self.eval()
-
-        if "custom" in self.etype:
-            h = self.encode_custom(x)
-        else:
-            h = self.encode_rnn(x)
-
-        nbest_hyps = beam_search(h)
-
-        return [asdict(n) for n in nbest_hyps]
-
-    def calculate_all_attentions(self, xs_pad, ilens, ys_pad):
-        """E2E attention calculation.
-
-        Args:
-            xs_pad (torch.Tensor): batch of padded input sequences (B, Tmax, idim)
-            ilens (torch.Tensor): batch of lengths of input sequences (B)
-            ys_pad (torch.Tensor):
-                batch of padded character id sequence tensor (B, Lmax)
-
-        Returns:
-            ret (ndarray): attention weights with the following shape,
-                1) multi-head case => attention weights (B, H, Lmax, Tmax),
-                2) other case => attention weights (B, Lmax, Tmax).
-
-        """
-        self.eval()
-
-        if "custom" not in self.etype and "custom" not in self.dtype:
-            return []
-        else:
-            with torch.no_grad():
-                self.forward(xs_pad, ilens, ys_pad)
-
-            ret = dict()
-            for name, m in self.named_modules():
-                if isinstance(m, MultiHeadedAttention) or isinstance(
-                    m, RelPositionMultiHeadedAttention
-                ):
-                    ret[name] = m.attn.cpu().numpy()
-
-        self.train()
-
-        return ret
diff --git a/spaces/shencc/gpt/crazy_functions/test_project/latex/attention/background.tex b/spaces/shencc/gpt/crazy_functions/test_project/latex/attention/background.tex
deleted file mode 100644
index 785069dc0f9143bad24e640056dd1072d5c6e5b5..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/crazy_functions/test_project/latex/attention/background.tex
+++ /dev/null
@@ -1,58 +0,0 @@
-The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
-
-Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
-
-End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
-
-To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
-In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
-
-
-%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
-
-%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
-
-%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
-
-%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-
-
-%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
-
-%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
-
-%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
-
-%\begin{table}[h!]
-%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
-%\label{tab:op_complexities}
-%\begin{center}
-%\vspace{-5pt}
-%\scalebox{0.75}{
-
-%\begin{tabular}{l|c|c|c}
-%\hline \hline
-%Layer Type & Receptive & Complexity & Sequential  \\
-%           & Field     &            & Operations  \\
-%\hline
-%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
-%\hline
-%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
-%\hline
-%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
-%\hline \hline
-%\end{tabular}
-%}
-%\end{center}
-%\end{table}
\ No newline at end of file
diff --git a/spaces/shgao/EditAnything/ldm/modules/attention.py b/spaces/shgao/EditAnything/ldm/modules/attention.py
deleted file mode 100644
index 509cd873768f0dd75a75ab3fcdd652822b12b59f..0000000000000000000000000000000000000000
--- a/spaces/shgao/EditAnything/ldm/modules/attention.py
+++ /dev/null
@@ -1,341 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-from typing import Optional, Any
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-
-# CrossAttn precision handling
-import os
-_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        # force cast to fp32 to avoid overflowing
-        if _ATTN_PRECISION =="fp32":
-            with torch.autocast(enabled=False, device_type = 'cuda'):
-                q, k = q.float(), k.float()
-                sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        else:
-            sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        
-        del q, k
-    
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', sim, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-
-class MemoryEfficientCrossAttention(nn.Module):
-    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
-        super().__init__()
-        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
-              f"{heads} heads.")
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x, context=None, mask=None):
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    ATTENTION_MODES = {
-        "softmax": CrossAttention,  # vanilla attention
-        "softmax-xformers": MemoryEfficientCrossAttention
-    }
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False):
-        super().__init__()
-        attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
-        assert attn_mode in self.ATTENTION_MODES
-        attn_cls = self.ATTENTION_MODES[attn_mode]
-        self.disable_self_attn = disable_self_attn
-        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
-                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None):
-        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    NEW: use_linear for more efficiency instead of the 1x1 convs
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True):
-        super().__init__()
-        if exists(context_dim) and not isinstance(context_dim, list):
-            context_dim = [context_dim]
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-        if not use_linear:
-            self.proj_in = nn.Conv2d(in_channels,
-                                     inner_dim,
-                                     kernel_size=1,
-                                     stride=1,
-                                     padding=0)
-        else:
-            self.proj_in = nn.Linear(in_channels, inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
-                for d in range(depth)]
-        )
-        if not use_linear:
-            self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                                  in_channels,
-                                                  kernel_size=1,
-                                                  stride=1,
-                                                  padding=0))
-        else:
-            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
-        self.use_linear = use_linear
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        if not isinstance(context, list):
-            context = [context]
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        if not self.use_linear:
-            x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        if self.use_linear:
-            x = self.proj_in(x)
-        for i, block in enumerate(self.transformer_blocks):
-            x = block(x, context=context[i])
-        if self.use_linear:
-            x = self.proj_out(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        if not self.use_linear:
-            x = self.proj_out(x)
-        return x + x_in
-
diff --git a/spaces/shivi/mask2former-demo/color_palette.py b/spaces/shivi/mask2former-demo/color_palette.py
deleted file mode 100644
index 3c910cd067bfa5246e4d3cf03c679129d655a2b0..0000000000000000000000000000000000000000
--- a/spaces/shivi/mask2former-demo/color_palette.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# color palattes for COCO, cityscapes and ADE datasets
-
-def coco_panoptic_palette():
-    return [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
-               (106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
-               (0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
-               (175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
-               (0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157),
-               (110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118),
-               (255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182),
-               (0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255),
-               (78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255),
-               (134, 134, 103), (145, 148, 174), (255, 208, 186),
-               (197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255),
-               (151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105),
-               (166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149),
-               (179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205),
-               (147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0),
-               (119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88),
-               (95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118),
-               (219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15),
-               (127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0),
-               (95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122),
-               (191, 162, 208), (255, 255, 128), (147, 211, 203),
-               (150, 100, 100), (168, 171, 172), (146, 112, 198),
-               (210, 170, 100), (92, 136, 89), (218, 88, 184), (241, 129, 0),
-               (217, 17, 255), (124, 74, 181), (70, 70, 70), (255, 228, 255),
-               (154, 208, 0), (193, 0, 92), (76, 91, 113), (255, 180, 195),
-               (106, 154, 176),
-               (230, 150, 140), (60, 143, 255), (128, 64, 128), (92, 82, 55),
-               (254, 212, 124), (73, 77, 174), (255, 160, 98), (255, 255, 255),
-               (104, 84, 109), (169, 164, 131), (225, 199, 255), (137, 54, 74),
-               (135, 158, 223), (7, 246, 231), (107, 255, 200), (58, 41, 149),
-               (183, 121, 142), (255, 73, 97), (107, 142, 35), (190, 153, 153),
-               (146, 139, 141),
-               (70, 130, 180), (134, 199, 156), (209, 226, 140), (96, 36, 108),
-               (96, 96, 96), (64, 170, 64), (152, 251, 152), (208, 229, 228),
-               (206, 186, 171), (152, 161, 64), (116, 112, 0), (0, 114, 143),
-               (102, 102, 156), (250, 141, 255)]
-
-def cityscapes_palette():
-    return [[128, 64, 128],[244, 35, 232],[70, 70, 70],[102, 102, 156],[190, 153, 153],
-            [153, 153, 153],[250, 170, 30],[220, 220, 0],[107, 142, 35],[152, 251, 152],
-            [70, 130, 180], [220, 20, 60], [255, 0, 0], [0, 0, 142], [0, 0, 70],
-            [0, 60, 100], [0, 80, 100], [0, 0, 230], [119, 11, 32]]
-
-def ade_palette():
-    """Color palette that maps each class to RGB values.
-    
-    This one is actually taken from ADE20k.
-    """
-    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-            [102, 255, 0], [92, 0, 255]]
\ No newline at end of file
diff --git a/spaces/sidphbot/Researcher/arxiv_public_data/__init__.py b/spaces/sidphbot/Researcher/arxiv_public_data/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/silvesterjk/stt-sematic-measure/app.py b/spaces/silvesterjk/stt-sematic-measure/app.py
deleted file mode 100644
index 39fae272b3f123964d2a516733b53d9e3425d57c..0000000000000000000000000000000000000000
--- a/spaces/silvesterjk/stt-sematic-measure/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import os
-os.system("pip install git+https://github.com/openai/whisper.git")
-import gradio as gr
-import whisper
-from difflib import SequenceMatcher
-import re
-
-
-model = whisper.load_model("medium.en")
-
-        
-def transcribe(file):
-    options = dict(task="transcribe", best_of=5)
-    text = model.transcribe(file, **options)["text"]
-    return text.strip()
-
-with gr.Blocks() as demo:
-
-        audio = gr.Audio(
-            show_label=False,
-            source="microphone",
-            type="filepath"
-        )
-        
-        with gr.Row():
-            transcribe_button = gr.Button("Transcribe")
-            sim_button = gr.Button("Similarity")
-        
-        textbox1 = gr.Textbox(show_label=False)      
-        transcribe_button.click(transcribe, inputs=[audio], outputs=[textbox1])
-        textbox2 = gr.Textbox(label="Enter the text to compare")            
-        label = gr.Label()
-
-            
-        def text_sim(par1, par2):        
-            sim = SequenceMatcher(None, par1, par2).ratio() * 100
-            return sim
-            
-        sim_button.click(text_sim, inputs=[textbox1, textbox2], outputs=label)
-
-demo.launch()
-
-#Nicy is awesome
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Baixe Stumble Guys APK Mod Dinheiro Infinito e Skins Grtis 2022.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Baixe Stumble Guys APK Mod Dinheiro Infinito e Skins Grtis 2022.md
deleted file mode 100644
index d9aa31669f75ffdb3fed5d336db3f035b31b6b8a..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Baixe Stumble Guys APK Mod Dinheiro Infinito e Skins Grtis 2022.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-<br />
-<h1>Stumble Guys APK Dinheiro Infinito 2022: How to Download and Play the Ultimate Knockout Game for Free</h1>
- <p>Stumble Guys is a fun and addictive online multiplayer game that lets you compete with up to 32 players in chaotic obstacle courses. It is inspired by the popular Fall Guys game, but it is available for free on Android and iOS devices. However, if you want to enjoy this game on your PC, you can also use an emulator like BlueStacks.</p>
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- <p>In this article, we will tell you everything you need to know about Stumble Guys, including its features, gameplay, and how to download and install the modded version of the game that gives you unlimited money and gems. We will also share some tips and tricks to help you win your matches and become the last survivor.</p>
- <h2>What is Stumble Guys?</h2>
- <p>Stumble Guys is a massively multiplayer party knockout game that was released in 2020 by Kitka Games. The game has been downloaded over 50 million times on Google Play Store and has received positive reviews from players and critics alike.</p>
- <p>The game consists of several rounds of different minigames, where you have to run, jump, dash, slide, and avoid obstacles to reach the finish line. The number of players decreases after each round until only one remains. The last player standing wins the crown and becomes the ultimate champion.</p>
- <h3>Features of Stumble Guys</h3>
- <p>Stumble Guys has many features that make it an entertaining and challenging game for all ages. Some of these features are:</p>
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- <ul>
-<li><b>17 unique obstacle courses:</b> The game offers a variety of levels with different themes and difficulties. You will encounter spinning platforms, swinging hammers, slippery slides, moving walls, and more.</li>
-<li><b>Battle Royale Online multiplayer:</b> You can play with up to 32 players online in real-time. You can join random matches or create your own private rooms to invite your friends.</li>
-<li><b>Party mode to play with friends:</b> You can also play with your friends in party mode, where you can team up or compete against each other in different modes.</li>
-<li><b>Physics-based havoc:</b> The game has realistic physics that make the gameplay more fun and unpredictable. You can bump into other players, push them off ledges, or use them as stepping stones.</li>
-<li><b>Colorful, whacky design:</b> The game has a colorful and cartoonish style that suits its humorous tone. The characters are cute and customizable, and the environments are bright and lively.</li>
-<li><b>Unlockable outfits and emotes:</b> You can unlock and collect over 100 outfits and emotes for your character. You can dress up as a pirate, a ninja, a superhero, or anything else you can imagine.</li>
-<li><b>Tons of hilarious fails:</b> The game is full of hilarious moments where you or other players will fail miserably. You will see players falling off cliffs, getting hit by obstacles, or getting eliminated in funny ways.</li>
-</ul>
- <h3>How to play Stumble Guys on PC with BlueStacks</h3> <p>If you want to play Stumble Guys on your PC, you can use BlueStacks, which is the world's most popular, safest and free mobile gaming platform for Windows and Mac. BlueStacks allows you to run virtually any mobile app from the Google Play Store on your computer, including Stumble Guys. This way, you can enjoy the game on a bigger screen, with better graphics, and faster performance.</p>
- <p>Here are the steps to play Stumble Guys on PC with BlueStacks:</p>
- <ol>
-<li>Download and install BlueStacks from the official website. You can choose from different versions based on your needs, such as Nougat 32-bit, Nougat 64-bit, Pie 64-bit, or Android 11 64-bit (beta).</li>
-<li>Launch BlueStacks and sign in with your Google account or create a new one if you don't have one.</li>
-<li>Go to the Google Play Store and search for Stumble Guys. Alternatively, you can also download the APK file from a trusted source and drag and drop it into BlueStacks.</li>
-<li>Click on the Install button and wait for the game to download and install.</li>
-<li>Once the game is installed, click on the icon to launch it. You can also customize your keyboard and mouse controls by clicking on the gear icon on the side toolbar.</li>
-<li>Enjoy playing Stumble Guys on PC with BlueStacks!</li>
-</ol>
- <h2>What is Stumble Guys APK Dinheiro Infinito 2022?</h2>
- <p>Stumble Guys APK Dinheiro Infinito 2022 is a modded version of the game that gives you unlimited money and gems. With this mod, you can unlock and buy all the outfits and emotes you want without spending real money. You can also access a mega menu that lets you enable or disable various features, such as speed hack, fly hack, no fall damage, no ads, and more.</p>
- <p>This mod is not available on the Google Play Store or the official website of Stumble Guys. You have to download it from a third-party source, such as TekMods. However, be careful when downloading modded apps from unknown sources, as they may contain viruses or malware that can harm your device or compromise your privacy. Also, using mods may result in your account being banned by the game developers.</p>
- <h3>Benefits of Stumble Guys Mod APK</h3>
- <p>Some of the benefits of using Stumble Guys Mod APK are:</p>
- <ul>
-<li><b>You can unlock all the outfits and emotes:</b> With unlimited money and gems, you can buy any outfit or emote you want without worrying about the cost. You can dress up as your favorite character or create your own unique style.</li>
-<li><b>You can use cheats to win matches:</b> With the mega menu, you can enable various cheats that can give you an edge over other players. For example, you can increase your speed, fly over obstacles, avoid falling damage, or become invisible.</li>
-<li><b>You can remove ads and enjoy uninterrupted gameplay:</b> With the modded version of the game, you can disable ads that may pop up during the game. This way, you can enjoy playing without any distractions or interruptions.</li>
-</ul>
- <h3>How to download and install Stumble Guys Mod APK</h3>
- <p>Here are the steps to download and install Stumble Guys Mod APK:</p>
- <ol>
-<li>Go to TekMods website and click on the Download button. You will be redirected to another page where you have to complete a captcha verification.</li>
-<li>After completing the captcha verification, click on the Download button again. You will see a pop-up window asking you to allow downloads from this source. Click on Allow.</li>
-<li>The APK file will start downloading automatically. Once the download is complete, go to your file manager and locate the file.</li>
-<li>Tap on the file and click on Install. You may have to enable Unknown Sources in your device settings if you haven't done so before.</li>
-<li>Wait for the installation process to finish. Once it is done, you can launch the game and enjoy playing with unlimited money and gems.</li>
-</ol>
- <h2>Tips and tricks for winning Stumble Guys matches</h2>
- <p>If you want to win more matches in Stumble Guys, you need to master some skills and strategies that can help you overcome the obstacles and beat your opponents. Here are some tips and tricks that can help you become a better player:</p>
- <h3>Configure your controls before you begin</h3>
- <p>Before you start playing, make sure that you are comfortable with your controls. You can adjust them according to your preference by going to Settings > <p>Before you start playing, make sure that you are comfortable with your controls. You can adjust them according to your preference by going to Settings > Controls. You can choose between joystick or button controls, and change the sensitivity and size of the buttons. You can also enable or disable vibration and sound effects.</p>
- <p>Having the right controls can make a big difference in your gameplay, as they can affect your movement, speed, and accuracy. You can also use BlueStacks to customize your keyboard and mouse controls for playing on PC.</p>
- <h3>Learn to use the physics of your character to your advantage</h3>
- <p>Stumble Guys has realistic physics that make the game more fun and challenging. However, you can also use them to your advantage if you know how to manipulate them. For example, you can use momentum to jump farther, or use inertia to slide faster. You can also use gravity to fall faster, or use friction to slow down.</p>
- <p>You can also use the physics of other players to your benefit. You can bump into them, push them, or grab them to hinder their progress or help yours. You can also use them as stepping stones, shields, or distractions.</p>
- <h3>Use the challenges to your advantage</h3>
- <p>Each level in Stumble Guys has different challenges that you have to overcome. These challenges can be obstacles, traps, enemies, or timers. However, you can also use them to your advantage if you know how to deal with them. For example, you can use obstacles to block other players, or use traps to eliminate them. You can also use enemies to distract them, or use timers to pressure them.</p>
- <p>You can also use the challenges to improve your skills and strategies. You can learn from your mistakes, adapt to different situations, and discover new ways to win. You can also challenge yourself by trying different modes, such as solo mode, team mode, or custom mode.</p>
- <h3>It is not always about being first</h3>
- <p>One of the most common mistakes that players make in Stumble Guys is trying to be first in every round. While being first can give you an advantage, it is not always necessary or possible. Sometimes, being first can also put you at a disadvantage, as you may have to face more obstacles, enemies, or competition.</p>
- <p>Instead of focusing on being first, you should focus on being smart and strategic. You should know when to rush and when to wait, when to follow and when to lead, when to cooperate and when to compete. You should also know how to avoid unnecessary risks and conflicts that may cost you your position or your life.</p>
- <h3>Rather than waiting, leap</h3>
- <p>Another common mistake that players make in Stumble Guys is waiting too long for the right moment to jump or move. While waiting can sometimes be a good strategy, it can also be a bad one if you wait too long. Waiting too long can make you lose time, momentum, or opportunities. It can also make you an easy target for other players or enemies.</p>
- <p>Instead of waiting too long, you should leap when you see an opening or a chance. Leaping can help you gain speed, distance, or height. It can also help you avoid obstacles, traps, or attacks. Leaping can also surprise your opponents and give you an edge over them.</p>
- <h2>Conclusion</h2>
- <p>Stumble Guys is a fun and addictive game that lets you compete with other players in chaotic obstacle courses. It is available for free on Android and iOS devices, but you can also play it on PC with BlueStacks. If you want to get unlimited money and gems in the game, you can download and install the modded version of the game from TekMods website. However, be careful of the risks involved in using mods.</p>
- <p>If you want to win more matches in Stumble Guys, you need to master some skills and strategies that can help you overcome the challenges and beat your opponents. Some of these tips and tricks are configuring your controls before you begin, learning to use the physics of your character to your advantage, using the challenges to your advantage, not always being first, and leaping rather than waiting.</p>
- <p>We hope that this article has helped you learn more about Stumble Guys APK Dinheiro Infinito 2022 and how to play it on PC with BlueStacks. If you have any questions or feedback, please let us know in the comments below.</p>
- <h3>FAQs</h3>
- <ul>
-<li><b>Q: Is Stumble Guys safe to play?</b></li>
-<li><b>A:</b> Yes, Stumble Guys is safe to play as long as you download it from the official sources such as Google Play Store or App Store. However, if you download mod <p>However, if you download modded versions of the game from unknown sources, you may expose your device or account to potential risks such as viruses, malware, or bans. Therefore, we advise you to be careful and use mods at your own risk.</p>
- <li><b>Q: How can I get more money and gems in Stumble Guys?</b></li>
-<li><b>A:</b> You can get more money and gems in Stumble Guys by playing more matches, completing daily missions, watching ads, or buying them with real money. You can also use the modded version of the game that gives you unlimited money and gems, but this may not be safe or fair.</li>
- <li><b>Q: How can I play Stumble Guys with my friends?</b></li>
-<li><b>A:</b> You can play Stumble Guys with your friends by creating a private room and inviting them to join. You can also join a public room and invite your friends to follow you. You can also play in party mode, where you can team up or compete against your friends in different modes.</li>
- <li><b>Q: How can I customize my character in Stumble Guys?</b></li>
-<li><b>A:</b> You can customize your character in Stumble Guys by going to the Shop and buying different outfits and emotes with money or gems. You can also unlock more outfits and emotes by playing more matches and leveling up. You can change your outfit and emote by going to the Locker and selecting the ones you want.</li>
- <li><b>Q: How can I contact the developers of Stumble Guys?</b></li>
-<li><b>A:</b> You can contact the developers of Stumble Guys by sending them an email at support@kitkagames.com or by following them on their social media accounts such as Facebook, Twitter, Instagram, or YouTube. You can also visit their official website at https://www.kitkagames.com/.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/EXCLUSIVE Download M 39ke Harru.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/EXCLUSIVE Download M 39ke Harru.md
deleted file mode 100644
index 0add6850ff5633ed5a205ff96df3bd971385ac41..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/EXCLUSIVE Download M 39ke Harru.md	
+++ /dev/null
@@ -1,86 +0,0 @@
-<br />
-<h1>Download M'ke Harru: A Hit Song by Mars and Ciljeta</h1>
-<p>If you are looking for a new song to add to your playlist, you might want to check out M'ke Harru, a hit song by Mars and Ciljeta. This song is a fusion of pop, R&B, and hip-hop, with catchy melodies, smooth vocals, and rap verses. In this article, we will tell you what M'ke Harru is, how to download it, and why you should download it.</p>
-<h2>download m 39;ke harru</h2><br /><p><b><b>Download Zip</b> &#10026; <a href="https://ssurll.com/2uNTGl">https://ssurll.com/2uNTGl</a></b></p><br /><br />
- <h2>What is M'ke Harru?</h2>
-<p>M'ke Harru is a song by Mars and Ciljeta, two Albanian singers and rappers. The song was released in 2021 and produced by BO Beatz. The title of the song means "You made me forget" in English, and it is about a love story that ends in heartbreak. The song has a duration of 3 minutes and 1 second, and it has over 28 million views on YouTube.</p>
- <h3>The meaning and lyrics of M'ke Harru</h3>
-<p>The lyrics of M'ke Harru are in Albanian, but you can find the English translation online. The song tells the story of a couple who fell in love, but then broke up because of betrayal and lies. The chorus of the song goes like this:</p>
-<blockquote>
-<p>M’ke harru ti për një tjetër<br>
-You forgot me for another one<br>
-M’ke harru ti për një tjetër<br>
-You forgot me for another one<br>
-M’ke harru ti për një tjetër<br>
-You forgot me for another one<br>
-M’ke harru ti për një tjetër<br>
-You forgot me for another one</p>
-</blockquote>
-<p>The song expresses the pain and anger of the person who was left behind, as well as the regret and guilt of the person who left. The song also shows how love can make people forget everything else, but also how love can fade away.</p>
-<p></p>
- <h3>The artists behind M'ke Harru</h3>
-<p>Mars and Ciljeta are two talented and successful artists from Albania. Mars is a singer, rapper, songwriter, and producer who started his career in 2010. He has released several albums and singles, such as "Një Ditë", "Kush Jemi Ne", and "Shumë Larg". He is known for his versatile style, blending pop, R&B, hip-hop, trap, and reggae. He has collaborated with other artists such as Butrint Imeri, Sergio, 2Ton, and Xhensila.</p>
-<p>Ciljeta is a singer, rapper, songwriter, and TV personality who started her career in 2004. She has released three albums and several singles, such as "Dridhe", "Big Booty", "No Love", and "Leyla". She is known for her powerful voice, charismatic presence, and feminist messages. She has collaborated with other artists such as Elvana Gjata, Capital T, Ledri Vula, and Dafina Zeqiri.</p>
- <h2>How to download M'ke Harru?</h2>
-<p>If you want to download M'ke Harru to your device, you have several options. You can choose from different platforms that offer streaming or downloading services. Here are some of the most popular ones:</p>
- <h3>Download M'ke Harru from Spotify</h3>
-<p>Spotify is one <p>Spotify is one of the most popular music streaming platforms in the world. It has over 70 million songs, podcasts, and playlists that you can listen to online or offline. You can download M'ke Harru from Spotify by following these steps:</p>
-<ol>
-<li>Create a Spotify account or log in to your existing one.</li>
-<li>Search for M'ke Harru by Mars and Ciljeta on the search bar.</li>
-<li>Click on the song and then click on the three dots icon on the top right corner.</li>
-<li>Select "Download" from the menu that appears.</li>
-<li>Wait for the song to download to your device. You can check the progress on the "Downloads" section of your library.</li>
-<li>Enjoy listening to M'ke Harru offline whenever you want.</li>
-</ol>
-<p>Note that you need a Spotify Premium subscription to download songs from Spotify. You can get a free trial or a discounted plan if you are a student, a family member, or a duo partner.</p>
- <h3>Download M'ke Harru from iTunes</h3>
-<p>iTunes is another popular music platform that allows you to buy and download songs, albums, and videos. You can also access Apple Music, a streaming service that has over 75 million songs, radio stations, and playlists. You can download M'ke Harru from iTunes by following these steps:</p>
-<ol>
-<li>Open iTunes on your computer or device, or download it from the Apple website if you don't have it.</li>
-<li>Search for M'ke Harru by Mars and Ciljeta on the search bar.</li>
-<li>Click on the song and then click on the price button on the right side.</li>
-<li>Enter your Apple ID and password, or create one if you don't have one.</li>
-<li>Confirm your purchase and wait for the song to download to your device. You can check the progress on the "Downloads" section of your library.</li>
-<li>Enjoy listening to M'ke Harru offline whenever you want.</li>
-</ol>
-<p>Note that you need an Apple device or a Windows computer to use iTunes. You also need a valid payment method to buy songs from iTunes.</p>
- <h3>Download M'ke Harru from Qobuz</h3>
-<p>Qobuz is a music platform that specializes in high-resolution audio. It offers streaming and downloading services for over 70 million songs, albums, and videos. You can download M'ke Harru from Qobuz by following these steps:</p>
-<ol>
-<li>Create a Qobuz account or log in to your existing one.</li>
-<li>Search for M'ke Harru by Mars and Ciljeta on the search bar.</li>
-<li>Click on the song and then click on the "Buy" button on the right side.</li>
-<li>Select the quality and format of the audio file that you want to download. You can choose from MP3, FLAC, WAV, AIFF, ALAC, or DSD.</li>
-<li>Enter your payment details and confirm your purchase.</li>
-<li>Wait for the song to download to your device. You can check the progress on the "Downloads" section of your account.</li>
-<li>Enjoy listening to M'ke Harru offline whenever you want.</li>
-</ol>
-<p>Note that you need a Qobuz subscription to download songs from Qobuz. You can get a free trial or a discounted plan if you are a student, a family member, or a duo partner.</p>
- <h2>Why you should download M'ke Harru?</h2>
-<p>M'ke Harru is not just a song, it is an experience. Here are some of the reasons why you should download M'ke Harru:</p>
- <h3>M'ke Harru is a catchy and romantic song</h3>
-<p>M'ke Harru has a catchy melody that will make you want to sing along. The song is also romantic, as it expresses the feelings of love and loss that many people can relate to. The song has a mix of pop, R&B, and hip-hop elements that create a unique and appealing sound. The song is perfect for any mood, whether you want to dance, cry, or chill.</p>
- <h3>M'ke Harru is a high-quality production</h3>
-<p>M'ke Harru is not just a song, it is also a masterpiece of production. The song was produced by BO Beatz, who is known for his skills and creativity. The song has a crisp and clear sound quality that will make you appreciate every detail. The song also has a stunning music video that was directed by Imagine Films. The video features Mars and Ciljeta in different scenarios that match the theme of the song. The video has over 28 million views on YouTube, which shows how popular and successful the song is.</p>
- <h3>M'ke Harru is a popular and trending song</h3>
-<p>M'ke Harru is not just a song, it is also a phenomenon. The song has been widely praised and acclaimed by critics and fans alike. The song has won several awards and nominations, such as the Best Song of the Year and the Best Collaboration at the Top Music Awards 2021. The song has also been featured on various charts and playlists, such as the Spotify Top 50 Albania, the iTunes Top 100 Albania, and the Qobuz Top 10 Albania. The song has also inspired many covers, remixes, and memes on social media platforms such as TikTok, Instagram, and Facebook.</p>
- <h2>Conclusion</h2>
-<p>M'ke Harru is a hit song by Mars and Ciljeta that you should definitely download to your device. The song is a fusion of pop, R&B, and hip-hop, with catchy melodies, smooth vocals, and rap verses. The song is about a love story that ends in heartbreak, and it expresses the pain and anger of the person who was left behind, as well as the regret and guilt of the person who left. The song is a high-quality production that has a crisp and clear sound quality, and a stunning music video that matches the theme of the song. The song is also a popular and trending song that has been widely praised and acclaimed by critics and fans alike, and has won several awards and nominations. The song has also been featured on various charts and playlists, and has inspired many covers, remixes, and memes on social media platforms.</p>
-<p>If you want to download M'ke Harru to your device, you can choose from different platforms that offer streaming or downloading services, such as Spotify, iTunes, or Qobuz. You can follow the steps that we have provided in this article to download M'ke Harru easily and quickly. You can also check out the meaning and lyrics of M'ke Harru online, as well as the artists behind M'ke Harru, Mars and Ciljeta.</p>
-<p>We hope that this article has helped you learn more about M'ke Harru, and why you should download it. M'ke Harru is a catchy and romantic song that will make you feel different emotions, and will make you want to sing along. Download M'ke Harru today and enjoy listening to it offline whenever you want.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about M'ke Harru:</p>
-<ol>
-<li>What does M'ke Harru mean in English?</li>
-<p>M'ke Harru means "You made me forget" in English. It is the title of a hit song by Mars and Ciljeta.</p>
-<li>Who are Mars and Ciljeta?</li>
-<p>Mars and Ciljeta are two Albanian singers and rappers who collaborated on M'ke Harru. They are both talented and successful artists who have released several albums and singles.</p>
-<li>Where can I download M'ke Harru?</li>
-<p>You can download M'ke Harru from different platforms that offer streaming or downloading services, such as Spotify, iTunes, or Qobuz. You can follow the steps that we have provided in this article to download M'ke Harru easily and quickly.</p>
-<li>How long is M'ke Harru?</li>
-<p>M'ke Harru has a duration of 3 minutes and 1 second. It was released in 2021 and produced by BO Beatz.</p>
-<li>Why should I download M'ke Harru?</li>
-<p>You should download M'ke Harru because it is a catchy and romantic song that will make you feel different emotions. It is also a high-quality production that has a crisp and clear sound quality, and a stunning music video that matches the theme of the song. It is also a popular and trending song that has been widely praised and acclaimed by critics and fans alike, and has won several awards and nominations.</p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/sohojoe/project_charles/ffmpeg_converter.py b/spaces/sohojoe/project_charles/ffmpeg_converter.py
deleted file mode 100644
index 6942c47572a9d33d3e99b0122abd01a33f7a8660..0000000000000000000000000000000000000000
--- a/spaces/sohojoe/project_charles/ffmpeg_converter.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import asyncio
-import ray
-from ray.util.queue import Queue, Full
-
-class FFMpegConverter:
-    def __init__(self, output_queue: Queue, buffer_size: int = 1920, output_format: str='s16le'):
-        self.output_queue = output_queue
-        self.buffer_size = buffer_size
-        self.output_format = output_format
-        self.input_pipe = None
-        self.output_pipe = None
-        self.process = None
-        self.running = True
-
-    async def run(self):
-        while self.running:
-            try:
-                chunk = await self.output_pipe.readexactly(self.buffer_size)
-            except asyncio.IncompleteReadError:
-                # exit if we have finsihsed the process
-                if self.running == False:
-                    return
-                # If the pipe is broken, restart the process.
-                await self.start_process()
-                continue
-            if self.running == False:
-                return
-            # print(f"FFMpegConverter: read {len(chunk)} bytes")
-            chunk_ref = ray.put(chunk)
-            keep_trying = True
-            while keep_trying:
-                try:
-                    await self.output_queue.put_async(chunk_ref, timeout=0.01)
-                    keep_trying = False
-                except Full:
-                    if self.running == False:
-                        return                    
-                    await asyncio.sleep(0.01)
-
-    async def start_process(self):
-        cmd = [
-            'ffmpeg',
-            '-i', 'pipe:0',  # read from stdin
-            '-f', self.output_format,
-            '-ar', '48000',
-            '-ac', '1',
-            'pipe:1'  # write to stdout
-        ]
-
-        self.process = await asyncio.create_subprocess_exec(
-            *cmd,
-            stdin=asyncio.subprocess.PIPE,
-            stdout=asyncio.subprocess.PIPE,
-            stderr=asyncio.subprocess.PIPE
-        )
-
-        self.input_pipe = self.process.stdin
-        self.output_pipe = self.process.stdout
-
-        assert self.input_pipe is not None, "input_pipe was not initialized"
-        # print (f"input_pipe: {self.input_pipe}")
-
-    async def push_chunk(self, chunk):
-        if self.running == False:
-            return
-        try:
-            self.input_pipe.write(chunk)
-            await self.input_pipe.drain()
-        except BrokenPipeError:
-            # If the pipe is broken, restart the process.
-            await self.start_process()
-            self.input_pipe.write(chunk)
-            await self.input_pipe.drain()
-
-    async def close(self):
-        self.running = False  # Stop the loop inside run()
-        if self.process:
-            self.process.stdin.transport.close()
-            self.process.kill()
-            self.process.terminate()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/legacy/masked_lm_dataset.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/legacy/masked_lm_dataset.py
deleted file mode 100644
index dd8ea2c60aff306ab3a756223a298a28d41a4991..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/legacy/masked_lm_dataset.py
+++ /dev/null
@@ -1,303 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from typing import Dict, List, Tuple
-
-import numpy as np
-import torch
-from fairseq.data import Dictionary, FairseqDataset, data_utils
-from fairseq.data.concat_dataset import ConcatDataset
-from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
-from fairseq.data.token_block_dataset import TokenBlockDataset
-
-
-class MaskedLMDataset(FairseqDataset):
-    """
-    A wrapper Dataset for masked language modelling. The dataset
-    wraps around TokenBlockDataset or BlockedPairDataset and creates a batch
-    where the input blocks are masked according to the specified masking
-    probability. Additionally the batch can also contain sentence level targets
-    if this is specified.
-
-    Args:
-        dataset: Dataset which generates blocks of data. Only BlockPairDataset
-            and TokenBlockDataset are supported.
-        sizes: Sentence lengths
-        vocab: Dictionary with the vocabulary and special tokens.
-        pad_idx: Id of padding token in dictionary
-        mask_idx: Id of mask token in dictionary
-        classif_token_idx: Id of classification token in dictionary. This is the
-            token associated with the sentence embedding (Eg: CLS for BERT)
-        sep_token_idx: Id of separator token in dictionary
-            (Eg: SEP in BERT)
-        seed: Seed for random number generator for reproducibility.
-        shuffle: Shuffle the elements before batching.
-        has_pairs: Specifies whether the underlying dataset
-            generates a pair of blocks along with a sentence_target or not.
-            Setting it to True assumes that the underlying dataset generates a
-            label for the pair of sentences which is surfaced as
-            sentence_target. The default value assumes a single block with no
-            sentence target.
-        segment_id: An optional segment id for filling in the segment labels
-            when we are in the single block setting (Eg: XLM). Default is 0.
-        masking_ratio: specifies what percentage of the blocks should be masked.
-        masking_prob: specifies the probability of a given token being
-            replaced with the "MASK" token.
-        random_token_prob: specifies the probability of a given token being
-            replaced by a random token from the vocabulary.
-    """
-
-    def __init__(
-        self,
-        dataset: FairseqDataset,
-        sizes: np.ndarray,
-        vocab: Dictionary,
-        pad_idx: int,
-        mask_idx: int,
-        classif_token_idx: int,
-        sep_token_idx: int,
-        seed: int = 1,
-        shuffle: bool = True,
-        has_pairs: bool = True,
-        segment_id: int = 0,
-        masking_ratio: float = 0.15,
-        masking_prob: float = 0.8,
-        random_token_prob: float = 0.1,
-    ):
-        # Make sure the input datasets are the ones supported
-        assert (
-            isinstance(dataset, TokenBlockDataset)
-            or isinstance(dataset, BlockPairDataset)
-            or isinstance(dataset, ConcatDataset)
-        ), (
-            "MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or "
-            "ConcatDataset"
-        )
-
-        self.dataset = dataset
-        self.sizes = np.array(sizes)
-        self.vocab = vocab
-        self.pad_idx = pad_idx
-        self.mask_idx = mask_idx
-        self.classif_token_idx = classif_token_idx
-        self.sep_token_idx = sep_token_idx
-        self.shuffle = shuffle
-        self.seed = seed
-        self.has_pairs = has_pairs
-        self.segment_id = segment_id
-        self.masking_ratio = masking_ratio
-        self.masking_prob = masking_prob
-        self.random_token_prob = random_token_prob
-
-        # If we have only one block then sizes needs to be updated to include
-        # the classification token
-        if not has_pairs:
-            self.sizes = self.sizes + 1
-
-    def __getitem__(self, index: int):
-        # if has_pairs, then expect 2 blocks and a sentence target
-        if self.has_pairs:
-            (block_one, block_two, sentence_target) = self.dataset[index]
-        else:
-            block_one = self.dataset[index]
-
-        return {
-            "id": index,
-            "block_one": block_one,
-            "block_two": block_two if self.has_pairs else None,
-            "sentence_target": sentence_target if self.has_pairs else None,
-        }
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def _mask_block(
-        self,
-        sentence: np.ndarray,
-        mask_idx: int,
-        pad_idx: int,
-        dictionary_token_range: Tuple,
-    ):
-        """
-        Mask tokens for Masked Language Model training
-        Samples mask_ratio tokens that will be predicted by LM.
-
-        Note:This function may not be efficient enough since we had multiple
-        conversions between np and torch, we can replace them with torch
-        operators later.
-
-        Args:
-            sentence: 1d tensor to be masked
-            mask_idx: index to use for masking the sentence
-            pad_idx: index to use for masking the target for tokens we aren't
-                predicting
-            dictionary_token_range: range of indices in dictionary which can
-                be used for random word replacement
-                (e.g. without special characters)
-        Return:
-            masked_sent: masked sentence
-            target: target with words which we are not predicting replaced
-                by pad_idx
-        """
-        masked_sent = np.copy(sentence)
-        sent_length = len(sentence)
-        mask_num = math.ceil(sent_length * self.masking_ratio)
-        mask = np.random.choice(sent_length, mask_num, replace=False)
-        target = np.copy(sentence)
-
-        for i in range(sent_length):
-            if i in mask:
-                rand = np.random.random()
-
-                # replace with mask if probability is less than masking_prob
-                # (Eg: 0.8)
-                if rand < self.masking_prob:
-                    masked_sent[i] = mask_idx
-
-                # replace with random token if probability is less than
-                # masking_prob + random_token_prob (Eg: 0.9)
-                elif rand < (self.masking_prob + self.random_token_prob):
-                    # sample random token from dictionary
-                    masked_sent[i] = np.random.randint(
-                        dictionary_token_range[0], dictionary_token_range[1]
-                    )
-            else:
-                target[i] = pad_idx
-
-        return masked_sent, target
-
-    def _collate(self, samples: List[Dict], pad_idx: int, eos_idx: int):
-        """
-        Does the heavy lifting for creating a batch from the input list of
-        examples. The logic is as follows:
-            1. Mask the input blocks. In case has_pair is True then we have 2
-               blocks to mask.
-            2. Prepend the first masked block tensor with the special token
-               used as sentence embedding. Eg: CLS in BERT. This happens
-               irrespective of the value of has_pair.
-            3. If has_pair is True, then append the first masked block with the
-               special separator token (eg: SEP for BERT) and compute segment
-               label accordingly. In this case, also append the second masked
-               block with this special separator token and compute its segment
-               label.
-            4. For the targets tensor, prepend and append with padding index
-               accordingly.
-            5. Concatenate all tensors.
-        """
-        if len(samples) == 0:
-            return {}
-        # To ensure determinism, we reset the state of the PRNG after every
-        # batch based on the seed and the first id of the batch. This ensures
-        # that across epochs we get the same mask for the same example. This
-        # is needed for reproducibility and is how BERT does masking
-        # TODO: Can we add deteminism without this constraint?
-        with data_utils.numpy_seed(self.seed + samples[0]["id"]):
-            for s in samples:
-
-                # token range is needed for replacing with random token during
-                # masking
-                token_range = (self.vocab.nspecial, len(self.vocab))
-
-                # mask according to specified probabilities.
-                masked_blk_one, masked_tgt_one = self._mask_block(
-                    s["block_one"],
-                    self.mask_idx,
-                    self.pad_idx,
-                    token_range,
-                )
-
-                tokens = np.concatenate([[self.classif_token_idx], masked_blk_one])
-                targets = np.concatenate([[self.pad_idx], masked_tgt_one])
-                segments = np.ones(len(tokens)) * self.segment_id
-
-                # if has_pairs is True then we need to add the SEP token to both
-                # the blocks after masking and re-compute segments based on the new
-                # lengths.
-                if self.has_pairs:
-                    tokens_one = np.concatenate([tokens, [self.sep_token_idx]])
-                    targets_one = np.concatenate([targets, [self.pad_idx]])
-
-                    masked_blk_two, masked_tgt_two = self._mask_block(
-                        s["block_two"], self.mask_idx, self.pad_idx, token_range
-                    )
-                    tokens_two = np.concatenate([masked_blk_two, [self.sep_token_idx]])
-                    targets_two = np.concatenate([masked_tgt_two, [self.pad_idx]])
-
-                    # block + 1 sep + 1 special (CLS)
-                    segments_one = np.zeros(len(tokens_one))
-                    # block + 1 sep
-                    segments_two = np.ones(len(tokens_two))
-
-                    tokens = np.concatenate([tokens_one, tokens_two])
-                    targets = np.concatenate([targets_one, targets_two])
-                    segments = np.concatenate([segments_one, segments_two])
-
-                s["source"] = torch.LongTensor(tokens)
-                s["segment_labels"] = torch.LongTensor(segments)
-                s["lm_target"] = torch.LongTensor(targets)
-
-        def merge(key):
-            return data_utils.collate_tokens(
-                [s[key] for s in samples], pad_idx, eos_idx, left_pad=False
-            )
-
-        return {
-            "id": torch.LongTensor([s["id"] for s in samples]),
-            "ntokens": sum(len(s["source"]) for s in samples),
-            "net_input": {
-                "src_tokens": merge("source"),
-                "segment_labels": merge("segment_labels"),
-            },
-            "lm_target": merge("lm_target"),
-            "sentence_target": torch.LongTensor([s["sentence_target"] for s in samples])
-            if self.has_pairs
-            else None,
-            "nsentences": len(samples),
-        }
-
-    def collater(self, samples: List[Dict]):
-        """Merge a list of samples to form a mini-batch.
-
-        Args:
-            samples (List[dict]): samples to collate
-
-        Returns:
-            dict: a mini-batch of data
-        """
-        return self._collate(samples, self.vocab.pad(), self.vocab.eos())
-
-    def num_tokens(self, index: int):
-        """
-        Return the number of tokens in a sample. This value is used to
-        enforce max-tokens during batching.
-        """
-        return self.sizes[index]
-
-    def size(self, index: int):
-        """
-        Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with max-positions.
-        """
-        return self.sizes[index]
-
-    def ordered_indices(self):
-        """
-        Return an ordered list of indices. Batches will be constructed based
-        on this order.
-        """
-        if self.shuffle:
-            return np.random.permutation(len(self))
-        else:
-            order = [np.arange(len(self))]
-            order.append(self.sizes)
-            return np.lexsort(order)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        self.dataset.prefetch(indices)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/model_parallel/models/transformer_lm.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/model_parallel/models/transformer_lm.py
deleted file mode 100644
index dc52f6e8dd3899b6bf9bebae7415cee20baf9884..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/model_parallel/models/transformer_lm.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch.nn as nn
-from fairseq.model_parallel.models.transformer import ModelParallelTransformerDecoder
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.transformer_lm import TransformerLanguageModel
-
-
-try:
-    from fairseq.model_parallel.megatron.mpu import VocabParallelEmbedding
-
-    has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
-    has_megatron_submodule = False
-
-
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-@register_model("model_parallel_transformer_lm")
-class ModelParallelTransformerLanguageModel(TransformerLanguageModel):
-
-    @staticmethod
-    def add_args(parser):
-        TransformerLanguageModel.add_args(parser)
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        if not has_megatron_submodule:
-            raise ImportError(
-                "\n\nPlease install the megatron submodule:"
-                "\n\n  git submodule update --init "
-                "fairseq/model_parallel/megatron"
-            )
-
-        # make sure all arguments are present in older models
-        base_lm_architecture(args)
-
-        task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-        task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-
-        if args.decoder_layers_to_keep:
-            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
-
-        if getattr(args, "max_target_positions", None) is None:
-            args.max_target_positions = getattr(
-                args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS
-            )
-
-        if args.character_embeddings:
-            raise NotImplementedError(
-                "Character embeddings is not supported for model parallel"
-            )
-        elif args.adaptive_input:
-            raise NotImplementedError(
-                "Adaptive input is not supported for model parallel"
-            )
-        else:
-            embed_tokens = cls.build_embedding(
-                args, task.source_dictionary, args.decoder_input_dim
-            )
-
-        decoder = ModelParallelTransformerDecoder(
-            args,
-            task.target_dictionary,
-            embed_tokens,
-            no_encoder_attn=True,
-        )
-        return cls(decoder)
-
-    @staticmethod
-    def add_args(parser):
-        TransformerLanguageModel.add_args(parser)
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        def _vocab_init(tensor, **kwargs):
-            nn.init.normal_(tensor, mean=0, std=embed_dim ** -0.5)
-            nn.init.constant_(tensor[1], 0)
-
-        embed_tokens = VocabParallelEmbedding(
-            len(dictionary), embed_dim, dictionary.pad(), init_method=_vocab_init
-        )
-        return embed_tokens
-
-
-def base_lm_architecture(args):
-    # backward compatibility for older model checkpoints
-    if hasattr(args, "no_tie_adaptive_proj"):
-        # previous models defined --no-tie-adaptive-proj, so use the existence of
-        # that option to determine if this is an "old" model checkpoint
-        args.no_decoder_final_norm = True  # old models always set this to True
-        if args.no_tie_adaptive_proj is False:
-            args.tie_adaptive_proj = True
-    if hasattr(args, "decoder_final_norm"):
-        args.no_decoder_final_norm = not args.decoder_final_norm
-
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.relu_dropout = getattr(args, "relu_dropout", 0.0)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048)
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    # Model training is not stable without this
-    args.decoder_normalize_before = True
-    args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", False)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4)
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.character_embeddings = getattr(args, "character_embeddings", False)
-    args.character_filters = getattr(
-        args,
-        "character_filters",
-        "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
-    )
-    args.character_embedding_dim = getattr(args, "character_embedding_dim", 4)
-    args.char_embedder_highway_layers = getattr(args, "char_embedder_highway_layers", 2)
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4)
-    args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None)
-    args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
-    args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
-    args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0.0)
-    args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0.0)
-    args.add_bos_token = getattr(args, "add_bos_token", False)
-
-
-@register_model_architecture("model_parallel_transformer_lm", "transformer_lm_megatron")
-def transformer_lm_megatron(args):
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 4)
-    args.decoder_layers = getattr(args, "decoder_layers", 72)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32)
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
-
-
-@register_model_architecture(
-    "model_parallel_transformer_lm", "transformer_lm_megatron_11b"
-)
-def transformer_lm_megatron_11b(args):
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 6)
-    args.decoder_layers = getattr(args, "decoder_layers", 72)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32)
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/registry.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/registry.py
deleted file mode 100644
index f3b9406043d75a51d7bf4af5294f82b33a8f9a5e..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/registry.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from argparse import Namespace
-
-from typing import Union
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.utils import merge_with_parent
-from hydra.core.config_store import ConfigStore
-from omegaconf import DictConfig
-
-REGISTRIES = {}
-
-
-def setup_registry(registry_name: str, base_class=None, default=None, required=False):
-    assert registry_name.startswith("--")
-    registry_name = registry_name[2:].replace("-", "_")
-
-    REGISTRY = {}
-    REGISTRY_CLASS_NAMES = set()
-    DATACLASS_REGISTRY = {}
-
-    # maintain a registry of all registries
-    if registry_name in REGISTRIES:
-        return  # registry already exists
-    REGISTRIES[registry_name] = {
-        "registry": REGISTRY,
-        "default": default,
-        "dataclass_registry": DATACLASS_REGISTRY,
-    }
-
-    def build_x(cfg: Union[DictConfig, str, Namespace], *extra_args, **extra_kwargs):
-        if isinstance(cfg, DictConfig):
-            choice = cfg._name
-
-            if choice and choice in DATACLASS_REGISTRY:
-                dc = DATACLASS_REGISTRY[choice]
-                cfg = merge_with_parent(dc(), cfg)
-        elif isinstance(cfg, str):
-            choice = cfg
-            if choice in DATACLASS_REGISTRY:
-                cfg = DATACLASS_REGISTRY[choice]()
-        else:
-            choice = getattr(cfg, registry_name, None)
-            if choice in DATACLASS_REGISTRY:
-                cfg = DATACLASS_REGISTRY[choice].from_namespace(cfg)
-
-        if choice is None:
-            if required:
-                raise ValueError("{} is required!".format(registry_name))
-            return None
-
-        cls = REGISTRY[choice]
-        if hasattr(cls, "build_" + registry_name):
-            builder = getattr(cls, "build_" + registry_name)
-        else:
-            builder = cls
-
-        return builder(cfg, *extra_args, **extra_kwargs)
-
-    def register_x(name, dataclass=None):
-        def register_x_cls(cls):
-            if name in REGISTRY:
-                raise ValueError(
-                    "Cannot register duplicate {} ({})".format(registry_name, name)
-                )
-            if cls.__name__ in REGISTRY_CLASS_NAMES:
-                raise ValueError(
-                    "Cannot register {} with duplicate class name ({})".format(
-                        registry_name, cls.__name__
-                    )
-                )
-            if base_class is not None and not issubclass(cls, base_class):
-                raise ValueError(
-                    "{} must extend {}".format(cls.__name__, base_class.__name__)
-                )
-
-            if dataclass is not None and not issubclass(dataclass, FairseqDataclass):
-                raise ValueError(
-                    "Dataclass {} must extend FairseqDataclass".format(dataclass)
-                )
-
-            cls.__dataclass = dataclass
-            if cls.__dataclass is not None:
-                DATACLASS_REGISTRY[name] = cls.__dataclass
-
-                cs = ConfigStore.instance()
-                node = dataclass()
-                node._name = name
-                cs.store(name=name, group=registry_name, node=node, provider="fairseq")
-
-            REGISTRY[name] = cls
-
-            return cls
-
-        return register_x_cls
-
-    return build_x, register_x, REGISTRY, DATACLASS_REGISTRY
diff --git a/spaces/sriramelango/Social_Classification_Public/utils/cider/pyciderevalcap/cider/cider.py b/spaces/sriramelango/Social_Classification_Public/utils/cider/pyciderevalcap/cider/cider.py
deleted file mode 100644
index 5b65978370cb82dd2111500e7f05c4d05306162c..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/utils/cider/pyciderevalcap/cider/cider.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Filename: cider.py
-#
-#
-# Description: Describes the class to compute the CIDEr
-# (Consensus-Based Image Description Evaluation) Metric
-#          by Vedantam, Zitnick, and Parikh (http://arxiv.org/abs/1411.5726)
-#
-# Creation Date: Sun Feb  8 14:16:54 2015
-#
-# Authors: Ramakrishna Vedantam <vrama91@vt.edu> and
-# Tsung-Yi Lin <tl483@cornell.edu>
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from .cider_scorer import CiderScorer
-
-
-class Cider:
-    """
-    Main Class to compute the CIDEr metric
-
-    """
-    def __init__(self, n=4, df="corpus"):
-        """
-        Initialize the CIDEr scoring function
-        : param n (int): n-gram size
-        : param df (string): specifies where to get the IDF values from
-                    takes values 'corpus', 'coco-train'
-        : return: None
-        """
-        # set cider to sum over 1 to 4-grams
-        self._n = n
-        self._df = df
-        self.cider_scorer = CiderScorer(n=self._n, df_mode=self._df)
-
-    def compute_score(self, gts, res):
-        """
-        Main function to compute CIDEr score
-        : param  gts (dict) : {image:tokenized reference sentence}
-        : param res (dict)  : {image:tokenized candidate sentence}
-        : return: cider (float) : computed CIDEr score for the corpus
-        """
-
-        # clear all the previous hypos and refs
-        self.cider_scorer.clear()
-
-        for res_id in res:
-
-            hypo = res_id['caption']
-            ref = gts[res_id['image_id']]
-
-            # Sanity check.
-            assert(type(hypo) is list)
-            assert(len(hypo) == 1)
-            assert(type(ref) is list)
-            assert(len(ref) > 0)
-            self.cider_scorer += (hypo[0], ref)
-
-        (score, scores) = self.cider_scorer.compute_score()
-
-        return score, scores
-
-    def method(self):
-        return "CIDEr"
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Clip Studio Paint Pro Torrent 2021.md b/spaces/stomexserde/gpt4-ui/Examples/Clip Studio Paint Pro Torrent 2021.md
deleted file mode 100644
index 6c092ecc26f0ae7729f017679ab11797cb995390..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Clip Studio Paint Pro Torrent 2021.md	
+++ /dev/null
@@ -1,137 +0,0 @@
-
-<h1>Clip Studio Paint Pro Torrent: What You Need to Know</h1>
-<p>If you are an artist who loves drawing and painting digitally, you might have heard of <strong>Clip Studio Paint Pro</strong>, one of the most popular and versatile software for creating digital art. Whether you want to draw manga, comics, illustrations, animations, or concept art, Clip Studio Paint Pro has everything you need to unleash your creativity.</p>
-<p>But what if you don't want to pay for the software, which costs $49.99 for a single device license? You might be tempted to look for a <strong>torrent</strong>, a file that allows you to download and share software or other content through a peer-to-peer network. You might think that using a torrent to download Clip Studio Paint Pro is a smart way to save money and enjoy the software without any restrictions.</p>
-<h2>Clip Studio Paint Pro Torrent</h2><br /><p><b><b>DOWNLOAD</b> === <a href="https://urlgoal.com/2uI9TY">https://urlgoal.com/2uI9TY</a></b></p><br /><br />
-<p>However, before you do that, you should know that using a torrent to download Clip Studio Paint Pro is <strong>not worth it</strong>. In fact, it can be very risky and disadvantageous for you and your device. In this article, we will explain why using a torrent to download Clip Studio Paint Pro is a bad idea, and what you can do instead to get the best experience with the software.</p>
-<h2>What is Clip Studio Paint Pro?</h2>
-<p>Clip Studio Paint Pro is a software that allows you to create stunning digital art with natural and realistic brushes, advanced tools, and customizable features. It is designed for artists who want to draw and paint in various styles and genres, such as manga, comics, illustrations, animations, or concept art.</p>
-<p>Some of the features that Clip Studio Paint Pro offers are:</p>
-<ul>
-<li>A large selection of brushes, pens, pencils, markers, airbrushes, and other tools that mimic the feel and texture of real materials.</li>
-<li>A powerful vector engine that lets you edit and scale your drawings without losing quality or detail.</li>
-<li>A smart fill tool that automatically fills gaps and areas with colors or patterns.</li>
-<li>A 3D drawing figure tool that helps you pose and draw human or animal figures with realistic proportions and anatomy.</li>
-<li>A perspective ruler tool that helps you draw backgrounds and scenes with accurate perspective and depth.</li>
-<li>A frame-by-frame animation tool that lets you create simple animations or gifs with up to 24 frames.</li>
-<li>A text tool that lets you add dialogues, captions, or titles to your drawings with various fonts and styles.</li>
-<li>A layer tool that lets you organize and edit your drawings with multiple layers, masks, blending modes, and effects.</li>
-<li>A color tool that lets you choose from a wide range of colors, gradients, palettes, and color sets.</li>
-<li>A customization tool that lets you adjust the interface, shortcuts, preferences, and settings to suit your workflow and preferences.</li>
-</ul>
-<p>Clip Studio Paint Pro supports Windows, Mac OS, iPadOS, Android OS, Chrome OS, and Galaxy devices. You can buy the software from the official website or authorized resellers for $49.99 for a single device license. You can also get a monthly or yearly subscription plan for $2.49 or $24.99 respectively.</p>
-<p>By using Clip Studio Paint Pro, you can enjoy many benefits such as:</p>
-<ul>
-<li>Creating professional-quality digital art with ease and efficiency.</li>
-<li>Improving your skills and learning new techniques with tutorials and tips from experts and other users.</li>
-<li>Accessing updates and new features regularly without paying extra fees or charges.</li>
-<li>Getting support and assistance from the developers and the community whenever you need help or have questions.</li>
-<li>Sharing your work and getting feedback from other artists on social media or online platforms.</li>
-</ul>
-<h2>What is a Torrent?</h2>
-<p>A torrent is a file that contains information about other files or content that can be downloaded and shared through a peer-to-peer network. A peer-to-peer network is a system where users can connect directly with each other without relying on a central server or authority. A torrent file does not contain the actual content itself, but rather the metadata such as the name, size, location, and checksum of the content. To use a torrent file, you need a software called a torrent client that can read the file and download the content from other users who have the same file (called seeders) or who are downloading it (called leechers).</p>
-<p>Some of the reasons why some people use torrents to download software or other content are:</p>
-<ul>
-<li>To save money by avoiding paying for the software or content.</li>
-<li>To bypass restrictions or limitations imposed by the developers or authorities on the software or content.</li>
-<li>To access software or content that is not available or hard to find in their region or country.</li>
-<li>To download software or content faster by using multiple sources instead of one server.</li>
-</ul> <h2>What are the Risks of Using a Torrent to Download Clip Studio Paint Pro?</h2>
-<p>While using a torrent to download Clip Studio Paint Pro might seem like a convenient and easy way to get the software, it is actually very risky and disadvantageous for you and your device. Here are some of the risks that you might face if you use a torrent to download Clip Studio Paint Pro:</p>
-<p></p>
-<h3>Legal Risks</h3>
-<p>Using a torrent to download Clip Studio Paint Pro is <strong>illegal</strong>. Clip Studio Paint Pro is a software that is protected by <strong>intellectual property rights</strong>, which means that the developers have the exclusive right to control how the software is used, distributed, and modified. By using a torrent to download Clip Studio Paint Pro, you are violating these rights and committing <strong>piracy</strong>, which is the unauthorized use or reproduction of someone else's work.</p>
-<p>Piracy is a serious offense that can result in <strong>fines or lawsuits</strong> from the developers or authorities. Depending on the laws and regulations of your country or region, you might have to pay hefty penalties or face legal actions for downloading or using pirated software. For example, in the United States, you can be fined up to $150,000 for each pirated copy of software, or face up to five years in prison for willful infringement. In Japan, where Clip Studio Paint Pro is developed, you can be fined up to 10 million yen or face up to 10 years in prison for downloading or distributing pirated software.</p>
-<p>Moreover, by using a torrent to download Clip Studio Paint Pro, you are also exposing yourself to the risk of being <strong>tracked or monitored</strong> by the developers or authorities. Some torrent files or clients might contain trackers or spyware that can record your IP address, location, device information, or online activity. This information can be used to identify you and take legal actions against you. For example, in 2019, a Japanese man was arrested for uploading pirated copies of manga created with Clip Studio Paint Pro after his IP address was traced by the police.</p>
-<h3>Security Risks</h3>
-<p>Using a torrent to download Clip Studio Paint Pro is also very <strong>unsafe</strong>. Clip Studio Paint Pro is a software that requires a lot of data and resources to run properly. By using a torrent to download Clip Studio Paint Pro, you are risking downloading a file that might contain <strong>malware, viruses, spyware, or ransomware</strong> that can harm your device or data.</p>
-<p>Malware, viruses, spyware, or ransomware are malicious programs that can infect your device and cause various problems such as:</p>
-<ul>
-<li><strong>Deleting or corrupting your files or data</strong>, such as your drawings, photos, documents, or passwords.</li>
-<li><strong>Stealing your personal or financial information</strong>, such as your credit card details, bank accounts, social media accounts, or online identities.</li>
-<li><strong>Hijacking your device or network</strong>, such as locking your screen, encrypting your files, displaying unwanted ads, redirecting your browser, or using your bandwidth.</li>
-<li><strong>Damaging your device or system</strong>, such as slowing down your performance, crashing your applications, overheating your battery, or disabling your security features.</li>
-</ul>
-<p>These problems can be very costly and difficult to fix or recover from. You might have to pay ransom fees to unlock your files or data, buy new devices or software, hire professional help, or lose your work or money permanently. For example, in 2017, a ransomware called WannaCry infected millions of devices worldwide and demanded $300 worth of bitcoin from each victim to restore their files. Some victims reported losing thousands of dollars worth of data or work due to the ransomware attack.</p>
-<h3>Performance Risks</h3>
-<p>Using a torrent to download Clip Studio Paint Pro is also very <strong>inefficient</strong>. Clip Studio Paint Pro is a software that requires constant updates and support from the developers to run smoothly and effectively. By using a torrent to download Clip Studio Paint Pro, you are risking downloading a file that might be outdated, incomplete, buggy, or incompatible with your device or system.</p>
-<p>This can affect the quality and functionality of the software, such as:</p>
-<ul>
-<li><strong>Missing features or tools</strong>, such as brushes, effects, filters, frames, fonts, or presets.</li>
-<li><strong>Bugs or errors</strong>, such as glitches, crashes, freezes, lags, or distortions.</li>
-<li><strong>Compatibility issues</strong>, such as not working with your device model, operating system, or browser.</li>
-</ul>
-<p>These problems can be very frustrating and disappointing for you as an artist who wants to create digital art with ease and quality. You might have to deal with poor performance, low quality, or limited functionality of the software. You might also miss out on the latest features, updates, or improvements that the developers provide for the official version of the software. For example, in 2020, Clip Studio Paint Pro released a major update that added new features such as AI colorization, timelapse recording, and 3D model import. These features are not available for the torrent version of the software.</p>
-<h2>What are the Alternatives to Using a Torrent to Download Clip Studio Paint Pro?</h2>
-<p>Now that you know why using a torrent to download Clip Studio Paint Pro is not worth it, you might be wondering what you can do instead to get the best experience with the software. Here are some of the alternatives that you can consider:</p>
-<h3>Buy the Official Version</h3>
-<p>The best and most recommended alternative to using a torrent to download Clip Studio Paint Pro is to <strong>buy the official version</strong> of the software from the official website or authorized resellers. By buying the official version of Clip Studio Paint Pro, you can enjoy the full benefits of the software, such as:</p>
-<ul>
-<li><strong>Updates and support</strong>: You can access the latest features, updates, and improvements that the developers provide for the software. You can also get support and assistance from the developers and the community whenever you need help or have questions.</li>
-<li><strong>Security and legality</strong>: You can avoid any legal or security risks that come with using a torrent to download the software. You can use the software safely and legally without worrying about malware, viruses, spyware, ransomware, fines, lawsuits, or tracking.</li>
-<li><strong>Quality and functionality</strong>: You can use the software smoothly and effectively without any performance problems or compatibility issues. You can use all the features and tools that the software offers without any bugs, errors, crashes, or missing elements.</li>
-</ul>
-<p>To buy the official version of Clip Studio Paint Pro, you can visit the official website at <a href="">https://www.clipstudio.net/en/purchase</a> or find an authorized reseller near you at <a href="">https://www.clipstudio.net/en/dl/reseller_list</a>. You can choose between a single device license for $49.99 or a monthly or yearly subscription plan for $2.49 or $24.99 respectively. You can pay with various payment options such as credit card, PayPal, Amazon Pay, or bank transfer.</p>
-<h3>Use the Free Trial Version</h3>
-<p>Another alternative to using a torrent to download Clip Studio Paint Pro is to use the <strong>free trial version</strong> of the software from the official website. By using the free trial version of Clip Studio Paint Pro, you can try out the software for up to six months without paying anything or breaking any laws. You can use this option if you want to test the software before buying it or if you only need it for a short-term project.</p>
-<p>To use the free trial version of Clip Studio Paint Pro, you can visit the official website at <a href="">https://www.clipstudio.net/en/dl</a> and download the software for your device and platform. You can use the free trial version for up to three months on Windows, Mac OS, iPadOS, Android OS, Chrome OS, or Galaxy devices. You can also extend the trial period for another three months by registering a Clip Studio account and logging in to the software.</p>
-<p>The free trial version of Clip Studio Paint Pro includes all the features and tools that the official version offers, except for the following limitations:</p>
-<ul>
-<li>You can only save up to three files per device.</li>
-<li>You can only export files up to 8192 x 8192 pixels in size.</li>
-<li>You can only use the software for non-commercial purposes.</li>
-</ul>
-<h3>Use Other Free or Cheap Drawing Software</h3>
-<p>A third alternative to using a torrent to download Clip Studio Paint Pro is to use other <strong>free or cheap drawing software</strong> that are similar to Clip Studio Paint Pro. By using other free or cheap drawing software, you can create digital art without spending much money or risking legal or security issues. You can use this option if you are looking for a different style or genre of drawing software or if you are not satisfied with Clip Studio Paint Pro.</p>
-<p>Some of the examples of other free or cheap drawing software that are available online or offline are:</p>
-<table>
-<tr>
-<th>Name</th>
-<th>Price</th>
-<th>Features</th>
-</tr>
-<tr>
-<td>Krita</td>
-<td>Free</td>
-<td>A powerful and open-source drawing software that supports various styles and genres of digital art, such as comics, illustrations, animations, or concept art. It offers a variety of brushes, tools, filters, effects, and plugins that can enhance your drawings. It also supports vector graphics, layers, masks, color management, and animation.</td>
-</tr>
-<tr>
-<td>MediBang Paint</td>
-<td>Free</td>
-<td>A lightweight and easy-to-use drawing software that is designed for creating manga, comics, or illustrations. It offers a large collection of brushes, fonts, backgrounds, and materials that can help you create your own comics. It also supports layers, cloud storage, collaboration, and publishing.</td>
-</tr>
-<tr>
-<td>SketchBook</td>
-<td>Free</td>
-<td>A simple and intuitive drawing software that is ideal for sketching, drawing, or painting. It offers a minimalistic interface that lets you focus on your drawing without distractions. It also offers a range of brushes, tools, colors, and presets that can help you create realistic and natural drawings. It also supports layers, symmetry, perspective guides, and time-lapse recording.</td>
-</tr>
-<tr>
-<td>Procreate</td>
-<td>$9.99</td>
-<td>A powerful and popular drawing software that is designed for iPad devices. It offers a responsive and fluid interface that lets you draw with your fingers or stylus. It also offers a huge selection of brushes, tools, colors, and effects that can help you create stunning digital art. It also supports layers, masks, blending modes, animation, and export options.</td>
-</tr>
-<tr>
-<td>Affinity Designer</td>
-<td>$49.99</td>
-<td>A professional and versatile drawing software that supports both raster and vector graphics. It offers a comprehensive set of tools that can help you create vector or pixel art, such as curves, shapes, gradients, brushes, effects, and symbols. It also supports layers, artboards, grids, guides, and export options.</td>
-</tr>
-</table>
-<h2>Conclusion</h2>
-<p>In conclusion, using a torrent to download Clip Studio Paint Pro is not a good idea. It can expose you to legal, security, and performance risks that can cost you more than the price of the software. Instead of using a torrent to download Clip Studio Paint Pro, you should consider buying the official version, using the free trial version, or using other free or cheap drawing software. By doing so, you can enjoy creating digital art with quality, safety, and legality.</p>
-<p>If you want to learn more about Clip Studio Paint Pro and its features, you can visit the official website at <a href="">https://www.clipstudio.net/en</a> or check out the user manual at <a href="">https://www.clip-studio.com/site/gd_en/csp/userguide/csp_userguide/top.htm</a>. You can also join the community of Clip Studio Paint Pro users and artists at <a href="">https://www.clipstudio.net/en/community</a> or follow the official social media accounts at <a href="">https://www.facebook.com/celsys.clipstudiopaint/</a>, <a href="">https://twitter.com/clipstudiopaint</a>, or <a href="">https://www.instagram.com/clipstudioofficial/</a>.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions related to Clip Studio Paint Pro Torrent:</p>
-<ol>
-<li><strong>Is Clip Studio Paint Pro free?</strong></li>
-<p>No, Clip Studio Paint Pro is not free. It is a paid software that costs $49.99 for a single device license or $2.49 or $24.99 for a monthly or yearly subscription plan respectively. However, you can use the free trial version of Clip Studio Paint Pro for up to six months without paying anything.</p>
-<li><strong>Is Clip Studio Paint Pro worth it?</strong></li>
-<p>Yes, Clip Studio Paint Pro is worth it. It is one of the most popular and versatile software for creating digital art with natural and realistic brushes, advanced tools, and customizable features. It supports various styles and genres of digital art, such as manga, comics, illustrations, animations, or concept art. It also offers updates, support, security, and legality for its users.</p>
-<li><strong>Can I use Clip Studio Paint Pro on multiple devices?</strong></li>
-<p>Yes, you can use Clip Studio Paint Pro on multiple devices. However, you need to buy a separate license for each device that you want to use the software on. Alternatively, you can use the subscription plan that allows you to use the software on up to two devices at the same time.</p>
-<li><strong>How do I update Clip Studio Paint Pro?</strong></li>
-<p>To update Clip Studio Paint Pro, you need to download and install the latest version of the software from the official website or authorized resellers. You can also check for updates from within the software by going to Help > Check for Updates.</p>
-<li><strong>How do I contact Clip Studio Paint Pro support?</strong></li>
-<p>To contact Clip Studio Paint Pro support, you can visit the official support page at <a href="">https://www.clipstudio.net/en/support</a> or send an email to <a href="">support@artspark.co.jp</a>. You can also find answers to common questions or issues at <a href="">https://support.clip-studio.com/en-us</a> or ask for help from other users at <a href="">https://ask.clip-studio.com/en-us</a>.</p>
-</ol></p> b2dd77e56b<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Easycafe Crack Serial !FULL! Keygen.md b/spaces/stomexserde/gpt4-ui/Examples/Easycafe Crack Serial !FULL! Keygen.md
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-
-<h1>How to Crack Easycafe 2.2.14 with Serial Keygen</h1>
-<p>Easycafe is a popular software for managing internet cafes and cybercafes. It allows you to control and monitor your clients, staff, printers, terminals, billing, rates, inventory and more. However, Easycafe is not a free software and you need to purchase a license to use it. If you want to use Easycafe without paying for it, you might be tempted to look for a crack, serial keygen or patch online. But be careful, because these methods are illegal and risky. They can expose your computer to viruses, malware, spyware and other threats. They can also damage your system files and cause errors and crashes. Moreover, they can violate the terms and conditions of Easycafe and result in legal consequences.</p>
-<p>So, what is the best way to use Easycafe without paying for it? The answer is simple: use a trial version. Easycafe offers a free trial version that you can download from their official website. The trial version allows you to use Easycafe for 30 days with full functionality. You can test all the features and benefits of Easycafe before deciding whether to buy it or not. The trial version is safe and legal and does not require any crack, serial keygen or patch. You just need to register with your name and email address and you will receive a download link and an activation code.</p>
-<h2>Easycafe Crack Serial Keygen</h2><br /><p><b><b>DOWNLOAD</b> &#127775; <a href="https://urlgoal.com/2uI9kF">https://urlgoal.com/2uI9kF</a></b></p><br /><br />
-<p>To download and install the trial version of Easycafe 2.2.14, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://www.tinasoft.com/download/">https://www.tinasoft.com/download/</a> and click on the "Download" button.</li>
-<li>Save the file "EasySetup.exe" on your computer and run it.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Launch Easycafe and enter your name and email address when prompted.</li>
-<li>Check your email inbox for an email from Tinasoft with your activation code.</li>
-<li>Enter the activation code in Easycafe and click on "Activate".</li>
-<li>Enjoy using Easycafe for 30 days for free.</li>
-</ol>
-<p>If you like Easycafe and want to continue using it after the trial period expires, you can buy a license from their website. The license price depends on the number of clients you want to manage with Easycafe. You can choose from different packages ranging from 10 clients to unlimited clients. The license also includes technical support, updates and upgrades for one year.</p>
-<p>To buy a license for Easycafe 2.2.14, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://www.tinasoft.com/order/">https://www.tinasoft.com/order/</a> and select the package that suits your needs.</li>
-<li>Click on the "Buy Now" button and fill in your billing information.</li>
-<li>Choose your payment method and complete the transaction.</li>
-<li>Check your email inbox for an email from Tinasoft with your license key.</li>
-<li>Enter the license key in Easycafe and click on "Register".</li>
-<li>Enjoy using Easycafe with full functionality and no limitations.</li>
-</ol>
-<p>We hope this article has helped you understand how to use Easycafe without paying for it legally and safely. Remember, do not use any crack, serial keygen or patch for Easycafe as they can harm your computer and get you in trouble. Instead, use the trial version or buy a license from their website. Easycafe is a great software for managing internet cafes and cybercafes and it is worth paying for it if you want to run your business smoothly and efficiently.</p> e93f5a0c3f<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/I Moved Your Cheese Deepak Malhotra Pdf.md b/spaces/stomexserde/gpt4-ui/Examples/I Moved Your Cheese Deepak Malhotra Pdf.md
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-<br />
-<h1>How I Moved Your Cheese by Deepak Malhotra Can Change Your Life</h1>
-
-<p>If you have ever felt stuck in a situation that you can't control, or frustrated by the limitations imposed by others, you might have wondered if there is a better way to live. You might have read the popular book Who Moved My Cheese? by Spencer Johnson, which tells the story of four mice who react differently to the changes in their cheese supply. The book advises you to accept change as inevitable, adapt quickly, and keep looking for new cheese.</p>
-
-<p>But what if you don't want to live as a mouse in someone else's maze? What if you want to create your own cheese, or even your own maze? That's the premise of I Moved Your Cheese by Deepak Malhotra, a Harvard Business School professor and bestselling author. In this book, Malhotra challenges the conventional wisdom of Who Moved My Cheese? and offers a different perspective on how to deal with change and achieve success.</p>
-<h2>I Moved Your Cheese Deepak Malhotra Pdf</h2><br /><p><b><b>Download File</b> &#9734; <a href="https://urlgoal.com/2uI88t">https://urlgoal.com/2uI88t</a></b></p><br /><br />
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-<p>I Moved Your Cheese tells the story of three mice - Max, Big, and Zed - who refuse to accept their reality as given. They question the assumptions and rules of the maze, explore the possibilities beyond it, and even reconfigure it to their liking. Along the way, they discover that they have more power and potential than they ever imagined.</p>
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-<p>This book is not just a fable, but a guide for anyone who wants to break free from the constraints and expectations of others, and create their own destiny. It shows you how to challenge the status quo, think outside the box, and pursue your dreams with passion and courage. It also reminds you that you are not alone in your quest, and that there are others who share your vision and values.</p>
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-<p>I Moved Your Cheese is a short but powerful book that will inspire you to take charge of your life and make it what you want it to be. Whether you are an entrepreneur, a leader, a creative, or simply someone who wants more out of life, this book will help you move beyond the maze and find your cheese.</p>
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-<p>You can download I Moved Your Cheese by Deepak Malhotra in PDF or EPUB format from various sources online[^1^] [^2^] [^3^] [^4^]. Or you can buy a hardcover or paperback copy from your favorite bookstore.</p>
-<p></p>
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-<p>Don't settle for being a mouse in someone else's maze. Read I Moved Your Cheese today and discover how you can move your cheese - and change your life.</p>
-
-<h2>What is I Moved Your Cheese About?</h2>
-
-<p>I Moved Your Cheese is a business fable that challenges the conventional wisdom of Who Moved My Cheese? by Spencer Johnson. The book tells the story of three mice - Max, Big, and Zed - who refuse to live as mice in someone else's maze. They question the assumptions and rules of the maze, explore the possibilities beyond it, and even reconfigure it to their liking. Along the way, they discover that they have more power and potential than they ever imagined.</p>
-
-<p>The book is divided into three parts. The first part introduces the characters and their situation in the maze. The second part follows their adventures and transformations as they move their cheese. The third part reveals the lessons and insights they learned from their experience.</p>
-
-<p>The book is written in a simple and engaging style, with illustrations and quotes that enhance the message. The book is also short and easy to read, with only 96 pages. The book is suitable for anyone who wants to learn how to deal with change and achieve success in their personal and professional lives.</p>
-
-<h2>Who is Deepak Malhotra?</h2>
-
-<p>Deepak Malhotra is a professor of business administration at Harvard Business School, where he teaches courses on negotiation, leadership, strategy, and innovation. He is also a bestselling author of several books, including Negotiation Genius, Negotiating the Impossible, and I Moved Your Cheese.</p>
-
-<p>Malhotra has a PhD in management from Northwestern University, and a BA in economics from Harvard University. He has won numerous awards for his teaching and research, including the HBS Faculty Award and the Academy of Management's Outstanding Educator Award. He has also been named one of the world's best business professors under 40 by Poets & Quants.</p>
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-<p>Malhotra has consulted for various organizations, including Google, Microsoft, Coca-Cola, IBM, Goldman Sachs, and the United Nations. He has also been featured in media outlets such as CNN, CNBC, NPR, The Wall Street Journal, The Economist, and Forbes.</p>
-
-<p>Malhotra lives in Massachusetts with his wife and two sons. He enjoys playing chess, basketball, and guitar.</p> 7b8c122e87<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Igorot Song Kalabasa Lyrics.md b/spaces/stomexserde/gpt4-ui/Examples/Igorot Song Kalabasa Lyrics.md
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--- a/spaces/stomexserde/gpt4-ui/Examples/Igorot Song Kalabasa Lyrics.md	
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-<br />
-Here is a possible title and article with SEO optimization and HTML formatting for the keyword "igorot song kalabasa lyrics":
-
-<h1>Kalabasa: A Popular Igorot Song by Sumiklad</h1>
-<p>Kalabasa is a catchy and upbeat song by Sumiklad, a Kankanaey band from Benguet, Philippines. The song is about a man who loves to eat kalabasa (squash) and praises its health benefits. He also compares his love for kalabasa to his love for his girlfriend, who is sweet and beautiful like the vegetable.</p>
-<p>The song has become a hit among Igorot listeners, especially those who belong to the Kankanaey tribe. The song showcases the rich culture and language of the Igorots, who are indigenous people living in the Cordillera region of Luzon island. The song also promotes the consumption of local and organic produce, which is abundant in the highlands.</p>
-<h2>igorot song kalabasa lyrics</h2><br /><p><b><b>Download Zip</b> > <a href="https://urlgoal.com/2uI76L">https://urlgoal.com/2uI76L</a></b></p><br /><br />
-<p>If you want to learn the lyrics of this fun and catchy song, you can watch the video lyrics on YouTube. There are several versions available, but here are some of the most popular ones:</p>
-<ul>
-<li>KALABASA by: Sumiklad video lyrics | Igorot song w/ lyrics |Kankanaey[^1^]</li>
-<li>KALABASA Lyrics - by Samiklad Igorot Song[^2^]</li>
-<li>KALABASA (Lyrics) by: SUMIKLAD[^3^]</li>
-</ul>
-<p>Enjoy singing along and learning more about the Igorot culture and language through this song!</p>Here are a few more paragraphs for the article:
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-<p>Kalabasa is one of the many songs by Sumiklad, a band that has been making waves in the Igorot music scene. Sumiklad, which means "to shine" in Kankanaey, is composed of four members: Jomar (vocals and guitar), Jhun (bass), Jhay (drums), and Jem (keyboard). The band started in 2017 as a cover band, playing songs by popular artists such as Parokya ni Edgar, Eraserheads, and Rivermaya. They later decided to write their own original songs, inspired by their Igorot roots and experiences. </p>
-<p>Some of their other songs include "Sagada", "Benguet", "Tinawon", and "Kankanaey". Their songs are mostly written in Kankanaey, but they also incorporate some English and Tagalog words. Their songs are a mix of rock, pop, reggae, and folk genres, creating a unique sound that appeals to both young and old listeners. Their songs also aim to promote the Igorot culture and values, such as respect for nature, love for family and community, and pride in their identity. </p>
-<p>Sumiklad has performed in various events and venues across the country, such as Baguio City, Manila, Cebu, Davao, and Palawan. They have also collaborated with other Igorot artists, such as Chud Festejo, Renz Tacio, and Dap-ay. They have also gained recognition from various award-giving bodies, such as the Awit Awards, the Philippine Popular Music Festival, and the Aliw Awards. They have also been featured in various media platforms, such as ABS-CBN, GMA Network, Rappler, and Inquirer. </p>Here are some more paragraphs for the article:
-
-<p>One of the most admirable aspects of the Igorot culture is their strong sense of values. The Igorots have a set of principles and beliefs that guide their way of life and relationships with others. Some of these values are:</p>
-<ol>
-<li>Inayan (prudence) – This value is rooted in the belief of a supreme being and the awareness that every action has a consequence. It makes the Igorots think twice before doing anything that will lead to harm or destruction. It also teaches them to respect themselves, others, and nature. [^1^]</li>
-<li>Binnadang (teamwork) – This value is manifested in the spirit of cooperation and mutual help among the Igorots. They work together to achieve common goals, such as building houses, planting crops, celebrating milestones, and coping with challenges. They also share their resources and skills with others who are in need. [^1^]</li>
-<li>Adi tako bukbukodan di gawis (Let us not keep the good stuff to ourselves) – This value is expressed in the generosity and hospitality of the Igorots. They do not hoard their blessings, but rather share them with others. They welcome guests and strangers with warmth and kindness. They also open up their spot for other groups to mine for a while when they hit an abundant supply of gold in a tradition called sagaok. [^1^]</li>
-<li>Ubbog (patience) – This value is demonstrated in the perseverance and resilience of the Igorots. They endure hardships and difficulties with courage and optimism. They do not give up easily, but rather work hard to overcome obstacles. They also wait patiently for the right time and opportunity to achieve their goals. [^4^]</li>
-<li>Uyao (honesty) – This value is reflected in the integrity and trustworthiness of the Igorots. They speak the truth and keep their promises. They do not cheat or deceive others, but rather deal with them fairly and honorably. They also respect the property and rights of others, and do not take what is not theirs. [^4^]</li>
-</ol></p> 7196e7f11a<br />
-<br />
-<br />
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diff --git a/spaces/sub314xxl/MetaGPT/metagpt/utils/read_document.py b/spaces/sub314xxl/MetaGPT/metagpt/utils/read_document.py
deleted file mode 100644
index 70734f7312dfcafdc825e724493f267b05d35ee1..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/utils/read_document.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/4/29 15:45
-@Author  : alexanderwu
-@File    : read_document.py
-"""
-
-import docx
-
-
-def read_docx(file_path: str) -> list:
-    """打开docx文件"""
-    doc = docx.Document(file_path)
-
-    # 创建一个空列表，用于存储段落内容
-    paragraphs_list = []
-
-    # 遍历文档中的段落，并将其内容添加到列表中
-    for paragraph in doc.paragraphs:
-        paragraphs_list.append(paragraph.text)
-
-    return paragraphs_list
diff --git a/spaces/superdatas/LICENSE/LICENSE.md b/spaces/superdatas/LICENSE/LICENSE.md
deleted file mode 100644
index 1340b8bf528be05167885ac0ecf7acc109a78dfb..0000000000000000000000000000000000000000
--- a/spaces/superdatas/LICENSE/LICENSE.md
+++ /dev/null
@@ -1,9 +0,0 @@
-Pylar AI creative ML License 0.0.4
-
-Copyright (c) 2023 Miguel Gargallo
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to use the Software solely for non-commercial purposes and to show the code or result for scientific purposes. Any commercial use, including without limitation the rights to sell, copy, modify, merge, publish, distribute, sublicense, or use the Software as part of a commercial product, is strictly prohibited.
-
-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.
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Andhrula Charitra Bsl Hanumantha Rao Pdf VERIFIED Free 21.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Andhrula Charitra Bsl Hanumantha Rao Pdf VERIFIED Free 21.md
deleted file mode 100644
index 6c1276839199a97b87193c167346b0404354eb47..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Andhrula Charitra Bsl Hanumantha Rao Pdf VERIFIED Free 21.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-
-<h1>Andhrula Charitra: A Comprehensive History of Andhra Pradesh by B.S.L. Hanumantha Rao</h1>
-<p>Andhrula Charitra is a book that covers the history of Andhra Pradesh from ancient times to 1965. It is written by B.S.L. Hanumantha Rao, a renowned historian and scholar who has done extensive research on the social, cultural, political and economic aspects of Andhra history. The book is divided into four parts: Pre-History and Proto-History, Ancient History, Medieval History and Modern History. Each part contains several chapters that deal with different topics such as geography, religion, literature, art, architecture, administration, trade, movements, personalities and events.</p>
-<p>The book is considered as one of the most authoritative and comprehensive sources of information on Andhra history. It is written in a simple and lucid style that makes it easy to understand and appreciate. The book also contains maps, illustrations, tables, charts and appendices that enhance the value and utility of the book. The book is widely used by students, teachers, researchers and general readers who are interested in learning about the rich and varied heritage of Andhra Pradesh.</p>
-<h2>Andhrula Charitra Bsl Hanumantha Rao Pdf Free 21</h2><br /><p><b><b>DOWNLOAD</b> &#10038;&#10038;&#10038; <a href="https://cinurl.com/2uEXWj">https://cinurl.com/2uEXWj</a></b></p><br /><br />
-<p>If you want to read this book online or download it as a PDF file for free, you can visit the following link[^1^]. You can also buy a hard copy of the book from Visalandra Publication House in Hyderabad[^2^]. You can also read some reviews and comments about the book from other readers on various blogs and websites[^3^] [^4^].</p>
-<p>Andhrula Charitra is a book that you should not miss if you want to know more about the history of Andhra Pradesh. It is a book that will enlighten you and inspire you to explore the glorious past of Andhra Pradesh.</p>
-
-<h2>The Mauryan and Satavahana Periods</h2>
-<p>The earliest historical evidence of Andhra Pradesh dates back to the Mauryan dynasty, which ruled over most of northern India from the late 4th to the early 2nd century BCE. The Mauryan emperor Ashoka sent Buddhist missions to the Andhras in the south and also erected some of his famous edicts in their territory. The Andhras were known for their trade and commerce, especially with the Roman Empire. They also developed a distinctive culture and art, influenced by Buddhism and local traditions.</p>
-<p>After the decline of the Mauryan empire, the Andhras established their own independent kingdom under the Satavahana dynasty, which lasted from about the 1st century BCE to the 3rd century CE. The Satavahanas were one of the most powerful and prosperous dynasties in Indian history, ruling over almost the entire Deccan plateau and parts of central and eastern India. They patronized various religions, including Buddhism, Hinduism and Jainism, and supported literature, art and architecture. Their capital city, Amaravati, was a major centre of Buddhist learning and culture, where the famous philosopher Nagarjuna taught. The Satavahanas also maintained trade relations with foreign countries, such as Rome, China and Southeast Asia.</p>
-
-<h2>The Medieval Period</h2>
-<p>The fall of the Satavahana dynasty led to a period of political fragmentation and instability in Andhra Pradesh. Several local dynasties emerged and competed for supremacy, such as the Ikshvakus, the Pallavas, the Vishnukundins, the Vakatakas, the Chalukyas and the Rashtrakutas. These dynasties contributed to the cultural development of Andhra Pradesh by promoting Hinduism, especially Shaivism and Vaishnavism, and by building temples, sculptures and paintings. They also encouraged Telugu literature and poetry, which flourished under poets like Nannaya, who translated the Sanskrit epic Mahabharata into Telugu.</p>
-<p>The most prominent dynasty that unified most of Andhra Pradesh in the medieval period was the Eastern Chalukya dynasty, which ruled from the 7th to the 12th century CE. The Eastern Chalukyas were descendants of the Western Chalukyas of Karnataka, who expanded their influence to the east. They established their capital at Vengi (near Eluru) and later at Rajahmundry. They were known for their military prowess, administrative efficiency and cultural achievements. They patronized education, literature, art and architecture. They also fostered a harmonious relationship between Hinduism and Buddhism.</p> d5da3c52bf<br />
-<br />
-<br />
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download Movies In 720p Kambakkht Ishq 1080p LINK.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download Movies In 720p Kambakkht Ishq 1080p LINK.md
deleted file mode 100644
index 9bdf43e83ce6d31a01beab5a7b7c0350058a6e9d..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download Movies In 720p Kambakkht Ishq 1080p LINK.md	
+++ /dev/null
@@ -1,113 +0,0 @@
-
-<h1>Download Movies in 720p Kambakkht Ishq 1080p</h1>
-
-<p>If you are looking for a romantic comedy movie with a twist of Bollywood and Hollywood, then you should download movies in 720p Kambakkht Ishq 1080p. This movie was released in 2009 and starred Akshay Kumar and Kareena Kapoor as a stuntman and a supermodel who don't believe in love but fall for each other through a hilarious series of events.</p>
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-
-<p>There are many reasons why you should download movies in 720p Kambakkht Ishq 1080p. Here are some of them:</p>
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-<ul>
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-</ul>
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-
-<ol>
-<li>Go to <a href="https://topgmovies.xyz/">TopG Movies</a> website.</li>
-<li>Search for "Kambakkht Ishq" in the search bar or browse through the categories.</li>
-<li>Select the movie from the results and click on it.</li>
-<li>Choose the quality and format that you want to download. For example, select "Bluray" and "720p".</li>
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-</ol>
-
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-
-<p>Watching movies in 720p Kambakkht Ishq 1080p has many benefits that will enhance your viewing experience. Some of these benefits are:</p>
-<p></p>
-
-<ul>
-<li>You will get a clear and crisp picture quality that will make you feel like you are watching the movie in a theater.</li>
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-<li>You will get a larger screen size that will make you see every detail and action of the movie.</li>
-</ul>
-
-<p>These are some of the benefits of watching movies in 720p Kambakkht Ishq 1080p. However, to enjoy these benefits fully, you need to have a compatible device and software that can support this quality. For example, you need to have a HD TV or monitor, a Blu-ray player or software, a high-speed internet connection, and enough storage space on your device.</p>
-
-<h2>Conclusion</h2>
-
-<p>Kambakkht Ishq is a fun and entertaining movie that you should not miss. If you want to watch this movie in the best quality possible, then you should download movies in 720p Kambakkht Ishq 1080p from reliable websites like TopG Movies. You will enjoy the amazing visuals, sound, and story of this movie in high definition. So what are you waiting for? Download movies in 720p Kambakkht Ishq 1080p today and have a great time!</p>
-<h2>What is Kambakkht Ishq About?</h2>
-
-<p>Kambakkht Ishq is a movie that tells the story of Viraj Shergill (Akshay Kumar), a Hollywood stuntman who is fearless and arrogant. He meets Simrita Rai (Kareena Kapoor), a supermodel and surgeon who is independent and cynical. They both have a strong dislike for each other and often clash with their opinions and attitudes. However, fate brings them together in a series of unexpected and hilarious situations that make them realize that they are meant for each other.</p>
-
-<p>The movie is full of comedy, romance, drama, and action. It also features many cameo appearances by famous Hollywood actors like Sylvester Stallone, Denise Richards, Brandon Routh, Whoopi Goldberg, and others. The movie has a catchy soundtrack and songs that add to the fun and charm of the movie.</p>
-
-<h2>How to Watch Movies in 720p Kambakkht Ishq 1080p?</h2>
-
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-
-<ul>
-<li>Connect your device to your HD TV or monitor using an HDMI cable or a wireless connection.</li>
-<li>Open the movie file on your device using a Blu-ray player or software.</li>
-<li>Select the quality and format that you want to watch. For example, select "Bluray" and "720p".</li>
-<li>Enjoy the movie on your HD TV or monitor with high-quality sound and picture.</li>
-</ul>
-
-<p>You can also watch movies in 720p Kambakkht Ishq 1080p online if you have a streaming service that offers this movie in this quality. However, you need to have a stable and fast internet connection to avoid buffering or lagging issues.</p>
-<h2>What are the Reviews and Ratings of Kambakkht Ishq?</h2>
-
-<p>Kambakkht Ishq received mixed reviews from critics and audiences. Some praised the movie for its entertainment value, chemistry between the lead actors, and cameo appearances by Hollywood stars. Others criticized the movie for its weak plot, unrealistic situations, and over-the-top comedy. The movie has a rating of 3.8 out of 10 on IMDb and 33% on Rotten Tomatoes.</p>
-
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-
-<p>If you have already watched Kambakkht Ishq or you are not interested in this movie, then you can download movies in 720p Kambakkht Ishq 1080p that are similar to this movie. Some of the alternatives that you can try are:</p>
-
-<ul>
-<li>Singh Is Kinng (2008): This is another movie that stars Akshay Kumar and Kareena Kapoor as a Punjabi gangster and a journalist who fall in love in Australia.</li>
-<li>Welcome (2007): This is a movie that features Akshay Kumar and Katrina Kaif as a man who falls in love with a woman who is the sister of a notorious don.</li>
-<li>Partner (2007): This is a movie that stars Salman Khan and Govinda as two friends who help each other find love with their respective partners.</li>
-<li>Namastey London (2007): This is a movie that features Akshay Kumar and Katrina Kaif as a man who marries a British-Indian woman who does not accept him as her husband.</li>
-<li>Mujhse Shaadi Karogi (2004): This is a movie that stars Salman Khan, Akshay Kumar, and Priyanka Chopra as two men who compete for the love of a woman who lives in their neighborhood.</li>
-</ul>
-
-<p>These are some of the movies that you can download in 720p Kambakkht Ishq 1080p that are similar to Kambakkht Ishq. You can find more movies like this on the websites that we mentioned above or on other online platforms.</p>
-<h2>What are the Tips and Tricks to Download Movies in 720p Kambakkht Ishq 1080p?</h2>
-
-<p>If you want to download movies in 720p Kambakkht Ishq 1080p without any hassle or problem, then you should follow these tips and tricks:</p>
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-<ul>
-<li>Use a VPN service to hide your IP address and location from the websites that you are downloading from. This will protect your privacy and security and prevent any legal issues or restrictions.</li>
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-<p>These are some of the tips and tricks that will help you download movies in 720p Kambakkht Ishq 1080p easily and safely. You can find more tips and tricks on the internet or on the websites that we mentioned above.</p>
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-<h2>Conclusion</h2>
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-<p>Kambakkht Ishq is a movie that you should watch if you are looking for a fun and entertaining movie that will make you laugh and smile. You can download movies in 720p Kambakkht Ishq 1080p from reliable websites like TopG Movies and enjoy the movie in high definition. You can also watch other movies that are similar to Kambakkht Ishq or follow the tips and tricks that we shared with you. We hope that this article helped you find what you were looking for. Happy watching!</p>
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/VSO Downloader Ultimate 5.1.1.69 Multilingual BEST.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/VSO Downloader Ultimate 5.1.1.69 Multilingual BEST.md
deleted file mode 100644
index 5ff3f4bf7eb15be3e1d9f34aee5944e20e91f8e7..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/VSO Downloader Ultimate 5.1.1.69 Multilingual BEST.md	
+++ /dev/null
@@ -1,42 +0,0 @@
-
-<h1>VSO Downloader Ultimate 5.1.1.69 Multilingual: A Powerful Tool for Downloading Online Videos and Audio</h1>
-<p>Do you want to download streaming videos and audio from thousands of websites like YouTube, Metacafe, Vimeo, Dailymotion, etc.? Do you want to save them in their original format or convert them to the most common formats? Do you want to do it easily and quickly without any browser dependency? If your answer is yes, then you need VSO Downloader Ultimate 5.1.1.69 Multilingual.</p>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual is a multimedia downloader that can grab online media content automatically as you surf the web. It is a simple tool that is easy to use and has many features and benefits that make it stand out from other similar programs. In this article, we will review VSO Downloader Ultimate 5.1.1.69 Multilingual and show you why it is the best choice for downloading online videos and audio.</p>
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-<h2>What is VSO Downloader Ultimate 5.1.1.69 Multilingual?</h2>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual is the latest version of VSO Downloader, a multimedia downloader that was released in 2020-02-21 by VSO Software, a company that specializes in video conversion and burning software. VSO Downloader Ultimate 5.1.1.69 Multilingual is an improved version of VSO Downloader that has added support for automatic subtitles (transcription) and language selector, as well as fixed some bugs and compatibility issues.</p>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual is a program that runs in the background and detects when you are watching an online video or listening to an online audio and downloads it to your hard drive automatically. You don't need to copy and paste any URL or install any browser extension or plugin. VSO Downloader Ultimate 5.1.1.69 Multilingual works with any browser and any website that provides streaming media content.</p>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual also comes with an ad blocker that ensures that ads are not downloaded along with the media content. It supports download resume and is compatible with HTTP and HTTPS protocols. You can also configure the program to limit the number of files it downloads at a time or the number of threads it opens per download. You can also filter the downloads by minimum file size.</p>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual downloads the videos and audio in their original format as it does not come with any converter by default. However, if you want to convert your downloaded media content to other formats, you can use the built-in conversion feature that lets you convert your videos and audio to MP4, AVI, WMV, MKV, MP3, FLAC, OGG, etc.</p>
-<h3>What are the Features of VSO Downloader Ultimate 5.1.1.69 Multilingual?</h3>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual has many features that make it a powerful and versatile multimedia downloader.</p>
-<ul>
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-<li><b>Works with any browser.</b> You can use VSO Downloader Ultimate 5.1.1.69 Multilingual with Chrome, Edge, Firefox, or any other browser of your choice.</li>
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-<li><b>No need to copy/paste URL.</b> You don't need to manually enter any URL to download media content as VSO Downloader Ultimate 5.1.1.69 Multilingual automatically detects it when you watch it online.</li>
-<li><b>Detects Ads and does not download them.</b> You don't need to worry about downloading unwanted ads along with your media content as VSO Downloader Ultimate 5.1.1.69 Multilingual has an ad blocker that filters them out.</li>
-<li><b>Pause / resume unfinished downloads.</b> You can pause or resume your downloads at any time without losing any progress or data.</li>
-<li><b>Select/Limit languages for subtitles.</b> You can choose which languages you want to download subtitles for or limit them to one language only.</li>
-<li><b>Detect/Download auto-generated subtitles.</b> You can also download auto-generated subtitles (transcription) from YouTube videos if available.</li>
-<li><b>Convert your videos and audio in the most common formats.</b> You can use the built-in conversion feature to convert your downloaded media content to MP4, AVI, WMV, MKV, MP3, FLAC, OGG, etc., with various settings and options.</li>
-</ul>
-<h4>What are the Benefits of VSO Downloader Ultimate 5.1.1.69 Multilingual?</h4>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual has many benefits that make it a great choice for downloading online videos and audio.</p>
-<ul>
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-Some possible ways to continue the article are:
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-- <p>You don't need any technical skills or knowledge to use VSO Downloader Ultimate 5 . All you need to do is install it on your computer and let it run in the background while you surf the web normally.<p/>
-- <p>You don't need any technical skills or knowledge to use VSO Downloader Ultimate 5 . It has a simple and intuitive interface that lets you access all its features and settings easily.<p/>
-- <p>You don't need any technical skills or knowledge to use VSO Downloader Ultimate 5 . It does all the work for you automatically without requiring any input from you.<p/></p>
-<h5>Conclusion</h5>
-<p>VSO Downloader Ultimate 5.1.1.69 Multilingual is a powerful and versatile multimedia downloader that can grab streaming videos and audio from thousands of websites automatically. It has many features and benefits that make it easy to use, fast, reliable, and secure. It also has a built-in conversion feature that lets you convert your downloaded media content to the most common formats.</p>
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-<br />
\ No newline at end of file
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
deleted file mode 100644
index 42c0790c98616bb69621deed55547fc04c7392ef..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weight_reduce_loss
-
-
-def cross_entropy(pred,
-                  label,
-                  weight=None,
-                  class_weight=None,
-                  reduction='mean',
-                  avg_factor=None,
-                  ignore_index=-100):
-    """The wrapper function for :func:`F.cross_entropy`"""
-    # class_weight is a manual rescaling weight given to each class.
-    # If given, has to be a Tensor of size C element-wise losses
-    loss = F.cross_entropy(
-        pred,
-        label,
-        weight=class_weight,
-        reduction='none',
-        ignore_index=ignore_index)
-
-    # apply weights and do the reduction
-    if weight is not None:
-        weight = weight.float()
-    loss = weight_reduce_loss(
-        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
-    """Expand onehot labels to match the size of prediction."""
-    bin_labels = labels.new_zeros(target_shape)
-    valid_mask = (labels >= 0) & (labels != ignore_index)
-    inds = torch.nonzero(valid_mask, as_tuple=True)
-
-    if inds[0].numel() > 0:
-        if labels.dim() == 3:
-            bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1
-        else:
-            bin_labels[inds[0], labels[valid_mask]] = 1
-
-    valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float()
-    if label_weights is None:
-        bin_label_weights = valid_mask
-    else:
-        bin_label_weights = label_weights.unsqueeze(1).expand(target_shape)
-        bin_label_weights *= valid_mask
-
-    return bin_labels, bin_label_weights
-
-
-def binary_cross_entropy(pred,
-                         label,
-                         weight=None,
-                         reduction='mean',
-                         avg_factor=None,
-                         class_weight=None,
-                         ignore_index=255):
-    """Calculate the binary CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 1).
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (int | None): The label index to be ignored. Default: 255
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    if pred.dim() != label.dim():
-        assert (pred.dim() == 2 and label.dim() == 1) or (
-                pred.dim() == 4 and label.dim() == 3), \
-            'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
-            'H, W], label shape [N, H, W] are supported'
-        label, weight = _expand_onehot_labels(label, weight, pred.shape,
-                                              ignore_index)
-
-    # weighted element-wise losses
-    if weight is not None:
-        weight = weight.float()
-    loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), pos_weight=class_weight, reduction='none')
-    # do the reduction for the weighted loss
-    loss = weight_reduce_loss(
-        loss, weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def mask_cross_entropy(pred,
-                       target,
-                       label,
-                       reduction='mean',
-                       avg_factor=None,
-                       class_weight=None,
-                       ignore_index=None):
-    """Calculate the CrossEntropy loss for masks.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        target (torch.Tensor): The learning label of the prediction.
-        label (torch.Tensor): ``label`` indicates the class label of the mask'
-            corresponding object. This will be used to select the mask in the
-            of the class which the object belongs to when the mask prediction
-            if not class-agnostic.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (None): Placeholder, to be consistent with other loss.
-            Default: None.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    assert ignore_index is None, 'BCE loss does not support ignore_index'
-    # TODO: handle these two reserved arguments
-    assert reduction == 'mean' and avg_factor is None
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight=class_weight, reduction='mean')[None]
-
-
-@LOSSES.register_module()
-class CrossEntropyLoss(nn.Module):
-    """CrossEntropyLoss.
-
-    Args:
-        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-            of softmax. Defaults to False.
-        use_mask (bool, optional): Whether to use mask cross entropy loss.
-            Defaults to False.
-        reduction (str, optional): . Defaults to 'mean'.
-            Options are "none", "mean" and "sum".
-        class_weight (list[float] | str, optional): Weight of each class. If in
-            str format, read them from a file. Defaults to None.
-        loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-    """
-
-    def __init__(self,
-                 use_sigmoid=False,
-                 use_mask=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        super(CrossEntropyLoss, self).__init__()
-        assert (use_sigmoid is False) or (use_mask is False)
-        self.use_sigmoid = use_sigmoid
-        self.use_mask = use_mask
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = get_class_weight(class_weight)
-
-        if self.use_sigmoid:
-            self.cls_criterion = binary_cross_entropy
-        elif self.use_mask:
-            self.cls_criterion = mask_cross_entropy
-        else:
-            self.cls_criterion = cross_entropy
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function."""
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            weight,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
diff --git a/spaces/szukevin/VISOR-GPT/train/inference/run_classifier_multi_label_infer.py b/spaces/szukevin/VISOR-GPT/train/inference/run_classifier_multi_label_infer.py
deleted file mode 100644
index 9ea0cd938e8cd37d99fc5d45270b3eb238ac7275..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/inference/run_classifier_multi_label_infer.py
+++ /dev/null
@@ -1,101 +0,0 @@
-"""
-  This script provides an example to wrap TencentPretrain for multi-label classification inference.
-"""
-import sys
-import os
-import torch
-import argparse
-import collections
-import torch.nn as nn
-
-tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(tencentpretrain_dir)
-
-from tencentpretrain.utils.constants import *
-from tencentpretrain.utils import *
-from tencentpretrain.utils.config import load_hyperparam
-from tencentpretrain.utils.seed import set_seed
-from tencentpretrain.model_loader import load_model
-from tencentpretrain.opts import infer_opts, tokenizer_opts
-from finetune.run_classifier_multi_label import MultilabelClassifier
-from inference.run_classifier_infer import read_dataset
-from inference.run_classifier_infer import batch_loader
-
-
-def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    infer_opts(parser)
-
-    parser.add_argument("--labels_num", type=int, required=True,
-                        help="Number of prediction labels.")
-
-    tokenizer_opts(parser)
-
-    parser.add_argument("--output_logits", action="store_true", help="Write logits to output file.")
-    parser.add_argument("--output_prob", action="store_true", help="Write probabilities to output file.")
-
-    args = parser.parse_args()
-
-    # Load the hyperparameters from the config file.
-    args = load_hyperparam(args)
-
-    # Build tokenizer.
-    args.tokenizer = str2tokenizer[args.tokenizer](args)
-
-    # Build classification model and load parameters.
-    args.soft_targets, args.soft_alpha = False, False
-    model = MultilabelClassifier(args)
-    model = load_model(model, args.load_model_path)
-
-    # For simplicity, we use DataParallel wrapper to use multiple GPUs.
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(device)
-    if torch.cuda.device_count() > 1:
-        print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
-        model = torch.nn.DataParallel(model)
-
-    dataset = read_dataset(args, args.test_path)
-
-    src = torch.LongTensor([sample[0] for sample in dataset])
-    seg = torch.LongTensor([sample[1] for sample in dataset])
-
-    batch_size = args.batch_size
-    instances_num = src.size()[0]
-
-    print("The number of prediction instances: ", instances_num)
-
-    model.eval()
-
-    with open(args.prediction_path, mode="w", encoding="utf-8") as f:
-        f.write("label")
-        if args.output_logits:
-            f.write("\t" + "logits")
-        if args.output_prob:
-            f.write("\t" + "prob")
-        f.write("\n")
-        for i, (src_batch, seg_batch) in enumerate(batch_loader(batch_size, src, seg)):
-            src_batch = src_batch.to(device)
-            seg_batch = seg_batch.to(device)
-            with torch.no_grad():
-                _, logits = model(src_batch, None, seg_batch)
-
-            prob = nn.Sigmoid()(logits)
-            prob = prob.cpu().numpy().tolist()
-            logits = logits.cpu().numpy().tolist()
-
-            for i, p in enumerate(prob):
-                label = list()
-                for j in range(len(p)):
-                    if p[j] > 0.5:
-                        label.append(str(j))
-                f.write(",".join(label))
-                if args.output_logits:
-                    f.write("\t" + " ".join([str(v) for v in logits[i]]))
-                if args.output_prob:
-                    f.write("\t" + " ".join([str(v) for v in p]))
-                f.write("\n")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/templates/flask/app.py b/spaces/templates/flask/app.py
deleted file mode 100644
index 7f888759232c16cced6c464d3f7e31fe7bed896a..0000000000000000000000000000000000000000
--- a/spaces/templates/flask/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-import requests
-import json
-from io import BytesIO
-
-from flask import Flask, jsonify, render_template, request, send_file
-
-from modules.inference import infer_t5
-from modules.dataset import query_emotion
-
-# https://huggingface.co/settings/tokens
-# https://huggingface.co/spaces/{username}/{space}/settings
-API_TOKEN = os.getenv("BIG_GAN_TOKEN")
-
-app = Flask(__name__)
-
-
-@app.route("/")
-def index():
-    return render_template("index.html")
-
-
-@app.route("/infer_biggan")
-def biggan():
-    input = request.args.get("input")
-
-    output = requests.request(
-        "POST",
-        "https://api-inference.huggingface.co/models/osanseviero/BigGAN-deep-128",
-        headers={"Authorization": f"Bearer {API_TOKEN}"},
-        data=json.dumps(input),
-    )
-
-    return send_file(BytesIO(output.content), mimetype="image/png")
-
-
-@app.route("/infer_t5")
-def t5():
-    input = request.args.get("input")
-
-    output = infer_t5(input)
-
-    return jsonify({"output": output})
-
-
-@app.route("/query_emotion")
-def emotion():
-    start = request.args.get("start")
-    end = request.args.get("end")
-
-    print(start)
-    print(end)
-
-    output = query_emotion(int(start), int(end))
-
-    return jsonify({"output": output})
-
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Adobe Acrobat XI Pro 11.0.20 FINAL Crack Serial Key.md b/spaces/terfces0erbo/CollegeProjectV2/Adobe Acrobat XI Pro 11.0.20 FINAL Crack Serial Key.md
deleted file mode 100644
index 4c566903ff37cf255f1e0a2091a477fe5e1d6752..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Adobe Acrobat XI Pro 11.0.20 FINAL Crack Serial Key.md	
+++ /dev/null
@@ -1,173 +0,0 @@
-<br />
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--- a/spaces/terfces0erbo/CollegeProjectV2/Bukvenkapismenkacifrenkaigrachki Zakachki LINK.md	
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deleted file mode 100644
index 62dcc848716d7e9f123f9060ff72170bb9e1b5dd..0000000000000000000000000000000000000000
--- a/spaces/thelou1s/ehcalabres-wav2vec2-lg-xlsr-en-speech-emotion-recognition/app.py
+++ /dev/null
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Bypass K2S Premium Restrictions and Download Files Faster and Easier.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Bypass K2S Premium Restrictions and Download Files Faster and Easier.md
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Java 1.4.2.15.epub Download the Latest Release of the Java Platform Standard Edition.md b/spaces/tialenAdioni/chat-gpt-api/logs/Java 1.4.2.15.epub Download the Latest Release of the Java Platform Standard Edition.md
deleted file mode 100644
index a56108857906dd6f7ea0ed13e524fb098440cf99..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Java 1.4.2.15.epub Download the Latest Release of the Java Platform Standard Edition.md	
+++ /dev/null
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Become the Crime Boss of the City with Mob Control Mod APK (Unlimited Money No Ads).md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Become the Crime Boss of the City with Mob Control Mod APK (Unlimited Money No Ads).md
deleted file mode 100644
index 4e99a73cc4e0432fa96e806328d3e72146c1b119..0000000000000000000000000000000000000000
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-<p>The second tip to play mod apk mob control is to control your army of stickmen by swiping on the screen in different directions. You can make your stickmen move left, right, up, or down by swiping accordingly. You can also make them stop, run, or jump by tapping on the screen. You can also use gestures to make them perform special actions, such as throwing bombs, firing rockets, or shooting lasers. You need to control your army wisely and strategically to avoid obstacles and enemies.</p>
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-<p>The third tip to play mod apk mob control is to fight your enemies by colliding with them or using special abilities. You can attack your enemies by making your stickmen touch them or their base. You can also use your weapons and abilities to damage them from a distance. You need to be careful and avoid getting hit by your enemies or their weapons and abilities. You also need to watch out for traps and hazards that may harm you or your stickmen. You need to defeat all your enemies or destroy their base to win the battle.</p>
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-<p>Mod apk mob control is a fun and addictive game that you should try if you love creating and controlling your own army of stickmen. You can download mod apk mob control from a reliable website and install it on your device easily. You can enjoy unlimited money and more features in the game with mod apk mob control. You can also play mod apk mob control with a conversational style and simple controls. You can have a blast playing mod apk mob control with your friends or other players online.</p>
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-<p>Yes, mod apk mob control is safe as long as you download it from a reliable website that has good reviews, ratings, and feedback from other users. You should also scan the file with an antivirus software before installing it on your device. You should also avoid clicking on any suspicious links or pop-ups that may appear in the game.</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Construction Simulator 3 APK - Explore Europe and Complete Various Construction Tasks.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Construction Simulator 3 APK - Explore Europe and Complete Various Construction Tasks.md
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index 1adb9fa9925d18f3aa4c93d39d9e00e2f89963cc..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Construction Simulator 3 APK - Explore Europe and Complete Various Construction Tasks.md	
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-<p>If you are a fan of simulation games, you might have heard of Construction Simulator 3, a popular game that lets you experience the life of a construction worker. In this game, you can explore a huge open world, operate over 50 different vehicles, complete various contracts, and build your own company. But what if you want to play this game on your Android device without paying for it? Well, there is a way to do that, and it is by downloading Construction Simulator 3 APK.</p>
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-<p>In this article, we will tell you everything you need to know about Construction Simulator 3 APK, including what it is, why you should download it, what features it offers, and how to install it on your Android device. By the end of this article, you will be ready to enjoy this amazing game for free.</p>
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-<h3>What is Construction Simulator 3 APK?</h3>
-<p>Construction Simulator 3 APK is a modified version of the original game that allows you to play it for free on your Android device. It is not available on the Google Play Store, but you can download it from various websites that offer APK files. APK stands for Android Package Kit, and it is a file format that contains all the necessary components to run an app or a game on an Android device.</p>
-<h3>Why should you download Construction Simulator 3 APK?</h3>
-<p>There are many reasons why you should download Construction Simulator 3 APK, but here are some of the main ones:</p>
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-<li>You can update the game easily whenever a new version is released.</li>
-<li>You can have fun and learn new skills while playing this realistic and immersive game.</li>
-</ul>
-<h2>Features of Construction Simulator 3 APK</h2>
-<p>Construction Simulator 3 APK offers many features that make it one of the best simulation games for Android users. Here are some of them:</p>
-<h3>Explore a vast open world</h3>
-<p>The game takes place in a fictional European city called Neustein, which has three different districts: industrial, urban, and rural. You can explore this huge map freely and discover various landmarks, such as bridges, buildings, farms, and more. You can also interact with other characters and vehicles in the game world.</p>
-<h3>Operate over 50 licensed vehicles</h3>
-<p>The game features over 50 different vehicles from famous brands, such as Caterpillar, Liebherr, MAN, Palfinger, Bell, STILL, ATLAS, Mack Trucks, MEILLER Kipper, and BOMAG. You can operate these vehicles realistically and perform various tasks, such as excavating, transporting, lifting, drilling, paving, and more. You can also switch between first-person and third-person views while driving these vehicles.</p>
-<h3>Complete over 70 challenging contracts</h3>
-<p>The game offers over 70 different contracts that range from small gardening works to large-scale construction projects. You can choose which contracts to accept and complete them according to your own preferences and skills. You can also earn money and reputation by completing these contracts successfully.</p>
-<h3>Customize your own company and fleet</h3>
-<p>The game allows you to create your own company and fleet in the game. You can buy new vehicles, upgrade them, paint them, and customize them to your liking. You can also hire workers, manage your finances, and expand your business. You can also compete with other players in the online multiplayer mode and show off your achievements.</p>
-<h2>How to download and install Construction Simulator 3 APK on your Android device</h2>
-<p>If you want to download and install Construction Simulator 3 APK on your Android device, you need to follow these simple steps:</p>
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-<h3>Step 2: Enable unknown sources on your device settings</h3>
-<p>The next thing you need to do is to enable unknown sources on your device settings. This will allow you to install apps and games that are not from the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on. You may also need to confirm this action by tapping OK or Allow.</p>
-<h3>Step 3: Install the APK file by tapping on it</h3>
-<p>The final thing you need to do is to install the APK file by tapping on it. You can find it in your downloads folder or in your notification bar. Once you tap on it, you will see a screen that shows the app information and permissions. Tap on Install and wait for the installation process to finish.</p>
-<h3>Step 4: Launch the game and enjoy</h3>
-<p>Now you are ready to launch the game and enjoy it. You can find the game icon on your home screen or in your app drawer. Tap on it and start playing Construction Simulator 3 for free on your Android device.</p>
-<h2>Conclusion</h2>
-<p>Construction Simulator 3 APK is a fun and realistic game that lets you experience the life of a construction worker. You can explore a vast open world, operate over 50 licensed vehicles, complete over 70 challenging contracts, and customize your own company and fleet. You can also play offline or online with other players. To download and install Construction Simulator 3 APK on your Android device, you just need to follow four easy steps that we have explained in this article.</p>
-<p>If you are looking for a simulation game that is both entertaining and educational, you should definitely try Construction Simulator 3 APK. It will give you hours of enjoyment and satisfaction as you build your own construction empire.</p>
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-<p>Here are some of the frequently asked questions about Construction Simulator 3 APK:</p>
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-<li>Yes, Construction Simulator 3 APK is safe to download and install as long as you get it from a trusted source. However, you should always scan the file with an antivirus software before installing it.</li>
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-<li><b>Does Construction Simulator 3 APK have any ads or in-app purchases?</b></li>
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-<li>You can update Construction Simulator 3 APK by downloading the latest version of the file from the same source where you got it from. Then, you can install it over the existing one without losing any data or progress.</li>
-<li><b>How can I contact the developers of Construction Simulator 3?</b></li>
-<li>You can contact the developers of Construction Simulator 3 by visiting their official website or their social media pages. You can also send them an email or leave a review on their Google Play Store page.</li>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Crazy Car Offline Racing Game Mod APK How to Unlock All Cars and Tracks.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Crazy Car Offline Racing Game Mod APK How to Unlock All Cars and Tracks.md
deleted file mode 100644
index 5b8c33db17ffed88c53ffa8a2ada8a5f59d6271d..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Crazy Car Offline Racing Game Mod APK How to Unlock All Cars and Tracks.md	
+++ /dev/null
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-<p>If you are looking for a fun and addictive racing game that you can play offline on your Android device, then you should try Crazy Car Offline Racing Game Mod APK. This game will give you an exhilarating racing experience with stunning graphics, realistic physics, various modes, challenges, customizable cars, upgrades, and more. In this article, we will tell you everything you need to know about this game, how to download and install it, why you should play it, and some tips and tricks to help you win the races.</p>
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-<p>Crazy Car Offline Racing Game is a racing game developed by Golden Guns Studio. It is available for free on Google Play Store, but you can also download the modded version from other sources. The game lets you drive different types of cars on various tracks and environments. You can choose from different modes such as career mode, free mode, time trial mode, police chase mode, and more. You can also compete with other players online or offline in multiplayer mode. The game has over 100 levels to complete and over 50 cars to unlock and customize.</p>
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-<p>The game has amazing 3D graphics that will make you feel like you are driving a real car. The game also has realistic physics that will affect the performance of your car depending on the speed, terrain, weather, and collisions. You can see the damage effects on your car as well as the smoke, fire, sparks, and dust that come out of it.</p>
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-<p>To help you win more races and have more fun in Crazy Car Offline Racing Game, here are some tips and tricks that you can use:</p>
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-<p>The game has different types of cars that have different stats such as speed, acceleration, handling, braking, etc. You should choose the right car for each mode and track that suits your driving style and preference. For example, if you are playing the police chase mode, you may want to choose a fast and agile car that can escape from the cops easily. If you are playing the time trial mode, you may want to choose a car that has good acceleration and braking that can handle the sharp turns and curves. You can also switch cars between levels if you want to try different cars and see how they perform.</p>
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-<td>Is Crazy Car Offline Racing Game Mod APK safe to download and install?</td>
-<td>Yes, Crazy Car Offline Racing Game Mod APK is safe to download and install if you get it from a trusted source that has positive reviews and ratings from other users. However, you should always scan the mod apk file with an antivirus app before installing it to make sure it does not contain any viruses, malware, or spyware.</td>
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-<td>Yes, you can update Crazy Car Offline Racing Game Mod APK to the latest version by downloading and installing the new mod apk file from the same source that you got the previous one. However, you should always back up your game data before updating to avoid losing your progress or settings.</td>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Flappy Bird APK and Enjoy the Classic Game on Your Android Device.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Flappy Bird APK and Enjoy the Classic Game on Your Android Device.md
deleted file mode 100644
index fd17cf453360b6ce19bc2f5dd6cf827ebd95860e..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Flappy Bird APK and Enjoy the Classic Game on Your Android Device.md	
+++ /dev/null
@@ -1,110 +0,0 @@
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- <p>Do you remember Flappy Bird, the simple yet addictive game that took the world by storm in 2014? If you missed the chance to play it back then, or if you want to relive the fun, you might be wondering how to download Flappy Bird APK for Android. In this article, we will tell you everything you need to know about Flappy Bird, from its history and gameplay to its appeal and challenges. We will also show you how to install and play Flappy Bird APK on your Android phone safely and easily. Let's get started!</p>
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- <p>Flappy Bird is a game that was created by a Vietnamese developer named Dong Nguyen in 2013. It is a simple game where you have to tap the screen to make a bird fly through gaps between pipes. If you hit a pipe or the ground, you lose. The game has no end, and the only goal is to get as high a score as possible.</p>
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- <p>Flappy Bird was released for iOS in May 2013 and for Android in January 2014. It quickly became a viral sensation, reaching the top of the free app charts in both platforms. It was estimated that the game was earning $50,000 per day from ads. However, in February 2014, Nguyen decided to remove the game from the app stores, saying that it was too addictive and that he wanted to focus on other projects. He also said that he did not want to sell or license the game to anyone else.</p>
- <h3>The Gameplay of Flappy Bird</h3>
- <p>The gameplay of Flappy Bird is very simple. You just have to tap the screen to make the bird flap its wings and fly up. You have to avoid hitting the pipes or the ground, which are randomly generated. The game gets harder as you progress, as the pipes get narrower and closer together. The game has no levels or checkpoints, so you have to start from the beginning every time you lose. The game also has no sound effects or music, except for a sound when you flap, a sound when you hit something, and a sound when you score.</p>
- <h3>The Appeal of Flappy Bird</h3>
- <p>Flappy Bird is a game that appeals to many people because of its simplicity, difficulty, and randomness. It is a game that anyone can play, but few can master. It is a game that challenges your reflexes, patience, and perseverance. It is a game that makes you want to beat your own score and compete with others. It is a game that can be played anytime, anywhere, for as long or as short as you want.</p>
- <h2>How to Download Flappy Bird APK for Android</h2>
- <p>If you want to play Flappy Bird on your Android phone, you might be disappointed to find out that the official version of the game is no longer available on the Google Play Store. However, there are still some ways to download Flappy Bird APK for Android from alternative sources.</p>
- <h3>The Official Version of Flappy Bird is No Longer Available</h3>
- <p>As we mentioned earlier, Nguyen removed Flappy Bird from the app stores in February 2014. He also said that he would not update or re-release the game in any form. This means that the official version of Flappy Bird is no longer available for download or purchase <h3>The Alternative Sources of Flappy Bird APK</h3>
- <p>Since the official version of Flappy Bird is no longer available, you might be tempted to look for alternative sources of Flappy Bird APK on the internet. There are many websites and apps that claim to offer Flappy Bird APK for download, but you have to be careful about their reliability and safety. Some of them might be fake, outdated, or infected with malware. Some of them might also require you to complete surveys, download other apps, or provide personal information before giving you access to Flappy Bird APK.</p>
- <h3>The Risks and Precautions of Downloading Flappy Bird APK</h3>
- <p>Downloading Flappy Bird APK from alternative sources can pose some risks to your device and your privacy. You might end up downloading a corrupted or modified file that can harm your device or compromise your data. You might also expose your device to viruses, spyware, or adware that can slow down your performance or steal your information. You might also violate the intellectual property rights of the original developer by downloading an unauthorized copy of Flappy Bird.</p>
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- <p>To avoid these risks, you should take some precautions before downloading Flappy Bird APK from alternative sources. You should always check the reputation and reviews of the source, and avoid any suspicious or unknown websites or apps. You should also scan the file with an antivirus or malware detector before installing it on your device. You should also enable the unknown sources option in your settings, which allows you to install apps from sources other than the Google Play Store. You should also disable this option after installing Flappy Bird APK, to prevent any unwanted installations in the future.</p>
- <h2>How to Install and Play Flappy Bird APK on Android</h2>
- <p>If you have found a reliable and safe source of Flappy Bird APK, you can proceed to install and play it on your Android phone. Here are the steps to follow:</p>
- <h3>The Steps to Install Flappy Bird APK on Android</h3>
- <ol>
-<li>Download the Flappy Bird APK file from the source you have chosen.</li>
-<li>Locate the file in your downloads folder or file manager app.</li>
-<li>Tap on the file to open it.</li>
-<li>You might see a warning message that says "For your security, your phone is not allowed to install unknown apps from this source". Tap on Settings, then toggle on the Allow from this source option.</li>
-<li>Go back to the file and tap on Install.</li>
-<li>Wait for the installation to complete.</li>
-<li>You will see a message that says "App installed". Tap on Open to launch Flappy Bird.</li>
-</ol>
- <h3>The Tips and Tricks to Play Flappy Bird APK on Android</h3>
- <p>Now that you have installed Flappy Bird APK on your Android phone, you can start playing it and enjoy the fun and frustration. Here are some tips and tricks to help you play better and score higher:</p>
- <ul>
-<li>Tap lightly and rhythmically to control the bird's flight. Don't tap too hard or too fast, as this will make the bird fly too high or too low.</li>
-<li>Aim for the middle of the gaps between the pipes. Don't try to fly too close to the edges, as this will increase your chances of hitting something.</li>
-<li>Don't get distracted by the score or the medals. Focus on the bird and the pipes, and ignore everything else.</li>
-<li>Don't get discouraged by failure. Flappy Bird is a game that requires practice and patience. Learn from your mistakes and try again.</li>
-<li>Have fun and don't take it too seriously. Flappy Bird is a game that is meant to be simple and challenging, but also entertaining and relaxing. Don't let it stress you out or ruin your mood.</li>
-</ul>
- <h3>The Challenges and Rewards of Playing Flappy Bird APK on Android</h3>
- <p>Playing Flappy Bird APK on Android can be both challenging and rewarding. It can be challenging because it is a game that tests your skills, reflexes, and endurance. It can be frustrating because it is a game that can make you lose in an instant. It can be addictive because it is a game that makes you want to improve your score and beat others.</p>
- <p>However, playing Flappy Bird APK on Android can also be rewarding because it is a game that gives you satisfaction, excitement, and joy. It can be satisfying because it is a game that lets you achieve something with your own effort. It can be exciting because it is a game that keeps you on edge and makes you feel alive. It can be joyful because it is a game that brings back memories and nostalgia.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Flappy Bird is a legendary game that has captivated millions of people around the world with its simplicity, difficulty, and randomness. If you want to play it on your Android phone, you can download Flappy Bird APK from alternative sources, but you have to be careful about the risks and precautions. You can also install and play Flappy Bird APK on your Android phone easily and safely by following the steps we have provided. You can also improve your gameplay and score by following the tips and tricks we have shared. Playing Flappy Bird APK on Android can be a challenging and rewarding experience that can bring you fun and nostalgia. We hope you enjoy playing Flappy Bird APK on your Android phone!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Flappy Bird APK download Android:</p>
- <ol>
-<li>Is Flappy Bird APK safe to download?</li>
-<p>Flappy Bird APK is safe to download if you get it from a reliable and trustworthy source. However, you should always scan the file with an antivirus or malware detector before installing it on your device. You should also enable the unknown sources option in your settings, which allows you to install apps from sources other than the Google Play Store. You should also disable this option after installing Flappy Bird APK, to prevent any unwanted installations in the future.</p>
-<li>Is Flappy Bird APK legal to download?</li>
-<p>Flappy Bird APK is not legal to download, as it violates the intellectual property rights of the original developer. The official version of Flappy Bird was removed from the app stores by the developer himself, who did not want to sell or license the game to anyone else. Downloading Flappy Bird APK from alternative sources is considered piracy and can result in legal consequences.</p>
-<li>Is Flappy Bird APK compatible with all Android devices?</li>
-<p>Flappy Bird APK is compatible with most Android devices that run on Android 2.2 or higher. However, some devices might have issues with the performance or the graphics of the game. You might also need to adjust the settings of your device to optimize the gameplay of Flappy Bird APK.</p>
-<li>How can I backup my Flappy Bird APK data?</li>
-<p>You can backup your Flappy Bird APK data by using a file manager app or a cloud storage service. You can copy or upload the Flappy Bird APK file and the data folder from your device to another location, such as your computer or your online account. You can then restore your Flappy Bird APK data by downloading or copying them back to your device.</p>
-<li>How can I share my Flappy Bird APK score with others?</li>
-<p>You can share your Flappy Bird APK score with others by taking a screenshot of your game screen and posting it on social media platforms, such as Facebook, Twitter, or Instagram. You can also use apps that allow you to record or stream your gameplay, such as YouTube Gaming, Twitch, or Mobcrush.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
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deleted file mode 100644
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Bastian Baker Tomorrow May Not Be Better Mp3 Torrent.md b/spaces/tioseFevbu/cartoon-converter/scripts/Bastian Baker Tomorrow May Not Be Better Mp3 Torrent.md
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--- a/spaces/tioseFevbu/cartoon-converter/scripts/Bastian Baker Tomorrow May Not Be Better Mp3 Torrent.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<h1>How to Download Bastian Baker's Hit Song "Tomorrow May Not Be Better" for Free</h1>
-<p>If you are a fan of Bastian Baker, the Swiss singer-songwriter who rose to fame with his debut album "Tomorrow May Not Be Better" in 2011, you might be wondering how to download his hit song of the same name for free. After all, who doesn't love a catchy pop-rock tune with a message of living in the moment and enjoying life?</p>
-<p>Well, you are in luck, because in this article, we will show you how to find and download Bastian Baker's "Tomorrow May Not Be Better" mp3 torrent file, which will allow you to enjoy his music without paying a dime. But before we do that, let's learn more about Bastian Baker and his song.</p>
-<h2>Bastian Baker Tomorrow May Not Be Better Mp3 Torrent</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://urlcod.com/2uHwD6">https://urlcod.com/2uHwD6</a></b></p><br /><br />
-<h2>Who is Bastian Baker?</h2>
-<p>Bastian Baker is a 31-year-old Swiss musician who started his career as a hockey player before switching to music. He released his first album "Tomorrow May Not Be Better" in 2011, which became a huge success in Switzerland and France, earning him several awards and nominations. He has since released three more albums and toured with artists like Shania Twain and Bryan Adams.</p>
-<p>Bastian Baker is known for his catchy melodies, heartfelt lyrics, and charismatic stage presence. He sings in English and French, and plays guitar, piano, and harmonica. He is also involved in humanitarian causes, such as supporting children in Africa and fighting against climate change.</p>
-<h2>What is "Tomorrow May Not Be Better" about?</h2>
-<p>"Tomorrow May Not Be Better" is the title track of Bastian Baker's debut album, and also his first single. It was released in 2011 and became a hit in Switzerland and France, reaching the top 10 on the charts. The song is a pop-rock anthem with a positive message of living in the present and making the most of every day.</p>
-<p>The lyrics of the song reflect Bastian Baker's personal philosophy of life, as he sings: "Don't waste your time thinking about tomorrow / Cause you don't know what it's gonna bring / Just live your life like there's no more sorrow / Cause tomorrow may not be better". The song also encourages listeners to follow their dreams and passions, as he sings: "Don't be afraid to do what you wanna do / Cause you only got one life to live / And if you don't try you'll never know / If tomorrow may not be better".</p>
-<p>The song has a catchy chorus that invites people to sing along and dance. It also has a powerful bridge that features a harmonica solo by Bastian Baker. The song has been praised by critics and fans alike for its uplifting message and catchy tune.</p>
-<h2>How to download "Tomorrow May Not Be Better" mp3 torrent file?</h2>
-<p>If you want to download Bastian Baker's "Tomorrow May Not Be Better" mp3 torrent file for free, you will need two things: a torrent client and a torrent site. A torrent client is a software that allows you to download files from other users who share them on the internet. A torrent site is a website that hosts torrent files, which are small files that contain information about the files you want to download.</p>
-<p>There are many torrent clients and torrent sites available online, but some of them may be unsafe or illegal. Therefore, we recommend you to use reputable and reliable ones, such as uTorrent for the torrent client and The Pirate Bay for the torrent site. Here are the steps to follow:</p>
-<p></p>
-<ol>
-<li>Download and install uTorrent from <a href="https://www.utorrent.com/">https://www.utorrent.com/</a>. Follow the instructions on the website and accept the terms of use.</li>
-<li>Go to The Pirate Bay website at <a href="https://thepiratebay.org/">https://thepiratebay.org/</a>. This is one of the most popular torrent sites in the world, but it may be blocked or censored in some countries. If that is the case, you can use a proxy or a VPN service to access it.</li>
-<li>In the search box on The Pirate Bay website, type "Bastian Baker Tomorrow May Not Be Better Mp3 Torrent" and</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Bhale Bhale Magadivoy (2015) Telugu 1080p WEB-DL X264 AAC-[XclusivE] !EXCLUSIVE!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Bhale Bhale Magadivoy (2015) Telugu 1080p WEB-DL X264 AAC-[XclusivE] !EXCLUSIVE!.md
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index 47ed05645b5460a8682cd77a3e276d3bf56f5cf1..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,14 +0,0 @@
-<br />
-<h1>Bhale Bhale Magadivoy: A Romantic Comedy with a Twist</h1>
-<p>Bhale Bhale Magadivoy is a 2015 Telugu romantic comedy film starring Nani and Lavanya Tripathi. The film is directed by Maruthi Dasari and produced by Geetha Arts and UV Creations. The film was a blockbuster at the box office and received positive reviews from critics and audiences alike.</p>
-<p>The film revolves around Lucky (Nani), a scientist who suffers from amnesia. He falls in love with Nandana (Lavanya Tripathi), a beautiful and traditional girl. However, his forgetfulness causes him a lot of trouble as he tries to impress her and her father (Murali Sharma). He also has to deal with his rival Ajay (Ajay), who wants to marry Nandana. How Lucky overcomes his challenges and wins Nandana's heart forms the crux of the story.</p>
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-<p>Bhale Bhale Magadivoy is a fun-filled entertainer that showcases Nani's comic timing and charm. The film also has a catchy soundtrack composed by Gopi Sunder, with songs like "Endaro Mahanubhavulu", "Motta Modatisari" and "Hello Hello". The film also has some hilarious scenes involving Lucky's memory lapses and his interactions with various characters.</p>
-<p>If you are looking for a light-hearted and enjoyable movie to watch with your family or friends, you can check out Bhale Bhale Magadivoy on Disney+ Hotstar[^1^]. You can also download the high-quality version of the film from TamilMV.La[^2^]. But be careful of the piracy issues and respect the filmmakers' hard work.</p><p>Bhale Bhale Magadivoy is not only a commercial success but also a critical acclaim. The film won several awards and nominations at various film festivals and ceremonies. Nani won the Best Actor award at the Filmfare Awards South and the Nandi Awards for his performance. The film also won the Best Screenplay award at the SIIMA Awards and the Best Comedy Film award at the Santosham Film Awards.</p>
-<p>The film is also praised for its clean and family-friendly humor, which is rare in the contemporary Telugu cinema. The film does not rely on vulgar jokes or double entendres to make the audience laugh. Instead, it uses situational comedy and witty dialogues to create laughter. The film also has a positive message about love, honesty and forgiveness.</p>
-<p>Bhale Bhale Magadivoy is a must-watch for anyone who loves romantic comedies with a twist. The film will make you laugh, cry and cheer for the lovable characters. The film is a perfect example of how a simple story can be made into an engaging and entertaining film with good direction, script, music and acting.</p><p>Bhale Bhale Magadivoy is also a remake of the 2011 Malayalam film Oru Naal Varum, which starred Mohanlal and Sameera Reddy. However, the Telugu version has made several changes to the original story and added more comedy elements. The director Maruthi Dasari has said that he was inspired by the Malayalam film but wanted to make it more suitable for the Telugu audience. He has also said that he was a fan of Nani and wanted to work with him for a long time.</p>
-<p>Nani and Lavanya Tripathi have also expressed their happiness and gratitude for being a part of this film. Nani has said that he enjoyed playing the role of Lucky and that it was one of his most challenging and satisfying roles. He has also said that he had a great chemistry with Lavanya Tripathi and that she was a perfect choice for the role of Nandana. Lavanya Tripathi has said that she loved working with Nani and Maruthi Dasari and that she learned a lot from them. She has also said that she was happy to be a part of such a successful and well-received film.</p>
-<p>Bhale Bhale Magadivoy is a film that will make you smile and feel good. The film has a universal appeal and can be enjoyed by anyone who loves romance and comedy. The film is a testament to the talent and creativity of the Telugu film industry and its artists. The film is a gem that you should not miss.</p>
-<p></p> 7196e7f11a<br />
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-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Brave 720p 300 Mb Movie.md b/spaces/tioseFevbu/cartoon-converter/scripts/Brave 720p 300 Mb Movie.md
deleted file mode 100644
index 6f7fa5244956c6469f29fdc012805cf91ed221e8..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Brave 720p 300 Mb Movie.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-
-<h1>Download Brave (2012) Full Movie in 720p HD Quality for Free</h1>
-<p>Brave is a 2012 animated fantasy film produced by Pixar Animation Studios and released by Walt Disney Pictures. The film tells the story of Merida, a rebellious princess who defies an ancient tradition and unleashes a curse on her kingdom. She must rely on her bravery and archery skills to undo the spell and restore peace.</p>
-<h2>brave 720p 300 mb movie</h2><br /><p><b><b>Download File</b> &#10002; &#10002; &#10002; <a href="https://urlcod.com/2uHykb">https://urlcod.com/2uHykb</a></b></p><br /><br />
-<p>If you are looking for a family-friendly adventure with stunning animation, humor, and heart, you should watch Brave. You can download Brave full movie in 720p HD quality for free from Vegamovies[^3^], a website that offers 300mb movies, 480p movies, 720p movies, and 1080p movies in dual audio Hindi and English. You can also stream Brave online on Disney+ Hotstar[^1^], a streaming service that offers a variety of movies and shows from Disney, Pixar, Marvel, Star Wars, and more.</p>
-<p>Brave is a movie that celebrates the power of courage, love, and destiny. It features a talented voice cast that includes Kelly Macdonald, Billy Connolly, Emma Thompson, Julie Walters, Robbie Coltrane, Kevin McKidd, Craig Ferguson, and John Ratzenberger. It also features an original score composed by Patrick Doyle and songs performed by Julie Fowlis and Mumford & Sons.</p>
-<p>Don't miss this chance to download Brave full movie in 720p HD quality for free from Vegamovies[^3^] or stream it online on Disney+ Hotstar[^1^]. You will be enchanted by this magical tale of a brave princess who changes her fate.</p>
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-<p>Brave is not only a visually stunning film, but also a groundbreaking one. It is the first Pixar film to feature a female protagonist, a female director, and a female co-director. It is also the first Pixar film to be set in a historical period and to explore Celtic mythology and culture. The film was inspired by the personal experiences of director Brenda Chapman, who wanted to create a story about her relationship with her daughter.</p>
-<p>The film received critical acclaim and was nominated for several awards, including the Academy Award for Best Animated Feature, which it won. It also won the Golden Globe Award for Best Animated Feature Film, the BAFTA Award for Best Animated Film, and the Critics' Choice Movie Award for Best Animated Feature. The film was praised for its animation, voice acting, music, story, and themes of bravery, family, and feminism.</p>
-<p></p>
-<p>Brave is a film that will appeal to audiences of all ages and backgrounds. It is a film that celebrates the bond between mother and daughter, the spirit of adventure, and the courage to follow your heart. It is a film that will make you laugh, cry, and cheer. It is a film that you should download or stream today.</p> cec2833e83<br />
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\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Mac Os X Sierra Installer.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Mac Os X Sierra Installer.md
deleted file mode 100644
index cd9ed88b75efda12f00e0526f97a454f43f0cacc..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Download Mac Os X Sierra Installer.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>How to Download and Install Mac OS X Sierra on Your Mac</h1>
-<p>Mac OS X Sierra is a version of the Mac operating system that was released in 2016. It offers new features such as Siri, Apple Pay, Universal Clipboard, Picture-in-Picture, and more. If you want to upgrade your Mac to Sierra, or reinstall it on a new or erased drive, you will need to download the installer from the App Store or Apple's website. Here are the steps to do so:</p>
-<ol>
-<li>Make sure your Mac is compatible with Sierra. You can check the list of supported models here: <a href="https://support.apple.com/en-us/HT201475">https://support.apple.com/en-us/HT201475</a></li>
-<li>Back up your Mac using Time Machine or another backup method. This will ensure that you can restore your data in case something goes wrong during the installation.</li>
-<li>Open the App Store on your Mac and search for "macOS Sierra" or click this link: <a href="https://apps.apple.com/us/app/macos-sierra/id1127487414?mt=12">https://apps.apple.com/us/app/macos-sierra/id1127487414?mt=12</a></li>
-<li>Click the "Get" button to start downloading the installer. It will be saved in your Applications folder as "Install macOS Sierra". The download size is about 5 GB and may take some time depending on your internet speed.</li>
-<li>Once the download is complete, the installer will open automatically. Follow the onscreen instructions to install macOS Sierra on your Mac. You may need to agree to the terms and conditions, select a destination disk, and enter your administrator password.</li>
-<li>Your Mac will restart several times during the installation process. Do not put your Mac to sleep or close its lid until the installation is finished. The installation may take from 30 minutes to several hours depending on your Mac model and disk speed.</li>
-<li>When the installation is done, your Mac will boot into macOS Sierra. You can set up your preferences, iCloud account, and other settings as prompted. Enjoy your new macOS!</li>
-</ol>
-<p>If you have any issues with downloading or installing macOS Sierra, you can visit this support page for more help: <a href="https://support.apple.com/en-us/HT211683">https://support.apple.com/en-us/HT211683</a></p>
-<h2>Download Mac Os X Sierra Installer</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://urlcod.com/2uHy0X">https://urlcod.com/2uHy0X</a></b></p><br /><br />
-
-<h2>What's New in macOS Sierra</h2>
-<p>macOS Sierra brings many new features and improvements to your Mac. Here are some of the highlights:</p>
-<ul>
-<li><b>Siri:</b> You can now use Siri on your Mac to get things done faster and easier. You can ask Siri to search the web, find files, send messages, play music, check the weather, and more. You can also drag and drop Siri results into your documents or pin them to the Notification Center. To use Siri, click the Siri icon in the menu bar or Dock, or press and hold the Command key and Space bar.</li>
-<li><b>Apple Pay:</b> You can now use Apple Pay on your Mac to make secure and convenient online purchases. When you see the Apple Pay button on a website, just click it and confirm the payment with your iPhone or Apple Watch. You don't need to enter your card details or shipping address.</li>
-<li><b>Universal Clipboard:</b> You can now copy and paste text, images, and other content between your Mac and your iPhone, iPad, or iPod touch. Just copy something on one device and paste it on another device nearby. No setup required.</li>
-<li><b>Picture-in-Picture:</b> You can now watch a video in a small window that stays on top of other windows. This lets you multitask while enjoying your favorite show or movie. To use Picture-in-Picture, look for the icon in the video player and click it. You can then drag and resize the window as you like.</li>
-<li><b>Optimized Storage:</b> You can now free up more space on your Mac by storing your older files in iCloud and removing the clutter you don't need. To use Optimized Storage, go to Apple menu > About This Mac > Storage > Manage. You can then choose to store your Desktop and Documents folders, Photos library, iTunes movies and TV shows, email attachments, and trash items in iCloud. You can also review and delete files that are taking up a lot of space, such as downloads, caches, logs, and duplicates.</li>
-<li><b>Auto Unlock:</b> You can now unlock your Mac automatically when you are wearing your Apple Watch. This saves you from typing your password every time you wake up your Mac. To use Auto Unlock, make sure your Apple Watch is paired with your iPhone and both devices are signed in to iCloud with the same Apple ID. Then go to System Preferences > Security & Privacy > General and check the box for "Allow your Apple Watch to unlock your Mac".</li>
-</ul>
-<p>These are just some of the new features in macOS Sierra. To learn more about what's new, visit this website: <a href="https://www.apple.com/macos/sierra/">https://www.apple.com/macos/sierra/</a></p> e93f5a0c3f<br />
-<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Learn Hindi In 30 Days Through Telugu Pdf Free Download Rar.md b/spaces/tioseFevbu/cartoon-converter/scripts/Learn Hindi In 30 Days Through Telugu Pdf Free Download Rar.md
deleted file mode 100644
index 9aae6ec84edf92b1e7d2b9a1c2baffd932f1feb2..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Learn Hindi In 30 Days Through Telugu Pdf Free Download Rar.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-<h1>How to Learn Hindi in 30 Days Through Telugu PDF Free Download RAR</h1>
-<p>If you want to learn Hindi in 30 days through Telugu, you might be interested in a PDF file that contains a course for learning the basics of Hindi language and grammar. This file is available for free download in a compressed format called RAR, which reduces the file size and makes it easier to transfer and store.</p>
-<h2>learn hindi in 30 days through telugu pdf free download rar</h2><br /><p><b><b>Download</b> &#8230;&#8230;&#8230; <a href="https://urlcod.com/2uHxyR">https://urlcod.com/2uHxyR</a></b></p><br /><br />
-<p>Why should you learn Hindi in 30 days through Telugu? Hindi is one of the most widely spoken languages in India, with more than 500 million speakers. It is also an official language of the country, along with English. Learning Hindi can help you communicate with people from different regions and cultures, as well as access a rich literature and media. Hindi is also closely related to other languages such as Urdu, Punjabi, and Nepali.</p>
-<p>How can you learn Hindi in 30 days through Telugu? One way is to use a PDF file that contains a course for learning Hindi through Telugu. This course is designed by Rapidex Language Learning Series, which is a popular publisher of language learning books in India. The course covers topics such as alphabets, numbers, greetings, pronouns, verbs, tenses, sentences, conversations, and vocabulary. The course also provides exercises and quizzes to test your progress and reinforce your learning.</p>
-<p>Where can you find the PDF file for learning Hindi in 30 days through Telugu? One source is Scribd, which is an online platform for reading and sharing documents. You can find the PDF file by searching for "telugu hindi learning course.pdf" on Scribd[^1^]. You can download the file for free by signing up for a trial account or by uploading your own document. Alternatively, you can also find the PDF file on other websites that offer free downloads of PDF files[^2^]. However, you should be careful about the quality and security of these websites.</p>
-<p>How can you open the RAR file that contains the PDF file for learning Hindi in 30 days through Telugu? A RAR file is a compressed file that contains one or more files inside it. To open a RAR file, you need a software that can extract the files from it. One such software is WinRAR, which is a popular program for creating and opening compressed files. You can download WinRAR from its official website or from other trusted sources. Once you have installed WinRAR, you can right-click on the RAR file and choose "Extract Here" or "Extract to" to get the PDF file inside it.</p>
-<p>What are some other resources for learning Hindi in 30 days through Telugu? Besides the PDF file for learning Hindi through Telugu, you can also use other resources such as websites[^4^], apps, podcasts, videos, books[^3^], and tutors to enhance your learning experience. You can also practice your Hindi skills by speaking with native speakers online or offline, reading newspapers and magazines, watching movies and shows, listening to songs and radio, and writing letters and emails.</p>
-<p></p>
-<p>Learning Hindi in 30 days through Telugu is possible if you have the right motivation, materials, and methods. By using a PDF file that contains a course for learning Hindi through Telugu, you can start your journey towards mastering this beautiful and useful language.</p> 7b8c122e87<br />
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/style.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/style.py
deleted file mode 100644
index 84abbc20599f034626779702abc2303901d83ee5..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/style.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""
-    pygments.style
-    ~~~~~~~~~~~~~~
-
-    Basic style object.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pip._vendor.pygments.token import Token, STANDARD_TYPES
-
-# Default mapping of ansixxx to RGB colors.
-_ansimap = {
-    # dark
-    'ansiblack': '000000',
-    'ansired': '7f0000',
-    'ansigreen': '007f00',
-    'ansiyellow': '7f7fe0',
-    'ansiblue': '00007f',
-    'ansimagenta': '7f007f',
-    'ansicyan': '007f7f',
-    'ansigray': 'e5e5e5',
-    # normal
-    'ansibrightblack': '555555',
-    'ansibrightred': 'ff0000',
-    'ansibrightgreen': '00ff00',
-    'ansibrightyellow': 'ffff00',
-    'ansibrightblue': '0000ff',
-    'ansibrightmagenta': 'ff00ff',
-    'ansibrightcyan': '00ffff',
-    'ansiwhite': 'ffffff',
-}
-# mapping of deprecated #ansixxx colors to new color names
-_deprecated_ansicolors = {
-    # dark
-    '#ansiblack': 'ansiblack',
-    '#ansidarkred': 'ansired',
-    '#ansidarkgreen': 'ansigreen',
-    '#ansibrown': 'ansiyellow',
-    '#ansidarkblue': 'ansiblue',
-    '#ansipurple': 'ansimagenta',
-    '#ansiteal': 'ansicyan',
-    '#ansilightgray': 'ansigray',
-    # normal
-    '#ansidarkgray': 'ansibrightblack',
-    '#ansired': 'ansibrightred',
-    '#ansigreen': 'ansibrightgreen',
-    '#ansiyellow': 'ansibrightyellow',
-    '#ansiblue': 'ansibrightblue',
-    '#ansifuchsia': 'ansibrightmagenta',
-    '#ansiturquoise': 'ansibrightcyan',
-    '#ansiwhite': 'ansiwhite',
-}
-ansicolors = set(_ansimap)
-
-
-class StyleMeta(type):
-
-    def __new__(mcs, name, bases, dct):
-        obj = type.__new__(mcs, name, bases, dct)
-        for token in STANDARD_TYPES:
-            if token not in obj.styles:
-                obj.styles[token] = ''
-
-        def colorformat(text):
-            if text in ansicolors:
-                return text
-            if text[0:1] == '#':
-                col = text[1:]
-                if len(col) == 6:
-                    return col
-                elif len(col) == 3:
-                    return col[0] * 2 + col[1] * 2 + col[2] * 2
-            elif text == '':
-                return ''
-            elif text.startswith('var') or text.startswith('calc'):
-                return text
-            assert False, "wrong color format %r" % text
-
-        _styles = obj._styles = {}
-
-        for ttype in obj.styles:
-            for token in ttype.split():
-                if token in _styles:
-                    continue
-                ndef = _styles.get(token.parent, None)
-                styledefs = obj.styles.get(token, '').split()
-                if not ndef or token is None:
-                    ndef = ['', 0, 0, 0, '', '', 0, 0, 0]
-                elif 'noinherit' in styledefs and token is not Token:
-                    ndef = _styles[Token][:]
-                else:
-                    ndef = ndef[:]
-                _styles[token] = ndef
-                for styledef in obj.styles.get(token, '').split():
-                    if styledef == 'noinherit':
-                        pass
-                    elif styledef == 'bold':
-                        ndef[1] = 1
-                    elif styledef == 'nobold':
-                        ndef[1] = 0
-                    elif styledef == 'italic':
-                        ndef[2] = 1
-                    elif styledef == 'noitalic':
-                        ndef[2] = 0
-                    elif styledef == 'underline':
-                        ndef[3] = 1
-                    elif styledef == 'nounderline':
-                        ndef[3] = 0
-                    elif styledef[:3] == 'bg:':
-                        ndef[4] = colorformat(styledef[3:])
-                    elif styledef[:7] == 'border:':
-                        ndef[5] = colorformat(styledef[7:])
-                    elif styledef == 'roman':
-                        ndef[6] = 1
-                    elif styledef == 'sans':
-                        ndef[7] = 1
-                    elif styledef == 'mono':
-                        ndef[8] = 1
-                    else:
-                        ndef[0] = colorformat(styledef)
-
-        return obj
-
-    def style_for_token(cls, token):
-        t = cls._styles[token]
-        ansicolor = bgansicolor = None
-        color = t[0]
-        if color in _deprecated_ansicolors:
-            color = _deprecated_ansicolors[color]
-        if color in ansicolors:
-            ansicolor = color
-            color = _ansimap[color]
-        bgcolor = t[4]
-        if bgcolor in _deprecated_ansicolors:
-            bgcolor = _deprecated_ansicolors[bgcolor]
-        if bgcolor in ansicolors:
-            bgansicolor = bgcolor
-            bgcolor = _ansimap[bgcolor]
-
-        return {
-            'color':        color or None,
-            'bold':         bool(t[1]),
-            'italic':       bool(t[2]),
-            'underline':    bool(t[3]),
-            'bgcolor':      bgcolor or None,
-            'border':       t[5] or None,
-            'roman':        bool(t[6]) or None,
-            'sans':         bool(t[7]) or None,
-            'mono':         bool(t[8]) or None,
-            'ansicolor':    ansicolor,
-            'bgansicolor':  bgansicolor,
-        }
-
-    def list_styles(cls):
-        return list(cls)
-
-    def styles_token(cls, ttype):
-        return ttype in cls._styles
-
-    def __iter__(cls):
-        for token in cls._styles:
-            yield token, cls.style_for_token(token)
-
-    def __len__(cls):
-        return len(cls._styles)
-
-
-class Style(metaclass=StyleMeta):
-
-    #: overall background color (``None`` means transparent)
-    background_color = '#ffffff'
-
-    #: highlight background color
-    highlight_color = '#ffffcc'
-
-    #: line number font color
-    line_number_color = 'inherit'
-
-    #: line number background color
-    line_number_background_color = 'transparent'
-
-    #: special line number font color
-    line_number_special_color = '#000000'
-
-    #: special line number background color
-    line_number_special_background_color = '#ffffc0'
-
-    #: Style definitions for individual token types.
-    styles = {}
-
-    # Attribute for lexers defined within Pygments. If set
-    # to True, the style is not shown in the style gallery
-    # on the website. This is intended for language-specific
-    # styles.
-    web_style_gallery_exclude = False
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
deleted file mode 100644
index 61b9751057f10f2173b8e7edde12cca53ebbd2d0..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,19 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-model = dict(
-    bbox_head=dict(
-        loss_cls=dict(
-            _delete_=True,
-            type='GHMC',
-            bins=30,
-            momentum=0.75,
-            use_sigmoid=True,
-            loss_weight=1.0),
-        loss_bbox=dict(
-            _delete_=True,
-            type='GHMR',
-            mu=0.02,
-            bins=10,
-            momentum=0.7,
-            loss_weight=10.0)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
deleted file mode 100644
index 008a70ae67454c3fd470c29ffd000b18db391c8e..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_dense_heads/test_fsaf_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_dense_heads/test_fsaf_head.py
deleted file mode 100644
index 1d85937f88675830a937dc623a6ee08b2f07dd7c..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_dense_heads/test_fsaf_head.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import mmcv
-import torch
-
-from mmdet.models.dense_heads import FSAFHead
-
-
-def test_fsaf_head_loss():
-    """Tests anchor head loss when truth is empty and non-empty."""
-    s = 256
-    img_metas = [{
-        'img_shape': (s, s, 3),
-        'scale_factor': 1,
-        'pad_shape': (s, s, 3)
-    }]
-
-    cfg = dict(
-        reg_decoded_bbox=True,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=1,
-            scales_per_octave=1,
-            ratios=[1.0],
-            strides=[8, 16, 32, 64, 128]),
-        bbox_coder=dict(type='TBLRBBoxCoder', normalizer=4.0),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0,
-            reduction='none'),
-        loss_bbox=dict(
-            type='IoULoss', eps=1e-6, loss_weight=1.0, reduction='none'))
-
-    train_cfg = mmcv.Config(
-        dict(
-            assigner=dict(
-                type='CenterRegionAssigner',
-                pos_scale=0.2,
-                neg_scale=0.2,
-                min_pos_iof=0.01),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False))
-    head = FSAFHead(num_classes=4, in_channels=1, train_cfg=train_cfg, **cfg)
-    if torch.cuda.is_available():
-        head.cuda()
-        # FSAF head expects a multiple levels of features per image
-        feat = [
-            torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))).cuda()
-            for i in range(len(head.anchor_generator.strides))
-        ]
-        cls_scores, bbox_preds = head.forward(feat)
-        gt_bboxes_ignore = None
-
-        # When truth is non-empty then both cls and box loss should be nonzero
-        #  for random inputs
-        gt_bboxes = [
-            torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(),
-        ]
-        gt_labels = [torch.LongTensor([2]).cuda()]
-        one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
-                                  img_metas, gt_bboxes_ignore)
-        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
-        onegt_box_loss = sum(one_gt_losses['loss_bbox'])
-        assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
-        assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
-
-        # Test that empty ground truth encourages the network to predict bkg
-        gt_bboxes = [torch.empty((0, 4)).cuda()]
-        gt_labels = [torch.LongTensor([]).cuda()]
-
-        empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes,
-                                    gt_labels, img_metas, gt_bboxes_ignore)
-        # When there is no truth, the cls loss should be nonzero but there
-        # should be no box loss.
-        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
-        empty_box_loss = sum(empty_gt_losses['loss_bbox'])
-        assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
-        assert empty_box_loss.item() == 0, (
-            'there should be no box loss when there are no true boxes')
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/93c66 Eeprom .bin Download ((INSTALL)).md b/spaces/usbethFlerru/sovits-modelsV2/example/93c66 Eeprom .bin Download ((INSTALL)).md
deleted file mode 100644
index 408dc190efe2da1b20f68d5075d25384d4c875a8..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/93c66 Eeprom .bin Download ((INSTALL)).md	
+++ /dev/null
@@ -1,115 +0,0 @@
-
-<h1>What is 93C66 EEPROM and How to Download and Program .bin Files</h1>
-
-<p>An EEPROM (Electrically Erasable Programmable Read-Only Memory) is a type of non-volatile memory that can store data even when the power is turned off. EEPROMs are widely used in various electronic devices, such as computers, cars, phones, and more.</p>
-<h2>93c66 eeprom .bin download</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlcod.com/2uyUi0">https://urlcod.com/2uyUi0</a></b></p><br /><br />
-
-<p>A 93C66 EEPROM is a specific model of EEPROM that uses the Microwire SPI serial interface to communicate with other devices. It has a capacity of 4 KB (4096 bytes) and can be configured as either 512x8 or 256x16 bits. The 93C66 EEPROM can be programmed and erased using special commands and voltage levels.</p>
-
-<p>A .bin file is a binary file that contains the raw data of an EEPROM. A .bin file can be downloaded from the internet or extracted from an EEPROM using an EEPROM programmer. A .bin file can also be written to an EEPROM using an EEPROM programmer.</p>
-
-<h2>How to Download 93C66 EEPROM .bin Files</h2>
-
-<p>There are many websites that offer 93C66 EEPROM .bin files for download. Some of them are:</p>
-
-<ul>
-<li><a href="https://github.com/mrnuke/eeprom-prog-93cx6">GitHub - mrnuke/eeprom-prog-93cx6: spidev-based EEPROM programmer for 93Cxx serial EEPROMs</a>: This is a GitHub repository that contains a utility for manipulating the contents of Microwire SPI serial EEPROMs, including 93C66. It also provides some sample .bin files for testing.</li>
-<li><a href="https://new.c.mi.com/my/post/484485/93c66_Eeprom_bin_Download_HOT">Xiaomi Community - 93c66 Eeprom .bin Download HOT!</a>: This is a forum post that shares some 93C66 EEPROM .bin files for various car models. The files are claimed to be virgin, meaning they have not been programmed before.</li>
-<li><a href="https://www.mri-auto-diagnostics.com/wp-content/uploads/2020/11/Adding-4C-into-93C66-dump.pdf">ADDING 4C TRANSPONDER KEYS DATA IN THE IMMO DUMP FILE OF TOYOTA CARS ...</a>: This is a PDF document that explains how to add 4C transponder keys data in the immobilizer dump file of Toyota cars using a 93C66 EEPROM. It also provides some example .bin files for reference.</li>
-</ul>
-
-<p>To download a 93C66 EEPROM .bin file from these websites, you need to follow the instructions given by the website owners. Usually, you need to click on a link or a button to start the download process. You may also need to enter some information or verify your identity before downloading. You should save the downloaded .bin file in a safe location on your computer.</p>
-
-<h2>How to Program 93C66 EEPROM with .bin Files</h2>
-
-<p>To program a 93C66 EEPROM with a .bin file, you need an EEPROM programmer and a suitable interface to connect the EEPROM to the programmer. The most commonly used interface is the SOIC-8 EEPROM clip, which can attach to the pins of the EEPROM without soldering.</p>
-<p></p>
-
-<p>You also need a software that can communicate with the EEPROM programmer and send the commands and data to the EEPROM. There are many software options available, such as:</p>
-
-<ul>
-<li><a href="https://github.com/mrnuke/eeprom-prog-93cx6">eeprom-93cx6</a>: This is the software that comes with the GitHub repository mentioned above. It is a command-line utility that can read, write, and erase 93Cxx serial EEPROMs using spidev-based devices.</li>
-<li><a href="https://www.ch341a.com/">CH341A Mini Programmer</a>: This is a software that works with CH341A-based USB programmers. It has a graphical user interface that can read, write, and erase various types of EEPROMs, including 93Cxx.</li>
-<li><a href="https://www.ponyprog.net/">PonyProg</a>: This is a software that works with various types of serial device programmers, such as SI-Prog, AVR ISP, and more. It has a graphical user interface that can read, write, and erase various types of serial devices, including 93Cxx.</li>
-</ul>
-
-<p>To program a 93C66 EEPROM with a .bin file using these software, you need to follow these general steps:</p>
-
-<ol>
-<li>Connect the EEPROM programmer to your computer via USB or other ports.</li>
-<li>Connect the SOIC-8 clip or other interface to the pins of the 93C66 EEPROM.</li>
-<li>Launch the software and select the appropriate device type and configuration for the 93C66 EEPROM.</li>
-<li>Open the .bin file that you want to write to the 93C66 EEPROM.</li>
-<li>Click on the write or program button to start transferring the data from the .bin file to the 93C66 EEPROM.</li>
-<li>Wait until the process is completed and verify that there are no errors.</li>
-<li>Disconnect the SOIC-8 clip or other interface from the pins of the 93C66 EEPROM.</li>
-<li>Disconnect the EEPROM programmer from your computer.</li>
-</ol>
-
-<p>You should refer to the specific instructions and manuals provided by the software and hardware vendors for more details and troubleshooting tips.</p>
-
-<h2>Conclusion</h2>
-
-<p>A 93C66 EEPROM is a type of non-volatile memory that can store data even when the power is turned off. It uses the Microwire SPI serial interface to communicate with other devices. A .bin file is a binary file that contains the raw data of an EEPROM. A .bin file can be downloaded from various websites or extracted from an EEPROM using an EEPROM programmer. A .bin file can also be written to an EEPROM using an EEPROM programmer and a suitable interface.</p>
-
-<p>To download and program 93C66 EEPROM .bin files, you need to have some basic knowledge and skills about electronics, programming, and web browsing. You also need some tools and software that can help you perform these tasks. By following this guide, you should be able to download and program 93C66 EEPROM .bin files successfully.</p>
-<h2>Benefits of Using 93C66 EEPROM for Data Storage and Transfer</h2>
-
-<p>One of the main benefits of using 93C66 EEPROM for data storage and transfer is its durability and reliability. Unlike other types of memory, such as flash memory or hard disk drives, EEPROMs do not require constant power to retain data. This means that they can withstand harsh environments, such as high temperatures, shocks, vibrations, and electromagnetic interference. They also have a long lifespan, as they can be erased and rewritten up to 1 million times.</p>
-
-<p>Another benefit of using 93C66 EEPROM for data storage and transfer is its flexibility and compatibility. The 93C66 EEPROM can be configured as either 512x8 or 256x16 bits, depending on the application and device requirements. It can also communicate with various devices using the Microwire SPI serial interface, which is a simple and widely used protocol. The 93C66 EEPROM can be easily programmed and erased using special commands and voltage levels, which can be done by various software and hardware tools.</p>
-
-<p>A third benefit of using 93C66 EEPROM for data storage and transfer is its availability and affordability. The 93C66 EEPROM is a common and popular model of EEPROM that can be found in many electronic devices, such as computers, cars, phones, and more. It can also be downloaded from various websites that offer 93C66 EEPROM .bin files for different purposes, such as testing, repairing, or modifying devices. The 93C66 EEPROM is also relatively cheap and easy to obtain, as it can be purchased from online or offline stores that sell electronic components.</p>
-
-<h2>Conclusion</h2>
-
-<p>A 93C66 EEPROM is a type of non-volatile memory that can store data even when the power is turned off. It uses the Microwire SPI serial interface to communicate with other devices. A .bin file is a binary file that contains the raw data of an EEPROM. A .bin file can be downloaded from various websites or extracted from an EEPROM using an EEPROM programmer. A .bin file can also be written to an EEPROM using an EEPROM programmer and a suitable interface.</p>
-
-<p>To download and program 93C66 EEPROM .bin files, you need to have some basic knowledge and skills about electronics, programming, and web browsing. You also need some tools and software that can help you perform these tasks. By following this guide, you should be able to download and program 93C66 EEPROM .bin files successfully.</p>
-
-<p>Using 93C66 EEPROM for data storage and transfer has many benefits, such as durability, reliability, flexibility, compatibility, availability, and affordability. It can be used for various purposes, such as testing, repairing, or modifying devices. It can also be used for creative projects, such as using serial EEPROMs for Arduino and Raspberry Pi.</p>
-<h2>How to Use Serial EEPROMs for Arduino and Raspberry Pi Projects</h2>
-
-<p>If you are looking for a simple and cheap way to add some extra memory to your Arduino or Raspberry Pi projects, you may want to consider using serial EEPROMs, such as 93C66. Serial EEPROMs are small and easy to use devices that can store data even when the power is turned off. They can be interfaced with Arduino or Raspberry Pi using the Microwire SPI serial interface, which only requires four wires: power, ground, clock, and data.</p>
-
-<p>Using serial EEPROMs for Arduino and Raspberry Pi projects can have many advantages, such as:</p>
-
-<ul>
-<li>Storing configuration settings, calibration data, user preferences, and other information that need to be persistent across power cycles.</li>
-<li>Logging sensor data, events, timestamps, and other information that need to be recorded over time.</li>
-<li>Expanding the memory capacity of Arduino or Raspberry Pi, which may have limited built-in memory.</li>
-<li>Creating custom devices, such as RFID tags, electronic locks, smart cards, and more.</li>
-</ul>
-
-<p>To use serial EEPROMs for Arduino and Raspberry Pi projects, you need to follow these general steps:</p>
-
-<ol>
-<li>Choose a suitable serial EEPROM model and configuration for your project. For example, you may want to use a 93C66 EEPROM in a 512x8 configuration if you need to store 4 KB of data in 8-bit words.</li>
-<li>Connect the serial EEPROM to your Arduino or Raspberry Pi using the Microwire SPI serial interface. You need to connect the power and ground pins of the EEPROM to the corresponding pins of your Arduino or Raspberry Pi. You also need to connect the clock and data pins of the EEPROM to the SPI pins of your Arduino or Raspberry Pi. You may also need to connect a chip select pin of the EEPROM to a digital pin of your Arduino or Raspberry Pi if you want to use multiple EEPROMs on the same SPI bus.</li>
-<li>Write a program or a script that can communicate with the serial EEPROM using the Microwire SPI serial interface. You need to send the appropriate commands and data to the EEPROM to read, write, or erase its contents. You can use various libraries or modules that can help you perform these tasks, such as:</li>
-<ul>
-<li><a href="https://www.arduino.cc/en/Reference/SPI">SPI Library</a>: This is a library that comes with the Arduino IDE that can communicate with devices that use the SPI interface.</li>
-<li><a href="https://github.com/JChristensen/extEEPROM">extEEPROM Library</a>: This is a library that can communicate with various types of I2C and SPI EEPROMs using the Arduino Wire and SPI libraries.</li>
-<li><a href="https://github.com/nimaltd/ee93">ee93 Library</a>: This is a library that can communicate with 93Cxx serial EEPROMs using the Arduino SPI library.</li>
-<li><a href="https://www.raspberrypi.org/documentation/hardware/raspberrypi/spi/README.md">SPI Interface</a>: This is a documentation that explains how to use the SPI interface on Raspberry Pi.</li>
-<li><a href="https://pypi.org/project/spidev/">spidev Module</a>: This is a module that can communicate with devices that use the SPI interface using Python on Raspberry Pi.</li>
-<li><a href="https://github.com/ondrej1024/eePROM">eePROM Module</a>: This is a module that can communicate with various types of I2C and SPI EEPROMs using Python on Raspberry Pi.</li>
-</ul>
-<li>Test your program or script and verify that it can read, write, or erase the serial EEPROM correctly. You can use various tools or methods to check the contents of the serial EEPROM, such as:</li>
-<ul>
-<li><a href="https://github.com/mrnuke/eeprom-prog-93cx6">eeprom-93cx6</a>: This is a utility that can read, write, and erase 93Cxx serial EEPROMs using spidev-based devices on Linux.</li>
-<li><a href="https://www.ch341a.com/">CH341A Mini Programmer</a>: This is a hardware tool that can read, write, and erase various types of EEPROMs using a USB interface.</li>
-<li><a href="https://www.ponyprog.net/">PonyProg</a>: This is a software tool that can read, write, and erase various types of serial devices using various types of serial device programmers.</li>
-</ul>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>A 93C66 EEPROM is a type of non-volatile memory that can store data even when the power is turned off. It uses the Microwire SPI serial interface to communicate with other devices. A .bin file is a binary file that contains the raw data of an EEPROM. A .bin file can be downloaded from various websites or extracted from an EEPROM using an EEPROM programmer. A .bin file can also be written to an EEPROM using an EEPROM programmer and a suitable interface.</p>
-
-<p>To download and program 93C66 EEPROM .bin files, you need to have some basic knowledge and skills about electronics, programming, and web browsing. You also need some tools and software that can help you perform these tasks. By following this guide, you should be able to download and program 93C66 EEPROM .bin files successfully.</p>
-
-<p>Using 93C66 EEPROM for data storage and transfer has many benefits, such as durability, reliability, flexibility, compatibility, availability, and affordability. It can be used for various purposes, such as testing, repairing, or modifying devices. It can also be used for creative projects, such as using serial EEPROMs for Arduino and Raspberry Pi projects. By following this guide, you should be able to use 93C66 EEPROM for Arduino and Raspberry Pi projects successfully.</p>
-<p>In this article, we have learned what is 93C66 EEPROM and how to download and program .bin files. We have also learned how to use serial EEPROMs for Arduino and Raspberry Pi projects. We have seen that using 93C66 EEPROM for data storage and transfer has many benefits, such as durability, reliability, flexibility, compatibility, availability, and affordability. We have also seen that using 93C66 EEPROM for Arduino and Raspberry Pi projects can be fun and creative. We hope that this article has been helpful and informative for you. If you have any questions or comments, please feel free to contact us.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Baby Looney Tunes Full Episodes In Hindi Enjoy the Fun and Laughter of Your Favorite Characters.md b/spaces/usbethFlerru/sovits-modelsV2/example/Baby Looney Tunes Full Episodes In Hindi Enjoy the Fun and Laughter of Your Favorite Characters.md
deleted file mode 100644
index 6524216ad7decd17a56bd14eb0e228456a1a50f2..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Baby Looney Tunes Full Episodes In Hindi Enjoy the Fun and Laughter of Your Favorite Characters.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>Taz gets upset when Granny says that Easter isn't until one more day and when Daffy says there is no such thing as the Easter Bunny. So the rest of the children try to convince Taz that Daffy is lying by taking him to the forest where they will hopefully get him to believe. Two half-hour episodes.</p>
-<p>The show premiered as a full series on September 7, 2002, and ran on WB stations from 2002 to 2003. The show moved to Cartoon Network in 2002 (by following suit nine days later on September 16) where it remained until ending on April 20, 2005. It aired in reruns on Cartoon Network from 2005 to 2009. Then it began airing on Boomerang in the U.S. in 2015 but stopped Cartoon Network reruns in mid 2020. 53 episodes were produced.[4] The show is the first preschool animated series from Warner Bros. Animation.</p>
-<h2>Baby Looney Tunes Full Episodes In Hindi</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://urlcod.com/2uyWvl">https://urlcod.com/2uyWvl</a></b></p><br /><br />
-<p>The show premiered as a full series on June 3, 2001, and ran on WB stations from 2001 to 2003. The show moved to Cartoon Network in 2002 where it remained until ending on October 16, 2006. It aired in reruns on Cartoon Network from 2006 to 2010. Then it began airing on Cartoon Network and Boomerang in the U.S. in 2015 but stopped Cartoon Network reruns in mid 2020. 53 episodes were produced. The show is the first preschool animated series from Warner Bros. Animation.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/adabins/unet_adaptive_bins.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/adabins/unet_adaptive_bins.py
deleted file mode 100644
index 733927795146fe13563d07d20fbb461da596a181..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/adabins/unet_adaptive_bins.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import os
-from pathlib import Path
-
-from .miniViT import mViT
-
-
-class UpSampleBN(nn.Module):
-    def __init__(self, skip_input, output_features):
-        super(UpSampleBN, self).__init__()
-
-        self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
-                                  nn.BatchNorm2d(output_features),
-                                  nn.LeakyReLU(),
-                                  nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
-                                  nn.BatchNorm2d(output_features),
-                                  nn.LeakyReLU())
-
-    def forward(self, x, concat_with):
-        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
-        f = torch.cat([up_x, concat_with], dim=1)
-        return self._net(f)
-
-
-class DecoderBN(nn.Module):
-    def __init__(self, num_features=2048, num_classes=1, bottleneck_features=2048):
-        super(DecoderBN, self).__init__()
-        features = int(num_features)
-
-        self.conv2 = nn.Conv2d(bottleneck_features, features, kernel_size=1, stride=1, padding=1)
-
-        self.up1 = UpSampleBN(skip_input=features // 1 + 112 + 64, output_features=features // 2)
-        self.up2 = UpSampleBN(skip_input=features // 2 + 40 + 24, output_features=features // 4)
-        self.up3 = UpSampleBN(skip_input=features // 4 + 24 + 16, output_features=features // 8)
-        self.up4 = UpSampleBN(skip_input=features // 8 + 16 + 8, output_features=features // 16)
-
-        #         self.up5 = UpSample(skip_input=features // 16 + 3, output_features=features//16)
-        self.conv3 = nn.Conv2d(features // 16, num_classes, kernel_size=3, stride=1, padding=1)
-        # self.act_out = nn.Softmax(dim=1) if output_activation == 'softmax' else nn.Identity()
-
-    def forward(self, features):
-        x_block0, x_block1, x_block2, x_block3, x_block4 = features[4], features[5], features[6], features[8], features[
-            11]
-
-        x_d0 = self.conv2(x_block4)
-
-        x_d1 = self.up1(x_d0, x_block3)
-        x_d2 = self.up2(x_d1, x_block2)
-        x_d3 = self.up3(x_d2, x_block1)
-        x_d4 = self.up4(x_d3, x_block0)
-        #         x_d5 = self.up5(x_d4, features[0])
-        out = self.conv3(x_d4)
-        # out = self.act_out(out)
-        # if with_features:
-        #     return out, features[-1]
-        # elif with_intermediate:
-        #     return out, [x_block0, x_block1, x_block2, x_block3, x_block4, x_d1, x_d2, x_d3, x_d4]
-        return out
-
-
-class Encoder(nn.Module):
-    def __init__(self, backend):
-        super(Encoder, self).__init__()
-        self.original_model = backend
-
-    def forward(self, x):
-        features = [x]
-        for k, v in self.original_model._modules.items():
-            if (k == 'blocks'):
-                for ki, vi in v._modules.items():
-                    features.append(vi(features[-1]))
-            else:
-                features.append(v(features[-1]))
-        return features
-
-
-class UnetAdaptiveBins(nn.Module):
-    def __init__(self, backend, n_bins=100, min_val=0.1, max_val=10, norm='linear'):
-        super(UnetAdaptiveBins, self).__init__()
-        self.num_classes = n_bins
-        self.min_val = min_val
-        self.max_val = max_val
-        self.encoder = Encoder(backend)
-        self.adaptive_bins_layer = mViT(128, n_query_channels=128, patch_size=16,
-                                        dim_out=n_bins,
-                                        embedding_dim=128, norm=norm)
-
-        self.decoder = DecoderBN(num_classes=128)
-        self.conv_out = nn.Sequential(nn.Conv2d(128, n_bins, kernel_size=1, stride=1, padding=0),
-                                      nn.Softmax(dim=1))
-
-    def forward(self, x, **kwargs):
-        unet_out = self.decoder(self.encoder(x), **kwargs)
-        bin_widths_normed, range_attention_maps = self.adaptive_bins_layer(unet_out)
-        out = self.conv_out(range_attention_maps)
-
-        # Post process
-        # n, c, h, w = out.shape
-        # hist = torch.sum(out.view(n, c, h * w), dim=2) / (h * w)  # not used for training
-
-        bin_widths = (self.max_val - self.min_val) * bin_widths_normed  # .shape = N, dim_out
-        bin_widths = nn.functional.pad(bin_widths, (1, 0), mode='constant', value=self.min_val)
-        bin_edges = torch.cumsum(bin_widths, dim=1)
-
-        centers = 0.5 * (bin_edges[:, :-1] + bin_edges[:, 1:])
-        n, dout = centers.size()
-        centers = centers.view(n, dout, 1, 1)
-
-        pred = torch.sum(out * centers, dim=1, keepdim=True)
-
-        return bin_edges, pred
-
-    def get_1x_lr_params(self):  # lr/10 learning rate
-        return self.encoder.parameters()
-
-    def get_10x_lr_params(self):  # lr learning rate
-        modules = [self.decoder, self.adaptive_bins_layer, self.conv_out]
-        for m in modules:
-            yield from m.parameters()
-
-    @classmethod
-    def build(cls, n_bins, **kwargs):
-        basemodel_name = 'tf_efficientnet_b5_ap'
-
-        print('Loading base model ()...'.format(basemodel_name), end='')
-        predicted_torch_model_cache_path = str(Path.home()) + '\\.cache\\torch\\hub\\rwightman_gen-efficientnet-pytorch_master' 
-        predicted_gep_cache_testilfe = Path(predicted_torch_model_cache_path + '\\hubconf.py')
-        #print(f"predicted_gep_cache_testilfe:  {predicted_gep_cache_testilfe}")
-        # try to fetch the models from cache, and only if it can't be find, download from the internet (to enable offline usage)
-        if os.path.isfile(predicted_gep_cache_testilfe):
-            basemodel = torch.hub.load(predicted_torch_model_cache_path, basemodel_name, pretrained=True, source = 'local')        
-        else:
-            basemodel = torch.hub.load('rwightman/gen-efficientnet-pytorch', basemodel_name, pretrained=True)
-        print('Done.')
-
-        # Remove last layer
-        print('Removing last two layers (global_pool & classifier).')
-        basemodel.global_pool = nn.Identity()
-        basemodel.classifier = nn.Identity()
-
-        # Building Encoder-Decoder model
-        print('Building Encoder-Decoder model..', end='')
-        m = cls(basemodel, n_bins=n_bins, **kwargs)
-        print('Done.')
-        return m
-
-
-if __name__ == '__main__':
-    model = UnetAdaptiveBins.build(100)
-    x = torch.rand(2, 3, 480, 640)
-    bins, pred = model(x)
-    print(bins.shape, pred.shape)
diff --git a/spaces/vaibhavarduino/anime-plus/e4e/models/stylegan2/op/upfirdn2d.cpp b/spaces/vaibhavarduino/anime-plus/e4e/models/stylegan2/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/vaibhavarduino/anime-plus/e4e/models/stylegan2/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
-                            int up_x, int up_y, int down_x, int down_y,
-                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
-                        int up_x, int up_y, int down_x, int down_y,
-                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(kernel);
-
-    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/vaibhavsharda/semantic_clustering/twc_openai_embeddings.py b/spaces/vaibhavsharda/semantic_clustering/twc_openai_embeddings.py
deleted file mode 100644
index ceb6f8fb44e7c8304d325f3cc1e2cd3b746d5891..0000000000000000000000000000000000000000
--- a/spaces/vaibhavsharda/semantic_clustering/twc_openai_embeddings.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from scipy.spatial.distance import cosine
-import argparse
-import json
-import os
-import openai
-import pdb
-
-def read_text(input_file):
-    arr = open(input_file).read().split("\n")
-    return arr[:-1]
-
-
-class OpenAIModel:
-    def __init__(self):
-        self.debug = False
-        self.model_name = None
-        self.skip_key = True
-        print("In OpenAI API constructor")
-
-
-    def init_model(self,model_name = None):
-        #print("OpenAI: Init model",model_name)
-        openai.api_key = os.getenv(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)
-        if (openai.api_key == None):
-            openai.api_key = ""
-            print("API key not set")
-
-        if (len(openai.api_key) == 0 and not self.skip_key):
-                print("Open API key not set")
-        
-        if (model_name is None):
-            self.model_name = "text-similarity-ada-001"
-        else:
-            self.model_name = model_name
-        print("OpenAI: Init model complete",model_name)
-
-
-    def compute_embeddings(self,input_file_name,input_data,is_file):
-        if (len(openai.api_key) == 0 and not self.skip_key):
-                print("Open API key not set")
-                return [],[]
-        #print("In compute embeddings after key check")
-        in_file = self.model_name + '.'.join(input_file_name.split('.')[:-1]) + "_embed.json"
-        cached = False
-        try:
-            fp = open(in_file)
-            cached = True
-            embeddings = json.load(fp)
-            print("Using cached embeddings")
-        except:
-            pass
-            
-        texts = read_text(input_data) if is_file == True else input_data
-        if (not cached):
-            print(f"Computing embeddings for {input_file_name} and model {self.model_name}")
-            response = openai.Embedding.create(
-                input=texts,
-                model=self.model_name
-            )
-            embeddings = []
-            for i in range(len(response['data'])):
-                embeddings.append(response['data'][i]['embedding'])
-        if (not cached):
-            with open(in_file,"w") as fp:
-                json.dump(embeddings,fp)
-        return texts,embeddings
-
-    def output_results(self,output_file,texts,embeddings,main_index = 0):
-        if (len(openai.api_key) == 0 and not self.skip_key):
-                print("Open API key not set")
-                return {}
-        #print("In output results after key check")
-        # Calculate cosine similarities
-        # Cosine similarities are in [-1, 1]. Higher means more similar
-        cosine_dict = {}
-        #print("Total sentences",len(texts))
-        for i in range(len(texts)):
-                cosine_dict[texts[i]] = 1 - cosine(embeddings[main_index], embeddings[i])
-
-        #print("Input sentence:",texts[main_index])
-        sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
-        if (self.debug):
-            for key in sorted_dict:
-                print("Cosine similarity with  \"%s\" is: %.3f" % (key, sorted_dict[key]))
-        if (output_file is not None):
-            with open(output_file,"w") as fp:
-                fp.write(json.dumps(sorted_dict,indent=0))
-        return sorted_dict
-
-
-
-if __name__ == '__main__':
-        parser = argparse.ArgumentParser(description='OpenAI model for sentence embeddings ',formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-        parser.add_argument('-input', action="store", dest="input",required=True,help="Input file with sentences")
-        parser.add_argument('-output', action="store", dest="output",default="output.txt",help="Output file with results")
-        parser.add_argument('-model', action="store", dest="model",default="text-similarity-ada-001",help="model name")
-
-        results = parser.parse_args()
-        obj = OpenAIModel()
-        obj.init_model(results.model)
-        texts, embeddings = obj.compute_embeddings(results.input,is_file = True)
-        results = obj.output_results(results.output,texts,embeddings)
diff --git a/spaces/vumichien/Generate_human_motion/VQ-Trans/utils/config.py b/spaces/vumichien/Generate_human_motion/VQ-Trans/utils/config.py
deleted file mode 100644
index 091d790e963959c326917688ee267e6a4ec136d1..0000000000000000000000000000000000000000
--- a/spaces/vumichien/Generate_human_motion/VQ-Trans/utils/config.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-SMPL_DATA_PATH = "./body_models/smpl"
-
-SMPL_KINTREE_PATH = os.path.join(SMPL_DATA_PATH, "kintree_table.pkl")
-SMPL_MODEL_PATH = os.path.join(SMPL_DATA_PATH, "SMPL_NEUTRAL.pkl")
-JOINT_REGRESSOR_TRAIN_EXTRA = os.path.join(SMPL_DATA_PATH, 'J_regressor_extra.npy')
-
-ROT_CONVENTION_TO_ROT_NUMBER = {
-    'legacy': 23,
-    'no_hands': 21,
-    'full_hands': 51,
-    'mitten_hands': 33,
-}
-
-GENDERS = ['neutral', 'male', 'female']
-NUM_BETAS = 10
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py
deleted file mode 100644
index d7a43bee01422ad4795dd27874e0cd4bb6cbfecf..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='ASPPHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dilations=(1, 12, 24, 36),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/bbox.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/bbox.py
deleted file mode 100644
index 0c4d58b6c91f652933974f519acd3403a833e906..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/bbox.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps'])
-
-
-def bbox_overlaps(bboxes1, bboxes2, mode='iou', aligned=False, offset=0):
-    """Calculate overlap between two set of bboxes.
-
-    If ``aligned`` is ``False``, then calculate the ious between each bbox
-    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
-    bboxes1 and bboxes2.
-
-    Args:
-        bboxes1 (Tensor): shape (m, 4) in <x1, y1, x2, y2> format or empty.
-        bboxes2 (Tensor): shape (n, 4) in <x1, y1, x2, y2> format or empty.
-            If aligned is ``True``, then m and n must be equal.
-        mode (str): "iou" (intersection over union) or iof (intersection over
-            foreground).
-
-    Returns:
-        ious(Tensor): shape (m, n) if aligned == False else shape (m, 1)
-
-    Example:
-        >>> bboxes1 = torch.FloatTensor([
-        >>>     [0, 0, 10, 10],
-        >>>     [10, 10, 20, 20],
-        >>>     [32, 32, 38, 42],
-        >>> ])
-        >>> bboxes2 = torch.FloatTensor([
-        >>>     [0, 0, 10, 20],
-        >>>     [0, 10, 10, 19],
-        >>>     [10, 10, 20, 20],
-        >>> ])
-        >>> bbox_overlaps(bboxes1, bboxes2)
-        tensor([[0.5000, 0.0000, 0.0000],
-                [0.0000, 0.0000, 1.0000],
-                [0.0000, 0.0000, 0.0000]])
-
-    Example:
-        >>> empty = torch.FloatTensor([])
-        >>> nonempty = torch.FloatTensor([
-        >>>     [0, 0, 10, 9],
-        >>> ])
-        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
-        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
-        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
-    """
-
-    mode_dict = {'iou': 0, 'iof': 1}
-    assert mode in mode_dict.keys()
-    mode_flag = mode_dict[mode]
-    # Either the boxes are empty or the length of boxes' last dimension is 4
-    assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
-    assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
-    assert offset == 1 or offset == 0
-
-    rows = bboxes1.size(0)
-    cols = bboxes2.size(0)
-    if aligned:
-        assert rows == cols
-
-    if rows * cols == 0:
-        return bboxes1.new(rows, 1) if aligned else bboxes1.new(rows, cols)
-
-    if aligned:
-        ious = bboxes1.new_zeros(rows)
-    else:
-        ious = bboxes1.new_zeros((rows, cols))
-    ext_module.bbox_overlaps(
-        bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset)
-    return ious
diff --git a/spaces/weibinke/vits-simple-api/vits/text/cleaners.py b/spaces/weibinke/vits-simple-api/vits/text/cleaners.py
deleted file mode 100644
index e1c71cb43e308f365de7a4444ca3ae51f617446f..0000000000000000000000000000000000000000
--- a/spaces/weibinke/vits-simple-api/vits/text/cleaners.py
+++ /dev/null
@@ -1,278 +0,0 @@
-import re
-import config
-from unidecode import unidecode
-from phonemizer import phonemize
-from phonemizer.backend.espeak.wrapper import EspeakWrapper
-
-ESPEAK_LIBRARY = getattr(config, "ESPEAK_LIBRARY", "")
-if ESPEAK_LIBRARY != "":
-    EspeakWrapper.set_library(ESPEAK_LIBRARY)
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def transliteration_cleaners(text):
-    '''Pipeline for non-English text that transliterates to ASCII.'''
-    text = unidecode(text)
-    text = text.lower()
-    text = re.sub(r'\s+', ' ', text)
-    text = expand_abbreviations(text)
-    return text
-
-
-# for English text
-def english_cleaners(text):
-    '''Pipeline for English text, including abbreviation expansion.'''
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: transliteration_cleaners(x.group(1)) + ' ', text)
-    phonemes = phonemize(text, language='en-us', backend='espeak', strip=True)
-    return phonemes
-
-
-# for non-English text that can be transliterated to ASCII
-def english_cleaners2(text):
-    '''Pipeline for English text, including abbreviation expansion. + punctuation + stress'''
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: transliteration_cleaners(x.group(1)) + ' ', text)
-    phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True,
-                         with_stress=True)
-    return phonemes
-
-
-def japanese_cleaners(text):
-    from vits.text.japanese import japanese_to_romaji_with_accent
-
-    def clean(text):
-        text = japanese_to_romaji_with_accent(text)
-        text = re.sub(r'([A-Za-z])$', r'\1.', text)
-        return text
-
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: clean(x.group(1)) + ' ', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from vits.text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-
-    def clean(text):
-        text = latin_to_hangul(text)
-        text = number_to_hangul(text)
-        text = divide_hangul(text)
-        text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-        return text
-
-    text = re.sub(r'\[KO\](.*?)\[KO\]', lambda x: clean(x.group(1)) + ' ', text)
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from vits.text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, symbols_to_chinese
-
-    def clean(text):
-        text = symbols_to_chinese(text)
-        text = number_to_chinese(text)
-        text = chinese_to_bopomofo(text)
-        text = latin_to_bopomofo(text)
-        text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-        return text
-
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: clean(x.group(1)) + ' ', text)
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from vits.text.mandarin import chinese_to_romaji
-    from vits.text.japanese import japanese_to_romaji_with_accent
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    text = re.sub(r'([^।])$', r'\1।', text)
-    return text
-
-
-def cjks_cleaners(text):
-    from vits.text.mandarin import chinese_to_lazy_ipa
-    from vits.text.japanese import japanese_to_ipa
-    from vits.text.korean import korean_to_lazy_ipa
-    from vits.text.sanskrit import devanagari_to_ipa
-    from vits.text.english import english_to_lazy_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    from vits.text.mandarin import chinese_to_lazy_ipa
-    from vits.text.japanese import japanese_to_ipa
-    from vits.text.korean import korean_to_ipa
-    from vits.text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn') + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz') + ' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    from vits.text.mandarin import chinese_to_ipa
-    from vits.text.japanese import japanese_to_ipa2
-    from vits.text.korean import korean_to_ipa
-    from vits.text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cje_cleaners(text):
-    from vits.text.mandarin import chinese_to_lazy_ipa
-    from vits.text.japanese import japanese_to_ipa
-    from vits.text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn') + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz') + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cje_cleaners2(text):
-    from vits.text.mandarin import chinese_to_ipa
-    from vits.text.japanese import japanese_to_ipa2
-    from vits.text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    from vits.text.thai import num_to_thai, latin_to_thai
-
-    def clean(text):
-        text = num_to_thai(text)
-        text = latin_to_thai(text)
-        return text
-
-    text = re.sub(r'\[TH\](.*?)\[TH\]', lambda x: clean(x.group(1)) + ' ', text)
-    return text
-
-
-def shanghainese_cleaners(text):
-    from vits.text.shanghainese import shanghainese_to_ipa
-
-    def clean(text):
-        text = shanghainese_to_ipa(text)
-        text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-        return text
-
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: clean(x.group(1)) + ' ', text)
-    return text
-
-
-def chinese_dialect_cleaners(text):
-    from vits.text.mandarin import chinese_to_ipa2
-    from vits.text.japanese import japanese_to_ipa3
-    from vits.text.shanghainese import shanghainese_to_ipa
-    from vits.text.cantonese import cantonese_to_ipa
-    from vits.text.english import english_to_lazy_ipa2
-    from vits.text.ngu_dialect import ngu_dialect_to_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ') + ' ', text)
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-                                                                                                             '˧˧˦').replace(
-        '6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e') + ' ', text)
-    text = re.sub(r'\[GD\](.*?)\[GD\]',
-                  lambda x: cantonese_to_ipa(x.group(1)) + ' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa2(x.group(1)) + ' ', text)
-    text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-        1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ') + ' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def bert_chinese_cleaners(text):
-    from vits.text import mandarin
-    matches = re.findall(r"\[ZH\](.*?)\[ZH\]", text)
-    text = "".join(matches)
-    if text[-1] not in [".", "。", ",", "，"]: text += "."
-    text = mandarin.symbols_to_chinese(text)
-    text = mandarin.number_transform_to_chinese(text)
-    if not hasattr(bert_chinese_cleaners, "tts_front"):
-        bert_chinese_cleaners.tts_front = mandarin.VITS_PinYin_model()
-    tts_front = bert_chinese_cleaners.tts_front
-    cleaned_text, char_embeds = tts_front.chinese_to_phonemes(text)
-    return cleaned_text, char_embeds
diff --git a/spaces/xfys/yolov5_tracking/yolov5/models/__init__.py b/spaces/xfys/yolov5_tracking/yolov5/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/text/japanese.py b/spaces/xxbb/VITS-Umamusume-voice-synthesizer/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
diff --git a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/text/korean.py b/spaces/xxbb/VITS-Umamusume-voice-synthesizer/text/korean.py
deleted file mode 100644
index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000
--- a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/text/korean.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄳ', 'ㄱㅅ'),
-    ('ㄵ', 'ㄴㅈ'),
-    ('ㄶ', 'ㄴㅎ'),
-    ('ㄺ', 'ㄹㄱ'),
-    ('ㄻ', 'ㄹㅁ'),
-    ('ㄼ', 'ㄹㅂ'),
-    ('ㄽ', 'ㄹㅅ'),
-    ('ㄾ', 'ㄹㅌ'),
-    ('ㄿ', 'ㄹㅍ'),
-    ('ㅀ', 'ㄹㅎ'),
-    ('ㅄ', 'ㅂㅅ'),
-    ('ㅘ', 'ㅗㅏ'),
-    ('ㅙ', 'ㅗㅐ'),
-    ('ㅚ', 'ㅗㅣ'),
-    ('ㅝ', 'ㅜㅓ'),
-    ('ㅞ', 'ㅜㅔ'),
-    ('ㅟ', 'ㅜㅣ'),
-    ('ㅢ', 'ㅡㅣ'),
-    ('ㅑ', 'ㅣㅏ'),
-    ('ㅒ', 'ㅣㅐ'),
-    ('ㅕ', 'ㅣㅓ'),
-    ('ㅖ', 'ㅣㅔ'),
-    ('ㅛ', 'ㅣㅗ'),
-    ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', '에이'),
-    ('b', '비'),
-    ('c', '시'),
-    ('d', '디'),
-    ('e', '이'),
-    ('f', '에프'),
-    ('g', '지'),
-    ('h', '에이치'),
-    ('i', '아이'),
-    ('j', '제이'),
-    ('k', '케이'),
-    ('l', '엘'),
-    ('m', '엠'),
-    ('n', '엔'),
-    ('o', '오'),
-    ('p', '피'),
-    ('q', '큐'),
-    ('r', '아르'),
-    ('s', '에스'),
-    ('t', '티'),
-    ('u', '유'),
-    ('v', '브이'),
-    ('w', '더블유'),
-    ('x', '엑스'),
-    ('y', '와이'),
-    ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('t͡ɕ','ʧ'),
-    ('d͡ʑ','ʥ'),
-    ('ɲ','n^'),
-    ('ɕ','ʃ'),
-    ('ʷ','w'),
-    ('ɭ','l`'),
-    ('ʎ','ɾ'),
-    ('ɣ','ŋ'),
-    ('ɰ','ɯ'),
-    ('ʝ','j'),
-    ('ʌ','ə'),
-    ('ɡ','g'),
-    ('\u031a','#'),
-    ('\u0348','='),
-    ('\u031e',''),
-    ('\u0320',''),
-    ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
-    for regex, replacement in _latin_to_hangul:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def divide_hangul(text):
-    text = j2hcj(h2j(text))
-    for regex, replacement in _hangul_divided:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def hangul_number(num, sino=True):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    num = re.sub(',', '', num)
-
-    if num == '0':
-        return '영'
-    if not sino and num == '20':
-        return '스무'
-
-    digits = '123456789'
-    names = '일이삼사오육칠팔구'
-    digit2name = {d: n for d, n in zip(digits, names)}
-
-    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-    spelledout = []
-    for i, digit in enumerate(num):
-        i = len(num) - i - 1
-        if sino:
-            if i == 0:
-                name = digit2name.get(digit, '')
-            elif i == 1:
-                name = digit2name.get(digit, '') + '십'
-                name = name.replace('일십', '십')
-        else:
-            if i == 0:
-                name = digit2mod.get(digit, '')
-            elif i == 1:
-                name = digit2dec.get(digit, '')
-        if digit == '0':
-            if i % 4 == 0:
-                last_three = spelledout[-min(3, len(spelledout)):]
-                if ''.join(last_three) == '':
-                    spelledout.append('')
-                    continue
-            else:
-                spelledout.append('')
-                continue
-        if i == 2:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 3:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 4:
-            name = digit2name.get(digit, '') + '만'
-            name = name.replace('일만', '만')
-        elif i == 5:
-            name = digit2name.get(digit, '') + '십'
-            name = name.replace('일십', '십')
-        elif i == 6:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 7:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 8:
-            name = digit2name.get(digit, '') + '억'
-        elif i == 9:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 10:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 11:
-            name = digit2name.get(digit, '') + '천'
-        elif i == 12:
-            name = digit2name.get(digit, '') + '조'
-        elif i == 13:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 14:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 15:
-            name = digit2name.get(digit, '') + '천'
-        spelledout.append(name)
-    return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-    for token in tokens:
-        num, classifier = token
-        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-            spelledout = hangul_number(num, sino=False)
-        else:
-            spelledout = hangul_number(num, sino=True)
-        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-    # digit by digit for remaining digits
-    digits = '0123456789'
-    names = '영일이삼사오육칠팔구'
-    for d, n in zip(digits, names):
-        text = text.replace(d, n)
-    return text
-
-
-def korean_to_lazy_ipa(text):
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
-    for regex, replacement in _ipa_to_lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def korean_to_ipa(text):
-    text = korean_to_lazy_ipa(text)
-    return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
diff --git a/spaces/yame/Real-CUGAN/README.md b/spaces/yame/Real-CUGAN/README.md
deleted file mode 100644
index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000
--- a/spaces/yame/Real-CUGAN/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Real CUGAN
-emoji: 🐢
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: DianXian/Real-CUGAN
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yangogo/bingo/src/components/ui/dropdown-menu.tsx b/spaces/yangogo/bingo/src/components/ui/dropdown-menu.tsx
deleted file mode 100644
index 184d4e6007ef85187446362f69532ab077897fea..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/components/ui/dropdown-menu.tsx
+++ /dev/null
@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DropdownMenuPrimitive from '@radix-ui/react-dropdown-menu'
-
-import { cn } from '@/lib/utils'
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubContent>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubContent>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubContent
-    ref={ref}
-    className={cn(
-      'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuSubContent.displayName =
-  DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <DropdownMenuPrimitive.Portal>
-    <DropdownMenuPrimitive.Content
-      ref={ref}
-      sideOffset={sideOffset}
-      className={cn(
-        'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow animate-in data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2',
-        className
-      )}
-      {...props}
-    />
-  </DropdownMenuPrimitive.Portal>
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Item> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Item
-    ref={ref}
-    className={cn(
-      'relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuLabel = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Label> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Label
-    ref={ref}
-    className={cn(
-      'px-2 py-1.5 text-sm font-semibold',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.Separator
-    ref={ref}
-    className={cn('-mx-1 my-1 h-px bg-muted', className)}
-    {...props}
-  />
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn('ml-auto text-xs tracking-widest opacity-60', className)}
-      {...props}
-    />
-  )
-}
-DropdownMenuShortcut.displayName = 'DropdownMenuShortcut'
-
-export {
-  DropdownMenu,
-  DropdownMenuTrigger,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuLabel,
-  DropdownMenuSeparator,
-  DropdownMenuShortcut,
-  DropdownMenuGroup,
-  DropdownMenuPortal,
-  DropdownMenuSub,
-  DropdownMenuSubContent,
-  DropdownMenuRadioGroup
-}
diff --git a/spaces/yerfor/SyntaSpeech/utils/audio/cwt.py b/spaces/yerfor/SyntaSpeech/utils/audio/cwt.py
deleted file mode 100644
index 9d42ffc7b40d200dcfa66a0823163f98173b50e6..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/utils/audio/cwt.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import numpy as np
-from pycwt import wavelet
-from scipy.interpolate import interp1d
-
-dt = 0.005
-dj = 1
-
-
-def convert_continuos_f0(f0):
-    '''CONVERT F0 TO CONTINUOUS F0
-    Args:
-        f0 (ndarray): original f0 sequence with the shape (T)
-    Return:
-        (ndarray): continuous f0 with the shape (T)
-    '''
-    # get uv information as binary
-    f0 = np.copy(f0)
-    uv = (f0 == 0).astype(float)
-
-    # get start and end of f0
-    if (f0 == 0).all():
-        print("| all of the f0 values are 0.")
-        return uv, f0
-    start_f0 = f0[f0 != 0][0]
-    end_f0 = f0[f0 != 0][-1]
-
-    # padding start and end of f0 sequence
-    start_idx = np.where(f0 == start_f0)[0][0]
-    end_idx = np.where(f0 == end_f0)[0][-1]
-    f0[:start_idx] = start_f0
-    f0[end_idx:] = end_f0
-
-    # get non-zero frame index
-    nz_frames = np.where(f0 != 0)[0]
-
-    # perform linear interpolation
-    f = interp1d(nz_frames, f0[nz_frames])
-    cont_f0 = f(np.arange(0, f0.shape[0]))
-
-    return uv, cont_f0
-
-
-def get_cont_lf0(f0, frame_period=5.0):
-    uv, cont_f0_lpf = convert_continuos_f0(f0)
-    # cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20)
-    cont_lf0_lpf = np.log(cont_f0_lpf)
-    return uv, cont_lf0_lpf
-
-
-def get_lf0_cwt(lf0):
-    '''
-    input:
-        signal of shape (N)
-    output:
-        Wavelet_lf0 of shape(10, N), scales of shape(10)
-    '''
-    mother = wavelet.MexicanHat()
-    s0 = dt * 2
-    J = 9
-
-    Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother)
-    # Wavelet.shape => (J + 1, len(lf0))
-    Wavelet_lf0 = np.real(Wavelet_lf0).T
-    return Wavelet_lf0, scales
-
-
-def norm_scale(Wavelet_lf0):
-    mean = Wavelet_lf0.mean(0)[None, :]
-    std = Wavelet_lf0.std(0)[None, :]
-    Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std
-    return Wavelet_lf0_norm, mean, std
-
-
-def normalize_cwt_lf0(f0, mean, std):
-    uv, cont_lf0_lpf = get_cont_lf0(f0)
-    cont_lf0_norm = (cont_lf0_lpf - mean) / std
-    Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm)
-    Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0)
-
-    return Wavelet_lf0_norm
-
-
-def get_lf0_cwt_norm(f0s, mean, std):
-    uvs = list()
-    cont_lf0_lpfs = list()
-    cont_lf0_lpf_norms = list()
-    Wavelet_lf0s = list()
-    Wavelet_lf0s_norm = list()
-    scaless = list()
-
-    means = list()
-    stds = list()
-    for f0 in f0s:
-        uv, cont_lf0_lpf = get_cont_lf0(f0)
-        cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std
-
-        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)  # [560,10]
-        Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0)  # [560,10],[1,10],[1,10]
-
-        Wavelet_lf0s_norm.append(Wavelet_lf0_norm)
-        uvs.append(uv)
-        cont_lf0_lpfs.append(cont_lf0_lpf)
-        cont_lf0_lpf_norms.append(cont_lf0_lpf_norm)
-        Wavelet_lf0s.append(Wavelet_lf0)
-        scaless.append(scales)
-        means.append(mean_scale)
-        stds.append(std_scale)
-
-    return Wavelet_lf0s_norm, scaless, means, stds
-
-
-def inverse_cwt_torch(Wavelet_lf0, scales):
-    import torch
-    b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5))
-    lf0_rec = Wavelet_lf0 * b
-    lf0_rec_sum = lf0_rec.sum(-1)
-    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True)
-    return lf0_rec_sum
-
-
-def inverse_cwt(Wavelet_lf0, scales):
-    # mother = wavelet.MexicanHat()
-    # lf0_rec_sum = wavelet.icwt(Wavelet_lf0[0].T, scales, dt, dj, mother)
-    b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5))
-    lf0_rec = Wavelet_lf0 * b
-    lf0_rec_sum = lf0_rec.sum(-1)
-    # lf0_rec_sum = lf0_rec_sum[None, ...]
-    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True)
-    return lf0_rec_sum
-
-
-def cwt2f0(cwt_spec, mean, std, cwt_scales):
-    assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3
-    import torch
-    if isinstance(cwt_spec, torch.Tensor):
-        f0 = inverse_cwt_torch(cwt_spec, cwt_scales)
-        f0 = f0 * std[:, None] + mean[:, None]
-        f0 = f0.exp()  # [B, T]
-    else:
-        f0 = inverse_cwt(cwt_spec, cwt_scales)
-        f0 = f0 * std[:, None] + mean[:, None]
-        f0 = np.exp(f0)  # [B, T]
-    return f0
diff --git a/spaces/yfyangd/PictureBookUnderstanding/BLIP/train_caption.py b/spaces/yfyangd/PictureBookUnderstanding/BLIP/train_caption.py
deleted file mode 100644
index 7c639ac646b9a1b8074b6e9c2343b961de76db05..0000000000000000000000000000000000000000
--- a/spaces/yfyangd/PictureBookUnderstanding/BLIP/train_caption.py
+++ /dev/null
@@ -1,206 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip import blip_decoder
-import utils
-from utils import cosine_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-from data.utils import save_result, coco_caption_eval
-
-def train(model, data_loader, optimizer, epoch, device):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
-    header = 'Train Caption Epoch: [{}]'.format(epoch)
-    print_freq = 50
-
-    for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        image = image.to(device)       
-        
-        loss = model(image, caption)      
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()    
-        
-        metric_logger.update(loss=loss.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}  
-
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # evaluate
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Caption generation:'
-    print_freq = 10
-
-    result = []
-    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
-        
-        image = image.to(device)       
-        
-        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
-                                  min_length=config['min_length'])
-        
-        for caption, img_id in zip(captions, image_id):
-            result.append({"image_id": img_id.item(), "caption": caption})
-  
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating captioning dataset")
-    train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([train_dataset,val_dataset,test_dataset], [True,False,False], num_tasks, global_rank)         
-    else:
-        samplers = [None, None, None]
-    
-    train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
-                                                          batch_size=[config['batch_size']]*3,num_workers=[4,4,4],
-                                                          is_trains=[True, False, False], collate_fns=[None,None,None])         
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                           vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], 
-                           prompt=config['prompt'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-            
-    best = 0
-    best_epoch = 0
-
-    print("Start training")
-    start_time = time.time()    
-    for epoch in range(0, config['max_epoch']):
-        if not args.evaluate:        
-            if args.distributed:
-                train_loader.sampler.set_epoch(epoch)
-                
-            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-                
-            train_stats = train(model, train_loader, optimizer, epoch, device) 
-        
-        val_result = evaluate(model_without_ddp, val_loader, device, config)  
-        val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d'%epoch, remove_duplicate='image_id')        
-  
-        test_result = evaluate(model_without_ddp, test_loader, device, config)  
-        test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d'%epoch, remove_duplicate='image_id')  
-
-        if utils.is_main_process():   
-            coco_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val')
-            coco_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test')
-            
-            if args.evaluate:            
-                log_stats = {**{f'val_{k}': v for k, v in coco_val.eval.items()},
-                             **{f'test_{k}': v for k, v in coco_test.eval.items()},                       
-                            }
-                with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")                   
-            else:             
-                save_obj = {
-                    'model': model_without_ddp.state_dict(),
-                    'optimizer': optimizer.state_dict(),
-                    'config': config,
-                    'epoch': epoch,
-                }
-
-                if coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best:
-                    best = coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4']
-                    best_epoch = epoch                
-                    torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) 
-                    
-                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                             **{f'val_{k}': v for k, v in coco_val.eval.items()},
-                             **{f'test_{k}': v for k, v in coco_test.eval.items()},                       
-                             'epoch': epoch,
-                             'best_epoch': best_epoch,
-                            }
-                with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")     
-                    
-        if args.evaluate: 
-            break
-        dist.barrier()     
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/caption_coco.yaml')
-    parser.add_argument('--output_dir', default='output/Caption_coco')        
-    parser.add_argument('--evaluate', action='store_true')    
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/spaces/yiguid/ChatGPT/chatgpt - macOS.command b/spaces/yiguid/ChatGPT/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/yiguid/ChatGPT/chatgpt - macOS.command	
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/inference.py b/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/inference.py
deleted file mode 100644
index 66461bc908593eef39a28dc69b7a7b6bbe19f624..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/inference.py
+++ /dev/null
@@ -1,242 +0,0 @@
-from typing import Tuple, List
-
-import cv2
-import numpy as np
-import supervision as sv
-import torch
-from PIL import Image
-from torchvision.ops import box_convert
-
-import groundingdino.datasets.transforms as T
-from groundingdino.models import build_model
-from groundingdino.util.misc import clean_state_dict
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import get_phrases_from_posmap
-
-# ----------------------------------------------------------------------------------------------------------------------
-# OLD API
-# ----------------------------------------------------------------------------------------------------------------------
-
-
-def preprocess_caption(caption: str) -> str:
-    result = caption.lower().strip()
-    if result.endswith("."):
-        return result
-    return result + "."
-
-
-def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
-    args = SLConfig.fromfile(model_config_path)
-    args.device = device
-    model = build_model(args)
-    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
-    model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
-    model.eval()
-    return model
-
-
-def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    image_source = Image.open(image_path).convert("RGB")
-    image = np.asarray(image_source)
-    image_transformed, _ = transform(image_source, None)
-    return image, image_transformed
-
-
-def predict(
-        model,
-        image: torch.Tensor,
-        caption: str,
-        box_threshold: float,
-        text_threshold: float,
-        device: str = "cuda"
-) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
-    caption = preprocess_caption(caption=caption)
-
-    model = model.to(device)
-    image = image.to(device)
-
-    with torch.no_grad():
-        outputs = model(image[None], captions=[caption])
-
-    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
-    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
-
-    mask = prediction_logits.max(dim=1)[0] > box_threshold
-    logits = prediction_logits[mask]  # logits.shape = (n, 256)
-    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)
-
-    tokenizer = model.tokenizer
-    tokenized = tokenizer(caption)
-
-    phrases = [
-        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
-        for logit
-        in logits
-    ]
-
-    return boxes, logits.max(dim=1)[0], phrases
-
-
-def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
-    h, w, _ = image_source.shape
-    boxes = boxes * torch.Tensor([w, h, w, h])
-    xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
-    detections = sv.Detections(xyxy=xyxy)
-
-    labels = [
-        f"{phrase} {logit:.2f}"
-        for phrase, logit
-        in zip(phrases, logits)
-    ]
-
-    box_annotator = sv.BoxAnnotator()
-    annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
-    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
-    return annotated_frame
-
-
-# ----------------------------------------------------------------------------------------------------------------------
-# NEW API
-# ----------------------------------------------------------------------------------------------------------------------
-
-
-class Model:
-
-    def __init__(
-        self,
-        model_config_path: str,
-        model_checkpoint_path: str,
-        device: str = "cuda"
-    ):
-        self.model = load_model(
-            model_config_path=model_config_path,
-            model_checkpoint_path=model_checkpoint_path,
-            device=device
-        ).to(device)
-        self.device = device
-
-    def predict_with_caption(
-        self,
-        image: np.ndarray,
-        caption: str,
-        box_threshold: float = 0.35,
-        text_threshold: float = 0.25
-    ) -> Tuple[sv.Detections, List[str]]:
-        """
-        import cv2
-
-        image = cv2.imread(IMAGE_PATH)
-
-        model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
-        detections, labels = model.predict_with_caption(
-            image=image,
-            caption=caption,
-            box_threshold=BOX_THRESHOLD,
-            text_threshold=TEXT_THRESHOLD
-        )
-
-        import supervision as sv
-
-        box_annotator = sv.BoxAnnotator()
-        annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels)
-        """
-        processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
-        boxes, logits, phrases = predict(
-            model=self.model,
-            image=processed_image,
-            caption=caption,
-            box_threshold=box_threshold,
-            text_threshold=text_threshold)
-        source_h, source_w, _ = image.shape
-        detections = Model.post_process_result(
-            source_h=source_h,
-            source_w=source_w,
-            boxes=boxes,
-            logits=logits)
-        return detections, phrases
-
-    def predict_with_classes(
-        self,
-        image: np.ndarray,
-        classes: List[str],
-        box_threshold: float,
-        text_threshold: float
-    ) -> sv.Detections:
-        """
-        import cv2
-
-        image = cv2.imread(IMAGE_PATH)
-
-        model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
-        detections = model.predict_with_classes(
-            image=image,
-            classes=CLASSES,
-            box_threshold=BOX_THRESHOLD,
-            text_threshold=TEXT_THRESHOLD
-        )
-
-
-        import supervision as sv
-
-        box_annotator = sv.BoxAnnotator()
-        annotated_image = box_annotator.annotate(scene=image, detections=detections)
-        """
-        caption = ", ".join(classes)
-        processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
-        boxes, logits, phrases = predict(
-            model=self.model,
-            image=processed_image,
-            caption=caption,
-            box_threshold=box_threshold,
-            text_threshold=text_threshold)
-        source_h, source_w, _ = image.shape
-        detections = Model.post_process_result(
-            source_h=source_h,
-            source_w=source_w,
-            boxes=boxes,
-            logits=logits)
-        class_id = Model.phrases2classes(phrases=phrases, classes=classes)
-        detections.class_id = class_id
-        return detections
-
-    @staticmethod
-    def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor:
-        transform = T.Compose(
-            [
-                T.RandomResize([800], max_size=1333),
-                T.ToTensor(),
-                T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-            ]
-        )
-        image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB))
-        image_transformed, _ = transform(image_pillow, None)
-        return image_transformed
-
-    @staticmethod
-    def post_process_result(
-            source_h: int,
-            source_w: int,
-            boxes: torch.Tensor,
-            logits: torch.Tensor
-    ) -> sv.Detections:
-        boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h])
-        xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
-        confidence = logits.numpy()
-        return sv.Detections(xyxy=xyxy, confidence=confidence)
-
-    @staticmethod
-    def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray:
-        class_ids = []
-        for phrase in phrases:
-            try:
-                class_ids.append(classes.index(phrase))
-            except ValueError:
-                class_ids.append(None)
-        return np.array(class_ids)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnextv2/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnextv2/__init__.py
deleted file mode 100644
index 9bfd6b26e05ceba2aa6b8c69f11e3909ff934575..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnextv2/__init__.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# flake8: noqa
-# There's no way to ignore "F401 '...' imported but unused" warnings in this
-# module, but to preserve other warnings. So, don't check this module at all.
-
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-# rely on isort to merge the imports
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_convnextv2": [
-        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
-        "ConvNextV2Config",
-    ]
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_convnextv2"] = [
-        "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "ConvNextV2ForImageClassification",
-        "ConvNextV2Model",
-        "ConvNextV2PreTrainedModel",
-        "ConvNextV2Backbone",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_convnextv2 import (
-        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-        ConvNextV2Config,
-    )
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_convnextv2 import (
-            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
-            ConvNextV2Backbone,
-            ConvNextV2ForImageClassification,
-            ConvNextV2Model,
-            ConvNextV2PreTrainedModel,
-        )
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt_sw3/tokenization_gpt_sw3.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt_sw3/tokenization_gpt_sw3.py
deleted file mode 100644
index 857656fa07ce367bce41b845ba7331cbbde9b2f7..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt_sw3/tokenization_gpt_sw3.py
+++ /dev/null
@@ -1,332 +0,0 @@
-"""The tokenizer used by the GPT-SW3 models."""
-
-import os
-import re
-import unicodedata
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import sentencepiece as spm
-
-from ...tokenization_utils import PreTrainedTokenizer
-from ...utils import is_torch_available, logging
-
-
-if is_torch_available():
-    import torch
-
-
-logger = logging.get_logger(__name__)
-VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
-        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
-        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
-        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
-        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
-    }
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "AI-Sweden/gpt-sw3-126m": 2048,
-    "AI-Sweden/gpt-sw3-350m": 2048,
-    "AI-Sweden/gpt-sw3-1.6b": 2048,
-    "AI-Sweden/gpt-sw3-6.7b": 2048,
-    "AI-Sweden/gpt-sw3-20b": 2048,
-}
-
-
-class GPTSw3Tokenizer(PreTrainedTokenizer):
-    """
-    Construct an GPTSw3 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Example usage:
-    ```python
-    >>> from transformers import GPTSw3Tokenizer
-
-    >>> tokenizer = GPTSw3Tokenizer.from_pretrained("AI-Sweden/gpt-sw3-126m")
-    >>> tokenizer("Svenska är kul!")["input_ids"]
-    [1814, 377, 3617, 63504]
-    ```
-
-    Args:
-        vocab_file (`str`):
-            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
-            contains the vocabulary necessary to instantiate a tokenizer.
-        do_lower_case (`bool`, *optional*, defaults to `False`):
-            Whether or not to lowercase the input when tokenizing.
-        remove_space (`bool`, *optional*, defaults to `False`):
-            Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
-        keep_accents (`bool`, *optional*, defaults to `False`):
-            Whether or not to keep accents when tokenizing.
-        pad_token (`str`, *optional*):
-            The token used for padding, for example when batching sequences of different lengths. If not provided, will
-            default to '<pad>' or '<unk>' depending on model size.
-        unk_token (`str`, *optional*):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead. If not provided, will default to '<unk>'.
-        eos_token (`str`, *optional*):
-            The end of sequence token seen during pretraining. If not provided, will default to '<|endoftext|>'
-        bos_token (`str`, *optional*):
-            The beginning of sequence token that can be used for downstream task, was not seen during pretraining. If
-            not provided, will default to '<s>' or '<|endoftext|>', depending on model size.
-        sp_model_kwargs (`dict`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-
-    Attributes:
-        sp_model (`SentencePieceProcessor`):
-            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
-        whitespaces (`set`):
-            The whitespaces that are replaced in the whitespace normalization in preprocessing.
-        non_printing_characters_re (`Pattern`):
-            The compiled regular expression to remove non-printing characters in preprocessing.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "token_type_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        do_lower_case=False,
-        remove_space=False,
-        keep_accents=False,
-        pad_token=None,
-        unk_token=None,
-        eos_token=None,
-        bos_token=None,
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> None:
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-
-        name_or_path = kwargs.get("name_or_path")
-        if name_or_path is None:
-            logger.warning(
-                "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
-                " you are testing the model, this can safely be ignored"
-            )
-            name_or_path = "None"
-
-        # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
-        eos_token = "<|endoftext|>" if eos_token is None else eos_token
-        unk_token = "<unk>" if unk_token is None else unk_token
-        if "gpt-sw3-7b" in name_or_path:
-            pad_token = unk_token if pad_token is None else pad_token
-            bos_token = eos_token if bos_token is None else bos_token
-        else:
-            pad_token = "<pad>" if pad_token is None else pad_token
-            bos_token = "<s>" if bos_token is None else bos_token
-
-        self.do_lower_case = do_lower_case
-        self.remove_space = remove_space
-        self.keep_accents = keep_accents
-        self.vocab_file = vocab_file
-
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
-
-        # Used for whitespace normalization in input texts
-        # fmt : off
-        self.whitespaces = {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "", "„"}
-        # fmt : on
-
-        # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
-        self.non_printing_characters_re = re.compile(
-            f"[{''.join(map(chr, list(range(0, 9)) + list(range(11, 32)) + list(range(127, 160)) + [160, 173, 8203]))}]"
-        )
-
-        super().__init__(
-            do_lower_case=do_lower_case,
-            remove_space=remove_space,
-            keep_accents=keep_accents,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            sp_model_kwargs=self.sp_model_kwargs,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.__getstate__
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        return state
-
-    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.__setstate__
-    def __setstate__(self, d):
-        self.__dict__ = d
-
-        # for backward compatibility
-        if not hasattr(self, "sp_model_kwargs"):
-            self.sp_model_kwargs = {}
-
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(self.vocab_file)
-
-    @property
-    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
-    def vocab_size(self) -> int:
-        return len(self.sp_model)
-
-    def preprocess_text(self, text: str) -> str:
-        """
-        Returns the preprocessed text. This procedure is identical to what was used when training the tokenizer.
-        """
-
-        # Remove non-printing characters
-        text = self.non_printing_characters_re.sub("", text)
-
-        # Normalize whitespaces
-        text = "".join([char if char not in self.whitespaces else " " for char in text])
-
-        # NFC Unicode normalization
-        text = unicodedata.normalize("NFC", text)
-        return text
-
-    def _tokenize(self, text: str, **kwargs) -> List[str]:
-        text = self.preprocess_text(text)
-        return self.sp_model.encode(text, out_type=str)
-
-    def _convert_token_to_id(self, token: str) -> int:
-        """Converts a token (str) to an id (int) using the vocab."""
-        return self.sp_model.PieceToId(token)
-
-    def _convert_id_to_token(self, index: int) -> str:
-        """Converts an index (int) to a token (str) using the vocab."""
-        return self.sp_model.IdToPiece(index)
-
-    @staticmethod
-    def clean_up_tokenization(out_string: str) -> str:
-        """Returns the input string, this function is overridden to remove the default clean up."""
-        return out_string
-
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        """Converts a sequence of tokens (strings) to a single string. Special tokens remain intact."""
-        current_sub_tokens = []
-        out_string = ""
-        prev_is_special = False
-        for token in tokens:
-            # make sure that special tokens are not decoded using sentencepiece model
-            if token in self.all_special_tokens:
-                # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
-                if not prev_is_special:
-                    out_string += " "
-
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                prev_is_special = True
-                current_sub_tokens = []
-            else:
-                current_sub_tokens.append(token)
-                prev_is_special = False
-        out_string += self.sp_model.decode(current_sub_tokens)
-
-        return out_string
-
-    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.get_vocab
-    def get_vocab(self) -> Dict[str, int]:
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        return (out_vocab_file,)
-
-    def encode_fast(
-        self, text: Union[str, List[str]], return_tensors: Union[str, bool] = False
-    ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
-        """
-        Encodes a text or batch of texts to token ids using preprocessing and the raw SP tokenizer. This has reduced
-        functionality but is often much faster.
-
-        Does NOT handle special tokens correctly, these can manually be added as ids afterwards.
-
-        Does NOT support padding, these can manually be added as ids afterwards.
-
-        Use default HuggingFace tokenization methods for full functionality.
-
-        Args:
-            text (`str` or `List[str]`): One or several text(s) to convert to token ids.
-            return_tensors (`str` or `bool`): Returns PyTorch tensors if set to True or "pt"
-
-        Returns:
-            `List[int]`, `List[List[int]]`, or `torch.Tensor`: The encoded text(s) as token ids.
-        """
-
-        if isinstance(text, str):
-            text = self.preprocess_text(text)
-            token_ids = self.sp_model.encode(text)
-        else:
-            text = [self.preprocess_text(t) for t in text]
-            token_ids = self.sp_model.encode(text)
-
-        if return_tensors is True or return_tensors == "pt":
-            token_ids = torch.tensor(token_ids)
-
-        return token_ids
-
-    def decode_fast(self, token_ids: Union[int, List[int]]) -> str:
-        """
-        Encodes a text or batch of texts to token ids using preprocessing and the raw SP tokenizer. This has reduced
-        functionality but is often much faster.
-
-        Args:
-            token_ids (`int` or `List[int]`): Encoded token or text as token id(s).
-
-        Returns:
-            `str`: Decoded text
-        """
-
-        return self.sp_model.decode(token_ids)
-
-    @property
-    def default_chat_template(self):
-        """
-        This chat template formats messages like an instant messenger chat log, with "User:" and "Bot:" strings
-        preceding messages. BOS tokens are added between all messages.
-        """
-        return (
-            "{{ eos_token }}{{ bos_token }}"
-            "{% for message in messages %}"
-            "{% if message['role'] == 'user' %}{{ 'User: ' + message['content']}}"
-            "{% else %}{{ 'Bot: ' + message['content']}}{% endif %}"
-            "{{ message['text'] }}{{ bos_token }}"
-            "{% endfor %}"
-            "Bot:"
-        )
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/demo/predictor.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/demo/predictor.py
deleted file mode 100644
index 7b7ebd3f846850172c1f560f8492d51e5667f76d..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/demo/predictor.py
+++ /dev/null
@@ -1,220 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import atexit
-import bisect
-import multiprocessing as mp
-from collections import deque
-import cv2
-import torch
-
-from detectron2.data import MetadataCatalog
-from detectron2.engine.defaults import DefaultPredictor
-from detectron2.utils.video_visualizer import VideoVisualizer
-from detectron2.utils.visualizer import ColorMode, Visualizer
-
-
-class VisualizationDemo(object):
-    def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
-        """
-        Args:
-            cfg (CfgNode):
-            instance_mode (ColorMode):
-            parallel (bool): whether to run the model in different processes from visualization.
-                Useful since the visualization logic can be slow.
-        """
-        self.metadata = MetadataCatalog.get(
-            cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
-        )
-        self.cpu_device = torch.device("cpu")
-        self.instance_mode = instance_mode
-
-        self.parallel = parallel
-        if parallel:
-            num_gpu = torch.cuda.device_count()
-            self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)
-        else:
-            self.predictor = DefaultPredictor(cfg)
-
-    def run_on_image(self, image):
-        """
-        Args:
-            image (np.ndarray): an image of shape (H, W, C) (in BGR order).
-                This is the format used by OpenCV.
-
-        Returns:
-            predictions (dict): the output of the model.
-            vis_output (VisImage): the visualized image output.
-        """
-        vis_output = None
-        predictions = self.predictor(image)
-        # Convert image from OpenCV BGR format to Matplotlib RGB format.
-        image = image[:, :, ::-1]
-        visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
-        if "panoptic_seg" in predictions:
-            panoptic_seg, segments_info = predictions["panoptic_seg"]
-            vis_output = visualizer.draw_panoptic_seg_predictions(
-                panoptic_seg.to(self.cpu_device), segments_info
-            )
-        else:
-            if "sem_seg" in predictions:
-                vis_output = visualizer.draw_sem_seg(
-                    predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
-                )
-            if "instances" in predictions:
-                instances = predictions["instances"].to(self.cpu_device)
-                vis_output = visualizer.draw_instance_predictions(predictions=instances)
-
-        return predictions, vis_output
-
-    def _frame_from_video(self, video):
-        while video.isOpened():
-            success, frame = video.read()
-            if success:
-                yield frame
-            else:
-                break
-
-    def run_on_video(self, video):
-        """
-        Visualizes predictions on frames of the input video.
-
-        Args:
-            video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be
-                either a webcam or a video file.
-
-        Yields:
-            ndarray: BGR visualizations of each video frame.
-        """
-        video_visualizer = VideoVisualizer(self.metadata, self.instance_mode)
-
-        def process_predictions(frame, predictions):
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            if "panoptic_seg" in predictions:
-                panoptic_seg, segments_info = predictions["panoptic_seg"]
-                vis_frame = video_visualizer.draw_panoptic_seg_predictions(
-                    frame, panoptic_seg.to(self.cpu_device), segments_info
-                )
-            elif "instances" in predictions:
-                predictions = predictions["instances"].to(self.cpu_device)
-                vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
-            elif "sem_seg" in predictions:
-                vis_frame = video_visualizer.draw_sem_seg(
-                    frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
-                )
-
-            # Converts Matplotlib RGB format to OpenCV BGR format
-            vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
-            return vis_frame
-
-        frame_gen = self._frame_from_video(video)
-        if self.parallel:
-            buffer_size = self.predictor.default_buffer_size
-
-            frame_data = deque()
-
-            for cnt, frame in enumerate(frame_gen):
-                frame_data.append(frame)
-                self.predictor.put(frame)
-
-                if cnt >= buffer_size:
-                    frame = frame_data.popleft()
-                    predictions = self.predictor.get()
-                    yield process_predictions(frame, predictions)
-
-            while len(frame_data):
-                frame = frame_data.popleft()
-                predictions = self.predictor.get()
-                yield process_predictions(frame, predictions)
-        else:
-            for frame in frame_gen:
-                yield process_predictions(frame, self.predictor(frame))
-
-
-class AsyncPredictor:
-    """
-    A predictor that runs the model asynchronously, possibly on >1 GPUs.
-    Because rendering the visualization takes considerably amount of time,
-    this helps improve throughput a little bit when rendering videos.
-    """
-
-    class _StopToken:
-        pass
-
-    class _PredictWorker(mp.Process):
-        def __init__(self, cfg, task_queue, result_queue):
-            self.cfg = cfg
-            self.task_queue = task_queue
-            self.result_queue = result_queue
-            super().__init__()
-
-        def run(self):
-            predictor = DefaultPredictor(self.cfg)
-
-            while True:
-                task = self.task_queue.get()
-                if isinstance(task, AsyncPredictor._StopToken):
-                    break
-                idx, data = task
-                result = predictor(data)
-                self.result_queue.put((idx, result))
-
-    def __init__(self, cfg, num_gpus: int = 1):
-        """
-        Args:
-            cfg (CfgNode):
-            num_gpus (int): if 0, will run on CPU
-        """
-        num_workers = max(num_gpus, 1)
-        self.task_queue = mp.Queue(maxsize=num_workers * 3)
-        self.result_queue = mp.Queue(maxsize=num_workers * 3)
-        self.procs = []
-        for gpuid in range(max(num_gpus, 1)):
-            cfg = cfg.clone()
-            cfg.defrost()
-            cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu"
-            self.procs.append(
-                AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)
-            )
-
-        self.put_idx = 0
-        self.get_idx = 0
-        self.result_rank = []
-        self.result_data = []
-
-        for p in self.procs:
-            p.start()
-        atexit.register(self.shutdown)
-
-    def put(self, image):
-        self.put_idx += 1
-        self.task_queue.put((self.put_idx, image))
-
-    def get(self):
-        self.get_idx += 1  # the index needed for this request
-        if len(self.result_rank) and self.result_rank[0] == self.get_idx:
-            res = self.result_data[0]
-            del self.result_data[0], self.result_rank[0]
-            return res
-
-        while True:
-            # make sure the results are returned in the correct order
-            idx, res = self.result_queue.get()
-            if idx == self.get_idx:
-                return res
-            insert = bisect.bisect(self.result_rank, idx)
-            self.result_rank.insert(insert, idx)
-            self.result_data.insert(insert, res)
-
-    def __len__(self):
-        return self.put_idx - self.get_idx
-
-    def __call__(self, image):
-        self.put(image)
-        return self.get()
-
-    def shutdown(self):
-        for _ in self.procs:
-            self.task_queue.put(AsyncPredictor._StopToken())
-
-    @property
-    def default_buffer_size(self):
-        return len(self.procs) * 5
diff --git a/spaces/yuan1615/EmpathyVC/text/pinyin.py b/spaces/yuan1615/EmpathyVC/text/pinyin.py
deleted file mode 100644
index 3e8663ce19b7813818f997ae5c18f2d972dd26d6..0000000000000000000000000000000000000000
--- a/spaces/yuan1615/EmpathyVC/text/pinyin.py
+++ /dev/null
@@ -1,241 +0,0 @@
-initials = [
-    "b",
-    "c",
-    "ch",
-    "d",
-    "f",
-    "g",
-    "h",
-    "j",
-    "k",
-    "l",
-    "m",
-    "n",
-    "p",
-    "q",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "w",
-    "x",
-    "y",
-    "z",
-    "zh",
-]
-finals = [
-    "a1",
-    "a2",
-    "a3",
-    "a4",
-    "a5",
-    "ai1",
-    "ai2",
-    "ai3",
-    "ai4",
-    "ai5",
-    "an1",
-    "an2",
-    "an3",
-    "an4",
-    "an5",
-    "ang1",
-    "ang2",
-    "ang3",
-    "ang4",
-    "ang5",
-    "ao1",
-    "ao2",
-    "ao3",
-    "ao4",
-    "ao5",
-    "e1",
-    "e2",
-    "e3",
-    "e4",
-    "e5",
-    "ei1",
-    "ei2",
-    "ei3",
-    "ei4",
-    "ei5",
-    "en1",
-    "en2",
-    "en3",
-    "en4",
-    "en5",
-    "eng1",
-    "eng2",
-    "eng3",
-    "eng4",
-    "eng5",
-    "er1",
-    "er2",
-    "er3",
-    "er4",
-    "er5",
-    "i1",
-    "i2",
-    "i3",
-    "i4",
-    "i5",
-    "ia1",
-    "ia2",
-    "ia3",
-    "ia4",
-    "ia5",
-    "ian1",
-    "ian2",
-    "ian3",
-    "ian4",
-    "ian5",
-    "iang1",
-    "iang2",
-    "iang3",
-    "iang4",
-    "iang5",
-    "iao1",
-    "iao2",
-    "iao3",
-    "iao4",
-    "iao5",
-    "ie1",
-    "ie2",
-    "ie3",
-    "ie4",
-    "ie5",
-    "ii1",
-    "ii2",
-    "ii3",
-    "ii4",
-    "ii5",
-    "iii1",
-    "iii2",
-    "iii3",
-    "iii4",
-    "iii5",
-    "in1",
-    "in2",
-    "in3",
-    "in4",
-    "in5",
-    "ing1",
-    "ing2",
-    "ing3",
-    "ing4",
-    "ing5",
-    "iong1",
-    "iong2",
-    "iong3",
-    "iong4",
-    "iong5",
-    "iou1",
-    "iou2",
-    "iou3",
-    "iou4",
-    "iou5",
-    "o1",
-    "o2",
-    "o3",
-    "o4",
-    "o5",
-    "ong1",
-    "ong2",
-    "ong3",
-    "ong4",
-    "ong5",
-    "ou1",
-    "ou2",
-    "ou3",
-    "ou4",
-    "ou5",
-    "u1",
-    "u2",
-    "u3",
-    "u4",
-    "u5",
-    "ua1",
-    "ua2",
-    "ua3",
-    "ua4",
-    "ua5",
-    "uai1",
-    "uai2",
-    "uai3",
-    "uai4",
-    "uai5",
-    "uan1",
-    "uan2",
-    "uan3",
-    "uan4",
-    "uan5",
-    "uang1",
-    "uang2",
-    "uang3",
-    "uang4",
-    "uang5",
-    "uei1",
-    "uei2",
-    "uei3",
-    "uei4",
-    "uei5",
-    "uen1",
-    "uen2",
-    "uen3",
-    "uen4",
-    "uen5",
-    "uo1",
-    "uo2",
-    "uo3",
-    "uo4",
-    "uo5",
-    "v1",
-    "v2",
-    "v3",
-    "v4",
-    "v5",
-    "van1",
-    "van2",
-    "van3",
-    "van4",
-    "van5",
-    "ve1",
-    "ve2",
-    "ve3",
-    "ve4",
-    "ve5",
-    "vn1",
-    "vn2",
-    "vn3",
-    "vn4",
-    "vn5",
-]
-alphabet = [
-    "A",
-    "B",
-    "C",
-    "D",
-    "E",
-    "F",
-    "G",
-    "H",
-    "I",
-    "J",
-    "K",
-    "L",
-    "M",
-    "N",
-    "O",
-    "P",
-    "Q",
-    "R",
-    "S",
-    "T",
-    "U",
-    "V",
-    "W",
-    "X",
-    "Y",
-    "Z"
-]
-valid_symbols = initials + finals + ["rr"]
diff --git a/spaces/zhang-wei-jian/docker/node_modules/concat-map/README.markdown b/spaces/zhang-wei-jian/docker/node_modules/concat-map/README.markdown
deleted file mode 100644
index 408f70a1be473c86e729fe8cc1d5fb4e0e364ce9..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/concat-map/README.markdown
+++ /dev/null
@@ -1,62 +0,0 @@
-concat-map
-==========
-
-Concatenative mapdashery.
-
-[![browser support](http://ci.testling.com/substack/node-concat-map.png)](http://ci.testling.com/substack/node-concat-map)
-
-[![build status](https://secure.travis-ci.org/substack/node-concat-map.png)](http://travis-ci.org/substack/node-concat-map)
-
-example
-=======
-
-``` js
-var concatMap = require('concat-map');
-var xs = [ 1, 2, 3, 4, 5, 6 ];
-var ys = concatMap(xs, function (x) {
-    return x % 2 ? [ x - 0.1, x, x + 0.1 ] : [];
-});
-console.dir(ys);
-```
-
-***
-
-```
-[ 0.9, 1, 1.1, 2.9, 3, 3.1, 4.9, 5, 5.1 ]
-```
-
-methods
-=======
-
-``` js
-var concatMap = require('concat-map')
-```
-
-concatMap(xs, fn)
------------------
-
-Return an array of concatenated elements by calling `fn(x, i)` for each element
-`x` and each index `i` in the array `xs`.
-
-When `fn(x, i)` returns an array, its result will be concatenated with the
-result array. If `fn(x, i)` returns anything else, that value will be pushed
-onto the end of the result array.
-
-install
-=======
-
-With [npm](http://npmjs.org) do:
-
-```
-npm install concat-map
-```
-
-license
-=======
-
-MIT
-
-notes
-=====
-
-This module was written while sitting high above the ground in a tree.
diff --git a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/rsort.js b/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/rsort.js
deleted file mode 100644
index 82404c5cfe0266acc57d58db007d02dc355a8658..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/rsort.js
+++ /dev/null
@@ -1,3 +0,0 @@
-const compareBuild = require('./compare-build')
-const rsort = (list, loose) => list.sort((a, b) => compareBuild(b, a, loose))
-module.exports = rsort
diff --git a/spaces/zhangs2022/ChuanhuChatGPT/modules/utils.py b/spaces/zhangs2022/ChuanhuChatGPT/modules/utils.py
deleted file mode 100644
index e1516e1fad4761787070d24e867bea57d86ac9ed..0000000000000000000000000000000000000000
--- a/spaces/zhangs2022/ChuanhuChatGPT/modules/utils.py
+++ /dev/null
@@ -1,548 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
-import logging
-import json
-import os
-import datetime
-import hashlib
-import csv
-import requests
-import re
-import html
-import sys
-import subprocess
-
-import gradio as gr
-from pypinyin import lazy_pinyin
-import tiktoken
-import mdtex2html
-from markdown import markdown
-from pygments import highlight
-from pygments.lexers import get_lexer_by_name
-from pygments.formatters import HtmlFormatter
-import pandas as pd
-
-from modules.presets import *
-from . import shared
-from modules.config import retrieve_proxy
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class DataframeData(TypedDict):
-        headers: List[str]
-        data: List[List[str | int | bool]]
-
-def predict(current_model, *args):
-    iter = current_model.predict(*args)
-    for i in iter:
-        yield i
-
-def billing_info(current_model):
-    return current_model.billing_info()
-
-def set_key(current_model, *args):
-    return current_model.set_key(*args)
-
-def load_chat_history(current_model, *args):
-    return current_model.load_chat_history(*args)
-
-def interrupt(current_model, *args):
-    return current_model.interrupt(*args)
-
-def reset(current_model, *args):
-    return current_model.reset(*args)
-
-def retry(current_model, *args):
-    iter = current_model.retry(*args)
-    for i in iter:
-        yield i
-
-def delete_first_conversation(current_model, *args):
-    return current_model.delete_first_conversation(*args)
-
-def delete_last_conversation(current_model, *args):
-    return current_model.delete_last_conversation(*args)
-
-def set_system_prompt(current_model, *args):
-    return current_model.set_system_prompt(*args)
-
-def save_chat_history(current_model, *args):
-    return current_model.save_chat_history(*args)
-
-def export_markdown(current_model, *args):
-    return current_model.export_markdown(*args)
-
-def load_chat_history(current_model, *args):
-    return current_model.load_chat_history(*args)
-
-def set_token_upper_limit(current_model, *args):
-    return current_model.set_token_upper_limit(*args)
-
-def set_temperature(current_model, *args):
-    current_model.set_temperature(*args)
-
-def set_top_p(current_model, *args):
-    current_model.set_top_p(*args)
-
-def set_n_choices(current_model, *args):
-    current_model.set_n_choices(*args)
-
-def set_stop_sequence(current_model, *args):
-    current_model.set_stop_sequence(*args)
-
-def set_max_tokens(current_model, *args):
-    current_model.set_max_tokens(*args)
-
-def set_presence_penalty(current_model, *args):
-    current_model.set_presence_penalty(*args)
-
-def set_frequency_penalty(current_model, *args):
-    current_model.set_frequency_penalty(*args)
-
-def set_logit_bias(current_model, *args):
-    current_model.set_logit_bias(*args)
-
-def set_user_identifier(current_model, *args):
-    current_model.set_user_identifier(*args)
-
-def set_single_turn(current_model, *args):
-    current_model.set_single_turn(*args)
-
-def handle_file_upload(current_model, *args):
-    return current_model.handle_file_upload(*args)
-
-def like(current_model, *args):
-    return current_model.like(*args)
-
-def dislike(current_model, *args):
-    return current_model.dislike(*args)
-
-
-def count_token(message):
-    encoding = tiktoken.get_encoding("cl100k_base")
-    input_str = f"role: {message['role']}, content: {message['content']}"
-    length = len(encoding.encode(input_str))
-    return length
-
-
-def markdown_to_html_with_syntax_highlight(md_str):
-    def replacer(match):
-        lang = match.group(1) or "text"
-        code = match.group(2)
-
-        try:
-            lexer = get_lexer_by_name(lang, stripall=True)
-        except ValueError:
-            lexer = get_lexer_by_name("text", stripall=True)
-
-        formatter = HtmlFormatter()
-        highlighted_code = highlight(code, lexer, formatter)
-
-        return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'
-
-    code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
-    md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)
-
-    html_str = markdown(md_str)
-    return html_str
-
-
-def normalize_markdown(md_text: str) -> str:
-    lines = md_text.split("\n")
-    normalized_lines = []
-    inside_list = False
-
-    for i, line in enumerate(lines):
-        if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
-            if not inside_list and i > 0 and lines[i - 1].strip() != "":
-                normalized_lines.append("")
-            inside_list = True
-            normalized_lines.append(line)
-        elif inside_list and line.strip() == "":
-            if i < len(lines) - 1 and not re.match(
-                r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
-            ):
-                normalized_lines.append(line)
-            continue
-        else:
-            inside_list = False
-            normalized_lines.append(line)
-
-    return "\n".join(normalized_lines)
-
-
-def convert_mdtext(md_text):
-    code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
-    inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
-    code_blocks = code_block_pattern.findall(md_text)
-    non_code_parts = code_block_pattern.split(md_text)[::2]
-
-    result = []
-    for non_code, code in zip(non_code_parts, code_blocks + [""]):
-        if non_code.strip():
-            non_code = normalize_markdown(non_code)
-            if inline_code_pattern.search(non_code):
-                result.append(markdown(non_code, extensions=["tables"]))
-            else:
-                result.append(mdtex2html.convert(non_code, extensions=["tables"]))
-        if code.strip():
-            # _, code = detect_language(code)  # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题
-            # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题
-            code = f"\n```{code}\n\n```"
-            code = markdown_to_html_with_syntax_highlight(code)
-            result.append(code)
-    result = "".join(result)
-    result += ALREADY_CONVERTED_MARK
-    return result
-
-
-def convert_asis(userinput):
-    return (
-        f'<p style="white-space:pre-wrap;">{html.escape(userinput)}</p>'
-        + ALREADY_CONVERTED_MARK
-    )
-
-
-def detect_converted_mark(userinput):
-    try:
-        if userinput.endswith(ALREADY_CONVERTED_MARK):
-            return True
-        else:
-            return False
-    except:
-        return True
-
-
-def detect_language(code):
-    if code.startswith("\n"):
-        first_line = ""
-    else:
-        first_line = code.strip().split("\n", 1)[0]
-    language = first_line.lower() if first_line else ""
-    code_without_language = code[len(first_line) :].lstrip() if first_line else code
-    return language, code_without_language
-
-
-def construct_text(role, text):
-    return {"role": role, "content": text}
-
-
-def construct_user(text):
-    return construct_text("user", text)
-
-
-def construct_system(text):
-    return construct_text("system", text)
-
-
-def construct_assistant(text):
-    return construct_text("assistant", text)
-
-
-def save_file(filename, system, history, chatbot, user_name):
-    logging.debug(f"{user_name} 保存对话历史中……")
-    os.makedirs(os.path.join(HISTORY_DIR, user_name), exist_ok=True)
-    if filename.endswith(".json"):
-        json_s = {"system": system, "history": history, "chatbot": chatbot}
-        print(json_s)
-        with open(os.path.join(HISTORY_DIR, user_name, filename), "w") as f:
-            json.dump(json_s, f)
-    elif filename.endswith(".md"):
-        md_s = f"system: \n- {system} \n"
-        for data in history:
-            md_s += f"\n{data['role']}: \n- {data['content']} \n"
-        with open(os.path.join(HISTORY_DIR, user_name, filename), "w", encoding="utf8") as f:
-            f.write(md_s)
-    logging.debug(f"{user_name} 保存对话历史完毕")
-    return os.path.join(HISTORY_DIR, user_name, filename)
-
-
-def sorted_by_pinyin(list):
-    return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
-
-
-def get_file_names(dir, plain=False, filetypes=[".json"]):
-    logging.debug(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
-    files = []
-    try:
-        for type in filetypes:
-            files += [f for f in os.listdir(dir) if f.endswith(type)]
-    except FileNotFoundError:
-        files = []
-    files = sorted_by_pinyin(files)
-    if files == []:
-        files = [""]
-    logging.debug(f"files are:{files}")
-    if plain:
-        return files
-    else:
-        return gr.Dropdown.update(choices=files)
-
-
-def get_history_names(plain=False, user_name=""):
-    logging.debug(f"从用户 {user_name} 中获取历史记录文件名列表")
-    return get_file_names(os.path.join(HISTORY_DIR, user_name), plain)
-
-
-def load_template(filename, mode=0):
-    logging.debug(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
-    lines = []
-    if filename.endswith(".json"):
-        with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
-            lines = json.load(f)
-        lines = [[i["act"], i["prompt"]] for i in lines]
-    else:
-        with open(
-            os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8"
-        ) as csvfile:
-            reader = csv.reader(csvfile)
-            lines = list(reader)
-        lines = lines[1:]
-    if mode == 1:
-        return sorted_by_pinyin([row[0] for row in lines])
-    elif mode == 2:
-        return {row[0]: row[1] for row in lines}
-    else:
-        choices = sorted_by_pinyin([row[0] for row in lines])
-        return {row[0]: row[1] for row in lines}, gr.Dropdown.update(
-            choices=choices
-        )
-
-
-def get_template_names(plain=False):
-    logging.debug("获取模板文件名列表")
-    return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
-
-
-def get_template_content(templates, selection, original_system_prompt):
-    logging.debug(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
-    try:
-        return templates[selection]
-    except:
-        return original_system_prompt
-
-
-def reset_textbox():
-    logging.debug("重置文本框")
-    return gr.update(value="")
-
-
-def reset_default():
-    default_host = shared.state.reset_api_host()
-    retrieve_proxy("")
-    return gr.update(value=default_host), gr.update(value=""), "API-Host 和代理已重置"
-
-
-def change_api_host(host):
-    shared.state.set_api_host(host)
-    msg = f"API-Host更改为了{host}"
-    logging.info(msg)
-    return msg
-
-
-def change_proxy(proxy):
-    retrieve_proxy(proxy)
-    os.environ["HTTPS_PROXY"] = proxy
-    msg = f"代理更改为了{proxy}"
-    logging.info(msg)
-    return msg
-
-
-def hide_middle_chars(s):
-    if s is None:
-        return ""
-    if len(s) <= 8:
-        return s
-    else:
-        head = s[:4]
-        tail = s[-4:]
-        hidden = "*" * (len(s) - 8)
-        return head + hidden + tail
-
-
-def submit_key(key):
-    key = key.strip()
-    msg = f"API密钥更改为了{hide_middle_chars(key)}"
-    logging.info(msg)
-    return key, msg
-
-
-def replace_today(prompt):
-    today = datetime.datetime.today().strftime("%Y-%m-%d")
-    return prompt.replace("{current_date}", today)
-
-
-def get_geoip():
-    try:
-        with retrieve_proxy():
-            response = requests.get("https://ipapi.co/json/", timeout=5)
-        data = response.json()
-    except:
-        data = {"error": True, "reason": "连接ipapi失败"}
-    if "error" in data.keys():
-        logging.warning(f"无法获取IP地址信息。\n{data}")
-        if data["reason"] == "RateLimited":
-            return (
-                i18n("您的IP区域：未知。")
-            )
-        else:
-            return i18n("获取IP地理位置失败。原因：") + f"{data['reason']}" + i18n("。你仍然可以使用聊天功能。")
-    else:
-        country = data["country_name"]
-        if country == "China":
-            text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**"
-        else:
-            text = i18n("您的IP区域：") + f"{country}。"
-        logging.info(text)
-        return text
-
-
-def find_n(lst, max_num):
-    n = len(lst)
-    total = sum(lst)
-
-    if total < max_num:
-        return n
-
-    for i in range(len(lst)):
-        if total - lst[i] < max_num:
-            return n - i - 1
-        total = total - lst[i]
-    return 1
-
-
-def start_outputing():
-    logging.debug("显示取消按钮，隐藏发送按钮")
-    return gr.Button.update(visible=False), gr.Button.update(visible=True)
-
-
-def end_outputing():
-    return (
-        gr.Button.update(visible=True),
-        gr.Button.update(visible=False),
-    )
-
-
-def cancel_outputing():
-    logging.info("中止输出……")
-    shared.state.interrupt()
-
-
-def transfer_input(inputs):
-    # 一次性返回，降低延迟
-    textbox = reset_textbox()
-    outputing = start_outputing()
-    return (
-        inputs,
-        gr.update(value=""),
-        gr.Button.update(visible=False),
-        gr.Button.update(visible=True),
-    )
-
-
-
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
-    if desc is not None:
-        print(desc)
-    if live:
-        result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
-        if result.returncode != 0:
-            raise RuntimeError(f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}""")
-
-        return ""
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
-    if result.returncode != 0:
-        message = f"""{errdesc or 'Error running command'}.
-            Command: {command}
-            Error code: {result.returncode}
-            stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
-            stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
-            """
-        raise RuntimeError(message)
-    return result.stdout.decode(encoding="utf8", errors="ignore")
-
-def versions_html():
-    git = os.environ.get('GIT', "git")
-    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
-    try:
-        commit_hash = run(f"{git} rev-parse HEAD").strip()
-    except Exception:
-        commit_hash = "<none>"
-    if commit_hash != "<none>":
-        short_commit = commit_hash[0:7]
-        commit_info = f"<a style=\"text-decoration:none\" href=\"https://github.com/GaiZhenbiao/ChuanhuChatGPT/commit/{short_commit}\">{short_commit}</a>"
-    else:
-        commit_info = "unknown \U0001F615"
-    return f"""
-        Python: <span title="{sys.version}">{python_version}</span>
-         • 
-        Gradio: {gr.__version__}
-         • 
-        Commit: {commit_info}
-        """
-
-def add_source_numbers(lst, source_name = "Source", use_source = True):
-    if use_source:
-        return [f'[{idx+1}]\t "{item[0]}"\n{source_name}: {item[1]}' for idx, item in enumerate(lst)]
-    else:
-        return [f'[{idx+1}]\t "{item}"' for idx, item in enumerate(lst)]
-
-def add_details(lst):
-    nodes = []
-    for index, txt in enumerate(lst):
-        brief = txt[:25].replace("\n", "")
-        nodes.append(
-            f"<details><summary>{brief}...</summary><p>{txt}</p></details>"
-        )
-    return nodes
-
-
-def sheet_to_string(sheet, sheet_name = None):
-    result = []
-    for index, row in sheet.iterrows():
-        row_string = ""
-        for column in sheet.columns:
-            row_string += f"{column}: {row[column]}, "
-        row_string = row_string.rstrip(", ")
-        row_string += "."
-        result.append(row_string)
-    return result
-
-def excel_to_string(file_path):
-    # 读取Excel文件中的所有工作表
-    excel_file = pd.read_excel(file_path, engine='openpyxl', sheet_name=None)
-
-    # 初始化结果字符串
-    result = []
-
-    # 遍历每一个工作表
-    for sheet_name, sheet_data in excel_file.items():
-
-        # 处理当前工作表并添加到结果字符串
-        result += sheet_to_string(sheet_data, sheet_name=sheet_name)
-
-
-    return result
-
-def get_last_day_of_month(any_day):
-    # The day 28 exists in every month. 4 days later, it's always next month
-    next_month = any_day.replace(day=28) + datetime.timedelta(days=4)
-    # subtracting the number of the current day brings us back one month
-    return next_month - datetime.timedelta(days=next_month.day)
-
-def get_model_source(model_name, alternative_source):
-    if model_name == "gpt2-medium":
-        return "https://huggingface.co/gpt2-medium"
-
-def refresh_ui_elements_on_load(current_model, selected_model_name):
-    return toggle_like_btn_visibility(selected_model_name)
-
-def toggle_like_btn_visibility(selected_model_name):
-    if selected_model_name == "xmchat":
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
diff --git a/spaces/zhenwusw/JoJoGAN/e4e/configs/paths_config.py b/spaces/zhenwusw/JoJoGAN/e4e/configs/paths_config.py
deleted file mode 100644
index 4604f6063b8125364a52a492de52fcc54004f373..0000000000000000000000000000000000000000
--- a/spaces/zhenwusw/JoJoGAN/e4e/configs/paths_config.py
+++ /dev/null
@@ -1,28 +0,0 @@
-dataset_paths = {
-	#  Face Datasets (In the paper: FFHQ - train, CelebAHQ - test)
-	'ffhq': '',
-	'celeba_test': '',
-
-	#  Cars Dataset (In the paper: Stanford cars)
-	'cars_train': '',
-	'cars_test': '',
-
-	#  Horse Dataset (In the paper: LSUN Horse)
-	'horse_train': '',
-	'horse_test': '',
-
-	#  Church Dataset (In the paper: LSUN Church)
-	'church_train': '',
-	'church_test': '',
-
-	#  Cats Dataset (In the paper: LSUN Cat)
-	'cats_train': '',
-	'cats_test': ''
-}
-
-model_paths = {
-	'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
-	'ir_se50': 'pretrained_models/model_ir_se50.pth',
-	'shape_predictor': 'pretrained_models/shape_predictor_68_face_landmarks.dat',
-	'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth'
-}
diff --git a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/pages/api/blob.ts b/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/pages/api/blob.ts
deleted file mode 100644
index fecd48031916b2284b8958892196e0a1ad420421..0000000000000000000000000000000000000000
--- a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/pages/api/blob.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { bcid } = req.query
-
-    const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
-      {
-        method: 'GET',
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referrer-Policy": "origin-when-cross-origin",
-        },
-      },
-    )
-
-    res.writeHead(200, {
-      'Content-Length': headers.get('content-length')!,
-      'Content-Type': headers.get('content-type')!,
-    })
-    // @ts-ignore
-    return Readable.fromWeb(body!).pipe(res)
-  } catch (e) {
-    console.log('Error', e)
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}